Public transportation accessibility can significantly affect real estate prices. However, the extent of this impact can greatly vary depending on the specific urban context. This study investigates the impact of accessibility to public transportation on apartment prices in two distinct districts of Shiraz, Iran: District 1, which has the highest vehicle ownership per capita, and District 8, which has the lowest. Using 182 transactions from District 1 and 65 transactions from District 8, a hedonic pricing model was employed to analyze data on apartment prices, area, age, number of bedrooms, and distances to the nearest bus stop, subway station, park, hospital, school, and the Central Business District. The findings revealed distinct patterns across the districts. In District 8, where vehicle ownership per capita is low, accessibility to bus stops significantly influenced apartment prices. Conversely, accessibility to subway stations did not significantly impact apartment prices in either district, nor did bus stop accessibility in District 1. This study underscores the varying influence of public transportation accessibility on property prices in different urban contexts, particularly in relation to vehicle ownership levels.
",1.0,https://aber.apacsci.com/index.php/ec/article/download/3406/3814,True,JournalArticle,,2025-05-12,5760fac704f0d25e4b375d62d83eaed6a39d809c,theimpactofpublictransportationaccessibilityonapartmentpricesinshirazacomparativestudyinadevelopingurbancontext,kashkooli,10.54517/ec3406,theimpactofpublictransportationaccessibilityonapartmentpricesinshirazacomparativestudyinadevelopingurbancontext|2025,1,2990,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate)",2,4,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,973124ea687da38bfcb0d526fb28745dae9e1e90,10.1016/j.spasta.2017.07.010,,MCMC Bayesian spatial filtering for hedonic models in real estate markets,2017.0,,P. Gargallo; Jesus Miguel; M. Salvador,"Abstract The traditional hedonic model postulates that housing prices depend on their characteristics and their location. However, this model assumes a constant relationship between the dependent and the independent variables. This assumption is unrealistic because empirical studies have shown that the regression coefficients depend on the housing location. For this reason, it is necessary to use models with spatially varying coefficients. The approaches proposed in the literature used to estimate this type of models do not incorporate the uncertainty associated with the estimation and selection of models and/or are computationally expensive. To improve these aspects, this paper proposes spatial filtering techniques to parsimoniously model the spatial dependencies of the hedonic coefficients and an adaptive MCMC Bayesian algorithm to select the most appropriate filters. The method is illustrated through an application to the real estate market of Zaragoza, and a comparison with alternative procedures is conducted. Our results show that our valuation methodology has better goodness of fit and predictive performance properties than alternative methods. Although our proposal assumes normality and homoscedasticity of the model error distribution, the method is easy to implement and not very computationally demanding, which makes this approach attractive and useful from a practical viewpoint.",6.0,http://zaguan.unizar.es/record/71190,True,,Computer Science,2017-11-01,973124ea687da38bfcb0d526fb28745dae9e1e90,mcmcbayesianspatialfilteringforhedonicmodelsinrealestatemarkets,gargallo,10.1016/j.spasta.2017.07.010,mcmcbayesianspatialfilteringforhedonicmodelsinrealestatemarkets|2017,1,2910,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic || real estate hedonic regression || spatial hedonic model housing",2,1,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,40a400adea28e3222d175da2aec907341e4b0664,10.2495/ut110261,,Validating Stated Preference Surveys through the Use of Hedonic Regression Models with an Application to Housing Prices around Transit Oriented Developments,2011.0,,Brett Smith; D. Olaru,,1.0,http://www.witpress.com/Secure/elibrary/papers/UT11/UT11026FU1.pdf,True,Review,Economics,2011-05-27,40a400adea28e3222d175da2aec907341e4b0664,validatingstatedpreferencesurveysthroughtheuseofhedonicregressionmodelswithanapplicationtohousingpricesaroundtransitorienteddevelopments,smith,10.2495/ut110261,validatingstatedpreferencesurveysthroughtheuseofhedonicregressionmodelswithanapplicationtohousingpricesaroundtransitorienteddevelopments|2011,1,1500,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic || real estate hedonic regression",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,6902792e014ff34816d6a002095db91af18cc0ab,10.6008/cbpc2179-6858.2020.006.0008,,"Urban green areas and real estate prices in Curitiba, Brazil",2020.0,,Alessandro Panasolo; F. Galvão; H. Higachi; E. D. de Oliveira; Fernando C. De Oliveira; Carlos Wroblewski; T. Gadda; C. Balbinot,"We attempted to identify to which extent the implicit ecosystem service values of urban green areas impact real estate values in the city of Curitiba, Brazil. The study is based on spatial econometrics techniques and hedonic price theory applied to 43 urban green areas, highlighting three units: the Airumã Private Natural Heritage Reserve, the Teresa Urban Ecological Station, and the President Getulio Vargas Refinery. Information was obtained on the structural characteristics of more than 5,300 apartments and houses. The results of exploratory spatial data analysis (ESDA) and estimates from hedonic regression model parameters show that the presence of urban green areas contribute to the final property prices. The effects of proximity to urban green areas on the price of urban residential property are not homogeneous and stationary throughout urban spaces and can generate distinct spatial clusters of real estate prices: high-high and low-low. The used methodology proved to be efficient to assess the value of urban green areas with regard to the use of information, processing, data analysis, and results generated. Furthermore, it measured the impact of these areas on property prices and provided easily interpretable data that can be relevant for payments for ecosystem services policies at the local level.",2.0,http://www.sustenere.co/index.php/rica/article/download/CBPC2179-6858.2020.006.0008/2279,True,,Geography,2020-07-06,6902792e014ff34816d6a002095db91af18cc0ab,urbangreenareasandrealestatepricesincuritibabrazil,panasolo,10.6008/cbpc2179-6858.2020.006.0008,urbangreenareasandrealestatepricesincuritibabrazil|2020,1,2824,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || house prices proximity hedonic || real estate hedonic regression",2,2,4,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,a14233ed745154acdd94225f1db0275e7381b56f,10.4236/jss.2020.83029,,An Analysis on Influence of the Realty Rents Brought by Urban Railway System: A Case Study of Guangzhou MTR 2nd Line,2020.0,Open Journal of Social Sciences,Q. Deng,"This paper took 215 samples along the Guangzhou metro line 2 as an example to research the impact of urban rail system on realty rental prices. Method of hedonic price model was employed to calculate the range of subway influencing the surrounding reality rental. Three conclusions can be drawn up: 1) Metro has no influence on the samples of the central zone, while it contributes to surrounding realty rental prices increment of the peripheral area; 2) For the peripheral area, rental houses are influenced within 1.5 km. Realty rental prices have a significantly positive correlation with the distance of rental house away from the nearest subway station; 3) Average appreciation rate of monthly rents in the impact range reaches to 52.37%. Furthermore, residential monthly rents of house within 0.5 km, 0.5 - 1 km, and 1 - 1.5 km of subway stations are separately 56.9%, 49.2%, and 51% higher than monthly rents of houses located outside the affected area. Based on the result above, several points of tactic advice to generate higher return in rentals for investors and to rent a proper house for tenants have been put forward.",0.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=99078,True,,Business,2020-03-04,a14233ed745154acdd94225f1db0275e7381b56f,ananalysisoninfluenceoftherealtyrentsbroughtbyurbanrailwaysystemacasestudyofguangzhoumtr2ndline,deng,10.4236/jss.2020.83029,ananalysisoninfluenceoftherealtyrentsbroughtbyurbanrailwaysystemacasestudyofguangzhoumtr2ndline|2020,1,2648,1,hedonic house price,2,2,4,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",229ad63adfebaa8ed206ca11b5bf0ca7a5209692,10.4236/eng.2011.312147,,The Bending of Rectangular Deep Beams with Fixed at Both Ends under Uniform Load,2011.0,,Ying-Jie Chen; B. Fu; Gang Li; Jie Wu; M. Bai; Xia Chen,"The Maroochy River, which is located on east coast of Australia, provides a variety of uses and values to the community. Changes in structure, function and management of the river will influence the value that the community derives from it. Therefore, critical to the river’s continued management is the development of policy relevant tools based on the community’s value of the river. This paper focuses on estimating the financial value the local residents derive from living close to the river through investigation of changes in residential property values due to attributes of the Maroochy River. It is a complex analysis since there are several confounding geographical and property variables. Given a large and complete dataset of 28,000 properties for the Maroochy region, Artificial Neural Networks (ANN) was applied to estimate the economic value of the properties. This ANN was then able to simulate scenarios for property values with respect to changes in environmental features. It showed the Maroochy River contributed AU$900,000,000 to the unimproved capital value of the whole region, a value that could not be estimated previously, and much higher than anticipated. Calculating potential annual payments to the Shire Council through land tax analysis from these property values, provides the council with means to justify expenditure to maintain a standard of water quality and ecosystem health.",1.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=9207,True,,Engineering,2011-12-22,229ad63adfebaa8ed206ca11b5bf0ca7a5209692,thebendingofrectangulardeepbeamswithfixedatbothendsunderuniformload,chen,10.4236/eng.2011.312147,thebendingofrectangulardeepbeamswithfixedatbothendsunderuniformload|2011,1,2912,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),6a582c18a0b55c4939a0e4812ae293fd34b678de,10.4236/chnstd.2018.74025,,Capitalization of Public School Quality into Housing Prices: An Empirical Analysis Based on School District Housing for Public Primary Schools in Shanghai,2018.0,,Tiancheng Zhou,"This paper examines the capitalization of public school quality into housing prices in the specific context of China’s compulsory education system and “district correspondence enrollment” policy, which stipulates a strict correspondence between residential estates and enrollment into public primary schools. Taking 344 neighborhoods in 7 main districts of Shanghai, China as its sample, this paper carries out detailed descriptive analysis of the data. It also employs the traditional Hedonic Price Model and 2SLS regression method to quantitatively calculate the exact capitalization rates while isolating eight non-school attributes affecting housing prices. It yields the conclusion that for every one-rank improvement in the quality of the corresponding public primary school, the average housing price of a neighborhood is projected to increase by 3.1%, 2.8%, and 1.9%, on the citywide, urban, and suburban scale, respectively. The capitalization effect of public school quality in housing prices is statistically significant, so the status-quo of the distribution of public educational resources in Shanghai is still considerably unequal. In the final section of this paper, the significance of this research is discussed and comprehensive policy recommendations and action plan are given in an attempt to mitigate the school district housing fever and educational inequality.",5.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=88416,True,,Business,2018-09-12,6a582c18a0b55c4939a0e4812ae293fd34b678de,capitalizationofpublicschoolqualityintohousingpricesanempiricalanalysisbasedonschooldistricthousingforpublicprimaryschoolsinshanghai,zhou,10.4236/chnstd.2018.74025,capitalizationofpublicschoolqualityintohousingpricesanempiricalanalysisbasedonschooldistricthousingforpublicprimaryschoolsinshanghai|2018,1,2883,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",df571658cee6c502caa5e7f109e8a17702d92322,10.4236/jbcpr.2020.82009,,"Inner-City Neighbourhood Changes Predicted from House Prices in Windsor, Ontario, since the Early- or Mid-1980s",2020.0,Journal of Building Construction and Planning Research,A. Phipps,"Changes in prices of homes are hypothesized as correlated with the times of their sale and resale and the attributes of their dwelling unit and neighbourhood and those of neighbouring homes. They may also be correlated with the occurrences of events inside the neighbourhoods caused by the activities of individuals and organizations outside the neighbourhoods, such as whether the local economy is in a recession or has a high unemployment rate. Calibrated hybrid housing price models predict precipitous decreases in house prices of approximately 2900 sold and resold homes in two inner-city neighbourhoods in Windsor, Ontario, during those events since 1981 or 1986. Overall modest predicted percentage increases in houses’ prices during more than 30 years therefore subsumed periods of inner-city neighbourhood deterioration in dispersed locations of unimproved and disimproved homes. Compensatory predictions however are of increasing prices for minorities of homes with improvements to several attributes of the dwelling unit and neighbourhood.",4.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=101190,True,JournalArticle,Geography,2020-04-30,df571658cee6c502caa5e7f109e8a17702d92322,innercityneighbourhoodchangespredictedfromhousepricesinwindsorontariosincetheearlyormid1980s,phipps,10.4236/jbcpr.2020.82009,innercityneighbourhoodchangespredictedfromhousepricesinwindsorontariosincetheearlyormid1980s|2020,1,2564,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,6,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",c1ca4e69c0ce476b2a947c812b051c2c1cbd2897,10.1016/j.jue.2021.103371,,The value of climate amenities: A comparison of hedonic and discrete choice approaches,2021.0,,P. Sinha; Martha L. Caulkins; M. Cropper,"Abstract Amenities that vary across cities are typically valued using either a hedonic model, in which amenities are capitalized into wages and housing prices, or a discrete model of household location choice. In this paper, we use the 2000 Public Use Microdata Sample (PUMS) to value climate amenities using both methods. We compare estimates of marginal willingness to pay (MWTP), allowing preferences for climate amenities to vary by location. We find that mean MWTP for warmer winters is about twice as large using the discrete choice approach as with the hedonic approach; mean MWTP for cooler summers is approximately the same. The two approaches differ, however, in their estimates of MWTP by location. These disparities lead to significant differences in estimates of willingness to pay to avoid the A2 and B1 SRES scenarios in 2020–2050 using the two approaches.",15.0,http://www.sciencedirect.com/science/article/pii/S009411902100053X,True,,Economics,2021-11-01,c1ca4e69c0ce476b2a947c812b051c2c1cbd2897,thevalueofclimateamenitiesacomparisonofhedonicanddiscretechoiceapproaches,sinha,10.1016/j.jue.2021.103371,thevalueofclimateamenitiesacomparisonofhedonicanddiscretechoiceapproaches|2021,1,2371,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,1,2,2,1,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,e3d40b94789507826f707f95f0b49aebd3b93c16,10.4067/s0718-50732019000200215,,Análisis de precios hedónicos de viviendas,2019.0,Revista ingeniería de construcción,S. Poeta; T. Gerhardt; M. González,"This paper presents a study about the housing pricing using hedonic modelling. The pricing models have several uses, such as determinate property taxes or to mortgage decision analysis. In the case of taxation, models must be broader, aiming at the evaluation of a set of housing units. It makes the work seem more complex. This issue is a barrier to widespread statistical inference in tax assessment, especially in small and medium-sized municipalities, which have fewer human resources. This paper presents a study in two medium-sized Brazilian cities, demonstrating the process of analysis and developing models to single-family properties. Data were collected on real estate agents and valuation reports. Statistical analysis allows finding the equations that best fitted observed data. Significance tests of variables and models were performed and indicated that the models have good explanation of prices. It can be concluded that these models could be used in evaluation with tax purposes",4.0,http://www.scielo.cl/pdf/ric/v34n2/en_0718-5073-ric-34-02-215.pdf,True,,Economics;Philosophy,2019-08-30,e3d40b94789507826f707f95f0b49aebd3b93c16,analisisdeprecioshedonicosdeviviendas,poeta,10.4067/s0718-50732019000200215,analisisdeprecioshedonicosdeviviendas|2019,1,2496,4,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic property valuation || precios hedónicos vivienda,3,0,3,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,precios hedónicos vivienda,0bc1fdb226dd86dfbdc941ce7dd8e45cacef3a9f,10.4067/s0718-915x2011000100008,,El impacto del policentrismo sobre la distribución espacial de los valores inmobiliarios: un análisis para la Región Metropolitana de Barcelona,2011.0,,Carlos Aguirre-Nuñez; Carlos Marmolejo,"La teoria de la renta ofertada, que prevalece en el ambito de la economia urbana, establece un trade-off entre los costes de transporte y la renta que se transfiere al suelo, de manera que cuanto mayor es la distancia entre un punto determinado del sistema urbano y el CBD, menor es la renta transferible al suelo. Sin embargo, esta teoria, en su acepcion original, parte del supuesto de un sistema monocentrico, en el cual todos los empleos estan situados en el centro, un paradigma, por tanto, alejado del actual. En este articulo se propone: 1) un conjunto de metodos que permiten detectar subcentros de empleo, para 2) probar hasta que punto estos ejercen una influencia en el valor inmobiliario de su entorno. A partir de datos de empleo de la Region Metropolitana de Barcelona (RMB) y de valores residenciales, se detectan entre 15 y 26 municipios como potenciales subcentros; a partir de aqui, mediante un modelo de precios hedonicos, se prueba la influencia de estos sobre la distribucion espacial del valor de las viviendas. Los resultados destacan la presencia de problemas de multicolinealidad, entretanto, la distancia al CBD y a algunos subcentros esta correlacionada. Sin embargo, el modelo construido a partir de los subcentros detectados mediante la funcion exponencial negativa minimiza este problema, y permite validar, en un escenario policentrico, la teoria de la renta ofertada. Palabras clave: Teoria de la renta ofertada, policentrismo, mercados inmobiliarios, precios hedonicos.",9.0,http://www.scielo.cl/pdf/rconst/v10n1/art08.pdf,True,,Philosophy,2011-08-01,0bc1fdb226dd86dfbdc941ce7dd8e45cacef3a9f,elimpactodelpolicentrismosobreladistribucionespacialdelosvaloresinmobiliariosunanalisisparalaregionmetropolitanadebarcelona,aguirre-nuñez,10.4067/s0718-915x2011000100008,elimpactodelpolicentrismosobreladistribucionespacialdelosvaloresinmobiliariosunanalisisparalaregionmetropolitanadebarcelona|2011,1,3005,2,modelo de precios hedónicos vivienda || precios hedónicos vivienda,2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c0ccfc24ab44ae1818f22e04a8d24eeeb2cb325e,10.5430/afr.v8n2p99,,Considerations for a Regression-Based Real Estate Valuation and Appraisal Model: A Pilot Study,2019.0,Accounting and Finance Research,Shawn L. Robey; Mark A. McKnight; M. R. Price; R. Coleman,"This paper advocates a more scientific approach to residential real estate valuation as opposed to more traditional approaches, which are flawed for two main reasons: (1) appraiser judgements are almost exclusively used and (2) appraisers’ sample sizes are too small to provide adequate estimated values. By using a regression model, this paper explores the impacts of different characteristics on market value. Three hundred and fifteen properties in Evansville, Indiana, were analyzed testing twelve different variables. This model suggests that 91.8% of the total market value variation is explained by four independent variables. These findings provide evidence that multiple linear regression could be used to better predict a property’s value.",2.0,http://www.sciedu.ca/journal/index.php/afr/article/download/14978/9485,True,,Mathematics,2019-03-30,c0ccfc24ab44ae1818f22e04a8d24eeeb2cb325e,considerationsforaregressionbasedrealestatevaluationandappraisalmodelapilotstudy,robey,10.5430/afr.v8n2p99,considerationsforaregressionbasedrealestatevaluationandappraisalmodelapilotstudy|2019,1,2249,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",65a1056b073b3f3445fae0a823c7193e36c8db97,10.22158/wjssr.v5n1p89,,"Temporal and Spatial Effects of Urban Center on Housing Price — A Case Study on Hangzhou, China",2018.0,,Luhong Chu; Hai-Zhen Wen,"With the acceleration of urbanization and the rapid development of real estate, people pay more and more attention to the change of urban housing prices. Over time, the change of city center will inevitably affect the urban land or housing prices, which is reflected in the spatial distribution of urban land or housing prices. Therefore, this article attempts to explore the impact of urban center on housing prices from the perspective of multi-center city and study separately from two aspects of time and space. This paper takes the six main urban districts of Hangzhou as the research scope. At the time level, we select the residential data from 2007 to 2015 to construct models respectively based on the hedonic price theory and find that the influence of different urban center on housing price shows a certain change with time. On the spatial level, this paper choses the residential data in 2012 to construct geographic weighted regression model and the result shows that the impact of three centers on housing prices shows a certain degree of spatial heterogeneity.",3.0,http://www.scholink.org/ojs/index.php/wjssr/article/download/1269/1429,True,,Geography,2018-02-28,65a1056b073b3f3445fae0a823c7193e36c8db97,temporalandspatialeffectsofurbancenteronhousingpriceacasestudyonhangzhouchina,chu,10.22158/wjssr.v5n1p89,temporalandspatialeffectsofurbancenteronhousingpriceacasestudyonhangzhouchina|2018,1,2576,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression || spatial hedonic model housing",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,011148667cf42b65807b050b2dc9f527a19d50ea,10.11606/1413-8050/ea147474,,Urban amenities and Real State Value: An Empirical Analysis for Recife City,2016.0,,Deborah Maria da Silva Seabra; Raul da Mota Silveira; T. D. de Menezes,"This study provides estimates of the amenities’ influence on the value of residential property in Recife, a city particularly marked by the presence of beaches and rivers. Therefore, a model of hedonic prices with correction for different types of spatial dependence was considered and a rich database provided by the City Hall was used, which includes information about sold properties and urban amenities. The evidence indicates that “ocean view” and proximity to the river are the most valued amenities in the city, while the value of real estate tends to be negatively affected by proximity to ZEIS, subways and violent places",7.0,http://www.revistas.usp.br/ecoa/article/download/111684/109857,True,,Geography,2016-03-07,011148667cf42b65807b050b2dc9f527a19d50ea,urbanamenitiesandrealstatevalueanempiricalanalysisforrecifecity,seabra,10.11606/1413-8050/ea147474,urbanamenitiesandrealstatevalueanempiricalanalysisforrecifecity|2016,1,2130,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic price model amenities residential,2,1,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",988d4549282509e934b142f99eac4c2da0dcdf34,10.1080/14445921.2007.11104222,,Combining Geographic Information Systems and Regression Models to Generate Locational Value Residual Surfaces in the Assessment of Residential Property Values,2007.0,,Amalina Izzati Abdul Hamid,,9.0,http://www.prres.net/Papers/PRPRJ_No_1_2007_Hamid.pdf,True,,Mathematics,2007-01-01,988d4549282509e934b142f99eac4c2da0dcdf34,combininggeographicinformationsystemsandregressionmodelstogeneratelocationalvalueresidualsurfacesintheassessmentofresidentialpropertyvalues,hamid,10.1080/14445921.2007.11104222,combininggeographicinformationsystemsandregressionmodelstogeneratelocationalvalueresidualsurfacesintheassessmentofresidentialpropertyvalues|2007,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",caa0d7c959640499d35d0aa03c4880cb4000d157,10.1080/14445921.2007.11104223,,Cell Phone Tower Proximity Impacts on House Prices: A New Zealand Case Study,2007.0,,S. Bond,,25.0,http://www.prres.net/Papers/PRPRJ_No_1_2007_Bond.pdf,True,,Business,2007-01-01,caa0d7c959640499d35d0aa03c4880cb4000d157,cellphonetowerproximityimpactsonhousepricesanewzealandcasestudy,bond,10.1080/14445921.2007.11104223,cellphonetowerproximityimpactsonhousepricesanewzealandcasestudy|2007,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4e3711cec97cc562f45b0e7df2afa12f9d4347b3,10.18306/dlkxjz.2021.02.009,,Influencing factors of housing price differentiation based on the spatial quantile model: A case study of Wuhan City,2021.0,Progress in Geography,Xin-Hai Lu; Dawei Cai; Chen Zeng,": The change of housing price is an important issue related to urbanization and high-quality social and economic development in China. In order to explore the influencing factors in terms of the quantiles of housing price, this study took Wuhan City as a case, which is the central city of central China, and used the spatial quantile regression (SQR) model for quantitative analysis. The two-stage least squares (2SLS) model result was compared with the SQR model output to reveal its superiority. The research shows that: 1) The SQR model not only can consider the spatial autocorrelation of the housing price, but also have the capability to embed the conditional distribution characteristics, which helps to better describe the driving effects of micro- factors on different housing prices. 2) In view of the quantiles, the spatial autocorrelation of the high prices is stronger than the low prices. The influencing factors show volatility and heterogeneity. Compared with the mean result of the 2SLS model, the influencing degree of each factor in the SQR model increases or decreases with the change of the quantile. There is a significant difference in the degree of influence on housing price of different levels. 3) Overall, age of the residential building and medical facilities are negative influencing factors, and building characteristics including floor area ratio, location, and neighborhood such as nearby educational facilities are positive influencing factors. Based on the results, reasonable increase of the floor area ratio and green space ratio of low- and middle- priced residential areas, and increased investment in rail transportation and educational facilities in low- priced residential areas can be taken as alternatives for the government to formulate differentiated policy measures for housing with different levels of prices.",2.0,http://www.progressingeography.com/CN/article/downloadArticleFile.do?attachType=PDF&id=49224,True,,Economics,,4e3711cec97cc562f45b0e7df2afa12f9d4347b3,influencingfactorsofhousingpricedifferentiationbasedonthespatialquantilemodelacasestudyofwuhancity,lu,10.18306/dlkxjz.2021.02.009,influencingfactorsofhousingpricedifferentiationbasedonthespatialquantilemodelacasestudyofwuhancity|2021,1,3357,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,12092f283369285b82ea77ca6ce1b199b74a5826,10.47836/pjssh.32.4.13,,Swiftlet Farming Industry—A White Gold in the Malaysian Housing Market?,2024.0,Pertanika Journal of Social Sciences and Humanities,N. H. Adi Maimun; Yeong Ka Choon,"The swiftlet farming industry is fast-growing and contributes to Malaysia’s economic growth. Nonetheless, dirty and noisy surroundings caused by bird droppings and audio systems are unattractive and may adversely impact the housing market. Hence, this study seeks to ascertain the price impacts of the swiftlet farming industry. The first objective defines the hedonic model by reviewing its definition, theoretical framework, methodological process, and house price applications. The second objective explores factors influencing house prices, including the animal farming industry, through a literature review. The third objective evaluates the impact of the swiftlet farming industry on house prices. Ten-year house transaction data and spatial data provided by the Valuation and Property Services Department and Municipal District of Bentong are used to construct the Hedonic model. The result is evaluated based on its logical, statistical and predictive performance. The study found that swiftlet farming in Bentong has a negative impact on house prices, with an inverse price impact that increases with distance from the farming area. Buyers are reluctant to pay for houses near farming due to noise pollution and obstruction. This study supports international and national agendas relating to environment and health sustainability.",2.0,http://www.pertanika.upm.edu.my/resources/files/Pertanika PAPERS/JSSH Vol. 32 (4) Dec. 2024/13 JSSH-8986-2023.pdf,True,JournalArticle;Review,,2024-12-16,12092f283369285b82ea77ca6ce1b199b74a5826,swiftletfarmingindustryawhitegoldinthemalaysianhousingmarket,maimun,10.47836/pjssh.32.4.13,swiftletfarmingindustryawhitegoldinthemalaysianhousingmarket|2024,1,2839,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || hedonic property valuation || spatial hedonic model housing",2,1,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),1a86b82ceb72684c2378cc378f74ff983b491b46,10.3386/w25984,,Hurricane Katrina Floods New Jersey: The Role of Information in the Market Response to Flood Risk,2019.0,,Nicholas Z. Muller; Caroline Hopkins,"This study uses hedonic property models to explore how coastal real estate markets subject to heterogeneous information treatments respond to flood risk. We identify reactions to flood risk, distinctly from price effects due to flood damage, by examining non-local flooding events. Utilizing a difference-in-difference methodology, we test whether the coastal real estate market in New Jersey responds to several well-publicized hurricanes and tropical storms that did not strike the Atlantic seaboard. We find that homes in high flood risk zones situated in towns that participate in public flood awareness activities incur a 7 to 16 percent decrease in price after the non-local shock.",20.0,http://www.nber.org/papers/w25984.pdf,True,,Geography,2019-06-01,1a86b82ceb72684c2378cc378f74ff983b491b46,hurricanekatrinafloodsnewjerseytheroleofinformationinthemarketresponsetofloodrisk,muller,10.3386/w25984,hurricanekatrinafloodsnewjerseytheroleofinformationinthemarketresponsetofloodrisk|2019,1,2187,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,0,2,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",db234f08b8a5dc3cafcd6e86797480ebccf67de6,10.1016/j.jpubeco.2019.104110,,Unlocking amenities: Estimating public good complementarity,2018.0,Journal of Public Economics,David Albouy; P. Christensen; Ignacio Sarmiento-Barbieri,"Abstract Public goods may exhibit complementarities that are essential for determining their individual value. Our results indicate that improving safety near parks can turn them from public bads to goods. Ignoring complementarities may lead to i) undervaluing the potential value of public goods; ii) overestimating heterogeneity in preferences; and iii) understating the value of public goods to minority households. Recent reductions in crime have “unlocked” almost $7 billion in property value in Chicago, New York and Philadelphia. Still, two-fifths of the potential value of park proximity, $10 billion, remains locked-in.",59.0,http://www.nber.org/papers/w25107.pdf,True,,Economics,2018-09-01,db234f08b8a5dc3cafcd6e86797480ebccf67de6,unlockingamenitiesestimatingpublicgoodcomplementarity,albouy,10.1016/j.jpubeco.2019.104110,unlockingamenitiesestimatingpublicgoodcomplementarity|2018,1,2128,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e8dc0e684d1cf0ef4b9a9d75b8804bb69e58405e,10.37043/jura.2013.5.1.4,,"SMART GROWTH AND THE CHALLENGE OF NIMBY: MULTIFAMILY DWELLINGS AND THEIR ASSOCIATION WITH SINGLE-FAMILY HOUSE SELLING PRICES IN TALLAHASSEE, FLORIDA, USA",2020.0,,H. Gibson; M. Becker,"IntroductionHomeownership is the single largest financial investment most people will make during their lifetime (United States Department of Housing and Urban Development 1995). Furthermore, housing choice can have implications for quality of life, so housing consumers make purchasing decisions based on many factors, including affordability, location, specific housing or property attributes, and/or the characteristics of the surrounding neighborhood (Tiebout 1956, Fischel 2000). Because external neighborhood qualities are part of the decision to buy a house, it can also influence the monetary value of housing. Thus, neighborhood activities are important to many homeowners. Accordingly, NIMBY (""not in my backyard"") activism is frequent amongst homeowners. NIMBY citizens will participate in local government to protest development in their neighborhoods when they consider the development undesirable. One potential type of neighborhood development often perceived as undesirable by single-family homeowners in the United States (US) is multifamily housing. US single-family homeowners will regularly protest proposed multifamily construction when it is close to their house because they fear property devaluation (Pendall 1999). However, no substantial empirical evidence shows higher density developments causes any real reduction to surrounding single-family property values. In fact, the opposite may be true.In 1997, the United States National Association of Home Builders (NAHB), using US Census and American Housing Survey data, found that the average value of a single-family house in the US within 91.44 meters (300 feet) of multifamily residences increased in value at a slightly higher rate than single-family houses without multifamily residences nearby, at 2.9% versus 2.7% per year (Haughey 2005, United States National Association of Home Builders 2004). Granted, a concerned single-family homeowner might be a bit skeptical of this statistic, not just because of the source, but because the statistic is based on a national dataset. To illustrate, many multifamily dwellings (e.g., condominiums) are often found on or near an amenity, such as a beach, ski resort, or a thriving urban node. Logically, single-family properties proximate to these same amenities will also be proximate to the multifamily residences; capitalizing on the same amenity will bring up the national average substantially. This logic follows the basic principle of Alonso's classic Bid Rent Theory, based on the von Thunen model of land use, stating that as distance increases from a central node, so do transportation costs/time, and therefore rents will decrease (Alonso 1960). Thus, a local amenity may act as a central point for housing price premiums, trumping, at the aggregate level, any nuisance imposed by multifamily dwellings.Either way, the fact remains that community residents across the nation regularly protest new multifamily housing developments proposed for their neighborhoods (Babcock 1966, Scott 1969, Plotkin 1987, Pendall 1999). An investigation of 182 proposed housing developments in the San Francisco Bay Area during the late 1980s showed that multifamily projects were most likely to encounter NIMBY resistance. In the San Francisco Bay Area study, Pendall found 61% of all projects prompted some form of public opposition, but multifamily projects were 42% more likely to generate public controversy than other proposed developments (Pendall 1999).Even though studies examining the effects of subsidized housing on surrounding property values generally show no monetary effect on surrounding property values (Haughey 2005, Nguyen 2005, Pollakowski 2005), one likely issue with multifamily housing in the US is that it can be associated with more ""affordable"" housing, attracting lower income and ethnic minority residents. Despite continuous and significant improvements in racial attitudes amongst US Whites, race still partly explains today's housing segregation (Charles 2005, Ihlanfeldt and Scafidi 2004). …",0.0,http://www.jurareview.ro/resources/pdf/volume_11_smart_growth_and_the_challenge_of_nimby:_multifamily_dwellings_and_their_association_with_single-family_house_selling_prices_in_tallahassee_florida_usa_abstract.pdf,True,Review,Economics,2020-10-01,e8dc0e684d1cf0ef4b9a9d75b8804bb69e58405e,smartgrowthandthechallengeofnimbymultifamilydwellingsandtheirassociationwithsinglefamilyhousesellingpricesintallahasseefloridausa,gibson,10.37043/jura.2013.5.1.4,smartgrowthandthechallengeofnimbymultifamilydwellingsandtheirassociationwithsinglefamilyhousesellingpricesintallahasseefloridausa|2020,1,5540,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,9,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,8a5b3edb7b173a27e3dc5cfeec621542af91409a,10.9798/kosham.2025.25.3.181,,Impact of Major Natural Disasters on Sale Prices of Single-Family Homes,2025.0,Journal of the Korean Society of Hazard Mitigation,Dalbyul Lee,"This study empirically analyzes the impact of major natural disasters on the sale prices of single-family houses. Employing 2023 transaction data from the public real estate transaction system of the Ministry of Land, Infrastructure, and Transport, a hedonic regression analysis was conducted to assess the relationship between disaster experience and housing prices. The results reveal that single-family house prices in disaster-affected areas were, on average, 12.8% lower than those in non-affected areas, with the decline being more significant in areas that experienced repeated disasters. Specifically, house prices in areas with two disaster experiences declined by an average of 18.7%, and recovery took longer. Additionally, price fluctuations varied by disaster, housing type, and the local housing market. This study provides empirical evidence regarding the impact of natural disasters on the sale prices of single-family houses and underscores the necessity for real estate market stabilization and housing recovery policies in disaster-prone areas.",0.0,http://www.j-kosham.or.kr/upload/pdf/KOSHAM-2025-25-3-181.pdf,True,JournalArticle,,2025-06-30,8a5b3edb7b173a27e3dc5cfeec621542af91409a,impactofmajornaturaldisastersonsalepricesofsinglefamilyhomes,lee,10.9798/kosham.2025.25.3.181,impactofmajornaturaldisastersonsalepricesofsinglefamilyhomes|2025,1,2563,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || real estate hedonic regression",2,3,6,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2ef589a2560d5f43fe4591a742ca78c152a82db5,10.53894/ijirss.v8i3.6982,,"Dynamic price estimation model for residential buildings per square meter in Surabaya: Analyzing the impact of construction costs, material quality, and Land Area",2025.0,International journal of innovative research and scientific studies,Kusnan Kusnan; Naniek Widayati; Dadang M. Ma’some,"This study develops a dynamic price estimation model for residential buildings per square meter in Surabaya, focusing on key variables such as construction costs, material quality, land area, and building size. Using multiple linear regression analysis and the backward elimination method, this research identifies the most significant factors that influence housing prices in urban and suburban areas. The results show that construction costs and material quality have a positive impact on housing prices, while land area exhibits an inverse relationship with the price per square meter, particularly in peripheral areas of the city. The final model achieved an R² value of 0.685, indicating strong explanatory power. The findings provide valuable insights for real estate developers, policymakers, and urban planners in optimizing housing development strategies, promoting affordable housing, and improving market transparency in rapidly urbanizing regions. This model can be applied in other emerging markets with similar urban dynamics.",0.0,http://www.ijirss.com/index.php/ijirss/article/download/6982/1423,True,JournalArticle,,2025-05-13,2ef589a2560d5f43fe4591a742ca78c152a82db5,dynamicpriceestimationmodelforresidentialbuildingspersquaremeterinsurabayaanalyzingtheimpactofconstructioncostsmaterialqualityandlandarea,kusnan,10.53894/ijirss.v8i3.6982,dynamicpriceestimationmodelforresidentialbuildingspersquaremeterinsurabayaanalyzingtheimpactofconstructioncostsmaterialqualityandlandarea|2025,1,2540,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",05ee370964c54632a49c9bef170c0b594c36d704,10.19139/soic-2310-5070-1764,,"Geoadditive Semiparametric Regression For Modeling Property Price In Surabaya, Indonesia Using Marketplace Data",2024.0,"Statistics, Optimization & Information Computing",Wahyu Wibowo; M. Khoiri; S. Wulandari; Fausania Hibatullah; Mochammad Reza Habibi; Harun Al Azies,"The price growth of the property in Surabaya is the highest among the other cities in East Java, but demand in the residential sub-sector is still there. The value of a property is described by its price. Property price is one of the important factors considered in making an investment decision. The market value is determined by its physical and micro-neighborhood factors. It consists of location and environmental factors. Mass appraisal is an efficient and cost-effective way to value property fairly, transparently, and consistently, as properties with the same attributes will receive equal value. The existence of a price property model is vital in the context of mass appraisal. The objective of mass appraisal is to value a group of properties using data, valuation methods, and statistical tests. Mass appraisal is invaluable for the government to formulate taxes based on the market value. In this research, the Geoaditive model is used to model property price based on its physical and location factors. The results show that the physical (number of bedrooms, number of bathrooms, land area, and building area) and location (longitude and latitude coordinates) factors significantly influence the property prices in Surabaya. The building area has more impact on the property price compared to the land area. The combined effect plot shows also that the properties located in the eastern of Surabaya have a relatively higher price than those in the western part",0.0,http://www.iapress.org/index.php/soic/article/download/1764/1098,True,JournalArticle,,2024-04-28,05ee370964c54632a49c9bef170c0b594c36d704,geoadditivesemiparametricregressionformodelingpropertypriceinsurabayaindonesiausingmarketplacedata,wibowo,10.19139/soic-2310-5070-1764,geoadditivesemiparametricregressionformodelingpropertypriceinsurabayaindonesiausingmarketplacedata|2024,1,2974,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,cca9b5019b0fc9142dc68dadaabdb567573c5fe6,10.13189/sa.2016.040709,,"House Prices and School Quality: Instruments, Signaling, Usage and Perceptions",2016.0,,David M. Brasington,"We identify commonly-available instrumental variables for housing hedonics for proficiency tests, school spending, and property taxes. We estimate the hedonic simultaneously with the reasons a person bought his house. We find larger housing value elasticities than previous studies: 0.47 for test scores, 1.07 for expenditures per pupil, and -0.37 for property taxes. We suggest schooling expenditures acts as a signaling mechanism. We find that people who use private schools and think their private school is excellent pay an additional 10% house price premium; this finding, along with capitalization of private school test scores, supports the theory of the marginal consumer.",5.0,http://www.hrpub.org/download/20160630/SA9-19606797.pdf,True,,Economics,2016-07-01,cca9b5019b0fc9142dc68dadaabdb567573c5fe6,housepricesandschoolqualityinstrumentssignalingusageandperceptions,brasington,10.13189/sa.2016.040709,housepricesandschoolqualityinstrumentssignalingusageandperceptions|2016,1,2180,3,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,603803520bbeb39521e65b1bdf222a3208585e31,10.3368/le.84.4.635,,"Intra-Regional Amenities, Wages, and Home Prices: The Role of Forests in the Southwest",2008.0,Land Economics,M. Hand; J. Thacher; Daniel W. Mccollum; R. P. Berrens,,40.0,http://www.fs.fed.us/rm/pubs_other/rmrs_2008_hand_m002.pdf,True,,Economics,2008-11-01,603803520bbeb39521e65b1bdf222a3208585e31,intraregionalamenitieswagesandhomepricestheroleofforestsinthesouthwest,hand,10.3368/le.84.4.635,intraregionalamenitieswagesandhomepricestheroleofforestsinthesouthwest|2008,1,1500,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || housing characteristics prices hedonic",0,1,1,1,1,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b531cfecedebdd4e4baa6ecc54f4ca643a46f7de,10.1016/j.regsciurbeco.2006.03.007,,"Open Space, Residential Property Values, and Spatial Context",2006.0,,S. Anderson; Sarah E. West,,565.0,http://www.feem-web.it/ess/ess05/files/Anderson.pdf,True,,Business,2006-11-01,b531cfecedebdd4e4baa6ecc54f4ca643a46f7de,openspaceresidentialpropertyvaluesandspatialcontext,anderson,10.1016/j.regsciurbeco.2006.03.007,openspaceresidentialpropertyvaluesandspatialcontext|2006,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bb333ded7cf46af47da2d5ef3020d3486c45bde1,10.30902/jrea.2020.6.1.19,,"Effects of Neighborhood Environmental Factors on Apartment Housing Prices",2020.0,Journal of Real Estate Analysis,So-Young Lee; M. Kim; Eun Jung Kim,"The purpose of this study is to examine correlations between neighborhood environments and housing prices in Gangnam-gu, Seoul, and Suseong-gu, Daegu. The study covers apartment complexes where transaction took place in 2017, and the total numbers of the complexes are 469 and 258 in Gangnam-gu and Suseong-gu, respectively. The dependent variable is the average transaction price per m2 of apartment, and independent variables comprise complex characteristics and neighborhood environment characteristics. The results of analysis showed that there were differences in environmental factors affecting housing prices between the two regions. The housing price was correlated with normalized difference vegetation index (NDVI) (+), mean slope (‒), and distance to river (‒) in Gangnam-gu, while it was associated with distance to high school (‒), number of private academies (+), high school quality (+), distance to subway station (‒), and the number of public sport facilities (+) in Suseong-gu. This study is differentiated in that it comparatively analyzed neighborhood environment factors affecting apartment prices between Gangnam-gu in Seoul Capital Area and Suseong-gu in non-Seoul Capital Area. Also, it is significant in that it can be used as basic data for responding to differences in housing prices arising from regional characteristics.",2.0,http://www.ejrea.org/download/download_pdf?pid=jrea-6-1-19,True,JournalArticle,Geography,2020-04-01,bb333ded7cf46af47da2d5ef3020d3486c45bde1,effectsofneighborhoodenvironmentalfactorsonapartmenthousingprices,lee,10.30902/jrea.2020.6.1.19,effectsofneighborhoodenvironmentalfactorsonapartmenthousingprices|2020,1,2849,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,8877fbe091f7a7ae39e4640c0cfe92e8569e6f87,10.30902/jrea.2018.4.2.85,,"A Study on the Impact of Large-Scale Complex Shopping Center on Apartment Prices - Case of Starfield Hanam -",2018.0,Journal of Real Estate Analysis,Jong-Kyu Hwang,"This study analyzes the effects of a large-scale complex shopping center on surrounding apartment prices. This study investigated Starfield Hanam, a large-scale complex shopping center located in Hanam City. Starfield Hanam has a total floor area of 460,000 square meters, and is a combination of department stores, discount stores and leisure facilities. For the analysis, the Hedonic price function was used. Apartment price was used as a dependent variable; and residential characteristics, complex characteristics, area characteristics were used as explanatory variables. The results of analysis show that the opening of a large complex shopping center positively affected the price of nearby apartments. According as the distance from the complex shopping center increased by 1m, the price of apartment per 3.3㎡ was reduced by 0.028 million won. The distance range affecting apartment price is estimated to be about",4.0,http://www.ejrea.org/download/download_pdf?pid=jrea-4-2-85,True,,Economics,2018-11-01,8877fbe091f7a7ae39e4640c0cfe92e8569e6f87,astudyontheimpactoflargescalecomplexshoppingcenteronapartmentpricescaseofstarfieldhanam,hwang,10.30902/jrea.2018.4.2.85,astudyontheimpactoflargescalecomplexshoppingcenteronapartmentpricescaseofstarfieldhanam|2018,1,2423,1,apartment prices hedonic,2,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2bc5488e24b916f691201ab5eea7753c17e6634b,10.30902/jrea.2025.11.1.179,,"An Analysis of Housing Price Characteristics in Areas Approved and Announced for Management Planning in Seoul’s Moa-Town",2025.0,Journal of Real Estate Analysis,S. Lee; Han-Ro Seung,,1.0,http://www.ejrea.org/download/download_pdf?pid=jrea-11-1-179,True,JournalArticle,,2025-04-01,2bc5488e24b916f691201ab5eea7753c17e6634b,ananalysisofhousingpricecharacteristicsinareasapprovedandannouncedformanagementplanninginseoulsmoatown,lee,10.30902/jrea.2025.11.1.179,ananalysisofhousingpricecharacteristicsinareasapprovedandannouncedformanagementplanninginseoulsmoatown|2025,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,579de02eb394b4140b2711b0446ce8b281d823f3,10.4467/2353737xct.18.097.8792,,Is the Energy Price Premium Spatially Aggregated? A Listing Price Analysis of the Residential Market in Barcelona,2018.0,,Ai Chen; Carlos Marmolejo-Duarte,"Abstract Building energy efficiency has aroused much discussion around the world. Energy Performance Certificates (EPCs) and relevant regulations and legislation have been established and enforced in the past 15 years due to the extreme 40% consumption of total energy and 38% of total CO2 emissions caused by residential buildings in Europe. This paper aims to confirm the energy premium in the Metropolitan Area of Barcelona (AMB) and the presence of spatial homogeneity of this energy premium with OLS hedonic prices and the GWR model. The results suggest that the energy premium causes a 12.2% housing price increase from Class G to Class A, or an implicit housing price rise of 1.9% with every ranking of EPC ordinal scale improvement. Furthermore, the areas with a higher incidence of energy labelling are situated in the middle and north-eastern parts of AMB that are inhabited by skilled professionals who more commonly have a higher university education.",1.0,http://www.ejournals.eu/pliki/art/12139/,True,,Economics,2018-07-01,579de02eb394b4140b2711b0446ce8b281d823f3,istheenergypricepremiumspatiallyaggregatedalistingpriceanalysisoftheresidentialmarketinbarcelona,chen,10.4467/2353737xct.18.097.8792,istheenergypricepremiumspatiallyaggregatedalistingpriceanalysisoftheresidentialmarketinbarcelona|2018,1,2463,2,hedonic housing prices || spatial hedonic model housing,2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9ffc586f651b3035ba2f9b5866eb0451f65ecbd1,10.38100/jhuf.2024.9.1.31,,"Analysis of the impact of urban planning facility locations on multifamily attached housing prices*",2024.0,Journal of Housing and Urban Finance,Yujeong Hwang; Y. Choi,"This study examines the correlation between multifamily attached housing prices and the significance of urban planning facilities. Using data on 12,228 actual transaction prices in Busan for 2022, provided by the Ministry of Land, Infrastructure, and Transport, we conducted an empirical analysis to identify factors affecting multifamily attached housing prices. To precisely analyze the regional characteristics at higher levels, we employed a Multilevel Model, which allows for hierarchical structuring and analysis. The level-2 regional factors in the model include area ratios of public, cultural, sports, spatial, transportation, distribution, supply, disaster prevention, health and hygiene, and environmental facilities. At the level-1 individual factors, all variables except for the floor area ratio significantly influenced multifamily attached housing prices. Positive effects were observed for all variables except for area and building age. At level-2, which considered the group characteristics of the lower-level data, spatial and transportation facilities were significant. Spatial facilities had a positive (+) effect, while transportation facilities unexpectedly showed a negative (−) relationship. This study highlights the need for policies aimed at increasing green spaces and parks, reducing traffic noise, and ensuring a balanced distribution of urban planning facilities.",1.0,http://www.ejhuf.org/download/download_pdf?pid=jhuf-9-1-31,True,JournalArticle,,2024-06-01,9ffc586f651b3035ba2f9b5866eb0451f65ecbd1,analysisoftheimpactofurbanplanningfacilitylocationsonmultifamilyattachedhousingprices,hwang,10.38100/jhuf.2024.9.1.31,analysisoftheimpactofurbanplanningfacilitylocationsonmultifamilyattachedhousingprices|2024,1,2896,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,92e7bed78640da66954d0cbce56d4e28bb729ba6,10.38100/jhuf.2026.11.2.61,,"Effects of industrial heritage adaptive reuse on apartment prices: Evidence from F1963 in Busan",2026.0,Journal of Housing and Urban Finance,J. Jang; Yeol Choi,"Industrial facilities created through industrialization often become idle as urban economies restructure, leaving underused industrial sites as persistent spatial challenges. In this context, industrial heritage has been increasingly reframed as a resource for urban regeneration. Here, a key strategy is adaptive reuse: reprogramming existing industrial buildings while retaining historical and physical traces. However, empirical evidence on whether and how such projects generate neighborhood-level externalities that are capitalized into housing prices remains limited. This study examines F1963, an adaptive reuse project completed in 2016 in Busan, South Korea, and estimates its effect on nearby apartment prices. We analyze apartment transaction records from 2008 – 2022 using a hedonic pricing framework combined with a quasi-experimental propensity score matching – difference-in-differences design. We find a statistically significant post-reuse price premium within the treatment area, with an estimated magnitude of approximately 1.5% per square meter. Thus, the adaptive reuse of industrial heritage can generate measurable neighborhood benefits that are capitalized into housing values. Furthermore, they support incorporating market-based indicators alongside internal operational metrics when evaluating the broader impacts of industrial heritage-led regeneration.",0.0,http://www.ejhuf.org/download/download_pdf?pid=jhuf-11-2-61,True,JournalArticle,,2026-06-01,92e7bed78640da66954d0cbce56d4e28bb729ba6,effectsofindustrialheritageadaptivereuseonapartmentpricesevidencefromf1963inbusan,jang,10.38100/jhuf.2026.11.2.61,effectsofindustrialheritageadaptivereuseonapartmentpricesevidencefromf1963inbusan|2026,1,2881,2,apartment prices hedonic || hedonic housing prices,2,3,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),9c795a54b59b8ceda6abf71343c4ee3b27414df9,10.23977/etss.2018.12529,,Measuring Green Value in Housing Market: Evidence from Hangzhou,2018.0,,Hai-Zhen Wen; Yuqing Han; Zaiyuan Gui,"With the rapid development of economy, homebuyers prefer to buy the residential complexes with green characteristics, such as living comfort and energy efficiency. As a new trend of housing development model, green housing which is consistent with the concept of sustainability has been promoted positively. More and more real estate developers have used marketing strategy and green words to advertise their residential products. Based on the Hangzhou newly-built housing transaction data in 2016, this study constructs the hedonic price model to quantitatively analyze whether such green marketing have an effect on housing prices. The empirical results show that green marketing really yields a price premium and allows developers to charge such premium that homebuyers have willingness to pay. This study can provide a theoretical basis for government to make relevant policies, as well as real estate developer to determine development decisions.",0.0,http://www.clausiuspress.com/conferences/AETP/ETSS 2018/A12529.pdf,True,,Business,2018-12-01,9c795a54b59b8ceda6abf71343c4ee3b27414df9,measuringgreenvalueinhousingmarketevidencefromhangzhou,wen,10.23977/etss.2018.12529,measuringgreenvalueinhousingmarketevidencefromhangzhou|2018,1,2451,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,1,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic property valuation,f0cc9b2effae1ea7c95822379df21e9f9ef26336,10.20448/journal.501.2020.72.115.125,,Effects of High-Voltage Overhead Power Lines (HVOPLs) on Residential Property Prices,2020.0,Asian Journal of Economics and Empirical Research,W. M. Jayantha; L. Abeydeera,"Research findings on the effects of high-voltage overhead power lines on values of neighborhood properties remain inconclusive and inadequately explored. This study aims to investigate the effects of HVOPLs on market values and sale-prices of residential properties in the vicinity. The empirical results based on a hedonic price model show that HVOTLs impart a significant negative effect on nearby residential properties. Results show that affected properties are selling at a discount price compared to properties without the influence of HVOPLs. The market value of a residential unit located within physical distance of 300-meters of power-lines, and pylon, are on average, 34.2% and 18% lower than comparable properties, respectively. Furthermore, results suggest that it is the physical distance to HVOPL structures that accounts more for the value diminution compared to the visual impact of the structure. The results imply that the market value of properties adjacent to HVOPL corridors is reduced significantly. This discourages potential property buyers. The study contributes to the knowledge in two-ways: (i) it shows the greater impact of HVOPLs on property prices with a larger reduction in valuation than previous studies; and (ii) effects of the different visual impact of pylons compared with that of cables are clearly distinguished.",1.0,http://www.asianonlinejournals.com/index.php/AJEER/article/download/1633/1504,True,,Computer Science,,f0cc9b2effae1ea7c95822379df21e9f9ef26336,effectsofhighvoltageoverheadpowerlineshvoplsonresidentialpropertyprices,jayantha,10.20448/journal.501.2020.72.115.125,effectsofhighvoltageoverheadpowerlineshvoplsonresidentialpropertyprices|2020,1,2853,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic property valuation",2,2,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c968aee072b3778a6e8832f9bbd33398e466d511,10.59139/ws.2013.05.3,,Zastosowanie modeli zmiennych jakościowych do badania ceny rynkowej mieszkań,2013.0,Wiadomości Statystyczne. The Polish Statistician,B. Batóg; I. Foryś,"The article presents the results of the housing market survey in Warsaw in 2009 and 2010. Offers were described by a set of several quantitative and qualitative variables, including the transaction price. In the studied years operated the ”Rodzina na swoim” (”Family on its own”) program, in which the housing price limits were introduced on the opportunity to apply for funding. However, the limits of budget expenditure resulted in the introduction of savings and announcement of the program realization in a modified formula. In this context, conducted by the authors the secondary housing market research shows the characteristics of a dwelling eligible for purchase by the state fund. The co-financing threshold in Warsaw was treated as a dichotomous variable (a feature which is included or not in the range of co-financing). Binomial logit models were used for so defined dependent variable. As independent variables were used both quantitative and qualitative housing characteristics. The use of this type of models allows to assess which variables have the greatest impact on the price of meeting the criteria of the State housing assistance program.",6.0,http://ws.stat.gov.pl/WS/2013/5/WS_05_2013__3_Barbara BATÓG Iwona FORYŚ — Zastosowanie modeli zmiennych jakościowych do badania ceny ry.pdf,True,JournalArticle;Review,Economics,2013-05-28,c968aee072b3778a6e8832f9bbd33398e466d511,zastosowaniemodelizmiennychjakosciowychdobadaniacenyrynkowejmieszkan,batóg,10.59139/ws.2013.05.3,zastosowaniemodelizmiennychjakosciowychdobadaniacenyrynkowejmieszkan|2013,1,2659,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,48903214e1c41c55e19b4dfc97ebba18e2a41fbe,10.1108/pm-07-2021-0052,,"Constructing segmented rental housing indices: evidence from Beijing, China",2022.0,Property Management,Zisheng Song; Mats Wilhelmsson; Zan Yang,"PurposeThis paper aims to construct rental housing indices and identify market segmentation for more effective property-management strategies.Design/methodology/approachThe hedonic model was employed to construct the rental indices. Using the k-means++ and REDCAP (Regionalisation with Dynamically Constrained Agglomerative Clustering and Partitioning) approaches, the authors conducted clustering analysis and identified different market segmentation. The empirical study relied on the database of 80,212 actual rental transactions in Beijing, China, spanning 2016–2018.FindingsRental housing market segmentation may distribute across administrative boundaries. Properly segmented indices could provide a better account for the heterogeneity and spatial continuity of rental housing and as well be crucial for effective property management.Research limitations/implicationsResidential rent might not only vary over space but also interplays with housing price. It would be worth studying how the rental market functions together with the owner-occupied sector in the future.Practical implicationsResidential rental indices are of great importance for policymakers to be able to evaluate housing policies and for property managers to implement competitive strategies in the rental market. Their constructions largely depend on the analysis of market segmentation, a trade-off between housing spatial heterogeneity and continuity.Originality/valueThis paper fills the gap in knowledge concerning segmented rental indices construction, particularly in China. The spatial constrained clustering approach (REDCAP) was also initially introduced to identify regionalised market segmentation due to its superior performance.",6.0,http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-312420,True,,,2022-03-08,48903214e1c41c55e19b4dfc97ebba18e2a41fbe,constructingsegmentedrentalhousingindicesevidencefrombeijingchina,song,10.1108/pm-07-2021-0052,constructingsegmentedrentalhousingindicesevidencefrombeijingchina|2022,1,3217,2,hedonic housing prices || spatial hedonic model housing,2,2,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cf16265d76cb2a334fa54d33f5e8cb817e67c45b,10.1108/ijhma-04-2017-0035,,"Park proximity, crime and apartment prices",2018.0,,Asifa Iqbal; Mats Wilhelmsson,,18.0,http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217512,True,,Geography,2018-05-25,cf16265d76cb2a334fa54d33f5e8cb817e67c45b,parkproximitycrimeandapartmentprices,iqbal,10.1108/ijhma-04-2017-0035,parkproximitycrimeandapartmentprices|2018,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,1cc9771f869ec8b0800957a96eb1404558abe9ef,10.1080/14043858.2015.1009674,,Does crime in parks affect apartment prices?,2015.0,,Asifa Iqbal; V. Ceccato,"A park is a desirable feature when people are purchasing a property. Buyers are ready to pay more for properties surrounded by natural amenities, such as a pleasant park. However, if a park is targeted by crime and disorder, then it may have a negative effect on people's appraisal. The aim of this study is to reach a better understanding of how parks and crime rates affect housing prices taking into account residential properties and parks type. Stockholm, the capital of Sweden, is used as the case study. The methodology has two stages. An extensive field work was performed in a selected number of parks to help characterize the parks in Stockholm. This information was gathered with secondary data about parks using Geographic Information System. This pre-assessment was later followed by the analysis of 2008s apartment sales using hedonic modelling to assess the effect of parks alone and in combination with crime rates. Results suggest that the effect of parks on prices vary by park type (increased with, e.g. features of cultural and national interest and decreased with ball games or boating facilities) while an aggregated measure of parks lowers overall apartment prices. More interestingly, findings show that the price of apartments tends to be discounted in areas where parks have relatively high rates of violence and vandalism.",26.0,http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167789,True,,Business,2015-01-02,1cc9771f869ec8b0800957a96eb1404558abe9ef,doescrimeinparksaffectapartmentprices,iqbal,10.1080/14043858.2015.1009674,doescrimeinparksaffectapartmentprices|2015,1,2849,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic housing prices || hedonic price model amenities residential",2,2,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",50ac40f65d7a95a32e6f4b3e077ff1ef1c26d015,10.14569/ijacsa.2017.081042,,"Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization Case Study : Malang, East Java, Indonesia",2017.0,,Adyan Nur Alfiyatin; Ruth Ema Febrita; Hilman Taufiq; W. Mahmudy,"House prices increase every year, so there is a need for a system to predict house prices in the future. House price prediction can help the developer determine the selling price of a house and can help the customer to arrange the right time to purchase a house. There are three factors that influence the price of a house which include physical conditions, concept and location. This research aims to predict house prices based on NJOP houses in Malang city with regression analysis and particle swarm optimization (PSO). PSO is used for selection of affect variables and regression analysis is used to determine the optimal coefficient in prediction. The result from this research proved combination regression and PSO is suitable and get the minimum prediction error obtained which is IDR 14.186.",74.0,http://thesai.org/Downloads/Volume8No10/Paper_42-Modeling_House_Price_Prediction_using_Linear_Regression.pdf,True,,Computer Science,,50ac40f65d7a95a32e6f4b3e077ff1ef1c26d015,modelinghousepricepredictionusingregressionanalysisandparticleswarmoptimizationcasestudymalangeastjavaindonesia,alfiyatin,10.14569/ijacsa.2017.081042,modelinghousepricepredictionusingregressionanalysisandparticleswarmoptimizationcasestudymalangeastjavaindonesia|2017,1,2299,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8361fd463be68fd23f0094abf8ee388289f61f44,10.30541/v62i4493-520,,Revisiting Urban Immovable Property Valuation: An Appraisal of Spatial Heterogeneities in Punjab Using Big Data,2023.0,Pakistan Development Review,Shoaib Khalid; Fariha Zameer,"This study employed big data and spatial analysis to assess property values in two cities, Lahore and Faisalabad. Traditional housing price models overlook spatial nuances, focusing solely on structural attributes. To address this, we constructed valuation models using ordinary least square regression and Fast Geographic Weighted Regression (FastGWR), implemented through Python and MPI, based on spatial variables. The models explained up to 75 percent of variance in Faisalabad and around 85 percent in Lahore. Factors like floor area, proximity of health facilities, recreational sites, and marketplaces add a premium to prices, while the nearness of educational institutions, worship places, and solid waste transfer stations or dumping sites lessen the property values in both cities. However, the proximity of industrial units and graveyards affects property values negatively in Lahore but positively in Faisalabad. This study highlights the critical significance of spatial factors in urban immovable property appraisal. As a result, it is recommended to integrate these factors into the process of policy formulation and urban planning. 1. INTRODUCTION Pakistan’s real estate market emerges as a potential economic powerhouse, with a substantial portion of the nation’s wealth concentrated within its real estate assets, estimated at 60-70 percent. While the sector contributes around 2 percent to the GDP, the combined impact of housing and construction reaches nearly 9 percent. The value of Pakistan’s real estate sector, evaluated at approximately $700 billion by the Federal Board of Revenue, signifies its economic significance. Impressively, returns on investment can soar beyond 100 percent (Ouattara et al., 2018). However, this promising market is juxtaposed against a backdrop of challenges and disparities. Pakistan’s population, surpassing 225 million, expands annually at a 2.4 percent rate, characterised by an average household size of 6.5 (Pakistan Bureau of Statistics, 2019). With a yearly housing requirement of 700,000 units, merely half of this demand is met, leading to an alarming gap of roughly 10 million units (Rizvi, 2018). This housing shortage necessitates innovative strategies, particularly in the realm of low-cost housing schemes. The intricacies of property valuation further complicate the real estate landscape. Government land acquisitions relying on DC valuation tables often incite public protests due to perceived undervaluation (Sabir et al., 2017). The importance of precise valuation for equitable compensation is underscored by research (Malaitham et al., 2020). Notably, property valuation is not just pivotal for buyers and sellers; it resonates with stakeholders such as investors, banks, agents, and insurers. The geographical location holds substantial influence over a property’s price, further emphasising the necessity for accurate valuation (Mankad, 2021). In dynamically growing cities, the accurate prediction of urban land use evolution plays a pivotal role in fostering sustainable urban planning (Liang et al., 2018) such as Lahore and Faisalabad in Pakistan. Notably, vast untapped potential resides within public properties, including Government Officers Residences (GORs) and railway lands, representing latent avenues for wealth creation. Leveraging these assets effectively can substantially contribute to economic prosperity. The implications of this study extend to policymakers, offering insights to navigate the intricate domains of housing and urban development. A robust housing market stands as a linchpin of a resilient economy; however, Pakistan’s housing sector faces an intricate array of challenges. Urbanisation and migration galvanise demand within urban centers, an issue compounded by insufficient supply catalysed by diverse factors. Shortcomings in land usage, planning, and property rights impede progress, while inadequate revenue collection from property taxes curtails infrastructure financing. The labyrinthine regulations further stall land development, exacerbating housing availability discrepancies, particularly pronounced in megacities like Karachi and Lahore. Notably, housing construction trails behind the meteoric pace of population expansion. Skyrocketing market conditions render housing unattainable for many, channeling them towards informal settlements (Dowall & Ellis, 2009; Haque, 2015; Wani et al., 2020; Yuen & Choi, 2012). The challenges are particularly pronounced within Punjab’s housing markets, accentuated in cities like Lahore and Faisalabad, grappling with deficits in affordable housing (Malik et al., 2020; Wajahat, 2012). A glaring obstacle lies in the hands of speculative investors who control 75 percent of residential plots, perpetuating this complex issue (Zaman & Baloch, 2011). This practice thrives on secure real estate investments and tax loopholes, compounding the predicament. Although plot prices surge significantly (Gul et al., 2018), official valuations lag behind, generating volatility in Pakistan’s property prices. The repercussions of such fluctuations extend to the public, shouldering the burden of investor gains. Despite intermittent housing policies, the issue remains inadequately addressed, emphasising the urgency of public sector interventions to stabilise spiraling prices (Ahmed et al., 2021). ",2.0,http://thepdr.pk/index.php/pdr/article/download/3243/3812,True,JournalArticle,,2023-11-29,8361fd463be68fd23f0094abf8ee388289f61f44,revisitingurbanimmovablepropertyvaluationanappraisalofspatialheterogeneitiesinpunjabusingbigdata,khalid,10.30541/v62i4493-520,revisitingurbanimmovablepropertyvaluationanappraisalofspatialheterogeneitiesinpunjabusingbigdata|2023,1,6857,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,3,3,6,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,d555b7523f1b2210b768288f3d94d621ed191fb4,10.34096/rtt.i20.6394,,Inversión pública y plusvalías urbanas. Análisis espacial y efectos no lineales de la proximidad de la red de subterráneos sobre los precios de la vivienda en Buenos Aires,2019.0,Revista transporte y territorio,S. Rosanovich; Ana Paula Di Giovambattista,"As regards the capture and recovery of urban capital gains, one of the requirements for the design of public policy is the identification not only of the measure of the effect but also of the universe of affected properties as a result of urban development. In this article, the selling offers of apartments in Buenos Aires are analyzed in relation to its localization and distance to the subway network. Furthermore, for the first time a non-linear hedonic price model with spatial dependence is implemented in order to show the impact over real-estate market using a new dataset of selling prices. The presence of a non-linear effect of the price per square meter based on the distance to the subway stations is verified, negative for the range between 0 and 200 meters (-5%) and positive for the range of 300 to 600 meters (+3,6%). In addition, a detailed description of the current heterogeneities between subway lines is included, stressing the differences in the spatial distribution of house prices, partially determined by the attributes (v.g. vigilance, swimming pool, gym) of the buildings in the different neighborhoods of the city.",2.0,http://revistascientificas.filo.uba.ar/index.php/rtt/article/download/6394/5646,True,,Geography,2019-04-01,d555b7523f1b2210b768288f3d94d621ed191fb4,inversionpublicayplusvaliasurbanasanalisisespacialyefectosnolinealesdelaproximidaddelareddesubterraneossobrelospreciosdelaviviendaenbuenosaires,rosanovich,10.34096/rtt.i20.6394,inversionpublicayplusvaliasurbanasanalisisespacialyefectosnolinealesdelaproximidaddelareddesubterraneossobrelospreciosdelaviviendaenbuenosaires|2019,1,2643,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || precios hedónicos vivienda",2,3,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",c27fb428313aa49bc8927386e4652dc31f3abb51,10.32468/be.508,,The cost of avoiding crime: the case of Bogota,2008.0,,Alejandro Gaviria; C. Medina; L. Morales; J. Nunez,"We use hedonic price models to estimate the value households are willing to pay to avoid violent crime in the city of Bogota. We find that households living in the highest socioeconomic level (stratum 6) pay up to 7.2% of their house values in order to prevent average homicide rates from increasing in one standard deviation. Households in stratum 5 pay up to 2.4% of their house values to prevent homicide rates from increasing. The results indicate the willingness to pay for security by households in Bogota, and additionally, reveal that a pure public good like security, ends up creating urban private markets that auction security. These markets imply different levels of access to public goods among the population, and actually, the exclusion of the poorest. We find as well evidence of negative capitalization of the rate of attacks against life, and positive capitalization of the presence of police authority.",29.0,http://repositorio.banrep.gov.co/bitstream/20.500.12134/5525/1/be_508.pdf,True,,Economics,2008-04-15,c27fb428313aa49bc8927386e4652dc31f3abb51,thecostofavoidingcrimethecaseofbogota,gaviria,10.32468/be.508,thecostofavoidingcrimethecaseofbogota|2008,1,2421,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price",2,2,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2344ebd24eeebb31c33011f3e72a12383c3c1e88,10.11114/ijsss.v5i9.2510,,"Hedonic Price Modeling of New Residential Property Values in Xi’an City, China",2015.0,,Kai Liu; T. Ichinose,"This study analyzed new residential property values of Xi’an City in March, 2014. Results show that accessibility indices, such as distance to CBD, have been well capitalized into the residential property values. Particularly, a within-zone housing unit is sold 9.4% more than if it was outside the attendance zone of a Key Primary School, i.e. home buyers have how much willingness-to-pay for the high-quality educational resource. Another corollary is got that the accessibility to subway stations has not significantly been capitalized, only with a low premium reflecting in the real estate market of Xi’an City. Considered that spatial local singularities caused by unobserved variables or estimation bias can be associated with multi-regression errors, this study herein has explored an unconventional viewpoint to residual problem, which combines the regional differences (coming from real world) and the spatial distributions of singularities (feedback from data). Furthermore, whole samples are classified into 5 agglomerations for revealing the underlying reasons about the future trend and variation of real estate market within each region. It is effective to provide scientific basis of decision making for the real estate investors and planners.",4.0,http://redfame.com/journal/index.php/ijsss/article/download/2510/2781,True,,Economics,2015-05-01,2344ebd24eeebb31c33011f3e72a12383c3c1e88,hedonicpricemodelingofnewresidentialpropertyvaluesinxiancitychina,liu,10.11114/ijsss.v5i9.2510,hedonicpricemodelingofnewresidentialpropertyvaluesinxiancitychina|2015,1,2758,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression || spatial hedonic model housing",2,1,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,cb1bc7e2c84417458119d53a40d6ec9c84167bc6,10.30585/icabml-cp.v2i1.209,,The House Price Dynamics and the Macro-Economy: an Empirical Perspective of UAE,2019.0,"International Conference on Advances in Business, Management and Law (ICABML)",Yahya Hamad Al Zaabi; Genanew Bekele,"Objective: The paper aims to examine house price drivers in Dubai, addressing the effect of internal and external factors afecting house prices Design/methedology/approach: Using the Hedonic price model, the study examined the implications of house size (space), the availability of bathrooms, bedrooms, waterfronts, and pool and cell phone towers within residential area as auxiliary determinant factors to housing price within developed cities by using the Hedonic Modelling. Also, study highlight the effect of the green strategies that been followed by developer on the housing prices. Findings: The study is expected to reveal results with significant ramifications for researchers, practitioners and policy makers. From a policy perspective, there is an obvious interest in understanding whether the price of housing is affected by different attributes differently along its distribution. Research limitations/implications: The data used in this study could be limited, and depends on information to be provided by the Dubai Land Department. There is a room for future research to include more data (such as on other house attributes such as house condition, plot numbers and configuration).",0.0,http://publications.ud.ac.ae/index.php/ICABML-CP/article/download/209/73,True,Conference,Economics,2019-02-03,cb1bc7e2c84417458119d53a40d6ec9c84167bc6,thehousepricedynamicsandthemacroeconomyanempiricalperspectiveofuae,zaabi,10.30585/icabml-cp.v2i1.209,thehousepricedynamicsandthemacroeconomyanempiricalperspectiveofuae|2019,1,2709,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices",2,3,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2ba20a88e43703e3c456f3dc2c6891b7c5ac372b,10.18267/j.pep.823,,Are the Effects of Opening New Mass Rapid Transit Segments in Taiwan on Nearby Housing Prices Positive?,2023.0,Prague Economic Papers,Ming-Te Lee; Ming-Long Lee; Yahang Cheng,"This study evaluates the effects of opening new segments of mass rapid transit (MRT) lines on housing prices near the MRT stations in Tucheng District and Xinzhuang District, New Taipei City, Taiwan. The effect of proximity to each MRT station is estimated separately with difference-in-differences regressions integrated with spatial econometrics with heteroscedasticity-robust standard errors. The opening of the new segment of the Blue Line, also known as Bannan Line of the Taipei Metro, does not significantly influence housing prices within 600-metre road network distance of the MRT stations, compared to prices outside the distance range. In contrast, and also unlike the findings of prior studies, although the segment and the stations are underground structures, the opening of the new segment of the Orange Line, also known as the Zhonghe-Xinlu Line of the Taipei Metro, significantly decreases housing prices within 600-metre road network distance of the MRT stations, compared to prices outside the distance range, perhaps because the opening of the stations is delayed about one and a half years to be used as a temporary storage area for MRT trains. The findings have implications for homebuyers, investors, mortgage lending institutions and tax assessment authorities.",0.0,http://pep.vse.cz/doi/10.18267/j.pep.823.pdf,True,,,2023-03-09,2ba20a88e43703e3c456f3dc2c6891b7c5ac372b,aretheeffectsofopeningnewmassrapidtransitsegmentsintaiwanonnearbyhousingpricespositive,lee,10.18267/j.pep.823,aretheeffectsofopeningnewmassrapidtransitsegmentsintaiwanonnearbyhousingpricespositive|2023,1,2784,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,10.1177/0042098013492234,,Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria,2014.0,,M. Helbich; Wolfgang A. Brunauer; E. Vaz; P. Nijkamp,,202.0,http://papers.tinbergen.nl/13171.pdf,True,JournalArticle,Economics,2014-02-01,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,spatialheterogeneityinhedonichousepricemodelsthecaseofaustria,helbich,10.1177/0042098013492234,spatialheterogeneityinhedonichousepricemodelsthecaseofaustria|2014,1,1500,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || real estate hedonic regression || spatial hedonic model housing",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",ed1a3097456f6aef513c029e3fd332c82d1401e8,10.4995/carma2020.2020.11410,,Investigating the impacts of street environment on pre-owned housing price in Shanghai using street-level images,2020.0,CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics,Waishan Qiu; Xiao-Kai Huang; Xiao-Jiang Li; Wen-Jing Li; Zi-Ye Zhang,"Studies considering street environment quality’s impact on housing value were limited to top-down variables such as the green ratio measured from satellite maps. In contrast, this study quantified street views’ impacts on the value of second-hand commodity residential properties in Shanghai based on analysis of street view imagery. (1) It applied computer vision to objectively measure street features from largely accessible street view imagery. (2) Based on the classical urban design measures frameworks, it applied machine learning to evaluate human perceived street quality as street scores systematically, in contrast to the common practice of doing so in a more intuition-based fashion. (3) It further identified important indicators from both human-centered street scores as well as the more objective street feature measures with positive or adverse effects on property values based on a hedonic modeling method. The estimation suggested both street scores and features are significant and non-negligible. For the perceived street scores (from 0-10 scale), neighborhoods with a unit increase in their “enclosure” or “safety” score enjoy price premium of 0.3% to 0.6%. Meanwhile, streets with 10% greater tree canopy exposure are attributable to a 0.2% increase in the property value. This study enriched our current understanding at a micro level of the factors that impact property values from the perspective of the built environment. It introduced human-centered perception of street scores and objective measures of street features as spatial variables into the analysis of neighborhood attribute vectors.",1.0,http://ocs.editorial.upv.es/index.php/CARMA/CARMA2020/paper/download/11410/5731,True,Conference,Business,2020-07-02,ed1a3097456f6aef513c029e3fd332c82d1401e8,investigatingtheimpactsofstreetenvironmentonpreownedhousingpriceinshanghaiusingstreetlevelimages,qiu,10.4995/carma2020.2020.11410,investigatingtheimpactsofstreetenvironmentonpreownedhousingpriceinshanghaiusingstreetlevelimages|2020,1,3120,6,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices || machine learning hedonic pricing housing || spatial hedonic model housing",2,4,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic price model amenities residential,b28e4c322c0b376810e95bff886bda3dec8a683e,10.1016/j.compenvurbsys.2009.10.007,,"Integrating lidar, GIS and hedonic price modeling to measure amenity values in urban beach residential property markets",2010.0,"Computers, Environment and Urban Systems",S. Hamilton; Ash Morgan,,104.0,http://libres.uncg.edu/ir/asu/f/Morgan_Ashton_2010_Integrating_Lidar_GIS_Hedonic.pdf,True,JournalArticle,Geography;Computer Science,2010-03-01,b28e4c322c0b376810e95bff886bda3dec8a683e,integratinglidargisandhedonicpricemodelingtomeasureamenityvaluesinurbanbeachresidentialpropertymarkets,hamilton,10.1016/j.compenvurbsys.2009.10.007,integratinglidargisandhedonicpricemodelingtomeasureamenityvaluesinurbanbeachresidentialpropertymarkets|2010,1,1500,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic price model amenities residential",2,0,1,1,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",d07278917cd10d3c330180bd32143c245b4f5639,10.1080/08920750903398287,,Estimating a Payment Vehicle for Financing Nourishment of Residential Beaches using a Spatial-Lag Hedonic Property Price Model,2010.0,,O. A. Morgan; S. Hamilton,,20.0,http://libres.uncg.edu/ir/asu/f/Morgan_Ashton_2010_Estimating_Payment_vehicle_financing.nourishment.pdf,True,,Economics,2010-01-22,d07278917cd10d3c330180bd32143c245b4f5639,estimatingapaymentvehicleforfinancingnourishmentofresidentialbeachesusingaspatiallaghedonicpropertypricemodel,morgan,10.1080/08920750903398287,estimatingapaymentvehicleforfinancingnourishmentofresidentialbeachesusingaspatiallaghedonicpropertypricemodel|2010,1,1500,2,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment rent hedonic",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,80958fd2638f21cfbb1a72def7e62ae46db5ef0f,10.3368/le.96.3.366,,Flood Risk Perception in the Housing Market and the Impact of a Major Flood Event,2020.0,Land Economics,Hannah Hennighausen; Jordan F. Suter,"The impact of flood events on flood risk perception has important implications for policy. Applying a novel dataset featuring the flooding extents from a severe event in Colorado, we disentangle inundated properties from “near misses,” defined as structures not directly flooded but located inside the 100-year floodplain. Using a triple-difference hedonic framework, we show that inundated properties inside the floodplain underwent a decrease in price after the flood, while near misses saw a relative price increase. We speculate that inundated properties are perceived as being riskier and near misses relatively less risky, suggesting the possible influence of the availability heuristic or Bayesian learning. (JEL Q54, Q58)",37.0,http://le.uwpress.org/content/96/3/366.full.pdf,True,,Environmental Science,2020-06-24,80958fd2638f21cfbb1a72def7e62ae46db5ef0f,floodriskperceptioninthehousingmarketandtheimpactofamajorfloodevent,hennighausen,10.3368/le.96.3.366,floodriskperceptioninthehousingmarketandtheimpactofamajorfloodevent|2020,1,2229,1,hedonic housing prices,2,0,1,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,bd5490155bc4cbf59ec2743cb41b1f32bc28ae48,10.13106/jafeb.2020.vol7.no8.033,,An Empirical Testing of a House Pricing Model in the Indian Market,2020.0,"Journal of Asian Finance, Economics and Business",Najmul Hoda; Syed Ashraf Jafri; Naim Ahmad; S. M. Hussain,"The main aim of the study is to test a house pricing model by combining hedonic and asset-based pricing models. An understanding of the relationship between house pricing and its return (the rental income) helps to establish houses as a significant asset class. The model tested the relationship between house pricing (dependent variable) and the house attributes (independent variables) derived from Freeman’s framework of housing attributes. This study uses a large data-set of 1,899 sample of new, high-end houses purchased between 2016 and 2019 collected from the national capital region of India (Delhi-NCR). The algorithm was built in R-Script, and stepwise multiple linear regression was used to analyze the model. The analysis of the model proves that the three significant variables, namely, carpet area, pay-off, and annual maintenance charges explain the price function. Further, the model is statistically fit. The major contribution of the study is to understand the key factors and their influence on the house pricing. The model will be helpful in risk assessment in the housing investment and enhance the chances of investment. Policy-makers can use information about the underlying valuation drivers of the house prices to stabilize the market and also in framing the tax policies.",6.0,http://koreascience.or.kr:80/article/JAKO202026061031534.pdf,True,JournalArticle,Economics,2020-08-31,bd5490155bc4cbf59ec2743cb41b1f32bc28ae48,anempiricaltestingofahousepricingmodelintheindianmarket,hoda,10.13106/jafeb.2020.vol7.no8.033,anempiricaltestingofahousepricingmodelintheindianmarket|2020,1,2798,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices",2,2,4,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",74d547d03ce09414cd47d49258d08d24dec223de,10.7465/jkdi.2017.28.1.173,,Application of geographical and temporal weighted regression model to the determination of house price,2017.0,,Saehee Park; Min-soo Kim; J. Baek,,1.0,http://koreascience.or.kr:80/article/JAKO201711656576813.pdf,True,,Economics,2017-01-31,74d547d03ce09414cd47d49258d08d24dec223de,applicationofgeographicalandtemporalweightedregressionmodeltothedeterminationofhouseprice,park,10.7465/jkdi.2017.28.1.173,applicationofgeographicalandtemporalweightedregressionmodeltothedeterminationofhouseprice|2017,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,4c7179dd84112b4960f502fd2402c7981bbf9d13,10.14249/eia.2016.25.6.457,,The Effect of an Urban Park View on the Price of Apartment - A Case of Songdo Central Park -,2016.0,,T. Jung; Y. Baek; Jihyun Sohn; Sunbin Yoo,": Around the world, a lot of people are migrating to the urban areas, and new cities are continuously being constructed. Currently about 54 percent of the world’s population live in the urban areas, and by 2050, it is expected to increase to 66 percent; thus, managing the urban areas is one of the most important challenges of sustainable development in the 21st century. The key to successful urban management is to preserve the urban green spaces, which provide aesthetic, psychological and health benefits to the urban citizens. However, the benefits of the urban green spaces are not fully appreciated within the societies due to the difficulty of economic valuation of the urban green spaces. This study examined whether the view of the Songdo Central Park has a positive influence on the prices of the surrounding apartments, using the hedonic pricing method. The results showed that a positive relation exists between the view of the Songdo Central Park and the price of apartment. The price of an apartment with the view of the Songdo Central Park was 5 percent higher than that of an apartment without the view. In addition, it was estimated that the proximity to the Songdo Central Park has an influence on the housing price as well.",2.0,http://koreascience.or.kr:80/article/JAKO201610235351775.pdf,True,,Geography,2016-12-31,4c7179dd84112b4960f502fd2402c7981bbf9d13,theeffectofanurbanparkviewonthepriceofapartmentacaseofsongdocentralpark,jung,10.14249/eia.2016.25.6.457,theeffectofanurbanparkviewonthepriceofapartmentacaseofsongdocentralpark|2016,1,2744,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic housing prices",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,real estate hedonic regression,c360b31fe2ed879c6a79bc1b35ce53f12d3212c6,10.6106/kjcem.2015.16.5.077,,"Using Ridge Regression to Improve the Accuracy and Interpretation of the Hedonic Pricing Model : Focusing on apartments in Guro-gu, Seoul",2015.0,,Bonsang Koo; Byungjin Shin,,3.0,http://koreascience.or.kr:80/article/JAKO201530261999206.pdf,True,,Economics,2015-09-30,c360b31fe2ed879c6a79bc1b35ce53f12d3212c6,usingridgeregressiontoimprovetheaccuracyandinterpretationofthehedonicpricingmodelfocusingonapartmentsinguroguseoul,koo,10.6106/kjcem.2015.16.5.077,usingridgeregressiontoimprovetheaccuracyandinterpretationofthehedonicpricingmodelfocusingonapartmentsinguroguseoul|2015,1,1500,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression",2,0,1,1,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5b1474c696f92c70b9414b7debe7fbb6f89215b2,10.31357/icremv.v5.5638,,DOES PROXIMITY TO WATER BODIES IMPACTS MARKET VALUES OF RESIDENTIAL PROPERTIES? CASE STUDY OF DIYAWANNAWA LAKE AREA,2022.0,Proceedings of International Conference on Real Estate Management and Valuation,J. G. L. D. Pathmasiri; T. Perera,"This study examined the impact of water bodies with a scenic view on residential property values around Diyawannawa lake located within Sri Jayewardenepura Kotte Municipal council (a suburban area of Colombo district). The study employed both primary (structural and environmental data related to properties, different types of benefits generated from waterbodies) and secondary data (market evidence) whilst the data analysis included content analysis and a regression analysis (hedonic pricing model). Water bodies with scenic views generate multiple utilities for residents such as observing nature, relaxation, and playing, pleasant views, appreciation of colour and sounds, relationships with family members and neighbors, a place to exercise and cycling, stress relief likewise. According to the study, between 2019-2020, a residential property with a scenic view of 600 or more to Diyawannawa Lake had a premium market value of Rs. 803,433.05 compared to other properties. Furthermore, the residential properties one meter away from such water body showed a decrease in market value of Rs. 23049.65. These findings will raise awareness on the benefits of water bodies with scenic views and it will lead to greater acceptance by residents, developers, local authorities to invest and protect them, which could contribute to overcoming one of the barriers to maintaining such water bodies in good condition. Keywords: water bodies; residential property; market value",1.0,http://journals.sjp.ac.lk/index.php/icremv/article/download/5638/4191,True,Conference,,2022-05-18,5b1474c696f92c70b9414b7debe7fbb6f89215b2,doesproximitytowaterbodiesimpactsmarketvaluesofresidentialpropertiescasestudyofdiyawannawalakearea,pathmasiri,10.31357/icremv.v5.5638,doesproximitytowaterbodiesimpactsmarketvaluesofresidentialpropertiescasestudyofdiyawannawalakearea|2022,1,2972,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",3f762774f55db72a1f5a56ea3393b24b78d3352e,10.5614/jts.2011.18.3.7,,The Influence of Railway Station on Residential Property Values-Spatial Hedonic Approach The Case of Serpong’s Railway Station,2011.0,,I. Syabri,"Abstract . Urban area management in developed countries such as the United States and Japan which have implemented “Transit Oriented Development” (TOD), shows that TOD seems capable of increasing the property values and and reducing household expenditure for transportation costs. However, applying the TOD concept in Indonesian cities such as Jakarta, Surabaya, and Medan, is a unique and considerable challenge, considering urban transportation condition in Indonesia that is far different from the conditions in urban areas in developed countries. One of important infrastructure policy issues for Jakarta and other rapidly growing cities in Indonesia is how to raise fiscal revenues through “value capture”, in which the increase the property values is due to better access and services from the existances of the railway stations. The research is based on a case study in Serpong, a neighborhood in Jakarta Metropolitan Area (JMA), Indonesia. The focus of this study is to test the basic viewpoint of TOD strategy which is how transport infrastructure such as railway stations influence the property values of a neighborhood. Following Anselin and Lozano-Gracia’s (2009) framework, this paper uses a spatial hedonic pricing model that allows to measure both “direct and in-direct effects” or “externality spillovers” from the existence of the railway stations. Abstrak . Pengelolaan kawasan perkotaan di negara-negara maju seperti Amerika Serikat, Jepang, dan lain-lain yang telah menerapkan konsep ""Pembangunan berorientasi Transit"" atau lazim disebut dengan istilah “transit oriented development” (TOD), menunjukkan bahwa TOD tampaknya mampu meningkatkan nilai properti dan dan mampu mengurangi pengeluaran rumah tangga untuk biaya transportasi. Namun demikian, menerapkan konsep TOD di kota-kota Indonesia seperti Jakarta, Surabaya, dan Medan, merupakan tantangan yang unik dimana kondisi tataruang kota dan transportasi perkotaan di Indonesia yang jauh berbeda dari kondisi di daerah perkotaan di negara-negara maju. Salah satu isu kebijakan infrastruktur penting untuk Jakarta dan kota-kota berkembang pesat di Indonesia adalah bagaimana memberikan jastifikasi meningkatkan pendapatan fiskal melalui konsep ""value capture"", di mana nilai properti meningkat karena akses dan layanan yang baik dari keberadaan stasiun kereta api. Penelitian ini didasarkan pada studi kasus di Serpong, sebuah lingkungan di Jakarta Metropolitan Area (JMA), Indonesia. Fokus dari penelitian ini adalah untuk menguji sudut pandang dasar strategi TOD yaitu bagaimana infrastruktur transportasi, seperti stasiun kereta api, mempengaruhi nilai properti yang ada disekitarnya. Dengan menggunakan kerangka metodoologi yang sama dengan Anselin dan Lozano-Gracia (2009),”spatial hedonic pricing models” studi ini mampu mengukur baik ""efek langsung” maupun “tidak langsung"" dari ""limpahan eksternalitas"" keberadaan stasiun kereta api.",11.0,http://journals.itb.ac.id/index.php/jts/article/download/2807/1400,True,,Geography,2011-12-01,3f762774f55db72a1f5a56ea3393b24b78d3352e,theinfluenceofrailwaystationonresidentialpropertyvaluesspatialhedonicapproachthecaseofserpongsrailwaystation,syabri,10.5614/jts.2011.18.3.7,theinfluenceofrailwaystationonresidentialpropertyvaluesspatialhedonicapproachthecaseofserpongsrailwaystation|2011,1,4414,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,d5598ba7050c114f960b47b504185e16d8369426,10.12928/optimum.v15i2.11478,,Model development for estimating sales adjusment magnitude in real estate appraisal using hedonic price model and paired data analysis,2025.0,Optimum Jurnal Ekonomi dan Pembangunan,Bagaskara Bagaskara; F. Prativi,"This study aims to develop a model that can identify a reference for determining the adjustment magnitude in the market approach used in appraisal practice. Currently, it is challenging for appraisers to accurately determine the extent of these adjustments. Data collection in this study employed purposive sampling, ensuring that the criteria and specifications closely mirrored the data collection process in appraisal practices. The researcher selected three districts to capture any possible variations in location affecting the adjustment magnitude. The collected data was regressed to build a hedonic price model, which was later analyzed using the paired data analysis method to meet ideal model conditions and specifications. The results of this study demonstrate that the adjustment magnitude in the market approach can be identified from price differences resulting from variations in a single attribute. The highly heterogeneous housing market conditions in Indonesia present significant challenges for conducting paired data analysis in a practical context. Artificial conditions were required to meet the specifications for paired data analysis. The implication of the study that appraisers in determining adjustments in the market approach. Additionally, the model/procedure developed in this study can be applied in research across different contexts and objects, contributing to the advancement of real estate valuation science.",1.0,http://journal2.uad.ac.id/index.php/optimum/article/download/11478/5669,True,JournalArticle,,2025-09-30,d5598ba7050c114f960b47b504185e16d8369426,modeldevelopmentforestimatingsalesadjusmentmagnitudeinrealestateappraisalusinghedonicpricemodelandpaireddataanalysis,bagaskara,10.12928/optimum.v15i2.11478,modeldevelopmentforestimatingsalesadjusmentmagnitudeinrealestateappraisalusinghedonicpricemodelandpaireddataanalysis|2025,1,2944,3,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression,2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c67ce856a98062e3ca8cde96abd879b34a2d52e2,10.4114/ia.v19i58.1156,,Artificial Intelligence (AI) techniques to analyze the determinants attributes in housing prices,2016.0,,J. Núñez,"The econometric approach to obtain the value of a property began with hedonic modelling, which were based on a set of property attributes, internal or external, associated to each particular dwelling. The final sale value can be estimated, and also the marginal prices of each exogenous explanatory variable. A good alternative to the hedonic approach is based on several Artificial Intelligence (AI) techniques, such as artificial neural networks (ANN), these tend to be more precise. Both methodologies are compared, and a case study is developed using data from Seville, the larger town in the South of Spain. Resumen El análisis abordado tiene como objetivo la obtención de modelos econométricos de valoración de vivienda. Partiendo de una serie de atributos referidos al inmueble -internos y externosse pretende obtener el precio de venta del mismo, estimando además el peso marginal de cada uno de los atributos que explican el precio global. Debido al carácter heterogéneo de estos bienes se utiliza desde hace varias décadas la Metodología de Precios Hedónicos. Sin embargo, las técnicas de Inteligencia Artificial (IA), en especial las Redes Neuronales Artificiales (RNA) suelen ser más precisas en este ámbito. Se efectuó una comparativa entre ambas técnicas en la ciudad de mayor tamaño del sur de España: Sevilla.",0.0,http://journal.iberamia.org/index.php/intartif/article/download/37/8/47,True,,Computer Science,,c67ce856a98062e3ca8cde96abd879b34a2d52e2,artificialintelligenceaitechniquestoanalyzethedeterminantsattributesinhousingprices,núñez,10.4114/ia.v19i58.1156,artificialintelligenceaitechniquestoanalyzethedeterminantsattributesinhousingprices|2016,1,2825,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices || modelo de precios hedónicos vivienda || precios hedónicos vivienda",3,1,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9a496965470f50c013a16a12e618400cd9357eb2,10.31671/dogus.2019.223,,Determinants of House Prices in Turkey : A Hedonic Regression Model,2008.0,,S. Selim,"Emlak degerlemede ve konut piyasasi arastirmalarinda konutun piyasa degeri genellikle mikro ekonomik teoriye dayanan hedonik model yoluyla analiz edilmektedir. Hedonik model, bir malin ozelliklerinin fiyat uzerindeki etkisini incelemektedir. Bu calismada Turkiye’de konut fiyatlarini belirleyen faktorler 2004 Hanehalki Butce Anketi kullanilarak analiz edilmistir. Konut fiyatlarini etkileyen en onemli degiskenler konutun tipi, yapi turu, oda sayisi konutun buyuklugu ve diger yapisal degiskenlerden konutun su sistemi, havuz, dogal gaza sahip olmasidir.",129.0,http://journal.dogus.edu.tr/index.php/duj/article/download/80/pdf_selim,True,,Economics,2008-07-27,9a496965470f50c013a16a12e618400cd9357eb2,determinantsofhousepricesinturkeyahedonicregressionmodel,selim,10.31671/dogus.2019.223,determinantsofhousepricesinturkeyahedonicregressionmodel|2008,1,2055,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonik fiyat konut",3,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic property valuation,a62e5c8ece3bbc335b334246ba72302fca27fb34,10.1108/jerer-07-2018-0033,,An analysis of regional house prices in Greece,2018.0,Journal of European Real Estate Research,M. White; D. Papastamos," Purpose This paper aims to examine the housing market in Greece after the Global Financial Crisis focussing on regional analysis and urban markets in Athens and Thessaloniki. Design/methodology/approach The paper uses a data set of over 70,750 property values from 2007 until 2014 incorporating characteristics variables upon which hedonic models are estimated. These form the bases for calculating value indices for mix adjusted houses/apartments by year and region. The indices are used in a panel model in which regional and economic variables are included as independent variables. Using advances in dynamic panel data modelling, a bias-corrected least squares dummy variable corrected (LSDVC) model is applied. Findings Results indicate the importance of macroeconomic variables in terms of the role of disposable income and significantly different regional effects. Examining the major urban markets, results indicate significant differences in the response of house values to exogenous demand side influences, consistent with the finding of significant regional differences in the LSDVC. Research limitations/implications While data on valuations are used that may contain smoothing, the data set covers a large sample of residential properties. As regional economic differences are significant and persistent, housing markets will also behave differently, and hence national policies, unless targeted, will have regionally differentiated effects. Practical implications Regional heterogeneity needs to be considered in model estimation. Social implications Policymakers should consider regional differences to improve policy effectiveness. Originality/value This is the first paper to use a large sample of residential properties in Greece and apply the LSDVC model to overcome estimation biases. ",2.0,http://irep.ntu.ac.uk/id/eprint/34855/1/12366_White.PDF,True,,Economics,2018-11-05,a62e5c8ece3bbc335b334246ba72302fca27fb34,ananalysisofregionalhousepricesingreece,white,10.1108/jerer-07-2018-0033,ananalysisofregionalhousepricesingreece|2018,1,3318,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic",2,4,5,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,87b010de156e63c9c351d7c1b248e4b57346941c,10.1061/(asce)up.1943-5444.0000734,,"Purchase Motivation, Landscape Preference, and Housing Prices: Quantile Hedonic Analysis in Guangzhou, China",2021.0,,Hai-Zhen Wen; Shu-Yuan Li; E. Hui; Shi-Jun Jia; Wenjun Cui,,7.0,http://ira.lib.polyu.edu.hk/bitstream/10397/97513/1/Hui_Purchase_Motivation_Landscape.pdf,True,,Economics,2021-09-01,87b010de156e63c9c351d7c1b248e4b57346941c,purchasemotivationlandscapepreferenceandhousingpricesquantilehedonicanalysisinguangzhouchina,wen,10.1061/(asce)up.1943-5444.0000734,purchasemotivationlandscapepreferenceandhousingpricesquantilehedonicanalysisinguangzhouchina|2021,1,1500,1,hedonic housing prices,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,8608769bf99b3ca08fd8a68ce2950f5a018d858e,10.1108/jchmsd-06-2018-0044,,Sustainable adaptive reuse – economic impact of cultural heritage,2019.0,Journal of Cultural Heritage Management and Sustainable Development,T. Kee," Purpose The purpose of this paper is to investigate the positive externalities of adaptive reuse of heritage buildings and the economic impact on adjacent residential property prices as adaptive reuse is emerging as a significant heritage management and cultural heritage conservation practice recognized by the International Council on Monuments and Sites. Design/methodology/approach Through mixed methodologies of hedonic price model and case studies of three tenement houses in Hong Kong, this paper argues that the adaptive reuse of heritage buildings increases the values of residential properties within the district and revitalizes the area economically and culturally because of the positive externalities generated from the cultural heritage. Findings The findings have identified key cultural heritage values of adaptive reuse via the case studies as well as the major intangible cultural values associated with the heritage assignment. On the other hand, the hedonic regression also verifies that key variables such as heritage completion and distance from heritage show significance to the property prices of adjacent residential units. Practical implications The research is useful for heritage conservationists, policy makers and urban planners in other cities with regards to management and implementation of sustainable cultural heritage revitalization schemes for economic benefits. Originality/value The research is original in its scope and context, and is one of the first of its kind for a high-density metropolitan context in Hong Kong and is significant in demonstrating the economic impact of the heritage practice of adaptive reuse. ",33.0,http://ira.lib.polyu.edu.hk/bitstream/10397/104607/1/Kee_Sustainable_Adaptive_Reuse.pdf,True,,Business,2019-05-20,8608769bf99b3ca08fd8a68ce2950f5a018d858e,sustainableadaptivereuseeconomicimpactofculturalheritage,kee,10.1108/jchmsd-06-2018-0044,sustainableadaptivereuseeconomicimpactofculturalheritage|2019,1,3168,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,1,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",c8845286678d010541443739ff169c50f1c3c20a,10.1016/j.habitatint.2020.102132,,An empirical study of the impact of vehicular traffic and floor level on property price,2020.0,,Hai-Zhen Wen; Zaiyuan Gui; Ling Zhang; E. Hui,"Abstract Urban road traffic often generates noise and air pollution, thereby resulting in a disamenity effect on surrounding residential property and subsequently affecting the willingness to pay of homebuyers. Given that the distribution of road-traffic externalities varies in vertical space, heterogeneous effects of road traffic result on properties situated in different floors. Based on data of 7590 multi-story and 4980 high-rise residential property in Hangzhou, China in 2017, this study constructs hedonic price and spatial econometric models to investigate the relationship among road-traffic externality, floor level, and property price. Empirical results show that road-traffic externalities have a significant disamenity effect on property price. Different from existing studies, we find that the floor level has a significant moderating effect on the disamenity effect of road traffic. In particular, effects on different submarkets reveal that capitalization rate is non-monotonic in vertical space and different in multi-story and high-rise buildings. Previous literature has largely ignored these issues, but the latter is crucial in estimating the influence of road-traffic externalities on property price.",27.0,http://ira.lib.polyu.edu.hk/bitstream/10397/103231/1/Hui_Empirical_Study_Impact.pdf,True,,Economics,2020-03-01,c8845286678d010541443739ff169c50f1c3c20a,anempiricalstudyoftheimpactofvehiculartrafficandfloorlevelonpropertyprice,wen,10.1016/j.habitatint.2020.102132,anempiricalstudyoftheimpactofvehiculartrafficandfloorlevelonpropertyprice|2020,1,2725,2,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,2,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,93fc93fc119e195243abe117c503f18d82c78f03,10.1016/j.ufug.2020.126769,,The housing market impacts of human activities in public spaces: The case of the square dancing,2020.0,,Yue Xiao; E. Hui; Hai-Zhen Wen,"Abstract Square dancing is a popular public activity performed in squares and parks in Chinese cities. Although dancers benefit from such activity physically and mentally, nearby residents may suffer from noise pollution and other negative externalities. However, only a few existing studies have explored the effects of square dancing on the value of residential properties and open spaces. To fill this knowledge gap, this study utilizes the hedonic price model and spatial econometric model to investigate the external effects of square dancing in the housing market of Hangzhou, China. Results show that nearby housing price drops by 5.8 % and 13.0 % when people dance in the nearest park and square. In addition, square dancing decreases the value of parks and squares, and this mediating effect shows spatial heterogeneity (e.g., the premium of living in the segments of 0–200 m and 200 m – 1 km from parks decreases from 0.231 to 0.047 and from 0.126 to 0.045, respectively; the premium of living in the segments of 0–800 m, 800 m – 1.5 km, and 1.5–3 km from squares decreases from 0.157 to 0.060, from 0.165 to 0.022, and from 0.166 to 0.033, respectively). The results of this study indicate that square dancing has a significant negative external effect on housing price and the value of open space. This study offers implications for policy makers to achieve a harmonious society and humanized urban planning. The findings and implications of this study ensure the benefits of residents and improve the value of residential properties and open spaces. In addition, the findings can be generalized across cities or countries where square dancing and other human activities (e.g., night market) are popular.",20.0,http://ira.lib.polyu.edu.hk/bitstream/10397/103184/1/Hui_Housing_Market_Impacts.pdf,True,,Economics,2020-10-01,93fc93fc119e195243abe117c503f18d82c78f03,thehousingmarketimpactsofhumanactivitiesinpublicspacesthecaseofthesquaredancing,xiao,10.1016/j.ufug.2020.126769,thehousingmarketimpactsofhumanactivitiesinpublicspacesthecaseofthesquaredancing|2020,1,3216,3,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,67b546a2a2e167541897a54ad1a4e3021e8635ea,10.1007/s11442-015-1223-6,,Impact of urban landscape and environmental externalities on spatial differentiation of housing prices in Yangzhou City,2015.0,Journal of Geographical Science,Yang Wang; Ling-Ling Zhao; L. Sobkowiak; Xingliang Guan; Shaojian Wang,"In this study, housing prices data for residential quarters from the period 2001–2012 were used and Global Differentiation Index (GDI) was established to measure the overall differentiation trend in housing prices in Yangzhou City, eastern China. Then the influence of the natural landscape and environment on prices of global housing market and housing submarkets was evaluated by the hedonic price model. The results are shown as follows. (1) There have been increasing gaps among housing prices since 2001. In this period, the differentiation trend has shown an upward fluctuation, which has been coupled with the annual growth rate of housing prices. (2) The spatial distribution of residential quarters of homogenous prices has changed from clustered in 2001 into dispersed in 2012. (3) Natural landscape and environmental externalities clearly influence spatial differentiation of housing prices. (4) In different housing submarkets, the influence of natural landscape and environmental externalities are varied. Natural landscape characteristics have significant impact on housing prices of ordinary commercial houses and indemnificatory houses, while the impact of environmental characteristics have obvious influence on housing prices of cottages and villas.",16.0,http://ir.igsnrr.ac.cn/handle/311030/38915,True,,Economics,2015-07-07,67b546a2a2e167541897a54ad1a4e3021e8635ea,impactofurbanlandscapeandenvironmentalexternalitiesonspatialdifferentiationofhousingpricesinyangzhoucity,wang,10.1007/s11442-015-1223-6,impactofurbanlandscapeandenvironmentalexternalitiesonspatialdifferentiationofhousingpricesinyangzhoucity|2015,1,2767,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,2,4,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,real estate hedonic regression,1a982c52a3a695b67a2c28687558f3b538f90e8d,10.3989/ic.68059,,La influencia de las características de las viviendas de segunda mano en sus precios de venta: evidencias en el mercado alicantino,2020.0,,V. Pérez-Sánchez; R. Mora-García; J. C. Pérez-Sánchez; M. F. Céspedes-López,"This paper estimates the impact that the characteristics of second-hand homes in Alicante have on the asking price. In order to carry out this research, information has been used from real estate portals, which offer information such as the characteristics of dwellings, the building, their geographical location and asking prices. The analysis is developed in the province of Alicante geographic context. A hedonic regression is estimated in which the dwellings characteristics and location act as independent variables, and the asking price as a dependent value. The results show significant differences in the dwelling asking price depending on its characteristics and location, suggesting higher prices for dwellings: located in the coastal regions of the north of Alicante, that have a swimming pool, garden or parking space. However, dwellings with lower asking prices are: those located in southern and inland regions of the province.",3.0,http://informesdelaconstruccion.revistas.csic.es/index.php/informesdelaconstruccion/article/download/6009/7303,True,,Geography,2020-06-11,1a982c52a3a695b67a2c28687558f3b538f90e8d,lainfluenciadelascaracteristicasdelasviviendasdesegundamanoensuspreciosdeventaevidenciasenelmercadoalicantino,pérez-sánchez,10.3989/ic.68059,lainfluenciadelascaracteristicasdelasviviendasdesegundamanoensuspreciosdeventaevidenciasenelmercadoalicantino|2020,1,2441,3,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || precios hedónicos vivienda || real estate hedonic regression,2,1,4,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),ccc840bf05d1dd777dcf52fa680f939ad8e923ef,10.3989/ic.16.053,,La incidencia de la calificación energética sobre los valores residenciales: un análisis para el mercado plurifamiliar en Barcelona,2016.0,,C. Duarte,"This paper studies in a novel way the incidence of the energy rating on residential values in Spain. Real estate sale values in metropolitan Barcelona are analyzed using a hedonic model. The results suggest that moving from a G to an A rating increases the value by 5.11 %, equivalent to an average of 16.07 euros/m2/step (although there is an extra for grade A housing).This surplus is, in relative terms, much lower than in other European cities and, in absolute terms, less than the previously reported extra costs expended on improving the energy rating. In addition, the huge lack of energy rating information in the market seems to respond to a strategy to hide the poor rating of the building stock and aggravate the information asymmetry. All together overshadows the effectiveness of the European Energy Efficiency Directive as has been implemented in our country, and calls for its redesign.",38.0,http://informesdelaconstruccion.revistas.csic.es/index.php/informesdelaconstruccion/article/download/5659/6534,True,,Economics,2016-09-30,ccc840bf05d1dd777dcf52fa680f939ad8e923ef,laincidenciadelacalificacionenergeticasobrelosvaloresresidencialesunanalisisparaelmercadoplurifamiliarenbarcelona,duarte,10.3989/ic.16.053,laincidenciadelacalificacionenergeticasobrelosvaloresresidencialesunanalisisparaelmercadoplurifamiliarenbarcelona|2016,1,2401,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,0,2,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),829c9d868f9d5f7ddb4f8ec3b71de990ffe4d917,10.20472/iac.2018.039.031,,Analysis of Spatial Dependence in Real Estate Prices: Evidence from an Emerging Market,2018.0,,Orcun Morali; Neslihan Yılmaz,"Real estate properties are naturally location-fixed, therefore, a spatial dependence is expected. When location-specific factors persist over time, spatial autocorrelation is likely to exist in a hedonic pricing regression. Spatial autocorrelation causes problems in the interpretation of the regression results due to inefficient estimators or complex models. The focus of this study is to identify and test the determinants of spatial dependence in the real estate market. Using a novel data set, our study contributes to the literature as we identify the specific spatial dependence factors through the analysis of different types of housing markets and extend the literature to an emerging market.",0.0,http://iises.net/proceedings/39th-international-academic-conference-amsterdam/table-of-content?cid=82&iid=031&rid=9533,True,,Economics,,829c9d868f9d5f7ddb4f8ec3b71de990ffe4d917,analysisofspatialdependenceinrealestatepricesevidencefromanemergingmarket,morali,10.20472/iac.2018.039.031,analysisofspatialdependenceinrealestatepricesevidencefromanemergingmarket|2018,1,2201,4,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression || spatial hedonic model housing,2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,5c034aee01fdf74ee728ac69389cea26ce9a974b,10.5171/2012.383101,,Multiple regressions in analysing house price variations,2012.0,,A. Yusof; Syuhaida Ismail,"An application of rigorous statistical analysis in aiding investment decision making gains momentum in the United States of America as well as the United Kingdom. Nonetheless in Malaysia the responses from the local academician are rather slow and the rate is even slower as far as the practitioners are concern. This paper illustrates how Multiple Regression Analysis (MRA) and its extension, Hedonic Regression Analysis been used in explaining price variation for selected houses in Malaysia. Each attribute that theoretically identified as price determinant is priced and the perceived contribution of each is explicitly shown. The paper demonstrates how the statistical analysis is capable of analyzing property investment by considering multiple determinants. The consideration of various characteristics which is more rigorous enables better investment decision making.",38.0,http://ibimapublishing.com/articles/CIBIMA/2012/383101/383101.pdf,True,,Economics,2012-03-01,5c034aee01fdf74ee728ac69389cea26ce9a974b,multipleregressionsinanalysinghousepricevariations,yusof,10.5171/2012.383101,multipleregressionsinanalysinghousepricevariations|2012,1,2375,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2661cf8b87138bea845c729bf21335a2eadce031,10.1111/1540-6229.12352,,Environmental hazards: The microgeography of land‐use negative externalities,2021.0,Real Estate Economics,T. Somerville; Jake Wetzel,"The decisions on the siting of hazardous facilities and compensation for nearby landowners depends on an accurate estimation of the negative externalities these facilities place on proximate land uses, primarily residential properties. In this paper we highlight the sensitivity of these estimates to the treatment of distance from the hazard and to the presence of other nearby land uses identified at a highly granular geographic level. Recent opposition to the expansion of North American pipeline capacity has been intense, mixing concerns about climate change, environmental damage, and local opposition to the physical presence of pipelines in their neighbourhoods. This paper studies the disamenity effects associated with the last factor. In doing so we generate results that more broadly address the specification and left out variable bias challenges of measuring the capitalization of negative location-specific environmental externalities. The key contributions of this paper are first showing that disamenity effects can be highly localized and easily susceptible to errors with parametric specifications. Second, that the magnitude of the effect on house prices arising from proximity are sensitive to land uses that are not the hazard in question, but whose presence may be correlated with the hazard. And third, that negative news about a hazard increases the assessment of risk and lowers nearby house values, but that this effect is temporary. We find that the quantitative effects of proximity to oil pipelines are relatively small: prices are lower by 5.7 percent ($39.3k) for properties with a pipeline easement, 2.1 percent ($14.4k) lower for those properties adjacent to a property with an easement, and 1.4 percent ($9.6k) for those adjacent to the former, one property further away from the pipeline. Though this last result is sensitive to specification choice. The prices of all residential properties further away from the pipeline in our data are unaffected. When expressed in cardinal distance, only the prices for residential properties within 100 meters of the pipeline easement are affected. The findings here suggest that care and flexibility with functional forms, the perception of hazards, and attention to land use contexts is necessary for an analysis of the negative externalities for residential property associated with proximity to environmental disamenities and that simple parametric treatments are highly likely to result in biased estimates. JEL Codes: Housing Demand (R21), Valuation of Environmental Effects (Q51), Government Policy (Q58), Other Spatial and Pricing Analysis (R32)",5.0,http://hdl.handle.net/2429/63153,True,,Economics,2021-04-30,2661cf8b87138bea845c729bf21335a2eadce031,environmentalhazardsthemicrogeographyoflandusenegativeexternalities,somerville,10.1111/1540-6229.12352,environmentalhazardsthemicrogeographyoflandusenegativeexternalities|2021,1,4129,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",71d296b73cf2513489f80c2160fa8bc67d4b9272,10.1109/melcon.2016.7495471,,Attitudes to systemic risk: The impact of flood risk on the housing market in Dublin,2016.0,Mediterranean Electrotechnical Conference,Salem S. Gharbia; O. Naughton; Vincent Farrelly; Ronan C. Lyons; F. Pilla,"The concatenated effects of increased frequency of intense precipitations due to climate change and anthropogenic impacts in the form of construction in floodplains, channel straightening and increased presence of impermeable surfaces are increasing the incidence of floods in urban areas. This paper investigates behavioral responses to a natural hazard (flooding) by examining residential property values. The results of this investigation can be used to develop benefit/cost studies to assess the economic merits of policies that mitigates the risk of floods by using the residential housing market as a proxy for estimating these values since the choice of where to live often includes the choice of hazard level. The methodology described here also provides a mechanism for testing consumer behavior under uncertainty. This study uses a hedonic property price function to estimate the effects of flood hazards on residential property values. The study utilizes data from 158,890 residential home sales in Dublin, Ireland between 2006 and 2015. This area experienced significant flooding in October 2011. GIS is used to spatially characterize the houses included in the analysis by linking them to the following set of parameters included into the baseline regression: house price, house type and size (number of bedrooms and bathrooms), when it was on the market, and its location. Once the baseline regression model is built, then the variables included in it are regressed against the flood-risk. The distance between a set of amenities and the properties is also calculated using GIS. Results show that a house located within a floodplain has a lower market value than an equivalent house located outside the floodplain. Finally, the benefits resulting from the use of GIS-based spatial indicators of properties in hedonic regression models to quantify the accessibility to amenities as network travel distances are also demonstrated.",3.0,http://hdl.handle.net/2262/76554,True,Conference,Geography,2016-04-18,71d296b73cf2513489f80c2160fa8bc67d4b9272,attitudestosystemicrisktheimpactoffloodriskonthehousingmarketindublin,gharbia,10.1109/melcon.2016.7495471,attitudestosystemicrisktheimpactoffloodriskonthehousingmarketindublin|2016,1,3442,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || hedonic price model amenities residential || spatial hedonic model housing",2,3,5,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bc7f6a5a027943acacebcba5ca30b9d77f1b75da,10.3828/jtep.2012.46.2.239,,A Hedonic Analysis of the Value of Rail Transport in the Greater Dublin Area,2008.0,Journal of Transport Economics and Policy,K. Mayor; S. Lyons; D. Duffy; R. Tol,"We use a hedonic house price model to estimate the value of transport networks to homeowners in the Dublin area. Using a data set of house sales and data on rail lines in Dublin, we assess the values assigned to different transport links by homeowners. We find that the value attributed to transport depends on its distance from a property and is affected by the availability of alternative transport options. There are differences in the values assigned to recently constructed tramlines compared to the traditional rapid transit train stations. The study also takes into account house characteristics and other environmental amenities.",43.0,http://hdl.handle.net/2262/27968,True,JournalArticle,Business;Geography,2008-11-20,bc7f6a5a027943acacebcba5ca30b9d77f1b75da,ahedonicanalysisofthevalueofrailtransportinthegreaterdublinarea,mayor,10.3828/jtep.2012.46.2.239,ahedonicanalysisofthevalueofrailtransportinthegreaterdublinarea|2008,1,2137,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5e2b1180b83c8a4300a6329dde6caf45b6769698,10.1007/978-3-030-23786-8_4,,Spatial Implications of EPC Rankings Over Residential Prices,2019.0,Values and Functions for Future Cities,Carlos Marmolejo-Duarte; Ai Chen; M. Bravi,"Empirical evidence has found a correlation between Energy Performance (EPC) rankings and housing prices. Though, little attention has been devoted to research the spatial implications of EPC marginal prices. This paper attempts to fill this gap using a geographical-weighted-regression, departing from listing prices for Barcelona. The results suggest the existence of a premium equivalent to 1.7% for each EPC ranking, which nearly double the previous reported evidence in this city. Also, the impact shows a non-stationary distribution across urban space. As a matter of fact, the higher the impact, the lower the price of apartments and their quality. The apartments portraying the larger impact are located in peripheral areas inhabited by medium and low-income population. So, the equity of less wealthy population witnesses the largest brown discount.",17.0,http://hdl.handle.net/2117/175313,True,,Economics,,5e2b1180b83c8a4300a6329dde6caf45b6769698,spatialimplicationsofepcrankingsoverresidentialprices,marmolejo-duarte,10.1007/978-3-030-23786-8_4,spatialimplicationsofepcrankingsoverresidentialprices|2019,1,2357,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,3,5,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,62be846525bcdce01e3d5e6a4cae5d81922a8228,10.1007/978-3-030-76059-5_15,,Understanding the Economic Value of Walkable Cities,2021.0,Urban Informatics and Future Cities,J. Roper; Christopher Pettit; M. Ng,"Improving walkability has the potential to play a major role in improving both accessibility and the public health dimensions of our cities. Walkability research to-date has focused on the influence of the built environment on physical activity associated with health and active transport outcomes. While the probable desirability of walkability to real estate value is recognised by the embedding of walkability indices such as WalkScore in property listing platforms, there is a paucity of research exploring this assumption. This research addresses this gap by undertaking an empirical study to quantify the economic value of walkability to residential property. Specifically, we demonstrate the use of hedonic price modelling to test measures of walkability with a case study. We find that WalkScore is positively related to prices for detached houses in Sydney, Australia but has no significant relationship to unit prices. Possible reasons and directions for future work are discussed.",3.0,http://hdl.handle.net/1959.4/unsworks_78366,True,,Business,,62be846525bcdce01e3d5e6a4cae5d81922a8228,understandingtheeconomicvalueofwalkablecities,roper,10.1007/978-3-030-76059-5_15,understandingtheeconomicvalueofwalkablecities|2021,1,2491,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price,2,0,3,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",5bf1856dda2d373ddda62e4b2baf234d6c827148,10.1177/0042098019879382,,A novel hedonic price modelling approach for estimating the impact of transportation infrastructure on property prices,2021.0,,S. Lieske; Ryan van den Nouwelant; Hoon-Han Jung; C. Pettit,"Hedonic estimations of the effect of transport infrastructure on property prices vary widely. This high variability demonstrates a deficit in our understanding of these relationships, limits the utility of econometrics for the valuation of urban property markets, and limits the development and implementation of effective and fair market-based policy tools. Several avenues may lead to improved consistency: re-consideration of accessibility, inclusion of urban design characteristics, assessment of spatial dependence and spatial heterogeneity, and consideration of geographic scale. This paper outlines the rationale and opportunities for inclusion of, and presents empirical tests for, these assertions using a case study in western Sydney, Australia. Results show a number of urban design characteristics to be significant determinants of residential property price. Street connectivity and higher density in areas surrounding residences negatively impact price, higher density close to train stations positively impacted price in one model. Park-and-ride stations led to decreases in property values. Smaller study area results indicate a nonlinear relationship between distance to train station and property price and a disamenity impact for residences within 400 m of train stations. Relative accessibility measured as frequency of peak hour trains is a significant and positive determinant of price in the larger study area. Incorporation of a price trend surface and estimation using a spatial error model reduce the extent to which spatial autocorrelation overstates the effect of a train station on prices. These conceptual and empirical improvements further develop our understanding of the effect of transport infrastructure on property values.",72.0,http://hdl.handle.net/1959.4/unsworks_60283,True,JournalArticle,Economics,2021-01-01,5bf1856dda2d373ddda62e4b2baf234d6c827148,anovelhedonicpricemodellingapproachforestimatingtheimpactoftransportationinfrastructureonpropertyprices,lieske,10.1177/0042098019879382,anovelhedonicpricemodellingapproachforestimatingtheimpactoftransportationinfrastructureonpropertyprices|2021,1,3258,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic property valuation || residential property price determinants",2,2,1,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),a39850091fe418201cd90802aa3a4dc8f9fb4196,10.1111/1540-6229.00737,,Assessing the performance of real estate auctions,1998.0,,C. Mayer,"This paper investigates the performance of real estate auctions in selling real estate relative to the more traditional method of negotiated sale. Estimates from auctions in Los Angeles during the boom of the mid 1980s show a discount that ranges between 0 and 9 percent, while similar sales in Dallas during the real estate bust of the late 1980s obtained discounts in the 9 to 21 percent range. This evidence is censistent with a theory that predicts larger percentage discounts in down markets. Although these results differ from previous studies of U.S. auctions that find much larger discounts, a comparison of methodologies suggests that previous papers that use a hedonic equation suffer from a selection bias problem, pushing auction coefficients towards finding larger discounts. Another interesting finding is that publishing a reserve price does not affect the estimated auction prices. Finally, the study notes that scattered-site auctions sell at a larger discount than the more homogeneous sales of single-site condominiums and finds no evidence of price declines over the course of an auction. The paper concludes that despite the discounts, auctions are still a viable sales strategy, especially for large sellers that face high holding costs and long average sales times, and for developers of single-site condominium complexes.",119.0,http://hdl.handle.net/1721.1/12704,True,,Economics,1998-03-01,a39850091fe418201cd90802aa3a4dc8f9fb4196,assessingtheperformanceofrealestateauctions,mayer,10.1111/1540-6229.00737,assessingtheperformanceofrealestateauctions|1998,1,2845,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,0,2,1,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",60a316c683571dfaa5c653f3d003e9f1f8b3464d,10.1007/s11146-016-9586-8,,The Housing Market Effects of Local Home Purchase Restrictions: Evidence from Beijing,2016.0,Journal of real estate finance and economics,Wei-Zeng Sun; Si-Qi Zheng; D. Geltner; Rui Wang,"Home prices have surged in major Chinese cities, leading to concerns of asset price bubbles and housing affordability. The policy of home purchase restrictions (HPR) has been one of China’s harshest housing market interventions to squeeze out speculative demand and dampen the soaring home prices. Beijing was the first city to implement the HPR. Employing the regression discontinuity design technique, we find that Beijing’s HPR policy triggered a 17–24 % decrease in resale price, a drop in the price-to-rent ratio of about a quarter of its mean value, and a deep (1/2 to 3/4) reduction in the transaction volume of the for-sale market, with no significant change in the rent or the transaction volume of rental units. In submarkets where housing supply was less elastic, the effects of the HPR were larger in price and smaller in quantity, suggesting that wealthy buyers likely benefited more from the HPR. The scope of the analysis does not allow conclusions regarding the persistence or longevity of these effects.",5.0,http://hdl.handle.net/1721.1/111622,True,,,2016-10-10,60a316c683571dfaa5c653f3d003e9f1f8b3464d,thehousingmarketeffectsoflocalhomepurchaserestrictionsevidencefrombeijing,sun,10.1007/s11146-016-9586-8,thehousingmarketeffectsoflocalhomepurchaserestrictionsevidencefrombeijing|2016,1,2520,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,e766ce42d20ce6d53c53ea3f54b8578337a1a02e,10.1177/0042098020927839,,"Examining building age, rental housing and price filtering for affordability in Melbourne, Australia",2020.0,Urban studies,Matthew Palm; Katrina Raynor; G. Warren-Myers,"Governments worldwide have responded to housing affordability challenges with supply-side solutions. Proponents of these approaches often draw on the notion of ‘filtering effects’ to argue that new supply naturally trickles down to lower-income households over time, improving affordability. This study examines the characteristics of Naturally Occurring Affordable Housing in Melbourne, Australia, analysing how dwelling age impacts rental cost. We specify a non-linear relationship between building age and rents through hedonic rent modelling, reflecting a premium for historic properties in inner Melbourne. We also conduct a cluster analysis of rental listings and measure affordability by cluster. Our results problematise the notion of filtering, finding that most contemporary affordable housing was initially built in the 1960s and 1970s as social housing or targeted at low-income households. We argue that filtering in this instance is not natural but is instead a reflection of historic government expenditure and past construction choices, or ‘filter up’.",15.0,http://hdl.handle.net/11343/244443,True,JournalArticle,Business,2020-06-24,e766ce42d20ce6d53c53ea3f54b8578337a1a02e,examiningbuildingagerentalhousingandpricefilteringforaffordabilityinmelbourneaustralia,palm,10.1177/0042098020927839,examiningbuildingagerentalhousingandpricefilteringforaffordabilityinmelbourneaustralia|2020,1,2568,2,hedonic housing prices || housing characteristics prices hedonic,2,1,4,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",8fb7d192681a4d59f3c4588795fd0deb8ee67fc4,10.1080/17421771003730703,,"Valuing Access to Water—A Spatial Hedonic Approach, with an Application to Bangalore, India",2010.0,,L. Anselin; Nancy Lozano-Gracia; U. Deichmann; Somik V. Lall,"Abstract An important infrastructure policy issue for rapidly growing cities in developing countries is how to raise fiscal revenues to finance basic services in a fair and efficient manner. This requires estimates of the potential benefits or positive welfare effects that may follow from improved infrastructure. In this paper, we take advantage of a unique geo-referenced household survey to carry out a hedonic analysis of housing values that explicitly accounts for spatial spillovers. We use this to derive an estimate of the value of improved access to water in the Indian city of Bangalore. The findings suggest that by limiting the focus to individual or private benefits only, we may underestimate the overall social welfare from investing in service supply. We further demonstrate how spatially explicit policy simulations based on these estimates provide insight into the total effects of targeted interventions. Appréciation de l'accès à l'eau—Une méthode hédonique spatiale appliquée à la ville de Bangalore, en Inde Rèsumè Un important problème de politique en matière d'infrastructure qui se pose pour les villes à expansion rapide de pays en voie de développement est comment lever des recettes fiscales, de façon à la fois équitable et efficace, pour financer des services essentiels. Ces villes doivent, pour ceci, procéder à des évaluations des bénéfices potentiels ou des effets sociaux positifs que pourrait engendrer une meilleure infrastructure. Dans la présente communication, nous faisons usage des résultats d'une étude géoréférencée unique sur les ménages pour effectuer une analyse hédonique des valeurs des habitations, qui tienne compte de façon explicite des débordements spatiaux. Nous utilisons ensuite ces résultats pour dériver une estimation de la valeur d'un meilleur accès à l'eau dans la ville de Bangalore, en Inde. Les conclusions indiquent qu'en limitant notre recherche aux bénéfices individuels ou privés seulement, nous risquons de sous-estimer les avantages sociaux généraux des investissements dans la fourniture de services. Nous démontrons également comment des simulations de la politique, aux implications spatiales implicites sur la base de ces évaluations, permettent d'obtenir des connaissances sur les effets totaux des interventions ciblées. Valorando el acceso a agua—Un planteamiento hedónico espacial con una aplicación a Bangalore, India Extracto Una cuestión importante en cuanto a política de infraestructura para las ciudades de rápido crecimiento en países en vías de desarrollo es cómo incrementar los ingresos fiscales para financiar servicios básicos de forma justa y eficiente. Esto requiere estimaciones de los beneficios potenciales o de los efectos positivos para el bienestar social que podrían derivar de una mejor infraestructura. En este trabajo aprovechamos un exclusivo estudio georreferenciado sobre la vivienda para aplicar un análisis hedónico que tiene en cuenta explícitamente excedentes espaciales. Lo utilizamos para derivar una estimación del valor de un mejor acceso a agua en la ciudad india de Bangalore. Los descubrimientos sugieren que limitando el enfoque a los beneficios individuales o privados podríamos subestimar el bienestar social general procedente de invertir en el suministro de servicios. También demostramos cómo las simulaciones de políticas espacialmente explícitas basadas en estas estimaciones proporcionan una visión interna de los efectos totales de intervenciones dirigidas.",42.0,http://hdl.handle.net/10986/5884,True,Review,Economics,2010-06-01,8fb7d192681a4d59f3c4588795fd0deb8ee67fc4,valuingaccesstowateraspatialhedonicapproachwithanapplicationtobangaloreindia,anselin,10.1080/17421771003730703,valuingaccesstowateraspatialhedonicapproachwithanapplicationtobangaloreindia|2010,1,4985,2,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment rent hedonic",4,1,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9712b6600e7e3252d6ca1484cd87f8c6846354cd,10.1016/j.tra.2009.02.003,,Capitalization of BRT network expansions effects into prices of non-expansion areas,2009.0,,D. Rodríguez; Carlos Mojica,"Abstract A before and after hedonic model is used to determine the property value impacts on properties already served by the transit system caused by extensions to Bogota’s bus rapid transit system. Asking prices of residential properties belonging to an intervention area ( N = 1407 before, 1570 after) or a control area ( N = 267 before, 732 after) and offered for sale between 2001 and 2006 are used to determine capitalization of the enhanced regional access provided by the extension. Properties offered during the year the extension was inaugurated and in subsequent years have asking prices that are between 13% and 14% higher than prices for properties in the control area, after adjusting for structural, neighborhood and regional accessibility characteristics of each property. Furthermore, the appreciation is similar for properties within 500 m and properties between 500 m and 1 km of the BRT.",172.0,http://hdl.handle.net/10986/4895,True,,Business,2009-06-01,9712b6600e7e3252d6ca1484cd87f8c6846354cd,capitalizationofbrtnetworkexpansionseffectsintopricesofnonexpansionareas,rodríguez,10.1016/j.tra.2009.02.003,capitalizationofbrtnetworkexpansionseffectsintopricesofnonexpansionareas|2009,1,2409,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,2,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),41b49c5dc53c1cf4bbb9bcbcdbfb056f93548daa,10.1016/j.tranpol.2018.04.010,,The impact of undesirable externalities on residential property values: spatial regressive models and an empirical study,2019.0,Transport Policy,Rubén Cordera; Vincenza Chiarazzo; M. Ottomanelli; L. dell'Olio; Angel Ibeas,"Abstract Pollutant emissions, noise and other externalities generated by heavy infrastructures, might impact negatively on real estate values. To test this effect, this paper presents the results of an analysis based on Hedonic Linear Regression, Spatial Hedonic Linear Regression and Hedonic Geographically Weighted Regression models, carried out for the study case of the province of Taranto (Italy). The biggest steel factory in Europe is located here, and some population movements have been observed in relation to the high levels of pollution in the areas close to the factory. The variables used to measure the impact of externalities are of two types: objective indicators such as the distance from the industrial area and the levels of NO2 and PM10, and subjective indicators such as the level of pollution and noise perceived by the population. Results show that the distance from factory was a positive factor in the real estate prices although not always clearly significant, and among pollution indicators, only high levels of NO2 had a negative effect. The accessibility to employment did not prove to be a significant variable in the real estate prices, which indicates that factors related to environmental quality have a greater weight in residential location. Moreover, models including subjective indicators do not show better estimates than models considering only objective indicators. Finally, spatial regression models were useful to analyse the spatial dependence and spatial heterogeneity observed in the data.",18.0,http://hdl.handle.net/10902/18147,True,,Economics,2019-08-01,41b49c5dc53c1cf4bbb9bcbcdbfb056f93548daa,theimpactofundesirableexternalitiesonresidentialpropertyvaluesspatialregressivemodelsandanempiricalstudy,cordera,10.1016/j.tranpol.2018.04.010,theimpactofundesirableexternalitiesonresidentialpropertyvaluesspatialregressivemodelsandanempiricalstudy|2019,1,3035,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression",2,2,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),92adc3800ede8fb7f518a0daf2ec05f5a0f71e8f,10.1016/j.tra.2018.07.015,,The impact of accessibility by public transport on real estate values: A comparison between the cities of Rome and Santander,2019.0,Transportation Research Part A: Policy and Practice,Rubén Cordera; P. Coppola; L. dell'Olio; Angel Ibeas,"Abstract Empirical evidence supports the hypothesis that greater accessibility to opportunities can have a positive impact on real estate values. However, this capitalisation of the benefits of accessibility could vary between different study areas. This research estimates hedonic and spatial hedonic models in two urban areas to compare if differences can be found in the impact accessibility using public transport has on residential property values. The two study areas chosen for comparison are a medium sized city without any major mobility problems (Santander, Spain) and a very large city with major congestion problems (Rome, Italy). The estimated hedonic models have considered the possible presence of spatial effects, a common occurrence with real estate data which may generate dependency in the residuals of hedonic models. Accessibility has been measured using two types of indicators: relative and gravity based. The results confirm that accessibility was a positive factor on property prices in both cities, although in Santander this was only true using the relative indicator to the city centre. These results are relevant for supporting the introduction of value capture policies which provide finance for new projects to extend and encourage greater use of public transport.",67.0,http://hdl.handle.net/10902/18146,True,,Geography,2019-07-01,92adc3800ede8fb7f518a0daf2ec05f5a0f71e8f,theimpactofaccessibilitybypublictransportonrealestatevaluesacomparisonbetweenthecitiesofromeandsantander,cordera,10.1016/j.tra.2018.07.015,theimpactofaccessibilitybypublictransportonrealestatevaluesacomparisonbetweenthecitiesofromeandsantander|2019,1,2795,3,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate)",2,3,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5190403a0c67cb1926699e1948736a36b7e65fd6,10.1016/j.enpol.2016.03.032,,The price of energy efficiency in the Spanish housing market,2016.0,,A. de Ayala; I. Galarraga; J. Spadaro,"The housing sector is a substantial consumer of energy, and therefore a focus for energy savings efforts. The Energy Performance Certificate (EPC) is the main EU policy to improve the energy performance of buildings. Following the implementation of the EPC Directive into Spanish law, from June 2013, all properties offered for sale or rented out in Spain are required to have an EPC. Given that this is a recently introduced regulation, unlike other European housing markets, the Spanish one lacks market data on energy efficiency (EE) labels and their impact on housing price. In order to overcome this gap, we determine the EE ratings of a sample of 1,507 homes across Spain on the basis of information collected previously through household surveys. This information allowed us to answer the question of whether or not, and to what extent, Spanish housing markets capitalise the value of EE. We apply the hedonic-price technique and observe that more energy efficient dwellings (meaning higher EE rating) have a price?premium between 5.4% and 9.8% compared to those with the same characteristics but lower EE level.",129.0,http://hdl.handle.net/10810/64325,True,Review,Economics,2016-07-01,5190403a0c67cb1926699e1948736a36b7e65fd6,thepriceofenergyefficiencyinthespanishhousingmarket,ayala,10.1016/j.enpol.2016.03.032,thepriceofenergyefficiencyinthespanishhousingmarket|2016,1,2619,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic",2,2,5,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,ddb3a8895ad5dfbe89c033d615bc7a80234d8c6c,10.1016/j.landurbplan.2016.06.010,,Environmental externalities of urban river pollution and restoration: A hedonic analysis in Guangzhou (China),2017.0,,Wendy Y. Chen,"Abstract The degradation of urban river ecosystems presents a serious threat to sustainable urban development. Consequently, extensive efforts have been devoted to the ecological restoration of urban rivers worldwide. This study evaluates the environmental externalities associated with water pollution and river restoration in Guangzhou in southern China. A basic hedonic pricing model is applied to test hypotheses using a sample of 968 apartment transaction records during July-December 2013. Results show that river restoration could reverse negative externalities of polluted watercourses to positive externalities, especially for those apartments located on the 10th floor or lower. Water quality improvement from polluted non-recreational water (Grade V or worse) to non-body contact recreational water (Grade IV) could increase apartment values by 0.9%. River restoration (including river bank greening and water quality improvement) could increase property values by 4.61%, demonstrating a preference of Guangzhou residents for greening riverscapes. This study could hopefully provide a scientific basis for urban river governance for communities and decision-makers, and serve as a reference case to elucidate human preferences about river restoration in rapidly developing countries.",114.0,http://hdl.handle.net/10722/245407,True,,Geography,,ddb3a8895ad5dfbe89c033d615bc7a80234d8c6c,environmentalexternalitiesofurbanriverpollutionandrestorationahedonicanalysisinguangzhouchina,chen,10.1016/j.landurbplan.2016.06.010,environmentalexternalitiesofurbanriverpollutionandrestorationahedonicanalysisinguangzhouchina|2017,1,2794,1,apartment prices hedonic,2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",ba026f6ecd8135f4df50f8f95091385c77ca1a52,10.1016/j.landurbplan.2017.01.012,,Cumulative impacts of polluted urban streams on property values: A 3-D spatial hedonic model at the micro-neighborhood level,2017.0,,Wendy Y. Chen; Xun Li,"Abstract Urban streams could concurrently bring about both positive amenities accruing to the view of waterscape and negative disamenities associated with water pollution. In this paper, we focus on a specific question that has not yet been explicitly answered in the extant literature: how to estimate the cumulative impacts of urban streams (which refer to the combined impacts of amenities and disamenities of multiple sources) in high density and high-rise urban contexts. A typical residential apartment complex (comprised of a number of apartment units located in dozens of mid- to high-rise commercial apartment buildings on a contiguous land parcel) in Guangzhou, south China, is used as a case study. A detailed palette of natural amenities and environmental disamenities of two bifurcated streams are quantified via the generalized spatial two-stage least squares (GS2SLS) model. In this model, an originally constructed 3-D spatial connectivity matrix, cube contiguity, is applied to address, in a sophisticated manner, an array of endogenous, exogenous, and error interactions along both horizontal and vertical dimensions that inherently exist in the spatial context of high-rise housing markets. Our results from spatial hedonic models at the micro-neighborhood level demonstrate that homebuyers tend to evaluate urban streams’ chemical, physical, and ecological features holistically. A cumulative impact is also found, as homebuyers would like to pay an extra premium for an apartment located farther away from both polluted streams. This study advances the extant literature by contributing to a novel and effective extension of the conventional two-dimensional spatial matrix which can capture the long-ignored spatial correlation existing amongst apartment units located on contiguous floors, as well as a robust estimation of the demand of urban natural and environmental amenities in the gradually rising mid- and high-rise housing market in both developing and developed countries.",38.0,http://hdl.handle.net/10722/245405,True,,Geography,2017-06-01,ba026f6ecd8135f4df50f8f95091385c77ca1a52,cumulativeimpactsofpollutedurbanstreamsonpropertyvaluesa3dspatialhedonicmodelatthemicroneighborhoodlevel,chen,10.1016/j.landurbplan.2017.01.012,cumulativeimpactsofpollutedurbanstreamsonpropertyvaluesa3dspatialhedonicmodelatthemicroneighborhoodlevel|2017,1,3503,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || spatial hedonic model housing",2,2,4,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,936c6590606e5669e7af8c0cb16273ac696d2d36,10.1016/j.landusepol.2016.03.001,,"Estimating the mediating effect of privately-supplied green space on the relationship between urban public green space and property value: Evidence from Shanghai, China",2016.0,,Xiao Yang; Zhigang Li; C. Webster,"Much of the housing in China’s cities built since the opening up of housing markets in the 1990s is organized in residential clubs, with neighbourhood civic goods and services constructed by private developers, financed by residents’ fees, managed by property-management companies and governed by resident associations. This means that there are now three important types of green space in Chinese cities: public green space (open-access parks), private green space (gardens, exclusive to a private home) and club green space (greenery exclusive to fee-paying estate residents). We ask a simple but profound question: how does this three-fold green space morphology and economy influence housing prices and, by implication, locational preferences in the city? More specifically, we test the hypothesis that the privately supplied green spaces withing club-communities substitute for publicly supplied green spaces in the public realm. We find evidence in support of this hypothesis, showing for example, that unlike other kinds of green space, public district parks have no measurable use value to Shanghai home-buyers, using a hedonic valuation model. If urban planners in China understood that their cities have become ‘cities of clubs’, then they would plan open space differently and scarce public funds could be redirected to other public goods and services with measurable demand.",137.0,http://hdl.handle.net/10722/231580,True,,Economics,2016-07-01,936c6590606e5669e7af8c0cb16273ac696d2d36,estimatingthemediatingeffectofprivatelysuppliedgreenspaceontherelationshipbetweenurbanpublicgreenspaceandpropertyvalueevidencefromshanghaichina,yang,10.1016/j.landusepol.2016.03.001,estimatingthemediatingeffectofprivatelysuppliedgreenspaceontherelationshipbetweenurbanpublicgreenspaceandpropertyvalueevidencefromshanghaichina|2016,1,2886,3,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic property valuation,2,3,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,6755b33aebe85f33284871490a049fe4f6f9d9d2,10.1007/s00181-011-0485-9,,Nonparametric neural network modeling of hedonic prices in the housing market,2011.0,Empirical Economics,M. Landajo; C. Bilbao; A. Bilbao,"This article addresses the contribution to hedonic modeling of a nonparametric approach based on artificial neural network (ANN) regressions. ANNs provide consistent estimates for the hedonic price of each attribute and permit a number of hypotheses on the hedonic price relationship to be tested nonparametrically. In particular, we exploit results by Stinchcombe and White (Econom Theory 14:295–324, 1998) in order to carry out misspecification testing in linear and semiloglinear hedonic models. The same approach directly applies to testing misspecification of any parametric specification for the hedonic relationship. A nonparametric significance test for the variables in the hedonic model is also proposed. The test extends the approach developed by Racine (J Bus Econ Stat 15(3):369–378, 1997) in kernel-based nonparametric testing to ANN-based inference. The finite sample performance of the proposed tests is analyzed through Monte Carlo experiments, and simulation-based algorithms for computation of the null distribution of the tests are proposed. Then, the performance of three classes of regression models—linear, semi-log, and ANNs—applied to hedonic price modeling in a Spanish regional housing market is compared. Our results indicate the presence of nonlinear behavior, as predicted by economic theory, with the ANN-based tests detecting statistically significant evidence of misspecification—both in the linear and the semilog specifications—and ANN regressions providing moderate improvement of predictive performance.",28.0,http://hdl.handle.net/10651/7104,True,,Economics,2011-05-29,6755b33aebe85f33284871490a049fe4f6f9d9d2,nonparametricneuralnetworkmodelingofhedonicpricesinthehousingmarket,landajo,10.1007/s00181-011-0485-9,nonparametricneuralnetworkmodelingofhedonicpricesinthehousingmarket|2011,1,3040,2,apartment rent hedonic || hedonic housing prices,2,0,1,2,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic house price,27ea51454849aec008c131dc7851b22308ce20da,10.1016/j.eneco.2022.105955,,How heterogeneous is the impact of energy efficiency on dwelling prices? Evidence from the application of the unconditional quantile hedonic model to the Portuguese residential market,2022.0,Energy Economics,Rui Evangelista; J. A. E. Silva; Esmeralda A. Ramalho,"This paper investigates the impact of energy efficiency along the unconditional distribution of residential property prices in Portugal. Using a dataset of more than 256,000 residential property sales from 2009 to 2013, a period that covers an economic depression, unconditional quantile regression analysis reveals that the responsiveness to energy efficiency improvements is different not only as we move from lowto high-priced residential units but also for apartments and houses. While the former show a downward trend in the magnitude of energy efficiency coefficient estimates, the opposite occurs for houses. The latter market segment exhibits clear market discounts at the lowest quantiles of the price distribution, something that is not observable thought conditional mean and quantile regression analysis. Results suggest the existence of a different responsiveness to energy efficiency improvements in the Lisbon region when compared to the rest of the country and that the impact of the Energy Performance Certificate label increases throughout time across all price quantiles. As a by-product of this paper, an unconditional quantile price index shows that the impact of the crisis was not the same across the different market segments, with price decreases being more severe for lowthan high-priced properties.",14.0,http://hdl.handle.net/10400.5/21677,True,,,2022-03-01,27ea51454849aec008c131dc7851b22308ce20da,howheterogeneousistheimpactofenergyefficiencyondwellingpricesevidencefromtheapplicationoftheunconditionalquantilehedonicmodeltotheportugueseresidentialmarket,evangelista,10.1016/j.eneco.2022.105955,howheterogeneousistheimpactofenergyefficiencyondwellingpricesevidencefromtheapplicationoftheunconditionalquantilehedonicmodeltotheportugueseresidentialmarket|2022,1,2825,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,3,5,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),9e73789b4a3462de8b6626ea39704e1db08403f8,10.1016/j.habitatint.2018.05.012,,"Education quality, accessibility, and housing price: Does spatial heterogeneity exist in education capitalization?",2018.0,Habitat International,Hai-Zhen Wen; Yue Xiao; E. Hui; Ling Zhang,"Abstract Evaluating the capitalization effect of educational facilities via the real estate market has elicited considerable research attention. Although the spatial attribute of housing data is generally considered when constructing a hedonic price model, only a few studies investigated the spatial heterogeneity of educational capitalization in depth. To fill this gap, this study uses the housing data of 516 communities in Hangzhou, as well as constructs the hedonic price, spatial econometric and geographically weighted models to quantitatively evaluate the capitalization effect of educational facilities and explore whether space plays an important role in the capitalization of education. Results confirm that, from the perspective of educational quality or accessibility, kindergarten, primary school, junior high school, senior high school, and university significantly affect housing prices. Specifically, the quality of primary school and junior high school as well as the accessibility of university profoundly influence housing prices. The geographically weighted regression model (GWR) further reveals the existence of spatial heterogeneity in educational capitalization. Different types of educational facilities show significant spatial differences in the scope of influence and capitalization rate, thereby implying that homebuyers have different preferences for educational resources in different areas of the city. Comparisons of the three models affirm that the spatial econometric model can better handle problems caused by the spatial dependence of housing price, whereas the GWR has unique advantages in dealing with spatial heterogeneity and can obtain results that are substantially detailed.",127.0,http://hdl.handle.net/10397/78296,True,,Economics,2018-08-01,9e73789b4a3462de8b6626ea39704e1db08403f8,educationqualityaccessibilityandhousingpricedoesspatialheterogeneityexistineducationcapitalization,wen,10.1016/j.habitatint.2018.05.012,educationqualityaccessibilityandhousingpricedoesspatialheterogeneityexistineducationcapitalization|2018,1,3220,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression || spatial hedonic model housing",2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",017b1a98d43f4b936bd29fd19f0d052996156b9f,10.1061/(asce)up.1943-5444.0000270,,Heterogeneity in Spatial Correlation and Influential Factors on Property Prices of Submarkets Categorized by Urban Dwelling Spaces,2016.0,,C. Hui; Jia-Wei Zhong; Ka Yu,,17.0,http://hdl.handle.net/10397/55872,True,,Geography,2016-03-01,017b1a98d43f4b936bd29fd19f0d052996156b9f,heterogeneityinspatialcorrelationandinfluentialfactorsonpropertypricesofsubmarketscategorizedbyurbandwellingspaces,hui,10.1061/(asce)up.1943-5444.0000270,heterogeneityinspatialcorrelationandinfluentialfactorsonpropertypricesofsubmarketscategorizedbyurbandwellingspaces|2016,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,0018c4e3a517312ec210f1879e51e790265ebfcb,10.1016/j.buildenv.2006.05.004,,Measuring the neighboring and environmental effects on residential property value: Using spatial weighting matrix,2007.0,,E. Hui; Kwan-Chau Chi; Li H. H. Pun; M. Y. Law,,216.0,http://hdl.handle.net/10397/11096,True,,Business,2007-06-01,0018c4e3a517312ec210f1879e51e790265ebfcb,measuringtheneighboringandenvironmentaleffectsonresidentialpropertyvalueusingspatialweightingmatrix,hui,10.1016/j.buildenv.2006.05.004,measuringtheneighboringandenvironmentaleffectsonresidentialpropertyvalueusingspatialweightingmatrix|2007,1,1500,9,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment prices hedonic || hedonic house price || hedonic housing prices || hedonic price model amenities residential || housing characteristics prices hedonic || spatial hedonic model housing || willingness to pay housing attributes hedonic",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",48d25d8068f17010f84dade2136f696403c9bedc,10.1016/j.regsciurbeco.2018.02.001,,"The Effects of Cultural Heritage on Residential Property Values: Evidence from Lisbon, Portugal",2018.0,,Sofia F. Franco; Jacob L. Macdonald,"This paper examines the impact of historic amenities on residential housing prices in the city of Lisbon, Portugal. Our study is directed towards identifying the spatial variation of amenity values for churches, palaces, lithic (stone) architecture and other historic amenities via the housing market, making use of both global and local spatial hedonic models.Our empirical evidence reveals that different types of historic and landmark amenities provide different housing premiums. While having a local non-landmark church within 100 meters increases housing prices by approximately 4.2%, higher concentrations of non-landmark churches within 1000 meters yield negative effects in the order of 0.1% of prices with landmark churches having a greater negative impact around 3.4%. In contrast, higher concentration of both landmark and non-landmark lithic structures positively influence housing prices in the order of 2.9% and 0.7% respectively.Global estimates indicate a negative effect of protected zones, however this significance is lost when accounting for heterogeneity within these areas. We see that the designation of historic zones may counteract negative effects on property values of nearby neglected buildings in historic neighborhoods by setting additional regulations ensuring that dilapidated buildings do not damage the city’s beauty or erode its historic heritage.Further, our results from a geographically weighted regression specification indicate the presence of spatial non-stationarity in the effects of different historic amenities across the city of Lisbon with variation between historic and more modern areas.",68.0,http://hdl.handle.net/10362/17162,True,,Geography,2018-05-01,48d25d8068f17010f84dade2136f696403c9bedc,theeffectsofculturalheritageonresidentialpropertyvaluesevidencefromlisbonportugal,franco,10.1016/j.regsciurbeco.2018.02.001,theeffectsofculturalheritageonresidentialpropertyvaluesevidencefromlisbonportugal|2018,1,3137,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices",2,3,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,0f0224520307b2dd2be4e6bbbb275f0944da01db,10.1080/02692171.2018.1518412,,"Natives, immigrants and social cohesion: intra-city analysis combining the hedonic approach and a framed field experiment",2018.0,International Review of Applied Economics,R. Borgoni; Giacomo Degli Antoni; Marco Faillo; A. Michelangeli,"ABSTRACT The literature on the hedonic price approach applied to housing highlights the existence of natives’ preferences against living in urban areas with high foreign-born population. At the same time, empirical and experimental evidence show that ethnic fragmentation reduces social cohesion in society. Mainly because of the difficulty to measure social cohesion at the neighborhood level, the correlation between these two phenomena is still largely unexplored. In this paper, we investigate natives’ preferences for immigrants following an original approach that combines the hedonic price approach and a framed field experiment. The latter allows us to collect a measure of cooperation at the neighborhood level. We apply this methodology to the city of Milan. Our findings show that natives prefer to not live in dense immigrants neighborhoods. However, this preference is not attributable to an erosion of social cohesion in those areas.",2.0,http://hdl.handle.net/10281/282096,True,,Economics,2018-11-21,0f0224520307b2dd2be4e6bbbb275f0944da01db,nativesimmigrantsandsocialcohesionintracityanalysiscombiningthehedonicapproachandaframedfieldexperiment,borgoni,10.1080/02692171.2018.1518412,nativesimmigrantsandsocialcohesionintracityanalysiscombiningthehedonicapproachandaframedfieldexperiment|2018,1,2447,1,hedonic housing prices,2,0,1,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,89bf8db09db03368715327c4e4d81e12fe3cbace,10.1080/19463138.2011.552942,,Contamination by the Israeli military industry and its impact on apartment sale prices in an adjacent Tel Aviv neighborhood: a hedonic pricing model study,2011.0,,Itai Shelem; D. Lavee; N. Becker,"This study quantifies the effect of environmental degradation due to contamination from Taas Magen, an Israeli Defense Force industrial facility, on the nearby housing market. A model was constructed so as to isolate the impact of the contamination from that of the facility itself by incorporating information regarding public awareness of the contamination. The resulting regression analysis suggests that distance from the site of the facility affected home prices only after the general public became aware of the contamination. Therefore, it is specifically the environmental contamination, rather than the facility as of itself, that negatively impacted prices. As a result of the contamination, apartment prices fell by an average of $24,665 (2006 dollars), equal to approximately 14% of the sales price of an average apartment in the sample area. Total losses to the surrounding housing market are accordingly estimated at between $267 and $287 million. These loss estimates serve as a lower bound for the total social and economic costs incurred by the greater community due to the contamination, which are estimated to total at least $358 million. Assuming the government were to fund the estimated $33 million cleanup costs, a minimum gain of 1.5% in sale prices within this $2.2 billion housing market would create the necessary economic benefit to offset the cost of decontaminating the site. Similarly, a more technologically advanced, yet expensive, remediation process would require a gain of 10.1% in housing market prices to offset its costs. Ultimately, reclaiming a lost aquifer, reduction in human health risks, restoration of environmental integrity, and further increases in housing market value are all benefits of remediation that may greatly outweigh the required cleanup costs.",6.0,http://hdl.handle.net/10161/482,True,,Economics,2011-03-09,89bf8db09db03368715327c4e4d81e12fe3cbace,contaminationbytheisraelimilitaryindustryanditsimpactonapartmentsalepricesinanadjacenttelavivneighborhoodahedonicpricingmodelstudy,shelem,10.1080/19463138.2011.552942,contaminationbytheisraelimilitaryindustryanditsimpactonapartmentsalepricesinanadjacenttelavivneighborhoodahedonicpricingmodelstudy|2011,1,3304,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || apartment rent hedonic || hedonic housing prices",2,4,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",4623143311a81f444f198fa8b902f93a9a5e0904,10.1016/j.jenvman.2022.114908,,Valuing the improvement of a decommissioned heritage site to a multifunctional water sensitive greenspace.,2022.0,Journal of Environmental Management,M. S. Iftekhar; M. Polyakov; A. Rogers,"In this paper, we assess residents' preferences for different options of large-scale water sensitive urban improvement of a heritage site in Melbourne, the Main Outfall Sewer (MOS) reserve. We use a hedonic pricing analysis and a discrete choice experiment to understand the benefits associated with improving the MOS reserve. We find that there are significant non-market benefits associated with various options, both in terms of the potential to reflect increases in house value and in terms of people's willingness to pay for improvements in amenity and environmental quality. This information will contribute to determining investment options for this important heritage site. Importantly, we demonstrate the combination of the hedonic and stated choice willingness to pay estimates to provide an aggregate estimate of welfare, revealing a useful approach for greenspace valuation applications where multiple non-market values should be considered.",8.0,http://hdl.handle.net/10072/423279,True,JournalArticle,Medicine,2022-04-04,4623143311a81f444f198fa8b902f93a9a5e0904,valuingtheimprovementofadecommissionedheritagesitetoamultifunctionalwatersensitivegreenspace,iftekhar,10.1016/j.jenvman.2022.114908,valuingtheimprovementofadecommissionedheritagesitetoamultifunctionalwatersensitivegreenspace|2022,1,2453,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price",2,2,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,0b1e0730d8a12b2c22c910c60c31596da2879f1e,10.1016/j.renene.2020.07.085,,"House price premium associated with residential solar photovoltaics and the effect from feed-in tariffs: A case study of Southport in Queensland, Australia",2020.0,,Haifeng Lan; Z. Gou; Lin-Chuan Yang,"Abstract This study aims to contribute a regional experience of establishing a credible estimation of the house price premium associated with solar photovoltaics (PV) and to reveal the change of the premium in response to the changing feed-in tariffs (FiTs). This paper used Python to crawl records of sold residential properties in Southport, Queensland from 2008 to 2018. An experimental group of 316 solar PV houses and a control group of 228 non-solar PV houses were extracted for analysis. Using the hedonic approach, the study found that the gross premium was 4.3% of the average house price; the average net premium (exclusive of the PV installation cost) was 21,403 AUD when the FiT was at 44 cents/kWh; when the FiT dropped to 8 cents/kWh, the gross premium fell to 2.4%, and the average net premium dropped to 5600 AUD. The study shows that a solar PV house does enjoy a premium in terms of property value and indicates that a higher solar rebate and subsidy leads to a higher premium. It is commendable to provide some financial incentives to keep the premium within a reasonable range to encourage solar PV adoption.",22.0,http://hdl.handle.net/10072/409925,True,,Economics,2020-08-01,0b1e0730d8a12b2c22c910c60c31596da2879f1e,housepricepremiumassociatedwithresidentialsolarphotovoltaicsandtheeffectfromfeedintariffsacasestudyofsouthportinqueenslandaustralia,lan,10.1016/j.renene.2020.07.085,housepricepremiumassociatedwithresidentialsolarphotovoltaicsandtheeffectfromfeedintariffsacasestudyofsouthportinqueenslandaustralia|2020,1,2628,1,hedonic house price,2,2,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,35f21aee2cab38f964100dbf7c3a3da4ea0ac8cb,10.1016/j.jtrangeo.2020.102850,,Accessibility and proximity effects of bus rapid transit on housing prices: Heterogeneity across price quantiles and space,2020.0,,Lin-Chuan Yang; Xiaoling Chu; Z. Gou; Hong-Tai Yang; Yi Lu; Wencheng Huang,"Abstract Bus rapid transit (BRT) syste ms have mushroomed worldwide in the last few decades. An enriched understanding of BRT capitalization effects is essential. Although the BRT accessibility effect on housing prices has been extensively explored, the effect of proximity to the BRT corridor (which may be related to unattractive landscape and noise pollution) has been little scrutinized. More importantly, whether and how the two effects vary across price levels and space have yet to be sufficiently studied. To this end, we estimate the effect of BRT accessibility and proximity on housing prices by applying a battery of econometric methods (including hedonic pricing models, spatial regression models, quantile regression models, and a geographically weighted regression model) to 5185 observations in the housing market in Xiamen Island, China. The results of this study are: (1) BRT accessibility premiums and proximity penalties simultaneously exist in the housing market; (2) buyers of high-priced housing have a greater willingness to pay for avoiding the nuisances attributed to proximity to the BRT corridor; (3) the effect of BRT on housing prices is spatially heterogeneous; (4) the BRT accessibility effect is larger in suburban areas than in urban areas; and (5) housing prices are more predictable near the city centers than outside the area, which may be because a greater proportion of the price of a house near the city centers is derived from the location (rather than the building structure). Finally, policy implications (e.g., building acoustic barriers and planting vegetation along the BRT corridor and improving the transit service in suburban areas) are discussed.",147.0,http://hdl.handle.net/10072/397458,True,,Business,2020-10-01,35f21aee2cab38f964100dbf7c3a3da4ea0ac8cb,accessibilityandproximityeffectsofbusrapidtransitonhousingpricesheterogeneityacrosspricequantilesandspace,yang,10.1016/j.jtrangeo.2020.102850,accessibilityandproximityeffectsofbusrapidtransitonhousingpricesheterogeneityacrosspricequantilesandspace|2020,1,3195,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices || house prices proximity hedonic || spatial hedonic model housing || willingness to pay housing attributes hedonic",2,1,2,6,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",d23f164194d63279863ed0d88eb4eafba8f3202f,10.1016/j.jtrangeo.2016.05.010,,"Residential property value impacts of proximity to transport infrastructure: An investigation of bus rapid transit and heavy rail networks in Brisbane, Australia",2016.0,,C. Mulley; Liang Ma; G. Clifton; Barbara T. H. Yen; M. Burke,"Public transport investment is normally targeted at increasing accessibility which land rent theory identifies and will in turn increase land values. There is a clear policy interest in how much land values increase following a new transport investment so as to establish if there is sufficient land value uplift to capture and to help pay or contribute to investment plans. Identifying an uplift for residential land has been well studied in the context of new light rail systems and bus rapid transit (BRT) systems in developing countries but there is little evidence for BRT in developed countries. This paper has two objectives. First, to examine long term impact of BRT in a developed world context in Brisbane, Australia. Brisbane's BRT uses an open system design which contrasts with the closed system design of the successful BRT systems in South America and elsewhere, including the BRT in the suburbs of Sydney, Australia. Second, BRT in Brisbane was introduced to a network already dominated by a radial heavy rail network and this investigation recognises that the uplift from BRT introduction may therefore be different to a BRT in a single mode city. A third motivation is to consider the spatial distribution of uplift which is an essential pre-requisite to understanding the distributional impact if uplift is used to contribute to infrastructure provision. Spatial modelling is used to examine the accessibility impacts of the BRT at a global level. This is followed by Geographical Weighted Regression, used to examine the spatial distribution of accessibility using a local model. The results show that there is greater uplift in Brisbane, as compared to that identified by studies of Sydney's BRT which is likely due to the greater network coverage of BRT in Brisbane and less strong competition of rail. Land value uplift is also spatially distributed over the network giving higher uplift in some areas than others and lower values than typically found with rail based systems in developed countries. However, the degree of uplift is relatively low, with proximity to BRT stations attracting more uplift than proximity to train stations.",118.0,http://hdl.handle.net/10072/142793,True,,Geography,2016-06-01,d23f164194d63279863ed0d88eb4eafba8f3202f,residentialpropertyvalueimpactsofproximitytotransportinfrastructureaninvestigationofbusrapidtransitandheavyrailnetworksinbrisbaneaustralia,mulley,10.1016/j.jtrangeo.2016.05.010,residentialpropertyvalueimpactsofproximitytotransportinfrastructureaninvestigationofbusrapidtransitandheavyrailnetworksinbrisbaneaustralia|2016,1,3668,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b9e94cc5a90b6472a16be368f1a7bbb01e160d6a,10.4108/eai.2-12-2022.2328696,,Impact of Air Quality on Housing Prices: A Machine Learning Approach,2023.0,"Proceedings of the 3rd International Conference on Big Data Economy and Information Management, BDEIM 2022, December 2-3, 2022, Zhengzhou, China",Zulin Feng; Xinlong Li,,1.0,http://eudl.eu/pdf/10.4108/eai.2-12-2022.2328696,True,Conference,,,b9e94cc5a90b6472a16be368f1a7bbb01e160d6a,impactofairqualityonhousingpricesamachinelearningapproach,feng,10.4108/eai.2-12-2022.2328696,impactofairqualityonhousingpricesamachinelearningapproach|2023,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,housing characteristics prices hedonic,5414750679bef3fcbaf446be0f3f65b5dd3c82f2,10.4108/eai.2-12-2022.2328005,,Research on Spatial Differentiation of housing Price and Its Influencing Factors from the Perspective of Aging ——A Hedonic Analysis in Guangzhou,2023.0,"Proceedings of the 2nd International Conference on Information, Control and Automation, ICICA 2022, December 2-4, 2022, Chongqing, China",Yihua Mao; Qingwen Chen; Zhao-Wen Zhang; Yu-Chen Hu,"-The architectural characteristics of the house itself determine the basic value of the house, and the house price is also often tied to resources such as education, medical care, transportation, and environment, etc. Under the trend of population aging, the aging transformation of living environment has become the focus of social attention. Using Hedonic Model, the paper built an Overall Housing Price Model and two Sub Models in order to examine the impact of the characteristics related to aging suitability on housing price. Then the paper compared the Sub models to the Overall Model to explore the spatial heterogeneity of housing price. Based on the statistical results, some suggestions were finally put forward for the construction of suitable old living environment.",1.0,http://eudl.eu/pdf/10.4108/eai.2-12-2022.2328005,True,Conference,,,5414750679bef3fcbaf446be0f3f65b5dd3c82f2,researchonspatialdifferentiationofhousingpriceanditsinfluencingfactorsfromtheperspectiveofagingahedonicanalysisinguangzhou,mao,10.4108/eai.2-12-2022.2328005,researchonspatialdifferentiationofhousingpriceanditsinfluencingfactorsfromtheperspectiveofagingahedonicanalysisinguangzhou|2023,1,2279,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,3,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7d1cab9cb4a23353a91e331ad601c8e52aeb0810,10.4108/eai.18-11-2022.2327165,,Research on Housing Price Forecasting Model Based on Multiple Linear Regression Model and Neural Network Model,2023.0,"Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China",Rui-Hong Xu,"——There is a huge demand for the housing market in China. Effective forecasting models can provide reference for potential house buyers, thus avoiding blind purchase. In addition, policymakers can adjust real estate policies based on model predictions to reduce the stagnancy of the real estate markets, prevent the occurrence of economic crisis and maximize the benefits1.[1] Based on the second-hand housing data of Bao 'an District, Shenzhen city, this paper establishes multiple linear regression model and neural network model respectively, and tests the model with test set data, and obtains the conclusion that the neural network model is better in the accuracy of describing experimental data and fitting effect. Besides, this paper studies the role of multiple linear regression models in helping consumers to buy houses on demand and in helping real estate practitioners to set house prices according to influencing factors. The multiple linear regression model can help consumers and real estate practitioners to intuitively understand the factors that significantly affect housing prices. Furthermore, consumers can combine their own needs based on these factors, select the houses with conditions and prices that meet expectations; real estate practitioners can combine reality, develop an appropriate price strategy.",1.0,http://eudl.eu/pdf/10.4108/eai.18-11-2022.2327165,True,Conference,,,7d1cab9cb4a23353a91e331ad601c8e52aeb0810,researchonhousingpriceforecastingmodelbasedonmultiplelinearregressionmodelandneuralnetworkmodel,xu,10.4108/eai.18-11-2022.2327165,researchonhousingpriceforecastingmodelbasedonmultiplelinearregressionmodelandneuralnetworkmodel|2023,1,2830,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1ce1eaf3983315cb017b5669a5fa56ba93a3f60d,10.1080/13658816.2013.865739,,Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data,2014.0,International Journal of Geographical Information Science,Binbin Lu; M. Charlton; P. Harris; A. Fotheringham,,387.0,http://eprints.maynoothuniversity.ie/7852/1/MC_hedon%202014.pdf,True,JournalArticle,Geography;Computer Science,2014-04-01,1ce1eaf3983315cb017b5669a5fa56ba93a3f60d,geographicallyweightedregressionwithanoneuclideandistancemetricacasestudyusinghedonichousepricedata,lu,10.1080/13658816.2013.865739,geographicallyweightedregressionwithanoneuclideandistancemetricacasestudyusinghedonichousepricedata|2014,1,1500,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cad4660b3043692c5ff46aec533e4cee4536b3b3,10.5935/1808-2785/rem.v20n1p.173-202,,Os determinantes fundamentais dos preços dos imóveis residenciais: um estudo para o Brasil,2023.0,Revista de Economia Mackenzie,Guilherme Moreira da Costa Lopes; Ari Francisco de Araujo; Luiz Carlos Day Gama,"This study aims to analyze the main determinants of housing prices in Brazil. Considering that real estate are very heterogenic different methodologies were adopted in order to analyze the residential real estate market, both from a macro and micro perspective. In the first approach, models by Pooled OLS, Fixed Effects and Random Effects were estimated",0.0,http://editorarevistas.mackenzie.br/index.php/rem/article/download/15777/11936,True,JournalArticle,,,cad4660b3043692c5ff46aec533e4cee4536b3b3,osdeterminantesfundamentaisdosprecosdosimoveisresidenciaisumestudoparaobrasil,lopes,10.5935/1808-2785/rem.v20n1p.173-202,osdeterminantesfundamentaisdosprecosdosimoveisresidenciaisumestudoparaobrasil|2023,1,1854,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || preços hedônicos imóveis",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a422b7522f572351a603966868023463890756e2,10.70088/8f92z263,,An Empirical Study on the Determinants of Housing Prices in Beijing and Model Optimization,2024.0,"Science, Technology and Social Development Proceedings Series",Xiang Lai,"This paper aims to investigate the potential factors influencing housing prices in Beijing and optimize the prediction model. Using the Kaggle dataset ""Housing Price in Beijing"", multiple factors affecting housing prices were examined, including square footage, bedroom count, bathroom count, follower count, the presence of an elevator, and subway proximity. The study initially established a multiple linear regression model (MLR) and then optimized the model through variable selection and hypothesis testing. The final model indicates that square footage, bathroom count, follower count, elevator presence, and subway accessibility significantly impact housing prices. Additionally, subway proximity positively correlates with housing prices, while increased square footage is negatively associated with price, possibly due to lower unit costs for larger properties and the higher market demand for smaller homes. By validating the model's performance using a test dataset, the final model demonstrates effective predictive capability and offers insights for future model improvements.",0.0,http://dx.doi.org/10.70088/8f92z263,True,JournalArticle,,2024-11-09,a422b7522f572351a603966868023463890756e2,anempiricalstudyonthedeterminantsofhousingpricesinbeijingandmodeloptimization,lai,10.70088/8f92z263,anempiricalstudyonthedeterminantsofhousingpricesinbeijingandmodeloptimization|2024,1,2589,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),4dadf8fb379cd36ae0e8cd674bf573d3ec2efb44,10.56065/ijusv-ess/2023.12.3.100,,Constructing a Hedonic Pricing Model for the Rental Market in Varna,2023.0,Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series,Svetlana Todorova,"The aim of the research is to apply a hedonic pricing model to an apartment rental market in the city of Varna, Bulgaria. This widespread model has been used extensively in the various aspects of the rental markets. The sample size of the study is 59 apartments for rent offered on the market in “Chaika” living estate in the city of Varna, published online at “ALO.bg” as of September 1, 2023. We have chosen this specific date, because at that time the market is very brisk. On one hand, the demand of the apartments increases, because of the beginning of the school year, and on the other hand, this period coincides with seasonal layoff unemployment and seasonal labor moving out of the apartments, which they have been renting for the summer jobs. The results of the study explain above 90 percent of variation in rents in Varna, emphasizing the most important factors.",0.0,http://dx.doi.org/10.56065/ijusv-ess/2023.12.3.100,True,JournalArticle,,2023-12-01,4dadf8fb379cd36ae0e8cd674bf573d3ec2efb44,constructingahedonicpricingmodelfortherentalmarketinvarna,todorova,10.56065/ijusv-ess/2023.12.3.100,constructingahedonicpricingmodelfortherentalmarketinvarna|2023,1,2374,2,apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,1,2,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",94366bb23458480f07b64e5b6f9cbf042da33b71,10.54097/z336qy32,,Study of Influential Factors on Housing Prices by Using MLR,2024.0,Highlights in Science Engineering and Technology,Chang Liu,"With the emergence of the 2019 pandemic, the global economy experienced a downturn, but the housing market quickly rebounded post-pandemic, heightening the anxiety surrounding home buying. Previous research predominantly employed regression models to forecast housing prices, but these studies largely focused on specific factors. Hence, this article aims to offer a more comprehensive viewpoint by exploring the impact of multiple independent variables on housing prices, particularly emphasizing the effects of the age of buildings, the surrounding environment, and architectural factors. The methodology used in this study is the Multiple Linear Regression (MLR) model. It analyzes the real estate market data in Taipei, meticulously constructing and validating multiple models. The findings reveal that the age of buildings and the distance to the nearest subway station negatively influence housing prices, whereas the number of convenience stores positively impacts them. Among these factors, the quantity of convenience stores exerts the most significant effect on housing prices. Overall, this article provides a novel perspective and tools for understanding and predicting housing prices, assisting real estate developers and buyers in making more informed decisions in the complex real estate market. This study highlights the multidimensional nature of real estate value and contributes to the sustainable development of the real estate market.",1.0,http://dx.doi.org/10.54097/z336qy32,True,JournalArticle,,2024-04-10,94366bb23458480f07b64e5b6f9cbf042da33b71,studyofinfluentialfactorsonhousingpricesbyusingmlr,liu,10.54097/z336qy32,studyofinfluentialfactorsonhousingpricesbyusingmlr|2024,1,2955,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",408d3bf9cac6f3c6436c9a7bfa765b8d468b9ea8,10.54097/k95s1e12,,Impact of School Quality on House Prices in London and Bristol: Shifts in Urban Housing Dynamics During the COVID-19,2024.0,"Highlights in Business, Economics and Management",Wanhe Li; Yuanzhe Li,"This study examines the impact of school quality on house prices in London and Bristol before, during, and after the COVID-19 pandemic. Using cross-sectional data, the analysis highlights the relationship between proximity to high-quality state schools and residential property values, revealing shifts in buyer priorities influenced by pandemic-related factors such as urban exodus and changing educational dynamics. The findings demonstrate that while primary school quality positively correlates with house prices, the strength of this relationship has weakened during the pandemic as buyers increasingly prioritize property-specific characteristics. In contrast, secondary school quality shows varied effects across large and small cities, reflecting differences in school admission criteria and urban characteristics. The persistence of a house price premium near good primary schools suggests ongoing educational inequalities, as access to high-quality education remains limited to wealthier families. These results underscore the need for targeted policy interventions to address the entrenched disparities in educational opportunities and housing affordability in post-pandemic England.",0.0,http://dx.doi.org/10.54097/k95s1e12,True,JournalArticle,,2024-11-19,408d3bf9cac6f3c6436c9a7bfa765b8d468b9ea8,impactofschoolqualityonhousepricesinlondonandbristolshiftsinurbanhousingdynamicsduringthecovid19,li,10.54097/k95s1e12,impactofschoolqualityonhousepricesinlondonandbristolshiftsinurbanhousingdynamicsduringthecovid19|2024,1,2694,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",44517755c481953c8725d3d3d8d06b6fc14f1b6a,10.54097/hkgqcv31,,Analysis of the Influencing Factors and Predictions of Poston house Prices based on a Multiple Linear Regression Model,2024.0,Highlights in Science Engineering and Technology,Liangyu Jiang,"In this study, the classic Boston house price data set is selected for the analysis of house price correlation. According to the variables in the Boston housing price data set, the linear regression model of Boston housing prices is established by using Python software. The regression equation and regression coefficient were tested for significance, excluding the variable of p >=0.5, multiple linear regression was carried out, and the regression equation with good fitting was obtained. It is found that there are too many variables after multiple linear regression, which is difficult to analyze and predict, so the correlation analysis of variables is carried out. This paper gets the percentage of lower status population, pupil-teacher ratio and average number of rooms per dwelling with medv (The median quoted price for an owner-occupied home, $1,000 per unit) has a significant relationship. Finally, a linear regression equation is established for the independent variable whose correlation coefficient is greater than 0.5, and the housing price is predicted.",0.0,http://dx.doi.org/10.54097/hkgqcv31,True,JournalArticle,,2024-04-10,44517755c481953c8725d3d3d8d06b6fc14f1b6a,analysisoftheinfluencingfactorsandpredictionsofpostonhousepricesbasedonamultiplelinearregressionmodel,jiang,10.54097/hkgqcv31,analysisoftheinfluencingfactorsandpredictionsofpostonhousepricesbasedonamultiplelinearregressionmodel|2024,1,2571,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,4,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6927ee2814dc88855faef41002779b2fa6f834fb,10.54097/ezaycc33,,Effect of Housing Factors on Price in Seattle During the COVID-19 Pandemic,2024.0,Highlights in Science Engineering and Technology,Hao-Quan Hu,"Due to the influence of the COVID-19 pandemic, global economic trends have faced an immense change. The major difficulty experienced by workers, laborers, and households are the tremendous changes in housing prices in every part of the world which are both hard to bear and almost impossible to forecast from current factors. With a particular dataset based on the housing price in Seattle, Oregon, USA. This research will use a multiple linear regression model to predict the housing price through various housing factors and to compare the accuracy of the prediction and the relationship between variables by contrasting the train and test set whilst also constructing a residual graph. The result ends up with a moderate accuracy due to the influence of multicollinearity between the factors, this can be seen through the variance Inflation Factor. The process requires the standardization of the data in order to make the test legitimate.",0.0,http://dx.doi.org/10.54097/ezaycc33,True,JournalArticle,,2024-02-27,6927ee2814dc88855faef41002779b2fa6f834fb,effectofhousingfactorsonpriceinseattleduringthecovid19pandemic,hu,10.54097/ezaycc33,effectofhousingfactorsonpriceinseattleduringthecovid19pandemic|2024,1,2442,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d75d9e4223d7ad93daf7829b2dd31ea5b1329ae8,10.54097/4pa78x23,,The Research on Factors Influencing Housing Prices-Take Beijing as an Example,2024.0,Highlights in Science Engineering and Technology,Bowen Chen; Yi Min; Siyu Yu,"This article aims to identify those factors that have an impact on housing prices. The method of Multiple Linear Regression is used to analyze the significant factors with 400 samples of Beijing from 2011 to 2017. Based on an assumption, 12 variables that were chosen do correlate with housing prices. This paper also considers the interaction effects between house squares and the number of living rooms, drawing rooms or bathrooms, and uses Forward Stepwise Regression to solve the covariance problem caused by adding interaction terms. In order to test the effectiveness of this operation, the research compares the VIF value and significance of those variables. It turns out that square, building type, elevator, construction time, renovation condition, and subway have a significant linear relationship with prices, while the number of living rooms, drawing rooms and bathrooms, building structure, property rights, and district fail the significance test. Overall, the volatility of housing prices in Beijing can be considered by the extent to which these factors affect them.",0.0,http://dx.doi.org/10.54097/4pa78x23,True,JournalArticle,,2024-03-29,d75d9e4223d7ad93daf7829b2dd31ea5b1329ae8,theresearchonfactorsinfluencinghousingpricestakebeijingasanexample,chen,10.54097/4pa78x23,theresearchonfactorsinfluencinghousingpricestakebeijingasanexample|2024,1,2582,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9e0670b378f7e6384baeef40d212142bf17182db,10.54097/2nz8xe97,,Research on the Relationship between Influencing Factors and House Prices Changes,2024.0,Highlights in Science Engineering and Technology,Zixuan Lyu,"In recent years, the change in house prices has also become a main factor affecting economic changes. There are lot of research shows that the relationship between economics and house prices. This study focuses on how house prices change and what factors can lead to the change in house prices. The data from the Kaggle website, containing property prices in Delhi. By using the linear regression model, calculate 12 factors which are Price, Area, Bedrooms, Bathrooms, stories, main road, guestroom, basement, hotwaterheating, airconditioning, parking, prefarea, and furnishing status to study the degree and trend of impact on house prices changes. The results show that area, bathrooms, stories, guestroom, basement, hotwaterheating, airconditioning, parking, prefarea will have a significant positive impact on price, and furnishing status will have a significant negative impact on price. Bedrooms have no impact on price. This study opens a new perspective and giving society a new thinking angle for the study of housing prices changes.",0.0,http://dx.doi.org/10.54097/2nz8xe97,True,JournalArticle,,2024-05-08,9e0670b378f7e6384baeef40d212142bf17182db,researchontherelationshipbetweeninfluencingfactorsandhousepriceschanges,lyu,10.54097/2nz8xe97,researchontherelationshipbetweeninfluencingfactorsandhousepriceschanges|2024,1,2542,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a33e87e08f5352946b6ca86f418d09212ce1b232,10.5220/0013055900004601,,The Study on the Influencing Factors of Housing Price,2024.0,"Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy",Zhiyi Chen,": This paper aims to find out the factors that affect the housing price. Multiple Linear Regression method was used to analyze the significant factors between May 2014 and May 2015 in King County. Through the graph test, 13 of the 15 variables selected are related to house prices. In the model analysis, the p values and the VIF values of these variables were compared, and the accuracy of the model was confirmed. The results showed that the number of bedrooms, number of bathrooms, living area, waterfront, view level, overall condition, building grade, above ground area, below ground area, year of renovation, there is a significant linear relationship between the living area and lot size of neighbors and the housing price, and there is no significance test between the number of floors of the building and the housing price. In general, the fluctuation of home prices in King County can be considered by the extent to which these factors affect home prices.",0.0,http://dx.doi.org/10.5220/0013055900004601,True,Conference,,,a33e87e08f5352946b6ca86f418d09212ce1b232,thestudyontheinfluencingfactorsofhousingprice,chen,10.5220/0013055900004601,thestudyontheinfluencingfactorsofhousingprice|2024,1,2465,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,residential property price determinants,4b369244d2927a31600a5733c54206c65d0a4272,10.46299/j.isjmef.20240303.05,,Statistical analysis of main determinant’s role in the value formation of residential property assets in Ukraine,2024.0,"International Science Journal of Management, Economics & Finance",V. Yakubovsky; Kateryna Zhuk; Maria Ankudovych,"The objective of this study is to investigate the influence of both qualitative and quantitative factors on real estate valuation within the Ukrainian residential market. Leveraging a comprehensive dataset from the Ukrainian real estate market, encompassing essential property characteristics and historical pricing data, specific indicators were identified to discern primary determinants impacting residential property values. Through the development of multivariate regression models, this research ranks qualitative indicators to facilitate their integration into the analysis. Regression analysis conducted over data spanning from 2021 to 2023 reveals significant factors affecting housing prices across different segments, including apartments, land plots, and households. Notable determinants include area, building type, renovation quality, layout, heating system, and natural gas availability for apartments; area, distance to the nearest city, soil type, location, natural surroundings, and land use for land plots; and property type, location, and area for households. This study contributes to the understanding of real estate valuation dynamics by employing regression analysis to scrutinize the influence of various factors on property prices. Utilizing data sourced from the continually updated information and analytical database of the Ukrainian real estate market, this research offers valuable insights into the drivers shaping property valuation trends in the region. Notably, the inclusion of individual factor sets comprehensively describing housing price formation, along with the utilization of factor analysis approaches across all three segments of the secondary real estate market in Ukraine, underscores the novelty and significance of this study.",0.0,http://dx.doi.org/10.46299/j.isjmef.20240303.05,True,JournalArticle,,2024-06-01,4b369244d2927a31600a5733c54206c65d0a4272,statisticalanalysisofmaindeterminantsroleinthevalueformationofresidentialpropertyassetsinukraine,yakubovsky,10.46299/j.isjmef.20240303.05,statisticalanalysisofmaindeterminantsroleinthevalueformationofresidentialpropertyassetsinukraine|2024,1,3275,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || residential property price determinants",0,4,4,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",19df264cd86957e8b1d3d02e2a84274c27d62d3c,10.37634/efp.2024.5.5,,Economic and mathematical model of residential property prices in Dnipro city,2024.0,Economics. Finances. Law,I. Pistunov; Ivan Kapinus,"The paper develops an economic-mathematical model of housing prices in Dnipro city. Data for 2024 were collected, which included: Price per square meter (US dollar); Distance from the center (km); Total area (square meter); Number of rooms; Floor; Type of walls; Repair; Availability of autonomous heating; Height difference; View of windows; Guest bathroom; View of the ceiling; View of the hall floor; Guest area; Bedroom; Floor in the kitchen; Bathroom floor; Type of heating batteries; Ceiling height; m; Residential area (square meter); Kitchen area (square meter); Balcony/loggia; Air conditioners; Bathroom; Kitchen furniture; Bathroom furniture; Bedroom furniture; Furniture in the living room; Machinery; Availability of Wi-Fi; Bathroom area (square meter); Lighting devices in the kitchen; Lighting devices in the bathroom; Lighting devices in the bedroom; Lighting devices in the living room; Year of construction of the building. 24 classifiers were created to determine quantitative signs of qualitative characteristics of housing, in which class numbers start from 1. Based on these data, k-means clustering was carried out, as a result, 6 different classes were formed. An economic-mathematical model was created for each class, built on the basis of neural networks implemented in the Statistica software package. Each grid included three layers, and the hidden layer had at least 50-100 neurons. The quality of the model for each class ranged from 75% to 100%. Classification by the method of neural networks was preceded by a check of the level of correspondence of the previously found class of the object. It was recognized that in 70% of the objects, the class coincided with the significant class by the k-means method. The calculation of the price of a new apartment begins with the classification of its parameters with the determination of the number and the choice of the cluster to which it should be assigned. Next, the price is calculated according to the model of the corresponding cluster. Since for some clusters from 2 to 5 models are calculated, the average of the predicted price is found, and its standard serves as a possible border for trading between the buyer and the seller. Checking the accuracy of the model on data that was not included in the construction of the model showed an average deviation within 7 %.",0.0,http://dx.doi.org/10.37634/efp.2024.5.5,True,JournalArticle,,2024-05-31,19df264cd86957e8b1d3d02e2a84274c27d62d3c,economicandmathematicalmodelofresidentialpropertypricesindniprocity,pistunov,10.37634/efp.2024.5.5,economicandmathematicalmodelofresidentialpropertypricesindniprocity|2024,1,3853,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,ce99fea2df84a3db4ec858a1d9b09e166bf15099,10.12928/jampe.v3i2.9335,,Application of Hedonic Pricing Model and Geographic Information System on Property Valuation in Yogyakarta,2024.0,JAMPE (Journal of Asset Management and Public Economy),Nurisqi Amalia; Nur Aini Yuniyarti; F. Prativi,"This study aims to: 1) identify the differences in housing choices between Generation Z (students) and the previous generation (parents or guardians); 2) determine the factors influencing housing purchase decisions; and 3) map the areas of interest for Universitas Gadjah Mada (UGM) students based on price. The research employs a combination of the Hedonic Pricing Model (HPM) and Geographic Information System (GIS), a method that remains limited in the field of property research. The findings reveal that significant factors affecting property prices are building area and location, with the majority of UGM students choosing to reside in Ngaglik District, Sleman Regency. Sleman Regency is also the predominant area where UGM students purchase housing in Yogyakarta. Key attributes influencing UGM students' housing decisions include a safe neighbourhood, residential environment, and distance from waste centers. This research contributes valuable information to property appraisers regarding property price distribution in Yogyakarta and its influencing factors, and provides crucial insights for effective decision-making and policy implementation in regional economic development planning, especially in less popular areas.",0.0,http://dx.doi.org/10.12928/jampe.v3i2.9335,True,JournalArticle,,2024-07-21,ce99fea2df84a3db4ec858a1d9b09e166bf15099,applicationofhedonicpricingmodelandgeographicinformationsystemonpropertyvaluationinyogyakarta,amalia,10.12928/jampe.v3i2.9335,applicationofhedonicpricingmodelandgeographicinformationsystemonpropertyvaluationinyogyakarta|2024,1,2732,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || cites:sirmans2006 || hedonic housing prices || hedonic property valuation",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,a8dc6e5d1f57e3ecbb8de52c7d58727bfcb7784c,10.1080/09599916.2024.2339813,2307.13232,"Changes in risk appreciation, and short memory of house buyers when the market is hot, a case study of Christchurch, New Zealand",2023.0,Journal of Property Research,E. Mendoza; Fabian Dunker; M. Reale,"ABSTRACT In this paper, house prices in Christchurch are analysed over three distinct periods of time: post-2011 earthquake, pre-COVID-19 lockdown, and post-COVID-19 lockdown. The first method used is the well-established hedonic price model, additionally a second method incorporates random effects into the model to capture unobserved heterogeneity in buyers’ preferences for certain features of a house. Results show that buyers, in periods that are temporally distant from the 2011 Christchurch earthquake, underappreciate the risk of potential earthquake damage to a property. We find that there are observable shifts in buyer preference across the different time periods, specifically in terms of section size. We also examine heterogeneity and correlations in buyers’ preferences for floor area, land area, and age of a property.",0.0,http://dx.doi.org/10.1080/09599916.2024.2339813,True,JournalArticle,Economics;Mathematics,2023-07-25,a8dc6e5d1f57e3ecbb8de52c7d58727bfcb7784c,changesinriskappreciationandshortmemoryofhousebuyerswhenthemarketishotacasestudyofchristchurchnewzealand,mendoza,10.1080/09599916.2024.2339813,changesinriskappreciationandshortmemoryofhousebuyerswhenthemarketishotacasestudyofchristchurchnewzealand|2023,1,2336,1,hedonic house price,2,2,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cb5a7b1e772084f00688ab23f5c51c41012553e1,10.1257/app.20220329,,The Simpler the Better? Threshold Effects of Energy Labels on Property Prices and Energy Efficiency Investments,2025.0,American Economic Journal: Applied Economics,Rodolfo Sejas-Portillo; Mirko Moro; Till Stowasser,"Using British data on over 7 million residential property sales, we find that the simplified energy efficiency (EE) information leads to important market effects. UK legislation requires the use of an energy label that shows an EE rating score (1–100) mapped into arbitrary color-coded rating bands (A–G). Applying a regression discontinuity design, we document significant price discontinuities at rating band thresholds, indicating heuristic thinking in the market. These price discontinuities influence market behavior as sellers are more likely to make EE investments if their property is just below a threshold. We discuss policy considerations and avenues for future research. (JEL D91, K32, Q48, R31)",13.0,http://dspace.stir.ac.uk/bitstream/1893/36477/2/The_Simpler_The_Better_%20Online_Appendix.pdf,True,JournalArticle,,2025-01-01,cb5a7b1e772084f00688ab23f5c51c41012553e1,thesimplerthebetterthresholdeffectsofenergylabelsonpropertypricesandenergyefficiencyinvestments,sejas-portillo,10.1257/app.20220329,thesimplerthebetterthresholdeffectsofenergylabelsonpropertypricesandenergyefficiencyinvestments|2025,1,2207,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",41e682d558ebafb0606827d8675a866843204a7b,10.1016/j.wasman.2016.10.036,,Monetising the impacts of waste incinerators sited on brownfield land using the hedonic pricing method.,2017.0,Waste Management,M. Rivas-Casado; Jan Serafini; J. Glen; A. Angus,"In England and Wales planning regulations require local governments to treat waste near its source. This policy principle alongside regional self-sufficiency and the logistical advantages of minimising distances for waste treatment mean that energy from waste incinerators have been built close to, or even within urban conurbations. There is a clear policy and research need to balance the benefits of energy production from waste incinerators against the negative externalities experienced by local residents. However, the monetary costs of nuisance emissions from incinerators are not immediately apparent. This study uses the Hedonic Pricing Method to estimate the monetary value of impacts associated with three incinerators in England. Once operational, the impact of the incinerators on local house prices ranged from approximately 0.4% to 1.3% of the mean house price for the respective areas. Each of the incinerators studied had been sited on previously industrialised land to minimise overall impact. To an extent this was achieved and results support the effectiveness of spatial planning strategies to reduce the impact on residents. However, negative impacts occurred in areas further afield from the incinerator, suggesting that more can be done to minimise the impacts of incinerators. The results also suggest that in some case the incinerator increased the value of houses within a specified distance of incinerators under specific circumstances, which requires further investigation.",36.0,http://dspace.lib.cranfield.ac.uk/handle/1826/11393,True,Review;JournalArticle,Medicine;Business,2017-03-01,41e682d558ebafb0606827d8675a866843204a7b,monetisingtheimpactsofwasteincineratorssitedonbrownfieldlandusingthehedonicpricingmethod,rivas-casado,10.1016/j.wasman.2016.10.036,monetisingtheimpactsofwasteincineratorssitedonbrownfieldlandusingthehedonicpricingmethod|2017,1,3002,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,de011ea262dd3008c96179205bb5c6159c58b619,10.31357/fmscmst.2014.00200,,Determinants of Urban Middle Income Housing Prices: A Hedonic Regression Analysis,2020.0,,A.B.C.S. Ariyasena,"In real estate housing valuation, determination of housing value supported on sales comparison approach for similar housing in a particular market area remains the most widely used method. Given this situation, the hedonic model should be seen as a solution to improve the quality of assessments, as it determines the price of housing according to the observable values of their different attributes. This dissertation aims to assess the determinants of urban middle income housing prices and finally develop real estate price indexes for urban middle income housing prices in Kurunegala Municiple council area. Two no of urban housing schemes named as Millennium City & Viltherea Uyana consist with total no of 284 housing units sold during the period of 2002-20 12 and 122 no of housing units from Millennium City and 85 no of housing units from Vilthera Uyana, selected as the sample for this study 16 no of variables in total, Quantitative variables are plot area, house area, age of house units, number of bedrooms, and number of bathrooms, distance from nearest bus stop, distance from nearest town/ shopping centers, distance to entertainment facility, distance to quality of public schools, distance to hospital & health centers and Qualitative variable are presence of garage, with a view of river or lake, close & view of mountains, affect noise from traffic, and affect crime & vandalism, security of housing scheme. It was",0.0,http://dr.lib.sjp.ac.lk/bitstream/123456789/4282/5/Determinants%20of%20urban%20middle%20income%20housing%20prices%20A%20Hedonic%20Regression%20Analysis%20-Case%20study%20in%20Kurunegala%20municipal%20council%20area-001-015.pdf,True,,,,de011ea262dd3008c96179205bb5c6159c58b619,determinantsofurbanmiddleincomehousingpricesahedonicregressionanalysis,ariyasena,10.31357/fmscmst.2014.00200,determinantsofurbanmiddleincomehousingpricesahedonicregressionanalysis|2020,1,2934,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || real estate hedonic regression",2,4,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,7ff3891712d8f4e4350bef70731a3e0b7d6018d2,10.1155/2016/1478413,,The Impact of Subway Lines on Residential Property Values in Tianjin: An Empirical Study Based on Hedonic Pricing Model,2016.0,,Hui Sun; Yu-Ning Wang; Qing-Bo Li,"The construction of subways has an external impact on the urban environment. Among them, the most important acceleration function lies in the property values of housing near subway stations. Tianjin, the largest open coastal city in Northern China, is selected as the subject of the research in this paper by virtue of its unique background. The Hedonic Pricing Model is used in analysing the change in the value of the properties located within 1,000 metres of completed subway line 3 stations. Using the theories of land rent and land location, and a model of the impact of urban traffic on the surrounding real estate prices, we analyse the sphere of influence of Tianjin Metro Line 3 on real estate prices. Finally, the paper stresses the importance of urban construction and subway building and finds that different development strategies should be used according to the characteristics of the subway in various regions of the city.",35.0,http://downloads.hindawi.com/journals/ddns/2016/1478413.pdf,True,,Mathematics,2016-09-08,7ff3891712d8f4e4350bef70731a3e0b7d6018d2,theimpactofsubwaylinesonresidentialpropertyvaluesintianjinanempiricalstudybasedonhedonicpricingmodel,sun,10.1155/2016/1478413,theimpactofsubwaylinesonresidentialpropertyvaluesintianjinanempiricalstudybasedonhedonicpricingmodel|2016,1,2437,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,3,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5a510ef6da8156877e7715936e3fa8cf89899540,10.48175/ijarsct-22034,,A Study on Housing Price Prediction: Determinants and Effects,2024.0,"International Journal of Advanced Research in Science, Communication and Technology",Rajeshwari; M. N,"This study explores key factors that influence housing prices and assesses their impact on property valuation. By analysing a range of attributes such as area, number of bedrooms and bathrooms, stories, presence of air conditioning, basement, parking, and proximity to preferred areas the project aims to build predictive models for accurate pricing. The study uses sample of 500 house prices from Kaggle datasets and makes use of statistical and machine learning techniques, including multiple regression analysis, this research identifies significant predictors and quantifies their effect on house prices. The study provides insights into how property features offers useful information for buyers, sellers, and real estate professionals.",0.0,http://doi.org/10.48175/ijarsct-22034,True,JournalArticle,,2024-11-07,5a510ef6da8156877e7715936e3fa8cf89899540,astudyonhousingpricepredictiondeterminantsandeffects,rajeshwari,10.48175/ijarsct-22034,astudyonhousingpricepredictiondeterminantsandeffects|2024,1,2241,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,5,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic housing prices,668d07bf051b66c27a6176fb59b2559a1aade831,10.3923/sscience.2010.559.564,,Does the Functional Form Matter in the Estimation of Hedonic Price Model for Housing Market,2010.0,,A. K. Bello; Alabi Moruf,,19.0,http://docsdrive.com/pdfs/medwelljournals/sscience/2010/559-564.pdf,True,,Economics,2010-06-01,668d07bf051b66c27a6176fb59b2559a1aade831,doesthefunctionalformmatterintheestimationofhedonicpricemodelforhousingmarket,bello,10.3923/sscience.2010.559.564,doesthefunctionalformmatterintheestimationofhedonicpricemodelforhousingmarket|2010,1,1500,2,apartment rent hedonic || hedonic housing prices,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",026fd0ca53ecb0f64931e41ecac683b270fa9c68,10.54740/ros.2022.005,,Influence of Railroad Infrastructure on Residential Property Prices on the Example of Kórnik Municipality,2022.0,Rocznik Ochrona Srodowiska,Cyprian Chwiałkowski; A. Zydroń,"The primary objective of the study was to analyze the way in which railroad infrastructure affects residential property prices on the example of the municipality of Kórnik. The analysis carried out includes 737 transactions, which were concluded in the years 2019-2021. Input data forming the basis of the study were obtained from the District Surveying and Cartographic Documentation Centre in Poznań. The most important part of the analyses conducted was performed using the Ordinary Least Squares (OLS) method and the Geographically Weighted Regression (GWR) method. The analysis helped to identify the attributes that had the most significant impact on price. The statistical tools used showed that the presence of a railroad line in the vicinity of residential properties negatively affected transaction prices. A special role in the study is also played by spatial analyses, whose priority is to increase transparency in the process of describing phenomena occurring within the real estate market.",0.0,http://dlibra.tu.koszalin.pl/Content/1885/2022_ROSnr24_54-73.pdf,True,Review,,,026fd0ca53ecb0f64931e41ecac683b270fa9c68,influenceofrailroadinfrastructureonresidentialpropertypricesontheexampleofkornikmunicipality,chwiałkowski,10.54740/ros.2022.005,influenceofrailroadinfrastructureonresidentialpropertypricesontheexampleofkornikmunicipality|2022,1,2503,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,e7d567fcbd1a2d25e8fe060df3608464c8f621f7,10.46298/cst.12146,,"Subcenters, mode choice and transport policies: evidence form Nantes",2015.0,Les Cahiers scientifiques du transport,Rémy Le Boennec; F. Sari,"To cite this paper: Le Boennec, R. & Sari, F. (2015). Nouvelles centralités, choix modal et politiques de déplacements : le cas nantais. Les Cahiers Scientifiques du Transport, 67, 55–86. National audience In this paper based on empirical microeconomic choices of households, we use the hedonic pricing method to define the urban form of Nantes Métropole. The number of Central Business Districts (CBDs) should guide the second generation of transport policies in agglomerations subject to the LAURE law (law on air and the rational use of energy). Thus, if improved accessibility is capitalized into residential property values close to public transport, then strengthening the current incentive policy in favor of public transport networks and active modes seems appropriate to continue to limit the negative externalities generated by car trips in urban areas. Conversely, if improved accessibility is capitalized close to major roads, then a more coercive policy can take over in the form of an urban toll: this will generate revenue to subsequently improving the public transport network. In fact, the Spatial Durbin Model (SDM) reveals that Nantes situation matches neither the one nor the other of these patterns. In line with the theoretical background, acting on travel costs would nevertheless lead to a higher demand for centrality by house purchasers. So the major component of the local environmental policy could be a prior land-use policy involving a better control of space consumption at any point of the city, coupled with the potential implementation of an urban toll. Dans cet article empirique fondé sur les choix micro-économiques des ménages, nous utilisons la méthode des prix hédoniques pour définir la forme urbaine de Nantes Métropole. Le nombre de centralités doit orienter la deuxième génération de politiques de déplacements des agglomérations soumises à la loi LAURE. Ainsi, si des gains d’accessibilité sont capitalisés dans les valeurs immobilières près des transports collectifs, alors le renforcement de l’actuelle politique incitative au bénéfice des réseaux collectifs et des modes doux paraît indiqué pour continuer à limiter les externalités négatives provoquées par les déplacements automobiles en milieu urbain. En revanche, si ces gains d’accessibilité sont capitalisés près des principaux axes routiers, alors une politique plus contraignante, de type péage urbain, pourra prendre le relais : elle dégagera les recettes nécessaires à l’amélioration ultérieure du réseau de transport collectif. En réalité, le modèle de Durbin spatial (SDM) révèle que la situation de Nantes ne semble correspondre ni à l’un, ni à l’autre de ces cas de figure. En accord avec la littérature théorique, une action sur les coûts de déplacement provoquerait néanmoins une plus forte demande de centralité par les acquéreurs de maisons. C’est donc une politique préalable de maîtrise foncière et de densification de l’espace en tout point de la ville, couplée à une étude d’opportunité sur la mise en place d’un péage urbain, qui pourrait constituer l’élément majeur de la politique environnementale locale.",0.0,http://cst.episciences.org/12146/pdf,True,JournalArticle,,2015-03-31,e7d567fcbd1a2d25e8fe060df3608464c8f621f7,subcentersmodechoiceandtransportpoliciesevidenceformnantes,boennec,10.46298/cst.12146,subcentersmodechoiceandtransportpoliciesevidenceformnantes|2015,1,4631,1,hedonic house price,3,2,2,1,1,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),60dab2b77d4e852cf61dc74b2fa33452d39eee51,10.46298/cst.12122,,Public transportation and real estate valuation: case study of Fès,2012.0,Les Cahiers scientifiques du transport,Youssef Tachfine; Ahmed Rhellou,"The aim of this paper is to assess the role of public transport in localization choices of households living in Fez, a city in the North-east of Morocco. We test if the proximity of the bus network is valued positively in the real estate (property) business prices. Based on a hedonic model applied to the market segment of ""modern Moroccan houses"", the study shows that it is possible to consider public transport as a factor enhancing the urban real estate business. More precisely, it is the quality of the public transport supply which allows such effect. An analysis of declared choices corroborated the results of the first approach. Overall, the research results foreground the role of public transport in urban planning policies. L'objectif de cet article est d'évaluer l’importance du transport collectif dans la localisation résidentielle des ménages de l'agglomération de Fès (ville au Nord-est du Maroc). La démarche consiste à voir si la proximité du réseau bus est valorisée positivement dans les prix immobiliers. L'étude montre qu'il est possible à partir d'un modèle hédonique portant sur le segment de marché des « maisons marocaines modernes » de considérer le transport en commun comme un facteur valorisant de l'immobilier urbain. Mais c'est surtout la qualité de l’offre de transport collectif qui permet cette valorisation. Une analyse des choix déclarés permet de corroborer les résultats de la première démarche. Dans l'ensemble, la recherche suggère l'utilisation du transport en commun dans les politiques d'aménagement urbain.",0.0,http://cst.episciences.org/12122/pdf,True,JournalArticle,,2012-11-30,60dab2b77d4e852cf61dc74b2fa33452d39eee51,publictransportationandrealestatevaluationcasestudyoffes,tachfine,10.46298/cst.12122,publictransportationandrealestatevaluationcasestudyoffes|2012,1,3055,4,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic property valuation || house prices proximity hedonic,3,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,rent hedonic regression housing,2b6b5228da7c0ecbc1eae785a1044f7fdbe27c7d,10.25071/1874-6322.1261,,Implicit Rents from Own-housing and Income Distribution: Econometric Estimates for Greater Buenos Aires,2004.0,Journal of Income Distribution,L. Gasparini; Walter Sosa Escudero,"Most income studies do not take into account the implicit rent obtained by households who inhabit their own dwellings, a fact that introduces a potentially relevant bias in inequality, poverty, and welfare measures. In this paper we estimate these implicit rents for the Greater Buenos Aires area from Argentina's National Household Expenditures Survey (ENGH) of 1996-1997. Based on a sample of households that rent their dwellings, quantile regressions are used to estimate observed rents from a hedonic model. Estimated coefficients are applied to households that do not rent their houses or apartments in order to predict the implicit rents derived from living in an owned house. Estimated implicit rents are added to the notion of household income and various inequality measures are re-estimated. We find that the consideration of these implicit rents reduces inequality due to an income elasticity in spending in housing less than one, and to the relatively large proportion of house owners in the lower strata of income distribution.",51.0,http://cedlas.econo.unlp.edu.ar/archivos_upload/doc_cedlas14.pdf,True,Review,Economics,2004-10-01,2b6b5228da7c0ecbc1eae785a1044f7fdbe27c7d,implicitrentsfromownhousingandincomedistributioneconometricestimatesforgreaterbuenosaires,gasparini,10.25071/1874-6322.1261,implicitrentsfromownhousingandincomedistributioneconometricestimatesforgreaterbuenosaires|2004,1,2540,1,rent hedonic regression housing,2,1,5,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,497dd9371af7d1b9ce0e0d5c3b0fe9dac4ef90eb,10.12660/rbfin.v9n2.2011.2899,,Modelagem dos Preços de Imóveis Residenciais Paulistanos,2011.0,,D. C. de Oliveira Alves; Joe Akira Yoshino; Paula Carvalho Pereda; Carla Jucá Amrein,"Hedonic modeling has become a benchmark for pricing real assets with several intrinsic characteristics. This work tests also others dimensions for asset pricing: the quality of life in the housing neighborhood and macroeconomic variables. The data is about the real estate market in Sao Paulo city from January 2001 to March 2008. The main results were: the longer the maturity of mortgage financing, the larger the housing price, but decreasing interest rate spread stimulate the real estate market, and the interactions between the dummy for the boom period and either housing characteristics or bank interest rates spread show that the hedonic model loses its relative importance for pricing, while market risk variables become much more relevant. Thus, these new findings suggests that for modeling a house price index it is not sufficient to consider only average prices or a hedonic approach, but both the market and credit risks as well.",1.0,http://bibliotecadigital.fgv.br/ojs/index.php/rbfin/article/download/2899/2225,True,,Economics,2011-07-08,497dd9371af7d1b9ce0e0d5c3b0fe9dac4ef90eb,modelagemdosprecosdeimoveisresidenciaispaulistanos,alves,10.12660/rbfin.v9n2.2011.2899,modelagemdosprecosdeimoveisresidenciaispaulistanos|2011,1,2444,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || preços hedônicos imóveis",2,2,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cf7e5777ace1f775abfb4c43b163dda1352a6063,10.54691/bcpbm.v26i.1935,,Empirical Evidence Based on Geographic Regression Discontinuity Analysis of Housing in Guangzhou School District,2022.0,BCP Business & Management,Y. Ouyang; Keyue Sun,"In recent years, the ""school district housing boom"" has become a general concern as it disrupted the housing market, undermining educational equity and class mobility. In view of the real problem of housing premium in school districts, this paper selects the housing data of primary school districts in Guangzhou as a research sample, quantifies it and discusses the impact of education supply on it, so as to provide references and suggestions for subsequent policy introduction. The main research conclusions of this paper are as follows: There is a premium for housing in Guangzhou’s school districts. After excluding the influence of location and physical factors, it is calculated that the housing price from Longdong School District to Yongping School District has increased by about 42.3%.",1.0,http://bcpublication.org/index.php/BM/article/download/1935/1933,True,,,2022-09-19,cf7e5777ace1f775abfb4c43b163dda1352a6063,empiricalevidencebasedongeographicregressiondiscontinuityanalysisofhousinginguangzhouschooldistrict,ouyang,10.54691/bcpbm.v26i.1935,empiricalevidencebasedongeographicregressiondiscontinuityanalysisofhousinginguangzhouschooldistrict|2022,1,2296,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",21c336f4a650a4a612747694c4605d293d7f040a,10.48550/arxiv.2305.08887,2305.08887,Covariate-distance Weighted Regression (CWR): A Case Study for Estimation of House Prices,2023.0,arXiv.org,Hone-Jay Chu; Po-Hung Chen; Sheng-Mao Chang; M. Ali; Sumriti Ranjan Patra,"Geographically weighted regression (GWR) is a popular tool for modeling spatial heterogeneity in a regression model. However, the current weighting function used in GWR only considers the geographical distance, while the attribute similarity is totally ignored. In this study, we proposed a covariate weighting function that combines the geographical distance and attribute distance. The covariate-distance weighted regression (CWR) is the extension of GWR including geographical distance and attribute distance. House prices are affected by numerous factors, such as house age, floor area, and land use. Prediction model is used to help understand the characteristics of regional house prices. The CWR was used to understand the relationship between the house price and controlling factors. The CWR can consider the geological and attribute distances, and produce accurate estimates of house price that preserve the weight matrix for geological and attribute distance functions. Results show that the house attributes/conditions and the characteristics of the house, such as floor area and house age, might affect the house price. After factor selection, in which only house age and floor area of a building are considered, the RMSE of the CWR model can be improved by 2.9%-26.3% for skyscrapers when compared to the GWR. CWR can effectively reduce estimation errors from traditional spatial regression models and provide novel and feasible models for spatial estimation.",0.0,http://arxiv.org/pdf/2305.08887,True,JournalArticle,Computer Science,2023-05-15,21c336f4a650a4a612747694c4605d293d7f040a,covariatedistanceweightedregressioncwracasestudyforestimationofhouseprices,chu,10.48550/arxiv.2305.08887,covariatedistanceweightedregressioncwracasestudyforestimationofhouseprices|2023,1,2972,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",54193afb4da3db05fdfee04faff9c521675f7e62,10.1145/3342240,1807.07155,Take a Look Around,2018.0,ACM Transactions on Intelligent Systems and Technology,S. Law; Brooks Paige; C. Russell,"When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable while others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have led to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size, and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract proxy variables for visual desirability of neighborhoods that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the visual appeal of London streets.",174.0,http://arxiv.org/abs/1807.07155,True,JournalArticle,Computer Science;Economics,2018-07-18,54193afb4da3db05fdfee04faff9c521675f7e62,takealookaround,law,10.1145/3342240,takealookaround|2018,1,3404,7,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic house price || hedonic regression housing || house price prediction machine learning || housing price text listing",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ad8b8e5158e5851705de2d7d6b6883b23f62c45f,10.11648/j.ajere.20200504.11,,Effect of Neighborhood Security on Housing Price in Lagos,2020.0,American Journal of Environmental and Resource Economics,Olalekan Dimeji Bamiteko; O. Adebiyi,"Effect of neighborhood security on housing price in Lagos State Nigeria. The nonstop increase in urban population has created a gap between unceasing demand and limited supply of housing units in Nigeria particularly Lagos State. Housing being an heterogenous commodity has a lot of independent variables attached to it such as structural, location and neighborhood attributes (including crime rate). The population growth equally presented unemployment and insecurity in most neighborhood going by high rate of crime in Lagos State. The inadequate provision of security for life and properties by successive governments over the years along with population growth have caused unimaginably increase in crime rates in the city. The need for safety and prevention of crimes resulted into neighborhood security. The study confirms that the rate of crime in a particular neighborhood have a direct effect on housing price. This study investigates the effect of neighborhood security on housing price in Lagos State using Amuwo-Odofin Local government Area. The study employed primary data and surveyed 140 residents of the LGA through multistage sampling technique. Ordinary Least Squares (OLS) was used to analyze the data. The result, shows that fenced apartment, services of local securities and vigilantes and the presence of gateman have positive influence on price of housing in Lagos state. While high crime rates have a negative influence on housing price. The study concluded that residents pay higher housing price to live in a secured and crime free neighborhood. The study recommends the need for community policing is highly supported by the outcomes of this study.",3.0,http://article.sciencepublishinggroup.com/pdf/10.11648.j.ajere.20200504.11.pdf,True,Review,Business,2020-11-09,ad8b8e5158e5851705de2d7d6b6883b23f62c45f,effectofneighborhoodsecurityonhousingpriceinlagos,bamiteko,10.11648/j.ajere.20200504.11,effectofneighborhoodsecurityonhousingpriceinlagos|2020,1,3174,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,d20a7e93c2e46eb15dcaf4684bea6e9d045deefd,10.1017/s107407080002335x,,Living on the Edge: Residential Property Values in the Urban-Rural Interface,2007.0,Journal of Agricultural and Applied Economics,M. Espey; F. Fakhruddin; L. Gering; Huiyan Lin,"This study estimates the contribution of both urban-rural fringe location and lake proximity on residential property values in three upstate counties of South Carolina through estimation of spatial hedonic housing price models. Location in the urban fringe and the urban–rural interface are found to have a positive impact on residential housing values relative to either urban or more rural locations. Lakes in the upstate contribute positively to housing values to the extent that the house has a view of a lake, lake access, or lake frontage.",12.0,http://ageconsearch.umn.edu/record/37055,True,,Geography,2007-12-01,d20a7e93c2e46eb15dcaf4684bea6e9d045deefd,livingontheedgeresidentialpropertyvaluesintheurbanruralinterface,espey,10.1017/s107407080002335x,livingontheedgeresidentialpropertyvaluesintheurbanruralinterface|2007,1,2045,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || house prices proximity hedonic || spatial hedonic model housing",2,3,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",20fa5e8d4965f63fe9eb50cccda331289eedd6ff,10.2307/1238121,,Measuring the Impacts of Solid Waste Disposal Site Location on Property Values,1971.0,,J. Havlicek; R. A. Richardson; L. Davies,"T HE large and growing quantity of solid wastes poses an increasingly complex disposal problem for rural and urban areas. One important effect of the location of disposal sites is the external effect on adjacent property values. This paper attempts to estimate external effects on property prices resulting from the location of landfill types of solid waste disposal sites. This has important implications for public policy on site location and possible compensation to internalize the resultant external effects. A single equation linear regression model for price estimation is hypothesized. The data sample is 182 observations of single-unit house sales over the period 1962-1970 in the neighborhoods around five solid waste disposal sites in Fort Wayne, Indiana. Date and price of sale, as well as location of the property and detailed information about dwelling characteristics, were obtained. Three general categories of predetermined variables are used: (1) Physical attributes of residential property-size of house and lot, number of bathrooms and bedrooms, age of house, amount of encumbrance and ownership-tenant occupancy (the key features of the quantity and quality of living service provided by a piece of residential property); (2) year of sale-the best available measure of the general level of cost of housing; (3) factors representing amenities and disamenities associated with solid waste disposal sites and neighborhood characteristics. Based on a diffusion phenomenon, the impacts of solid waste disposal sites on residential property prices were measured by distance from the nearest disposal site and the absolute degrees that the resiential property is away from downwind (prevailing) of the disposal site. Zero-one variables were used to represent differences among disposal sites. The predetermined variables in the linear model explained approximately 78 percent of the variation in residential property prices. Coefficients of the physical attribute variables were all s g ificantly different from zero at the .05 level and all had the hypothesized sign except the number of bedrooms. The distance and angle v riables are key policy variables in the model. Both coefficients were positive as hypothesized, suggesting that a premium is placed on being a g eater distance away and being away from downwind of disposal sites. The estimated coefficients indicate that on the average the price of residential property increases 61 cents per foot of distance and $10.30 per degree away from downwind of a disposal site. This first attempt to measure external effects f solid waste disposal sites on adjacent property values has important implications for community development and policy. The empirical results suggest magnitudes of costs imposed and indicate the nature of their distribution around solid waste disposal sites. In decisions concerning disposal site location, external social cost and benefit are an integral and possibly major factor. Further research is therefore warranted in refining the model and its variables to more accuately measure these effects.",48.0,http://ageconsearch.umn.edu/record/284441,True,,Environmental Science,1971-12-01,20fa5e8d4965f63fe9eb50cccda331289eedd6ff,measuringtheimpactsofsolidwastedisposalsitelocationonpropertyvalues,havlicek,10.2307/1238121,measuringtheimpactsofsolidwastedisposalsitelocationonpropertyvalues|1971,1,4594,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,4,5,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",7afa40d17066b5d6525370fbcd6ea005ee482112,10.1086/717265,,Comparing Pollution Where You Live and Play: A Hedonic Analysis of Enterococcus in the Long Island Sound,2021.0,Marine Resource Economics,M. Kung; D. Guignet; P. Walsh,"Hedonic property value methods typically examine the effect of water quality on home prices by focusing on waters nearest a home. While this captures any aesthetic values households may hold for water quality improvements, it may not fully reflect recreational values, particularly for nearby residents that do not live on the waterfront. This study is the first to compare the conventional approach of examining how property prices vary with the quality of waters closest to a home, versus water quality levels at the closest point of access for recreation (i.e., the beach). Using spatial econometric models, we conduct a hedonic analysis of residences within five kilometers of the Long Island Sound. Due to an aging infrastructure, high levels of precipitation often lead to sewage overflows, resulting in high counts of enterococcus – a bacterial indicator of fecal pollution. We also estimate the effect of subsequent beach closures, which we posit as an alternative and more salient signal of local water quality to residents. In line with previous literature, we find that enterococcus levels at waters nearest a home negatively affect home prices within 1 kilometer. However, this effect becomes insignificant when controlling for levels at the nearest beach. In contrast, enterococcus at the closest beach yields a negative 0.03% to 0.02% elasticity that extends 2.5 km. Controlling for beach closures suggests negative effects as far as 3.5 km from beaches. Our findings demonstrate that the impact of water quality on home prices may extend further than previously suggested by the literature, at least at large iconic waterbodies like the Sound.",5.0,http://ageconsearch.umn.edu/record/280943,True,,Geography,2021-12-03,7afa40d17066b5d6525370fbcd6ea005ee482112,comparingpollutionwhereyouliveandplayahedonicanalysisofenterococcusinthelongislandsound,kung,10.1086/717265,comparingpollutionwhereyouliveandplayahedonicanalysisofenterococcusinthelongislandsound|2021,1,3158,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",2,3,2,1,1,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3c3cee5be2c3e34192bd9f0eb856c38c4a3b7dfe,10.1016/j.ecolecon.2016.12.014,,Modeling the Property Price Impact of Water Quality in 14 Chesapeake Bay Counties,2017.0,,P. Walsh; C. Griffiths; D. Guignet; H. Klemick,"The Chesapeake Bay and its tributaries provide a range of recreational and aesthetic amenities, such as swimming, fishing, boating, wildlife viewing, and scenic vistas. Living in close proximity to the Bay improves access to these amenities and should be capitalized into local housing markets. We investigate these impacts in the largest hedonic analysis of water quality ever completed, with over 200,000 property sales across 14 Maryland counties. We use a spatially explicit water quality dataset, along with a wealth of landscape, economic, geographic, and demographic variables. These data allow a comprehensive exploration of the value of water quality, while controlling for a multitude of other influences. We also estimate several variants of the models most popular in current literature, with a focus on the temporal average of water quality. In comparing 1 year and 3 year averages, the 3 year averages generally have a larger implicit price. Overall, results indicate that water quality improvements in the Bay, such as those required by EPA’s Total Maximum Daily Load, could yield significant benefits to waterfront and near-waterfront homeowners.",66.0,http://ageconsearch.umn.edu/record/280930,True,,Geography,2017-05-01,3c3cee5be2c3e34192bd9f0eb856c38c4a3b7dfe,modelingthepropertypriceimpactofwaterqualityin14chesapeakebaycounties,walsh,10.1016/j.ecolecon.2016.12.014,modelingthepropertypriceimpactofwaterqualityin14chesapeakebaycounties|2017,1,2662,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,2,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",f8f77ddfaeaa028ca11286c74729edeab5b8f1c6,10.1086/689201,,The Implicit Price of Aquatic Grasses,2014.0,Marine Resource Economics,D. Guignet; C. Griffiths; H. Klemick; P. Walsh,"ABSTRACT Submerged aquatic vegetation (SAV) in the Chesapeake Bay is well below half of its historic levels, largely due to excessive nutrient and sediment loads degrading water quality. SAV provides important ecosystem functions, many of which are beneficial to local residents. To understand the implicit value residents place on SAV and the ecosystem services it provides, we undertake a hedonic property value study using residential transactions in 11 Maryland counties adjacent to the Chesapeake Bay. These data are matched to highresolution maps of SAV coverage. We pose a quasi-experimental comparison and examine how the prices of homes near the waterfront vary with the presence of SAV. On average, waterfront and near-waterfront homes within 200 meters of the shore sell at about a 6.5% premium when SAV is present. Applying these estimates to the 185,000-acre SAV attainment goal suggests total property value gains on the order of $436 million. JEL Codes: Q51, Q53.",17.0,http://ageconsearch.umn.edu/record/280922,True,,Environmental Science,2014-12-01,f8f77ddfaeaa028ca11286c74729edeab5b8f1c6,theimplicitpriceofaquaticgrasses,guignet,10.1086/689201,theimplicitpriceofaquaticgrasses|2014,1,2478,1,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",2,1,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W1587476646,10.22004/ag.econ.19547,,Hedonic Housing Prices and Agricultural Pollution: An Empirical Investigation on Semiparametric Models,2005.0,RePEc: Research Papers in Economics,"Christophe Bontemps; Michel Simioni; Yves Surry; Bontemps, Christophe; Simioni, Michel; Surry, Yves R.","Many empirical studies challenged the parametric approach to estimate nonlinear house price models and to use instead nonparametric or semiparametric model specifications. In the area of environmental analysis using house price models, Stock (1991) and McMillen and Thorsen (2000) applied semi-parametric models to estimate the impact of hazardous wastes on house prices. Furthermore, estimating nonparametricaly house price models is gaining now additional momentum because Ekeland et al. (2002) provided a theoretical justification to use such an empirical approach to estimate hedonic price models. The objective of this paper is to assess the impact of agricultural pollution in a livestock intensive region of France using alternative non-parametric and semi-parametric hedonic price models. We propose a two stage estimation procedure were the first step is based on the partially linear model introduced by Robinson (1988). In the second step, once the parametric part of the model has been estimated on the house characteristics, we propose four specifications for the remaining functional form on the environmental variables. A parametric, a fully nonparametric, an additive, and a single index specifications are proposed and estimated using appropriate econometric procedures similar to those developed by Horowitz and Lee (2001), Iwata et al. (2000) and Robinson (1988). The performances of these models are examined, compared with respect to the benchmark nonparametric model and specification tests are proposed.",1.0,http://ageconsearch.umn.edu/record/24709,True,preprint,,2005-01-01,https://openalex.org/W1587476646,hedonichousingpricesandagriculturalpollutionanempiricalinvestigationonsemiparametricmodels,bontemps,10.22004/ag.econ.19547,hedonichousingpricesandagriculturalpollutionanempiricalinvestigationonsemiparametricmodels|2005,1,3026,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",91adc77e3ed0a0c3cdebd9547cf3d57d8d7ea529,10.5399/osu/jtrf.47.3.2146,,Hedonic Value of Transit Accessibility: An Empirical Analysis in a Small Urban Area,2012.0,,X. Cao; J. Hough,"Many programs of the SAFETEA-LU have pointed to improving transit services and individuals' accessibility in small urban areas. Urban economic theory suggests that improving accessibility by investing in transportation has the potential to drive up bids for land. However, will the improved transit accessibility in small urban areas increase property values? A number of studies have investigated the impact of rail transit on home sales but produced mixed results. Further, few studies have explored how transit influences the lease rate of apartments. This question is more relevant because transit accessibility tends to have a greater impact on apartment dwellers than home owners. Using about 400 apartment dwellers in Fargo, North Dakota, this study developed a hedonic price model to determine implicit price of proximity to bus routes. We found a negative impact of bus transit on apartment rent after controlling for other factors, however. We speculated that in a small urban area, this negative relationship could be mainly attributable to spurious relationships from other causal factors, as well as nuisance effects of bus transit itself.",33.0,http://ageconsearch.umn.edu/record/206969,True,,Geography,2012-02-27,91adc77e3ed0a0c3cdebd9547cf3d57d8d7ea529,hedonicvalueoftransitaccessibilityanempiricalanalysisinasmallurbanarea,cao,10.5399/osu/jtrf.47.3.2146,hedonicvalueoftransitaccessibilityanempiricalanalysisinasmallurbanarea|2012,1,2652,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || apartment prices hedonic",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",71ea609ea5158164fd5c87866f10e17e2fc6d696,10.1017/s0163548400003046,,"Effects of the accident at Three Mile Island on residential property values and sales. Final report, April 1980-January 1981",1981.0,,H. B. Gamble; R. Downing,"The study examined the effects of the accident at Three Mile Island on residential property values and number of sales within a 25-mile radius of the plant. Regression analyses, using data on 583 actual market sales of single family homes from 1977 through 1979, examined the effects before and after the accident on the basis of distance and direction from the plant and on three different property value classes. All valid single family property sales between 1975 and 1979 within the 25-mile area were examined in a time series analysis. Interviews were conducted with realtors, financial institution officials and building contractors in the area. The accident had no measurable effects, positive or negative, on the value of single family residential properties within a 25-mile radius of the plant, or in any direction from the plant, or on low, medium, or high value properties. The plant had no measurable effects on residential property values for the 2 years prior to the accident. Immediately following the accident there was a sharp decline in the number of residential sales within 10 miles of the plant, but the real estate market returned to near normal conditions within 4-8 weeks. The interviews basically confirmed themore » above findings.« less",8.0,http://ageconsearch.umn.edu/record/159815,True,,Business;Engineering,1981-04-01,71ea609ea5158164fd5c87866f10e17e2fc6d696,effectsoftheaccidentatthreemileislandonresidentialpropertyvaluesandsalesfinalreportapril1980january1981,gamble,10.1017/s0163548400003046,effectsoftheaccidentatthreemileislandonresidentialpropertyvaluesandsalesfinalreportapril1980january1981|1981,1,2764,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1746638ba682894ded3ef6e1c902371141e8fdc3,10.1017/s0163548400003125,,Benefits From Urban Open Space and Recreational Parks: A Case Study,1982.0,,John K. Hagerty; T. Stevens; P. Allen; T. More,"The hedonic pricing technique was used to measure that component of house price attributable to proximity to a city park, using data on sale prices and characteristics of houses in Worcester, MA. Aggregation of these residual values over all houses in the neighborhood of a park provided an estimate of the value of the park, to which was added an estimate of recreation benefits from extrazonal users. '!he policy question of park system development or contraction was addressed by comparing these benefits with operating costs to gain a net measure of parkland value.",12.0,http://ageconsearch.umn.edu/record/159780,True,,Business,,1746638ba682894ded3ef6e1c902371141e8fdc3,benefitsfromurbanopenspaceandrecreationalparksacasestudy,hagerty,10.1017/s0163548400003125,benefitsfromurbanopenspaceandrecreationalparksacasestudy|1982,1,2069,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || house prices proximity hedonic",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,implicit prices housing attributes,b6401bedb59a7356e471bfe6cba4b53c93fef01d,10.1017/s0163548400004118,,Land Use Conflicts in the Coastal Zone: An Approach for the Analysis of the Opportunity Costs of Protecting Coastal Resources,1984.0,,S. F. Edwards; G. Anderson,"The implicit price (hedonic) equation for the housing market in a coastal town in southern Rhode Island was estimated using a conditional Box-Cox maximum likelihood procedure. Linear, log-linear, and semi-log functional forms were rejected with 95% confidence. Estimates of marginal implicit prices for water related attributes (view of, frontage on, and proximity to a coastal salt water pond) derived from these rejected models were quite different from those determined from the optimal functional form. This result has important ramifications for public policy, as is shown in an example, since these attributes were found to be highly valued in the housing market.",20.0,http://ageconsearch.umn.edu/record/159270,True,,Geography,1984-04-01,b6401bedb59a7356e471bfe6cba4b53c93fef01d,landuseconflictsinthecoastalzoneanapproachfortheanalysisoftheopportunitycostsofprotectingcoastalresources,edwards,10.1017/s0163548400004118,landuseconflictsinthecoastalzoneanapproachfortheanalysisoftheopportunitycostsofprotectingcoastalresources|1984,1,2169,3,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic housing prices || implicit prices housing attributes",2,1,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8fbd66059d714aa0ed810fca8981507535512405,10.1017/s016354840000042x,,USING MULTIPLE LISTING SERVICE DATA TO ANALYZE DETERMINANTS OF RESIDENTIAL PROPERTY VALUES,1972.0,,J. Penn; G. Irwin,,0.0,http://ageconsearch.umn.edu/record/158052,True,,Business,1972-10-01,8fbd66059d714aa0ed810fca8981507535512405,usingmultiplelistingservicedatatoanalyzedeterminantsofresidentialpropertyvalues,penn,10.1017/s016354840000042x,usingmultiplelistingservicedatatoanalyzedeterminantsofresidentialpropertyvalues|1972,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e7fa54a50b1dd0e4a7a88975159fe81b9e3b4401,10.1017/s1068280500004433,,Spatial Hedonic Valuation of a Multi-use Urban Wetland in Southern California,2013.0,Agricultural and Resource Economics Review,Elaine F. Frey; Marissa B. Palin; P. Walsh; C. Whitcraft,"Most of Southern California's wetlands have disappeared despite being critical ecosystems with many valuable attributes. Many of the wetlands that remain are in relatively urban areas, are severely degraded, and may not function properly. Using hedonic spatial error models, we measure the economic value of living near an urban multi-use wetland in Long Beach, California. Both sales prices and estimated values are used in the analysis. Results show that proximity to wetlands increases residential property values in the focus area. This analysis provides important information for policymakers to justify ongoing restoration projects and prevent further degradation of urbanized natural resources.",15.0,http://ageconsearch.umn.edu/record/155318,True,,Economics,2013-08-01,e7fa54a50b1dd0e4a7a88975159fe81b9e3b4401,spatialhedonicvaluationofamultiuseurbanwetlandinsoutherncalifornia,frey,10.1017/s1068280500004433,spatialhedonicvaluationofamultiuseurbanwetlandinsoutherncalifornia|2013,1,2201,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic property valuation",2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,1b2d27fe76aef4a9ca8a0dbcbfe7ec076f98fe39,10.1017/s1068280500002835,,Spatially Delineated Public Goods and Spatially Located Public Bads: A Hedonic Approach to Measuring Urban Revitalization,2011.0,Agricultural and Resource Economics Review,John E. M. Brown; J. Geoghegan,"A regression discontinuity approach is used to measure the impact of public-goods creating programs in a declining inner city neighborhood of Worcester Massachusetts. Using GIS data, we develop a hedonic model of residential sales, using a parcel-level GIS tax assessment and land use database linked to property sales data for the years 1988 through 2007, to test the effect of the creation of a new high-performing public school, as well as other locational amenities and disamenities on neighborhood housing prices, by comparing properties adjacent to either side of the school catchment area boundary.",5.0,http://ageconsearch.umn.edu/record/120683,True,,Geography,2011-12-01,1b2d27fe76aef4a9ca8a0dbcbfe7ec076f98fe39,spatiallydelineatedpublicgoodsandspatiallylocatedpublicbadsahedonicapproachtomeasuringurbanrevitalization,brown,10.1017/s1068280500002835,spatiallydelineatedpublicgoodsandspatiallylocatedpublicbadsahedonicapproachtomeasuringurbanrevitalization|2011,1,2105,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || hedonic price model amenities residential || spatial hedonic model housing",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2792b4c943809eb914728ff2391864089ea9f685,10.11118/actaun201866040991,,Determinants of Real Estate Prices in the Statutory City of Brno,2018.0,Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis,Vlastimil Reichel; Petr Zimcik,"The real estate market demand for both houses and flats has been growing recently. This trend is one of the factors, which are able to influence the price of real estate. Our paper introduces determinants of housing price and their influence on actual real estate prices in the statutory city of Brno. The aim of this paper is to present key determinants of house price and find the hedonic price model, which describes, how determinants affect house price best. Realized prices were analyzed for housing units located in different districts of Brno. Key determinants in this research are a year of sale, an area of flat in square meters, number of rooms, location in districts of Brno, type of masonry and reconstruction. The dataset covers the time period between years 2012 and 2015. Hedonic price model is estimated by the method of ordinary least square. Besides main aim, four assumptions were verified, which should determine the influence on the price of individual determinants in the statutory city of Brno.",3.0,http://acta.mendelu.cz/doi/10.11118/actaun201866040991.pdf,True,,Business,2018-08-31,2792b4c943809eb914728ff2391864089ea9f685,determinantsofrealestatepricesinthestatutorycityofbrno,reichel,10.11118/actaun201866040991,determinantsofrealestatepricesinthestatutorycityofbrno|2018,1,2517,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || cites:sirmans2006 || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices",2,4,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",8d6ee38b69ae5a8fddf4e870776a92c2d71461d0,10.1080/13658810802672469,,Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices,2010.0,International Journal of Geographical Information Science,Bo Huang; Bo Wu; M. Barry,,1403.0,,False,JournalArticle,Geography;Computer Science,2010-03-01,8d6ee38b69ae5a8fddf4e870776a92c2d71461d0,geographicallyandtemporallyweightedregressionformodelingspatiotemporalvariationinhouseprices,huang,10.1080/13658810802672469,geographicallyandtemporallyweightedregressionformodelingspatiotemporalvariationinhouseprices|2010,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0095-0696(78)90006-2,,Hedonic housing prices and the demand for clean air,1978.0,Journal of Environmental Economics and Management,David Harrison; Daniel L Rubinfeld,,1347.0,,,journal-article,,,doi_10_1016_0095_0696_78_90006_2,hedonichousingpricesandthedemandforcleanair,harrison,10.1016/0095-0696(78)90006-2,hedonichousingpricesandthedemandforcleanair|1978,1,1000,3,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic housing prices",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W1582846375,10.2307/3145857,,Micro-Neighborhood Externalities and Hedonic Housing Prices,1980.0,Land Economics,Mingche M. Li; H. James Brown,"The evaluation of numerous school reforms requires an understanding of the value of better schools. Given the difficulty of calculating the relationship between school quality and student outcomes, I turn to another method and use house prices to infer ...",522.0,,False,article,,1980-05-01,https://openalex.org/W1582846375,microneighborhoodexternalitiesandhedonichousingprices,li,10.2307/3145857,microneighborhoodexternalitiesandhedonichousingprices|1980,1,1256,3,apartment rent hedonic || hedonic house price || hedonic housing prices,2,2,2,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,hedonic housing prices,,10.1023/a:1007703229507,,Analysis of Spatial Autocorrelation in House Prices,1998.0,The Journal of Real Estate Finance and Economics,Sabyasachi Basu; Thomas G. Thibodeau,,428.0,,,journal-article,,,doi_10_1023_a_1007703229507,analysisofspatialautocorrelationinhouseprices,basu,10.1023/a:1007703229507,analysisofspatialautocorrelationinhouseprices|1998,1,1000,2,hedonic house price || hedonic housing prices,0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,residential property price determinants,f89a85edea7844498302e22eff996e5047ce6634,10.1177/000312249906400308,,"“Property Values Drop When Blacks Move In, Because…”: Racial and Socioeconomic Determinants of Neighborhood Desirability",1999.0,American Sociological Review,D. Harris,"Are housing prices lower in neighborhoods with high concentrations of black residents? If so, is this relationship evidence of pure discrimination, or can it be explained by considering nonracial neighborhood traits? These questions derive their importance from the link between mobility patterns and residential segregation, and the consequent relationship between high levels of segregation and a host of deleterious outcomes. I assess the magnitude and motivations of racial aversion by conducting a hedonic price analysis of geocoded data from the Panel Study of Income Dynamics. I find clear evidence of lower property values in neighborhoods with relatively high proportions of black residents. However, whether it is blacks’ race or their socioeconomic status that affects property values depends on whether housing units are rented or owner-occupied.",357.0,,False,JournalArticle,Geography,1999-06-01,f89a85edea7844498302e22eff996e5047ce6634,propertyvaluesdropwhenblacksmoveinbecauseracialandsocioeconomicdeterminantsofneighborhooddesirability,harris,10.1177/000312249906400308,propertyvaluesdropwhenblacksmoveinbecauseracialandsocioeconomicdeterminantsofneighborhooddesirability|1999,1,1858,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || residential property price determinants",2,3,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",c2f692f275affede3cfd581711b8348be5c84583,10.1016/j.landurbplan.2013.08.009,,Classification and valuation of urban green spaces—A hedonic house price valuation,2013.0,,T. Panduro; Kathrine Lausted Veie,,319.0,,False,,Economics,2013-12-01,c2f692f275affede3cfd581711b8348be5c84583,classificationandvaluationofurbangreenspacesahedonichousepricevaluation,panduro,10.1016/j.landurbplan.2013.08.009,classificationandvaluationofurbangreenspacesahedonichousepricevaluation|2013,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || spatial hedonic model housing",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,df2c6c14cf5ff301ab5eeeabc8990f18e6094eae,10.1007/s11146-008-9159-6,,A Spatial Autocorrelation Approach for Examining the Effects of Urban Greenspace on Residential Property Values,2010.0,,D. A. Conway; Christina Q. Li; J. Wolch; Christoph Kahle; M. Jerrett,,301.0,,False,,Geography,2010-04-05,df2c6c14cf5ff301ab5eeeabc8990f18e6094eae,aspatialautocorrelationapproachforexaminingtheeffectsofurbangreenspaceonresidentialpropertyvalues,conway,10.1007/s11146-008-9159-6,aspatialautocorrelationapproachforexaminingtheeffectsofurbangreenspaceonresidentialpropertyvalues|2010,1,1000,4,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,a64864aa575b0c7e918a1b5361c1076e215cbb83,10.1016/j.cities.2018.11.016,,"Analyzing housing prices in Shanghai with open data: Amenity, accessibility and urban structure",2019.0,Cities,Han-Duo Li; Y. Wei; Yangyi Wu; Guang Tian,"Abstract Skyrocketing housing prices in China's megacities have generated broad concerns. By integrating open data from Lianjia.com , Dianping.com , Mobike.com , and Baidu Map POI, we analyze spatial patterns of apartment prices and their association with local attributes in Shanghai. We find that Shanghai's residential market still has a monocentric structure because of the centralized distribution of public transport facilities and amenities. Hedonic models further confirm that structural attributes, accessibility, as well as public and private service amenities significantly shape the real estate market. These factors also are differentiated so as to form a pattern of concentric rings. In the inner-city and expanded inner-city areas, public service amenities such as parks, schools, hospitals, and banks, as well as private service amenities such as entertainment, shopping, and residential service facilities, boost housing prices. In the suburbs, better access to bike sharing, bus stops, and metro stations are the top preferences for apartment buyers. Our study also indicates that the Chinese government needs to make public and private services more accessible, not only spatially in urban peripheries and villages, but also institutionally to lower income families who cannot afford apartments in expensive neighborhoods.",294.0,,False,,Business,2019-08-01,a64864aa575b0c7e918a1b5361c1076e215cbb83,analyzinghousingpricesinshanghaiwithopendataamenityaccessibilityandurbanstructure,li,10.1016/j.cities.2018.11.016,analyzinghousingpricesinshanghaiwithopendataamenityaccessibilityandurbanstructure|2019,1,2341,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic price model amenities residential || spatial hedonic model housing",2,3,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/j.eswa.2008.01.044,,Determinants of house prices in Turkey: Hedonic regression versus artificial neural network,2009.0,Expert Systems with Applications,Hasan Selim,,271.0,,,journal-article,,,doi_10_1016_j_eswa_2008_01_044,determinantsofhousepricesinturkeyhedonicregressionversusartificialneuralnetwork,selim,10.1016/j.eswa.2008.01.044,determinantsofhousepricesinturkeyhedonicregressionversusartificialneuralnetwork|2009,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || apartment rent hedonic || hedonic house price || hedonic housing prices || hedonic property valuation",2,1,1,3,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,1 openalex,hedonic housing prices,https://openalex.org/W1967087580,10.2307/1911956,,An Estimate of a Structural Hedonic Price Model of the Housing Market: An Application of Rosen's Theory of Implicit Markets,1979.0,Econometrica,Ann Dryden Witte; Howard J. Sumka; Homer Erekson,"Ann D. Witte, Howard J. Sumka, Homer Erekson, An Estimate of a Structural Hedonic Price Model of the Housing Market: An Application of Rosen's Theory of Implicit Markets, Econometrica, Vol. 47, No. 5 (Sep., 1979), pp. 1151-1173",270.0,,False,article,,1979-09-01,https://openalex.org/W1967087580,anestimateofastructuralhedonicpricemodelofthehousingmarketanapplicationofrosenstheoryofimplicitmarkets,witte,10.2307/1911956,anestimateofastructuralhedonicpricemodelofthehousingmarketanapplicationofrosenstheoryofimplicitmarkets|1979,1,1227,4,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || apartment rent hedonic || hedonic housing prices || implicit prices housing attributes",2,0,2,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),37670ea65e08543150679a7f7914aeae1fd09617,10.1016/j.trb.2014.02.007,,A Spatial Difference-in-Differences Estimator to Evaluate the Effect of Change in Public Mass Transit Systems on House Prices,2014.0,,Jean Dubé; Diègo Legros; M. Thériault; François Des Rosiers,,246.0,,False,,Geography;Economics,2014-06-01,37670ea65e08543150679a7f7914aeae1fd09617,aspatialdifferenceindifferencesestimatortoevaluatetheeffectofchangeinpublicmasstransitsystemsonhouseprices,dubé,10.1016/j.trb.2014.02.007,aspatialdifferenceindifferencesestimatortoevaluatetheeffectofchangeinpublicmasstransitsystemsonhouseprices|2014,1,1000,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price,0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,hedonic housing prices,,10.1016/s0094-1190(02)00010-4,,The impact of school characteristics on house prices: Chicago 1987–1991,2002.0,Journal of Urban Economics,Thomas A. Downes; Jeffrey E. Zabel,,232.0,,,journal-article,,,doi_10_1016_s0094_1190_02_00010_4,theimpactofschoolcharacteristicsonhousepriceschicago19871991,downes,10.1016/s0094-1190(02)00010-4,theimpactofschoolcharacteristicsonhousepriceschicago19871991|2002,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/j.landurbplan.2009.01.009,,Value of scenic views: Hedonic assessment of private housing in Hong Kong,2009.0,Landscape and Urban Planning,C.Y. Jim; Wendy Y. Chen,,230.0,,,journal-article,,,doi_10_1016_j_landurbplan_2009_01_009,valueofscenicviewshedonicassessmentofprivatehousinginhongkong,jim,10.1016/j.landurbplan.2009.01.009,valueofscenicviewshedonicassessmentofprivatehousinginhongkong|2009,1,1000,5,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment prices hedonic || apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1007/s00181-007-0152-3,,Errors in variables and spatial effects in hedonic house price models of ambient air quality,2007.0,Empirical Economics,Luc Anselin; Nancy Lozano-Gracia,,227.0,,,journal-article,,,doi_10_1007_s00181_007_0152_3,errorsinvariablesandspatialeffectsinhedonichousepricemodelsofambientairquality,anselin,10.1007/s00181-007-0152-3,errorsinvariablesandspatialeffectsinhedonichousepricemodelsofambientairquality|2007,1,1000,4,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic house price",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",64265d2ad7835e3be4855ed60dd439671b2397b0,10.1016/j.trd.2020.102387,,"Accessibility to transit, by transit, and property prices: Spatially varying relationships",2020.0,,Lin-Chuan Yang; K. W. Chau; W. Szeto; Xu Cui; Xu Wang,"Abstract Accessibility to transit facilities is perceived to affect property prices. However, accessibility by transit has rarely elicited adequate scholarly attention in property price analyses. Additionally, previous studies on how transit accessibility affects property prices mainly focused on rail and bus rapid transit systems, while conventional bus transit, which is very popular in many contexts, has seldom been investigated. Moreover, whether there is spatial heterogeneity in the price (or capitalization) effects of conventional bus accessibility remains to be explored. To fill these gaps, this study aims to investigate the role of accessibility to and by bus in determining housing prices in a bus-dependent city where urban transit service is offered mainly by a bus system rather than other transit systems. Using a database of 4966 condominium units in Xiamen, China, this study develops a battery of spatial econometric models to estimate global and local relationships between to-bus and by-bus accessibility and housing prices. The findings are as below: (1) to-bus accessibility (measured by the number of nearby bus stops) is positively associated with nearby housing prices; (2) by-bus accessibility (measured by travel time to city centers by bus and bus frequency) significantly affects nearby housing prices; (3) spatial heterogeneity exists in the price effects of bus accessibility; and (4) bus frequency exerts a larger price effect in the peripheral area than in the central. Finally, practical and policy implications are discussed.",225.0,,False,,Business,2020-08-01,64265d2ad7835e3be4855ed60dd439671b2397b0,accessibilitytotransitbytransitandpropertypricesspatiallyvaryingrelationships,yang,10.1016/j.trd.2020.102387,accessibilitytotransitbytransitandpropertypricesspatiallyvaryingrelationships|2020,1,2565,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,house prices proximity hedonic,,10.1080/095999196368899,,"Hedonic modelling, housing submarkets and residential valuation",1996.0,Journal of Property Research,A. S. Adair; J. N. Berry; W. S. McGreal,,216.0,,,journal-article,,,doi_10_1080_095999196368899,hedonicmodellinghousingsubmarketsandresidentialvaluation,adair,10.1080/095999196368899,hedonicmodellinghousingsubmarketsandresidentialvaluation|1996,1,1000,6,apartment rent hedonic || hedonic housing prices || hedonic property valuation residential || house prices proximity hedonic || implicit prices housing attributes || spatial hedonic model housing,2,0,2,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",90d123d23d3a8056f93170c4c292f2a2aead40ec,10.1023/b:real.0000035309.60607.53,,Modeling Spatial and Temporal House Price Patterns: A Comparison of Four Models,2004.0,,B. Case; J. Clapp; R. Dubin; Mauricio Rodríguez,,214.0,,False,,Computer Science,2004-01-27,90d123d23d3a8056f93170c4c292f2a2aead40ec,modelingspatialandtemporalhousepricepatternsacomparisonoffourmodels,case,10.1023/b:real.0000035309.60607.53,modelingspatialandtemporalhousepricepatternsacomparisonoffourmodels|2004,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4acd3ea1b225db4078b10d54b4e10b55d5e86b0c,10.1016/j.habitatint.2017.03.010,,"Spatial effects of accessibility to parks on housing prices in Shenzhen, China",2017.0,,Chao Wu; Xin-Yue Ye; Qingyun Du; P. Luo,,209.0,,False,,Economics,2017-05-01,4acd3ea1b225db4078b10d54b4e10b55d5e86b0c,spatialeffectsofaccessibilitytoparksonhousingpricesinshenzhenchina,wu,10.1016/j.habitatint.2017.03.010,spatialeffectsofaccessibilitytoparksonhousingpricesinshenzhenchina|2017,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",293598d214c9c0cbe70f29732ad1e246e3ad58a2,10.1016/j.regsciurbeco.2017.08.006,,Spatial-difference-in-differences models for impact of new mass rapid transit line on private housing values,2017.0,,M. Diao; Delon Leonard; T. Sing,,207.0,,False,,Environmental Science,2017-11-01,293598d214c9c0cbe70f29732ad1e246e3ad58a2,spatialdifferenceindifferencesmodelsforimpactofnewmassrapidtransitlineonprivatehousingvalues,diao,10.1016/j.regsciurbeco.2017.08.006,spatialdifferenceindifferencesmodelsforimpactofnewmassrapidtransitlineonprivatehousingvalues|2017,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",92d7ef83720ccbc959e3aa190d5c3dc6c0554545,10.1046/j.1080-8620.2003.00079.x,,Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach,2003.0,,T. Fik; David C. Ling; Gordon F. Mulligan,,200.0,,False,,Economics,2003-12-01,92d7ef83720ccbc959e3aa190d5c3dc6c0554545,modelingspatialvariationinhousingpricesavariableinteractionapproach,fik,10.1046/j.1080-8620.2003.00079.x,modelingspatialvariationinhousingpricesavariableinteractionapproach|2003,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || residential property price determinants",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W1978712773,10.1111/1540-6229.00555,,Nonparametric Estimation of Dynamic Hedonic Price Models and the Construction of Residential Housing Price Indices,1991.0,Real Estate Economics,Richard A. Meese; Nancy E. Wallace,"Parametric specifications for hedonic price equations are estimated using a data set from Alameda and San Francisco Counties and are compared to estimates using a nonparametric technique called locally weighted regression, LWR. LWR permits flexible estimation of the hedonic's curvature at median attributes and is less sensitive than standard regression techniques to the influence of unusual observations. The technique also avoids imposing a single functional form across time and municipalities. The LWR estimates of municipality‐specific hedonics are then used to obtain implicit prices for housing attributes and to derive municipality‐specific price indices. The results of extensive diagnostic checks of our technique are also reported.",197.0,,False,article,,1991-09-01,https://openalex.org/W1978712773,nonparametricestimationofdynamichedonicpricemodelsandtheconstructionofresidentialhousingpriceindices,meese,10.1111/1540-6229.00555,nonparametricestimationofdynamichedonicpricemodelsandtheconstructionofresidentialhousingpriceindices|1991,1,1744,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || apartment rent hedonic || hedonic housing prices || implicit prices housing attributes",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0094-1190(80)90055-8,,Some empirical results on the nature of the hedonic price function for the urban housing market,1980.0,Journal of Urban Economics,Peter Linneman,,197.0,,,journal-article,,,doi_10_1016_0094_1190_80_90055_8,someempiricalresultsonthenatureofthehedonicpricefunctionfortheurbanhousingmarket,linneman,10.1016/0094-1190(80)90055-8,someempiricalresultsonthenatureofthehedonicpricefunctionfortheurbanhousingmarket|1980,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_citations,cites:sirmans2006,a289a62de4e9f34c166950d208e0878d2e993527,10.1016/j.landurbplan.2011.11.012,,"Estimating the value of urban green areas: A hedonic pricing analysis of the single family housing market in Los Angeles, CA",2012.0,,J. Saphores; Wei Li,,197.0,,False,,Geography,2012-03-15,a289a62de4e9f34c166950d208e0878d2e993527,estimatingthevalueofurbangreenareasahedonicpricinganalysisofthesinglefamilyhousingmarketinlosangelesca,saphores,10.1016/j.landurbplan.2011.11.012,estimatingthevalueofurbangreenareasahedonicpricinganalysisofthesinglefamilyhousingmarketinlosangelesca|2012,1,1000,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || cites:sirmans2006 || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",84b875c83cbfda9867894fd542d13cad33554a68,10.1080/13658816.2018.1545158,,Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices,2018.0,International Journal of Geographical Information Science,Chao Wu; F. Ren; Weinbin Hu; Qingyun Du,"ABSTRACT Understanding scale effects is important and indispensable for geography studies. However, spatial and spatiotemporal statistical tools for measuring the operational scales of different processes are rather limited. This article extends the popular geographically and temporally weighted regression (GTWR) model to consider operational scale effects by proposing multiscale GTWR (MGTWR), which offers a flexible and scalable framework for identifying and analysing multiscale processes by specifying flexible bandwidths for various covariates. Then, MGTWR is employed to explore spatiotemporal variations and how influential factors are associated with housing prices in Shenzhen. This article attempts to extend GTWR to MGTWR in consideration of scale effects, thereby highlighting the importance of different levels of spatiotemporal heterogeneity. Furthermore, the empirical results of this study can provide valuable policy implications for real estate development in areas where urban planning should address multiscale effects in both temporal and spatial dimensions.",190.0,,False,JournalArticle,Geography;Computer Science,2018-11-28,84b875c83cbfda9867894fd542d13cad33554a68,multiscalegeographicallyandtemporallyweightedregressionexploringthespatiotemporaldeterminantsofhousingprices,wu,10.1080/13658816.2018.1545158,multiscalegeographicallyandtemporallyweightedregressionexploringthespatiotemporaldeterminantsofhousingprices|2018,1,2082,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c48b0e1b9df9d2ca997b90fd77c5ee7fc68af2a9,10.1016/j.jue.2008.06.002,,"Changes in the distribution of house prices over time: Structural characteristics, neighborhood, or coefficients?",2008.0,,Daniel P. McMillen,,168.0,,False,,Economics,2008-11-01,c48b0e1b9df9d2ca997b90fd77c5ee7fc68af2a9,changesinthedistributionofhousepricesovertimestructuralcharacteristicsneighborhoodorcoefficients,mcmillen,10.1016/j.jue.2008.06.002,changesinthedistributionofhousepricesovertimestructuralcharacteristicsneighborhoodorcoefficients|2008,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ff67e0f67e71d0324ad2684ee7aba4eae2cba2e0,10.1111/j.1475-5890.2008.00074.x,,The Anticipated Capitalisation Effect of a New Metro Line on Housing Prices,2008.0,,Claudio A. Agostini; G. Palmucci,,168.0,,False,,Economics,2008-06-01,ff67e0f67e71d0324ad2684ee7aba4eae2cba2e0,theanticipatedcapitalisationeffectofanewmetrolineonhousingprices,agostini,10.1111/j.1475-5890.2008.00074.x,theanticipatedcapitalisationeffectofanewmetrolineonhousingprices|2008,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,273af7ef337bda05d42a29e1058fcc78b5b9c5c3,10.22004/ag.econ.8630,,Measuring the Contribution of Water and Green Space Amenities to Housing Values: An Application and Comparison of Spatially Weighted Hedonic Models,2006.0,,Seong-Hoon Cho; J. Bowker; W. M. Park,,167.0,,False,,Economics,2006-12-01,273af7ef337bda05d42a29e1058fcc78b5b9c5c3,measuringthecontributionofwaterandgreenspaceamenitiestohousingvaluesanapplicationandcomparisonofspatiallyweightedhedonicmodels,cho,10.22004/ag.econ.8630,measuringthecontributionofwaterandgreenspaceamenitiestohousingvaluesanapplicationandcomparisonofspatiallyweightedhedonicmodels|2006,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || hedonic price model amenities residential || spatial hedonic model housing",2,1,1,2,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1023/a:1007715917198,,"The Construction of Residential Housing Price Indices: A Comparison of Repeat-Sales, Hedonic-Regression, and Hybrid Approaches",1997.0,The Journal of Real Estate Finance and Economics,Nancy E. Wallace; Richard A. Meese,,155.0,,,journal-article,,,doi_10_1023_a_1007715917198,theconstructionofresidentialhousingpriceindicesacomparisonofrepeatsaleshedonicregressionandhybridapproaches,wallace,10.1023/a:1007715917198,theconstructionofresidentialhousingpriceindicesacomparisonofrepeatsaleshedonicregressionandhybridapproaches|1997,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || apartment rent hedonic || hedonic housing prices || implicit prices housing attributes",2,1,2,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/j.jtrangeo.2008.10.012,,Does high-speed rail accessibility influence residential property prices? Hedonic estimates from southern Taiwan,2010.0,Journal of Transport Geography,David Emanuel Andersson; Oliver F. Shyr; Johnson Fu,,153.0,,,journal-article,,,doi_10_1016_j_jtrangeo_2008_10_012,doeshighspeedrailaccessibilityinfluenceresidentialpropertypriceshedonicestimatesfromsoutherntaiwan,andersson,10.1016/j.jtrangeo.2008.10.012,doeshighspeedrailaccessibilityinfluenceresidentialpropertypriceshedonicestimatesfromsoutherntaiwan|2010,1,1000,3,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || apartment rent hedonic || hedonic house price",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0094-1190(78)90016-5,,"Residential choice, hedonic prices, and the demand for urban air quality",1978.0,Journal of Urban Economics,Jon P. Nelson,,153.0,,,journal-article,,,doi_10_1016_0094_1190_78_90016_5,residentialchoicehedonicpricesandthedemandforurbanairquality,nelson,10.1016/0094-1190(78)90016-5,residentialchoicehedonicpricesandthedemandforurbanairquality|1978,1,1000,1,apartment rent hedonic,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,house prices proximity hedonic,d25958c697343bc63cd4bd06a7554d1e9ab6ae61,10.1016/j.jtrangeo.2014.08.003,,"Combined impacts of highways and light rail transit on residential property values: a spatial hedonic price model for Phoenix, Arizona",2014.0,,Kihwan Seo; A. Golub; M. Kuby,,151.0,,False,,Environmental Science,2014-12-01,d25958c697343bc63cd4bd06a7554d1e9ab6ae61,combinedimpactsofhighwaysandlightrailtransitonresidentialpropertyvaluesaspatialhedonicpricemodelforphoenixarizona,seo,10.1016/j.jtrangeo.2014.08.003,combinedimpactsofhighwaysandlightrailtransitonresidentialpropertyvaluesaspatialhedonicpricemodelforphoenixarizona|2014,1,1000,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || hedonic price model amenities residential || house prices proximity hedonic || spatial hedonic model housing",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W245041798,10.1016/j.landurbplan.2015.04.007,,"Valuation of landscape amenities: A hedonic pricing analysis of housing rents in urban, suburban and periurban Switzerland",2015.0,Landscape and Urban Planning,Felix Schläpfer; Fabian Waltert; Lorena Segura; Felix Kienast,,149.0,,False,article,,2015-05-16,https://openalex.org/W245041798,valuationoflandscapeamenitiesahedonicpricinganalysisofhousingrentsinurbansuburbanandperiurbanswitzerland,schläpfer,10.1016/j.landurbplan.2015.04.007,valuationoflandscapeamenitiesahedonicpricinganalysisofhousingrentsinurbansuburbanandperiurbanswitzerland|2015,1,1000,2,apartment prices hedonic || hedonic housing prices,2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,implicit prices housing attributes,,10.1108/14635789710163793,,Estimating the influence of transport on house prices: evidence from Hong Kong,1997.0,Journal of Property Valuation and Investment,H.M. So; R.Y.C. Tse; S. Ganesan,"Attempts to analyse the importance of transportation in determining house prices in Hong Kong. Residential property is sold as a package of inherent attributes that are valued by consumers and this analysis classifies them into the following categories: structural, physical and environmental. Specifically, these determinants of housing price include structural attributes such as size, age and floor level of the housing units; amenities such as accessibility to various means of transport; and factors determining the quality of the environment such as availability of recreational facilities. In this study, data were obtained from a large residential area in Hong Kong. Uses a Hedonic price model for estimating the importance of each of the defined utility‐bearing attributes on house prices.",146.0,,,journal-article,,,doi_10_1108_14635789710163793,estimatingtheinfluenceoftransportonhousepricesevidencefromhongkong,so,10.1108/14635789710163793,estimatingtheinfluenceoftransportonhousepricesevidencefromhongkong|1997,1,1798,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || hedonic price model amenities residential || house prices proximity hedonic || implicit prices housing attributes || residential property price determinants",2,3,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1080/00420980220135545,,Estimating Neighbourhood Effects in House Prices: Towards a New Hedonic Model Approach,2002.0,Urban Studies,Raymond Y. C. Tse,"A hedonic equation considers OLS models with independently and identically distributed errors. However, quality of property and location tend to exhibit highly auto-regressive correlation due to spatial dependence and heterogeneity. A modification is made to multiple regression analysis based on the land-rent concept so that the modified hedonic house price function can be determined. This paper develops a stochastic approach which is able to correct autocorrelation bias in the hedonic function. Others tend to model the spatial autocorrelation through the error terms, while this study models it through the constant term. The sample was orientated by location to reflect the neighbourhood effects. Thus the neighbourhood effects can be separated from the random disturbance. Our model, using data from Hong Kong, incorporates adjustments reflecting net floor area ratio, age, floor level, views, transport accessibility and amenities such as availability of recreational facilities. The stochastic model is a more flexible application of hedonic price regressions, making it an appealing alternative to spatial problems.",140.0,,,journal-article,,,doi_10_1080_00420980220135545,estimatingneighbourhoodeffectsinhousepricestowardsanewhedonicmodelapproach,tse,10.1080/00420980220135545,estimatingneighbourhoodeffectsinhousepricestowardsanewhedonicmodelapproach|2002,1,2127,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic house price || hedonic housing prices",2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",94b1f2830d00dabc5e72e47b87211acaffa9afad,10.1016/j.cities.2016.07.001,,"Amenity, accessibility and housing values in metropolitan USA: A study of Salt Lake County, Utah",2016.0,,Han-Duo Li; Y. Wei; Zhou Yu; Guang Tian,,139.0,,False,,Geography,2016-11-01,94b1f2830d00dabc5e72e47b87211acaffa9afad,amenityaccessibilityandhousingvaluesinmetropolitanusaastudyofsaltlakecountyutah,li,10.1016/j.cities.2016.07.001,amenityaccessibilityandhousingvaluesinmetropolitanusaastudyofsaltlakecountyutah|2016,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/j.geoforum.2006.10.002,,Consumption preferences and environmental externalities: A hedonic analysis of the housing market in Guangzhou,2007.0,Geoforum,C.Y. Jim; Wendy Y. Chen,,138.0,,,journal-article,,,doi_10_1016_j_geoforum_2006_10_002,consumptionpreferencesandenvironmentalexternalitiesahedonicanalysisofthehousingmarketinguangzhou,jim,10.1016/j.geoforum.2006.10.002,consumptionpreferencesandenvironmentalexternalitiesahedonicanalysisofthehousingmarketinguangzhou|2007,1,1000,5,apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || house prices proximity hedonic,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",0576e0b28cb69e2becb32cc79dcb9b1b00da49a5,10.1007/bf01258322,,Residential property values in a multinodal urban area: New evidence on the implicit price of location,1993.0,,P. Waddell; B. Berry; I. Hoch,,137.0,,False,,Economics;Geography,1993-09-01,0576e0b28cb69e2becb32cc79dcb9b1b00da49a5,residentialpropertyvaluesinamultinodalurbanareanewevidenceontheimplicitpriceoflocation,waddell,10.1007/bf01258322,residentialpropertyvaluesinamultinodalurbanareanewevidenceontheimplicitpriceoflocation|1993,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic housing prices || hedonic price model amenities residential || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.2307/3146079,,The Specification of Hedonic Indexes for Urban Housing,1982.0,Land Economics,Richard V. Butler,,134.0,,,journal-article,,,doi_10_2307_3146079,thespecificationofhedonicindexesforurbanhousing,butler,10.2307/3146079,thespecificationofhedonicindexesforurbanhousing|1982,1,1000,2,apartment rent hedonic || hedonic housing prices,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0094-1190(82)90016-x,,"Permanent income, hedonic prices, and demand for housing: New evidence",1982.0,Journal of Urban Economics,Allen C. Goodman; Masahiro Kawai,,132.0,,,journal-article,,,doi_10_1016_0094_1190_82_90016_x,permanentincomehedonicpricesanddemandforhousingnewevidence,goodman,10.1016/0094-1190(82)90016-x,permanentincomehedonicpricesanddemandforhousingnewevidence|1982,1,1000,2,apartment rent hedonic || hedonic housing prices,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,b11422e15014cb4b89c0bc53b8999740701dfe89,10.1016/j.jhe.2004.01.001,,A prediction comparison of housing sales prices by parametric versus semi-parametric regressions,2004.0,,Okmyung Bin,,132.0,,False,,Economics,2004-03-01,b11422e15014cb4b89c0bc53b8999740701dfe89,apredictioncomparisonofhousingsalespricesbyparametricversussemiparametricregressions,bin,10.1016/j.jhe.2004.01.001,apredictioncomparisonofhousingsalespricesbyparametricversussemiparametricregressions|2004,1,1000,2,hedonic house price || hedonic housing prices,0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",15bb4feffbf1a6725234c8865e3dec84f429f4be,10.1016/j.cities.2019.102433,,"Amenity effects of urban facilities on housing prices in China: Accessibility, scarcity, and urban spaces",2020.0,,Feng-Lin Yuan; Y. Wei; Jia-Wei Wu,"Abstract Housing values are heavily influenced by urban facilities. However, the amenity effects of urban facilities have not been fully assessed, and their spatial heterogeneity has largely been neglected. Taking Nanjing as an example, this study adopts a synthetic approach that considers scarcity, accessibility, and submarkets, and highlights the influence of the idiosyncratic characteristics of urban facilities and heterogeneous urban spaces on housing prices. We divide urban facilities into two categories based on scarcity: irreplaceable and replaceable facilities. We find that the influence of urban facilities differs among different categories and submarkets, and the influence of irreplaceable facilities is highly dependent on accessibility in all submarkets, while that of replaceable facilities relies on both the accessibility and scarcity of those facilities. This study contributes to our understanding of the importance of and differences among the effects of various public facilities on property values in the intra-urban environment.",129.0,,False,,Business,,15bb4feffbf1a6725234c8865e3dec84f429f4be,amenityeffectsofurbanfacilitiesonhousingpricesinchinaaccessibilityscarcityandurbanspaces,yuan,10.1016/j.cities.2019.102433,amenityeffectsofurbanfacilitiesonhousingpricesinchinaaccessibilityscarcityandurbanspaces|2020,1,2058,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,1,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",51b5b1c1588ea45c2faf233faa9c41575e282e78,10.1016/j.regsciurbeco.2016.06.005,,Flood hazards impact on neighborhood house prices: A spatial quantile regression analysis,2016.0,,Lei Zhang,,129.0,,False,,Geography,2016-09-01,51b5b1c1588ea45c2faf233faa9c41575e282e78,floodhazardsimpactonneighborhoodhousepricesaspatialquantileregressionanalysis,zhang,10.1016/j.regsciurbeco.2016.06.005,floodhazardsimpactonneighborhoodhousepricesaspatialquantileregressionanalysis|2016,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W864328887,10.1016/j.habitatint.2015.06.005,,A study of the effect of a high-speed rail station on spatial variations in housing price based on the hedonic model,2015.0,Habitat International,Bin Geng; Haijun Bao; Ying Liang,,127.0,,False,article,,2015-06-19,https://openalex.org/W864328887,astudyoftheeffectofahighspeedrailstationonspatialvariationsinhousingpricebasedonthehedonicmodel,geng,10.1016/j.habitatint.2015.06.005,astudyoftheeffectofahighspeedrailstationonspatialvariationsinhousingpricebasedonthehedonicmodel|2015,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || spatial hedonic model housing",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e8a3d204da39c6ba6ff566893f41dda864696115,10.1068/b32119,,"Modeling Spatial Dimensions of Housing Prices in Milwaukee, WI",2007.0,,Danlin Yu; Y. Wei; Chang-Shan Wu,,125.0,,False,JournalArticle,Mathematics,2007-11-01,e8a3d204da39c6ba6ff566893f41dda864696115,modelingspatialdimensionsofhousingpricesinmilwaukeewi,yu,10.1068/b32119,modelingspatialdimensionsofhousingpricesinmilwaukeewi|2007,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,house prices proximity hedonic,d015f0b776c392781354396e005d138d2c4fdfd4,10.1016/j.ufug.2015.09.013,,"Assessing amenity effects of urban landscapes on housing price in Hangzhou, China.",2015.0,,Hai-Zhen Wen; Yan Zhang; Ling Zhang,,124.0,,False,,Geography,,d015f0b776c392781354396e005d138d2c4fdfd4,assessingamenityeffectsofurbanlandscapesonhousingpriceinhangzhouchina,wen,10.1016/j.ufug.2015.09.013,assessingamenityeffectsofurbanlandscapesonhousingpriceinhangzhouchina|2015,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices || house prices proximity hedonic",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d24166f00700f9016f06cfeea4b5a863ed0cc8a0,10.1007/bf00219506,,Housing prices and flood risk: An examination using spline regression,1991.0,,J. Speyrer; Wade R. Ragas,,123.0,,False,,Business,1991-12-01,d24166f00700f9016f06cfeea4b5a863ed0cc8a0,housingpricesandfloodriskanexaminationusingsplineregression,speyrer,10.1007/bf00219506,housingpricesandfloodriskanexaminationusingsplineregression|1991,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),485efdd3d5d6971cc88e8a0452c2750fdc6fcf3a,10.1016/j.cities.2018.07.005,,Does bus accessibility affect property prices?,2019.0,Cities,Lin-Chuan Yang; Jiang-Ping Zhou; Oliver F. Shyr; Da Huo,"Abstract Existing studies have yet reached consistent conclusions on accessibility benefits of buses. Most existing studies have been conducted in the context of the West, where bus patronage is generally low. In this study, we used a database of 22,586 secondhand residential properties in 358 residential estates in Xiamen, China to develop four non-spatial hedonic pricing models (one standard and three Box-Cox transformed) and two spatial econometric models to quantify the effects of bus accessibility on property prices and analyze how the introduction of spatial econometric models would influence estimates of such benefits. Our findings are as follows. (1) Access to bus stops is positively correlated with property prices. This outcome is in contrast with findings of mainstream research (or conventional wisdom). For every bus stop within 500 m, the price of a property is 0.5% higher, all else being equal. (2) Bus travel times to essential destinations significantly influence housing prices. (3) Spatial econometric models that account for spatial autocorrelation outperform traditional hedonic pricing models. A few robustness check analyses further guarantee the plausibility of this study. However, the price premiums offered by bus accessibility may be gradually decreased, even eventually discarded, because of declining attractiveness for bus travel and continuous transit service enhancement in the forthcoming years.",121.0,,False,,Economics,,485efdd3d5d6971cc88e8a0452c2750fdc6fcf3a,doesbusaccessibilityaffectpropertyprices,yang,10.1016/j.cities.2018.07.005,doesbusaccessibilityaffectpropertyprices|2019,1,2439,4,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || spatial hedonic model housing",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2022052296,10.1111/0017-4815.00171,,Theoretical and Empirical Considerations Regarding Space in Hedonic Housing Price Model Applications,2001.0,Growth and Change,William M. Bowen; Brian A. Mikelbank; Dean M. Prestegaard,"Recent advances in spatial data analysis are making their way into a variety of applied research settings. Once purely the domain of specialists, increased availability of both spatial data and the software with which to handle them, spatial analysis techniques are diffusing into other areas of research. This article first details the rationale and need for spatial considerations in hedonic price models and focuses on the link between the context of the housing market and the statistical considerations necessary when dealing with spatial data. These issues are then explored via an application to the housing market of Cuyahoga County, Ohio. It was found, first, that explicit modeling of space is not always warranted. One of our two models shows no substantial signs of spatial misspecification. However, in the second model, where diagnostic tests call for the explicit modeling of space, some drastic differences were found between the space‐neglected model and the more correctly specified spatial hedonic model. This highlights the need to include spatial diagnostics as part of the standard model‐fitting procedure for hedonic house price applications.",119.0,,False,article,,2001-01-01,https://openalex.org/W2022052296,theoreticalandempiricalconsiderationsregardingspaceinhedonichousingpricemodelapplications,bowen,10.1111/0017-4815.00171,theoreticalandempiricalconsiderationsregardingspaceinhedonichousingpricemodelapplications|2001,1,2165,5,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment rent hedonic || hedonic house price || hedonic housing prices || spatial hedonic model housing",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1467-9787.1987.tb01184.x,,"A HEDONIC MODEL OF INTERREGIONAL WAGES, RENTS, AND AMENITY VALUES*",1987.0,Journal of Regional Science,John P. Hoehn; Mark C. Berger; Glenn C. Blomquist,"ABSTRACT This paper develops a general multimarket hedonic model appropriate for a national, interregional study of wages, housing prices, and location‐specific amenities. The model encompasses the effects of interregional location, intraurban location, and city size. Typically, hedonic studies focus on a single market such as labor or housing and ignore interactions implicit in a more global compensation mechanism. Examination of the comparative statics of our model indicates that single‐market differentials are partial prices and are unreliable measures of amenity values in an interregional context. Unbiased amenity values are estimated for a comprehensive set of amenities using data on housing prices for 34,414 households and wages for 46,004 workers from the 1980 Census of Population and Housing . Statistically significant differences in housing prices and wages are found due to amenities.",118.0,,,journal-article,,,doi_10_1111_j_1467_9787_1987_tb01184_x,ahedonicmodelofinterregionalwagesrentsandamenityvalues,hoehn,10.1111/j.1467-9787.1987.tb01184.x,ahedonicmodelofinterregionalwagesrentsandamenityvalues|1987,1,1906,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic housing prices",2,2,2,1,1,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1b545b4d8aaf1533c3203f1f46187202d33711d6,10.1177/1078087418824672,,Upzoning Chicago: Impacts of a Zoning Reform on Property Values and Housing Construction,2020.0,Urban Affairs Review,Y. Freemark,"What are the local-level impacts of zoning change? I study recent Chicago upzonings that increased allowed densities and reduced parking requirements in a manner exogenous of development plans and neighborhood characteristics. To evaluate outcomes, I use difference-in-differences tests on property transaction prices and housing-unit construction permits. I detect significant, robust increases in values for transactions on parcels that received a boost in allowed building size. I also identify value increases for residential condominiums, indicating that upzoning increased prices of existing housing units. I find no impacts of the reforms, however, on the number of newly permitted dwellings over five years. As such, I demonstrate that the short-term, local-level impacts of upzoning are higher property prices but no additional new housing construction.",117.0,,False,JournalArticle,Business,2020-05-01,1b545b4d8aaf1533c3203f1f46187202d33711d6,upzoningchicagoimpactsofazoningreformonpropertyvaluesandhousingconstruction,freemark,10.1177/1078087418824672,upzoningchicagoimpactsofazoningreformonpropertyvaluesandhousingconstruction|2020,1,1862,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,5,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5b5c65900fc3c7e539bc1408b8b5baff3633db65,10.1016/j.habitatint.2015.07.004,,"Retail accessibility and proximity effects on housing prices in Seoul, Korea: A retail type and housing submarket approach",2015.0,,Myungjun Jang; Chang-Deok Kang,,116.0,,False,,Business,2015-10-01,5b5c65900fc3c7e539bc1408b8b5baff3633db65,retailaccessibilityandproximityeffectsonhousingpricesinseoulkoreaaretailtypeandhousingsubmarketapproach,jang,10.1016/j.habitatint.2015.07.004,retailaccessibilityandproximityeffectsonhousingpricesinseoulkoreaaretailtypeandhousingsubmarketapproach|2015,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f94f19e4ae3c623a6181ee4868a28358b1a0879b,10.1016/j.landurbplan.2020.103900,,Eco-gentrification and who benefits from urban green amenities: NYC’s high Line,2020.0,,Katie Jo Black; M. Richards,"Abstract This study estimates the impact of the introduction of New York’s High Line, an elevated walkway in Manhattan by relating the increase in housing values to the issue of eco-gentrification in urban areas. Studies have shown that increasing the amount of green space can lead to gentrification: rising property values, displacement of existing residents, and a large in-migration of wealthy populations. We utilize Geographic Information Systems (GIS) to spatially model the effects of the High Line on residential property values. We exploit spatial and temporal differences of the High Line’s opening to estimate its impact on residential home values. We find that homes closest to the High Line experienced a 35.3% increase in housing values. We estimate the driving force behind this premium by using a triple difference approach by analyzing elevation levels and find that homes with relatively the same height as the High Line receive the largest premium. This study serves to inform policy makers regarding potential inequality of green space provisions. We provide policy recommendations for minimizing the housing price impact of green space creation.",114.0,,False,,Geography,2020-12-01,f94f19e4ae3c623a6181ee4868a28358b1a0879b,ecogentrificationandwhobenefitsfromurbangreenamenitiesnycshighline,black,10.1016/j.landurbplan.2020.103900,ecogentrificationandwhobenefitsfromurbangreenamenitiesnycshighline|2020,1,2167,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,4,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/j.reseneeco.2004.11.003,,The impact of oil and natural gas facilities on rural residential property values: a spatial hedonic analysis,2005.0,Resource and Energy Economics,Peter C. Boxall; Wing H. Chan; Melville L. McMillan,,113.0,,,journal-article,,,doi_10_1016_j_reseneeco_2004_11_003,theimpactofoilandnaturalgasfacilitiesonruralresidentialpropertyvaluesaspatialhedonicanalysis,boxall,10.1016/j.reseneeco.2004.11.003,theimpactofoilandnaturalgasfacilitiesonruralresidentialpropertyvaluesaspatialhedonicanalysis|2005,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment rent hedonic",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,f78c2cf2d2e1b14ec3ca2f4661148b448cdb083c,10.1016/j.eneco.2018.04.041,,Does voluntary disclosure create a green lemon problem? Energy-efficiency ratings and house prices,2018.0,Energy Economics,F. Fuerst; G. Warren-Myers,"This paper seeks to elucidate whether high levels of non-disclosure lead to adverse market outcomes in the form of the well-known lemons problem. It also empirically tests whether energy-efficiency ratings (EERs) are reflected in both housing sales prices and rents in the Australian Capital Territory, the only Australian housing market with mandatory ratings for all dwellings at point of sale or lease. Using a comprehensive dataset of sale and lease transactions during the period 2011–2016, a hedonic framework is applied. The analysis confirms that both the reported energy-efficiency levels and other sustainability-related characteristics that are not part of the formal rating assessment influence the pricing of both sales and rental transactions. Characteristics such as heating and cooling systems and the presence of solar power generators are significantly reflected in rents and sales prices, as tenants and buyers are likely to estimate their expected utility costs based on the EER. It is also shown that the option of leaving the EER of a rental property unreported presents a moral hazard for landlords of sub-standard properties, in that the likelihood of EER disclosure increases in line with the number of energy-efficient features of a property as revealed in the marketing material. The analysis also reveals that socio-economically disadvantaged areas suffer from disproportionately higher levels of EER non-disclosure, potentially constituting a ‘double disadvantage’ of non-disclosure and low–energy efficiency dwelling stock. From a market and asset-pricing perspective, it thus seems preferable to extend the requirement to obtain and present a valid EER to the rental market.",111.0,,False,,Business,2018-08-01,f78c2cf2d2e1b14ec3ca2f4661148b448cdb083c,doesvoluntarydisclosurecreateagreenlemonproblemenergyefficiencyratingsandhouseprices,fuerst,10.1016/j.eneco.2018.04.041,doesvoluntarydisclosurecreateagreenlemonproblemenergyefficiencyratingsandhouseprices|2018,1,2705,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,3,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,hedonic property valuation residential,,10.1080/00420980120080880,,Historic Preservation and Residential Property Values: An Analysis of Texas Cities,2001.0,Urban Studies,Robin M. Leichenko; N. Edward Coulson; David Listokin,"Designation of historic districts is increasingly used as a tool to revive or halt the deterioration of central-city neighbourhoods. While historic designation is generally thought to have a positive impact on property values, evidence on this issue is mixed. One limitation of previous research is that it typically focuses on historic neighbourhoods in one city and thus bases its conclusions on a very limited sample. This study expands upon previous work by examining the effects of designation on property values across a larger set of cities. The study employs hedonic regression models to estimate housing prices in historic districts and comparable neighbourhoods in nine Texas cities. Results suggest that, in most cases, historic designation is associated with higher property values.",110.0,,,journal-article,,,doi_10_1080_00420980120080880,historicpreservationandresidentialpropertyvaluesananalysisoftexascities,leichenko,10.1080/00420980120080880,historicpreservationandresidentialpropertyvaluesananalysisoftexascities|2001,1,1794,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || hedonic property valuation residential",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2074808384,10.1002/j.2325-8012.2002.tb00492.x,,The Market Value of Reducing Cancer Risk: Hedonic Housing Prices with Changing Information,2002.0,Southern Economic Journal,Ted Gayer; James T. Hamilton; W. Kip Viscusi,"In this paper, we use housing price changes occurring after the release of a regulatory agency's environmental risk information to estimate the value people place on cancer risk reduction. Using a large original data set on the repeal sales of houses, matched with detailed data on hazardous waste cancer risk and newspaper publicity, we find that housing prices respond in a rational manner to changes in information about risk. Since the new information indicated that the sites in our sample pose relatively low cancer risk, the informational release led residents to lower their risk beliefs, resulting in an average housing price increase of $56 to $87. This price change implies a statistical value per case of cancer of $4.3 million to $8.3 million, which is similar to the estimates obtained in labor market studies of the value of a statistical life. Newspaper publicity about the local sites increased housing prices, suggesting that residents perceived the news as good.",108.0,,False,article,,2002-10-01,https://openalex.org/W2074808384,themarketvalueofreducingcancerriskhedonichousingpriceswithchanginginformation,gayer,10.1002/j.2325-8012.2002.tb00492.x,themarketvalueofreducingcancerriskhedonichousingpriceswithchanginginformation|2002,1,1981,3,apartment rent hedonic || hedonic house price || hedonic housing prices,2,1,3,0,1,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1007/s11146-007-9083-1,,Do Repeated Wildfires Change Homebuyers’ Demand for Homes in High-Risk Areas? A Hedonic Analysis of the Short and Long-Term Effects of Repeated Wildfires on House Prices in Southern California,2007.0,The Journal of Real Estate Finance and Economics,Julie Mueller; John Loomis; Armando González-Cabán,,106.0,,,journal-article,,,doi_10_1007_s11146_007_9083_1,dorepeatedwildfireschangehomebuyersdemandforhomesinhighriskareasahedonicanalysisoftheshortandlongtermeffectsofrepeatedwildfiresonhousepricesinsoutherncalifornia,mueller,10.1007/s11146-007-9083-1,dorepeatedwildfireschangehomebuyersdemandforhomesinhighriskareasahedonicanalysisoftheshortandlongtermeffectsofrepeatedwildfiresonhousepricesinsoutherncalifornia|2007,1,1000,2,apartment rent hedonic || hedonic house price,2,1,3,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1467-8276.2005.00724.x,,The Amenity and Disamenity Impacts of Agriculture: Estimates from a Hedonic Pricing Model,2005.0,American Journal of Agricultural Economics,Richard C. Ready; Charles W. Abdalla,"Abstract The positive and negative externalities from farmland are increasingly a focus of public policy discussion about agriculture and land use. A GIS‐based hedonic pricing model shows that agricultural open space increases nearby residential property values, but larger‐scale animal operations and mushroom production have negative impacts. Animal production facilities with as few as 200 animal equivalent units reduce nearby property values, but larger facilities do not necessarily generate larger impacts. Because they tend to occur together, the negative impacts of animal agriculture and the positive impacts of open space must be simultaneously modeled to avoid omitted variable bias.",105.0,,,journal-article,,,doi_10_1111_j_1467_8276_2005_00724_x,theamenityanddisamenityimpactsofagricultureestimatesfromahedonicpricingmodel,ready,10.1111/j.1467-8276.2005.00724.x,theamenityanddisamenityimpactsofagricultureestimatesfromahedonicpricingmodel|2005,1,1695,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic price model amenities residential",2,1,1,1,1,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,b0dedb27afcdb0c1659d68d171565fee7f6e6a02,10.1016/j.trd.2018.04.001,,Walking accessibility and property prices,2018.0,Transportation Research Part D: Transport and Environment,Lin-Chuan Yang; Bo Wang; Jiang-Ping Zhou; Xu Wang,"Abstract Walking is an imperative travel mode, especially for short trips. Walking accessibility, which is defined as the ease of reaching essential destinations in the walk-in catchment area, may affect property prices because residents are more likely to be willing to pay for this attribute. In addition, different categories of public services may have varied influencing directions and magnitude. These two hypotheses are tested in this study. Taking Xiamen, China as a case study, we estimate the cumulative opportunities of public services on foot and develop a set of hedonic pricing models (more specifically, two pre-specified ordinary least squares models, four Box-Cox transformed models, and two spatial econometric models) to estimate, whether and to what extent, walking accessibility contributes to price premiums (or discounts). Using a database of 22,586 second-hand residential properties in 358 multi- or high-storey residential complexes, we find that (1) walking accessibility to public services contributes to the variations in housing prices and plays a role in determining housing prices; (2) different categories of services have vastly divergent, even opposite, influencing impacts; and (3) walking accessibility to primary schools, commercial centers, and sports and cultural centers have positive effects on house prices whereas walking accessibility to comprehensive hospitals adversely affects housing prices. Methodologically, we confirm that spatial econometric methods improve estimation accuracy and have more explanatory power relative to the standard non-spatial models. Robustness check analysis further guarantees the plausibility of this study.",104.0,,False,,Economics,2018-07-01,b0dedb27afcdb0c1659d68d171565fee7f6e6a02,walkingaccessibilityandpropertyprices,yang,10.1016/j.trd.2018.04.001,walkingaccessibilityandpropertyprices|2018,1,2684,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices || spatial hedonic model housing || willingness to pay housing attributes hedonic",2,4,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/j.regsciurbeco.2008.11.001,,How informative are average effects? Hedonic regression and amenity capitalization in complex urban housing markets,2009.0,Regional Science and Urban Economics,Christian L. Redfearn,,103.0,,,journal-article,,,doi_10_1016_j_regsciurbeco_2008_11_001,howinformativeareaverageeffectshedonicregressionandamenitycapitalizationincomplexurbanhousingmarkets,redfearn,10.1016/j.regsciurbeco.2008.11.001,howinformativeareaverageeffectshedonicregressionandamenitycapitalizationincomplexurbanhousingmarkets|2009,1,1000,9,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || implicit prices housing attributes || real estate hedonic regression || spatial hedonic model housing",2,0,1,3,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",a05f46f80241aefc123b8f16ff33a12dc9d62184,10.1016/j.landusepol.2020.104889,,A mass appraisal assessment study using machine learning based on multiple regression and random forest,2020.0,,Seckin Yilmazer; S. Kocaman,"Abstract Mass appraisal is a complex matter because it depends on several categorical and continuously changing or constant parameters. In addition, development of new assessment approaches for mass appraisal of real estate properties in highly complex urban environments is desirable. The advancements in geospatial technologies and machine learning algorithms open up new horizons. For this reason, the purpose of the present study is to compare one conventional stepwise linear multiple regression (MRA) and one more automated machine learning approach, random forest (RF), for mass appraisal in an urban residential area where commercial properties are also available. A part of Mamak District, Ankara, Turkey is selected as the study area since the property values are diverse and representative. Additionally, the district has a complex and developing urban structure. The data employed in the study were compiled under a cadastral modernization project of General Directorate of the Land Registry and Cadastre of Turkey (GDLRC) and were based on the reports of licensed experts (∼50 %), court reports (∼20 %), field surveys, or a combined analysis of all. Consequently, the data used in the study has a high level of confidence. The initial set of parameters used in both methods reflect the most frequently observed characteristics of the real estate properties in the study area that are also effective on the values. The stepwise MRA required manual adjustments of the final parameter set by the expert, whereas RF eliminated unusable parameters fully automatically. The method performance was assessed by using a subset of the training data as a random test. According to the accuracy assessment results, the RF (Adjusted R² 0.734; the total variance explained from the model) slightly outperforms the MRA (Adjusted R² 0.696) where the optimal parameters were set by the human expert. Finally, the results exhibited are promising for quick assessment of mass appraisal and a comprehensive discussion is presented in the study.",103.0,,False,Review,Computer Science,2020-12-01,a05f46f80241aefc123b8f16ff33a12dc9d62184,amassappraisalassessmentstudyusingmachinelearningbasedonmultipleregressionandrandomforest,yilmazer,10.1016/j.landusepol.2020.104889,amassappraisalassessmentstudyusingmachinelearningbasedonmultipleregressionandrandomforest|2020,1,3037,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,5,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),7b4e7f35d7b3d5553757b7fef045645f4e3b8d8c,10.1016/j.landurbplan.2018.12.007,,"Understanding the local impact of urban park plans and park typology on housing price: A case study of the Busan metropolitan region, Korea",2019.0,Landscape and Urban Planning,Hyunsoo Kim; Go-Eun Lee; Jaesong Lee; Yeol Choi,"Abstract The valuation of urban parks using hedonic pricing has been thoroughly documented in a number of studies set in many cities. Just as the value of existing parks can be estimated by investigating the relationship between housing prices and parks, the value of future parks can also be estimated using this approach. This study recognized that a series of planning processes and land use transitions to create urban parks not only influences the perceptions of real estate buyers but also the quality of the local environment. From the apartment sales data for the Busan metropolitan region of Korea, data on 11,498 sales of apartments within walking distance to planned parks (as well as existing parks) were collected. These data were applied in an analysis comprising three categories describing different planning contexts for the parks: “comprehensive plans,” “implementation plans,” and “completed parks.” A set of explanatory variables representing housing, park, and neighborhood characteristics was selected, with additional remarks regarding the planning specifications that might influence apartment buyers’ perceptions, such as the park type and the age of a plan, as defined in the official plans of the city. The results showed that smaller park types in residential settings were preferred throughout the processes of park planning, while all types were preferred once the processes were completed. The preference in park types is likely associated with the quality of the planning guidelines as well as the size of the project and the duration of planning.",102.0,,False,,Geography,2019-04-01,7b4e7f35d7b3d5553757b7fef045645f4e3b8d8c,understandingthelocalimpactofurbanparkplansandparktypologyonhousingpriceacasestudyofthebusanmetropolitanregionkorea,kim,10.1016/j.landurbplan.2018.12.007,understandingthelocalimpactofurbanparkplansandparktypologyonhousingpriceacasestudyofthebusanmetropolitanregionkorea|2019,1,2579,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,2,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,731283f3e5d3ed191e0d86aaa61184ca95314623,10.1080/2167034x.2003.12461362,,Has Falling Crime Driven New York City’s Real Estate Boom?,2003.0,Journal of Housing Research,A. Schwartz; Scott J. Susin; Ioan Voicu,"We investigate whether falling crime has driven New York City’s post-1994 real estate boom, as media reports suggest. We address this by decomposing trends in the city’s property values from 1988 to 1998 into components due to crime, the city’s investment in subsidized low-income housing, the quality of public schools, and other factors. We use rich data and employ both hedonic and repeat-sales house price models, which allow us to control for unobservable neighborhood and building-specific effects. We find that the popular story touting the overwhelming importance of crime rates has some truth to it. Falling crime rates are responsible for about a third of the post-1994 boom in property values. However, this story is incomplete because it ignores the revitalization of New York City’s poorer communities and the large role that housing subsidies played in mitigating the earlier bust.",101.0,,False,JournalArticle,,2003-01-01,731283f3e5d3ed191e0d86aaa61184ca95314623,hasfallingcrimedrivennewyorkcitysrealestateboom,schwartz,10.1080/2167034x.2003.12461362,hasfallingcrimedrivennewyorkcitysrealestateboom|2003,1,1895,3,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices,2,2,2,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",46fdcf9794cd8ad3ce5a38e5501a1680aeb34c03,10.1016/j.habitatint.2018.09.004,,"The effects of locational factors on the housing prices of residential communities: The case of Ningbo, China",2018.0,Habitat International,Xiaojin Liang; Yao-Lin Liu; Tianqi Qiu; Ying Jing; F. Fang,"Abstract Residential communities are the basic living units in Chinese cities. Housing prices are closely associated with the community location and surrounding support facilities. When selecting satisfactory residential accommodation, potential real estate purchasers prioritize the community location in a city at the macro-level and then consider other micro-factors (i.e., the floor, orientation, structure, etc.). This paper attempts to explore the relationship between housing prices and locational factors at the community level. We collect the current market prices of 545 residential communities built in the last decade in Ningbo, the second largest city in Zhejiang Province. Then, thirteen locational factors of five dimensions are identified to research their influences on housing prices. In the process of selecting certain locational variables, both extant features and additional features (i.e., planned ones) are considered. The geographic field model is introduced to quantify the external effects of locational factors, due to its advantages of producing more accurate results than that of traditional distance-based measure methods. Then, regression analysis is performed based on the average housing prices of residential communities and explanatory variables by the ordinary least squares model and the geographically weighted regression. The regression coefficients demonstrate that the externalities of parks, lakes, department stores, banks, secondary schools and rail transit have significant but spatially non-stationary effects on housing prices. The results provide references for local real estate planning departments and potential real estate purchasers.",101.0,,False,,Business,2018-11-01,46fdcf9794cd8ad3ce5a38e5501a1680aeb34c03,theeffectsoflocationalfactorsonthehousingpricesofresidentialcommunitiesthecaseofningbochina,liang,10.1016/j.habitatint.2018.09.004,theeffectsoflocationalfactorsonthehousingpricesofresidentialcommunitiesthecaseofningbochina|2018,1,2687,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0094-1190(89)90043-0,,Estimating the implicit price of energy efficiency improvements in the residential housing market: A hedonic approach,1989.0,Journal of Urban Economics,Terry M. Dinan; John A. Miranowski,,100.0,,,journal-article,,,doi_10_1016_0094_1190_89_90043_0,estimatingtheimplicitpriceofenergyefficiencyimprovementsintheresidentialhousingmarketahedonicapproach,dinan,10.1016/0094-1190(89)90043-0,estimatingtheimplicitpriceofenergyefficiencyimprovementsintheresidentialhousingmarketahedonicapproach|1989,1,1000,4,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || apartment rent hedonic || hedonic house price || hedonic housing prices",2,0,2,1,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9415dcf906b8cafd44032d14500fdf0eacad1c2e,10.1016/j.cities.2019.01.019,,Quantile effect of educational facilities on housing price: Do homebuyers of higher-priced housing pay more for educational resources?,2019.0,Cities,Hai-Zhen Wen; Yue Xiao; E. Hui,"Abstract Educational facilities are valued highly by citizens and play an important part in improving housing prices. Most existing studies applied the hedonic price model to obtain the average effects of educational facilities on housing prices. However, these effects may be heterogeneous in different conditional quantiles of housing prices. Aside from the average effects, scholars are also interested in the different behaviors of high-, medium-, and low-priced homebuyers, behaviors that cannot be simply explained by ordinary least-squares estimation without sample truncation. To fill this gap, this study utilizes quantile and spatial quantile regression models to obtain a complete description of the entire distribution. On the basis of 2015 housing transaction data in Hangzhou, substantial differences are identified in the effects of educational facilities on housing price across the conditional distribution. In particular, primary and secondary schools considerably improve housing prices over the entire conditional distribution, whereas kindergartens are only valued by homebuyers of lower- and higher-priced housing. Furthermore, only households of high-priced housing are willing to pay a premium for the accessibility of senior high schools and universities. The effect of spatial dependence on higher-priced housing is more pronounced than that on lower-priced housing, and the consideration of spatial attributes improves the explanatory ability. Empirical results indicate that the average effects of educational facilities on housing prices obtained by existing studies that used the traditional hedonic price model is far from adequate in shedding light on this real-world phenomenon. In particular, the quantile effect should be further revealed to obtain an in-depth understanding.",99.0,,False,,Economics,2019-07-01,9415dcf906b8cafd44032d14500fdf0eacad1c2e,quantileeffectofeducationalfacilitiesonhousingpricedohomebuyersofhigherpricedhousingpaymoreforeducationalresources,wen,10.1016/j.cities.2019.01.019,quantileeffectofeducationalfacilitiesonhousingpricedohomebuyersofhigherpricedhousingpaymoreforeducationalresources|2019,1,2810,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing || willingness to pay housing attributes hedonic",2,2,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8181714441658704d7e0aa062ef27a92aa8305ae,10.1016/j.habitatint.2019.03.008,,"Effects of floor level and landscape proximity on housing price: A hedonic analysis in Hangzhou, China",2019.0,Habitat International,Yue Xiao; E. Hui; Hai-Zhen Wen,"Abstract Floor level is an important factor influencing housing price. However, most existing literature just treated floor level as a control variable and simply obtained a linear relationship between floor level and housing price. To fill this gap, this study uses the hedonic price model to deeply investigate floor level premium. The emphasis of this study is on landscape proximity and also the interactive effect of floor level and landscapes. A better understanding between floor level, landscape proximity and housing price is provided. Contrary to most existing studies, the findings suggest that the relationship between floor level and housing price is non-linear and is different in multi-storey buildings and high-rise buildings. As for the interactive effect between floor level and landscape proximity, we find that the amenity value of landscapes shows vertical heterogeneity at different floor levels within a building. On the other hand, landscape proximity significantly affects the relationship between floor level and housing price. This study addresses issues that were not studied before but are crucial to estimate the value of floor level and landscapes. It provides important implications for a better living environment and urban planning.",99.0,,False,,Geography,2019-05-01,8181714441658704d7e0aa062ef27a92aa8305ae,effectsoffloorlevelandlandscapeproximityonhousingpriceahedonicanalysisinhangzhouchina,xiao,10.1016/j.habitatint.2019.03.008,effectsoffloorlevelandlandscapeproximityonhousingpriceahedonicanalysisinhangzhouchina|2019,1,2266,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f994559f690250ac6eb77d1e14251291e09c21c0,10.1016/j.landusepol.2018.03.045,,"Policy change, amenity, and spatiotemporal dynamics of housing prices in Nanjing, China",2018.0,Land Use Policy,Feng-Lin Yuan; Jia-Wei Wu; Y. Wei; Lei Wang,"Understanding the spatiotemporal heterogeneity of the housing market is critical for formulating land/housing policies and achieving sustainable urbanization. This study analyzes the housing transactions at the city, community, and apartment levels in Nanjing, focusing on the effects of government policy and amenity. We find that housing prices have appreciated significantly in Chinese cities, and prices also vary within cities. We also find that changes in government policies, especially policies for the development of new urban districts and public facilities, significantly influence the spatiotemporal heterogeneity and dynamics of housing prices. Moreover, high-quality schools provided by governments are evidently exerting an increasing influence on housing values, while proximities to parks, (sub-)CBDs and government service centers also emerge as important factors. We can therefore summarize that housing price variation within Chinese cities is largely institutionally driven, and the Chinese government is the dominant agent of creating uneven urban development in China. We suggest that development policies should consider the need and location of residential areas and improve their access to public facilities to promote intra-urban equality in the housing market.",99.0,,False,,Business,2018-06-01,f994559f690250ac6eb77d1e14251291e09c21c0,policychangeamenityandspatiotemporaldynamicsofhousingpricesinnanjingchina,yuan,10.1016/j.landusepol.2018.03.045,policychangeamenityandspatiotemporaldynamicsofhousingpricesinnanjingchina|2018,1,2288,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1046/j.1080-8620.2003.00078.x,,Estimating Bargaining Effects in Hedonic Models: Evidence from the Housing Market,2003.0,Real Estate Economics,John P. Harding; John R. Knight; C.F. Sirmans,"Bargaining is common in markets for heterogeneous goods and differences in bargaining power between buyer and seller affect the negotiated transaction price. Previous research has found systematic evidence in the housing markets that weak buyers pay higher prices and weak sellers receive lower prices for their homes. Earlier work has modeled the bargaining effect as a parallel shift in the hedonic function, implicitly assuming that attribute shadow prices were unaffected by the bargaining process. In this paper, we use a sample of home sales where the seller's bargaining power is weakened by the fact that the home is vacant at the time of sale to test whether the effect of bargaining is best captured by a shift in the hedonic constant or whether the attribute shadow prices vary as well. The question is significant for property valuation where estimation of the marginal value of an attribute is commonly used to adjust comparable sales data. We find strong confirmation that bargaining power influences the negotiated price. We also find evidence that bargaining power alters attribute prices, although we do not find a consistent pattern across markets.",98.0,,,journal-article,,,doi_10_1046_j_1080_8620_2003_00078_x,estimatingbargainingeffectsinhedonicmodelsevidencefromthehousingmarket,harding,10.1046/j.1080-8620.2003.00078.x,estimatingbargainingeffectsinhedonicmodelsevidencefromthehousingmarket|2003,1,2166,5,apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic property valuation || implicit prices housing attributes,2,1,3,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",65d48061efcc2ee43e9951cf6ba9e7781bef4946,10.3828/jtep.1990.24.1.49,,Aircraft Noise and Residential Property Values Adjacent to Manchester International Airport,1990.0,Journal of Transport Economics and Policy,G. Pennington; N. Topham; R. Ward,Lower market values of properties affected by aircraft noise are found to be wholly attributable to the neighbourhood and other characteristics of the properties - the effects of noise are insignificant. The authors question the need for the proposed ban on aircraft with acoustical certificates issued before 1977.,98.0,,False,JournalArticle,Economics,1990-01-01,65d48061efcc2ee43e9951cf6ba9e7781bef4946,aircraftnoiseandresidentialpropertyvaluesadjacenttomanchesterinternationalairport,pennington,10.3828/jtep.1990.24.1.49,aircraftnoiseandresidentialpropertyvaluesadjacenttomanchesterinternationalairport|1990,1,1315,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2735574852,10.1016/j.habitatint.2017.07.002,,Spatial and hedonic analysis of housing prices in Shanghai,2017.0,Habitat International,Zezhou Huang; Ruishan Chen; Di Xu; Wei Zhou,,97.0,,False,article,,2017-07-11,https://openalex.org/W2735574852,spatialandhedonicanalysisofhousingpricesinshanghai,huang,10.1016/j.habitatint.2017.07.002,spatialandhedonicanalysisofhousingpricesinshanghai|2017,1,1000,1,hedonic housing prices,2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,2f2f4935810c94b36fefa44c90d1841663e0b2f3,10.2307/252950,,Flood Hazard Pricing and Insurance Premium Differentials: Evidence From the Housing Market,1990.0,,Don N. Macdonald; Harry L. White; P. M. Taube; W. Huth,,97.0,,False,,Economics,1990-12-01,2f2f4935810c94b36fefa44c90d1841663e0b2f3,floodhazardpricingandinsurancepremiumdifferentialsevidencefromthehousingmarket,macdonald,10.2307/252950,floodhazardpricingandinsurancepremiumdifferentialsevidencefromthehousingmarket|1990,1,1000,3,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.3141/2115-16,,Effects of Transportation Accessibility on Residential Property Values,2009.0,Transportation Research Record: Journal of the Transportation Research Board,L. Miguel Martínez; José Manuel Viegas,"The aim of this paper is to examine the relationship between the availability of transportation infrastructure and services and the pattern of house prices in an urban area and to assess whether public investment in transportation can modify residential property values. This study was developed for the Lisbon, Portugal, metropolitan area (LMA) as part of a broader study that intends to develop new value-capture financing schemes for public transportation in the LMA. The paper focuses on three central municipalities in Portugal (Amadora, Lisbon, and Odivelas), where these effects could be more easily measured because of the existence of a significant variability of public transportation services. The paper tries to determine, with different spatial hedonic pricing models, the extent to which access to transportation infrastructure currently is capitalized into house prices and isolates the influence of three different transportation infrastructures: metro, rail, and road. The results suggest that the proximity to one or two metro lines leads to significant property value changes. Results further indicate that the classic hedonic price model (ordinary least-squares estimation) leads to similar coefficient values of the local accessibility dummy variables compared with the spatial lag model and thus provides a steady basis to forecast the property value changes derived from transportation investment for the study area in the absence of a significant property value database.",96.0,,,journal-article,,,doi_10_3141_2115_16,effectsoftransportationaccessibilityonresidentialpropertyvalues,martínez,10.3141/2115-16,effectsoftransportationaccessibilityonresidentialpropertyvalues|2009,1,2495,9,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment prices hedonic || apartment rent hedonic || hedonic house price || hedonic housing prices || house prices proximity hedonic || residential property price determinants || spatial hedonic model housing",2,3,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1475-4959.2010.00358.x,,Amenities and disamenities: a hedonic analysis of the heterogeneous urban landscape in Shenzhen (China),2010.0,The Geographical Journal,WENDY Y CHEN; C Y JIM,"Landscape amenities and disamenities are important factors for households in making residence decision. This study presents a hedonic analysis of the amenity and disamenity effects arising from the heterogeneous urban landscape in a Chinese city. The sample is composed of 358 apartment characteristics and transaction data collected directly from home owners in the central built‐up area of Shenzhen. Four types of urban landscapes were included, namely urban parks, residential gardens, Shenzhen Bay, and urban villages. We applied a three‐dimensional model (availability, accessibility, and visibility) to estimate the amenity–disamenity effects of these urban landscape features. The results indicated that residential gardens were the most attractive landscape (an average increase of 17.2% of housing price) and urban villages had disamenity effects (a decrease of 3.72% for visibility and 2.5% for availability). The visibility of landscape was more valued than the accessibility. The lower weight accorded to the availability variable suggested: both benefits and possible negative impacts related to urban parks and Shenzhen Bay could be embodied in home buyers' behaviour; and the unavoidability of urban villages. The findings could provide insights to the location and design of residential areas vis‐à‐vis amenity–disamenity landscape features in Chinese cities that are rapidly expanding and redeveloping.",96.0,,,journal-article,,,doi_10_1111_j_1475_4959_2010_00358_x,amenitiesanddisamenitiesahedonicanalysisoftheheterogeneousurbanlandscapeinshenzhenchina,chen,10.1111/j.1475-4959.2010.00358.x,amenitiesanddisamenitiesahedonicanalysisoftheheterogeneousurbanlandscapeinshenzhenchina|2010,1,2419,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || apartment rent hedonic || hedonic housing prices || hedonic price model amenities residential || housing characteristics prices hedonic",2,1,5,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1007/s10109-005-0011-8,,Heterogeneity in hedonic modelling of house prices: looking at buyers’ household profiles,2005.0,Journal of Geographical Systems,Yan Kestens; Marius Thériault; François Des Rosiers,,96.0,,,journal-article,,,doi_10_1007_s10109_005_0011_8,heterogeneityinhedonicmodellingofhousepriceslookingatbuyershouseholdprofiles,kestens,10.1007/s10109-005-0011-8,heterogeneityinhedonicmodellingofhousepriceslookingatbuyershouseholdprofiles|2005,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment rent hedonic || hedonic house price",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0094-1190(82)90078-x,,An alternative approach to housing market segmentation using hedonic price data,1982.0,Journal of Urban Economics,David Dale-Johnson,,95.0,,,journal-article,,,doi_10_1016_0094_1190_82_90078_x,analternativeapproachtohousingmarketsegmentationusinghedonicpricedata,dale-johnson,10.1016/0094-1190(82)90078-x,analternativeapproachtohousingmarketsegmentationusinghedonicpricedata|1982,1,1000,2,apartment rent hedonic || hedonic housing prices,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1509ad55dc2d4ef3b8195f43dae5eac5d4c0ace6,10.1177/0013916509334564,,Hazard Proximity or Risk Perception? Evaluating Effects of Natural and Technological Hazards on Housing Values,2010.0,,Yang Zhang; S. Hwang; M. Lindell,,94.0,,False,JournalArticle,Engineering,2010-09-01,1509ad55dc2d4ef3b8195f43dae5eac5d4c0ace6,hazardproximityorriskperceptionevaluatingeffectsofnaturalandtechnologicalhazardsonhousingvalues,zhang,10.1177/0013916509334564,hazardproximityorriskperceptionevaluatingeffectsofnaturalandtechnologicalhazardsonhousingvalues|2010,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,hedonic housing prices,,10.1108/14635789810212913,,Transport investment and house prices,1998.0,Journal of Property Valuation and Investment,John Henneberry,"The paper describes the use of hedonic analysis to examine the impact of the South Yorkshire Supertram on house prices in Sheffield. The approach was distinguished by the use of asking prices rather than transaction prices, the use of dummy variables to capture neighbourhood quality effects and the use of a GIS to calculate continuous distance variables. Compared with similar previous studies the equations performed well and identified, for the first time in a British context, a small, discrete transport induced price effect. Supertram initially depressed the prices of nearby houses but prices show signs of recovery. Further research is needed to examine the longer term impact of Supertram on the housing market.",92.0,,,journal-article,,,doi_10_1108_14635789810212913,transportinvestmentandhouseprices,henneberry,10.1108/14635789810212913,transportinvestmentandhouseprices|1998,1,1721,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || house prices proximity hedonic || implicit prices housing attributes",2,3,3,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8c47d5caab58baa3f592023102bbfa6419c66471,10.1016/j.landusepol.2020.104528,,"Effect of the housing purchase restriction policy on the Real Estate Market: Evidence from a typical suburb of Beijing, China",2020.0,,Yao-Yao Li; Dao-Lin Zhu; Jiangmeng Zhao; Xiaodong Zheng; Lan-Yue Zhang,"Abstract To curb excessive rises in housing prices, the government has instituted a series of macro-control measures in the real estate market. The housing purchase restriction (HPR) policy, one of the most severe policies, aims to curb a rapid rise in housing prices by excluding investment demand for housing. The real estate market of Langfang City was a particularly pertinent case because it is adjacent to Beijing, the capital of China. This study collected 11,597 sets of second-hand housing transaction data from five counties around Beijing in Langfang. A regression discontinuity design was then used to indicate that the HPR policy of Langfang did not significantly reduce the price of second-hand houses in the study area. In addition, the effect of the HPR policy on housing prices had an obvious spatial heterogeneity. Within 500 m of the buffer zone around Beijing, the implementation of the HPR policy significantly increased the price by 8.07 %. Within 1000 m of the buffer zone, the implementation of the policy increased the price by an average of 6.70 %. Although the HPR policy played a role in reducing the price further than 1000 m, it was not robust. Finally, the paper highlighted that the policy makers should pay attention to the heterogeneity and connection of different cities, balance the relationship between multiple stakeholders, and improve and implement relevant systems, including a real estate tax regime and land spatial planning regime.",92.0,,False,,Business,2020-05-01,8c47d5caab58baa3f592023102bbfa6419c66471,effectofthehousingpurchaserestrictionpolicyontherealestatemarketevidencefromatypicalsuburbofbeijingchina,li,10.1016/j.landusepol.2020.104528,effectofthehousingpurchaserestrictionpolicyontherealestatemarketevidencefromatypicalsuburbofbeijingchina|2020,1,2475,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,9d094736be6766aef01899b2898a5e3b3588c0ec,10.1016/j.apgeog.2021.102429,,"Built environment, special economic zone, and housing prices in Shenzhen, China",2021.0,,Han-Duo Li; Peijun Chen; Richard J. Grant,"Abstract Shenzhen's meteoric urban growth is unprecedented, and its housing prices position it among the world's top five most expensive cities. Commonly understood as a ‘miracle city,’ ‘instant city,’ and ‘model city’ orchestrated by the central state in modern experimental urban planning, the local context and the role of the SEZ are a neglected spatiality in housing studies focusing on Shenzhen. Drawing on open data ( Lianjia.com ), we examine Shenzhen's spatial patterns of residential apartment prices and their associated local attributes. Based on a hedonic model, our results affirm a dichotomous spatial structure shaped by the special economic zones' (SEZs') associated unbalanced distribution of both accessibility and public/private service amenities. Inside the original SEZ (INSEZ), cultural amenities, green space, shopping malls, and a central business district significantly affect housing prices. Outside the original SEZ (OUTSEZ), only proximity to metro stations positively affects housing prices. Urban redevelopment should concentrate more on collaborative redevelopment in urban villages and equity in private and public service provision to narrow the inequality between INSEZ and OUTSEZ.",91.0,,False,,Geography,2021-04-01,9d094736be6766aef01899b2898a5e3b3588c0ec,builtenvironmentspecialeconomiczoneandhousingpricesinshenzhenchina,li,10.1016/j.apgeog.2021.102429,builtenvironmentspecialeconomiczoneandhousingpricesinshenzhenchina|2021,1,2216,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic housing prices || hedonic price model amenities residential || spatial hedonic model housing",2,2,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",16a455e8a0d3dfee552c4804b15c0a7eaf5424d8,10.1016/j.landusepol.2022.106183,,A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul,2022.0,Land Use Policy,S. Sisman; A. Aydinoglu,,90.0,,False,,,2022-08-01,16a455e8a0d3dfee552c4804b15c0a7eaf5424d8,amodellingapproachwithgeographicallyweightedregressionmethodsfordetermininggeographicvariationandinfluencingfactorsinhousingpriceacaseinistanbul,sisman,10.1016/j.landusepol.2022.106183,amodellingapproachwithgeographicallyweightedregressionmethodsfordetermininggeographicvariationandinfluencingfactorsinhousingpriceacaseinistanbul|2022,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",670de17a2cea9033a002b2058faeb759a64133f0,10.1016/j.habitatint.2017.03.007,,"Spatial effect of river landscape on housing price: An empirical study on the Grand Canal in Hangzhou, China",2017.0,,Hai-Zhen Wen; Yue Xiao; Ling Zhang,,89.0,,False,,Economics,2017-05-01,670de17a2cea9033a002b2058faeb759a64133f0,spatialeffectofriverlandscapeonhousingpriceanempiricalstudyonthegrandcanalinhangzhouchina,wen,10.1016/j.habitatint.2017.03.007,spatialeffectofriverlandscapeonhousingpriceanempiricalstudyonthegrandcanalinhangzhouchina|2017,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,hedonic property valuation residential,,10.1080/0042098984961,,Noise versus Access: The Impact of an Airport in an Urban Property Market,1998.0,Urban Studies,J. Tomkins; N. Topham; J. Twomey; R. Ward,"The effects of a major airport are unlikely to exhibit a uniform spatial distribution. The benefits to industries and individual households may extend well beyond the local economy, whereas many of the costs are spatially concentrated in the immediate environment. In particular, the problems of noise and traffic generation can be expected to fall principally upon adjacent populations. This paper addresses the general question of whether the costs to local economies of airport proximity, which are in the nature of externalities, outweigh the benefits of access, employment and improved infrastructure. Based on data relating to Manchester airport and its surrounding areas, the specific approach adopted in the paper involves an investigation of the extent to which such proximity effects are capitalised into residential property prices. Our results provide some evidence to suggest that circumstances may exist where positive attributes, such as improved access and employment opportunities, may be more highly valued by local residents than the negative externality effects of airport proximity.",88.0,,,journal-article,,,doi_10_1080_0042098984961,noiseversusaccesstheimpactofanairportinanurbanpropertymarket,tomkins,10.1080/0042098984961,noiseversusaccesstheimpactofanairportinanurbanpropertymarket|1998,1,2103,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic property valuation residential",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5df0565bc4448a98ef97cd8b41451c370afac57c,10.1016/j.habitatint.2021.102374,,"Addressing the macroeconomic and hedonic determinants of housing prices in Beijing Metropolitan Area, China",2021.0,Habitat International,Jinlong Duan; Guang-Jin Tian; Lan Yang; Tao Zhou,"Abstract This paper examined both the macroeconomic and hedonic determinants of housing prices in Beijing. The vector autoregression (VAR) model was used to explain the dynamic impacts of macroeconomic indicators on housing prices, and the geographical weighted regression (GWR) model was used to reveal the spatial variations in the effects of hedonic attributes on housing prices. The VAR results suggest that shocks to the macroeconomic indicators produce different dynamic changes in housing prices. Both an increase in the money supply and a decrease in the mortgage rates lead a rise in housing prices in the long term. Therefore, monetary policies and mortgage rates could be used as long-term financial tools to regulate the dynamics of housing prices. The GWR results show that the effects of hedonic attributes vary spatially. The size of a house has a negative impact on housing prices, especially in the downtown areas. The age of a house also has a negative impact on housing prices, especially in the suburbs. The effect of the number of bedrooms changes from positive in the downtown areas to negative in the suburbs. Furthermore, convenient accessibilities to critical amenities such as commercial centers, metro stations and high-quality schools contribute greatly to high housing prices in the downtown areas. The hedonic analysis of housing prices provides great implications for forming policy on urban planning, building design and public services construction. The core problem concerning urban planning, building design and public services construction in Beijing is the mismatch between working and living spaces. Therefore, relevant policy makings should focus on reducing the mismatch between working and living spaces, which is conductive to alleviating the spatial imbalance between housing supply and demand, narrowing the development gap within the city, and solving the urban diseases. Therefore, one important policy suggestion is that urban planning and public services construction should be upgraded to introduce more governmental offices, job opportunities, high-quality schools, commercial facilities and infrastructures into the suburbs. Another important policy suggestion is that the design for the floor area ratio and internal structures of new residential buildings should be planned to accommodate more residents and form better living environment in the downtown areas. Those suggestions can provide some guidance for solving urban problems in China and in other countries.",87.0,,False,,Business,2021-07-01,5df0565bc4448a98ef97cd8b41451c370afac57c,addressingthemacroeconomicandhedonicdeterminantsofhousingpricesinbeijingmetropolitanareachina,duan,10.1016/j.habitatint.2021.102374,addressingthemacroeconomicandhedonicdeterminantsofhousingpricesinbeijingmetropolitanareachina|2021,1,3518,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || hedonic price model amenities residential || spatial hedonic model housing",2,2,3,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",4016a68ad871f943235e95c02162687224352516,10.1016/j.reseneeco.2012.05.006,,"A Hedonic Analysis of the Impact of LUST Sites on House Prices in Frederick, Baltimore, and Baltimore City Counties",2012.0,,Jeffrey E. Zabel; D. Guignet,,85.0,,False,,Environmental Science;Geography,2012-11-01,4016a68ad871f943235e95c02162687224352516,ahedonicanalysisoftheimpactoflustsitesonhousepricesinfrederickbaltimoreandbaltimorecitycounties,zabel,10.1016/j.reseneeco.2012.05.006,ahedonicanalysisoftheimpactoflustsitesonhousepricesinfrederickbaltimoreandbaltimorecitycounties|2012,1,1000,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",854af7f74b252934e128c8fe799c99aced84fc17,10.1016/j.jtrangeo.2019.102488,,"The effect of new metro stations on local land use and housing prices: The case of Wuhan, China",2019.0,Journal of Transport Geography,Rong-Hui Tan; Qingsong He; Ke-Hao Zhou; Peng Xie,"The metro system is an important means of transportation for urban residents in mega-cities in China, where not all families can afford an automobile and there are policy restrictions on private cars. Numerous empirical studies have investigated the implicit prices of metro stations in China. However, few have systematically examined the influence of the proximity of a metro station on housing prices. More importantly, the potential endogeneity of metro station placement is always neglected, which means that the validity of the reported causal effects of the distance to the metro station on housing prices must be questioned. To investigate whether households value being close to a metro station because closeness generates fundamentally positive externalities, we analyze the main changes in land use around metro stations before and after new metro lines are built, based on high-resolution images of Wuhan in China provided by Google Earth. The results suggest that the opening of new metro stations revitalizes land around suburban stations more than it does around central stations. However, the area around central stations exhibits a greater population increase than does the area surrounding suburban stations before a new line opens. The results for suburban and central stations seem to be paradoxical, suggesting that the cross-sectional regression using the full sample may be misleading. An additional before-and-after evaluation based on second-hand housing sales data in Wuhan indicates that households do value metro station proximity. Using the case of line 6, we show that opening new metro stations significantly increases housing prices in nearby areas. This influence extends to 1600 m from new metro stations. Moreover, second-hand housing within 1600 m of metro stations is 7–14% more valuable than its more remotely located counterparts. These findings suggest that local metro planners in China should not only recognize the importance of the metro for raising land-sector revenue but also focus on the location choice and spatial distribution of metro stations from the perspective of residents' interests.",85.0,,False,,Geography,2019-07-01,854af7f74b252934e128c8fe799c99aced84fc17,theeffectofnewmetrostationsonlocallanduseandhousingpricesthecaseofwuhanchina,tan,10.1016/j.jtrangeo.2019.102488,theeffectofnewmetrostationsonlocallanduseandhousingpricesthecaseofwuhanchina|2019,1,3140,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",0,1,2,5,1,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c9931b4640cad4fa14b30afa7c444830a75a7eae,10.1177/0042098011404936,,Historic Preservation and Residential Property Values: Evidence from Quantile Regression,2012.0,,Velma Zahirovic-Herbert; Swarn Chatterjee,,83.0,,False,JournalArticle,Geography,2012-02-01,c9931b4640cad4fa14b30afa7c444830a75a7eae,historicpreservationandresidentialpropertyvaluesevidencefromquantileregression,zahirovic-herbert,10.1177/0042098011404936,historicpreservationandresidentialpropertyvaluesevidencefromquantileregression|2012,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,real estate hedonic regression,3f8df2d3649e2138cb1c3f4664e236a6d4e282c6,10.1080/09599916.2019.1587489,,A machine learning approach to big data regression analysis of real estate prices for inferential and predictive purposes,2019.0,Journal of Property Research,J. Pérez-Rave; J. C. Correa-Morales; Favián González-Echavarría,"ABSTRACT The hedonic price regressions have mainly been used for inference. In contrast, machine learning employed on big data has a great potential for prediction. To contribute to the integration of these two strategies, this article proposes a machine learning approach to the regression analysis of big data, viz. real estate prices, for both inferential and predictive purposes. The methodology incorporates a new procedure of selecting variables, called ‘incremental sample with resampling’ (MINREM). The methodology is tested on two cases. The first is data from web advertisements selling used homes in Colombia (61,826 observations). The second considers the data (58,888 observations) from a sample of the Metropolitan American Housing Survey 2011 obtained and prepared by a reference study. The methodology consists of two stages. The first chooses the important variables under MINREM; the second focuses on the traditional training and validation procedure for machine learning, adding three activities. In both test cases, the methodology shows its value for obtaining highly parsimonious and stable models for different sample sizes, as well as taking advantage of the inferential and predictive use of the obtained regression functions. This paper contributes to an original methodology for big data regression analysis.",82.0,,False,Review,Computer Science,2019-01-02,3f8df2d3649e2138cb1c3f4664e236a6d4e282c6,amachinelearningapproachtobigdataregressionanalysisofrealestatepricesforinferentialandpredictivepurposes,pérez-rave,10.1080/09599916.2019.1587489,amachinelearningapproachtobigdataregressionanalysisofrealestatepricesforinferentialandpredictivepurposes|2019,1,2336,4,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || machine learning hedonic pricing housing || real estate hedonic regression,2,2,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 crossref,house prices proximity hedonic,,10.1080/095999100367930,,Heteroscedasticity in hedonic house price models,2000.0,Journal of Property Research,M. Fletcher; P. Gallimore; J. Mangan,,82.0,,,journal-article,,,doi_10_1080_095999100367930,heteroscedasticityinhedonichousepricemodels,fletcher,10.1080/095999100367930,heteroscedasticityinhedonichousepricemodels|2000,1,1000,5,apartment rent hedonic || hedonic house price || hedonic housing prices || hedonic property valuation residential || house prices proximity hedonic,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0094-1190(81)90041-3,,"Hedonic prices, demands for urban housing amenities, and benefit estimates",1981.0,Journal of Urban Economics,Glenn Blomquist; Lawrence Worley,,82.0,,,journal-article,,,doi_10_1016_0094_1190_81_90041_3,hedonicpricesdemandsforurbanhousingamenitiesandbenefitestimates,blomquist,10.1016/0094-1190(81)90041-3,hedonicpricesdemandsforurbanhousingamenitiesandbenefitestimates|1981,1,1000,2,apartment rent hedonic || hedonic housing prices,2,0,1,1,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1006/juec.1997.2076,,Residential Hedonic Models: A Rational Expectations Approach to Age Effects,1998.0,Journal of Urban Economics,John M. Clapp; Carmelo Giaccotto,,82.0,,,journal-article,,,doi_10_1006_juec_1997_2076,residentialhedonicmodelsarationalexpectationsapproachtoageeffects,clapp,10.1006/juec.1997.2076,residentialhedonicmodelsarationalexpectationsapproachtoageeffects|1998,1,1000,2,apartment rent hedonic || hedonic housing prices,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1467-8489.2009.00464.x,,"Capitalized amenity value of urban wetlands: a hedonic property price approach to urban wetlands in Perth, Western Australia*",2009.0,Australian Journal of Agricultural and Resource Economics,Sorada Tapsuwan; Gordon Ingram; Michael Burton; Donna Brennan,"Up to 60 per cent of potable water supplied to Perth, Western Australia, is extracted from the groundwater system that lies below the northern part of the metropolitan area. Many of the urban wetlands are groundwater‐dependent and excessive groundwater extraction and climate change have resulted in a decline in water levels in the wetlands. In order to inform decisions on conserving existing urban wetlands, it is beneficial to be able to estimate the economic value of the urban wetlands. Applying the Hedonic Property Price approach to value urban wetlands, we found that distance to the nearest wetland and the number of wetlands within 1.5 km of a property significantly influence house sales price. For a property that is 943 m away from the nearest wetland, which is the average distance to the wetland in this study, reducing the wetland distance by 1 m will increase the property price by AU$42.40. Similarly, the existence of an additional wetland within 1.5 km of the property will increase the sales price by AU$6976. For a randomly selected wetland, assuming a 20 ha isolated circular wetland surrounded by uniform density housing, the total sales premium to surrounding properties was estimated to be around AU$140 million (AU$40 million and AU$230 million).",81.0,,,journal-article,,,doi_10_1111_j_1467_8489_2009_00464_x,capitalizedamenityvalueofurbanwetlandsahedonicpropertypriceapproachtourbanwetlandsinperthwesternaustralia,tapsuwan,10.1111/j.1467-8489.2009.00464.x,capitalizedamenityvalueofurbanwetlandsahedonicpropertypriceapproachtourbanwetlandsinperthwesternaustralia|2009,1,2274,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic house price || hedonic housing prices",2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2073483673,10.1631/jzus.2005.a0907,,"Hedonic price analysis of urban housing: An empirical research on Hangzhou, China",2005.0,Journal of Zhejiang University. Science A,Haizhen Wen; Shenghua Jia; Guo Xiao-yu,The hedonic price model is widely applied to study the urban housing market because of the heterogeneity of housing products. Literature indicated that the hedonic price theory mainly includes two parts: Lancaster's partiality theory and Rosen's characteristic market equilibrium analysis. This paper chose 18 characteristics as independent variables and set up a linear hedonic price model for Hangzhou City. The model was tested with 2473 housing samples and field survey data of 290 housing communities. This research found that 14 out of 18 characteristics had significant influence on housing price. They were classified into 5 groups according to their impact degree.,81.0,,False,article,,2005-07-13,https://openalex.org/W2073483673,hedonicpriceanalysisofurbanhousinganempiricalresearchonhangzhouchina,wen,10.1631/jzus.2005.a0907,hedonicpriceanalysisofurbanhousinganempiricalresearchonhangzhouchina|2005,1,1673,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic housing prices || housing characteristics prices hedonic",2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,deb934e30e2e3416eb127b6f11aaf6f3305e2351,10.3828/jtep.1994.28.2.199,,Modelling the Effects of Airport Noise on Residential Housing Markets: A Case Study of Winnipeg International Airport,1994.0,Journal of Transport Economics and Policy,T. Levesque,"This hedonic price study uncovers relationships with the number of events and the mean and variance of the loudness of the events. The results suggest models that include cumulative energy noise measures, such as the Noise Exposure Forecast, do not perform as well as models that use the number and loudness of aircraft noise incidents.",80.0,,False,JournalArticle,Economics,1994-05-01,deb934e30e2e3416eb127b6f11aaf6f3305e2351,modellingtheeffectsofairportnoiseonresidentialhousingmarketsacasestudyofwinnipeginternationalairport,levesque,10.3828/jtep.1994.28.2.199,modellingtheeffectsofairportnoiseonresidentialhousingmarketsacasestudyofwinnipeginternationalairport|1994,1,1336,1,hedonic housing prices,2,0,2,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",901384212a5df697d2baa9b6c59ee01d6dfd54d6,10.1111/j.0033-0124.1981.00216.x,,THE EFFECT OF ENVIRONMENTAL AMENITIES ON HOUSE VALUES: THE EXAMPLE OF A VIEW LOT,1981.0,,Q. Gillard,,79.0,,False,,Economics,1981-05-01,901384212a5df697d2baa9b6c59ee01d6dfd54d6,theeffectofenvironmentalamenitiesonhousevaluestheexampleofaviewlot,gillard,10.1111/j.0033-0124.1981.00216.x,theeffectofenvironmentalamenitiesonhousevaluestheexampleofaviewlot|1981,1,1000,2,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic house price",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",47c3f3440ec06190a2d4f88a4e151abc21ccdaf4,10.1111/1540-6229.00048,,Predicting Spatial Patterns of House Prices Using LPR and Bayesian Smoothing,2002.0,,J. Clapp; Hyon‐Jung Kim; A. Gelfand,,79.0,,False,,Economics,,47c3f3440ec06190a2d4f88a4e151abc21ccdaf4,predictingspatialpatternsofhousepricesusinglprandbayesiansmoothing,clapp,10.1111/1540-6229.00048,predictingspatialpatternsofhousepricesusinglprandbayesiansmoothing|2002,1,1000,3,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,c78318b0e4d4bfd8a796d0a47bb9f39c7ac3ae9e,10.1016/j.scs.2020.102140,,"Place-varying impacts of urban rail transit on property prices in Shenzhen, China: Insights for value capture",2020.0,,Lin-Chuan Yang; Yang Chen; Nenglai Xu; R. Zhao; K. Chau; Shi-Jian Hong,"Abstract Urban rail transit (URT) is shown to mitigate numerous contemporary urban problems (e.g., traffic congestion and environmental degradation). To reap such gains, many Chinese cities have actively constructed URT and/or have been on their way of doing so. However, these URT projects often encumber local governments with debt. Value capture schemes can be used to finance URT development. To implement such schemes, we need to understand the relationship between URT and property prices. Despite numerous studies on this topic in other countries, limited empirical studies in urban China have been conducted. Moreover, most previous studies are silent on two issues that are important to value capture, namely: (1) whether transfer stations provide a larger housing price premiums than regular stations, and (2) whether the price effect of URT accessibility is more perceptible in the suburban area than that in the urban. This study contributes to our understanding of these issues by constructing a set of hedonic pricing models using price data from 722 residential complexes in the vicinity of a URT line in Shenzhen, China. Our empirical results show that (1) URT has a positive impact on nearby property prices; (2) the positive impact diminishes quickly as the distance to URT station increases; (3) transfer stations have a larger positive effect on nearby property prices than regular stations; and (4) the price effects of URT accessibility are stronger in the suburban area than in the urban. The first two findings are in line with most previous studies, but the last two findings are seldom reported in existing literature. Practical implications of our findings, such as differential, location-specific value capture schemes, are further discussed.",78.0,,False,,Business,2020-07-01,c78318b0e4d4bfd8a796d0a47bb9f39c7ac3ae9e,placevaryingimpactsofurbanrailtransitonpropertypricesinshenzhenchinainsightsforvaluecapture,yang,10.1016/j.scs.2020.102140,placevaryingimpactsofurbanrailtransitonpropertypricesinshenzhenchinainsightsforvaluecapture|2020,1,2770,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,3,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",79775b166642eb096d0be262f1026c1e5b243890,10.3828/jtep.1993.27.1.3,,Density of Residential Land Use and the Impact of Airport Noise,1991.0,Journal of Transport Economics and Policy,D. Uyeno; S. Hamilton; A. Biggs,"Although communities have shown an increasing concern for the environment, hedonic regression shows little change over time in the relationship of airport noise to the property values of detached dwellings. The effects of noise on the value of condominiums and on the value of vacant land are found to be higher than for detached houses and are statistically significant.",78.0,,False,JournalArticle,Geography,1991-07-01,79775b166642eb096d0be262f1026c1e5b243890,densityofresidentiallanduseandtheimpactofairportnoise,uyeno,10.3828/jtep.1993.27.1.3,densityofresidentiallanduseandtheimpactofairportnoise|1991,1,1371,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2,1,6,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c0c761275295ad1801b144a79ec5e98ef8d1c41e,10.1016/j.habitatint.2014.05.001,,"Spatial effect of lake landscape on housing price: A case study of the West Lake in Hangzhou, China",2014.0,,Hai-Zhen Wen; Xiao-Qing Bu; Zhong-Fu Qin,,78.0,,False,,Geography,2014-10-01,c0c761275295ad1801b144a79ec5e98ef8d1c41e,spatialeffectoflakelandscapeonhousingpriceacasestudyofthewestlakeinhangzhouchina,wen,10.1016/j.habitatint.2014.05.001,spatialeffectoflakelandscapeonhousingpriceacasestudyofthewestlakeinhangzhouchina|2014,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,e03d4fa501b21f71dc522a652cf17ec776dff133,10.1016/j.ecolecon.2019.03.025,,Can proximity to urban green spaces be considered a luxury? Classifying a non-tradable good with the use of hedonic pricing method,2019.0,Ecological Economics,E. Łaszkiewicz; Piotr Czembrowski; J. Kronenberg,"Abstract Environmental goods are sometimes considered a luxury that only the rich can afford. This is no less true in the case of green spaces. However, a typical microeconomic lens is not relevant to study the potential luxury of non-tradable goods. Typically, the distributional interpretation of the income elasticity of the willingness to pay (WTP) for such goods would involve stated preferences valuation methods. The possibility to describe the luxury of green space proximity based on revealed preferences methods has not been evaluated yet. Through this study, we would like to fill this gap. We proposed a three-step analysis of the apartment market in Lodz (Poland), based on the hedonic pricing method, to check how the marginal WTP (MWTP) for proximity to parks/forests differs among apartment price sub-segments. We discuss whether and to what extent the variability of the MWTP could be linked with the presumed luxury of green space proximity. We found that proximity does not result in increased apartment prices for all parks and forests. The estimated MWTP for the proximity to selected parks rises in consecutive apartment price sub-segments. This could be interpreted as a sign of the luxury of living close to these parks by apartment buyers.",77.0,,False,,Economics,2019-07-01,e03d4fa501b21f71dc522a652cf17ec776dff133,canproximitytourbangreenspacesbeconsideredaluxuryclassifyinganontradablegoodwiththeuseofhedonicpricingmethod,łaszkiewicz,10.1016/j.ecolecon.2019.03.025,canproximitytourbangreenspacesbeconsideredaluxuryclassifyinganontradablegoodwiththeuseofhedonicpricingmethod|2019,1,2264,1,apartment prices hedonic,2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",83ba64d9f847b27c2fec2269c21bce255277a528,10.3828/jtep.1994.28.2.175,,Aircraft Noise and Residential Property Values: An Artificial Neural Network Approach,1994.0,Journal of Transport Economics and Policy,Alan Collins; Alec Evans,The properties of Artificial Neural Networks (ANNs) are outlined and shown to discriminate the effect of aircraft noise from other value determinants in a study of residential property values adjacent to Manchester International Airport. The varying effects of noise on different property types and neighbourhoods are identified. The results are contrasted with earlier hedonic regression-based studies.,77.0,,False,JournalArticle,Engineering,1994-05-01,83ba64d9f847b27c2fec2269c21bce255277a528,aircraftnoiseandresidentialpropertyvaluesanartificialneuralnetworkapproach,collins,10.3828/jtep.1994.28.2.175,aircraftnoiseandresidentialpropertyvaluesanartificialneuralnetworkapproach|1994,1,1403,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,house prices proximity hedonic,d41645b6dabef4ba6d0adfbeb1c35c29abb7a96d,10.1016/j.regsciurbeco.2017.03.002,,"Measurement and valuation of urban greenness: Remote sensing and hedonic applications to Lisbon, Portugal",2017.0,Regional Science and Urban Economics,Sofia Franco; Jacob L. Macdonald,"Abstract This paper explores the role of remote sensing techniques in capturing urban environmental data in the form of tree canopy coverage and measures of urban greenery. Using a classification algorithm, we identify tree canopy coverage in Lisbon, Portugal, to be approximately 8%. Our results have an accuracy rating of approximately 90% highlighting the benefits of this technique in capturing novel forms of data. Using these measures aggregated to the neighborhood level, we explore the impact of open space accessibility and urban greenness on the residential property market in Lisbon. We capture the heterogeneity of open spaces through their size and average vegetation level, and further explore how the greenness of a resident’s neighborhood may elicit complementary or substitutability behavior in house pricing relative to proximity to urban open spaces and other urban ecological variables. Our results indicate that proximity to both large urban forests and smaller neighbourhood parks are capitalized through residential prices. These effects are dependent on neighborhood green composition with neighborhoods which have a higher proportion of sparse or low lying vegetation willing to trade-off proximity to parks (where this type of vegetation is abundant) and have a preference for being closer to urban forests (where there is greater diversity in vegetation from the neighborhood). Overall tree canopy coverage is positively valued with a square kilometer increase in the relative size of tree canopy valued at 0.20% of dwelling prices, or approximately €400 per dwelling. These results highlight the importance of capturing the heterogeneity of urban greenery and the interacting effects with the local ecology and the built environment.",76.0,,False,,Geography,2017-03-06,d41645b6dabef4ba6d0adfbeb1c35c29abb7a96d,measurementandvaluationofurbangreennessremotesensingandhedonicapplicationstolisbonportugal,franco,10.1016/j.regsciurbeco.2017.03.002,measurementandvaluationofurbangreennessremotesensingandhedonicapplicationstolisbonportugal|2017,1,2761,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic property valuation || house prices proximity hedonic",2,1,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,hedonic property valuation residential,,10.1108/14635780010338245,,Sorting out access and neighbourhood factors in hedonic price modelling,2000.0,Journal of Property Investment & Finance,François Des Rosiers; Marius Thériault; Paul‐Y Villeneuve,"This paper investigates the analytical potential of factor analysis for sorting out neighbourhood and access factors in hedonic modelling using a simulation procedure that combines GIS technology and spatial statistics. An application to the housing market of the Quebec Urban Community (575,000 in population; study based on some 2,400 cottages transacted from 1993 to 1997) illustrates the relevance of this approach. In the first place, accessibility from each home to selected activity places is computed on the basis of minimum travelling time using the TransCAD transportation‐oriented GIS software. The spatial autocorrelation issue is then addressed and a general modelling procedure developed. Following a five‐step approach, property specifics are first introduced in the model; proximity and neighbourhood attributes are then successively added on. Finally, factor analyses are performed on each set of access and census variables, thereby reducing to six principal components an array of 49 individual attributes. Substituting the resulting factors for the initial descriptors leads to high model performances, controlled collinearity and stable hedonic prices, although remaining spatial autocorrelation is still detected in the residuals.",76.0,,,journal-article,,,doi_10_1108_14635780010338245,sortingoutaccessandneighbourhoodfactorsinhedonicpricemodelling,rosiers,10.1108/14635780010338245,sortingoutaccessandneighbourhoodfactorsinhedonicpricemodelling|2000,1,2252,5,apartment rent hedonic || hedonic housing prices || hedonic property valuation residential || house prices proximity hedonic || spatial hedonic model housing,2,0,2,3,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/j.ecolecon.2008.09.011,,"Does clean air matter in developing countries' megacities? A hedonic price analysis of the Jakarta housing market, Indonesia",2009.0,Ecological Economics,Arief Anshory Yusuf; Budy P. Resosudarmo,,76.0,,,journal-article,,,doi_10_1016_j_ecolecon_2008_09_011,doescleanairmatterindevelopingcountriesmegacitiesahedonicpriceanalysisofthejakartahousingmarketindonesia,yusuf,10.1016/j.ecolecon.2008.09.011,doescleanairmatterindevelopingcountriesmegacitiesahedonicpriceanalysisofthejakartahousingmarketindonesia|2009,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic housing prices || housing characteristics prices hedonic",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1080/00420988720080331,,Spatial Variation in the Price of Housing: Rent Gradients in Non-Monocentric Cities,1987.0,Urban Studies,Robin A. Dubin; Chein-Hsing Sung,,75.0,,,journal-article,,,doi_10_1080_00420988720080331,spatialvariationinthepriceofhousingrentgradientsinnonmonocentriccities,dubin,10.1080/00420988720080331,spatialvariationinthepriceofhousingrentgradientsinnonmonocentriccities|1987,1,1000,1,apartment rent hedonic,0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W1541727578,10.1108/17538271011080655,,Structural and temporal changes in the housing market and hedonic housing price indices,2010.0,International Journal of Housing Markets and Analysis,Chihiro Shimizu; Hideoki Takatsuji; Hiroya Ono; Kiyohiko Giichi Nishimura,"Purpose An economic indicator faces two requirements. It should be reported in a timely manner and should not be significantly altered afterward to avoid erroneous messages. At the same time, it should reflect changing market conditions constantly and appropriately. These requirements are particularly challenging for housing price indices, since housing markets are subject to large temporal/seasonal changes and occasional structural changes. The purpose of this paper is to estimate a hedonic price index of condominiums of Tokyo, taking account of seasonal sample selection biases and structural changes in a way it enables us to report the index in a manner which is timely and not subject to change after reporting. Design/methodology/approach The paper proposes an overlapping‐period hedonic model (OPHM), in which a hedonic price index is calculated every month based on data in the “window” of a year ending this month (this month and previous 11 months). It also estimates standard hedonic housing price indexes under alternative assumptions: no structural change (“structurally restricted”: restricted hedonic model) and different structure for every month (“structurally unrestricted”: unrestricted hedonic model). Findings Results suggest that the structure of the housing market, including seasonality, changes over time, and these changes occur continuously over time. It is also demonstrated that structurally restricted indices that do not account for structural changes involve a large time lag compared with indices that do account for structural changes during periods with significant price fluctuations. Social implications Following the financial crisis triggered by the US housing market, housing price index guidelines are currently being developed, with the United Nations, International Monetary Fund, and Organization for Economic Co‐operation and Development leading the way. These guidelines recommend that indices be estimated based on the hedonic method. We believe that the hedonic method proposed here will serve as a reference for countries that develop hedonic method‐based housing price indices in future. Originality/value In the many studies involving conventional housing price indices, whether those using the repeat‐sales method or hedonic method, there are few that have analyzed the problem of market structural changes. This paper is the first to construct a large database and systematically estimate the effect that changes in market structure have on housing price indices.",74.0,,False,article,,2010-10-02,https://openalex.org/W1541727578,structuralandtemporalchangesinthehousingmarketandhedonichousingpriceindices,shimizu,10.1108/17538271011080655,structuralandtemporalchangesinthehousingmarketandhedonichousingpriceindices|2010,1,3522,2,apartment rent hedonic || hedonic housing prices,2,2,3,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1467-8489.2009.00483.x,,Flood prone risk and amenity values: a spatial hedonic analysis,2010.0,Australian Journal of Agricultural and Resource Economics,Oshadhi Samarasinghe; Basil Sharp,"This study examines the impact of flood‐hazard zone location on residential property prices. The study utilises data from over 2000 private residential property sales occurred during 2006 in North Shore City, New Zealand. A spatial autoregressive hedonic model is developed to provide efficient estimates of the marginal effect of flood prone risks on property prices. Results suggest that the sale price of a residential property within a flood prone area is lower than an equivalent property outside the flood prone area. The flood plain location discount is reduced by the release of public information regarding flood risk.",70.0,,,journal-article,,,doi_10_1111_j_1467_8489_2009_00483_x,floodproneriskandamenityvaluesaspatialhedonicanalysis,samarasinghe,10.1111/j.1467-8489.2009.00483.x,floodproneriskandamenityvaluesaspatialhedonicanalysis|2010,1,1627,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment rent hedonic || hedonic house price || hedonic price model amenities residential",2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.2307/3145138,,A Hedonic Model of Prices and Assessments for Single-Family Homes: Does the Assessor Follow the Market or the Market Follow the Assessor?,1975.0,Land Economics,Brian J. L. Berry; Robert S. Bednarz,,70.0,,,journal-article,,,doi_10_2307_3145138,ahedonicmodelofpricesandassessmentsforsinglefamilyhomesdoestheassessorfollowthemarketorthemarketfollowtheassessor,berry,10.2307/3145138,ahedonicmodelofpricesandassessmentsforsinglefamilyhomesdoestheassessorfollowthemarketorthemarketfollowtheassessor|1975,1,1000,1,apartment rent hedonic,2,0,3,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),0d6170622fea22e550ad06a5b91535f61513c81f,10.1007/s11135-009-9296-x,,Determinants of house prices in Istanbul: a quantile regression approach,2011.0,,Çağlayan Ebru; A. Eban,,70.0,,False,Review,Economics,2011-02-01,0d6170622fea22e550ad06a5b91535f61513c81f,determinantsofhousepricesinistanbulaquantileregressionapproach,ebru,10.1007/s11135-009-9296-x,determinantsofhousepricesinistanbulaquantileregressionapproach|2011,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || real estate hedonic regression",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W1521910555,10.2307/3145991,,A Comparison of Block Group and Census Tract Data in a Hedonic Housing Price Model,1977.0,Land Economics,Allen C. Goodman,"The consideration of neighborhood effects in urban and regional models has been constrained by data inadequacies. Analysts have customarily used data aggregated at the census tract level to characterize areas differentiated by public service provision or socioeconomic composition. This paper introduces use of the block group, an aggregation unit typically 20 to 25 percent of the size of a census tract, formulated for general use in the 1970 censuses for all urbanized areas. 1",69.0,,False,article,,1977-11-01,https://openalex.org/W1521910555,acomparisonofblockgroupandcensustractdatainahedonichousingpricemodel,goodman,10.2307/3145991,acomparisonofblockgroupandcensustractdatainahedonichousingpricemodel|1977,1,1480,2,apartment rent hedonic || hedonic housing prices,2,2,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",07eda75ca316b4e9eac3dc5dd37c2cdeda051629,10.1016/j.jdeveco.2019.102381,,Valuing the urban hukou in China: Evidence from a regression discontinuity design for housing prices,2019.0,Journal of Development Economics,Yu Chen; Shaobin Shi; Yu-Gang Tang,"This paper explores the demand side of hukou (household registration) acquisition in China by estimating the market valuation of urban hukou. According to the provisions in Jinan City’s “Acquiring Hukou by Purchasing Houses” policy, this paper uses houses slightly larger than the minimum required floor area as the treatment group, and houses slightly smaller than the minimum required floor area as the control group to perform regression discontinuity design. Empirical results show that residents’ willingness to pay for urban hukou in Jinan City was between approximately 90,000 and 126,000 yuan (RMB) in 2017. Our finding is robust to both parametric and nonparametric estimates and different model specifications. We also perform falsification tests by assuming a false policy introduction date and placebo tests based on rental data. Our analysis also offers insights for reform of the hukou system and designs for the provision of public services or welfare.",69.0,,False,,Economics,2019-09-20,07eda75ca316b4e9eac3dc5dd37c2cdeda051629,valuingtheurbanhukouinchinaevidencefromaregressiondiscontinuitydesignforhousingprices,chen,10.1016/j.jdeveco.2019.102381,valuingtheurbanhukouinchinaevidencefromaregressiondiscontinuitydesignforhousingprices|2019,1,1967,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,4,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W1584826612,10.2307/3146095,,The Effect of Rent Control on the Price of Rental Housing: An Hedonic Approach,1984.0,Land Economics,Denton Marks,It is widely believed that rent control leads to a decline in the quality of rental housing. This study examines the effect of rent control on the quality of rental housing in New York City. Quality change is linked to the suppression of rent below ...,68.0,,False,article,,1984-02-01,https://openalex.org/W1584826612,theeffectofrentcontrolonthepriceofrentalhousinganhedonicapproach,marks,10.2307/3146095,theeffectofrentcontrolonthepriceofrentalhousinganhedonicapproach|1984,1,1252,2,apartment rent hedonic || hedonic housing prices,2,1,2,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4a34e9ecd024b2e2aca73c4c99a6327daddcc341,10.1016/j.landusepol.2018.02.011,,"Spatial and temporal effects of urban wetlands on housing prices: Evidence from Hangzhou, China",2018.0,,Xue-Jun Du; Zhong-Hua Huang,,68.0,,False,,Geography,2018-04-01,4a34e9ecd024b2e2aca73c4c99a6327daddcc341,spatialandtemporaleffectsofurbanwetlandsonhousingpricesevidencefromhangzhouchina,du,10.1016/j.landusepol.2018.02.011,spatialandtemporaleffectsofurbanwetlandsonhousingpricesevidencefromhangzhouchina|2018,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",592df8d3047587abb29dd4493da075e6413de193,10.1111/j.1467-9663.2008.00472.x,,RESIDENTIAL ENVIRONMENT AND SPATIAL VARIATION IN HOUSE PRICES IN THE NETHERLANDS,2008.0,,P. Visser; F. Van Dam; P. Hooimeijer,,67.0,,False,,Economics,2008-07-01,592df8d3047587abb29dd4493da075e6413de193,residentialenvironmentandspatialvariationinhousepricesinthenetherlands,visser,10.1111/j.1467-9663.2008.00472.x,residentialenvironmentandspatialvariationinhousepricesinthenetherlands|2008,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,a503c4495a90c3ec33e5aabfd1aff30f85bab9ac,10.1016/j.habitatint.2018.09.002,,The value of a south-facing orientation: A hedonic pricing analysis of the Shanghai housing market,2018.0,Habitat International,Jia-Jun Lu,"Abstract The relationship between the view orientation of an apartment and its property value is examined in the context of Shanghai housing market. Using a hedonic pricing model and a unique dataset, comprised of apartment attributes, ambient environmental indicators and urban spatial structure, this paper shows that a south-facing orientation is associated on average with a 14% premium in property value. Among possible view orientations, due south reflects the highest price. A view of one Shanghai's landmark increases property values by 6%, with this premium being boosted a further 4% by south-facing orientation. Dust pollution lowers prices and shrinks the value of a south-facing view, while PM2.5, which is not visible, has little influence on either. Preference for height in apartment buildings is strongly influenced by the presence of elevators. Internal apartment attributes have the expected signs, but magnitudes can be misleading unless the larger set of apartment attributes is controlled for.",66.0,,False,,Economics,2018-11-01,a503c4495a90c3ec33e5aabfd1aff30f85bab9ac,thevalueofasouthfacingorientationahedonicpricinganalysisoftheshanghaihousingmarket,lu,10.1016/j.habitatint.2018.09.002,thevalueofasouthfacingorientationahedonicpricinganalysisoftheshanghaihousingmarket|2018,1,2015,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic housing prices || spatial hedonic model housing",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1007/bf01257055,,Valuation of education and crime neighborhood characteristics through hedonic housing prices,1982.0,Population and Environment,Robin A. Dubin; Allen C. Goodman,,65.0,,,journal-article,,,doi_10_1007_bf01257055,valuationofeducationandcrimeneighborhoodcharacteristicsthroughhedonichousingprices,dubin,10.1007/bf01257055,valuationofeducationandcrimeneighborhoodcharacteristicsthroughhedonichousingprices|1982,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",92b3b40a7d627a20990f6a2bce357f2cf608d07f,10.1016/j.retrec.2019.01.003,,Estimating land value uplift around light rail transit stations in Greater Kuala Lumpur: An empirical study based on geographically weighted regression (GWR),2019.0,Research on Transport Economics,M. Dziauddin,"Abstract Introducing a rail transit system into an urban region is expected to increase land values, and subsequently, residential property values. Despite this general belief, little is known about the spatial distributional effects of land value uplift. Thus, the goal of this paper is to provide new insights on how proximity to light rail transit stations may affect residential property values in Greater Kuala Lumpur, Malaysia using geographically weighted regression (GWR). The results provide information on spatial variations in the effects of a light rail transit system on residential property values. This study utilises GWR to account for spatial heterogeneity and spatial dependence. The results suggest that residential property values benefit from the provision of a light rail transit system but with considerable spatial variation over a geographical area. Evidently, proximity to the nearest light rail transit station gives positive premiums of up to 8% for a majority of properties located in lower-middle and upper-middle income neighbourhoods such as Wangsa Maju, Setapak, Keramat, Setiawangsa, Ampang and Sentul. In contrast, the impact of proximity to the nearest light rail transit station for properties located in high-income neighbourhoods such as Petaling Jaya was found to be non-significant. These findings can assist policy makers to better understand how properties around light rail transit stations accrue benefits. However, since the benefit of a light rail transit system on nearby properties varies over a geographical area (and where a positive premium can swing from positive to negative depending on the area), it may be difficult for policy makers to impose a single-value tax if there is a land value capture policy to be considered for implementation.",65.0,,False,,Geography,2019-05-01,92b3b40a7d627a20990f6a2bce357f2cf608d07f,estimatinglandvalueupliftaroundlightrailtransitstationsingreaterkualalumpuranempiricalstudybasedongeographicallyweightedregressiongwr,dziauddin,10.1016/j.retrec.2019.01.003,estimatinglandvalueupliftaroundlightrailtransitstationsingreaterkualalumpuranempiricalstudybasedongeographicallyweightedregressiongwr|2019,1,2796,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e9b4ccaad20af53fdc133a4cb4a3f7e599e36a3a,10.1016/j.retrec.2020.100813,,"Effects of transit-oriented development (TOD) on housing prices: A case study in Wuhan, China",2020.0,,Jian-Yi Li; Hao-Cheng Huang,"Abstract Rail transit is becoming significant to urban growth and sustainability in China since China is investing more than 2 trillion yuan in urban rail transit during the 13th Five-Year Plan (2016–2020). Transit-oriented development (TOD) emphasizes using built-environment factors such as mixed-use, density, and walkability, to maximize the benefits of improving transit accessibility. Although studies illustrate how transit-oriented built-environments amplify the rail transit impact on housing prices within the American context, little attention has been paid to Chinese cities. Using a case study of Wuhan City in China, this study shows that a higher transit proximity premium of housing prices is more likely to occur in the place with more mixed land uses. Policy implications on TOD planning and land value capture near transit station areas are derived.",63.0,,False,,Business,2020-05-01,e9b4ccaad20af53fdc133a4cb4a3f7e599e36a3a,effectsoftransitorienteddevelopmenttodonhousingpricesacasestudyinwuhanchina,li,10.1016/j.retrec.2020.100813,effectsoftransitorienteddevelopmenttodonhousingpricesacasestudyinwuhanchina|2020,1,1868,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.3141/1617-05,,Measuring the Impact of Light Rail Systems on Single-Family Home Values: A Hedonic Approach with Geographic Information System Application,1998.0,Transportation Research Record: Journal of the Transportation Research Board,Hong Chen; Anthony Rufolo; Kenneth J. Dueker,"In theory, proximity to light rail transit (LRT) may have two different effects on residential property values. On the one hand, accessibility (proximity to LRT stations) may increase property values. On the other hand, nuisance effects (proximity to the LRT line and stations) may decrease property values. Existing empirical studies are inconclusive, and failure to separate the effects of accessibility from the nuisance effects may explain some of the ambiguity. An examination is presented of the impact of the light rail system (MAX) in Portland, Oregon, on single-family home values using distance to rail stations as a proxy for accessibility and distance to the line itself as a proxy for nuisance effects. Geographic information system techniques are employed to create spatial-related variables and merge data from various sources. The study results confirm the hypothesis that the light rail has both a positive effect (accessibility effect) and a negative effect (nuisance effect) on single-family home values. The positive effect dominates the negative effect, which implies a declining price gradient as one moves away from LRT stations for several hundred meters. Without controlling for the nuisance effect of the distance to the rail line, the estimated coefficients on distance from stations appear to be biased and would underestimate the accessibility effect. The finding of an independent nuisance effect suggests that previous hedonic models may have reached contradictory results because the nuisance effect differs with different types of rail or other local characteristics.",62.0,,,journal-article,,,doi_10_3141_1617_05,measuringtheimpactoflightrailsystemsonsinglefamilyhomevaluesahedonicapproachwithgeographicinformationsystemapplication,chen,10.3141/1617-05,measuringtheimpactoflightrailsystemsonsinglefamilyhomevaluesahedonicapproachwithgeographicinformationsystemapplication|1998,1,2600,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic",2,3,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e71760b25631f15b2ea003f09c4561c103cee9cd,10.1016/j.ufug.2020.126643,,The capitalized amenity of green infrastructure in single-family housing values: An application of the spatial hedonic pricing method,2020.0,,Wonmin Sohn; Hyun Kim; Jun-Hyun Kim; Ming-Han Li,"Abstract The environmental benefits of having retention/detention ponds as major stormwater management facilities in a neighborhood have been well documented since the adoption of green infrastructure strategies in early 1990s. However, the capitalization effect of stormwater treatment ponds in real estate values still remains unclear. This study developed classic hedonic pricing and spatial econometric models to examine the capitalization effects of neighborhood-level detention and retention ponds using both cross-sectional and longitudinal housing prices. The housing market value from 2007 to 2016 was assessed in four subdivisions in Houston, Texas. A subdivision, located in the upper neighborhood, had retention ponds which were converted from detention ponds in 2011, while three subdivisions in the lower neighborhood had detention ponds over the same study period. The study results show that living near retention ponds has positive capitalization impacts on single-family housing prices, not only cross-sectionally but also longitudinally. In contrast, maintaining detention ponds depreciates housing values, and living nearby lowers the price increase over a decade. The findings in this study imply the importance of converting stormwater facilities into a park-like amenity to enhance aesthetic value and generate long-term economic benefits to a neighborhood.",61.0,,False,,Business,2020-03-01,e71760b25631f15b2ea003f09c4561c103cee9cd,thecapitalizedamenityofgreeninfrastructureinsinglefamilyhousingvaluesanapplicationofthespatialhedonicpricingmethod,sohn,10.1016/j.ufug.2020.126643,thecapitalizedamenityofgreeninfrastructureinsinglefamilyhousingvaluesanapplicationofthespatialhedonicpricingmethod|2020,1,2380,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || spatial hedonic model housing",2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/j.jhe.2004.04.004,,New empirical evidence on heteroscedasticity in hedonic housing models,2004.0,Journal of Housing Economics,Simon Stevenson,,60.0,,,journal-article,,,doi_10_1016_j_jhe_2004_04_004,newempiricalevidenceonheteroscedasticityinhedonichousingmodels,stevenson,10.1016/j.jhe.2004.04.004,newempiricalevidenceonheteroscedasticityinhedonichousingmodels|2004,1,1000,3,apartment rent hedonic || hedonic house price || hedonic housing prices,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/j.forpol.2010.05.004,,Insect infestation and residential property values: A hedonic analysis of the mountain pine beetle epidemic,2010.0,Forest Policy and Economics,James I. Price; Daniel W. McCollum; Robert P. Berrens,,59.0,,,journal-article,,,doi_10_1016_j_forpol_2010_05_004,insectinfestationandresidentialpropertyvaluesahedonicanalysisofthemountainpinebeetleepidemic,price,10.1016/j.forpol.2010.05.004,insectinfestationandresidentialpropertyvaluesahedonicanalysisofthemountainpinebeetleepidemic|2010,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic price model amenities residential",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8e2d3bf87dc36eb587637e693545e338f3ad866f,10.1016/j.landusepol.2019.04.004,,Analyzing the private rental housing market in Shanghai with open data,2019.0,Land Use Policy,Han-Duo Li; Y. Wei; Yangyi Wu,"The failure of public housing policies and skyrocketing housing prices have made the private rental sector an increasingly important part of the housing system in Chinese metropolises. However, due to the lack of data, the private rental housing market remains poorly understood. By integrating open data, this paper comprehensively examines the spatial patterns and determinants of residential rents in Shanghai at the intra-urban level. We find that high rents are concentrated heavily in the inner-city area, which not only forces low-income families to live in the suburbs, where rents are still relatively affordable, but also makes housing costs a heavy burden to non-native young professionals. The results of spatial regressions suggest that the rental housing market in Shanghai is shaped by regional level factors such as job opportunities, salary levels, and the size of the “floating” population, and neighborhood level indicators like public transport facilities and service amenities. Such factors are differentiated across space. In the areas with better infrastructure, the roles of high salaries and attractive amenities are highly significant. In suburban districts, the presence of a larger “floating” population, better access to metro stations, sharing bikes, bus stops, and shopping facilities tend to raise rents. These patterns indicate that the geographical inequality of infrastructures, spatially imbalanced job accessibility, and institutions that make housing unaffordable have reinforced the rental and commuting burdens on economically disadvantaged groups, which also undermines their social mobility. Thus, our study suggests that the Chinese government should devote more effort to decentralize high-paying jobs, improve employment accessibility in urban peripheries and villages, as well as reform public housing policies and provide more public housing to make rental housing more affordable.",59.0,,False,,Business,2019-06-01,8e2d3bf87dc36eb587637e693545e338f3ad866f,analyzingtheprivaterentalhousingmarketinshanghaiwithopendata,li,10.1016/j.landusepol.2019.04.004,analyzingtheprivaterentalhousingmarketinshanghaiwithopendata|2019,1,2928,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,33509043102139028caa3453d5c1905a5e6cd3d8,10.1016/j.ecolecon.2018.03.012,,Tropical Vegetation and Residential Property Value: A Hedonic Pricing Analysis in Singapore,2018.0,Ecological Economics,Richard N. Belcher; R. Chisholm,"Abstract Effective urban planning depends on knowing homebuyers' preferences for neighbourhood features that provide different amenities, such as managed parks and trees. As the expansion of tropical urban areas into biodiversity hotspots is predicted to more than double by 2030, knowing homebuyers utility from different vegetation types can contribute to global biodiversity conservation strategies. We used the hedonic pricing method to estimate the economic value of managed, spontaneous and high conservation value vegetation to Singapore public housing using a mixed effects model. On average vegetation had positive effects on property selling price, accounting for 3% of the average property's value, or a total of S$179 million for all public housing apartments sold over 13 months. These effects were almost entirely driven by managed vegetation, which had positive marginal effects on price for 98.1% of properties. The estimated marginal effects of high conservation value vegetation were mostly negative (90.5%% properties), but positive for properties without much managed vegetation nearby. The estimated marginal effects of spontaneous vegetation were mixed and mostly small. To reconcile the goals of protecting high conservation value vegetation and maximising homeowner utility, new public housing developments should contain more managed vegetation but be away from high conservation value vegetation.",57.0,,False,,Geography,2018-07-01,33509043102139028caa3453d5c1905a5e6cd3d8,tropicalvegetationandresidentialpropertyvalueahedonicpricinganalysisinsingapore,belcher,10.1016/j.ecolecon.2018.03.012,tropicalvegetationandresidentialpropertyvalueahedonicpricinganalysisinsingapore|2018,1,2422,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || hedonic price model amenities residential",2,3,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,house prices proximity hedonic,,10.1080/0042098975691,,The Choice of Functional Form and Variables in the Hedonic Price Model in Seoul,1997.0,Urban Studies,Serim Huh; Seung-Jun Kwak,"This study demonstrates that the important part of exploring the proper functional form of the hedonic price model would include investigating a dissimilar and unique hedonic price structure when the hedonic price model is applied to different housing markets. Based on the adjustment to the usual technique necessitated by the regional and cultural setting of Seoul, the criteria for significance of implicit price, root-mean-square-percentage-error, and the log-likelihood ratio test are applied to the choice of a useful functional form. We conclude that investigating the structure of a local housing market and choosing proper variables reflecting regional and cultural characteristics, both key parts of such a study, require significant adjustment to the reality of Seoul.",57.0,,,journal-article,,,doi_10_1080_0042098975691,thechoiceoffunctionalformandvariablesinthehedonicpricemodelinseoul,huh,10.1080/0042098975691,thechoiceoffunctionalformandvariablesinthehedonicpricemodelinseoul|1997,1,1779,6,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || apartment rent hedonic || hedonic housing prices || hedonic property valuation residential || house prices proximity hedonic || housing characteristics prices hedonic",2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W1220857210,10.1007/s11146-015-9520-5,,Estimating School Proximity Effects on Housing Prices: the Importance of Robust Spatial Controls in Hedonic Estimations,2015.0,The Journal of Real Estate Finance and Economics,Vivek Sah; Stephen J. Conroy; Andrew Narwold,,57.0,,False,article,,2015-08-01,https://openalex.org/W1220857210,estimatingschoolproximityeffectsonhousingpricestheimportanceofrobustspatialcontrolsinhedonicestimations,sah,10.1007/s11146-015-9520-5,estimatingschoolproximityeffectsonhousingpricestheimportanceofrobustspatialcontrolsinhedonicestimations|2015,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,1,1,2,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1080-8620.2004.00100.x,,On the Use of Spline Smoothing in Estimating Hedonic Housing Price Models: Empirical Evidence Using Hong Kong Data,2004.0,Real Estate Economics,Helen X.H. Bao; Alan T.K. Wan,"Traditionally in estimating hedonic housing price functions, investigators use parametric models involving specific functional forms and a finite number of unknown parameters. Some investigators have questioned whether the underlying theory is capable of conveying sufficient information to enable a correct and successful specification of parametric models and have instead proposed the less restrictive semiparametric approach to the problem. In this paper, we illustrate how the technique of smoothing splines can be used to estimate hedonic housing price models. Smoothing splines are a powerful approach to the analysis of housing data as they are exceptionally flexible in their functional forms and provide a computationally tractable method even with a large number of explanatory variables. Our illustration takes the form of a rather limited, but very promising, application with Hong Kong data. In the forecasting comparison, the spline smoothing procedure outperforms the traditional parametric Box–Cox model in mean square error terms for out‐of‐sample predictions. Our results also suggest that the distortion caused by underfitting the model is smaller for spline smoothing than for the kernel and k‐nearest‐neighbor semiparametric procedures.",56.0,,,journal-article,,,doi_10_1111_j_1080_8620_2004_00100_x,ontheuseofsplinesmoothinginestimatinghedonichousingpricemodelsempiricalevidenceusinghongkongdata,bao,10.1111/j.1080-8620.2004.00100.x,ontheuseofsplinesmoothinginestimatinghedonichousingpricemodelsempiricalevidenceusinghongkongdata|2004,1,2258,3,apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices,2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5bcfb772a2f546c3887de2fcf0652512074252b5,10.1016/j.jue.2019.103183,,Valuing primary schools in urban China,2020.0,,Jimmy Chan; Xian Fang; Zhi Wang; Xianhua Zai; Qing-Hua Zhang,"Abstract We study the effect of school quality on housing prices in urban China. Our objective is to provide an estimate of the school-quality premium based on the best available data. To account for unobserved neighborhood characteristics, we adopt the boundary-discontinuity design of Black (1999) and the matching strategy proposed by Fack and Grenet (2010). The results suggest that parents value public primary schools that have outstanding records in academic tournaments. The school-quality premium is highly non-linear. While a tournament superstar—a school above the 90th percentile in tournament performance—causes housing prices in its neighborhood to increase by 14 percent, or about 430,000 RMB, the price difference between non-superstar schools is small.",56.0,,False,,Economics,,5bcfb772a2f546c3887de2fcf0652512074252b5,valuingprimaryschoolsinurbanchina,chan,10.1016/j.jue.2019.103183,valuingprimaryschoolsinurbanchina|2020,1,1769,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,03bbc721ab8acc01e0b706f421692cfed86c99be,10.1080/10835547.2004.12091603,,A Hierarchical Linear Model Approach for Assessing the Effects of House and Neighborhood Characteristics on Housing Prices,2004.0,,K. Brown; Bulent Uyar,,56.0,,False,,Economics,,03bbc721ab8acc01e0b706f421692cfed86c99be,ahierarchicallinearmodelapproachforassessingtheeffectsofhouseandneighborhoodcharacteristicsonhousingprices,brown,10.1080/10835547.2004.12091603,ahierarchicallinearmodelapproachforassessingtheeffectsofhouseandneighborhoodcharacteristicsonhousingprices|2004,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,d0184a9b536a06e158b4ae91721f420d0a1b2e74,10.1016/j.ufug.2021.127370,,"Spatial Impacts of Multimodal Accessibility to Green Spaces on Housing Price in Cook County, Illinois",2021.0,Urban Forestry & Urban Greening,Si Chen; Le Zhang; Yi Huang; G. Mosey; Brian Deal,"Abstract This study explores the impacts of multimodal accessibility to green space on housing price. Quantifying the benefits of green space accessibility is important for supporting green infrastructure planning and guiding land use development. In this study, we calculate multimodal travel times (walking and driving) from each residential property in Cook County (Chicago metro), Illinois to each articulated (public or significant private) green space. A gravity-model based method is used to compute accessibility (by travel mode), which considers the access to multiple green spaces and weights prioritization. Green spaces are divided into seven categories depending on their type and size to differentiate their potential benefits. Hedonic models using housing structural features, locational attributes, socio-economic factors and green space accessibility as explanatory variables, are used to evaluate housing price (using housing transactions records from 2010 sales in the county). The spatial effects of green space accessibilities on housing prices are explored by an Ordinary Least Squares (OLS) regression, with and without fixed locational effects, and a Geographically Weighted Regression (GWR). Results show walking and driving accessibility to all sizes of recreational, medium conversational and private green spaces present positive impacts on housing price, with some negative impacts to larger (and smaller) conservation areas. The relationship also exhibits different heterogeneous spatial pattern over the study area between walking and driving accessibility to green space, possibly related to economic variation.",55.0,,False,,Geography,2021-10-01,d0184a9b536a06e158b4ae91721f420d0a1b2e74,spatialimpactsofmultimodalaccessibilitytogreenspacesonhousingpriceincookcountyillinois,chen,10.1016/j.ufug.2021.127370,spatialimpactsofmultimodalaccessibilitytogreenspacesonhousingpriceincookcountyillinois|2021,1,2643,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),52c76b68c43da3a5db5c8806d05e9e1bdb189d77,10.3368/le.87.1.75,,Climate Change and Housing Prices: Hedonic Estimates for Ski Resorts in Western North America,2011.0,Land Economics,V. Butsic; Ellen Hanak; Robert G. Valletta,,55.0,,False,,Economics,2011-02-01,52c76b68c43da3a5db5c8806d05e9e1bdb189d77,climatechangeandhousingpriceshedonicestimatesforskiresortsinwesternnorthamerica,butsic,10.3368/le.87.1.75,climatechangeandhousingpriceshedonicestimatesforskiresortsinwesternnorthamerica|2011,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2e66a8be19a7895a3c7f09c82d1c1a9c5c54d18f,10.1016/j.jhe.2007.12.003,,The effect of educational test scores on house prices in a model with spatial dependence,2008.0,,Norman H. Sedgley; N. Williams; F. W. Derrick,,55.0,,False,,Economics,2008-06-01,2e66a8be19a7895a3c7f09c82d1c1a9c5c54d18f,theeffectofeducationaltestscoresonhousepricesinamodelwithspatialdependence,sedgley,10.1016/j.jhe.2007.12.003,theeffectofeducationaltestscoresonhousepricesinamodelwithspatialdependence|2008,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,12ff1a3635d6a4f3f37d0eb8f15532fcfcd91dc7,10.1016/j.landurbplan.2018.11.002,,Does converting abandoned railways to greenways impact neighboring housing prices?,2019.0,Landscape and Urban Planning,Youngre Noh,"Abstract This research examines the housing market before and after abandoned railways are converted into greenways. Two different hedonic pricing models are employed to analyze changes in the impact and trend of the change—spatial regressions for before and after the conversion and the Adjusted Interrupted Time Series-Difference in Differences (AITS-DID) model. Analyzing 2005–2012 single-family home sale transactions in the City of Whittier, California the results show that converting the abandoned railway into a greenway increases property values. Upon the completion of the greenway, the positive association with the greenway proximity increased in both models. The premium also exists in the pre-conversion period. The sellers’ and buyers’ expectation of the greenway being an amenity factored into the housing market even during the construction of the greenway. After the conversion, properties near the greenway experienced a negative trend in their value.",54.0,,False,,Economics,2019-03-01,12ff1a3635d6a4f3f37d0eb8f15532fcfcd91dc7,doesconvertingabandonedrailwaystogreenwaysimpactneighboringhousingprices,noh,10.1016/j.landurbplan.2018.11.002,doesconvertingabandonedrailwaystogreenwaysimpactneighboringhousingprices|2019,1,1970,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || spatial hedonic model housing",2,3,3,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1068/a160389,,The Demand for Local Amenity: An Hedonic Price Analysis,1984.0,Environment and Planning A: Economy and Space,P B McLeod,"In this paper the author investigates the demand for housing characteristics and proximity to local amenities in Perth, Western Australia. By application of hedonic price theory to a sample of house transactions the implicit prices for individual characteristics are estimated and used to estimate inverse demand curves for these characteristics in a second stage analysis. The results confirm the importance of proximity of river, parks, and highways in determining house prices. River views are found to be particularly important being valued at 28% of average selling price. From the inverse demand curves income elasticities of willingness to pay are found to range from 1.0 for age of house to 0.43 for proximity to the river.",54.0,,,journal-article,,,doi_10_1068_a160389,thedemandforlocalamenityanhedonicpriceanalysis,mcleod,10.1068/a160389,thedemandforlocalamenityanhedonicpriceanalysis|1984,1,1731,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic house price || hedonic housing prices || house prices proximity hedonic || housing characteristics prices hedonic",2,2,2,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2743729757,10.1007/s10109-017-0257-y,,Housing price prediction: parametric versus semi-parametric spatial hedonic models,2017.0,Journal of Geographical Systems,José‐María Montero; Román Mı́nguez; Gema Fernández‐Avilés,,54.0,,False,article,,2017-08-03,https://openalex.org/W2743729757,housingpricepredictionparametricversussemiparametricspatialhedonicmodels,montero,10.1007/s10109-017-0257-y,housingpricepredictionparametricversussemiparametricspatialhedonicmodels|2017,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ce524372dfdf46587504ee0767b0664b01eee941,10.1016/j.landusepol.2019.104287,,Airport noise and house prices: A quasi-experimental design study,2020.0,,Xian Zheng; Wen-Wei Peng; Mingzhi Hu,"Abstract In this study, we investigate the benefits of reducing airport noise externalities based on a natural quasi-experiment on exogenous variation in aircraft noise from the relocated Hong Kong Kai Tak Airport in 1998. We collect housing transaction data from 1993 to 2006 in residential areas near this airport and employ the difference-in-difference method to measure the capitalization of noise externality into local house prices. Results show that the disappearance of aircraft noise leads to an average 24.43% increase in the prices of houses surrounding the airport. In addition, the influence of airport relocation decreases with the distance to the airport. These results remain consistent across various model specifications. Moreover, the prices of houses near the airport have become on the rise after the airport noise pollution has been eradicated. Our results justify the government compensation for households subjected to airport noise exposure. Quantifying the implicit price of noise pollution also helps to formulate additional appropriate compensation mechanisms and urban planning projects.",53.0,,False,,Business,,ce524372dfdf46587504ee0767b0664b01eee941,airportnoiseandhousepricesaquasiexperimentaldesignstudy,zheng,10.1016/j.landusepol.2019.104287,airportnoiseandhousepricesaquasiexperimentaldesignstudy|2020,1,2116,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",0,1,4,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d1bd80466d984d465e1c21776f00f4af4c4f4a7d,10.1016/j.regsciurbeco.2017.12.008,,"Fading premiums: The effect of light rail on residential property values in Minneapolis, Minnesota",2018.0,,Clemens A. Pilgram; Sarah E. West,,53.0,,False,,Geography,2018-03-01,d1bd80466d984d465e1c21776f00f4af4c4f4a7d,fadingpremiumstheeffectoflightrailonresidentialpropertyvaluesinminneapolisminnesota,pilgram,10.1016/j.regsciurbeco.2017.12.008,fadingpremiumstheeffectoflightrailonresidentialpropertyvaluesinminneapolisminnesota|2018,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2063469316,10.2307/2109495,,"Search, Hedonic Prices and Housing Demand",1992.0,The Review of Economics and Statistics,Sunwoong Kim,"The conventional model of the housing market does not take into account the search process for a suitable housing unit. Based on a dynamic search theory, this paper develops and estimates a truncated regression model of the rental housing market with stochastic and unobserved truncation points. The author's model provides a joint estimation of the hedonic rice and the reservation rent equations. The results turn out to be superior to the ordinary least squares estimates of either the traditional housing demand function or the hedonic price equation. Copyright 1992 by MIT Press.",52.0,,False,article,,1992-08-01,https://openalex.org/W2063469316,searchhedonicpricesandhousingdemand,kim,10.2307/2109495,searchhedonicpricesandhousingdemand|1992,1,1584,3,apartment rent hedonic || hedonic housing prices || rent hedonic regression housing,2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",eb3d446d75c0272b0527038e815e4665036e338f,10.1080/13658816.2021.1912348,,Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing,2021.0,International Journal of Geographical Information Science,Zhi Zhang; Jing Li; T. Fung; Hua-Yi Yu; Chang-Lin Mei; Y. Leung; Yu Zhou,"ABSTRACT Geographically and temporally weighted regression (GTWR) has been demonstrated as an effective tool for exploring spatiotemporal data under spatial and temporal heterogeneity. Exploiting the advantages of the two most popular GTWR methods, we propose an alternative GTWR with a good balance between complexity and interpretability via a unilateral temporal weighting scheme called unilateral GTWR (UGTWR). When compared to the other two popular GTWR methods, the simulation experiment shows that UGTWR has comparable estimation accuracy and model fit, but it is more efficient. Furthermore, we propose its multiscale extension, coined multiscale UGTWR (MUGTWR), to characterize the spatiotemporal dynamic regression relationships at multiple scales. The proposed MUGTWR was applied to the analysis of house prices in the period of 2014–2018 in Beijing as a case study. Our analysis reveals that MUGTWR can effectively capture different levels of spatiotemporal heterogeneity in selected factors affecting house prices at different scales. Therefore, this study is useful for the formulation of housing policy in which the spatiotemporal dynamics of house prices with respect to specific factors can be considered.",52.0,,False,JournalArticle,Computer Science,2021-05-12,eb3d446d75c0272b0527038e815e4665036e338f,multiscalegeographicallyandtemporallyweightedregressionwithaunilateraltemporalweightingschemeanditsapplicationintheanalysisofspatiotemporalcharacteristicsofhousepricesinbeijing,zhang,10.1080/13658816.2021.1912348,multiscalegeographicallyandtemporallyweightedregressionwithaunilateraltemporalweightingschemeanditsapplicationintheanalysisofspatiotemporalcharacteristicsofhousepricesinbeijing|2021,1,2222,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,implicit prices housing attributes,,10.1080/00420989550012492,,Spatial Estimation of Housing Prices and Locational Rents,1995.0,Urban Studies,Jorge Chica Olmo,"This work stems from the widely accepted hypothesis that housing price is determined by two types of characteristic: structural and locational. Under this hypothesis, a methodology is presented for the spatial estimation of housing prices and locational rents: the Iterative Residual Kriging (IRK) method. The methodological aspects are complemented by the presentation of an application of this methodology to the case of housing in Granada, Spain.",52.0,,,journal-article,,,doi_10_1080_00420989550012492,spatialestimationofhousingpricesandlocationalrents,olmo,10.1080/00420989550012492,spatialestimationofhousingpricesandlocationalrents|1995,1,1449,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || house prices proximity hedonic || implicit prices housing attributes",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0095-0696(79)90011-1,,"Airport noise, location rent, and the market for residential amenities",1979.0,Journal of Environmental Economics and Management,Jon P Nelson,,52.0,,,journal-article,,,doi_10_1016_0095_0696_79_90011_1,airportnoiselocationrentandthemarketforresidentialamenities,nelson,10.1016/0095-0696(79)90011-1,airportnoiselocationrentandthemarketforresidentialamenities|1979,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",dcba4c5fcac5270a8ce3be18c57405b47c1560be,10.1016/j.landusepol.2014.05.008,,Does the estimated impact of wildfires vary with the housing price distribution? A quantile regression approach,2013.0,,Julie M. Mueller; J. Loomis,,52.0,,False,,Economics,2013-09-01,dcba4c5fcac5270a8ce3be18c57405b47c1560be,doestheestimatedimpactofwildfiresvarywiththehousingpricedistributionaquantileregressionapproach,mueller,10.1016/j.landusepol.2014.05.008,doestheestimatedimpactofwildfiresvarywiththehousingpricedistributionaquantileregressionapproach|2013,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2086455107,10.1080/17421772.2011.647058,,"Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal",2012.0,Spatial Economic Analysis,Arnab Bhattacharjee; Eduardo Castro; João Lourenço Marques,"Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes the approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, applied to the urban housing market of Aveiro (Portugal) at two different spatial scales, provides exciting inferences on the spatial structure of the housing market. RÉSUMÉ L'hétérogénéité spatiale, la dépendance spatiale et l’échelle spatiale sont des caractéristiques clé de l'analyse spatiale dans les marchés de l'immobilier. Toutefois, la pratique habituelle de la modélisation de la dépendance spatiale comme étant le résultat d'interactions spatiales par le biais d'une matrice de poids spatiaux n'est souvent pas satisfaisante. Alors que les estimateurs existants des matrices de poids spatiaux sont basés sur des données de panel ou des ventes répétées, la présente communication adopte le principe d'un cadre transversal. Plus spécifiquement, sur la base d'une définition à priori des sub-marchés de l'immobilier, et de l'hypothèse d'un modèle multifactoriel, nous créons une méthodologie de probabilité maximale pour estimer des modèles hédoniques qui facilitent les connaissances de l'hétérogénéité spatiale et des interactions spatiales. Cette méthodologie, basée sur une analyse des facteurs orthogonaux, appliquée au secteur de l'immobilier urbain à Aveiro (Portugal) à deux échelles spatiales différentes, fournit des inférences excitantes en ce qui concerne la structure spatiale du secteur de l'immobilier. EXTRACTO La heterogeneidad, dependencia y escala espaciales constituyen características clave del análisis espacial de los mercados de la vivienda. No obstante, la práctica común de modelar la dependencia espacial como algo generado por interacciones espaciales a través de una matriz conocida de pesos espaciales, a menudo, no es satisfactoria. Aunque los estimadores existentes de matrices de pesos espaciales se basan en ventas repetidas o datos de panel, este estudio lleva el planteamiento a un marco de corte transversal. Específicamente, basados en una definición a priori de los submercados de la vivienda y en la presuposición de un modelo de múltiples factores, desarrollamos una metodología de probabilidad máxima para estimar modelos hedónicos, que facilita la comprensión de la heterogeneidad espacial y las interacciones espaciales. La metodología, basada en el análisis estadístico de factores ortogonales y aplicada al mercado de la vivienda urbana de Aveiro (Portugal) en dos escalas espaciales diferentes, proporciona interesantes inferencias sobre la estructura espacial del mercado de la vivienda.",51.0,,False,article,,2012-02-14,https://openalex.org/W2086455107,spatialinteractionsinhedonicpricingmodelstheurbanhousingmarketofaveiroportugal,bhattacharjee,10.1080/17421772.2011.647058,spatialinteractionsinhedonicpricingmodelstheurbanhousingmarketofaveiroportugal|2012,1,4222,2,hedonic housing prices || spatial hedonic model housing,4,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2054876535,10.1080/14765280802283584,,The impacts of distance to CBD on housing prices in Shanghai: a hedonic analysis,2008.0,Journal of Chinese Economic and Business Studies,Jie Chen; Qianjin Hao,"It is widely recognized that location is the primary determining factor of housing price. But to what extent the variation of housing price in Shanghai can be explained by the locational factor has not been empirically examined. In this paper, we examine the power of applying the hedonic method to the spatial-statistical analysis of housing prices in Shanghai. The data we use covers all new commercial residential housings sold in Shanghai during July 2004 and June 2006. The main focus in this paper is to examine the effect of geographical distance to city centre on the selling price of residential housings in Shanghai. We also discuss how the price gradient varies at different directions in Shanghai. Finally, we demonstrate the importance of applying quality control on the development of a housing price index. The statistical methodology and empirical results obtained in this paper carry interesting implications for other cities in China as well.",51.0,,False,article,,2008-07-31,https://openalex.org/W2054876535,theimpactsofdistancetocbdonhousingpricesinshanghaiahedonicanalysis,chen,10.1080/14765280802283584,theimpactsofdistancetocbdonhousingpricesinshanghaiahedonicanalysis|2008,1,1960,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic housing prices",2,3,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/j.landurbplan.2008.09.002,,The impact of detention basin design on residential property value: Case studies using GIS in the hedonic price modeling,2009.0,Landscape and Urban Planning,Jae Su Lee; Ming-Han Li,,51.0,,,journal-article,,,doi_10_1016_j_landurbplan_2008_09_002,theimpactofdetentionbasindesignonresidentialpropertyvaluecasestudiesusinggisinthehedonicpricemodeling,lee,10.1016/j.landurbplan.2008.09.002,theimpactofdetentionbasindesignonresidentialpropertyvaluecasestudiesusinggisinthehedonicpricemodeling|2009,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic price model amenities residential",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),4bf39dd9e083e82630131ca9cc727e72105efddf,10.1016/j.landusepol.2019.03.002,,"Accessibility, air pollution, and congestion: Capturing spatial trade-offs from agglomeration in the property market",2019.0,Land Use Policy,Christopher D. Higgins; M. Adams; W. Réquia; M. Mohamed,"Abstract The economies of scale and network effects from agglomeration form the foundation of the economic geography of cities. These agglomerations can be associated with transportation accessibility benefits and negative externalities from increased levels of harmful emissions, both of which are impacted by congestion. Such factors imply a tension between the economies and diseconomies of agglomeration in cities. This research employs real estate transaction data to examine whether spatial trade-offs occur between the benefits and negative impacts of agglomeration in the single-detached property market around two highways in Hamilton, Canada. Our novel disaggregate approach operationalizes agglomeration using empirical estimates of accessibility to employment with calibrated impedance functions; micro-scale dispersion modelling of harmful emissions of particulate matter (PM2.5); and congestion effects in each. Descriptive analyses reveal areas of transport advantage and environmental disadvantage and cross-sectional spatio-temporal hedonic models find evidence of an agglomeration trade-off in the study area. Results suggest that households value high levels of accessibility in locations that do not experience the corresponding environmental costs from traffic. In contrast, for locations with high access and high exposure, the access benefits of agglomeration are offset by environmental costs. This approach can be used to examine the dynamics of locational advantage, spatial justice, and how trade-offs between the economies and diseconomies of urban agglomeration inform the urban spatial structure of cities.",51.0,,False,,Economics,2019-05-01,4bf39dd9e083e82630131ca9cc727e72105efddf,accessibilityairpollutionandcongestioncapturingspatialtradeoffsfromagglomerationinthepropertymarket,higgins,10.1016/j.landusepol.2019.03.002,accessibilityairpollutionandcongestioncapturingspatialtradeoffsfromagglomerationinthepropertymarket|2019,1,2636,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,0,1,1,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/j.regsciurbeco.2010.07.002,,Hedonic analysis with locally weighted regression: An application to the shadow cost of housing regulation in Southern California,2010.0,Regional Science and Urban Economics,David L. Sunding; Aaron M. Swoboda,,51.0,,,journal-article,,,doi_10_1016_j_regsciurbeco_2010_07_002,hedonicanalysiswithlocallyweightedregressionanapplicationtotheshadowcostofhousingregulationinsoutherncalifornia,sunding,10.1016/j.regsciurbeco.2010.07.002,hedonicanalysiswithlocallyweightedregressionanapplicationtotheshadowcostofhousingregulationinsoutherncalifornia|2010,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic housing prices",2,0,1,1,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d4979b0847198cc7b7900e5a0c165a6d490c4d7b,10.2139/ssrn.3867764,,Does Building New Housing Cause Displacement?: The Supply and Demand Effects of Construction in San Francisco,2021.0,Social Science Research Network,K. Pennington,"San Francisco is gentrifying rapidly as an influx of high-income newcomers drives up housing prices and displaces lower-income incumbent residents. In theory, increasing the supply of housing should mitigate increases in rents. However, new construction could also increase demand for nearby housing by improving neighborhood quality. The net impact on nearby rents depends on the relative sizes of these supply and demand effects. This paper identifies the causal impact of new construction on nearby rents, displacement, and gentrification by exploiting random variation in the location of new construction induced by serious building fires. I combine parcel-level data on fires and new construction with an original dataset of historic Craigslist rents and panel data on individual migration histories to test the impact of proximity to new construction. I find that rents fall by 2% for parcels within 100m of new construction. Renters’ risk of being displaced to a lower-income neighborhood falls by 17%. Both effects decay linearly to zero within 1.5km. Next, I show evidence of a hyperlocal demand effect, with building renovations and business turnover spiking and then returning to zero after 100m. Gentrification follows the pattern of this demand effect: parcels within 100m of new construction are 2.5 percentage points (29.5%) more likely to experience a net increase in richer residents. Affordable housing and endogenously located construction do not affect displacement or gentrification. These findings suggest that increasing the supply of market rate housing has beneficial spillover effects for incumbent residents, reducing rents and displacement pressures while improving neighborhood quality.",51.0,,False,,,,d4979b0847198cc7b7900e5a0c165a6d490c4d7b,doesbuildingnewhousingcausedisplacementthesupplyanddemandeffectsofconstructioninsanfrancisco,pennington,10.2139/ssrn.3867764,doesbuildingnewhousingcausedisplacementthesupplyanddemandeffectsofconstructioninsanfrancisco|2021,1,2715,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1002/jae.929,,Nonparametric estimation of a hedonic price function,2007.0,Journal of Applied Econometrics,Christopher F. Parmeter; Daniel J. Henderson; Subal C. Kumbhakar,"Abstract Rosen's ( 1974 ) theory of hedonic prices is implemented econometrically using recently developed nonparametric techniques to examine the influence of qualitative factors on the price of a house. Our ability to smooth categorical variables leads to greater generalization in the valuation process and provides a canvas for interactions between categorical and continuous variables that is difficult to exploit in parametric and semiparametric models. This is illustrated with a replication of a previously used partially linear model specification. Copyright © 2007 John Wiley & Sons, Ltd.",51.0,,,journal-article,,,doi_10_1002_jae_929,nonparametricestimationofahedonicpricefunction,parmeter,10.1002/jae.929,nonparametricestimationofahedonicpricefunction|2007,1,1602,2,apartment rent hedonic || hedonic house price,2,0,1,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1007/s11146-010-9262-3,,Spatial Variation in the Determinants of House Prices and Apartment Rents in China,2010.0,The Journal of Real Estate Finance and Economics,Dean M. Hanink; Robert G. Cromley; Avraham Y. Ebenstein,,51.0,,,journal-article,,,doi_10_1007_s11146_010_9262_3,spatialvariationinthedeterminantsofhousepricesandapartmentrentsinchina,hanink,10.1007/s11146-010-9262-3,spatialvariationinthedeterminantsofhousepricesandapartmentrentsinchina|2010,1,1000,12,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment prices hedonic || apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || hedonic price model amenities residential || housing characteristics prices hedonic || real estate hedonic regression || rent hedonic regression housing || spatial hedonic model housing",0,2,2,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,house prices proximity hedonic,,10.1080/09599910110039905,,Size and proximity effects of primary schools on surrounding house values,2001.0,Journal of Property Research,Francois Des Rosiers; Antonio Lagana; Marius Theriault,,51.0,,,journal-article,,,doi_10_1080_09599910110039905,sizeandproximityeffectsofprimaryschoolsonsurroundinghousevalues,rosiers,10.1080/09599910110039905,sizeandproximityeffectsofprimaryschoolsonsurroundinghousevalues|2001,1,1000,4,hedonic house price || hedonic housing prices || hedonic property valuation residential || house prices proximity hedonic,0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,house prices proximity hedonic,,10.1086/261050,,The Impact of Proposition 13 on House Prices in Northern California: A Test of the Interjurisdictional Capitalization Hypothesis,1982.0,Journal of Political Economy,Kenneth T. Rosen,,51.0,,,journal-article,,,doi_10_1086_261050,theimpactofproposition13onhousepricesinnortherncaliforniaatestoftheinterjurisdictionalcapitalizationhypothesis,rosen,10.1086/261050,theimpactofproposition13onhousepricesinnortherncaliforniaatestoftheinterjurisdictionalcapitalizationhypothesis|1982,1,1000,3,hedonic housing prices || house prices proximity hedonic || implicit prices housing attributes,0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,9c3ea6cb26bd264ba7b2bdfebc7b4f2d654b2e3c,10.1111/jori.12041,,THE CAPITALIZATION OF INSURANCE PREMIUMS IN HOUSE PRICES,2015.0,,Charles M. Nyce; Randy E. Dumm; G. Sirmans; Greg T. Smersh,,51.0,,False,,Economics,2015-12-01,9c3ea6cb26bd264ba7b2bdfebc7b4f2d654b2e3c,thecapitalizationofinsurancepremiumsinhouseprices,nyce,10.1111/jori.12041,thecapitalizationofinsurancepremiumsinhouseprices|2015,1,1000,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",c406e688e08ce472e4cb9f8b2c06f013473db4bb,10.1016/j.cities.2018.11.004,,Trade-off relationship between public transportation accessibility and household economy: Analysis of subway access values by housing size,2019.0,Cities,Wonseok Seo; Hyunggwon Nam,"Abstract This study examines whether different income groups have different trade-off preferences between public transportation accessibility and income and economic power. In this study, apartments, the most typical housing type in Seoul, were categorized according to size (small, medium, and large) based on the premise that apartment size is a measure of the economic status of an individual or household. Subsequently, the effect of subway accessibility on apartment prices for the three types of apartments was examined through the conventional hedonic price model and the spatial autoregressive combined model, followed by an investigation of the spatial patterns of the effect with a focus on a geographically weighted regression. As a result, this study finds that, although subway accessibility serves as a positive factor in housing location decisions for most socioeconomic classes, the positive effect is not spatially unanimous, indicating that households with relatively robust economic stability preferred housing locations further away from subway stations in pursuit of a more attractive residential environment. Although high-income households trade greater accessibility to public transportation for a more attractive neighborhood environment, middle-income households more actively pursued convenience and the greenness of their neighborhoods. Households with less economic stability preferred neighborhoods featuring relatively low-cost housing and greater accessibility. These results confirm the prevalence of trade-off between public transportation accessibility and housing size in Seoul, Korea. This also verifies that the spatial division along economic class lines is intensifying, and that perspectives on subway accessibility vary depending on consumers' economic status. The results suggest the need for consumer-tailored public transportation policies that consider the residential location patterns of the socioeconomic classes that are dependent on public transportation. This perspective can improve our understanding of how to distribute mass transportation systems spaces in large cities, particularly where the wealth gap is relatively high.",50.0,,False,,Business,2019-04-01,c406e688e08ce472e4cb9f8b2c06f013473db4bb,tradeoffrelationshipbetweenpublictransportationaccessibilityandhouseholdeconomyanalysisofsubwayaccessvaluesbyhousingsize,seo,10.1016/j.cities.2018.11.004,tradeoffrelationshipbetweenpublictransportationaccessibilityandhouseholdeconomyanalysisofsubwayaccessvaluesbyhousingsize|2019,1,3180,4,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment prices hedonic || hedonic housing prices || spatial hedonic model housing",2,2,4,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,implicit prices housing attributes,,10.1108/14635789910258543,,Property valuation and the structure of urban housing markets,1999.0,Journal of Property Investment & Finance,Craig Watkins,"Since the 1980s UK academics have promoted the use of multiple regression analysis in property valuation. Recently, however, there has been growing recognition that regression models will be subject to aggregation bias if they fail to accommodate the existence of housing market segmentation (submarkets). In this study, we compare the empirical performance of a standard hedonic house price regression model for the city of Glasgow with a segmented model which recognises the importance of understanding the underlying market structure and, in particular, the existence of submarkets for different dwelling types. The results show that the (weighted) standard error of the segmented model is significantly lower than that of the market wide model. Consequently, we propose a two‐stage approach to the application of MRA techniques to residential valuation. First, following traditional institutional analysis of housing markets, the market should be subdivided into distinct structurally differentiated market segments. These segments can usefully be identified by principal components factor analysis which allows the identification of the most important common components in the housing bundle. Second, separate house price equations should be estimated for each market segment. Although the best‐fit equation may vary from sector to sector this is likely to reflect the behavioural realities of the property market, and will provide the basis for more accurate valuations.",50.0,,,journal-article,,,doi_10_1108_14635789910258543,propertyvaluationandthestructureofurbanhousingmarkets,watkins,10.1108/14635789910258543,propertyvaluationandthestructureofurbanhousingmarkets|1999,1,2476,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || hedonic property valuation || hedonic property valuation residential || implicit prices housing attributes",2,2,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_citations,cites:sirmans2006,668f045adfd0488efe88950eac2f8843da52c7ec,10.1177/0042098011429486,,"A Spatial Hedonic Analysis of the Value of Urban Land Cover in the Multifamily Housing Market in Los Angeles, CA",2012.0,,Wei Li; J. Saphores,,50.0,,False,JournalArticle,Economics,2012-09-01,668f045adfd0488efe88950eac2f8843da52c7ec,aspatialhedonicanalysisofthevalueofurbanlandcoverinthemultifamilyhousingmarketinlosangelesca,li,10.1177/0042098011429486,aspatialhedonicanalysisofthevalueofurbanlandcoverinthemultifamilyhousingmarketinlosangelesca|2012,1,1000,3,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || cites:sirmans2006 || spatial hedonic model housing",2,0,2,1,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1080/00420980123880,,Estimating Distance Gradients for Apartment Properties,2001.0,Urban Studies,Bo Söderberg; Christian Janssen,"The effect of distance from the city centre on selling price, tax assessment and gross income is investigated for income property in proximity to the city centre. Hedonic estimation with a multiplicative model specification is used to estimate the relationship between property characteristics on the one hand and price, assessment and income on the other. The sample consists of centrally located apartment buildings in downtown Stockholm sold during a recent three-year period. The application is specific to Stockholm, but the methodology and the insights gained are general. The results include the estimation of price gradients for the dependent variables. The models are used for estimating the implicit capitalisation rates in the property market as a function of the distance to the centre. We also estimated where the market appears to place the city centre as implied by the observations. Although no rent gradient was found, we were able to detect a significant negative price gradient. The former is reasonable considering the rent control ordinances. The latter indicates the existence of a negative land rent gradient.",49.0,,,journal-article,,,doi_10_1080_00420980123880,estimatingdistancegradientsforapartmentproperties,söderberg,10.1080/00420980123880,estimatingdistancegradientsforapartmentproperties|2001,1,2132,3,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment prices hedonic || apartment rent hedonic",2,2,1,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,implicit prices housing attributes,,10.1108/14635780010345436,,The modelling of housing submarkets,2000.0,Journal of Property Investment & Finance,Mike Fletcher; Paul Gallimore; Jean Mangan,"This paper is concerned as to whether it is more appropriate to use aggregate or disaggregate models in forecasting house prices using hedonic modelling. It is accepted that the implicit pricing of some of the attributes is not stable between locations, property types and ages but it is argued that this can be effectively modelled with an aggregate method. The models are developed using a dataset of nearly 18,000 transactions in the UK Midlands region in 1994. The comparative performance of these models is then considered using two approaches. Chow tests of the error differences between actual price and the price predicted by the models suggest that the submarket models lead to statistically significant, though small, improvements. A second approach, using comparison of the root mean square errors, is conducted on the models’ forecasts for a 10 per cent sample of nearly 2,000 transactions excluded from the modelling process. This shows little practical difference in the forecasting ability between the two approaches. Great care needs to be taken over sample size if a disaggregate model is used.",49.0,,,journal-article,,,doi_10_1108_14635780010345436,themodellingofhousingsubmarkets,fletcher,10.1108/14635780010345436,themodellingofhousingsubmarkets|2000,1,2111,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || hedonic property valuation residential || implicit prices housing attributes",2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.2307/2109351,,A Hedonic Approach to Residential Succession,1990.0,The Review of Economics and Statistics,N. Edward Coulson; Eric W. Bond,,49.0,,,journal-article,,,doi_10_2307_2109351,ahedonicapproachtoresidentialsuccession,coulson,10.2307/2109351,ahedonicapproachtoresidentialsuccession|1990,1,1000,1,apartment rent hedonic,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W4385633781,10.1016/j.eswa.2023.121059,,Improving hedonic housing price models by integrating optimal accessibility indices into regression and random forest analyses,2023.0,Expert Systems with Applications,David Rey-Blanco; Jose Luis Zofio; Julio González Arias,"Location indices are key in explaining variation in house prices. However, the definition of comprehensive indices capturing all locational features, along with their efficient and timely calculation, is usually one of the most complex dimensions of house price modeling. Existing difficulties result in partial location specifications, mostly due to three hurdles: (1) there is not a consensus on the best method to construct these indices, (2) what features (variables) to include: labor, demographic, commuting, etc., and (3) its creation requires granular and updated datasets. We introduce a methodology based on computer algorithms to create car and walk accessibility indices that address the previous concerns and capture location interactions among a wide range of variables. The selection of variables is based on an automated search of the best performing utility bearing gravitational accessibility indices for price prediction. Once these optimal indices are obtained, the method applies principal components analysis to secure their orthogonality. Using a unique dataset from a leading real estate portal in Europe, we illustrate and test for the city of Madrid their applicability in several house asking price models that are estimated using regression analysis and random forests (as a representative family of machine learning techniques). The experimental analysis reveals that using the optimal indices results in significant improvements in accuracy, for both regression-based models (13%) and random forests (21.6%), while achieving a substantial reduction in spatial autocorrelation (around 35%). The generated indices are clearly interpretable, which makes them a valuable tool for urban analyses (planning, transportation, sustainability, etc.). Finally, the methodology can be extended to other types of real state (commercial, industrial, etc.) and location (country, region, etc.).",48.0,,True,article,,2023-08-07,https://openalex.org/W4385633781,improvinghedonichousingpricemodelsbyintegratingoptimalaccessibilityindicesintoregressionandrandomforestanalyses,rey-blanco,10.1016/j.eswa.2023.121059,improvinghedonichousingpricemodelsbyintegratingoptimalaccessibilityindicesintoregressionandrandomforestanalyses|2023,1,2909,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices",2,3,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",2db3ff90fef8790d00b80b5962cdd08304f33637,10.1068/b38093,,Local Hedonic House-Price Modelling for Urban Planners: Advantages of Using Local Regression Techniques,2013.0,,R. Crespo; A. Grêt-Regamey,,48.0,,False,JournalArticle,Economics,2013-08-01,2db3ff90fef8790d00b80b5962cdd08304f33637,localhedonichousepricemodellingforurbanplannersadvantagesofusinglocalregressiontechniques,crespo,10.1068/b38093,localhedonichousepricemodellingforurbanplannersadvantagesofusinglocalregressiontechniques|2013,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices || spatial hedonic model housing",2,0,1,1,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",71fe66595c78203bde395eb777963e89d6c03b0a,10.1080/10835547.2018.12091505,,Impact of Artificial Neural Networks Training Algorithms on Accurate Prediction of Property Values,2018.0,Journal of Real Estate Research,Joseph Awoamim Yacim; D. Boshoff,"This study extended the use of artificial neural networks (ANNs) training algorithms in mass appraisal. The goal was to verify the comparative performance of ANNs with linear, semi-log, and log-log models. The methods were applied to a dataset of 3,232 single-family dwellings sold in Cape Town, South Africa. The results reveal that the semi-log model and the Levenberg-Marquardt trained artificial neural networks (LMANNs) performed best in their respective categories. The best performing models were tested in terms of prediction accuracy within the 10% and 20% of the assessed values, performance, and reliability ranking, and explicit explainability ranking order. The LMANNs outperform the semi-log model in the first two tests, but fail the explainability ranking order test. The results demonstrate the semi-log model as the most preferred technique due to its simplicity, consistency, transparency, locational advantage, and ease of application within the mass appraisal environment. The black box nature of th...",48.0,,False,,Economics,2018-07-01,71fe66595c78203bde395eb777963e89d6c03b0a,impactofartificialneuralnetworkstrainingalgorithmsonaccuratepredictionofpropertyvalues,yacim,10.1080/10835547.2018.12091505,impactofartificialneuralnetworkstrainingalgorithmsonaccuratepredictionofpropertyvalues|2018,1,2023,1,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 crossref,hedonic property valuation residential,,10.1108/14635780010324321,,The application of surface generated interpolation models for the prediction of residential property values,2000.0,Journal of Property Investment & Finance,William J. McCluskey; William G. Deddis; Ian G. Lamont; Richard A. Borst,The aim of this paper is to attempt to measure the effect of location on residential house prices and to endeavour to integrate spatial and aspatial data in terms of developing a hybrid predictive model. The research methodology investigates the traditional hedonic approach to modelling location using multiple regression techniques. Alternative approaches are considered which specifically model the spatial distribution of house prices with the objective of developing location adjustment factors. These approaches are based on the development of surface response techniques such as inverse distance weighting and universal kriging. The results generated from the surfaces created are then calibrated within MRA.,47.0,,,journal-article,,,doi_10_1108_14635780010324321,theapplicationofsurfacegeneratedinterpolationmodelsforthepredictionofresidentialpropertyvalues,mccluskey,10.1108/14635780010324321,theapplicationofsurfacegeneratedinterpolationmodelsforthepredictionofresidentialpropertyvalues|2000,1,1715,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic property valuation residential",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,3e6f3f4dd63d35f3aa533ac77718d43d8cb25d95,10.3368/le.90.1.61,,Amenity Values versus Land Constraints: The Spatial Effects of Natural Landscape Features on Housing Values,2014.0,Land Economics,E. Irwin; P. Jeanty; M. Partridge,,47.0,,False,,Economics,2014-01-06,3e6f3f4dd63d35f3aa533ac77718d43d8cb25d95,amenityvaluesversuslandconstraintsthespatialeffectsofnaturallandscapefeaturesonhousingvalues,irwin,10.3368/le.90.1.61,amenityvaluesversuslandconstraintsthespatialeffectsofnaturallandscapefeaturesonhousingvalues|2014,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,0f5576ed54667ae3880c744236fb522e112902ad,10.1016/j.landusepol.2019.02.024,,The changing face of environmental amenities: Heterogeneity across housing submarkets and time,2019.0,Land Use Policy,M. Fernandez; S. Bucaram,"Abstract The value of environmental amenities has been studied for decades in the hedonic housing prices literature. Numerous studies have found that amenities such as marine, river and woodland habitats, national and regional reserves, urban parks, or wetlands contribute positively on the formation of housing prices. Work is scarce when considering heterogeneity across submarkets and time. Auckland, New Zealand’s largest city, has a unique landscape with open spaces, waterways, coastal areas and volcanic features. Local housing market is highly segmented and the city has gone through a bust, recovery and boom cycle in the last decade. This paper incorporates heterogeneity across submarkets and time, and examines how they affect the capitalisation of amenities into housing prices. We construct a dataset of about 280 thousand sale transactions between 2000 and 2016 in Auckland and estimate hedonic models based on unconditional quantile regressions. We find diverse capitalisation patterns across housing submarkets. Beaches may add price premiums of 5.1% in houses in the upper-end of the distribution, but also price discounts of 2.1% in houses in the lower-end of the distribution. We use a detailed categorisation of parks and find that volcanic parks may add premiums for houses in the 70% percentile and above of the price distribution, but also imply price discounts values for houses around the median price and below. We argue that the differing effects occur because of trade-offs between regulations to protect amenities and the ecosystem services they provide, conditional to specifics of the location of houses.",46.0,,False,,Geography,2019-04-01,0f5576ed54667ae3880c744236fb522e112902ad,thechangingfaceofenvironmentalamenitiesheterogeneityacrosshousingsubmarketsandtime,fernandez,10.1016/j.landusepol.2019.02.024,thechangingfaceofenvironmentalamenitiesheterogeneityacrosshousingsubmarketsandtime|2019,1,2636,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices",2,1,3,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1007/s10640-008-9221-0,,A Spatial Hedonic Approach to Assess the Impact of Swine Production on Residential Property Values,2008.0,Environmental and Resource Economics,Jungik Kim; Peter Goldsmith,,46.0,,,journal-article,,,doi_10_1007_s10640_008_9221_0,aspatialhedonicapproachtoassesstheimpactofswineproductiononresidentialpropertyvalues,kim,10.1007/s10640-008-9221-0,aspatialhedonicapproachtoassesstheimpactofswineproductiononresidentialpropertyvalues|2008,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment rent hedonic",2,1,1,1,1,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),f48b8dbddcb45df1b8030d1b4b07743eccfc70f6,10.1111/jors.12187,,HEDONIC ANALYSIS OVER TIME AND SPACE: THE CASE OF HOUSE PRICES AND TRAFFIC NOISE,2015.0,,Aaron M. Swoboda; T. Nega; M.A. Timm,,46.0,,False,,Computer Science,2015-09-01,f48b8dbddcb45df1b8030d1b4b07743eccfc70f6,hedonicanalysisovertimeandspacethecaseofhousepricesandtrafficnoise,swoboda,10.1111/jors.12187,hedonicanalysisovertimeandspacethecaseofhousepricesandtrafficnoise|2015,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || real estate hedonic regression",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1029/92wr02995,,Water source as a housing characteristic: Hedonic property valuation and willingness to pay for water,1993.0,Water Resources Research,J. H. North; C. C. Griffin,"Using data from a large representative sample of rural households in one region of the Philippines, we estimate the determinants of the rental value of dwellings using the bid‐rent approach to the hedonic price model. Our particular interest is in the relative valuation these households place on owning a private source of water and distance to a public or communal source. We find that low‐, middle‐, and high‐income households value an in‐house piped water source highly relative to other characteristics of their homes. Middle‐ and high‐income households value a deep well or piped water in the yard, although at a substantially lower level than piped water in the house. It is somewhat surprising to find that, except in the case of high‐income families, households appear to gain little or no utility from having a communal source of water, such as a river, lake, or public tap, closer to their homes. As a consequence, public water policies that emphasize improving the quality and proximity of communal sources would be inappropriate for the region represented by this sample.",45.0,,,journal-article,,,doi_10_1029_92wr02995,watersourceasahousingcharacteristichedonicpropertyvaluationandwillingnesstopayforwater,north,10.1029/92wr02995,watersourceasahousingcharacteristichedonicpropertyvaluationandwillingnesstopayforwater|1993,1,2084,7,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic house price || hedonic housing prices || hedonic property valuation || house prices proximity hedonic || housing characteristics prices hedonic",2,1,6,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",433fc152c87a1e087157e21d8f30cf502674a58c,10.1016/j.habitatint.2015.08.036,,"Housing valuation of different towns using the hedonic model: A case of Faisalabad city, Pakistan",2015.0,,Ghaffar Ali; M. Bashir; H. Ali,,45.0,,False,,Geography,2015-12-01,433fc152c87a1e087157e21d8f30cf502674a58c,housingvaluationofdifferenttownsusingthehedonicmodelacaseoffaisalabadcitypakistan,ali,10.1016/j.habitatint.2015.08.036,housingvaluationofdifferenttownsusingthehedonicmodelacaseoffaisalabadcitypakistan|2015,1,1000,1,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1002/jae.3950020103,,A Bayesian approach to assessing the robustness of hedonic property value studies,1987.0,Journal of Applied Econometrics,Scott E. Atkinson; Thomas D. Crocker,"Abstract Hedonic price models are widely employed to estimate implicit prices for bundled attributes. Residential property value studies dominate these applications. Using a representative cross‐sectional property value data set, we employ Bayesian methods to translate a range of priors in covariate selection typical of hedonic property value studies into a range of posterior estimates. We also formulate priors regarding measurement error in individual covariates and compute the ranges of resulting posterior means. Finally, we empirically demonstrate that a greater and more systematic use of prior information drawn from one's own data and from other studies can break the collinearity deadlock in this data.",44.0,,,journal-article,,,doi_10_1002_jae_3950020103,abayesianapproachtoassessingtherobustnessofhedonicpropertyvaluestudies,atkinson,10.1002/jae.3950020103,abayesianapproachtoassessingtherobustnessofhedonicpropertyvaluestudies|1987,1,1715,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || apartment rent hedonic",2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8b995887e852f71f9974ee388dd1bad3a9367136,10.1016/j.tra.2019.09.053,,Valuing walkability: New evidence from computer vision methods,2019.0,,Christopher Yencha,"Abstract Walkability describes the efficiency and pleasure of walking in an area and is an aspect of urban design that has received much attention. Frequently used measures of walkability largely ignore the quality of nearby pedestrian pathing, such as sidewalks, in quantifying walkability. This paper expands upon the literature's understanding of walkability by supplementing current measures of walkability with data gathered from street-level images using computer vision techniques. Using hedonic methods and a sample of almost 60,000 house transactions in Ohio, I find that nearby establishments are an amenity capitalized into home prices only when there also exists access to adequate pedestrian pathing, and that walkability measures derived from computer vision methods contain information not found in other commonly used measures of walkability. With cities increasingly pushing towards creating more walkable neighborhoods, this paper provides evidence that walkable space is indeed valued by residents.",43.0,,False,,Geography,2019-12-01,8b995887e852f71f9974ee388dd1bad3a9367136,valuingwalkabilitynewevidencefromcomputervisionmethods,yencha,10.1016/j.tra.2019.09.053,valuingwalkabilitynewevidencefromcomputervisionmethods|2019,1,2017,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic price model amenities residential,f309cdeb8fe17983f88e290a159e1df28144de5e,10.1016/j.cities.2016.11.002,,Measuring the effect of greenbelt proximity on apartment rents in Seoul,2017.0,,M. Jun; Hee-Jae Kim,,43.0,,False,,Geography,2017-02-01,f309cdeb8fe17983f88e290a159e1df28144de5e,measuringtheeffectofgreenbeltproximityonapartmentrentsinseoul,jun,10.1016/j.cities.2016.11.002,measuringtheeffectofgreenbeltproximityonapartmentrentsinseoul|2017,1,1000,3,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment prices hedonic || hedonic price model amenities residential",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a6d1c488b09c82140870cba8426bcd5bb60abf3a,10.1177/1078087410376614,,The Spatial-Temporal Impact of Residential Foreclosures on Single-Family Residential Property Values,2011.0,,Timothy F. Kobie; Sugie Lee,,43.0,,False,,Economics,2011-01-01,a6d1c488b09c82140870cba8426bcd5bb60abf3a,thespatialtemporalimpactofresidentialforeclosuresonsinglefamilyresidentialpropertyvalues,kobie,10.1177/1078087410376614,thespatialtemporalimpactofresidentialforeclosuresonsinglefamilyresidentialpropertyvalues|2011,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b612cfeefe79b3fb631c316ff160895751d62e9e,10.1016/j.jhe.2016.05.003,,"Transit development, consumer amenities and home values: Evidence from Beijing's subway neighborhoods",2016.0,,Si-Qi Zheng; Yang Xu; Xiao-Nan Zhang; Rui Wang,,43.0,,False,,Business,2016-09-01,b612cfeefe79b3fb631c316ff160895751d62e9e,transitdevelopmentconsumeramenitiesandhomevaluesevidencefrombeijingssubwayneighborhoods,zheng,10.1016/j.jhe.2016.05.003,transitdevelopmentconsumeramenitiesandhomevaluesevidencefrombeijingssubwayneighborhoods|2016,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1002/jae.1022,,Semiparametric hedonic price models: assessing the effects of agricultural nonpoint source pollution,2008.0,Journal of Applied Econometrics,Christophe Bontemps; Michel Simioni; Yves Surry,"Abstract In the area of environmental analysis using hedonic price models, we investigate the performance of various nonparametric and semiparametric specifications. The proposed model specifications are made up of two parts: a linear component for house characteristics and a non‐(semi)parametric component representing the nonlinear influence of environmental indicators on house prices. We adopt a general‐to‐specific search procedure, based on recent specification tests comparing the proposed specifications with a fully nonparametric benchmark model, to select the best model specification. An application of these semiparametric models to rural districts indicates that pollution resulting from intensive livestock farming has a significant nonlinear impact on house prices. Copyright © 2008 John Wiley & Sons, Ltd.",42.0,,,journal-article,,,doi_10_1002_jae_1022,semiparametrichedonicpricemodelsassessingtheeffectsofagriculturalnonpointsourcepollution,bontemps,10.1002/jae.1022,semiparametrichedonicpricemodelsassessingtheeffectsofagriculturalnonpointsourcepollution|2008,1,1826,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic house price",2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1538-4632.1993.tb00277.x,,Apartment Rents: Another Challenge to the Monocentric Model,1993.0,Geographical Analysis,Irving Hoch; Paul Waddell,Extension of hedonic modeling strategy to the case of apartment rents in the Dallas region reveals the influence of a complex network of activity centers and highway axes. Nodes other than the CBD exert greater influence on rents than the CBD and amenity variables and externalities have both positive and negative effects. These findings demand reevaluation of the place normally assigned to higher‐density rental housing in urban models.,42.0,,,journal-article,,,doi_10_1111_j_1538_4632_1993_tb00277_x,apartmentrentsanotherchallengetothemonocentricmodel,hoch,10.1111/j.1538-4632.1993.tb00277.x,apartmentrentsanotherchallengetothemonocentricmodel|1993,1,1439,1,apartment rent hedonic,2,1,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0094-1190(85)90039-7,,Hedonic housing price indices and secondary employment centers,1985.0,Journal of Urban Economics,Bruce Bender; Hae-Shin Hwang,,42.0,,,journal-article,,,doi_10_1016_0094_1190_85_90039_7,hedonichousingpriceindicesandsecondaryemploymentcenters,bender,10.1016/0094-1190(85)90039-7,hedonichousingpriceindicesandsecondaryemploymentcenters|1985,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic housing prices || hedonic price model amenities residential",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a7994232e9de0d259914c149f19b3754a7f3b324,10.3233/sju-1985-3301,,The application of hedonic indexing methods: A study of house prices in the United Kingdom,1985.0,,M. Fleming; J. Nellis,,42.0,,False,,Economics,1985-08-01,a7994232e9de0d259914c149f19b3754a7f3b324,theapplicationofhedonicindexingmethodsastudyofhousepricesintheunitedkingdom,fleming,10.3233/sju-1985-3301,theapplicationofhedonicindexingmethodsastudyofhousepricesintheunitedkingdom|1985,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9b9f9b81641cca03cd602ed12b5bb79c297f06f7,10.1016/j.habitatint.2019.102068,,"Temporal and spatial effects of a ‘Shan Shui’ landscape on housing price: A case study of Chongqing, China",2019.0,,Gui-Wen Liu; Xizi Wang; Jian-Ping Gu; Yong Liu; Tao Zhou,"Abstract A residential environment with a ‘Shan Shui’ landscape (river and mountain landscapes) not only has high aesthetic value but also indicates good fortune in traditional Chinese geomantic omen theory. As determined at the 19th National Congress, the national strategic goal of China is to achieve green development and ecological construction for urbanization. To comprehensively recognize the economic value of a ‘Shan Shui’ landscape is a win-win solution for real estate developers, as well as for the sustainable urbanization of China. Most of the literature related to the amenity value of urban landscapes has focused on the value of individual landscapes while ignoring the interactions between landscapes, leading to controversy around the amenity value of the landscape as determined in different empirical case studies. This study takes the ‘city of Shan Shui’, Chongqing, as the case study to explore the interactions of river and mountain landscapes and the temporal and spatial variances in the value of ‘Shan Shui’. The findings suggest that access to the ‘Shan Shui’ landscapes of neighboring houses would increase the price of a house by 15.01%, which is much higher than the values for a river or mountain landscape independently, since the place where mountains and rivers meet is a blessing in Chinese geomantic omen theory, and the interactions between landscapes will create a new symbol containing greater value. In addition, ‘Shan Shui’ landscapes are more valued in the market boom because they are perceived to represent luxury, and the value of ‘Shan Shui’ has spatial spillover effects. The findings contribute to the broader discussion about the valuation of interactions among urban landscapes, and recommendations are made to the local government and for further sustainable urbanization in urban China.",41.0,,False,,Geography,2019-12-01,9b9f9b81641cca03cd602ed12b5bb79c297f06f7,temporalandspatialeffectsofashanshuilandscapeonhousingpriceacasestudyofchongqingchina,liu,10.1016/j.habitatint.2019.102068,temporalandspatialeffectsofashanshuilandscapeonhousingpriceacasestudyofchongqingchina|2019,1,2840,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,4,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,d0ef3bbf286defaf326a1f02572878402364b70c,10.1080/10511482.2019.1681016,,"Examining the Impact of Short-Term Rentals on Housing Prices in Washington, DC: Implications for Housing Policy and Equity",2020.0,,Zhen-Peng Zou,"ABSTRACT As on-demand short-term rentals (STRs) grow popular with the rise of sharing platforms like Airbnb, regulations for the STR market have become the center of a debate among policymakers, housing interest groups, the hotel and lodging industry, and STR platforms. Washington, DC, the nation’s capital and one of the most popular tourist destinations in the United States, is on the front lines of legalizing and regulating the STR business. With the heated policy debate over whether STRs disrupt the rental housing market in DC, a concrete discussion about what STRs impose on the owner housing market is left out. Using web-scraped data from Airbnb and property-level data from the city, I investigated the net impact of STRs on single-family property prices through a series of hedonic analyses. The results suggest that having Airbnb establishments in the neighborhood can significantly inflate property prices. Because of the uneven spatial market penetration of STRs, such price impact could inequitably affect low-income homebuyers and add another hurdle to resolving the housing affordability issue faced by policymakers in Washington, DC.",40.0,,False,,Business,2020-03-03,d0ef3bbf286defaf326a1f02572878402364b70c,examiningtheimpactofshorttermrentalsonhousingpricesinwashingtondcimplicationsforhousingpolicyandequity,zou,10.1080/10511482.2019.1681016,examiningtheimpactofshorttermrentalsonhousingpricesinwashingtondcimplicationsforhousingpolicyandequity|2020,1,2154,1,hedonic housing prices,2,3,4,1,1,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,af26f9f316a196a8566bceb8ef0c63ec7db95c15,10.1177/2399808317721184,,"Effects of spatial access to neighborhood land-use density on housing prices: Evidence from a multilevel hedonic analysis in Seoul, South Korea",2019.0,,Chang-Deok Kang,,40.0,,False,JournalArticle,Business,2019-05-01,af26f9f316a196a8566bceb8ef0c63ec7db95c15,effectsofspatialaccesstoneighborhoodlandusedensityonhousingpricesevidencefromamultilevelhedonicanalysisinseoulsouthkorea,kang,10.1177/2399808317721184,effectsofspatialaccesstoneighborhoodlandusedensityonhousingpricesevidencefromamultilevelhedonicanalysisinseoulsouthkorea|2019,1,1000,2,hedonic housing prices || spatial hedonic model housing,2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,b74d93f9fabc2a91919abdebe4d39182e2434094,10.1093/jeg/lbw040,,"The impact of aircraft noise on apartment prices: a differences-in-differences hedonic approach for Frankfurt, Germany",2017.0,,Tim Winke,,40.0,,False,,Economics,2017-11-01,b74d93f9fabc2a91919abdebe4d39182e2434094,theimpactofaircraftnoiseonapartmentpricesadifferencesindifferenceshedonicapproachforfrankfurtgermany,winke,10.1093/jeg/lbw040,theimpactofaircraftnoiseonapartmentpricesadifferencesindifferenceshedonicapproachforfrankfurtgermany|2017,1,1000,1,apartment prices hedonic,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,7e9e6bc7ee241fc22bf724eec26a5bd6be0df5e9,10.1016/j.regsciurbeco.2018.09.001,,Eliciting preferences for urban parks,2018.0,Regional Science and Urban Economics,T. Panduro; C. U. Jensen; T. Lundhede; Kathrine von Graevenitz; B. Thorsen,"Abstract The hedonic pricing method has been used extensively to obtain implicit prices for availability of urban green space, but few have obtained households' preference parameters. We elicit preferences and estimate willingness to pay functions for park availability in Copenhagen using an approach that places identifying restrictions on the utility function. We do this for two different measures of park availability and examine sources of preference heterogeneity. We find that the implicit price of another hectare of park within a 1000 m radius is 53.25 EUR per ha per year for the average apartment corresponding to an increase in annual rent of 0.33% per additional ha. For reducing distance to the nearest park by a meter, the price is 0.59 EUR per meter per year, corresponding to an increase in annual rent of 0.03‰ per meter. We apply our results to a policy scenario reducing the park area available in an area of central Copenhagen and show how estimates of aggregate welfare changes are highly sensitive to the measure of park availability applied. The findings stress the importance of paying attention to how public goods are defined when undertaking welfare economic policy analyses.",39.0,,False,,Economics,2018-11-01,7e9e6bc7ee241fc22bf724eec26a5bd6be0df5e9,elicitingpreferencesforurbanparks,panduro,10.1016/j.regsciurbeco.2018.09.001,elicitingpreferencesforurbanparks|2018,1,2204,1,apartment prices hedonic,2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1752-1688.1989.tb03095.x,,HEDONIC PRICE ANALYSIS OF THE EFFECT OF A FLOODPLAIN ON PROPERTY VALUES1,1989.0,JAWRA Journal of the American Water Resources Association,William A. Donnelly,"ABSTRACT: The effect of a floodplain location on the market value for property is controversial, with the conventional wisdom being that buyers are myopic. Previous parametric and nonparametric statistical tests have proven inconclusive in deciding the issue. Herein, a theoretical model, that of hedonic price indexes, is posited and tested, using housing sales characteristics data obtained from a Multi‐List Service cooperative. This study suggests that home buyers do adjust the purchase price for houses within a floodplain, and this amounts to, on average, just over 12 percent. The data are from a small midwestern town in which the last severe flooding occurred a decade ago.",39.0,,,journal-article,,,doi_10_1111_j_1752_1688_1989_tb03095_x,hedonicpriceanalysisoftheeffectofafloodplainonpropertyvaluessup1sup,donnelly,10.1111/j.1752-1688.1989.tb03095.x,hedonicpriceanalysisoftheeffectofafloodplainonpropertyvaluessup1sup|1989,1,1683,4,apartment rent hedonic || hedonic house price || hedonic housing prices || housing characteristics prices hedonic,2,1,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2219511693,10.1080/08941920.2015.1024810,,"Are Housing Prices Pulled Down or Pushed Up by Fracked Oil and Gas Wells? A Hedonic Price Analysis of Housing Values in Weld County, Colorado",2015.0,Society & Natural Resources,Ashley Bennett; John B. Loomis,"As fracking moved into neighborhoods it has created concerns by homeowners about environmental effects and reduced property values. However, the increase in drilling activity results in gains in employment and increased the demand for housing. We apply the hedonic price method to a heavily fracked county in Colorado to test for the net effects of these two factors on house prices. Results across 12 different models shows that in seven of these the count of wells being fracked within a half-mile of a house has a small (−1% per well being drilled) but significant effect on house prices in the in Weld County and Greeley but not in rural areas. In models without year fixed effects, employment in the oil and gas sector has a relatively small but significant positive effect on house prices in the whole county and urban area model specifications, but not in the rural model specifications.",39.0,,False,article,,2015-07-01,https://openalex.org/W2219511693,arehousingpricespulleddownorpushedupbyfrackedoilandgaswellsahedonicpriceanalysisofhousingvaluesinweldcountycolorado,bennett,10.1080/08941920.2015.1024810,arehousingpricespulleddownorpushedupbyfrackedoilandgaswellsahedonicpriceanalysisofhousingvaluesinweldcountycolorado|2015,1,1894,2,hedonic house price || hedonic housing prices,2,5,3,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,house prices proximity hedonic,91a44e2cd5a021b353ef15f6637207fbc3662ee4,10.1108/17538271111153022,,The impact of proximity to cell phone towers on residential property values,2011.0,,O. Filippova; Michael Rehm,,39.0,,False,,Economics,2011-08-09,91a44e2cd5a021b353ef15f6637207fbc3662ee4,theimpactofproximitytocellphonetowersonresidentialpropertyvalues,filippova,10.1108/17538271111153022,theimpactofproximitytocellphonetowersonresidentialpropertyvalues|2011,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic property valuation || house prices proximity hedonic",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1467-8276.2009.01316.x,,Riparian Buffers and Hedonic Prices: A Quasi‐Experimental Analysis of Residential Property Values in the Neuse River Basin,2009.0,American Journal of Agricultural Economics,Okmyung Bin; Craig E. Landry; Gregory F. Meyer,"Abstract Riparian buffers, the strips of vegetation along banks of rivers and streams, have been proposed as a key instrument to protect water quality in the United States. Riparian buffers impose a restriction on the use of private property limiting harvest and development, but buffers can also provide for aesthetic and recreational benefits that may accrue to property owners. With data from the Neuse River Basin in North Carolina, this study attempts to provide empirical evidence on the effect of a mandatory buffer rule on the value of riparian properties. Spatial autoregressive hedonic models are estimated within a quasi‐experimental framework using the imposition of the buffer rule as the treatment and nonriparian properties as a control group. Results indicate that a riparian property generally commands a high premium. We find no evidence, however, that the mandatory buffer rule has had a significant impact on riparian property values when compared with the control group.",38.0,,,journal-article,,,doi_10_1111_j_1467_8276_2009_01316_x,riparianbuffersandhedonicpricesaquasiexperimentalanalysisofresidentialpropertyvaluesintheneuseriverbasin,bin,10.1111/j.1467-8276.2009.01316.x,riparianbuffersandhedonicpricesaquasiexperimentalanalysisofresidentialpropertyvaluesintheneuseriverbasin|2009,1,1991,1,apartment rent hedonic,2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1540-6229.2006.00160.x,,The Impact of Environmental Contamination on Condo Prices: A Hybrid Repeat‐Sale/Hedonic Approach,2006.0,Real Estate Economics,Bradford Case; Peter F. Colwell; Chris Leishman; Craig Watkins,"We extend the literature on the impact of externalities using an approach based on a hybrid of hedonic and repeat‐sales methods. The externality in question is groundwater contamination in Scottsdale, Arizona. The use of condominium sales allows us to assume that major physical characteristics remain unchanged, but location parameters may be altered by urban growth and development as well as contamination. We find an economically significant discount for properties located in the contaminated area. Interestingly, it does not appear until several years after the contamination becomes publicly known, and it seems to have disappeared before the end of the study period.",38.0,,,journal-article,,,doi_10_1111_j_1540_6229_2006_00160_x,theimpactofenvironmentalcontaminationoncondopricesahybridrepeatsalehedonicapproach,case,10.1111/j.1540-6229.2006.00160.x,theimpactofenvironmentalcontaminationoncondopricesahybridrepeatsalehedonicapproach|2006,1,1674,1,apartment rent hedonic,2,0,2,2,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,house prices proximity hedonic,,10.1080/0042098975475,,House and Land Prices in Sydney from 1931 to 1989,1997.0,Urban Studies,Peter Abelson,"This paper describes and explains house and land prices in Sydney from 1931 to 1989. Throughout this period, house and land prices fell, generally exponentially, with distance to the CBD. However, the price gradients were not constant. Between 1931 and 1968 the gradients flattened. On the other hand, between the mid 1970s and 1989 they became steeper again. The changes in the gradients were caused mainly by changes in travel times and costs by road and rail. Real travel costs fell in the early period and rose later on. Other factors that affected the price gradients were changes in car ownership and the supply of urban services in the early period and gentrification of inner-city areas and the greater increase in housing supply on the urban fringe in the later period. The paper also shows how house size and environmental factors influenced house prices in the 1970s and 1980s.",38.0,,,journal-article,,,doi_10_1080_0042098975475,houseandlandpricesinsydneyfrom1931to1989,abelson,10.1080/0042098975475,houseandlandpricesinsydneyfrom1931to1989|1997,1,1888,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || house prices proximity hedonic || implicit prices housing attributes",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8891481973de1ade675a35e628140568686d59da,10.1016/j.jue.2019.05.001,,The rise and effects of homeowners associations,2019.0,Journal of Urban Economics,Wyatt Clarke; Matthew Freedman,"In the U.S., nearly 60% of recently built single-family houses, and 80% of houses in new subdivisions, are part of a homeowners association (HOA). We construct the first near-national map of HOAs using publicly recorded mortgage records for single-family homes. We use these data to document the growth and characteristics of HOAs as well as to examine their relationship with housing prices. We find that houses in HOAs have prices that are on average at least 4%, or $13,500, greater than observably similar houses outside of HOAs. The HOA premium correlates with the stringency of local land use regulation, local government spending on public goods, and measures of social attitudes toward race. The data also paint a detailed picture of the people living in HOA neighborhoods, who are on average more affluent and racially segregated than those living in other nearby neighborhoods.",38.0,,False,,Business,2019-07-01,8891481973de1ade675a35e628140568686d59da,theriseandeffectsofhomeownersassociations,clarke,10.1016/j.jue.2019.05.001,theriseandeffectsofhomeownersassociations|2019,1,1887,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,6,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0094-1190(84)90024-x,,Hedonic analysis of a housing market in disequilibrium,1984.0,Journal of Urban Economics,Alex Anas; Sung Jick Eum,,38.0,,,journal-article,,,doi_10_1016_0094_1190_84_90024_x,hedonicanalysisofahousingmarketindisequilibrium,anas,10.1016/0094-1190(84)90024-x,hedonicanalysisofahousingmarketindisequilibrium|1984,1,1000,2,apartment rent hedonic || hedonic housing prices,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",65c899cce84e99c0088a6d325c72650cdc3034ff,10.1111/j.1468-2257.2006.00332.x,,Externality Effects on Residential Property Values: The Example of Noise Disamenities,2006.0,,D. E. Clark,,38.0,,False,,Economics,2006-09-01,65c899cce84e99c0088a6d325c72650cdc3034ff,externalityeffectsonresidentialpropertyvaluestheexampleofnoisedisamenities,clark,10.1111/j.1468-2257.2006.00332.x,externalityeffectsonresidentialpropertyvaluestheexampleofnoisedisamenities|2006,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",eb4a6bf84eada4b6f16faea99b2c9b338120e625,10.1080/09654313.2012.673569,,Spatial Determinants of Housing Price Values in Istanbul,2012.0,,T. K. Koramaz; V. Dokmeci,,38.0,,False,,Economics,2012-07-01,eb4a6bf84eada4b6f16faea99b2c9b338120e625,spatialdeterminantsofhousingpricevaluesinistanbul,koramaz,10.1080/09654313.2012.673569,spatialdeterminantsofhousingpricevaluesinistanbul|2012,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e9a01934bb6af48f72ed97e6d6cebc3c4f8ebd62,10.1108/13664381211211046,,Spatial variation as a determinant of house price: Incorporating a geographically weighted regression approach within the Belfast housing market,2012.0,,M. McCord; P. Davis; M. Haran; S. McGreal; David McIlhatton,,38.0,,False,Review,Economics,2012-04-13,e9a01934bb6af48f72ed97e6d6cebc3c4f8ebd62,spatialvariationasadeterminantofhousepriceincorporatingageographicallyweightedregressionapproachwithinthebelfasthousingmarket,mccord,10.1108/13664381211211046,spatialvariationasadeterminantofhousepriceincorporatingageographicallyweightedregressionapproachwithinthebelfasthousingmarket|2012,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || residential property price determinants || spatial hedonic model housing",0,1,2,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1467-9787.1980.tb00661.x,,CROSS‐SECTIONAL VARIATION IN THE HEDONIC RELATIONSHIP FOR URBAN HOUSING MARKETS*,1980.0,Journal of Regional Science,Richard V. Butler,,37.0,,,journal-article,,,doi_10_1111_j_1467_9787_1980_tb00661_x,crosssectionalvariationinthehedonicrelationshipforurbanhousingmarkets,butler,10.1111/j.1467-9787.1980.tb00661.x,crosssectionalvariationinthehedonicrelationshipforurbanhousingmarkets|1980,1,1000,1,apartment rent hedonic,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0094-1190(88)90049-6,,Estimating willingness to pay for housing characteristics: An application of the Ellickson bid-rent model,1988.0,Journal of Urban Economics,David J. Gross,,37.0,,,journal-article,,,doi_10_1016_0094_1190_88_90049_6,estimatingwillingnesstopayforhousingcharacteristicsanapplicationoftheellicksonbidrentmodel,gross,10.1016/0094-1190(88)90049-6,estimatingwillingnesstopayforhousingcharacteristicsanapplicationoftheellicksonbidrentmodel|1988,1,1000,5,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic housing prices || housing characteristics prices hedonic || willingness to pay housing attributes hedonic",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d79aff3af45de5fe5fb16fc423114a546ba2bfd5,10.1016/j.econmod.2021.03.015,,A spatial difference-in-differences approach to evaluate the impact of light rail transit on property values,2021.0,Economic Modelling,F. Qiu; Q. Tong,"Abstract How to evaluate the comprehensive costs and benefits of urban light trains is a critical issue. The difference-in-differences technique is widely used in the literature. However, the conventional approach assumes the project does not affect the control group, which might be violated in real life due to social and economic interactions. We refine the DID approach and relax the no-spillover assumption. Through an example of impacts of a new light rail transit on house prices in Edmonton, Canada, we illustrate how the approach can be used to calculate three different treatment effects. The results indicate that the train system has negative impacts on values of single-detached houses nearby the train stations, and the negative effects also spill over to properties outside the treatment zone. Our findings show that the direction and magnitude of project impacts depend on the city size, the design of the train system, and relevant neighborhood characteristics.",36.0,,False,,Economics,2021-06-01,d79aff3af45de5fe5fb16fc423114a546ba2bfd5,aspatialdifferenceindifferencesapproachtoevaluatetheimpactoflightrailtransitonpropertyvalues,qiu,10.1016/j.econmod.2021.03.015,aspatialdifferenceindifferencesapproachtoevaluatetheimpactoflightrailtransitonpropertyvalues|2021,1,1978,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1647e7d65abbd9a6325e660efa1358895cf21905,10.1257/app.20200467,,The Impact of School Facility Investments on Students and Homeowners: Evidence from Los Angeles,2022.0,American Economic Journal: Applied Economics,Julien Lafortune; David Schönholzer,"We study school facility investments using administrative records from Los Angeles. Exploiting quasi-random variation in the timing of new facility openings and using a residential assignment instrument, we find positive impacts on test scores, attendance, and house prices. Effects are not driven by changes in class size, peers, teachers, or principals, but some evidence points toward increased facility quality. We evaluate program efficiency using implied future earnings and housing capitalization. For each dollar spent, the program generated $1.62 in household value, with about 24 percent coming directly through test score gains and 76 percent from capitalization of non-test-score amenities. (JEL H75, H76, I21, I26, J31, R31, R53)",36.0,,False,,,2022-07-01,1647e7d65abbd9a6325e660efa1358895cf21905,theimpactofschoolfacilityinvestmentsonstudentsandhomeownersevidencefromlosangeles,lafortune,10.1257/app.20200467,theimpactofschoolfacilityinvestmentsonstudentsandhomeownersevidencefromlosangeles|2022,1,1742,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,4,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c9e1ea7fa46747a0d33a78c660fe5e7a2b44bbc5,10.1111/jors.12447,,House prices and school choice: Evidence from Chicago's magnet schools’ proximity lottery,2019.0,Journal of Regional Science,Leonardo Bonilla-Mejía; Esteban López; Daniel P. McMillen,"Studies of open school policies predict house prices to rise in areas that gain access to high‐quality schools. However, excess demand may limit access to high‐quality schools. We take advantage of changes in Chicago's schools’ admissions policies to test whether a higher probability of admission to magnet schools for students living within 1.5 miles leads to higher house prices. Results indicate that the 1997 and 2009 reforms increased house prices for homes within the 1.5 mile radius by about 4% and 12.6%, respectively. The higher probability of admission for black students after a consent decree was vacated in 2009 led to a significant increase in prices in predominantly African‐American areas on the south side.",36.0,,False,,Economics,2019-05-20,c9e1ea7fa46747a0d33a78c660fe5e7a2b44bbc5,housepricesandschoolchoiceevidencefromchicagosmagnetschoolsproximitylottery,bonilla-mejía,10.1111/jors.12447,housepricesandschoolchoiceevidencefromchicagosmagnetschoolsproximitylottery|2019,1,1724,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",915683aac8519f9307fa14065e1c8149e5381fa8,10.1068/a050357,,Measuring the Determinants of Relative House Prices,1973.0,,R. Wilkinson; C. Archer,,36.0,,False,JournalArticle,Economics,1973-06-01,915683aac8519f9307fa14065e1c8149e5381fa8,measuringthedeterminantsofrelativehouseprices,wilkinson,10.1068/a050357,measuringthedeterminantsofrelativehouseprices|1973,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/1540-6229.00435,,Explaining the Variability of Apartment Rents,1987.0,Real Estate Economics,Karl L. Guntermann; Stefan Norrbin,"The research reported here uses regression analysis to analyze rent variations in a sample of apartment data from the Phoenix metropolitan area. Many of the variables used in hedonic price studies of houses are found to be significant in explaining variations in apartment rent. There are differences between hedonic studies of houses and apartments particularly with respect to common area features or amenities. The analysis of various submarkets also produced interesting results. Various uses can be made of the results of this and similar studies by appraisers (market‐derived adjustments), property managers (setting rents) and feasibility analysts (the design of apartment projects).",35.0,,,journal-article,,,doi_10_1111_1540_6229_00435,explainingthevariabilityofapartmentrents,guntermann,10.1111/1540-6229.00435,explainingthevariabilityofapartmentrents|1987,1,1690,5,apartment prices hedonic || apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || real estate hedonic regression,2,1,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1467-9787.1990.tb00083.x,,"HEDONIC PRICES, IDENTIFICATION, AND THE DEMAND FOR PUBLIC SAFETY*",1990.0,Journal of Regional Science,David E. Clark; James C. Cosgrove,"ABSTRACT In this paper, we use a two‐stage intercity hedonic model to estimate household demand for public safety. This approach is shown to readily address the identification problem inherent in the hedonic model. Data from the 1980 Public Use Microdata Sample are used to estimate a willingness‐to‐pay function for the public‐safety good. Income is found to be the primary determinant of willingness to pay. Indeed, the influence of income outweighs the combined impact of family life‐cycle considerations.",35.0,,,journal-article,,,doi_10_1111_j_1467_9787_1990_tb00083_x,hedonicpricesidentificationandthedemandforpublicsafety,clark,10.1111/j.1467-9787.1990.tb00083.x,hedonicpricesidentificationandthedemandforpublicsafety|1990,1,1508,1,apartment rent hedonic,2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2592948154,10.4018/ijaeis.2017040105,,Hedonic Analysis of Housing Sales Prices with Semiparametric Methods,2017.0,International Journal of Agricultural and Environmental Information Systems,Vincenzo Del Giudice; Benedetto Manganelli; Pierfrancesco De Paola,"This study estimates a hedonic price function using a semiparametric regression based on Penalized Spline Smoothing, and compares the price prediction performance with conventional parametric models. The excellent results obtained show that the semiparametric models allow to obtain a significant improvement in the prediction of housing sales prices.",35.0,,False,article,,2017-03-10,https://openalex.org/W2592948154,hedonicanalysisofhousingsalespriceswithsemiparametricmethods,giudice,10.4018/ijaeis.2017040105,hedonicanalysisofhousingsalespriceswithsemiparametricmethods|2017,1,1351,1,hedonic housing prices,2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.2148/benv.35.4.563,,Floods and Residential Property Values: A Hedonic Price Analysis for the Netherlands,2009.0,Built Environment,Vanessa E Daniel; Raymond J.G.M Florax; Piet Rietveld,,35.0,,,journal-article,,,doi_10_2148_benv_35_4_563,floodsandresidentialpropertyvaluesahedonicpriceanalysisforthenetherlands,daniel,10.2148/benv.35.4.563,floodsandresidentialpropertyvaluesahedonicpriceanalysisforthenetherlands|2009,1,1000,2,apartment rent hedonic || hedonic house price,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1023/a:1017924503356,,House building and product differentiation: An hedonic price approach,2001.0,Journal of Housing and the Built Environment,Chris Leishman,,35.0,,,journal-article,,,doi_10_1023_a_1017924503356,housebuildingandproductdifferentiationanhedonicpriceapproach,leishman,10.1023/a:1017924503356,housebuildingandproductdifferentiationanhedonicpriceapproach|2001,1,1000,3,apartment rent hedonic || hedonic house price || hedonic housing prices,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0d7a2255ec63e59a48a54551f7551249c1f6a5f1,10.1177/0042098014547368,,Registered sex offenders and house prices: An hedonic analysis,2015.0,,S. B. Caudill; Ermanno Affuso; Ming Yang,,35.0,,False,JournalArticle,Psychology,2015-10-01,0d7a2255ec63e59a48a54551f7551249c1f6a5f1,registeredsexoffendersandhousepricesanhedonicanalysis,caudill,10.1177/0042098014547368,registeredsexoffendersandhousepricesanhedonicanalysis|2015,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",675f5c74687318016dea93671393315268d40e34,10.1016/j.forpol.2021.102420,,Urban green spaces and housing prices in developing countries: A Two-stage quantile spatial regression analysis,2021.0,,Manuel A. Zambrano-Monserrate; M. A. Ruano; Cristina Yoong-Parraga; C. Silva,"Abstract Urban Green Spaces (UGS) provide various environmental benefits to nature and are essential for people's overall well-being. UGS regulate temperature and humidity, absorb pollutants, and reduce soil erosion. Also, they improve the emotional and physical well-being of people and increase the environmental awareness of citizens. Due to its great importance, various studies have sought to economically quantify its value through the housing market. However, the households' heterogeneity and the vegetation index of the UGS have not been analyzed together. In this study, we show that a developing country's population values the UGS (economically). Still, that valuation depends on the housing submarket and the interaction between “distance” and vegetation index. For example, for small parks, the interaction between “distance” and vegetation index is significant only in the highest and lowest conditional quantiles. For medium and large parks, the interaction is significant only from the 50th and 30th percentiles, respectively. On the other hand, for small, medium, and large forests, the interactive variable is significant starting from the 50th, 70th, and 80th percentiles, respectively. We, also make recommendations for public policy.",34.0,,False,,Geography,2021-04-01,675f5c74687318016dea93671393315268d40e34,urbangreenspacesandhousingpricesindevelopingcountriesatwostagequantilespatialregressionanalysis,zambrano-monserrate,10.1016/j.forpol.2021.102420,urbangreenspacesandhousingpricesindevelopingcountriesatwostagequantilespatialregressionanalysis|2021,1,2255,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",07b8b25f7b3d73d4b16980b495c5e8bc7ea0a08f,10.1080/14445921.2015.1058031,,Determinants of house prices in Seoul: A quantile regression approach,2015.0,,Heeho Kim; Sae-Won Park; Sunhae Lee; Xingqun Xue,,34.0,,False,,Economics,2015-05-04,07b8b25f7b3d73d4b16980b495c5e8bc7ea0a08f,determinantsofhousepricesinseoulaquantileregressionapproach,kim,10.1080/14445921.2015.1058031,determinantsofhousepricesinseoulaquantileregressionapproach|2015,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || house prices proximity hedonic || housing characteristics prices hedonic",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",eccbfc2a261f59024fbb2a7c9400041b513ba5b5,10.1016/j.scs.2019.101561,,Accessibility-based premiums and proximity-induced discounts stemming from bus rapid transit in China: Empirical evidence and policy implications,2019.0,Sustainable cities and society,Lin-Chuan Yang; K. W. Chau; Xiaoling Chu,"Abstract Bus rapid transit (BRT) is popular worldwide because it provides high-quality transit services at low costs. China is not an exception in this regard, and it tops the world rankings in BRT network expansion rate. Many studies conducted in the West have shown that BRT accessibility has a positive impact on nearby property prices (accessibility effect). Comparatively, few studies have been conducted in urban China. Moreover, there are even fewer empirical studies on the undesirable factors associated with proximity to the BRT corridor, such as noise, air pollution, and unpleasant visual scenes (proximity effect). Based on 16,165 housing samples in Xiamen (China), we estimated a set of, non-spatial and spatial, hedonic pricing models to simultaneously investigate the accessibility and proximity effects of BRT and tested whether or not accessibility-based premiums and proximity-induced discounts coexist in the housing market. Empirical results of this study suggest that: (1) accessibility to BRT positively affects nearby property prices; (2) proximity to the BRT corridor negatively affects property prices; and (3) accessibility to BRT has a stronger positive impact on property prices in peripheral areas than in central areas. Though the first outcome has been well recognized, the other two have rarely been studied in existing literature, thereby representing the potential contributions of this study. Finally, we proposed policy implications from these results.",33.0,,False,,Business,2019-07-01,eccbfc2a261f59024fbb2a7c9400041b513ba5b5,accessibilitybasedpremiumsandproximityinduceddiscountsstemmingfrombusrapidtransitinchinaempiricalevidenceandpolicyimplications,yang,10.1016/j.scs.2019.101561,accessibilitybasedpremiumsandproximityinduceddiscountsstemmingfrombusrapidtransitinchinaempiricalevidenceandpolicyimplications|2019,1,2489,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,1,1,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9c71480b4307bcf9a70c22b2d0be8cfb0147a81e,10.1080/13683500.2019.1711027,,Airbnb and VRBO: the impact of short-term tourist rentals on residential property pricing,2020.0,Current Issues in Tourism,D. Wyman; Chris Mothorpe; B. McLeod,"ABSTRACT The contemporary phenomenon of Airbnb, VRBO and peer-to-peer home sharing markets has generated considerable controversy regarding positive and negative community impacts. Our study examines the economic motives and costs of short-term rental properties. Using home sales data from the City of Isle of Palms, SC, we find short-term rental properties sell at a price premium relative to long-term rentals and owner-occupied properties providing confirmatory evidence for why residential property owners select into short-term residential property operation. We also find no pricing impact associated with proximity to short-term rental properties indicating homebuyers are not concerned with the perceived community nuisances. The positive economic return accruing to short-term rental properties suggests the short-term rental marketplace will continue to develop as a strategic competitor to traditional tourist lodging operators.",33.0,,False,,Business,2020-01-29,9c71480b4307bcf9a70c22b2d0be8cfb0147a81e,airbnbandvrbotheimpactofshorttermtouristrentalsonresidentialpropertypricing,wyman,10.1080/13683500.2019.1711027,airbnbandvrbotheimpactofshorttermtouristrentalsonresidentialpropertypricing|2020,1,1940,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8a8d2017d0b92a9ee363c4d5bd1da1d781f9babd,10.1016/j.landusepol.2018.09.011,,Impact of an urban growth boundary across the entire house price spectrum: The two-stage quantile spatial regression approach,2019.0,,S. Mathur,,33.0,,False,,Economics,,8a8d2017d0b92a9ee363c4d5bd1da1d781f9babd,impactofanurbangrowthboundaryacrosstheentirehousepricespectrumthetwostagequantilespatialregressionapproach,mathur,10.1016/j.landusepol.2018.09.011,impactofanurbangrowthboundaryacrosstheentirehousepricespectrumthetwostagequantilespatialregressionapproach|2019,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",07ae3775ca1d622a731895ab8aa1409a97810a95,10.1016/j.regsciurbeco.2019.02.001,,Seismic risk and house prices: Evidence from earthquake fault zoning,2019.0,Regional Science and Urban Economics,Ruchi Singh,"In 1972, the Alquist-Priolo Zoning Act provided for the publication of earthquake fault maps in California. I exploit revisions in these official maps over time to estimate the rate of capitalization of seismic risk into property values using a difference-in-differences framework. Using geographically consistent data from 1970 to 2010 at the census tract level, I find that on average property values decline by 6.6 percent after the delineation of the fault zone, while rents decline by around 3.3 percent. I also examine the risk gradient and heterogeneity in willingness to pay using individual sales transactions, assessors' records, and publicly available mortgage data for the City of Los Angeles. The analysis of micro transactions data from 1997 to 2016 reveals that, on average, house prices increase by 1.8 percent for a one-mile increase in distance from the fault zone. The evidence also indicates that holding income and gender constant, Blacks and Hispanics are less willing than Whites to trade other forms of consumption to avoid earthquake risk.",32.0,,False,,Geography,2019-03-01,07ae3775ca1d622a731895ab8aa1409a97810a95,seismicriskandhousepricesevidencefromearthquakefaultzoning,singh,10.1016/j.regsciurbeco.2019.02.001,seismicriskandhousepricesevidencefromearthquakefaultzoning|2019,1,2064,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",40a8f9670773d4621da4506ed892504f82a9eaf6,10.1016/j.retrec.2018.07.006,,The impact of metro accessibility on residential property values: An empirical analysis,2018.0,Research on Transport Economics,Li Zheng,"Abstract This paper investigates the influence of metro accessibility on Xi'an residential property values. The key finding is that proximity to metro stations tends to have a positive but nonlinear effect. Within two-kilometre road network distances from metro stations, property value uplift is lowest in areas immediately adjacent to metro stations, explained by negative externalities around metro stations outweighing accessibility gains. The highest effect is revealed at distances between 300 and 1200 metres away from stations. Other significant influences include the distance to the central business district, the size of the property, the age of the property, interior decoration, residential building type and geographical location. The evidence suggests that the catchment area size should be adjusted according to access mode shares. Policy implications are also provided for potential future decision making.",32.0,,False,,Economics,2018-10-01,40a8f9670773d4621da4506ed892504f82a9eaf6,theimpactofmetroaccessibilityonresidentialpropertyvaluesanempiricalanalysis,zheng,10.1016/j.retrec.2018.07.006,theimpactofmetroaccessibilityonresidentialpropertyvaluesanempiricalanalysis|2018,1,1923,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.2307/3146080,,The Impact of Rent Control on Tenure Discounts and Residential Mobility,1982.0,Land Economics,W. A. V. Clark; Allan D. Heskin,,32.0,,,journal-article,,,doi_10_2307_3146080,theimpactofrentcontrolontenurediscountsandresidentialmobility,clark,10.2307/3146080,theimpactofrentcontrolontenurediscountsandresidentialmobility|1982,1,1000,1,apartment rent hedonic,0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,69c865e882cfde6eb8748b0af6993e4c4b6321df,10.1080/08941920601171998,,Influence of Natural Amenities on Residential Property Values in a Rural Setting,2007.0,,E. White; L. Leefers,,32.0,,False,,Economics,2007-06-15,69c865e882cfde6eb8748b0af6993e4c4b6321df,influenceofnaturalamenitiesonresidentialpropertyvaluesinaruralsetting,white,10.1080/08941920601171998,influenceofnaturalamenitiesonresidentialpropertyvaluesinaruralsetting|2007,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic price model amenities residential",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,af10017f963f97c8eac5cd0564ba6fae460f64f9,10.1108/ijhma-10-2016-0072,,Location and neighbourhood conditions for housing choice and its rental value,2017.0,,U. De; Vitsosie Vupru,"Purpose The purpose of the study is to understand the relative impacts of structural characteristics of house, its locational attributes and neighbourhood characteristics on the choice of house and the rent paid by the individual tenants. The micro level study helps in understanding the issues of urban housing and help in policy formulation. Design/methodology/approach This paper tried to identify the socio-economic, locational and neighbourhood factors that influence tenant households in determining their residential choices in an urban area of North-east India. Also, the extent of impacts of those characteristics on the monthly rent for housing is analysed. The analysis is based on the primary data collected from the sample residents of Dimapur Town in Nagaland. The sample units are selected by cluster sampling technique from all the wards. Regression technique is used under hedonic pricing technique to examine the impacts of various potential factors on the rent. Findings The analysis reveals that family size, income, education of the head of family, water availability, security, convenience to access workplace, road conditions, etc. have significant positive impacts on the monthly rent. However, the impacts of some locational and neighbourhood variables vary across social and economic groups. Research limitations/implications Time series data on the growth of house price are not available, so that the authors could examine the escalation of house price and rising scarcity of houses in the selected town. Practical implications Rental housing accounts for a significant percentage of housing in many urban centres. It is particularly important for the migrants and urban poor for whom it is the only source of accommodation. Location choice depending on capability, availability, requirement and neighbourhood conditions are integral parts of selection of accommodation in a city. Hence the present study has its relevant practical implications. Social implications Housing choice in a hilly urban area like Dimapur assumes a special significance for the presence of heterogeneous ethnic, social and cultural groups with majority of Tribal inhabitants with varied requirements and lifestyle. Originality/value No study on housing choice or pricing of residential accommodation has been done in North-East India. Here along with the socio-economic characteristics of the individuals, structural characteristics of house, neighbourhood and locational characteristics are used simultaneously to find out the impact of various factors on the total prices through hedonic pricing method.",31.0,,False,,Economics,2017-07-17,af10017f963f97c8eac5cd0564ba6fae460f64f9,locationandneighbourhoodconditionsforhousingchoiceanditsrentalvalue,de,10.1108/ijhma-10-2016-0072,locationandneighbourhoodconditionsforhousingchoiceanditsrentalvalue|2017,1,3727,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || rent hedonic regression housing",2,2,5,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,5848a13e3b376c7b4886f9e1267454f2216aa9cd,10.1177/00420980231208560,,New metro and housing price and rent premiums: A natural experiment in China,2023.0,Urban studies,Dongsheng He; Gui-Bo Sun; Ling-Yu Li; Chris Webster,"Causal evidence of housing premiums of new metro lines is indispensable for financing and governing infrastructure investments. Previous studies have investigated the housing effects of urban rail transit with varying methods, while causality remains unsettled. This study used a natural experiment to estimate the causal effects of the new metro interventions on housing premiums in Shenzhen, China. We used metro planning knowledge, reasoning on pursuits in land finance and engineering efficiency to verify the as-if randomness of the treatment–control group assignment in the natural experiment to reinforce the power of causal inference. We applied hedonic difference-in-difference (DID) models to estimate the average treatment effects based on the longitudinal housing price and rent data. We found that housing rents increased significantly and consistently after the metro entered operation, but the price premium varied. In addition, the rent premiums around new metro lines showed a price gradient over the distance to stations. Our findings provide scientific evidence for designing value capture mechanisms (e.g. value-added property tax and rent revenue) to recover metro investment costs in China.",31.0,,False,JournalArticle,,2023-12-07,5848a13e3b376c7b4886f9e1267454f2216aa9cd,newmetroandhousingpriceandrentpremiumsanaturalexperimentinchina,he,10.1177/00420980231208560,newmetroandhousingpriceandrentpremiumsanaturalexperimentinchina|2023,1,2212,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,2,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,cbbbfe27d3d53c6d8b11cd72d705360f6429c554,10.1108/jfm-05-2013-0027,,Hedonic values of physical infrastructure in house rentals,2014.0,,F. Famuyiwa; G. Babawale,,31.0,,False,Review,Economics,2014-08-21,cbbbfe27d3d53c6d8b11cd72d705360f6429c554,hedonicvaluesofphysicalinfrastructureinhouserentals,famuyiwa,10.1108/jfm-05-2013-0027,hedonicvaluesofphysicalinfrastructureinhouserentals|2014,1,1000,1,hedonic house price,2,1,2,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/1540-6229.00541,,An Empirical Analysis of Hedonic Regression and Grid‐Adjustment Techniques in Real Estate Appraisal,1991.0,Real Estate Economics,Han‐Bin Kang; Alan K. Reichert,"Multiple regression analysis has become increasingly popular when appraising residential properties for tax purposes. Alternatively, most fee appraisers and real estate brokers use the traditional sales comparison approach. This study combines the two techniques and uses multiple regression to generate the adjustment coefficients used in the grid adjustment method. The study compares the combined grid‐regression method with ordinary regression and defines the market conditions under which each method is likely to be more effective. The grid‐regression method is found to be more accurate for relatively homogeneous housing markets, and the multiplicative percentage adjustment method (MPAM) the preferred approach.",31.0,,,journal-article,,,doi_10_1111_1540_6229_00541,anempiricalanalysisofhedonicregressionandgridadjustmenttechniquesinrealestateappraisal,kang,10.1111/1540-6229.00541,anempiricalanalysisofhedonicregressionandgridadjustmenttechniquesinrealestateappraisal|1991,1,1720,3,apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression,2,0,2,1,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1080/00420989220080601,,"Hedonic Prices and the Demand for Housing Attributes in a Third World City: The Case of Ibadan, Nigeria",1992.0,Urban Studies,Ben C. Arimah,"This paper estimates the demand functions for a set of housing attributes for the city of Ibadan, Nigeria, using Rosen's two-step estimation procedure. The empirical results reveal that the most important determinants of the demand for housing attributes are: income, price of the attribute in question, household size and the occupational status of the head of household. The paper further indicates that the demand for housing attributes is inelastic as all estimated elasticities are below unity.",31.0,,,journal-article,,,doi_10_1080_00420989220080601,hedonicpricesandthedemandforhousingattributesinathirdworldcitythecaseofibadannigeria,arimah,10.1080/00420989220080601,hedonicpricesandthedemandforhousingattributesinathirdworldcitythecaseofibadannigeria|1992,1,1499,2,apartment rent hedonic || hedonic housing prices,2,0,2,2,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f551b1ea9b4f3dbf7359755fbb74d57e2bc1d550,10.1016/j.jfe.2016.09.001,,The impact of agricultural conservation easement on nearby house prices: Incorporating spatial autocorrelation and spatial heterogeneity,2016.0,,James J. Yoo; R. Ready,,31.0,,False,,Geography,2016-12-01,f551b1ea9b4f3dbf7359755fbb74d57e2bc1d550,theimpactofagriculturalconservationeasementonnearbyhousepricesincorporatingspatialautocorrelationandspatialheterogeneity,yoo,10.1016/j.jfe.2016.09.001,theimpactofagriculturalconservationeasementonnearbyhousepricesincorporatingspatialautocorrelationandspatialheterogeneity|2016,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/1467-8470.00041,,The Value of an Ocean View: an Example of Hedonic Property Amenity Valuation,1998.0,Australian Geographical Studies,Rob Fraser; Geoff Spencer,"This paper presents a method of quantifying variation in quality in an amenity as a component of an hedonic property amenity valuation. The paper considers the residential land amenity of an ocean view, a situation where there is typically substantial variation in the quality of the view between sites. The method is effectively a scoring system based on three sub‐characteristics of the ocean view. It is illustrated by application to a coastal housing subdivision in Western Australia. The results provide an estimate of the increase in the sale price of a site attributable to the quality of the site’s ocean view. The method has a potential role in coastal land use management decisions.",30.0,,,journal-article,,,doi_10_1111_1467_8470_00041,thevalueofanoceanviewanexampleofhedonicpropertyamenityvaluation,fraser,10.1111/1467-8470.00041,thevalueofanoceanviewanexampleofhedonicpropertyamenityvaluation|1998,1,1692,4,apartment rent hedonic || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic,2,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,implicit prices housing attributes,,10.1080/0042098975538,,The Contribution of Environmental Amenities to Low-income Housing: A Comparative Study of Bangkok and Jakarta,1997.0,Urban Studies,Randall Crane; Amrita Daniere; Stacy Harwood,Central and local governments and their creditors are increasingly interested in cost recovery for public services. These strategies have two aims: increasing revenues and making a better connection between benefits received and consumer bills. This paper estimates a hedonic model for household-level data in a rare contrast of slums in two Asian mega-cities to provide comparative information about how the poor value environmental amenities and basic infrastructure access. The results suggest that slum housing prices do reflect differentials in public service access and that rough estimates of the value of access can be cheaply and usefully obtained for planning purposes.,30.0,,,journal-article,,,doi_10_1080_0042098975538,thecontributionofenvironmentalamenitiestolowincomehousingacomparativestudyofbangkokandjakarta,crane,10.1080/0042098975538,thecontributionofenvironmentalamenitiestolowincomehousingacomparativestudyofbangkokandjakarta|1997,1,1679,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || implicit prices housing attributes",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1080/00420988920080551,,"A Hedonic Index Model: The Housing Market of Jos, Nigeria",1989.0,Urban Studies,Isaac F. Megbolugbe,"This paper demonstrates the usefulness of the hedonic index approach to the study of housing markets in developing countries. The study broadens the geographical scope of the various economic studies of these markets. The current practice of extrapolating empirical findings from studies of housing markets in Latin America and Asia to the less studied markets of African cities is improved on by incorporating direct evidence on housing market conditions from African case studies. This paper uses data from Jos, Nigeria to provide empirical evidence on market parameters describing the hedonic relationships for housing. For the Jos sample, variables measuring the quality of housing structure and of public services in various neighbourhoods are the local hedonic structure descriptors. Some uses of housing hedonic index numbers for housing market policy analysis are presented.",30.0,,,journal-article,,,doi_10_1080_00420988920080551,ahedonicindexmodelthehousingmarketofjosnigeria,megbolugbe,10.1080/00420988920080551,ahedonicindexmodelthehousingmarketofjosnigeria|1989,1,1882,1,apartment rent hedonic,2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.2307/135026,,Price Effects of Rent Control on Controlled and Uncontrolled Rental Housing in Toronto: A Hedonic Index Approach,1985.0,The Canadian Journal of Economics,George Fallis; Lawrence B. Smith,,29.0,,,journal-article,,,doi_10_2307_135026,priceeffectsofrentcontroloncontrolledanduncontrolledrentalhousingintorontoahedonicindexapproach,fallis,10.2307/135026,priceeffectsofrentcontroloncontrolledanduncontrolledrentalhousingintorontoahedonicindexapproach|1985,1,1000,3,apartment rent hedonic || hedonic housing prices || housing characteristics prices hedonic,2,1,2,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",dd4c845a6fa1712ce6f79824837b5f6b8278689d,10.1016/j.landusepol.2020.104828,,Impact of transit stations on house prices across entire price spectrum: A quantile regression approach,2020.0,,S. Mathur,"Abstract This study provides evidence on the impact of a heavy-rail-based transit station on house prices by employing quantile regression method on a dataset that inventories sales transactions of single-family houses during the January 2000–April 2018 period, and within eight kilometers (five miles) of the Warm Springs station of the San Francisco Bay Area Rapid Transit system. The station is located in Fremont, CA. The results show that a) the station increased house prices across the entire price spectrum, and b) the price increase began more than ten years before the rail service commenced. On a larger note, this study should strengthen efforts to a) provide or extend heavy-rail-based rapid transit systems in areas of high need and b) explore the use of value capture tools to fund transit.",29.0,,False,,Environmental Science,2020-12-01,dd4c845a6fa1712ce6f79824837b5f6b8278689d,impactoftransitstationsonhousepricesacrossentirepricespectrumaquantileregressionapproach,mathur,10.1016/j.landusepol.2020.104828,impactoftransitstationsonhousepricesacrossentirepricespectrumaquantileregressionapproach|2020,1,1805,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",17abd6845dac273c6245231fc2bbaf394108eaf5,10.1146/annurev-resource-091912-151759,,What Can We Learn from Hedonic Models When Housing Markets Are Dominated by Foreclosures,2013.0,,N. E. Coulson; Jeffrey E. Zabel,,29.0,,False,,Economics,2013-10-07,17abd6845dac273c6245231fc2bbaf394108eaf5,whatcanwelearnfromhedonicmodelswhenhousingmarketsaredominatedbyforeclosures,coulson,10.1146/annurev-resource-091912-151759,whatcanwelearnfromhedonicmodelswhenhousingmarketsaredominatedbyforeclosures|2013,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6b688875a221680f7ac6d3cabf04bbfc9b04a866,10.1016/j.ufug.2019.01.014,,"Urban trees, house price, and redevelopment pressure in Tampa, Florida",2019.0,Urban Forestry & Urban Greening,Geoffrey H. Donovan; Shawn M. Landry; C. Winter,"Abstract We examined the relationship between urban trees and the sales price of single-family homes in Tampa, Florida. We chose Tampa, because the city is facing major redevelopment pressure that may impact the association between trees and house price. In particular, a frequently voiced view in Tampa’s development community is that trees adversely affect the value of houses that are being sold for redevelopment. We estimated hedonic models of sales price controlling for house and neighborhood characteristics and correcting for spatial autocorrelation (n = 1,924). We found that trees within 152m (500 feet) of a house’s lot were significantly associated with higher sales prices. Specifically, a 1-percentage point increase in tree-canopy cover was associated with a total increase in sales price of $9,271 to $9,836 (results were largely insensitive to correction for spatial autocorrelation). Our results demonstrate that, even in a city facing major redevelopment pressure, trees are associated with higher sales prices.",28.0,,False,,Business,2019-02-01,6b688875a221680f7ac6d3cabf04bbfc9b04a866,urbantreeshousepriceandredevelopmentpressureintampaflorida,donovan,10.1016/j.ufug.2019.01.014,urbantreeshousepriceandredevelopmentpressureintampaflorida|2019,1,2031,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,2,5,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,fc2aa1e144d9381c67f04862beb40fca4fbcf19c,10.1177/0739456x18787011,,The Impact of Transit-oriented Development on Housing Value Resilience: Evidence from the City of Atlanta,2018.0,Journal of planning education and research,Wenwen Zhang; Fangru Wang; Camille Barchers; Yong-Su Lee,"This research examines whether transit-oriented development (TOD) contributes to housing value resilience in Atlanta, Georgia. We developed two spatial hedonic models using property sale records to examine whether being located in TOD areas contributes to the resilience of housing prices during and after the Great Recession, compared to being located in transit-adjacent development areas. Our results suggest that there is a growing demand for TOD properties in recent years, which contributes to maintaining and faster regaining property values in TOD areas. Our results support future investment in TOD areas and also suggest challenges to incorporating affordable housing in TOD designs.",28.0,,False,JournalArticle,Economics;Business,2018-07-17,fc2aa1e144d9381c67f04862beb40fca4fbcf19c,theimpactoftransitorienteddevelopmentonhousingvalueresilienceevidencefromthecityofatlanta,zhang,10.1177/0739456x18787011,theimpactoftransitorienteddevelopmentonhousingvalueresilienceevidencefromthecityofatlanta|2018,1,1693,3,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,3,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1029/2002wr001799,,Estimating the benefits of maintaining adequate lake levels to homeowners using the hedonic property method,2003.0,Water Resources Research,John Loomis; Marvin Feldman,"The hedonic property method was used to estimate residents' economic benefits from maintaining high and stable lake levels at Lake Almanor, California. Nearly a thousand property transactions over a 14‐year period from 1987 to 2001 were analyzed. The linear hedonic property regression explained more than 60% of the variation in‐house prices. Property prices were negatively and significantly related to the number of linear feet of exposed lake shoreline. Each additional one foot of exposed shoreline reduces the property price by $108–$119. A view of the lake added nearly $31,000 to house prices, while lakefront properties sold for $209,000 more than non‐lake front properties.",28.0,,,journal-article,,,doi_10_1029_2002wr001799,estimatingthebenefitsofmaintainingadequatelakelevelstohomeownersusingthehedonicpropertymethod,loomis,10.1029/2002wr001799,estimatingthebenefitsofmaintainingadequatelakelevelstohomeownersusingthehedonicpropertymethod|2003,1,1683,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic house price",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",373f1ae3e5c3d29bb1ce5b49942c9462e64dc45f,10.5750/jgbe.v1i2.512,,The Impact of Casino Gambling on Housing Markets: A Hedonic Approach,2013.0,,M. Wenz,,28.0,,False,,Economics,2013-01-02,373f1ae3e5c3d29bb1ce5b49942c9462e64dc45f,theimpactofcasinogamblingonhousingmarketsahedonicapproach,wenz,10.5750/jgbe.v1i2.512,theimpactofcasinogamblingonhousingmarketsahedonicapproach|2013,1,1000,2,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic housing prices",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,rent hedonic regression housing,23a44dc59a57633db481bb6c2791607cd9db4d67,10.1108/ijhma-09-2012-0043,,Understanding rental prices in the UK: a comparative application of spatial modelling approaches,2014.0,,M. McCord; P. Davis; M. Haran; David McIlhatton; John McCord,,28.0,,False,,Economics,2014-08-03,23a44dc59a57633db481bb6c2791607cd9db4d67,understandingrentalpricesintheukacomparativeapplicationofspatialmodellingapproaches,mccord,10.1108/ijhma-09-2012-0043,understandingrentalpricesintheukacomparativeapplicationofspatialmodellingapproaches|2014,1,1000,5,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || housing characteristics prices hedonic || rent hedonic regression housing || spatial hedonic model housing",0,2,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",8ae979208845a8022dcbe8b230598e858795e69a,10.1016/j.landurbplan.2020.104016,,Who demands peri-urban nature? A second stage hedonic house price estimation of household’s preference for peri-urban nature,2021.0,Landscape and Urban Planning,C. U. Jensen; T. Panduro; T. Lundhede; Kathrine von Graevenitz; B. Thorsen,"Abstract We analyze housing markets in a suburb north of the Danish capital Copenhagen. There the concentration of affluent households decreases rapidly with distance to nature. This indicates systematic differences in preferences that are highly correlated with demographics. We assess if and to what extent this is the case by conducting a second-stage hedonic house price study, where we recover household-specific preferences for the availability of peri-urban nature. Preference parameters are identified locally through restrictions on household utility functions. We assess the relationship between demographic factors and household Willingness To Pay for nature availability across the Willingness To Pay distribution. The results of the analysis show that households paid on average 968 EUR per year for the level of peri-urban nature they have available from their home. In extreme cases, some households paid more than seven times the average amount. Willingness To Pay for peri-urban nature increases with income and wealth across the entire population. However, an increase in income will increase Willingness To Pay more for households at the lower end of the willingness to pay distribution, compared to the effect at the high end of the Willingness To Pay distribution. Furthermore, increases in education levels are related to higher willingness to pay in the middle of the willingness to pay distribution, whereas further education has less impact on households with high willingness to pay. Single parent status, car ownership, and other factors also affect Willingness To Pay for nature availability. Our study contributes to the discussion of the distributional aspects of environmental policies with results based on a revealed-preference method where households face a real and binding budget constraint. Our results show that the socio-economic distribution of changes in amenity values is a relevant factor to consider when evaluating policies that affect the provision of nature close to urban areas.",27.0,,False,,Economics,2021-03-01,8ae979208845a8022dcbe8b230598e858795e69a,whodemandsperiurbannatureasecondstagehedonichousepriceestimationofhouseholdspreferenceforperiurbannature,jensen,10.1016/j.landurbplan.2020.104016,whodemandsperiurbannatureasecondstagehedonichousepriceestimationofhouseholdspreferenceforperiurbannature|2021,1,3026,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices",2,2,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,b2316b1cfbeb148f129fe9d0b8d00bcb44f57a54,10.1016/j.cities.2019.01.015,,Market versus public provision of local goods: An analysis of amenity capitalization within the Metropolitan Region of Santiago de Chile,2019.0,Cities,Yasna Cortés; Víctor Iturra,"Abstract The liberalization of the Chilean economy since 1974 has generated important modifications on its urban development policies. However, little attention has been paid to estimate how urban amenities are capitalized in housing prices under this new scenario. In this paper, we explore this relationship through a hedonic pricing model and 27 urban amenities for the Chilean main urban agglomeration, the Metropolitan Region of Santiago (MR). We recognize that the capitalization of urban amenities into housing prices is spatially heterogeneous, therefore we study this relationship by using the geographically weighted regression (GWR). The main results suggest that urban amenities play an important role in determining housing prices with a significant spatial heterogeneity, where notably differences between public and private local services are found. Particularly, private services such as schools, shopping centers, healthcare centers or restaurants have positive capitalizations on housing prices. In contrast, public schools, public hospitals and public kindergartens have negative valuations in housing prices, suggesting that living closer to these services reduces housing values. Our results provide policy makers with valuable elements when designing urban policies in terms of the importance of heterogeneous valuations of urban amenities across neighborhoods, how to allocate resources more efficiently to foster the quality of life on new urban residents and the need of considering the economic segregation triggered by heterogeneous preferences to amenities across space.",27.0,,False,,Economics,2019-06-01,b2316b1cfbeb148f129fe9d0b8d00bcb44f57a54,marketversuspublicprovisionoflocalgoodsananalysisofamenitycapitalizationwithinthemetropolitanregionofsantiagodechile,cortés,10.1016/j.cities.2019.01.015,marketversuspublicprovisionoflocalgoodsananalysisofamenitycapitalizationwithinthemetropolitanregionofsantiagodechile|2019,1,2598,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,2,1,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W3136516155,10.1061/(asce)up.1943-5444.0000698,,"Determinants of Housing Prices in Dalian City, China: Empirical Study Based on Hedonic Price Model",2021.0,Journal of Urban Planning and Development,Huanhuan Luo; Shengchuan Zhao; Ronghan Yao,"Housing price is one of the most popular topics in urban studies. Among all the determinants of housing price, transportation accessibility is an essential factor that people often consider when purchasing a house. Using a semilogarithmic hedonic price model, this paper discusses the effects of structural attributes and accessibility of neighboring destinations on housing prices. The study area targets the core urban area of Dalian city, China. The data used in this study consist of sales prices of commercial apartments and houses and the geographic coordinates of corresponding residential areas. Estimation results of the hedonic price model show that structural attributes and accessibility to neighboring destinations significantly affect housing prices. The most influential factors to housing prices are the location of apartment or house, the nearby quality of higher education resources, and proximity to public transit service. These findings are significant for real estate developers, urban planners, and transportation planners to better understand the real estate market and to promote transit-oriented development.",27.0,,False,article,,2021-03-18,https://openalex.org/W3136516155,determinantsofhousingpricesindaliancitychinaempiricalstudybasedonhedonicpricemodel,luo,10.1061/(asce)up.1943-5444.0000698,determinantsofhousingpricesindaliancitychinaempiricalstudybasedonhedonicpricemodel|2021,1,2134,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,2,5,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,449b8af1e98d0318647429318b2d66506460aa91,10.1016/j.retrec.2020.100844,,"Subway boosts housing values, for whom: A quasi-experimental analysis",2020.0,,Chuan-Hao Tian; Ying Peng; Hai-Zhen Wen; W. Yue; Li Fang,"Abstract This study investigates the property price premium brought by the opening of a subway to illustrate temporal dynamics and heterogeneous mechanism of property value effects. The estimation of changes caused by subways on property value can aid in assessing the benefits of public transit investments well. On the basis of residential property transaction records in Hangzhou, China in 2009–2013, hedonic models in a difference-in-differences framework are applied to handle certain endogeneity problems of estimation by eliminating unobserved factors. Results show that treatment groups located within 1000 m of a subway have an average price increase of 444 yuan per m2 from the opening of Line1. High-cost houses constantly gain significant increment and their price premium regress during the research period. However, for their counterparts in low-cost communities, the insignificant price effects are negative for a short time, and then become positive. The generalized results are robust when subway radius is adjusted.",27.0,,False,,Economics,2020-04-21,449b8af1e98d0318647429318b2d66506460aa91,subwayboostshousingvaluesforwhomaquasiexperimentalanalysis,tian,10.1016/j.retrec.2020.100844,subwayboostshousingvaluesforwhomaquasiexperimentalanalysis|2020,1,2033,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices",2,3,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1dd8f9e39322403f1fa99f3aac44113709c6794e,10.1016/j.jeem.2022.102717,,National expenditures on local amenities,2022.0,Journal of Environmental Economics and Management,David S. Bieri; N. Kuminoff; Jaren C. Pope,"A significant fraction of GDP is generated by implicit expenditures on spatially varying nonmarket amenities such as climate, public goods, urban infrastructure, and pollution. People pay for these amenities indirectly, through spatial variation in housing prices, wages and property taxes. In this paper we construct a new database of 75 amenities, match it to location choices made by 5 million households, and estimate their amenity expenditures in a way that is consistent with principles of satellite accounting and fundamentals of spatial sorting behavior. We find that amenity expenditures were $562 billion in 2000—8% of all U.S. personal consumption expenditures.",27.0,,False,,Economics,2022-08-01,1dd8f9e39322403f1fa99f3aac44113709c6794e,nationalexpendituresonlocalamenities,bieri,10.1016/j.jeem.2022.102717,nationalexpendituresonlocalamenities|2022,1,1672,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,2,1,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",516bcc9addca455f8a1568c1056ae294bbc3bb69,10.1016/j.ecotra.2022.100272,,Exploring the nonlinear impact of air pollution on housing prices: A machine learning approach,2022.0,Economics of Transportation,Guojian Zou; Ziliang Lai; Ye Li; Xing-Hua Liu; Wenxiang Li,,27.0,,False,,,2022-09-01,516bcc9addca455f8a1568c1056ae294bbc3bb69,exploringthenonlinearimpactofairpollutiononhousingpricesamachinelearningapproach,zou,10.1016/j.ecotra.2022.100272,exploringthenonlinearimpactofairpollutiononhousingpricesamachinelearningapproach|2022,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,1 crossref,apartment rent hedonic,,10.1111/1468-2257.00066,,Measuring Amenity Benefits from Farmland: Hedonic Pricing vs. Contingent Valuation,1997.0,Growth and Change,RICHARD C. READY; MARK C. BERGER; GLENN C. BLOMQUIST,"ABSTRACT The amenity value to Kentucky residents from horse farm land was estimated using both the contingent valuation method and the hedonic pricing method. The hedonic pricing model included both the housing and labor markets. A value function estimated from dichotomous choice contingent valuation responses showed that the value of a change in the level of the horse farm amenity was sensitive to the size of the change, with no evidence of value that is independent of the size of the change. The two methods generated estimates of the external benefits from horse farm land that were within 20 percent of each other.",27.0,,,journal-article,,,doi_10_1111_1468_2257_00066,measuringamenitybenefitsfromfarmlandhedonicpricingvscontingentvaluation,ready,10.1111/1468-2257.00066,measuringamenitybenefitsfromfarmlandhedonicpricingvscontingentvaluation|1997,1,1623,2,apartment rent hedonic || hedonic housing prices,2,0,1,1,2,0,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,77f34967a32c096b6870564c6d4ee776b33afe76,10.1080/14445921.2019.1626543,,The price of a view: estimating the impact of view on house prices,2019.0,Pacific Rim Property Research Journal,A. S. Jayasekare; S. Herath; R. Wickramasuriya; P. Perez,"ABSTRACT Houses with desired aesthetic views have a price premium over similar houses without such views. This article raises the following questions in relation to view as a location-specific amenity: How can we calculate a reliable indicator for view using available information? Are dwellings with a view sold for a premium compared to otherwise similar properties? Using more than 5,000 house transactions in the Illawarra region in Australia, and applying hedonic price method, the study analyses the price effects of key aesthetic views. Due to unavailability of view as a housing characteristic within historical records, spatial analysis tools were used to estimate views for housing locations. The results confirm the significance of aesthetic views in explaining house prices. Beach view is the most important aesthetic view. An increase of 1% of beach view drives house prices up by nearly 2–3%. Significant positive contributions to prices are also evident from sea, conservation area and inland water views. In addition to the views, other location-specific attributes also influence house prices. Methods developed in this study to quantify the value of aesthetic views and location-specific characteristics associated with residential locations form an important contribution to urban planning and policy development.",26.0,,False,,Economics,2019-05-04,77f34967a32c096b6870564c6d4ee776b33afe76,thepriceofaviewestimatingtheimpactofviewonhouseprices,jayasekare,10.1080/14445921.2019.1626543,thepriceofaviewestimatingtheimpactofviewonhouseprices|2019,1,2332,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,1,5,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,f01c5cdbf2768b00564b2dea6af4603e5aa3f2c6,10.1016/j.regsciurbeco.2018.07.006,,Spatial interactive effects on housing prices in Shanghai and Beijing,2019.0,Regional Science and Urban Economics,Juncong Guo; X. Qu,"Abstract When connected through economic, financial, and policy shocks, cities can be considered to be neighbors in the social-economic sense. Therefore, a spatial correlation may exist in cities that are not geographically contiguous. The paper simultaneously studies the spatial correlation within each city and the spatial interactive effects among different cities on housing prices. We build a spatial autoregressive hedonic pricing model in a system of interrelated networks, which allows for multiple spatial interactive effects among housing units inside the same city and from other cities. For estimation, we propose a two-stage least squares (2SLS) method and the maximum likelihood estimation (MLE). Finite sample properties of these two methods are investigated in a Monte Carlo simulation. After applying these steps to March 2016 housing prices in Shanghai and Beijing, the empirical findings show that the spatial correlations within each city are significantly large, i.e., approximately 0.6–0.8, and spatial interactive effects between the two cities exist, although the magnitude is smaller, i.e., approximately 0.1–0.2.",26.0,,False,,Mathematics,2019-05-01,f01c5cdbf2768b00564b2dea6af4603e5aa3f2c6,spatialinteractiveeffectsonhousingpricesinshanghaiandbeijing,guo,10.1016/j.regsciurbeco.2018.07.006,spatialinteractiveeffectsonhousingpricesinshanghaiandbeijing|2019,1,2139,2,hedonic housing prices || spatial hedonic model housing,2,2,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f23c142b0745d160d7217c8d265c27c6e2e3579e,10.1061/(asce)up.1943-5444.0000458,,Capitalization of Urban Green Vegetation in a Housing Market with Poor Environmental Quality: Evidence from Beijing,2018.0,Journal of urban planning and development,Ying-Dan Mei; Xiao-Li Zhao; Lu Lin; Li Gao,AbstractUrban green vegetation provides amenity value for urban residents and improves the living environment. These changes in value can be reflected in the variation of house prices. This paper u...,26.0,,False,,Geography,2018-09-01,f23c142b0745d160d7217c8d265c27c6e2e3579e,capitalizationofurbangreenvegetationinahousingmarketwithpoorenvironmentalqualityevidencefrombeijing,mei,10.1061/(asce)up.1943-5444.0000458,capitalizationofurbangreenvegetationinahousingmarketwithpoorenvironmentalqualityevidencefrombeijing|2018,1,1200,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),19f0baff000054331262cd25e6b8352110d9f791,10.3141/2451-16,,Capitalization Effects of Rail Transit and Bus Rapid Transit on Residential Property Values in a Booming Economy,2014.0,,Liang Ma; R. Ye; H. Titheridge,,26.0,,False,JournalArticle,Business;Engineering,2014-01-01,19f0baff000054331262cd25e6b8352110d9f791,capitalizationeffectsofrailtransitandbusrapidtransitonresidentialpropertyvaluesinaboomingeconomy,ma,10.3141/2451-16,capitalizationeffectsofrailtransitandbusrapidtransitonresidentialpropertyvaluesinaboomingeconomy|2014,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",745746c9054e08a7daa0c52111e272463dbf91a8,10.1080/02723638.2015.1057397,,The modifiable areal unit problem in hedonic house-price models,2016.0,,Gunhak Lee; Daeheon Cho; Kamyoung Kim,,26.0,,False,,Mathematics,2016-02-17,745746c9054e08a7daa0c52111e272463dbf91a8,themodifiablearealunitprobleminhedonichousepricemodels,lee,10.1080/02723638.2015.1057397,themodifiablearealunitprobleminhedonichousepricemodels|2016,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",3eccc8181f1ca899ac3b2087dd76005869e5572e,10.1080/09599916.2017.1400575,,House price determinants in Athens: a spatial econometric approach,2017.0,,Marianthi Stamou; A. Mimis; Antonis Rovolis,,26.0,,False,,Economics,2017-10-02,3eccc8181f1ca899ac3b2087dd76005869e5572e,housepricedeterminantsinathensaspatialeconometricapproach,stamou,10.1080/09599916.2017.1400575,housepricedeterminantsinathensaspatialeconometricapproach|2017,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",98fc3cf59851eba5b1923758e4fad0e45895d23e,10.1080/00036849200000085,,Development of standardized indices for measuring house price inflation incorporating physical and locational characteristics,1992.0,,M. Fleming; J. Nellis,,26.0,,False,,Economics,1992-09-01,98fc3cf59851eba5b1923758e4fad0e45895d23e,developmentofstandardizedindicesformeasuringhousepriceinflationincorporatingphysicalandlocationalcharacteristics,fleming,10.1080/00036849200000085,developmentofstandardizedindicesformeasuringhousepriceinflationincorporatingphysicalandlocationalcharacteristics|1992,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",9fda34e5f379104dec7d9bd135e2fd792336625c,10.1016/j.retrec.2019.06.001,,Are low-end housing purchasers more willing to pay for access to basic public services? Evidence from China,2019.0,Research on Transport Economics,Lin-Chuan Yang; K. Chau; Xu Wang,"Abstract Previous studies suggest that generally, access to public services has a positive impact on housing price. However, most of them are silent on whether the impact varies across different housing submarkets. In some contexts like China, compared with high-end counterparts, low-end housing purchasers, generally, have lower income and thus lower car availability (and transportation mobility). As such, they may prefer and attach a higher value to living in the proximity of basic public services (which are essential to life, such as transit). This is likely to be applicable to contexts where purchasing and/or using cars are not fully affordable for low-income households. We use 22,586 second-hand housing data in Xiamen (China) to assess the differential price impacts of access to basic public services in low and high-end submarkets. Results from two-sample two-tailed t -tests and F -tests and hedonic modeling confirm several findings: (1) there are systematic differences between access to basic public services in low- and high-end properties; (2) the two housing submarkets do not follow the same pricing mechanism; (3) access to transit, commercial centers, shopping centers, and sports and cultural centers has positive and significant price impacts in the low-end housing submarket. Yet, this is not the case in the high-end counterpart; (4) the effect of access to basic public services on housing price varies across the two submarkets: typically, access to basic public services has a larger positive price impact on low-end properties than on high-end ones; (5) interestingly, access to shopping centers has a larger price impact in the high-end housing submarket than in the low-end. The findings of this study basically support our hypothesis and also benefit policy prescription. As such, practical implications of the findings are discussed finally. Notably, our argument is that low-end properties buyers are more willing to pay for access to “basic” public services rather than that to premium/high-quality services. By contrast, based on scattered evidence from this study and existing literature, we suspect, though definitely cannot conclude, that high-end properties buyers have a predilection for premium/high-quality amenities, such as natural beauty and prestigious schools.",25.0,,False,,Business,2019-06-14,9fda34e5f379104dec7d9bd135e2fd792336625c,arelowendhousingpurchasersmorewillingtopayforaccesstobasicpublicservicesevidencefromchina,yang,10.1016/j.retrec.2019.06.001,arelowendhousingpurchasersmorewillingtopayforaccesstobasicpublicservicesevidencefromchina|2019,1,3313,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e8141f49e8f72ae194a062730665b01aa1006400,10.1016/j.cities.2020.103080,,The impact of mixes of transportation options on residential property values: Synergistic effects of walkability,2021.0,,Kwangyul Choi; H. Park; James R. Dewald,"Abstract Calgary offers many public transportation options, including light rail transit (LRT), bus rapid transit (BRT), and local buses as well as facilities for non-motorized travelers. Yet the individual and synergistic effects of these mobility options on residential property values in Calgary have not been clearly understood. By using a multilevel hedonic modeling approach, we examine the independent and interactive economic impacts of different transportation options on Calgary's residential properties in 2017. Our findings suggest that proximity to LRT and better neighborhood walkability can appreciate residential property values, whereas proximity to major roads and bus service tend to depreciate the values, although the degree and significance vary by property type. When proximity to LRT is coupled with high walkability, more appreciation of property values is expected, particularly in the case of attached houses and apartment properties. Our results imply that urban planners and city officials should strive to create safe and pedestrian-friendly environments near LRT stations that could lead to more dynamic civic activities.",25.0,,False,,Business,2021-04-01,e8141f49e8f72ae194a062730665b01aa1006400,theimpactofmixesoftransportationoptionsonresidentialpropertyvaluessynergisticeffectsofwalkability,choi,10.1016/j.cities.2020.103080,theimpactofmixesoftransportationoptionsonresidentialpropertyvaluessynergisticeffectsofwalkability|2021,1,2152,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2,2,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,6664bea9b26a60249f7f27ac8a88f302c52a81c6,10.1080/08920753.2020.1732799,,Valuing Recreational Beaches: A Spatial Hedonic Pricing Approach,2020.0,,Jinwon Kim; Sukjoon Yoon; Eunjung Yang; B. Thapa,"Abstract Despite several studies that have estimated the economic value of beach proximity on housing values, previous linear hedonic pricing models (HPMs) have yet to explore the spatially heterogeneous beach premiums for housing prices. To address the issue, this study demonstrated the feasibility of spatial HPM (S-HPM) using geographically weighted regression (GWR) analysis of 152 properties in Jacksonville Beach, Florida. Specifically, this study (1) investigated the spatial associations between multiple beach proximity attributes and housing values, (2) explored local variations in modeling housing values, and (3) assessed whether GWR-based S-HPM outperformed previous linear HPM. Results indicated that the GWR-based S-HPM revealed spatially heterogeneous beach premiums for housing values, with improvements in model performance over the corresponding linear HPM. Findings of this study can contribute to understanding the local patterns of beach premiums for housing prices, ultimately providing guidelines for location-based beach access planning and management.",25.0,,False,,Geography,2020-03-03,6664bea9b26a60249f7f27ac8a88f302c52a81c6,valuingrecreationalbeachesaspatialhedonicpricingapproach,kim,10.1080/08920753.2020.1732799,valuingrecreationalbeachesaspatialhedonicpricingapproach|2020,1,2079,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,2,1,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1108/02637470110366202,,An application of the hedonic price model with uncertain attribute ‐ The case of the People’s Republic of China,2001.0,Property Management,Zan Yang,"The implicit prices of housing characteristics concerning the physical structure, location, environment and the risk of construction quality in China are examined in this paper. The hedonic price model with uncertainty of construction quality is adopted. Data is collected from the Beijing residential market. The results indicate that consumers have different preferences for choosing residences in the different parts of Beijing and the marginal price of public facilities is negative. The most important preference suggests households in Beijing are willing to pay additional expenditures to protect themselves from low construction quality. An obvious political implication of this study is that further residential reform should be accompanied by improved construction quality under legal supervision and by encouraging real estate developers to allocate units according to the preferences of households.",25.0,,,journal-article,,,doi_10_1108_02637470110366202,anapplicationofthehedonicpricemodelwithuncertainattributethecaseofthepeoplesrepublicofchina,yang,10.1108/02637470110366202,anapplicationofthehedonicpricemodelwithuncertainattributethecaseofthepeoplesrepublicofchina|2001,1,1909,8,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic || implicit prices housing attributes || willingness to pay housing attributes hedonic",2,1,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,1337ef0d44a7b275ff8e86d43738d53d7b895368,10.1061/(asce)up.1943-5444.0000651,,Spatial Autoregressive Analysis and Modeling of Housing Prices in City of Toronto,2021.0,,Yu Zhang; Da-Chuan Zhang; E. Miller,"Abstract Previous housing price studies based on hedonic price modeling have mainly focused on applying various factors, including built environment variables in the analysis, without establishing ...",25.0,,False,,Economics,2021-03-01,1337ef0d44a7b275ff8e86d43738d53d7b895368,spatialautoregressiveanalysisandmodelingofhousingpricesincityoftoronto,zhang,10.1061/(asce)up.1943-5444.0000651,spatialautoregressiveanalysisandmodelingofhousingpricesincityoftoronto|2021,1,1200,2,hedonic housing prices || spatial hedonic model housing,2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1007/s10584-006-3486-5,,Hedonic Pricing of Climate Change Impacts to Households in Great Britain,2006.0,Climatic Change,Katrin Rehdanz,,25.0,,,journal-article,,,doi_10_1007_s10584_006_3486_5,hedonicpricingofclimatechangeimpactstohouseholdsingreatbritain,rehdanz,10.1007/s10584-006-3486-5,hedonicpricingofclimatechangeimpactstohouseholdsingreatbritain|2006,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic house price",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",f6937e775386d05ff6ac41c049e145a1a3b9eb46,10.1016/j.trd.2020.102640,,Capitalisation of accessibility to dockless bike sharing in housing rentals: Evidence from Beijing,2021.0,,Si Qiao; Anthony Gar-On Yeh; Meng-Zhu Zhang,"Abstract Chinese cities have seen a rapid development of dockless bike sharing (DBS) in recent years. Numerous studies have examined the positive social effects of DBS, but a crucial question remains: if docklessness has successfully reworked shared bikes into a desired and valuable amenity, will unequal access to DBS affect housing rentals? The present study examines this question by considering the case of Beijing. A big data approach is employed to analyse the listed rents of 75,197 houses in Beijing and their attributes that affect housing rentals. Measurement of accessibility to DBS at the housing level is constructed from 3.2 million DBS trips generated over two weeks. Results of the hedonic model show that every one-point increase in the accessibility to DBS generates a premium worth 28.02 CNY in housing rental, thereby supporting the hypothesis that docklessness has turned DBS into a valuable amenity for renters. These findings suggest that although low- and middle-income workers benefit from the convenience of DBS in the short term, the benefits are gradually eroded and transferred to landlords through rental premiums. Unequal accessibility to DBS may induce gentrification that displaces or excludes low-income renters in certain neighbourhoods.",24.0,,False,,Business,,f6937e775386d05ff6ac41c049e145a1a3b9eb46,capitalisationofaccessibilitytodocklessbikesharinginhousingrentalsevidencefrombeijing,qiao,10.1016/j.trd.2020.102640,capitalisationofaccessibilitytodocklessbikesharinginhousingrentalsevidencefrombeijing|2021,1,2273,1,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,1,4,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,5980ecb9e6e20a6ad30c449feb6304985c7cc1e1,10.1177/0160017620918652,,Home Is Where the Internet Is? High-speed Internet’s Impact on Rural Housing Values,2020.0,,Kelsey L. Conley; Brian E. Whitacre,"The deployment of faster household Internet speeds enables new opportunities for entertainment, social interaction, and personal development, and many consider such access an essential component of everyday life. However, rural residents face lower availability, slower speeds and limited provider options, putting them at a disadvantage when compared to their urban counterparts. Connected rural households, especially those with higher speeds, may experience a premium on their home value. Data from the National Broadband Map, the Federal Communications Commission, and over 2,700 housing transactions from June 2011 to June 2017 are used to examine the impact of broadband availability on housing values in two rural Oklahoma counties via a hedonic price model. The results find no support for the existence of a broadband premium, and stress that differences across counties are crucial in assessing rural housing prices.",24.0,,False,JournalArticle,Business,2020-04-29,5980ecb9e6e20a6ad30c449feb6304985c7cc1e1,homeiswheretheinternetishighspeedinternetsimpactonruralhousingvalues,conley,10.1177/0160017620918652,homeiswheretheinternetishighspeedinternetsimpactonruralhousingvalues|2020,1,1926,1,hedonic housing prices,2,3,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3db01bac6d153608cb3a370bba9afc622ae6f889,10.1002/soej.12341,,Measuring Heterogeneous Price Effects for Home Acquisition Programs in At‐Risk Regions,2019.0,Southern Economic Journal,E. Frimpong; J. Kruse; Gregory Howard; R. Davidson; J. Trainor; L. Nozick,"Any entity offering flood insurance, whether it is private or government‐administered such as the National Flood Insurance Program (NFIP), faces the challenge of solvency. This is especially true for the NFIP, where homeowner affordability criteria limit the opportunity to charge fully risk‐based premiums. One solution is to remove the highest flood risk properties from the insurer's book of business. Acquisition (buyout) of flood‐prone structures is a potentially permanent solution that eliminates the highest risk properties while providing homeowners with financial assistance to relocate in a less risky location. To encourage participation, homeowners are offered a preflood fair market value of their damaged (or at risk of damage) structures. Although many factors have been shown to affect a homeowner's decision to accept an acquisition offer, very little research has been devoted to the influence of price or monetary incentive offered on homeowners' willingness to participate in acquisition programs. We estimate a pooled probit model and employ a bootstrap methodology to determine the effects of hypothetical home price offers on homeowners' acquisition decisions. We do so while controlling for environmental factors, property characteristics, and homeowner sociodemographic characteristics. Results show that price indeed has a positive effect on likelihood of accepting an acquisition contract. Furthermore, estimated homeowner supply curves differ significantly based on the damage status of the acquisition offer, as well as homeowner and property characteristics.",24.0,,False,,Economics,2019-04-01,3db01bac6d153608cb3a370bba9afc622ae6f889,measuringheterogeneouspriceeffectsforhomeacquisitionprogramsinatriskregions,frimpong,10.1002/soej.12341,measuringheterogeneouspriceeffectsforhomeacquisitionprogramsinatriskregions|2019,1,2589,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",04f9b045d617136cf4130e6572229ef66b2d5fe5,10.1177/23998083211063885,,Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression,2022.0,Environment and Planning B Urban Analytics and City Science,Yi-Gong Hu; Binbin Lu; Yong Ge; Guanpeng Dong,"Spatial heterogeneity is important for exploring data relationships between real estate price and its influential factors. The geographically weighted regression (GWR) technique has been frequently adopted for this purpose. In this study, we collected a second-hand real estate house price data set of Wuhan, in which each property is located the same as the community it belongs to. Thus, this data set possesses a typical characteristic, that is, dozens or even hundreds of observations could be allocated to one pair of coordinates, but vary in their attributes. This specific feature might lead to serious problems with bandwidth optimisations and coefficient estimates for calibrating the GWR model. We then proposed an extension by combining the hierarchical linear model (HLM) and GWR, namely HLM-GWR to cope with these problems. Results show that the HLM-GWR performs much better than the conventional GWR and HLM technique in terms of bandwidth optimisation, coefficient estimates. With a controlled simulation test, we again validated the advantage of the HLM-GWR model in comparison to both the HLM and GWR in handling this specific scenario. Overall, HLM-GWR is workable and should be recommended in this case or other scenarios with observations of similar spatial distributions.",24.0,,False,JournalArticle,,2022-01-22,04f9b045d617136cf4130e6572229ef66b2d5fe5,uncoveringspatialheterogeneityinrealestatepricesviacombinedhierarchicallinearmodelandgeographicallyweightedregression,hu,10.1177/23998083211063885,uncoveringspatialheterogeneityinrealestatepricesviacombinedhierarchicallinearmodelandgeographicallyweightedregression|2022,1,2292,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0b140003669227c448b3051166a4488a77b2145a,10.1177/0042098016669473,,"A hierarchical estimation of school quality capitalisation in house prices in Orange County, California",2017.0,,Sylvia Y. He,,24.0,,False,JournalArticle,Geography,2017-11-01,0b140003669227c448b3051166a4488a77b2145a,ahierarchicalestimationofschoolqualitycapitalisationinhousepricesinorangecountycalifornia,he,10.1177/0042098016669473,ahierarchicalestimationofschoolqualitycapitalisationinhousepricesinorangecountycalifornia|2017,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,47abb2fa071d6a535110d22ce13e9234d0c4e2bf,10.1016/j.jenvman.2020.110301,,Willingness to pay for staying away from haze: Evidence from a quasi-natural experiment in Xi'an.,2020.0,Journal of Environmental Management,F. Lan; Jing Lv; Jie Chen; Xiao-Ling Zhang; Zhuo-Hui Zhao; D. Pui,"Previous studies of the residents' willingness to pay (WTP) for air quality improvement are likely suffering various endogeneity biases. Exploiting the unique opportunity of a recent quasi-experiment operation of the world's first SFT (Smog Free Tower) in China's northwestern city Xi'an, this paper reliably calibrates local residents' WTP for less exposure to haze by analyzing the price responses of housings in the neighborhoods of SFT. The hedonic regression is applied in the DID (difference-in-difference) setting, with further assistances of the PSM (propensity score matching) method, quantile regression and placebo test. The regression results consistently suggest that housing price responded positively to the installment of the SFT, and the impact estimates are closely centered around 4%. The study also indicates high-priced housings response much more to the operation of the SFT than cheap-priced housings, suggesting uneven distribution of welfare gains of air quality improvement. A cost-benefit analysis of the SFT project indicates that the economic benefits of this project far outweigh the costs involved. The paper concludes with discussions of policy implications of this paper for environment governance.",23.0,,False,JournalArticle,Medicine;Economics,2020-05-15,47abb2fa071d6a535110d22ce13e9234d0c4e2bf,willingnesstopayforstayingawayfromhazeevidencefromaquasinaturalexperimentinxian,lan,10.1016/j.jenvman.2020.110301,willingnesstopayforstayingawayfromhazeevidencefromaquasinaturalexperimentinxian|2020,1,2231,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,3ecb662b17387fe6a3a519f040d627271b3e22b6,10.1080/00036846.2019.1631443,,High-speed Internet access and housing values,2019.0,Applied Economics,G. Molnár; Scott J. Savage; D. Sicker,"ABSTRACT A hedonic model is estimated that relates house values to high-speed Internet access while controlling for the potential endogeneity of Internet access. Results show that single-family homes with access to a 25 Mbps broadband connection have a price that is about $5,977, or 3%, more than similar homes in neighborhoods with 1 Mbps. The rural premium is lower at $5,099. A cost-benefit exercise on the viability of rural broadband shows that demand will generally not support private investment, but that the revenue gap from upgrading legacy networks could be readily covered by the Universal Service Fund and other public subsidies.",23.0,,False,,Economics,2019-06-19,3ecb662b17387fe6a3a519f040d627271b3e22b6,highspeedinternetaccessandhousingvalues,molnár,10.1080/00036846.2019.1631443,highspeedinternetaccessandhousingvalues|2019,1,1643,2,hedonic house price || hedonic housing prices,2,2,5,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ba48c13ae125f1895e4ea41405199dc1b200d247,10.1080/24694452.2023.2227683,,From Proximity to Quality: The Capitalization of Public Facilities into Housing Prices,2023.0,Annals of the American Association of Geographers,D. Tong; Yue Shen; Xiao-Guang Wang; Yiyu Sun; I. MacLachlan; Xin Li,"Housing prices are significantly influenced by the presence of public facilities, such as schools, parks, and transport infrastructure. Whereas existing literature has mainly focused on the proximity of public facilities, this study goes beyond proximity and introduces the concept of quality metrics to evaluate public facilities. By employing the gradient-boosting decision trees approach, we analyze the nonlinear relationships between public facilities and property values in Shenzhen, China. Our study not only quantifies the extent to which the quality of these facilities is capitalized in housing prices, but also examines the interaction effects of quality and proximity on housing prices. Our results reveal that quality variables exhibit a greater relative importance than proximity variables in determining housing prices, and this relationship follows a nonlinear pattern. Furthermore, we investigate the moderating effects of quality on the relationship between proximity and housing prices. We find that the amplifying effects of higher quality are particularly evident in metro stations and public middle schools, whereas the impact of park quality on housing prices is less pronounced. These findings highlight the need to consider both quality and proximity in the supply of public facilities, as they have synergistic effects on housing prices. The nonlinear effects observed in our study can serve as a valuable tool for identifying deficiencies in the supply of public facilities. Additionally, the distinction between proximity and quality, as well as their interaction effects, contributes to our understanding of how the value of public facilities is capitalized in housing markets.",23.0,,False,JournalArticle,,2023-08-10,ba48c13ae125f1895e4ea41405199dc1b200d247,fromproximitytoqualitythecapitalizationofpublicfacilitiesintohousingprices,tong,10.1080/24694452.2023.2227683,fromproximitytoqualitythecapitalizationofpublicfacilitiesintohousingprices|2023,1,2706,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",aabfbb500e4203cdd6f3b6441bde32e72de639c4,10.1016/j.jhe.2018.06.008,,Intra-school district capitalization of property tax rates,2018.0,Journal of Housing Economics,Mitchell R. Livy,"Abstract While previous studies have attempted to measure property tax rate capitalizations by proximate housing prices, disentangling school quality, and its associated amenities, from tax rates has impeded accurate estimation. I overcome this issue by investigating school districts containing varying tax rates. Identification is derived from the use of school and spatial-temporal boundary fixed effects to eliminate unobserved variation associated with tax districts that can confound capitalization estimates. Results from the primary model provide evidence of a negative capitalization of tax rate levels that corresponds to full capitalization at discount rate of nearly 3.5 percent. Further, property fixed effects are uniquely incorporated within the boundary identification method to show that tax rate changes are capitalized similar to baseline tax rate differences.",23.0,,False,,Economics,2018-09-01,aabfbb500e4203cdd6f3b6441bde32e72de639c4,intraschooldistrictcapitalizationofpropertytaxrates,livy,10.1016/j.jhe.2018.06.008,intraschooldistrictcapitalizationofpropertytaxrates|2018,1,1879,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0094-1190(77)90007-9,,Hedonic price and depreciation indexes for residential housing: A longitudinal approach,1977.0,Journal of Urban Economics,Peter T. Chinloy,,23.0,,,journal-article,,,doi_10_1016_0094_1190_77_90007_9,hedonicpriceanddepreciationindexesforresidentialhousingalongitudinalapproach,chinloy,10.1016/0094-1190(77)90007-9,hedonicpriceanddepreciationindexesforresidentialhousingalongitudinalapproach|1977,1,1000,3,apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices,2,0,2,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,d309e0674e949172ac323df2983c2c7b28535a37,10.1108/17538270910939583,,The influence of submarkets on water view house price premiums in New Zealand,2009.0,,O. Filippova,,23.0,,False,,Economics,2009-03-06,d309e0674e949172ac323df2983c2c7b28535a37,theinfluenceofsubmarketsonwaterviewhousepricepremiumsinnewzealand,filippova,10.1108/17538270910939583,theinfluenceofsubmarketsonwaterviewhousepricepremiumsinnewzealand|2009,1,1000,5,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic price model amenities residential || hedonic property valuation",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,7c1efd712862032c21d48358f51a955ed9a1ea0c,10.1029/2017wr022195,,Using Matching Methods to Estimate Impacts of Wildfire and Postwildfire Flooding on House Prices,2018.0,Water Resources Research,Julie M. Mueller; Ryan E. Lima; A. Springer; E. Schiefer,"The increasing frequency and severity of landscape‐scale wildfire poses new challenges for forested watershed management. The Schultz Fire burned 6,100 ha on the steep slopes of San Francisco Mountain near Flagstaff, AZ, USA in 2010. Within a month of the wildfire, summer monsoon rains created flooding and debris flows 1–2 orders of magnitude greater than prewildfire rainfall events. We estimate a hedonic property model to determine the loss in house prices from the wildfire and postwildfire flooding. While several studies examine the effects of wildfires and flooding on housing prices separately, to our knowledge, no study has determined the effects of postwildfire flooding in a fire‐adapted watershed. Most hedonic property models estimating impacts of wildfire or flooding use regression analysis to predict impacts. We also contribute to the hedonic property model literature by using nonparametric matching techniques. We find a negative and statistically significant average treatment effect of wildfire and post‐wildfire flooding. The treatment effect, however, differs relative to distance to wildfire perimeter and distance to flood zones. For example, houses located within 5 km of the Schultz Fire perimeter have an estimated 31% loss, while houses within 20 km have an estimated 6% loss. In addition, prices for houses located within 100 m of the 5‐year flood drop by 10% after the flood, whereas prices for houses within 2 km drop by 5%. Our results indicate that careful consideration of the impacts of both wildfire and postwildfire flooding is essential for understanding the net benefits of forested watershed restoration.",22.0,,False,,Environmental Science,2018-09-01,7c1efd712862032c21d48358f51a955ed9a1ea0c,usingmatchingmethodstoestimateimpactsofwildfireandpostwildfirefloodingonhouseprices,mueller,10.1029/2017wr022195,usingmatchingmethodstoestimateimpactsofwildfireandpostwildfirefloodingonhouseprices|2018,1,2648,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices",2,2,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2e466f1f0431f91f3bbdc635e2745f84ea7d8828,10.1016/j.marpol.2020.103866,,"Non-market valuation of beach quality: Using spatial hedonic price modeling in Hilton Head Island, SC",2020.0,,Serkan Catma,"Abstract Climate change has been impacting beach quality negatively for years. The gradual increase in sea level has been slowly diminishing the Atlantic coastline by eroding beaches and worsening coastal flooding. Narrowing beach width, one of the most important attributes of beach quality, has been a major concern for policy makers, local residents and visitors as lower beach quality has environmental and economic consequences. Filling the beaches with sand under beach replenishment projects has been widely used to treat this problem. The purpose of this paper is to estimate the economic value of beach width using the past residential property prices in Hilton Head Island, SC. Spatial error hedonic price method was adopted to account for spatial dependency among error terms. The results of this study show that beach width has significant influence on the values of neighboring properties that are located within 633 feet of the shoreline. An additional foot of beach width would add as much as $3,012 to the value of an oceanfront residential property. Without an accurate estimation of the economic value of beach width, cost-benefit analyses of beach nourishment and other shoreline management projects would be incomplete.",21.0,,False,,Environmental Science,2020-05-01,2e466f1f0431f91f3bbdc635e2745f84ea7d8828,nonmarketvaluationofbeachqualityusingspatialhedonicpricemodelinginhiltonheadislandsc,catma,10.1016/j.marpol.2020.103866,nonmarketvaluationofbeachqualityusingspatialhedonicpricemodelinginhiltonheadislandsc|2020,1,2239,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic property valuation",2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",91fb574910d5af782fa64c04e672e965418ac9fe,10.1016/j.landurbplan.2018.09.010,,Capitalization of interconnected active transportation infrastructure,2019.0,Landscape and Urban Planning,Cristina Connolly; Mitchell R. Livy; Y. Qiu; H. Klaiber,"Abstract This paper investigates the capitalized value of interconnected active transportation infrastructure in Franklin County, Ohio. We expand on the existing literature on preferences for local amenities by examining the impact of connections between local amenities and active transportation infrastructure on the sales price of single family homes. Hedonic results indicate that proximity to bike facilities results in positive capitalization, with on-road facilities driving this result. Extending the analysis to examine interconnectivity, we find that bike facility capitalization is heterogeneous depending on the types of local amenities and infrastructure links. For example, on-road facility connections with bus stop locations decrease nearby home values, while on-road facilities linked to local open space increase the value of proximate homes. Together, these results provide evidence that connectivity is an important input to active transportation planning.",21.0,,False,,Business,2019-02-01,91fb574910d5af782fa64c04e672e965418ac9fe,capitalizationofinterconnectedactivetransportationinfrastructure,connolly,10.1016/j.landurbplan.2018.09.010,capitalizationofinterconnectedactivetransportationinfrastructure|2019,1,1976,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,e2c1dd758e5c49eb93fe7bfcaa7a7bee27299cdf,10.1061/(asce)up.1943-5444.0000394,,"Spatial and Hedonic Analysis of House Price Dynamics in Warsaw, Poland",2017.0,,K. Olszewski; J. Waszczuk; Marta Widłak,,21.0,,False,,Economics,2017-09-01,e2c1dd758e5c49eb93fe7bfcaa7a7bee27299cdf,spatialandhedonicanalysisofhousepricedynamicsinwarsawpoland,olszewski,10.1061/(asce)up.1943-5444.0000394,spatialandhedonicanalysisofhousepricedynamicsinwarsawpoland|2017,1,1000,2,hedonic house price || hedonic housing prices,2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),97b1cb9fb256ffcaaecdd670e0ff961932b3f692,10.1177/2399808317702147,,"Scenic landscapes, visual accessibility and premium values in a single family housing market: A spatial hedonic approach",2019.0,,Jay Mittal; Sweta Byahut,,21.0,,False,JournalArticle,Economics,2019-01-01,97b1cb9fb256ffcaaecdd670e0ff961932b3f692,sceniclandscapesvisualaccessibilityandpremiumvaluesinasinglefamilyhousingmarketaspatialhedonicapproach,mittal,10.1177/2399808317702147,sceniclandscapesvisualaccessibilityandpremiumvaluesinasinglefamilyhousingmarketaspatialhedonicapproach|2019,1,1000,10,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || cites:sirmans2006 || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || house prices proximity hedonic || housing characteristics prices hedonic || real estate hedonic regression || spatial hedonic model housing",2,0,1,2,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e9c6a16fffab7ab3c2562ec99af0b0539cd3ee8c,10.3368/le.95.3.391,,Is Airport Proximity an Amenity or Disamenity? An Empirical Investigation Based on House Prices,2019.0,Land Economics,Ermanno Affuso; S. B. Caudill; F. Mixon; Kevin W. Starnes,"We use a spatial autoregressive model with directional effects to assess the impact of airport noise on housing values in Memphis, Tennessee, home of the busiest cargo airport in the United States. Our model is combined with a spatial dataset that contains information on noise levels, property characteristics, and neighborhood characteristics for 9,606 properties sold between 2011 and 2016. Results of our research suggest that the Memphis International Airport is perceived as a disamenity, with areas of the city affected to different degrees, with a potential average external cost of $4,795 per decibel of noise per household. (JEL C31, D62)",21.0,,False,,Economics,2019-07-01,e9c6a16fffab7ab3c2562ec99af0b0539cd3ee8c,isairportproximityanamenityordisamenityanempiricalinvestigationbasedonhouseprices,affuso,10.3368/le.95.3.391,isairportproximityanamenityordisamenityanempiricalinvestigationbasedonhouseprices|2019,1,1648,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,3,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",df474e76bff5e71bbfe9bdf218977ef7de66f758,10.1086/208992,,Conservation Characteristics among Determinants of Residential Property Value,1984.0,,Molly Longstreth; Anne R. Coveney; J. Bowers,,21.0,,False,,Economics,1984-06-01,df474e76bff5e71bbfe9bdf218977ef7de66f758,conservationcharacteristicsamongdeterminantsofresidentialpropertyvalue,longstreth,10.1086/208992,conservationcharacteristicsamongdeterminantsofresidentialpropertyvalue|1984,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic housing prices || hedonic property valuation residential || housing characteristics prices hedonic || residential property price determinants",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0a1f4157df5ae10cbac8a525c78d671b2ad52711,10.1007/s10901-023-10075-5,,"Examination of the impacts of hospital accessibility on housing prices: heterogeneity across attributes, space and price quantiles",2023.0,Journal of Housing and the Built Environment,Zong-Ni Gu; Mi Tang; Xiao-Long Luo; Jian-Xi Feng,,21.0,,False,JournalArticle,,2023-10-16,0a1f4157df5ae10cbac8a525c78d671b2ad52711,examinationoftheimpactsofhospitalaccessibilityonhousingpricesheterogeneityacrossattributesspaceandpricequantiles,gu,10.1007/s10901-023-10075-5,examinationoftheimpactsofhospitalaccessibilityonhousingpricesheterogeneityacrossattributesspaceandpricequantiles|2023,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,83065cd815d2559b2215e83d342d4c6f11b55c69,10.1016/j.habitatint.2021.102323,,"On innovation capitalization: Empirical evidence from Guangzhou, China",2021.0,,Kang-Min Wu; Yang Wang; Hong'ou Zhang; Yi Liu; Yu-Ling Zhang,"Abstract With the innovation-oriented transformation of the global economy, innovation and the knowledge-based economy have become two of the most distinguishing features of urbanization. The relationship between innovation and urban space has attracted considerable attention in existing theoretical research. However, little is known about the capitalization of innovation. Based on the capitalization hypothesis and hedonic price theory, we develop the concept of innovation capitalization and elaborate its mechanism. Based on housing data of 1668 communities in Guangzhou, a combination of the spatial regression model and the geodetector method were applied to confirm that innovation has a capitalization effect. This research is among the first attempts to quantitatively test the capitalization effect of innovation. The spatial regression results indicate that the innovation has significant positive impact on housing prices. The geodetector results indicate that innovation has an important impact on the spatial heterogeneity of housing prices. This research has critical theoretical and empirical value. We argue that from the innovation capitalization perspective, our understanding of the relationship between innovation and urban space can be deepened and that the impact of innovation on urban space development can be quantified, which can further help us understand the important role that innovation plays in urbanization and urban space development.",20.0,,False,,Economics,2021-03-01,83065cd815d2559b2215e83d342d4c6f11b55c69,oninnovationcapitalizationempiricalevidencefromguangzhouchina,wu,10.1016/j.habitatint.2021.102323,oninnovationcapitalizationempiricalevidencefromguangzhouchina|2021,1,2471,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,1,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1108/02637470410544986,,Comparing hedonic models for estimating and forecasting house prices,2004.0,Property Management,Mike Fletcher; Jean Mangan; Emily Raeburn,"In published work on hedonic house price estimation it is not uncommon to examine some of the conditions required for the estimators to have desirable properties such as minimum variance and unbiasedness, in particular spatial autocorrelation. However, other conditions that can give rise to similar difficulties with the estimates are often ignored. If these technical conditions are not met, it is sometimes because the model is misspecified in some way. This paper argues that a wider range of diagnostic statistics should be used in the specification search for a good model, in particular, but not exclusively, those concerned with predictive stability. The paper illustrates this approach by examining both in‐sample and out‐of‐sample diagnostic tests of various specifications of a hedonic house price model using data taken from the sale of over 1,600 properties in the Midlands of the UK in 1999/2000. The models investigated include various specifications of the dependent and independent variables, including models that are non‐linear in the parameters. The paper concludes that such statistics can often help in model selection and should be more widely employed.",20.0,,,journal-article,,,doi_10_1108_02637470410544986,comparinghedonicmodelsforestimatingandforecastinghouseprices,fletcher,10.1108/02637470410544986,comparinghedonicmodelsforestimatingandforecastinghouseprices|2004,1,2176,2,apartment rent hedonic || hedonic house price,2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,10775abe80013ecff14e229bbec570dc6a870392,10.1016/j.jhe.2018.04.003,,What contributes to the rising house prices in Beijing? A decomposition approach,2018.0,Journal of Housing Economics,Lei Zhang; Yi-Min Yi,"The average real house price in Beijing rose by roughly 41.8% between 2012 and 2015, and the appreciation differs across low- versus high-priced homes. The rising house price might be caused by changes in the characteristics of housing units, and it might also be induced by the changing returns to housing characteristics in the underlying hedonic price functions. In this paper, we use a comprehensive housing transaction dataset and analyze the changes in the distribution of house prices over time through a decomposition approach. The result of Oaxaca-Blinder mean decomposition suggests that the proportion of the price gap between 2012 and 2015 due to altered returns over time is greater than the proportion caused by changes in housing characteristics. The quantile decomposition shows that the decomposition effects are heterogenous across the complete distribution of house prices. We also find that low-priced homeowners are exposed to a decline in house prices, while there are deteriorating housing conditions from 2012 to 2015. The high-priced homeowners experience an improvement in living conditions, but a rise in house prices.",20.0,,False,,Economics,2018-09-01,10775abe80013ecff14e229bbec570dc6a870392,whatcontributestotherisinghousepricesinbeijingadecompositionapproach,zhang,10.1016/j.jhe.2018.04.003,whatcontributestotherisinghousepricesinbeijingadecompositionapproach|2018,1,2145,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,1,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",80b006b5172cb65140e593af6591ed3790ec0198,10.1016/j.landusepol.2020.104748,,"The demand for education: The impacts of good schools on property values in Brisbane, Australia",2020.0,Land Use Policy,D. Rajapaksa; Marcel Gono; C. Wilson; Shunsuke Managi; Bo-On L. Lee; Viet-Ngu Hoang,"The demand for education is partly revealed by a higher demand for properties around good schools, which has been the basis for a number of empirical studies. This research attempts to explore the nexus of school quality and the property market when school ranking is clearly available to the public. The spatial hedonic property valuation method was employed to analyze selected Brisbane public (state) and private schools consisting of those with high, middle and low ranking in terms of average student academic school performance. The results imply the capitalization of school values in both property and rental prices. The research also demonstrates the spillover impacts of school development. Such impacts are higher in comparison to previous studies, which are supported by the average school performance considered in individual assessments. The results of this study can be a valuable input into school policy reforms. (JEL: H23, I20, I28, R21)",20.0,,False,,Economics,2020-09-01,80b006b5172cb65140e593af6591ed3790ec0198,thedemandforeducationtheimpactsofgoodschoolsonpropertyvaluesinbrisbaneaustralia,rajapaksa,10.1016/j.landusepol.2020.104748,thedemandforeducationtheimpactsofgoodschoolsonpropertyvaluesinbrisbaneaustralia|2020,1,1955,3,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic property valuation",2,3,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),d95b158824a0f257011222a22aaa46512eb5e45d,10.1080/19491247.2022.2059844,,Supply side effects of the Berlin rent freeze,2022.0,International Journal of Housing Policy,Pekka Sagner; M. Voigtländer,"Abstract On 23 February 2020, the Berlin Senate introduced the Berlin rent freeze (‘Mietendeckel’). The law was repealed on 25 March 2021. The Berlin rent freeze was an unprecedented market intervention in the German housing market. We analyse how the rent cap part of the legislation which fixed rents at below market levels affected the supply side in the short term. We find rent decreases accompanied by decreases in supply five times as large. We further investigate spillover effects on the purchase market, regionally heterogeneous effects as well as different effects by dwelling characteristics. We find the rent freeze did not have spillover effects on dwellings for sale which point to a ‘wait-and-see-attitude’ on the investors’ side. We make use of a rich dataset of real estate advertisements and employ hedonic difference-in-difference and triple-difference estimation strategies.",20.0,,False,,,2022-05-05,d95b158824a0f257011222a22aaa46512eb5e45d,supplysideeffectsoftheberlinrentfreeze,sagner,10.1080/19491247.2022.2059844,supplysideeffectsoftheberlinrentfreeze|2022,1,1895,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1017/s1355770x05002548,,Willingness to pay for water and location bias in hedonic price analysis: evidence from the Indonesian housing market,2005.0,Environment and Development Economics,ARIEF ANSHORY YUSUF; PHOEBE KOUNDOURI,"Hedonic valuation of quality attributes can be misleading when the assumption that these attributes are exogenous to sample selection is violated. This paper considers the simultaneity between hedonic valuation and sample selection in the context of a model of consumer behavior over packaged goods and investigates empirically how the decision on house location (urban/rural) affects the household's valuation of water-related characteristics of the house in question. The empirical analysis uses data from the Indonesian housing market and suggests that households value access to safe and improved domestic water sources. However, failing to correct for sample selection results in a biased valuation of willingness to pay for house characteristics. This might misguide policy recommendations for improved provision of domestic water, based on cost–benefit analysis.",20.0,,,journal-article,,,doi_10_1017_s1355770x05002548,willingnesstopayforwaterandlocationbiasinhedonicpriceanalysisevidencefromtheindonesianhousingmarket,yusuf,10.1017/s1355770x05002548,willingnesstopayforwaterandlocationbiasinhedonicpriceanalysisevidencefromtheindonesianhousingmarket|2005,1,1869,6,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || willingness to pay housing attributes hedonic",2,0,2,3,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2f2c4c9104ab5f35fc4278e11f3a1d6ccb5a75ca,10.22300/1949-8276.11.1.84,,The Impact of Water front Location on Residential Home Values Considering Flood Risks,2019.0,The Journal of Sustainable Real Estate,N. Miller; Jeremy Gabe; Michael A. Sklarz,"We confirm existing findings of significant premiums for waterfront proximity, more so for oceans, bays, and large lakes than rivers. We then expand research on housing price trends immediately bef...",20.0,,False,,Environmental Science,,2f2c4c9104ab5f35fc4278e11f3a1d6ccb5a75ca,theimpactofwaterfrontlocationonresidentialhomevaluesconsideringfloodrisks,miller,10.22300/1949-8276.11.1.84,theimpactofwaterfrontlocationonresidentialhomevaluesconsideringfloodrisks|2019,1,1200,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",dc47cac81189f5e880b4065fdfbf15a92754cdc8,10.1080/01446193.2012.677542,,Housing attributes and Hong Kong real estate prices: a quantile regression analysis,2012.0,,L. Choy; Winky K.O. Ho; Stephen Mak,,20.0,,False,,Economics,2012-04-30,dc47cac81189f5e880b4065fdfbf15a92754cdc8,housingattributesandhongkongrealestatepricesaquantileregressionanalysis,choy,10.1080/01446193.2012.677542,housingattributesandhongkongrealestatepricesaquantileregressionanalysis|2012,1,1000,2,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || implicit prices housing attributes",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d08df985d975e985376df89c2c16beca6f9387b8,10.1007/s10018-015-0136-z,,Disamenity impact of Nala Lai (open sewer) on house rent in Rawalpindi city,2017.0,,I. Muhammad,,20.0,,False,,Business,,d08df985d975e985376df89c2c16beca6f9387b8,disamenityimpactofnalalaiopenseweronhouserentinrawalpindicity,muhammad,10.1007/s10018-015-0136-z,disamenityimpactofnalalaiopenseweronhouserentinrawalpindicity|2017,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f96ae327517a8457e3c1f01c66ddc882f4fe9efd,10.1007/s10901-020-09742-8,,"A geographically weighted regression approach to investigating local built-environment effects on home prices in the housing downturn, recovery, and subsequent increases",2020.0,Journal of Housing and the Built Environment,Chih-Hao Wang; Na Chen,,20.0,,False,,Geography,2020-04-15,f96ae327517a8457e3c1f01c66ddc882f4fe9efd,ageographicallyweightedregressionapproachtoinvestigatinglocalbuiltenvironmenteffectsonhomepricesinthehousingdownturnrecoveryandsubsequentincreases,wang,10.1007/s10901-020-09742-8,ageographicallyweightedregressionapproachtoinvestigatinglocalbuiltenvironmenteffectsonhomepricesinthehousingdownturnrecoveryandsubsequentincreases|2020,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),7ee843b18f0fa45518c49c8fc64c9e24875d34e0,10.1016/j.landurbplan.2019.103644,,Shared landscapes increase condominium unit selling price in a high-density city,2019.0,,Richard N. Belcher; Ester Suen; Sacha Menz; T. Schroepfer,"Abstract High-density buildings can benefit city sustainability by reducing expansion into biodiverse and often public natural ecosystems, reducing travel-time to work-and-play and improving energy efficiency. However, city densification can also remove vegetation which provides ecosystem services that enhance resident's wellbeing. Some architects design shared landscapes on and around large-scale buildings which are excludable to the public, like private gardens, but can be rivalrous between residents, like parks. This potentially mitigates public vegetation and subsequent ecosystem service loss. However, little evidence exists on whether condominium unit buyers pay more for shared landscapes. To address this knowledge gap, we used the hedonic pricing method to explain condominium unit resale value with unbuilt and vegetated space, accounting for other price determinants, spatial and temporal random effects using a robust regression. Leaf area above 184 m2 and unbuilt area (condominium estate area minus the building footprint) above 62.3 m2 per person had significant positive effects on condominium unit selling price. This provides evidence that economic returns can be gained by having vegetation in accessible landscapes on and around buildings, should they be provided above a certain quantity. When supplied at more abundant quantities condominium estate vegetation also had a bigger effect than nearby managed parks, suggesting condominium unit buyers see more economic value in having nearer more private vegetation. Planners and building developers should consider these effects when deciding whether to encourage vegetated landscapes at condominium estates, alongside how vegetation can provide other monetary and non-monetary ecosystem services.",19.0,,False,,Business,2019-12-01,7ee843b18f0fa45518c49c8fc64c9e24875d34e0,sharedlandscapesincreasecondominiumunitsellingpriceinahighdensitycity,belcher,10.1016/j.landurbplan.2019.103644,sharedlandscapesincreasecondominiumunitsellingpriceinahighdensitycity|2019,1,2773,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0fc6edd4df184c789fc1f8ba8aa41e33271577cf,10.1177/0042098020926034,,"The association between residential housing prices, bicycle infrastructure and ridership volumes",2020.0,,L. Conrow; S. Mooney; E. Wentz,"City officials and planners have shown increased interest in pedestrian- and bicycle-friendly designs aimed at addressing urban problems such as traffic congestion, pollution, sprawl and housing availability. An important planning consideration is the economic impact associated with existing or planned infrastructure, especially in relation to home property values. In this study, we use measures of infrastructure and ridership to evaluate the relationship between bicycling infrastructure and activity and single-family home values in Tempe, Arizona. We apply a hedonic modelling approach and find that bicycle infrastructure density is positively associated with home sale price, while ridership density around home locations has no significant relationship with sale price. Our results inform discourse related to the potential economic values of residential bicycle infrastructure, especially in areas where property tax is a source of local public finance revenue. We show that the characteristics of bicycle-friendly design may be the same characteristics valued by homebuyers and the resulting increased home sale values may lead to increased property tax revenue in Tempe, Arizona.",19.0,,False,JournalArticle,Business,2020-06-27,0fc6edd4df184c789fc1f8ba8aa41e33271577cf,theassociationbetweenresidentialhousingpricesbicycleinfrastructureandridershipvolumes,conrow,10.1177/0042098020926034,theassociationbetweenresidentialhousingpricesbicycleinfrastructureandridershipvolumes|2020,1,2192,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic",2,4,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1068/a190059,,Households' Utilities and Hedonic Prices for Inner-City Homes,1987.0,Environment and Planning A: Economy and Space,A G Phipps,"The housing-consumption preferences of a sample of inner-city owner-occupants in Saskatoon are recovered from their unconstrained residential preferences for housing and neighbourhood types. These unconstrained utilities are quantitatively compared with implicit prices for the same housing and neighborhood types, computed from a hedonic housing price model. A first finding is that the households preferred the levels of eight of the twelve residential attributes that were also the higher priced. In contrast, a second finding was that their unconstrained utilities were negatively monotonically related to the hedonic price scales for four attributes, neighborhood housing, renovation/basement, age of construction, and family life cycle of the neighborhood people. The salience of these four divergences between the scales of value, in terms of future land-use and social change in inner-city neighborhoods, is discussed.",19.0,,,journal-article,,,doi_10_1068_a190059,householdsutilitiesandhedonicpricesforinnercityhomes,phipps,10.1068/a190059,householdsutilitiesandhedonicpricesforinnercityhomes|1987,1,1926,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || apartment rent hedonic || hedonic housing prices || implicit prices housing attributes",2,1,5,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,0539758cbc1492190975b2900d11f2e8eb552429,10.1016/j.jenvman.2022.115012,,Rethinking urban park irrigation under climate change.,2022.0,Journal of Environmental Management,Claire A. Doll; M. Polyakov; D. Pannell; M. Burton,"Climate change is forcing cities to reassess their water management practices, particularly for water-intensive applications like park irrigation. If water scarcity requires governments to deviate from current park management norms and allocate less water towards parks, it is essential that park managers design spaces that maintain community wellbeing. We apply the hedonic pricing method and use detailed park management information to assess the value of parks in a region where local climatic conditions require extensive irrigation to keep turf green, and where climate change is further constraining water supplies. Here we show that the impacts of irrigation on the value of parks differ depending on the dwelling types of the nearby housing populations that they serve. In most cases, the convention that parks have to be irrigated to deliver ecosystem services to the public is supported. However, we find that non-irrigated park areas are also valued positively by nearby apartment dwellers. Accelerating rates of urbanization and shifts towards high-density living may support the development of more diverse park options that are less water-intensive. Increased visibility of these alternative park forms, which could include more areas of native vegetation that do not require irrigation, may subsequently influence public expectations for landscape design.",19.0,,False,JournalArticle,Medicine,2022-04-21,0539758cbc1492190975b2900d11f2e8eb552429,rethinkingurbanparkirrigationunderclimatechange,doll,10.1016/j.jenvman.2022.115012,rethinkingurbanparkirrigationunderclimatechange|2022,1,2371,2,apartment prices hedonic || hedonic housing prices,2,1,3,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1002/jae.1186,,On nonparametric estimation of a hedonic price function,2010.0,Journal of Applied Econometrics,Harry Haupt; Joachim Schnurbus; Rolf Tschernig,"Abstract Recently, using mixed data on Canadian housing, Parmeter, Henderson, and Kumbhakar ( Journal of Applied Econometrics 2007; 22 : 695–699) found that a nonparametric approach for estimating a hedonic house price function is superior to formerly suggested parametric and semiparametric specifications. We carefully reanalyze these specifications for this dataset by applying a recent nonparametric specification test and simulation‐based prediction comparisons. For the case at issue our results suggest that a previously proposed parametric specification does not have to be rejected and we illustrate how nonparametric methods provide valuable insights during all modeling steps. Copyright © 2010 John Wiley & Sons, Ltd.",19.0,,,journal-article,,,doi_10_1002_jae_1186,onnonparametricestimationofahedonicpricefunction,haupt,10.1002/jae.1186,onnonparametricestimationofahedonicpricefunction|2010,1,1732,3,apartment rent hedonic || hedonic house price || hedonic housing prices,2,1,2,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),fb5a043f15efd825817b54a5cd4de0369c3b6a03,10.1016/j.landusepol.2017.12.058,,The effect of conservation policy and varied open space on residential property values: A dynamic hedonic analysis,2018.0,,Linda M. Fernandez; Monobina Mukherjee; T. Scott,,19.0,,False,,Geography,2018-04-01,fb5a043f15efd825817b54a5cd4de0369c3b6a03,theeffectofconservationpolicyandvariedopenspaceonresidentialpropertyvaluesadynamichedonicanalysis,fernandez,10.1016/j.landusepol.2017.12.058,theeffectofconservationpolicyandvariedopenspaceonresidentialpropertyvaluesadynamichedonicanalysis|2018,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate)",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",408a53cd464aa0f7ddf74d92de4db259d6b20048,10.1016/j.cities.2018.07.007,,Quantifying welfare loss due to longer commute times in Seoul: A two-stage hedonic price approach,2019.0,Cities,M. Jun,"Abstract The relationship between commute duration and commuter well-being is an essential component of transport policy evaluation and planning, but few studies have considered it. This study evaluates the degree of capitalization of commute time in the housing market and measures the welfare loss caused by longer commute times using the Seoul metropolitan area (SMA) in Korea as a case study. We estimate a demand function between the observed commute time and its implicit price and evaluate the welfare loss for the average increase in commute time in the SMA between 1995 and 2010. The empirical findings show that the documented 12.5% increase in commute time caused a welfare loss of $1.3 billion for employees in the SMA. This evidence clearly links longer commute times with declining commuter well-being.",19.0,,False,,Economics,2019-01-01,408a53cd464aa0f7ddf74d92de4db259d6b20048,quantifyingwelfarelossduetolongercommutetimesinseoulatwostagehedonicpriceapproach,jun,10.1016/j.cities.2018.07.007,quantifyingwelfarelossduetolongercommutetimesinseoulatwostagehedonicpriceapproach|2019,1,1816,3,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,0,1,2,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b14f7815b7a0280ab2775b5d243b2cc75a1ec3f6,10.1016/j.buildenv.2022.109577,,Value capitalization effects of green buildings: A new insight through time trends and differences in various price levels,2022.0,Building and Environment,I-Chun Tsaim,"While many studies have explored green buildings, there remain many unsolved questions about how to expand this section of the housing market, such as how the capitalization effect of green buildings varies across time and location. This study investigates the price premiums of green buildings in Taipei City, the largest metropolis in Taiwan, to examine how they have changed over time, and assesses the magnitude of the capitalization effects under varying housing price levels. The empirical results indicate that Taipei residents are willing to pay higher prices for houses with green-label certification. The higher the certification level, the higher the price that residents are willing to pay. This study additionally identifies two characteristics of the capitalization effects in this market. The first is that the price premiums vary with the market condition. For example, during housing market downturns (e.g., 2014-2015), building green premiums may be unremarkable or non-existent. The second effect identified is that green buildings tend to exhibit an 'aristocratic trend.' This means that high-priced houses (i.e., houses priced above the 80% quantile) in the high-end label category attract the highest premiums, implying that the willingness to pay for green features tends to only exist among wealthy buyers. The result of this paper provides evidence to explain the heterogeneity of green build premiums. Regarding the specificity of these premiums, this paper provides recommendations for builders and managers.",19.0,,False,,Medicine,2022-09-01,b14f7815b7a0280ab2775b5d243b2cc75a1ec3f6,valuecapitalizationeffectsofgreenbuildingsanewinsightthroughtimetrendsanddifferencesinvariouspricelevels,tsaim,10.1016/j.buildenv.2022.109577,valuecapitalizationeffectsofgreenbuildingsanewinsightthroughtimetrendsanddifferencesinvariouspricelevels|2022,1,2535,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,implicit prices housing attributes,,10.1080/00420989650011555,,Demand for Housing Attributes in Developing Countries: A Study of Pakistan,1996.0,Urban Studies,Hafiz A. Pasha; Mohammad S. Butt,"The paper has applied a conventional framework of analysis of implicit markets to determine the characteristics of demand for housing attributes of quantity and quality in the urban areas of a large, low-income developing country like Pakistan. Income elasticities generally appear to be low, although somewhat higher for indicators of quality. Own-price elasticities are high while the cross-price effects with respect to different attribute prices reveal the high degree of substitutibility among these attributes. Methodological innovations in the paper include the use of a weighted factor score for the measurement of housing quality and incorporation of the effect of changes in non-housing prices on demand for housing attributes. Given the large magnitude of own-price effects and the negative impact of a rise in non-housing prices, it appears that in the face of slow growth in real incomes and double-digit inflation in Pakistan residential overcrowding conditions are likely to worsen over time.",19.0,,,journal-article,,,doi_10_1080_00420989650011555,demandforhousingattributesindevelopingcountriesastudyofpakistan,pasha,10.1080/00420989650011555,demandforhousingattributesindevelopingcountriesastudyofpakistan|1996,1,2007,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || implicit prices housing attributes",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,rent hedonic regression housing,36d7ed75000f783229ab78a6124ae893a6605dad,10.1177/23998083221122790,,A spatial autoregressive geographically weighted quantile regression to explore housing rent determinants in Amsterdam and Warsaw,2022.0,Environment and Planning B Urban Analytics and City Science,Mateusz Tomal; M. Helbich,"A hedonic approach is typically performed to identify housing rental or sales price determinants. However, standard hedonic regression models disregard spatial autocorrelation of prices and heterogeneity of housing preferences across space and over price segments. We developed a spatial autoregressive geographically weighted quantile regression (GWQR-SAR) to address these shortcomings. Using data on the determinants of residential rental prices in Warsaw (Poland) and Amsterdam (The Netherlands) as case studies, we applied GWQR-SAR and rigorously compared its performance with alternative mean and quantile hedonic regressions. The results revealed that GWQR-SAR outperforms other models in terms of fitting accuracy. Compared with mean regressions, GWQR-SAR performs better, especially at the tails of the dependent variable distribution, where non-quantile models overestimate low rent values and underestimate high ones. Policy recommendations for the development of private residential rental markets are provided based on our results, which incorporate spatial effects and price segment requirements.",18.0,,False,JournalArticle,,2022-08-26,36d7ed75000f783229ab78a6124ae893a6605dad,aspatialautoregressivegeographicallyweightedquantileregressiontoexplorehousingrentdeterminantsinamsterdamandwarsaw,tomal,10.1177/23998083221122790,aspatialautoregressivegeographicallyweightedquantileregressiontoexplorehousingrentdeterminantsinamsterdamandwarsaw|2022,1,2110,3,hedonic housing prices || rent hedonic regression housing || spatial hedonic model housing,2,3,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,prix hédoniques logement,,10.3917/reru.092.0369,,Formation des prix immobiliers et consentements à payer pour une amélioration de l'environnement urbain : l'exemple rennais,2009.0,Revue d’Économie Régionale & Urbaine,Marc Baudry; Alain Guengant; Sophie Larribeau; Matthieu Leprince,"Ce travail a pour objectif d’illustrer l’apport de la théorie économique et des méthodes économétriques, plus particulièrement l’économétrie spatiale, à la conception et à l’évaluation d’opérations de renouvellement urbain. Dans ce but, l’approche par les prix hédoniques est retenue. Elle procède en deux temps. Dans un premier temps, sont identifiées, parmi un ensemble de caractéristiques de localisation données a priori, celles qui exercent un rôle discriminant sur la valeur des logements. Dans un second temps, les consentements à payer des habitants pour une amélioration des caractéristiques discriminantes sont calculés. L’application aux mutations d’appartements à Rennes entre les années 1994 et 2001 montre qu’il existe des externalités de voisinage significatives qui justifient une intervention publique. Les consentements à payer pour une amélioration des caractéristiques environnementales discriminantes apparaissent assez hétérogènes entre individus et variables d’une caractéristique à l’autre.",18.0,,,journal-article,,,doi_10_3917_reru_092_0369,formationdespriximmobiliersetconsentementsapayerpouruneameliorationdelenvironnementurbainlexemplerennais,baudry,10.3917/reru.092.0369,formationdespriximmobiliersetconsentementsapayerpouruneameliorationdelenvironnementurbainlexemplerennais|2009,1,2014,1,prix hédoniques logement,1,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),d314c6a46f6ceb0fcf338474e9546ec201a241b3,10.1080/15623599.2019.1695097,,"Effects of locations, structures and neighbourhoods to housing price: an empirical study in Shanghai, China",2019.0,International Journal of Construction Management,V. Tam; I. Fung; Jing Wang; Mingxue Ma,"Abstract Since Chinese housing market has been reformed, the increasing housing price has been a focus of the whole society. Shanghai, as the economic and financial center in China, is one of the cities which have the highest housing price. However, the high housing price has gradually become an important economic problem, as it could create huge challenges to maintain a healthy real estate industry in China. A comprehensive understanding of the relationship between housing market and its impactors is in need to control the housing price. This paper will focus on Shanghai housing market and analyse relationship between 12 housing characteristics and housing price, using hedonic price model. It is found that the 12 housing characteristics impact Shanghai housing price differently. This paper could provide newest and largest amount of data as references to researchers and investors who concern about Shanghai housing market, and help government to formulate a proper housing price policy.",18.0,,False,,Economics,2019-11-27,d314c6a46f6ceb0fcf338474e9546ec201a241b3,effectsoflocationsstructuresandneighbourhoodstohousingpriceanempiricalstudyinshanghaichina,tam,10.1080/15623599.2019.1695097,effectsoflocationsstructuresandneighbourhoodstohousingpriceanempiricalstudyinshanghaichina|2019,1,1999,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,2,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",a64f7b3fbf465474fe48e94e76e850dbf3c81309,10.1061/(asce)up.1943-5444.0000595,,"Walkability, Safety, and Housing Values in Shrinking Cities: Spatial Hedonic Study in Buffalo, Pittsburgh, and Detroit",2020.0,,Li Yin; Hao Zhang; K. Patterson; R. Silverman; Liang Wu,Abstract The growing market demand for pedestrian and transit-oriented communities can be capitalized into higher housing values and can generate much-needed revenue for shrinking cities. Few studi...,18.0,,False,,Geography,2020-09-01,a64f7b3fbf465474fe48e94e76e850dbf3c81309,walkabilitysafetyandhousingvaluesinshrinkingcitiesspatialhedonicstudyinbuffalopittsburghanddetroit,yin,10.1061/(asce)up.1943-5444.0000595,walkabilitysafetyandhousingvaluesinshrinkingcitiesspatialhedonicstudyinbuffalopittsburghanddetroit|2020,1,1200,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1541-1338.2005.00175.x,,Hedonic Estimation and Policy Significance of the Impact of HOPE VI on Neighborhood Property Values,2005.0,Review of Policy Research,Edward Bair; John M. Fitzgerald,Abstract HOPE VI was designed as a program to revitalize distressed public housing. This study uses hedonic methods to test whether projects that are rebuilt with HOPE VI funds have a positive effect on surrounding property values. Comparisons are made between HOPE VI and other types of public housing programs using data on property values by census block groups from the 2000 census. We find that HOPE VI had a statistically significant positive impact on surrounding property values on the order of 8–10% for every quarter‐mile closer that a housing unit was located to the development. Other public housing developments were found to have little if any effect on property values.,18.0,,,journal-article,,,doi_10_1111_j_1541_1338_2005_00175_x,hedonicestimationandpolicysignificanceoftheimpactofhopevionneighborhoodpropertyvalues,bair,10.1111/j.1541-1338.2005.00175.x,hedonicestimationandpolicysignificanceoftheimpactofhopevionneighborhoodpropertyvalues|2005,1,1684,1,apartment rent hedonic,2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),7502cfea42d1ca8f4d106f44cc9b6c8ea0c68bac,10.1007/s12517-017-3331-3,,The use of hedonic pricing method to determine the parameters affecting residential real estate prices,2017.0,Arabian Journal of Geosciences,Ayşe Yavuz Özalp; H. Akıncı,,18.0,,False,,Business,2017-12-16,7502cfea42d1ca8f4d106f44cc9b6c8ea0c68bac,theuseofhedonicpricingmethodtodeterminetheparametersaffectingresidentialrealestateprices,özalp,10.1007/s12517-017-3331-3,theuseofhedonicpricingmethodtodeterminetheparametersaffectingresidentialrealestateprices|2017,1,1000,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cf103c1ff77ecff33cc3dcde7cfa30d40e2c90e8,10.1108/ijhma-11-2020-0133,,"Factors affecting housing prices in Izmir, Turkey: a quantile regression approach",2021.0,,Onur Özsoy; H. Sahin," Purpose The purpose of this paper is to investigate empirically the main factors that affect the house prices in Izmir, Turkey using the quantile regression and ordinary least square approaches. Design/methodology/approach Sample data about the housing market for Izmir collected from the web pages of various real estate agencies during June 2018. Following this, the quantile regression method is used to estimate all possible effects of variables on each interested quantile to determine the factors that affect house prices to guide the potential consumers, house developers, city planners and the policymakers in Izmir, Turkey. Findings Results show that the age of the house, central heating and parking have no significant effect on prices. The size of the house, the existence of an elevator, fire and security have a positive and significant effect on prices. The number of rooms has lower values for high-priced houses, while the floor, the number of balconies, air conditioning, proximity to schools have a higher value for high-priced houses. The number of toilets, the number of bathrooms and the distance to the hospital have a lower value on the high-priced housing. The value of the distance from the city center and the shopping center is almost uniform in all quantiles and lowers the value of the higher-priced houses. With the exception of the value of the houses in the 10th percentile in Balcova district, the value of the houses in Konak, Balcova and Narlidere is lower prices in Karsiyaka. Originality/value This is the first comprehensive research to determine the major factors that affect house prices in Izmir. The second contribution of this paper is that it includes all possible variables and accordingly derives adequate policy implications, which could be used both by the public housing authority and private housing constructing companies in designing and implementing effective housing policies. ",18.0,,False,,Economics,2021-03-18,cf103c1ff77ecff33cc3dcde7cfa30d40e2c90e8,factorsaffectinghousingpricesinizmirturkeyaquantileregressionapproach,özsoy,10.1108/ijhma-11-2020-0133,factorsaffectinghousingpricesinizmirturkeyaquantileregressionapproach|2021,1,2939,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1a18b0a2989548006da6847a281b5c4164757bcc,10.1177/0042098019896977,,"The spatial varying relationship between services of the train network and residential property values in Melbourne, Australia",2020.0,,Qian-Yao Li; Junwu Wang; J. Callanan; Binbin Lu; Zeng Guo,"This study investigates the spatial effects of the service frequency and transport interchange facilities of rail stations on residential property values for the entire metropolitan train network in Melbourne. Residential properties are classified as either detached or attached dwellings. Given that a traditional hedonic price model cannot handle the spatial dependence and spatial non-stationarity of the housing market, several geographically weighted regression (GWR) models are used and multicollinearity is considered; the model with the Euclidean distance metric outperforms others. Results indicate that the service frequency and facilities of the stations influence the residential property values in a spatially variable way. For every 1 km closer to the train stations, an increase in the frequency of the train services per unit results in a change in the residential property values ranging from −4.01% to 2.71%; an improvement in the transport interchange facilities per unit results in a change in the residential property values ranging from −29.93% to 47.04%. Crime and retail activities that indirectly affect the relationship between rail stations and residential property values are also identified. For every 1 km closer to the train stations, the crime density increases significantly from 5.64% to 42.88% and this occurs in one-fifth of the areas in Melbourne. In contrast, the relationship between retail activities and train stations remains spatially stable. This study complements the relatively scarce literature on the link between railway service levels and residential property values while extending the case study to the local level.",17.0,,False,JournalArticle,Geography,2020-02-10,1a18b0a2989548006da6847a281b5c4164757bcc,thespatialvaryingrelationshipbetweenservicesofthetrainnetworkandresidentialpropertyvaluesinmelbourneaustralia,li,10.1177/0042098019896977,thespatialvaryingrelationshipbetweenservicesofthetrainnetworkandresidentialpropertyvaluesinmelbourneaustralia|2020,1,2667,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,1,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),85ef5e533b05b7fdfa0d30e4429b6c6891b5b434,10.1016/j.eap.2019.09.004,,Implementing hedonic pricing models for valuing the visual impact of wind farms in Greece,2019.0,,Konstantinos Skenteris; S. Mirasgedis; Christos Tourkolias,"Abstract Although wind energy is a pollution-free and infinitely sustainable form of energy, there is considerable concern over certain environmental effects resulting from its development. Criticism focuses primarily on the visual impact of wind turbines and transmission lines, which may result in perceived deterioration of the landscape and also harm other economic activities such as tourism and real estate. This study applies the hedonic pricing method to estimate the value of environmental externalities associated with large-scale exploitation of wind power at a local level. It examines the characteristics of approximately 1,800 sales of single-family homes surrounding 17 existing wind facilities in two Greek islands, namely Evia and Kefalonia. Four different hedonic price models are developed and applied, with diverging results in the two areas. We find that in Evia, the per unit floor area sales price decreased for dwellings located within a 2 km radius of the wind farms, while in Kefalonia, the distance of the house to the wind turbines had no statistically significant effect on the sales price.",17.0,,False,,Economics,2019-12-01,85ef5e533b05b7fdfa0d30e4429b6c6891b5b434,implementinghedonicpricingmodelsforvaluingthevisualimpactofwindfarmsingreece,skenteris,10.1016/j.eap.2019.09.004,implementinghedonicpricingmodelsforvaluingthevisualimpactofwindfarmsingreece|2019,1,2119,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price,2,2,5,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,918708af65f53bc2092ee315626609025100f13f,10.1016/j.regsciurbeco.2021.103680,,"Property tax capitalization, a case study of Dallas County",2021.0,Regional Science and Urban Economics,Seth H. Giertz; Rasoul Ramezani; Kurt J. Beron,"Abstract We estimate the degree to which local property taxes are capitalized into house prices using administrative data from the Dallas Central Appraisal District (DCAD) from 2014 to 2016. Capitalization rates are measured using a hedonic price regression model. To control for unobservable neighborhood characteristics, we employ a border discontinuity research design, restricting our sample to homes within half-mile blocks of jurisdictional boundaries. With respect to school district taxes, we estimate that more than 70 percent of the increase in the present value of property tax liabilities resulting from a tax increase is capitalized into current house prices. The null hypothesis of full capitalization cannot be rejected. For city taxes, capitalization rates are even larger. We also estimate the willingness to pay for public schools, finding that a one standard deviation increase in test scores results in a roughly four-percent increase in house prices.",17.0,,False,,Economics,2021-07-01,918708af65f53bc2092ee315626609025100f13f,propertytaxcapitalizationacasestudyofdallascounty,giertz,10.1016/j.regsciurbeco.2021.103680,propertytaxcapitalizationacasestudyofdallascounty|2021,1,1971,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price",2,2,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,e31cae63ae1727f46acb28746be8df350845a64d,10.1177/0361198118774153,,Spatio-Temporal Hedonic Price Model to Investigate the Dynamics of Housing Prices in Contexts of Urban Form and Transportation Services in Toronto,2018.0,Transportation Research Record,Jason Hawkins; K. N. Habib,"A spatio-temporal hedonic price model is developed for the Greater Toronto area to examine the effects of urban configurations and proximity to transit services on housing price. A spatial Durbin panel model is utilized to account for both spatial and temporal autocorrelation. This model is shown to have advantages through its ability to reduce the number of explanatory variables required to obtain a strong fit with empirical data. Analysis is completed for the period of 1996 to 2017 and distinctions are made in housing stock between single-family houses, townhouses, and condominiums. It is shown that heterogeneities exist between the hedonic representations of each dwelling type and that separate models should be employed for each. In all cases, the average income of the community, its distance to the central business district (CBD), and population and employment density are found to be significant factors in the determination of price.",17.0,,False,JournalArticle,Economics,2018-06-08,e31cae63ae1727f46acb28746be8df350845a64d,spatiotemporalhedonicpricemodeltoinvestigatethedynamicsofhousingpricesincontextsofurbanformandtransportationservicesintoronto,hawkins,10.1177/0361198118774153,spatiotemporalhedonicpricemodeltoinvestigatethedynamicsofhousingpricesincontextsofurbanformandtransportationservicesintoronto|2018,1,1951,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || house prices proximity hedonic || spatial hedonic model housing",2,1,7,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,prix hédoniques logement,,10.3406/ecop.1995.5766,,Prix hédoniques et estimation d'un modèle structurel d'offre et de demande de caractéristiques,1995.0,Économie & prévision,Olivier Marchand; Eric Skhiri,"Hedonic Prices and a Structural Model Estimate of the Supply and Demand of Characteristics: Application to the French Rental Housing Market, by Olivier Marchand and Eric Skhiri . This hedonic price analysis is one of the first to be made of the French rental housing market. The data are taken from cross-sectional surveys in eight French towns (excluding the Paris area). A Box-Cox transformation is used to obtain the best functional form. The hedonic regression is found to be fairly satisfactory with 66% of variance explained by the model. Furthermore, a structural model of the supply and demand of characteristics is validated. The problem of simultaneity is solved by using the town dummy as an instrumental variable. We therefore make the first attempt to estimate the factors of the supply and demand of characteristics in France.",17.0,,,journal-article,,,doi_10_3406_ecop_1995_5766,prixhedoniquesetestimationdunmodelestructureldoffreetdedemandedecaracteristiques,marchand,10.3406/ecop.1995.5766,prixhedoniquesetestimationdunmodelestructureldoffreetdedemandedecaracteristiques|1995,1,1840,3,hedonic housing prices || housing characteristics prices hedonic || prix hédoniques logement,3,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",62968afe3a42647a4ec2a8d56d3697bd0d445b65,10.1016/j.regsciurbeco.2020.103583,,"The political economy of historic districts: The private, the public, and the collective",2021.0,,Yang Zhou,"Abstract This paper studies the political economy of historic district designation, with a focus on how historic district designation influences housing prices. I provide transaction-level empirical evidence from Denver, Colorado from 1990 to 2016 using hedonic price models with multiple fixed-effects. Results suggest that being in a historic district generates a 12-23% premium for house transactions after designation. Given the institutional context of Denver, I argue that one possible mechanism of the premium is the collective action problem in the historic district designation process. Meanwhile, a 10-20% positive spillover to the neighboring homes of historic districts is found, and the specific characteristics of historic districts influence the spillovers.",17.0,,False,,Business,,62968afe3a42647a4ec2a8d56d3697bd0d445b65,thepoliticaleconomyofhistoricdistrictstheprivatethepublicandthecollective,zhou,10.1016/j.regsciurbeco.2020.103583,thepoliticaleconomyofhistoricdistrictstheprivatethepublicandthecollective|2021,1,1772,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,1,4,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e3046e1599fd70135579f81ffd387c2d5949e20c,10.1016/j.landurbplan.2019.103608,,Value of playgrounds relative to green spaces: Matching evidence from property prices in Australia,2019.0,Landscape and Urban Planning,Robert V. Breunig; S. Hasan; Kym Whiteoak,"Abstract We examine the effect on house prices of the presence of a small playground relative to an empty green space using a matching approach combined with hedonic regression analysis. Using a data set of 33,521 property sales in urban Australia, we match properties near small playgrounds to similar properties near two empty, open green spaces which are candidates for playground construction. We control for property characteristics and distance to a wide range of urban amenities and other open spaces. We find that the presence of a playground within 300 metres adds about AU$20,000 (4.6 per cent) to the average property price. The price effect of a playground is larger for houses than apartments and falls with distance from the playground.",17.0,,False,,Mathematics,2019-10-01,e3046e1599fd70135579f81ffd387c2d5949e20c,valueofplaygroundsrelativetogreenspacesmatchingevidencefrompropertypricesinaustralia,breunig,10.1016/j.landurbplan.2019.103608,valueofplaygroundsrelativetogreenspacesmatchingevidencefrompropertypricesinaustralia|2019,1,1750,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,2,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,prix hédoniques logement,,10.3917/reco.645.0883,,La contribution des effets de voisinage à la formation des prix du logement,2013.0,Revue économique,Aurélien Décamps; Frédéric Gaschet,"Cet article se propose d’évaluer le rôle des effets de voisinage dans la formation des prix du logement, en prenant appui sur l’exemple de l’agglomération bordelaise. Compte tenu des limites des approches hédoniques traditionnelles dans la prise en compte de ces effets, nous mettons en œuvre une modélisation hédonique multiniveaux permettant une décomposition de la variance due aux différents déterminants identifiés par la théorie économique, à deux échelles spatiales. Les résultats confirment le rôle significatif joué par les effets de voisinage dans la formation des prix immobiliers, de même que la primauté de ces déterminants sur les facteurs traditionnels d’accessibilité intra-urbaine.",17.0,,,journal-article,,,doi_10_3917_reco_645_0883,lacontributiondeseffetsdevoisinagealaformationdesprixdulogement,décamps,10.3917/reco.645.0883,lacontributiondeseffetsdevoisinagealaformationdesprixdulogement|2013,1,1698,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || prix hédoniques logement",1,2,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",395c5d2a8a1503c507d1e5afde3efe9eed74bfcd,10.1016/j.cities.2021.103238,,Pricing the value of the chance to gain admission to an elite senior high school in Beijing: The effect of the LDHSE policy on resale housing prices,2021.0,,Jun-su Han; Liyuan Cui; Hua-Yi Yu,"Abstract Elite schools are highly sought after worldwide. Measuring the influence of elite schools on housing prices is an important issue not only to researchers but also to all residents. On March 19, 2015, the Beijing municipal government enacted a policy called locking districts for senior-high-school admission examination (hitherto referred to as LDHSE). This policy prevents senior high schools in the Xicheng, Dongcheng, and Haidian districts, where the most elite senior high schools in Beijing are located, from admitting students from other districts. This study explores the effect of the LDHSE policy on housing prices, and estimates the value of the chance to gain admission to an elite senior high school in Beijing, China. Using the difference-in-difference (DID) + regression discontinuity design (RD) methodology, we find an overall 5.4% rise in housing prices in locked districts. After narrowing the bandwidths, we find a rise of approximately 6.4%, 3.4%, 2.8%, 3.5%, and 2.7% for bandwidths within 5 km, 2 km, 1 km, IK optimal bandwidth 2.687 km, and CCT optimal bandwidth 1.680 km, respectively. Furthermore, the LDHSE increases the transaction volume, supporting the argument that the policy definitively increases the demand for housing in districts with high-quality schools. The value of the chance to gain admission to an elite senior high school is estimated to be approximately 1.18–1.37 million CNY. These results reflect the fact that households not only attach great importance to children's education, but also choose to pay a hefty price to create future opportunities of access to elite high schools for their children.",17.0,,False,,Business,2021-08-01,395c5d2a8a1503c507d1e5afde3efe9eed74bfcd,pricingthevalueofthechancetogainadmissiontoaneliteseniorhighschoolinbeijingtheeffectoftheldhsepolicyonresalehousingprices,han,10.1016/j.cities.2021.103238,pricingthevalueofthechancetogainadmissiontoaneliteseniorhighschoolinbeijingtheeffectoftheldhsepolicyonresalehousingprices|2021,1,2655,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ab191331e36cbad09e63b15fe6718ff9ef7bb99d,10.1016/j.cities.2021.103237,,Does school choice program affect local housing prices?: Inter- vs. intra-district choice program,2021.0,,Hyunjun Park; Alan Tidwell; Sungho Yun; Chang-Ha Jin,"Abstract We examine the effect of inter-district and intra-district high school programs on the residential real estate market in Seoul, South Korea, focusing on the switching effect from intra-district zoning to inter-district school choice. Data consist of 85,744 transaction-based housing observations corresponding to the 2008–2012 period surrounding South Korea's education policy change that directly affects 24 high schools. We employ a difference-in-differences (DID) methodology overcoming potential mis-specification bias while controlling for school, locational, and housing characteristics; and, find that switching from intra-district to an inter-district high school choice program decreases housing prices by an average of 4.7% within 0.5 km after the policy change was announced and another 5.3% after school admission. The observed price discount is ameliorated as the location boundary surrounding the affected schools is extended. This result suggests that the policy change reduced the economic barrier of higher housing prices near top performing schools and increased socio-economic diversity and geographic educational parity across the socio-economic spectrum. However, the creation of two different school systems can favor inter- over intra-district schools over time so care must be taken to avoid inequality between the two systems.",17.0,,False,,Business,2021-08-01,ab191331e36cbad09e63b15fe6718ff9ef7bb99d,doesschoolchoiceprogramaffectlocalhousingpricesintervsintradistrictchoiceprogram,park,10.1016/j.cities.2021.103237,doesschoolchoiceprogramaffectlocalhousingpricesintervsintradistrictchoiceprogram|2021,1,2360,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fbca687495ec1c96bddbdc0ba4ca22d0417a1385,10.1080/09599916.2018.1513057,,House prices and proximity to kindergarten – costs of distance and external effects?,2018.0,Journal of Property Research,Theis Theisen; A. Emblem,"ABSTRACT Parents accompany children to day-care, implying costs of time and money. Distance to kindergarten may therefore be an important locational attribute, which is likely to be discounted into house prices. We account for this through a theoretical model of house price formation, incorporating not only monetary and time costs associated with accompanying children to a kindergarten, but also possibly negative external effects of kindergartens on their immediate vicinity. Our theoretical model predicts that house prices increase as distance to kindergarten decreases, reach a peak, and then decline as one come very close to a kindergarten. We use a large sample of house transactions from a Norwegian town to explore the relationship between house prices and the distance to kindergarten. The empirical results support the prediction that house prices decline as distance to kindergarten increases, but we find no significant drop in house prices in the immediate vicinity of kindergartens. The results may be of interest to several actors in real-estate markets, perhaps particularly to urban planners and real-estate developers when considering the location of kindergartens.",17.0,,False,,Economics,2018-10-02,fbca687495ec1c96bddbdc0ba4ca22d0417a1385,housepricesandproximitytokindergartencostsofdistanceandexternaleffects,theisen,10.1080/09599916.2018.1513057,housepricesandproximitytokindergartencostsofdistanceandexternaleffects|2018,1,2187,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",03c70028d8f9e9e8a1486b12a093d1fac7daa1a0,10.1111/1540-6229.12302,,Getting High and Low Prices: Marijuana Dispensaries and Home Values,2019.0,Real Estate Economics,Justin Tyndall,"Laws concerning marijuana have recently undergone liberalization in many North American markets. The changing legal environment has enabled the establishment of retail marijuana dispensaries. Local externalities generated by dispensaries may impact home values, particularly by influencing demand for the surrounding neighbourhood. Recent empirical evidence has found a positive effect of dispensaries on home values. I use unique data on 84 dispensaries and 62,000 repeat home transactions from Vancouver, Canada to estimate the effect of dispensaries on home prices. Historical Google Street View images are used to construct a longitudinal record of dispensary activity. I find no evidence that dispensaries increase local home values in Vancouver and some evidence of a negative price effect for homes located within 100 meters of a dispensary. JEL: R21, R30, R31, R38 Housing, Marijuana, Local Amenity, Real Estate",17.0,,False,,Economics,2019-09-12,03c70028d8f9e9e8a1486b12a093d1fac7daa1a0,gettinghighandlowpricesmarijuanadispensariesandhomevalues,tyndall,10.1111/1540-6229.12302,gettinghighandlowpricesmarijuanadispensariesandhomevalues|2019,1,1919,2,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),05ad9d8be0cb3e8795591d551590f6dbe54c5011,10.1108/ijhma-03-2015-0016,,Hedonic appraisal of apartments in Tirana,2016.0,,Marsela Thanasi,,17.0,,False,Review,Economics,2016-07-01,05ad9d8be0cb3e8795591d551590f6dbe54c5011,hedonicappraisalofapartmentsintirana,thanasi,10.1108/ijhma-03-2015-0016,hedonicappraisalofapartmentsintirana|2016,1,1000,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic || real estate hedonic regression",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",443a582a1db48a2aad10dfd2ff51939e5b5bb3bb,10.1080/02673030701608183,,Landscaping Attributes and Property Buyers' Profiles: Their Joint Effect on House Prices,2007.0,,F. D. Rosiers; M. Thériault; Y. Kestens; P. Villeneuve,,17.0,,False,Review,Business,2007-11-01,443a582a1db48a2aad10dfd2ff51939e5b5bb3bb,landscapingattributesandpropertybuyersprofilestheirjointeffectonhouseprices,rosiers,10.1080/02673030701608183,landscapingattributesandpropertybuyersprofilestheirjointeffectonhouseprices|2007,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",29983f9ade705f981b012495b6339aac7ac62359,10.3368/le.84.2.188,,Finding the Missing Premium: An Explanation of Home Values within Residential Community Associations,2008.0,Land Economics,J. Groves,,17.0,,False,,Geography,2008-05-01,29983f9ade705f981b012495b6339aac7ac62359,findingthemissingpremiumanexplanationofhomevalueswithinresidentialcommunityassociations,groves,10.3368/le.84.2.188,findingthemissingpremiumanexplanationofhomevalueswithinresidentialcommunityassociations|2008,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,real estate hedonic regression,ac12b67b8c5e189983ceb5febdcf0b91db7f62d2,10.1016/j.landusepol.2020.105183,,Identifying the effects of a land-use policy on willingness to pay for open space using an endogenous switching regression model,2021.0,,Yang Cao; B. Swallow; F. Qiu,"Abstract An open space offers a stream of economic, social and environmental benefits. Population growth and extensive forms of economic growth have encroached onto large amounts of open space, converting forests, parks, grasslands and crop fields to developed uses, including residential, commercial and industrial. Analysts have used hedonic pricing techniques to estimate the capitalization of the values of open space into residential property values and the effects of changes in land-use policies on property values. Few of those studies have considered the possibility that property transactions may not occur randomly under different policy regimes. House sellers, buyers and developers respond to land use policies in their sale and purchase behaviors, thus changing the portfolio of houses for which sales data are available in a particular period. Such non-randomness can further bias the policy assessment using regression analysis. We address this issue by estimating the effects of a new development policy on WTP for open space as well as property values while correcting the selection bias that can arise as the result of self-selection. The empirical case is the Town of Okotoks, Alberta, Canada, where a conservation-oriented land use policy was replaced by a more development-oriented policy in 2012. An endogenous switching regression model is utilized to address the bias that could arise through self-selection. Key findings include: (1) selection does have an impact on estimating people's WTPs, and ignoring this issue leads to biased welfare calculations; (2) properties with nearby pastures and croplands were less likely to be traded on the real estate market after the introduction of the policy; (3) WTP for nearby cropland declined by about 1% (CAD 5148) of the average property price, due to the implementation of the new pro-development land policy; and (4) the pro-development policy led to a 8.4 % reduction in the value of Okotoks residential real estate.",16.0,,False,,Business,2021-03-01,ac12b67b8c5e189983ceb5febdcf0b91db7f62d2,identifyingtheeffectsofalandusepolicyonwillingnesstopayforopenspaceusinganendogenousswitchingregressionmodel,cao,10.1016/j.landusepol.2020.105183,identifyingtheeffectsofalandusepolicyonwillingnesstopayforopenspaceusinganendogenousswitchingregressionmodel|2021,1,2990,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || real estate hedonic regression",2,3,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,a363d6cac121c29186feb15d46840faab8796f4f,10.1016/j.enpol.2018.09.017,,The value of residential energy efficiency in interior Alaska: A hedonic pricing analysis,2018.0,Energy Policy,Dominique J. Pride; Joseph M. Little; M. Mueller-stoffels,"Abstract Residents of Interior Alaska are faced with a cold climate and relatively high energy prices, which results in high home energy expenditures. Increasing the energy efficiency of the housing stock can help reduce household energy expenditures. Following a spike in oil prices in 2008, legislation was passed, which created the Home Energy Rebate Program. Homeowners participating in the program were eligible to receive up to a $10,000 rebate for preapproved home energy efficiency improvements. This paper examines the effect of the Home Energy Rebate program on the selling prices of single-family residences in the Fairbanks North Star Borough from 2008 through 2015 using a hedonic pricing analysis. The results show that homes that completed the Home Energy Rebate program in the Fairbanks North Star Borough sell for a 15.1–16.5% price premium over similar homes that did not complete the program, which indicates that investments in residential energy efficiency are compensated. This is the first study to examine the impact of energy efficiency on house prices in a market with a subarctic climate.",16.0,,False,,Business,2018-12-01,a363d6cac121c29186feb15d46840faab8796f4f,thevalueofresidentialenergyefficiencyininterioralaskaahedonicpricinganalysis,pride,10.1016/j.enpol.2018.09.017,thevalueofresidentialenergyefficiencyininterioralaskaahedonicpricinganalysis|2018,1,2115,2,hedonic house price || hedonic housing prices,2,2,6,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,machine learning hedonic pricing housing,74fb81e360438d95b72d6619e7446bb4efda2951,10.1080/17421772.2024.2418906,,Determinants of housing rental prices in Seoul: applying explainable AI,2024.0,Spatial Economic Analysis,Tae-Young Kim; Eunil Park; Doojin Ryu,"ABSTRACT Though previous analyses of housing rental prices rely on hedonic pricing or ordinary least squares models, they cannot fully account for the nonlinearity and complexity inherent in the determination of market prices. We exploit machine-learning techniques to accurately understand the determinants of housing rental prices and emphasise explainable artificial intelligence (XAI) to overcome the limitations of classical machine learning with black-box features. To analyse the determinants of housing rental prices in Seoul, we apply extreme gradient boosting (XGBoost) and Tree Shapley additive explanations (TreeSHAP) models which learn and explain nonlinear features of the rental prices. XGBoost predicts rental prices more accurately than regressions and is robust to multicollinearity. TreeSHAP identifies local and global features affecting the determination of rental prices. Our analyses identify the individual impacts of various determinant variables on rental prices and underscore the multifaceted influences that these variables exert on housing affordability.",16.0,,False,JournalArticle,,2024-12-04,74fb81e360438d95b72d6619e7446bb4efda2951,determinantsofhousingrentalpricesinseoulapplyingexplainableai,kim,10.1080/17421772.2024.2418906,determinantsofhousingrentalpricesinseoulapplyingexplainableai|2024,1,2084,2,hedonic housing prices || machine learning hedonic pricing housing,2,2,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 openalex,hedonic housing prices,https://openalex.org/W2765508957,10.20965/jaciii.2017.p1040,,Housing Market Hedonic Price Study Based on Boosting Regression Tree,2017.0,Journal of Advanced Computational Intelligence and Intelligent Informatics,Guangtong Gu; Bing Xu,"Based on the purchase price data of new real estate markets three cities in China, Beijing, Shanghai, and Guangzhou, including architectural features, neighborhood property features, and location features, in this study a boosting regression tree model was built to study the factors and the influence path of housing prices from the microcosmic perspective. First, a classical hedonic price model was constructed to analyze and compare the significant effect factors on housing prices in the market segments of the three cities. Second, the gradient boosting regression tree method that is proposed in this paper was applied to the three markets in combination to analyze the influence paths and factors and the importance of the type of housing hedonic price. The influence paths of housing hedonic prices and decision tree rules are visualized. The significant housing features are effectively extracted. Finally, we present three main conclusions and several suggestions for policy makers to improve urban functions while stabilizing real estate prices.",16.0,,True,article,,2017-10-20,https://openalex.org/W2765508957,housingmarkethedonicpricestudybasedonboostingregressiontree,gu,10.20965/jaciii.2017.p1040,housingmarkethedonicpricestudybasedonboostingregressiontree|2017,1,2057,1,hedonic housing prices,2,2,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),5969eaa11840cbf2194d8cc2d24399e4a440a8b6,10.1080/10511482.2020.1768573,,The Commodity Effects of Decommodification: Community Land Trusts and Neighborhood Property Values,2020.0,,Katharine L. Nelson; J. DeFilippis; Richard Kruger; Olivia Williams; Joseph Pierce; Deborah G. Martin; Azadeh Hadizadeh Esfahani,"ABSTRACT This article explores the impacts of community land trust (CLT) properties on the real estate prices of nearby homes through a case study of a relatively large CLT in Minneapolis, Minnesota. We use hedonic regression and a difference-in-difference estimation with spatial error correction to measure price effects. The number of developments citywide is insufficient to yield significant results. However, we find evidence that clustering CLTs stemmed the decline in sales prices during the foreclosure crisis. The introduction of the first nearby CLT had no measurable price impact, but each additional CLT was associated with a 5% higher sales price in North Minneapolis, and 3% higher in Central Minneapolis. In the postrecession period, we estimate that the introduction of CLTs in North Minneapolis was associated with a 10.9% increase in nearby sales prices. These results suggest that, contrary to common assumptions, price effects are strongest when affordable properties are spatially clustered.",16.0,,False,,Business,2020-08-04,5969eaa11840cbf2194d8cc2d24399e4a440a8b6,thecommodityeffectsofdecommodificationcommunitylandtrustsandneighborhoodpropertyvalues,nelson,10.1080/10511482.2020.1768573,thecommodityeffectsofdecommodificationcommunitylandtrustsandneighborhoodpropertyvalues|2020,1,2013,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression",2,3,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1068/a42422,,Spatial Variation in Job Accessibility and Gender: An Intraregional Analysis using Hedonic House-Price Estimation,2010.0,Environment and Planning A: Economy and Space,Liv Osland,"Using data on single-family detached houses from a Norwegian region, in this paper I study the existence of gender differences in estimated labour-market accessibility between locations. Different specifications of a gravity-based job-accessibility measure are included in hedonic house-price models. I test for significant gender-related differences in the distance-deterrence parameters found in the labour-market accessibility indicator. The estimated distance-deterrence parameters are lower for women than for men. This difference is, however, not significant, given the data. The main gender-related differences relate to the spatial variation in job accessibility as measured here. The supply of relevant jobs is significantly higher for women in the administrative centres of municipalities in the area, and significantly higher for men outside these administrative centres. The attractive forces of the centres could be relevant as an explanation of differentiated gender-related commuting patterns.",16.0,,,journal-article,,,doi_10_1068_a42422,spatialvariationinjobaccessibilityandgenderanintraregionalanalysisusinghedonichousepriceestimation,osland,10.1068/a42422,spatialvariationinjobaccessibilityandgenderanintraregionalanalysisusinghedonichousepriceestimation|2010,1,2008,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic house price",2,1,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,house prices proximity hedonic,1eecd785e1bace005f982d0271dc15d2de2dee67,10.1177/15270025211018258,,The Amenity Value of Sports Facilities: Evidence From the Staples Center in Los Angeles,2021.0,,Zachary T. Keeler; H. Stephens; B. Humphreys,"U.S. cities have recently experienced a boom in new sports facility construction. Although these facilities can provide benefits to local residents, they may also generate negative externalities, making dwellings near a facility less desirable. Using a hedonic spatial difference-in-differences model, we analyze the impact of proximity to the Staples Center, a sports and entertainment venue in downtown Los Angeles, California, on house prices. Results indicate that the arena opening increased nearby house prices and that there were also positive “anticipation” effects associated with the announcement of the new arena location and local government approval.",16.0,,False,JournalArticle,,2021-10-01,1eecd785e1bace005f982d0271dc15d2de2dee67,theamenityvalueofsportsfacilitiesevidencefromthestaplescenterinlosangeles,keeler,10.1177/15270025211018258,theamenityvalueofsportsfacilitiesevidencefromthestaplescenterinlosangeles|2021,1,1663,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || house prices proximity hedonic",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e25664df9de1f78dcde6595e7a7d834098ae3ff8,10.1108/ijhma-08-2018-0062,,Determinants of housing prices in Baku: empirical analyses,2019.0,International Journal of Housing Markets and Analysis,Khatai Aliyev; Mehin Amiraslanova; N. Bakirova; Narmin Eynizada," Purpose This paper aims to reveal major factors affecting housing prices (flats and houses) in Baku, the capital of Azerbaijan Republic. Design/methodology/approach Based on cross-sectional data set of 497 flats and 443 houses, polynomial regression models are estimated for flats and houses separately. Regression models are estimated by using ordinary least squares. Findings Location, largeness, repair level and existence of bill of sale are major price determinants for flats. For houses, number of rooms also matters. Findings reveals that houses are land intensive (more floors, less land area) toward city center, and vice versa. Price difference due to existence of bill of sale diminishes significantly toward the surrounding areas. Research limitations/implications The data set represents view of sellers and does not take into consideration price bargaining in time of sale; probability of information asymmetries exists which not could accounted for, and urgency of sale is not considered. Practical implications Estimation results can be used for housing valuation by real estate market participants and investors. Social implications Research findings reveal importance of bill of sale as a major price determinant and expected to attract policymakers’ attention to solve such a big social problem. Additionally, models can be based for price estimations in Baku housing market. Originality/value The study contributes to the literature by empirically analyzing housing market in Baku, Azerbaijan. Research produces new practically valuable findings. ",16.0,,False,,Economics,2019-02-14,e25664df9de1f78dcde6595e7a7d834098ae3ff8,determinantsofhousingpricesinbakuempiricalanalyses,aliyev,10.1108/ijhma-08-2018-0062,determinantsofhousingpricesinbakuempiricalanalyses|2019,1,2580,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || cites:sirmans2006",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5a417a0b487d4c73da3ce68d2f114a1f9cad1c44,10.1016/j.habitatint.2019.04.001,,"Tourism and housing prices in Santa Marta, Colombia: Spatial determinants and interactions",2019.0,Habitat International,Nestor Garza; Maria Carolina Ovalle,"Abstract We analyze Real Estate effects of Tourism-Oriented Development in Santa Marta, a touristic Caribbean city. We hypothesize that non-local demand affects the spatial distribution of housing prices and their determinants, for both –high and low-income groups. This is the first spatial economic analysis of the relationship between Tourism-Oriented Development and Real Estate Markets in a Global South city. We use 2016 housing transaction data to identify price determinants for different income groups using Spatial Quantile Regression. The regression results go beyond describing differentiated submarkets by income group, more common in the literature, identifying predicted prices by income groups in contiguous locations. These results are refined using qualitative information that confirms that Tourism-Oriented-Development affects the spatial distribution of prices, while increasing the everyday transport hardship in the urban agglomeration.",16.0,,False,,Geography,2019-05-01,5a417a0b487d4c73da3ce68d2f114a1f9cad1c44,tourismandhousingpricesinsantamartacolombiaspatialdeterminantsandinteractions,garza,10.1016/j.habitatint.2019.04.001,tourismandhousingpricesinsantamartacolombiaspatialdeterminantsandinteractions|2019,1,1959,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",024be83eb57dd223f6419c3ab0e10b1fb5409a60,10.1016/j.jhe.2021.101807,,Foreign buyer taxes and house prices in Canada: A tale of two cities,2021.0,Journal of Housing Economics,Zai-Chao Du; Hua Yin; Lin Zhang,"Abstract In August 2016, the government of British Columbia imposed a tax of 15% on foreign entities buying residential property in Vancouver. A similar tax was imposed in Toronto in April 2017. This paper empirically evaluates the effects of foreign buyer taxes (FBTs) on house prices in Vancouver and Toronto, using a variant of the panel program evaluation method with city-level data and a regression discontinuity design method with house-level transaction data. Our results show that the FBT reduced the house prices in Vancouver by around 5% from 2016:M08 to 2017:M12, and by something within the range of 7% to 9% in Toronto from 2017:M04 to 2017:M12. These effects were concentrated in the single-detached home segment in both cities.",16.0,,False,,Business,2021-09-01,024be83eb57dd223f6419c3ab0e10b1fb5409a60,foreignbuyertaxesandhousepricesincanadaataleoftwocities,du,10.1016/j.jhe.2021.101807,foreignbuyertaxesandhousepricesincanadaataleoftwocities|2021,1,1743,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",125777e71d438fe33364e306e7ff7e0698ed1710,10.15396/eres2016_227,,Do the Determinants of House Prices Change over Time? Evidence from 200 Years of Transactions Data,2016.0,,Martijn Droes; A. van de Minne,,16.0,,False,,Business,,125777e71d438fe33364e306e7ff7e0698ed1710,dothedeterminantsofhousepriceschangeovertimeevidencefrom200yearsoftransactionsdata,droes,10.15396/eres2016_227,dothedeterminantsofhousepriceschangeovertimeevidencefrom200yearsoftransactionsdata|2016,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,79c9f07bb0c94e267b809bbc0b9bdf89c7eb16dc,10.1016/j.landurbplan.2020.104037,,Threshold effects of incremental redevelopment of an industrial property on a residential neighbourhood,2021.0,,B. Tang; K. Wong,"Abstract The threshold effect refers to the presence of a tipping point at which incremental action triggers an abrupt change. It is ubiquitous in both the environment and society. In this study, we identify the threshold effects of the continuous renewal of an industrial area on nearby housing in Hong Kong. Rather than using hedonic or difference-in-differences models, threshold regression models are used to detect the changes in the price-distance gradient of apartments near an industrial redevelopment project over two decades. We found a significant change in the value of apartments located in close proximity to the redevelopment site after a critical mass of new office space was built. Differential responses are also found across geography and for different building types. A possible explanation for these effects is the gentrification of the entire neighborhood. It is likely that a new spatial mix of blue- and white-collar households emerged in the surrounding neighborhoods along with the development of industrial property. Different building features appeal to different housing classes, resulting in dissimilar price-distance responses of the apartments. Integrative spatial design and good connectivity of industrial redevelopment with its neighborhood can possibly generate a wider geographical spread of its positive spillover in urban regeneration.",15.0,,False,,Geography,2021-04-01,79c9f07bb0c94e267b809bbc0b9bdf89c7eb16dc,thresholdeffectsofincrementalredevelopmentofanindustrialpropertyonaresidentialneighbourhood,tang,10.1016/j.landurbplan.2020.104037,thresholdeffectsofincrementalredevelopmentofanindustrialpropertyonaresidentialneighbourhood|2021,1,2374,2,hedonic housing prices || spatial hedonic model housing,2,1,4,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bbb8674354e766e47727d9838744cf79f968dae1,10.1108/ijhma-02-2022-0025,,Explainable housing price prediction with determinant analysis,2022.0,International Journal of Housing Markets and Analysis,Ean Zou Teoh; Wei-Chuen Yau; T. Ong; Tee Connie," Purpose This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices. Design/methodology/approach Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R2 of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers. Findings By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making. Research limitations/implications A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable. Originality/value The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers. ",15.0,,False,,,2022-08-08,bbb8674354e766e47727d9838744cf79f968dae1,explainablehousingpricepredictionwithdeterminantanalysis,teoh,10.1108/ijhma-02-2022-0025,explainablehousingpricepredictionwithdeterminantanalysis|2022,1,4396,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,6,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",aaf6de3c96739d358234d1744b5d34cb144f9300,10.1177/0042098016665955,,Market heterogeneity and the determinants of Paris apartment prices: A quantile regression approach,2017.0,,Charles-Olivier Amédée-Manesme; Michel Baroni; Fabrice Barthélémy; Francois Des Rosiers,,15.0,,False,JournalArticle,Economics,2017-11-01,aaf6de3c96739d358234d1744b5d34cb144f9300,marketheterogeneityandthedeterminantsofparisapartmentpricesaquantileregressionapproach,amédée-manesme,10.1177/0042098016665955,marketheterogeneityandthedeterminantsofparisapartmentpricesaquantileregressionapproach|2017,1,1000,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment prices hedonic || hedonic house price || hedonic housing prices || implicit prices housing attributes",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",8f5757703a010ac3f39fe05e4616e3d438640bce,10.1007/s12061-021-09383-6,,"Identifying the Spatial Heterogeneity in the Effects of the Social Environment on Housing Rents in Guangzhou, China",2021.0,Applied spatial analysis and policy,Yang Wang; Kang-Min Wu; Lixia Jin; Geng-Zhi Huang; Yu-Ling Zhang; Yong-Xian Su; Hong'ou Zhang; Jing Qin,,15.0,,False,,Business,2021-04-27,8f5757703a010ac3f39fe05e4616e3d438640bce,identifyingthespatialheterogeneityintheeffectsofthesocialenvironmentonhousingrentsinguangzhouchina,wang,10.1007/s12061-021-09383-6,identifyingthespatialheterogeneityintheeffectsofthesocialenvironmentonhousingrentsinguangzhouchina|2021,1,1000,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cce28f52d1753b3c8515b61bbd8995005ed2865b,10.1007/s11116-021-10233-0,,Evaluating the spatial and temporal distribution of beltway effects on housing prices using difference-in-differences methods,2021.0,Transportation,J. Murray; Eleni Bardaka,,15.0,,False,,Mathematics,2021-09-30,cce28f52d1753b3c8515b61bbd8995005ed2865b,evaluatingthespatialandtemporaldistributionofbeltwayeffectsonhousingpricesusingdifferenceindifferencesmethods,murray,10.1007/s11116-021-10233-0,evaluatingthespatialandtemporaldistributionofbeltwayeffectsonhousingpricesusingdifferenceindifferencesmethods|2021,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",805b1f41a73870d0d17451e1c7328afce971915a,10.1007/s12061-023-09536-9,,"Urban Structure, Housing Prices and the Double Role of Amenity: A Study of Nanjing, China",2023.0,Applied spatial analysis and policy,Meitong Liu; Y. Wei; Yangyi Wu,,15.0,,False,JournalArticle,,2023-07-26,805b1f41a73870d0d17451e1c7328afce971915a,urbanstructurehousingpricesandthedoubleroleofamenityastudyofnanjingchina,liu,10.1007/s12061-023-09536-9,urbanstructurehousingpricesandthedoubleroleofamenityastudyofnanjingchina|2023,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",045ff0f8219d9fbe3eb7a54c3ddea8277108cc97,10.1061/jupddm.upeng-4434,,"Multilevel Impacts of Urban Amenities on Housing Price in Tehran, Iran",2023.0,Journal of urban planning and development,A. Soltani; N. Zali; Roghayeh Mirzaei; M. Heydari; Ali Rahimi; Arman Hamidi,,15.0,,False,JournalArticle,,,045ff0f8219d9fbe3eb7a54c3ddea8277108cc97,multilevelimpactsofurbanamenitiesonhousingpriceintehraniran,soltani,10.1061/jupddm.upeng-4434,multilevelimpactsofurbanamenitiesonhousingpriceintehraniran|2023,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",e231749528dff7451238577bcd44062a15fc6ab9,10.1016/j.landurbplan.2020.103883,,3-D spatial hedonic modelling: Environmental impacts of polluted urban river in a high-rise apartment market,2020.0,,Xun Li; Wendy Y. Chen; Frankie Hin Ting Cho,"Abstract The vertical dimension of the residential apartment market has been largely overlooked in spatial hedonic studies. To adequately quantify the environmental impacts brought by various urban natural elements on apartment prices using spatial hedonic models, the selection of an appropriate weights matrix, which could adequately approximate complex spatial relationship amongst apartment units in high-rise housing markets, is an important methodological challenge. Using a micro high-rise neighbourhood in Guangzhou (China) as a case, we compare the model performance of a set of 3-D spatial weights matrices in capturing the environmental impacts of a polluted river running by the residential neighbourhood on apartment transaction prices. Three 3-D spatial weights schemes are examined, including distance-based, contiguity-based, and a hybrid distance-contiguity weights matrix. Our Monte Carlo simulations and empirical comparison consistently confirm that contiguity-based weights matrices outperform distance-based and hybrid weights alternatives. Adopting the appropriate second-order queen’s cube contiguity matrix in a 3-D Spatial Autoregressive Combined (SAC) model, the spatial heterogeneity of urban river’s environmental impacts along both horizontal and vertical dimensions is revealed. We find significant negative impacts associated with the proximity and visibility of the polluted river, which are strengthened for apartments located on the 10th floor and below. Our findings highlight the urgency of restoring polluted urban rivers and provide evidence-based information for local authorities and private housing developers to restore polluted rivers in order to improve environmental amenities.",14.0,,False,,Environmental Science,2020-11-01,e231749528dff7451238577bcd44062a15fc6ab9,3dspatialhedonicmodellingenvironmentalimpactsofpollutedurbanriverinahighriseapartmentmarket,li,10.1016/j.landurbplan.2020.103883,3dspatialhedonicmodellingenvironmentalimpactsofpollutedurbanriverinahighriseapartmentmarket|2020,1,2723,5,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment prices hedonic || hedonic housing prices || hedonic price model amenities residential || spatial hedonic model housing",2,2,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,1c1f68420485e15e36e9afff78c3e2e41c2706c3,10.1016/j.landusepol.2019.104283,,Hedonic valuation of harmful algal bloom pollution: Why econometrics matters?,2019.0,,A. Osseni; F. Bareille; P. Dupraz,"Abstract Harmful algal bloom (HAB) pollution is a major environmental threat worldwide. This is the case in Brittany, where approximately half of the coastal municipalities has been polluted in the two last decades. This pollution affects the welfare of the population by decreasing the accessibility of beaches for several professional and recreational users (anglers, walkers, joggers, etc.). Here, we estimated the willingness to pay (WTP) to reduce HAB pollution levels using the hedonic pricing method for the particular case of the Breton rural housing market between 2010 and 2012. We found that, on average, residents that were located 20 km from a polluted beach were willing to pay approximately 208€/inhabitant/year to reduce HABs to the minimal observed levels on Breton seacoasts. This estimate controlled for three sources of identified potential biases biathat may affect the quality of the estimation: the agricultural characteristics of upstream watersheds, the spatial unobserved heterogeneity and the adjacency effects. Indeed, the estimated WTP was 18% to 46% higher than the WTP that was estimated with a nonspatialized econometric model that did not account for these features. In particular, the adjacency effect on house prices due to housing market functioning explained one-third of the total effect. This study illustrates the importance of using suitable econometric specifications when applying revealed preference estimations to house prices.",14.0,,False,,Geography,2019-11-18,1c1f68420485e15e36e9afff78c3e2e41c2706c3,hedonicvaluationofharmfulalgalbloompollutionwhyeconometricsmatters,osseni,10.1016/j.landusepol.2019.104283,hedonicvaluationofharmfulalgalbloompollutionwhyeconometricsmatters|2019,1,2472,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",b92821ec71d50918ddd6fab4de773d00823b6d77,10.1080/09599916.2018.1510428,,Hedonic pricing and the spatial structure of housing data – an application to Berlin,2018.0,Journal of Property Research,Sören Gröbel; Lorenz Thomschke,"ABSTRACT Housing prices are largely determined by physical location. By applying the outsample prediction accuracy of rental prices as evaluation criteria, we examine whether the choice of the hedonic model additionally depends on the spatial structure of housing data, i.e. accounting for locational effects by either district fixed effects or spatial econometric modelling. Our results show that a generalised spatio-temporal model outperforms a district fixed effects model only if the spatial density – the weighted mean distance to nearest neighbours – is relatively small. Moreover, we use the required density thresholds to deduce a pseudo indifference curve, thereby showing that the ratio of the weighted spatial distance-to-the mean district diameter increases with the mean sample size per district. This emphasises the role of data structure and district choices for model selection. Differences in data can thereby serve as an explanation for contradictory findings in literature, whether spatial econometric methods or simple district fixed effects are used.",14.0,,False,,Economics,2018-07-03,b92821ec71d50918ddd6fab4de773d00823b6d77,hedonicpricingandthespatialstructureofhousingdataanapplicationtoberlin,gröbel,10.1080/09599916.2018.1510428,hedonicpricingandthespatialstructureofhousingdataanapplicationtoberlin|2018,1,2072,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,3,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",83b7b4b6ec09fdb53a25df4f854330bf334428ef,10.1086/726157,,Hazardous Waste and Home Values: An Analysis of Treatment and Disposal Sites in the United States,2023.0,Journal of the Association of Environmental and Resource Economists,D. Guignet; C. Nolte,"There are 2,389 treatment, storage, and disposal facilities (TSDFs) of hazardous waste in the contiguous United States. We frame two sets of difference-in-differences and triple differences quasi-experiments that exploit the spatial and temporal variation in contamination and cleanup events and estimate hedonic price regressions using a sample of 9.7 million home transactions from 2000–2018. The discovery of contamination and subsequent investigation is associated with up to a 5% depreciation in the value of homes within 750 meters of a TSDF, but the evidence is mixed. In contrast, we find strong evidence that the completion of cleanup leads to an average 6%–7% appreciation. A total capitalization of $295 million can be attributed to the 195 TSDFs that were remediated since the inception of the Resource Conservation and Recovery Act. We estimate that the completion of cleanup yields an average lower-bound, ex post benefit of about $14,000 per household.",14.0,,False,JournalArticle,,2023-06-05,83b7b4b6ec09fdb53a25df4f854330bf334428ef,hazardouswasteandhomevaluesananalysisoftreatmentanddisposalsitesintheunitedstates,guignet,10.1086/726157,hazardouswasteandhomevaluesananalysisoftreatmentanddisposalsitesintheunitedstates|2023,1,1967,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2,1,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d5717997f637cad56ed83f58063333e979ade328,10.11175/eastpro.2009.0.192.0,,Influence of Urban Rail Transit on House Value: Spatial Hedonic Analysis in Bangkok,2009.0,,Varameth Vichiensan; K. Miyamoto,,14.0,,False,,Geography,,d5717997f637cad56ed83f58063333e979ade328,influenceofurbanrailtransitonhousevaluespatialhedonicanalysisinbangkok,vichiensan,10.11175/eastpro.2009.0.192.0,influenceofurbanrailtransitonhousevaluespatialhedonicanalysisinbangkok|2009,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || spatial hedonic model housing",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),674687f59bdec96ac2b378f81aa0535ac950ffb3,10.1007/s41324-021-00416-3,,Comparing OLS based hedonic model and ANN in house price estimation using relative location,2021.0,Spatial Information Research,Mudit D. Mankad,,14.0,,False,,,2021-09-24,674687f59bdec96ac2b378f81aa0535ac950ffb3,comparingolsbasedhedonicmodelandanninhousepriceestimationusingrelativelocation,mankad,10.1007/s41324-021-00416-3,comparingolsbasedhedonicmodelandanninhousepriceestimationusingrelativelocation|2021,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || real estate hedonic regression",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,1 openalex,hedonic housing prices,https://openalex.org/W2150485309,10.1007/s10182-011-0173-2,,A functional connectivity approach for modeling cross-sectional dependence with an application to the estimation of hedonic housing prices in Paris,2011.0,AStA Advances in Statistical Analysis,Georges Bresson; Chêng Hsiao,,14.0,,False,article,,2011-10-26,https://openalex.org/W2150485309,afunctionalconnectivityapproachformodelingcrosssectionaldependencewithanapplicationtotheestimationofhedonichousingpricesinparis,bresson,10.1007/s10182-011-0173-2,afunctionalconnectivityapproachformodelingcrosssectionaldependencewithanapplicationtotheestimationofhedonichousingpricesinparis|2011,1,1000,4,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || spatial hedonic model housing",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/1080-8620.00023,,Do Tax‐Deferred Exchanges Impact Purchase Price? Evidence from the Phoenix Apartment Market,2001.0,Real Estate Economics,Andrew Holmes; Barrett A. Slade,"Many authors have commented on the compliance risk associated with tax‐deferred exchanges. However, no published studies explicitly address whether the risks associated with the exchange process impact the price at which exchanged assets trade. Using a unique data set that documents transactions for nondirect exchanges, this study examines the price impact of tax‐deferred exchanges on apartment transactions in the Phoenix, Arizona, market. Consistent with the price pressure hypothesis originally developed by Scholes (1972) and Kraus and Stoll (1972) and the tax capitalization hypothesis proposed by Oates (1969), the data show that exchange participants pay an economically significant premium to acquire replacement assets. A conventional hedonic price index is generated to investigate the rational bounds of the exchange premium.",14.0,,,journal-article,,,doi_10_1111_1080_8620_00023,dotaxdeferredexchangesimpactpurchasepriceevidencefromthephoenixapartmentmarket,holmes,10.1111/1080-8620.00023,dotaxdeferredexchangesimpactpurchasepriceevidencefromthephoenixapartmentmarket|2001,1,1839,2,apartment prices hedonic || apartment rent hedonic,2,0,1,2,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b85028275b011deb999436d9eb9aa1a2d46dcd7a,10.1108/ijhma-06-2020-0076,,The capitalization of spatial healthcare accessibility into house prices in Taiwan: an application of spatial quantile regression,2020.0,International Journal of Housing Markets and Analysis,Ti-Ching Peng,"PurposePopulation ageing is fast becoming a major social concern across the globe. This ageing trend unavoidably fuels elders’ demand for healthcare services. As the main users of health care service, whether the healthcare is geographically approachable in local areas is more imperative to senior residents with restricted mobility. This paper proposes to examine the effect of elders’ healthcare accessibility on property prices of Taipei Metropolis, Taiwan.Design/methodology/approachLuo and Qi’s (2009) enhanced two-step floating catchment area method – taking both healthcare demand and supply into account – was used to measure three types of healthcare services: “physician-to-elder ratio”, “hospital bed-to-elder ratio” and “ambulance-to-elder ratio”. Spatial quantile regression (SQR) model was then used to examine the spatial effect of healthcare accessibility on different property price ranges.FindingsThe “physician-to-elder ratio” and “hospital bed-to-elder ratio” demonstrated expected consistent positive effects across all quantiles of property prices (p< 0.01) in SQR, and its effects aggravated as the quantiles of property prices rose. The “ambulance-to-elder ratio” demonstrated a non-linear influence on property prices (i.e. a negative effect on lowest quantile prices but a positive on higher quantile prices) possibly due to the semi-obnoxious characteristic of the ambulance. That is, residents living in lower priced neighbourhoods may dislike ambulances’ annoying sound of sirens (i.e. ambulances’ disamenity), while residents living in higher valued neighbourhoods may on the contrary appreciate ambulances’ healthcare services (i.e. amenity).Practical implicationsThese findings are expected to offer some insights for government’s policies in providing elders in their later years with good residential quality and easy access to healthcare resource.Originality/valueThis paper is one of the few studies that consider the capitalization of the spatial healthcare accessibility to elders into property prices. In this ageing trend across the globe, although all the accessibility to medical resources should be equally critical, the application of spatial quantile regression revealed residents’ inconsistent tendency against semi-obnoxious ambulances. It provides a different perspective in defining the importance of healthcare accessibility in neighbourhoods.",14.0,,False,,Business,2020-12-07,b85028275b011deb999436d9eb9aa1a2d46dcd7a,thecapitalizationofspatialhealthcareaccessibilityintohousepricesintaiwananapplicationofspatialquantileregression,peng,10.1108/ijhma-06-2020-0076,thecapitalizationofspatialhealthcareaccessibilityintohousepricesintaiwananapplicationofspatialquantileregression|2020,1,3394,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",9b167268c26bd9296b02d777ed26f0e9b5134e42,10.1080/09599916.2019.1651755,,"Sales comparison approach, multiple regression analysis and the implicit prices of housing",2019.0,Journal of Property Research,G. Lisi,"ABSTRACT By using two of the main evaluation methods, namely the sales comparison approach and the multiple regression analysis, this paper points out the key role of implicit prices of housing characteristics in real estate appraisal. Firstly, this paper confirms the close link existing between the sales comparison approach and the multiple regression analysis. Furthermore, unlike examples in the related literature, this paper highlights the difference between marginal and implicit prices of housing characteristics. Although the related literature expresses awareness of the methodology for calculating marginal and implicit prices, there appears to be less awareness that implicit prices can: 1) lead to an estimate of the house price that is different from the regression model-predicted price; 2) replace the crucial and non-trivial phase of adjustment and reconciliation that characterises the main appraisal method, namely, the popular Sales Comparison Approach. Finally, an empirical analysis provides evidence of the key role of implicit prices in estimating house value.",14.0,,False,,Computer Science,2019-07-03,9b167268c26bd9296b02d777ed26f0e9b5134e42,salescomparisonapproachmultipleregressionanalysisandtheimplicitpricesofhousing,lisi,10.1080/09599916.2019.1651755,salescomparisonapproachmultipleregressionanalysisandtheimplicitpricesofhousing|2019,1,2085,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d58ecfd119a34ae0419e39082b7114991b030168,10.1109/geoinformatics.2011.5981032,,Geographically Weighted Regression model (GWR) based spatial analysis of house price in Shenzhen,2011.0,GEOINFORMATICS,Ji-Jin Geng; K. Cao; Le Yu; Yong Tang,,14.0,,False,Conference;Review,Geography,2011-06-24,d58ecfd119a34ae0419e39082b7114991b030168,geographicallyweightedregressionmodelgwrbasedspatialanalysisofhousepriceinshenzhen,geng,10.1109/geoinformatics.2011.5981032,geographicallyweightedregressionmodelgwrbasedspatialanalysisofhousepriceinshenzhen|2011,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",3bb67a0d4a803ad7bbd3731273974291274d9727,10.5040/9781509933495.0043,,Article 35,2021.0,The EU Charter of Fundamental Rights,Christopher G. Leggett; N. Bockstael; Anne Arundel,"This paper examines the effect water quality has on property values in Maryland's Anne Arundel County, located along the western shore of Chesapeake Bay. The study uses hedonic pricing techniques to accomplish this, which was an area lacking examination at the time this paper was written. The problem lies in how scientific water quality variables used (such as dissolved oxygen, nitrogen, and phosphorus) cannot be perceived by homeowners like air particulates can in air pollution. It is only when algae blooms and fish kills occur that homeowners notice, which is related to the perceived water clarity. The water quality variable used is fecal coliform bacteria and willingness to pay (WTP) is estimated for marginal improvements in this variable and a onetime improvement in a particular area. The model estimates fecal coliform count significantly decreases property values. The study was also able to address many of the statistical faults previously encountered in hedonic pricing techniques when reaching all of these values. A change of 100 fecal coliform counts per 100 mL is estimated to produce a 1.5% negative change in property prices. This is a significant amount of change in the fecal coliform count, but a wide variety of values was measured and must be accounted for. For the eight specifications of the variables lot size and water quality, the range of mean effect on predicted price for a 100 count change is from a low of $5,114 to a high of $9,824. To test the specific site improvement now that fecal coliform levels are shown to depress property values, a particular area of 41 residential parcels suffering from fecal coliform counts ranging from 50 counts per 100 mL to 240 counts per 100 mL is used to demonstrate the model's effectiveness. The projected increase in property values based on a 100 counts per 100 mL decrease was approximately $230,000, or about 2% of the aggregate assessed value over $10 million dollars. In order for all properties in the area to achieve 200 counts per 100 mL, or the state standard, 494 properties would need improvement that would benefit by $12.145 million dollars in property value increase (although the estimate is likely an upper bound).",13.0,,False,,,,3bb67a0d4a803ad7bbd3731273974291274d9727,article35,leggett,10.5040/9781509933495.0043,article35|2021,1,3211,1,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,hedonic property valuation residential,,10.1080/00420989550012645,,A Hedonic Analysis of Rent and Rental Revenue in the Subsidised and Unsubsidised Housing Sectors in Geneva,1995.0,Urban Studies,Sandra Buchel; Martin Hoesli,"In this paper, an analysis is made of what would be the rent difference for the tenants of subsidised units if these units were rented as unsubsidised units. Furthermore, the existence of any pecuniary disadvantage for the owner of a subsidised unit is investigated by considering his rental revenue (i.e. the amount paid by the tenant and the subsidy). It is found that there exists a premium between an unsubsidised and a subsidised unit when using the amount paid by the tenant (i.e. the rent) as the dependent variable. Also, the subsidy system in Geneva has an influence on the pricing of each characteristic of a unit. Furthermore, it is found that when using rental revenue as the dependent variable, both sub-samples can be used in a single hedonic equation. Thus, owners of subsidised units are not at a disadvantage due to the granting of a subsidy. As a result, Geneva's subsidy system matches the goals of agents on both the demand and supply sides.",13.0,,,journal-article,,,doi_10_1080_00420989550012645,ahedonicanalysisofrentandrentalrevenueinthesubsidisedandunsubsidisedhousingsectorsingeneva,buchel,10.1080/00420989550012645,ahedonicanalysisofrentandrentalrevenueinthesubsidisedandunsubsidisedhousingsectorsingeneva|1995,1,1961,6,apartment rent hedonic || hedonic housing prices || hedonic property valuation residential || house prices proximity hedonic || housing characteristics prices hedonic || implicit prices housing attributes,2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1467-9787.1992.tb00177.x,,THE EXCHANGEABILITY OF HEDONIC PROPERTY PRICE STUDIES*,1992.0,Journal of Regional Science,Scott E. Atkinson; Thomas D. Crocker,"ABSTRACT. Observations drawn from multiple markets are essential to the construction of indices of housing costs and to measures of demand for housing attributes. It is not evident when multiple markets exist or whether such markets exist for some attributes but not for others. We test for multiple markets by using Bayesian methods to assess the transferability (random exchangeability) of entire hedonic price expressions from one site and time to another, the transferability of hedonic price functions for particular attributes, and the degrees of similarity that hedonic price functions must have in order to be transferable. In our illustrations, price functions for structural housing attributes are generally transferable; prices for neighborhood attributes are not. Therefore, in our illustrations, the desired price indices and demand functions should be estimable for neighborhood attributes, but not for structural ones.",13.0,,,journal-article,,,doi_10_1111_j_1467_9787_1992_tb00177_x,theexchangeabilityofhedonicpropertypricestudies,atkinson,10.1111/j.1467-9787.1992.tb00177.x,theexchangeabilityofhedonicpropertypricestudies|1992,1,1933,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic housing prices",2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,8dad89f88ec2e117aec31bdf5a8262267aa351c5,10.1061/(asce)up.1943-5444.0000507,,"Effects of Urban Road Centrality on Property Values: Spatial Hedonic Analysis of the Housing Market in Wuhan, China",2019.0,Journal of urban planning and development,Rong-Hui Tan; Ke-Hao Zhou; Hengzhou Xu,"AbstractRoad infrastructure plays a crucial role in shaping urban spatial configurations and may generate price premiums in the housing market. In hedonic studies, the externalities of road infrast...",13.0,,False,,Economics,2019-06-01,8dad89f88ec2e117aec31bdf5a8262267aa351c5,effectsofurbanroadcentralityonpropertyvaluesspatialhedonicanalysisofthehousingmarketinwuhanchina,tan,10.1061/(asce)up.1943-5444.0000507,effectsofurbanroadcentralityonpropertyvaluesspatialhedonicanalysisofthehousingmarketinwuhanchina|2019,1,1200,2,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices",2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,028976180a5f01c74d70cc4e37e97dadb10004c7,10.1080/23754931.2018.1446354,,"Estimation of Spatial Association Between Housing Price and Local Environmental Amenities in Kolkata, India Using Hedonic Local Regression",2018.0,,M. Bera; B. Mondal; Gour Dolui; Suman Chakraborti,,13.0,,False,,Geography,2018-05-02,028976180a5f01c74d70cc4e37e97dadb10004c7,estimationofspatialassociationbetweenhousingpriceandlocalenvironmentalamenitiesinkolkataindiausinghedoniclocalregression,bera,10.1080/23754931.2018.1446354,estimationofspatialassociationbetweenhousingpriceandlocalenvironmentalamenitiesinkolkataindiausinghedoniclocalregression|2018,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || hedonic price model amenities residential || spatial hedonic model housing",2,1,1,3,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",c1a39182b9c4eb50b954ac2eee3c5b5d5846a743,10.11175/eastpro.2011.0.168.0,,Hedonic Analysis of Residential Property Values in Bangkok: Spatial Dependence and Nonstationarity Effects,2011.0,,Varameth Vichiensan; Sathita Malaitham; K. Miyamoto,,13.0,,False,,Economics,,c1a39182b9c4eb50b954ac2eee3c5b5d5846a743,hedonicanalysisofresidentialpropertyvaluesinbangkokspatialdependenceandnonstationarityeffects,vichiensan,10.11175/eastpro.2011.0.168.0,hedonicanalysisofresidentialpropertyvaluesinbangkokspatialdependenceandnonstationarityeffects|2011,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/s1051-1377(05)80037-3,,Zoning and hedonic housing price models,1991.0,Journal of Housing Economics,J.M. Pogodzinski; Tim R. Sass,,13.0,,,journal-article,,,doi_10_1016_s1051_1377_05_80037_3,zoningandhedonichousingpricemodels,pogodzinski,10.1016/s1051-1377(05)80037-3,zoningandhedonichousingpricemodels|1991,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic housing prices || housing characteristics prices hedonic",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/1540-6229.t01-20-00201,,Hedonic Property Valuation Models: Are Subjective Measures of Neighborhood Amenities Needed?,1979.0,Real Estate Economics,James R. Lang; Wesley H. Jones,"Common applications of hedonic residential property valuation models have relied on proxy measures of neighborhood amenities rather than directly measured subjective evaluations. While this approach simplifies data collection, there is reason to suspect that the use of proxy measures has degraded the accuracy of hedonic valuation models. The extent to which the use of proxy measures may decrease predictive accuracy, and fail to capture the effect of neighborhood amenities, has not been reported previously. The results presented here show that direct subjective measures yield only modest improvements in price prediction. Careful use of proxy measures for neighborhood amenities appears to be cost efficient for large‐scale appraisal work.",13.0,,,journal-article,,,doi_10_1111_1540_6229_t01_20_00201,hedonicpropertyvaluationmodelsaresubjectivemeasuresofneighborhoodamenitiesneeded,lang,10.1111/1540-6229.t01-20-00201,hedonicpropertyvaluationmodelsaresubjectivemeasuresofneighborhoodamenitiesneeded|1979,1,1745,4,apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic price model amenities residential || hedonic property valuation,2,0,1,2,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5e83261e49e7715cfa061ba4d497b1dab4327631,10.1108/ijhma-10-2023-0145,,Assessing the effect of housing attributes and green certification on Malaysian house price,2024.0,International Journal of Housing Markets and Analysis,Nor Nazihah Chuweni; N. Fauzi; Asmma' Che Kasim; Sekar Mayangsari; N. Wardhani," Purpose Sustainability represents innovative elements in determining the profitability of real estate investments, among other factors, including the green component in real estate. Evidence from the literature has pointed out that incorporating green features into residential buildings can reduce operational costs and increase the building’s value. Although green real estate is considered the future trend of choice, it is still being determined whether prospective buyers are willing to accept the extra cost of green residential investment. Therefore, this study aims to investigate the effect of housing attributes and green certification on residential real estate prices. Design/methodology/approach The impact of the housing attribute and green certification in the residential sectors was assessed using a transaction data set comprising approximately 861 residential units sold in Selangor, Malaysia, between 2014 and 2022. Linear and quantile regression were used in this study by using SPSS software for a robust result. Findings The findings indicate that the market price of residential properties in Malaysia is influenced by housing attributes, transaction types and Green Building Index certification. The empirical evidence from this study suggests that green certification significantly affects the sales price of residential properties in Malaysia. The findings of this research will help investors identify measurable factors that affect the transaction prices of green-certified residential real estate. These identifications will facilitate the development of strategic plans aimed at achieving sustainable rates of return in the sustainable residential real estate market. Practical implications Specifically, this research will contribute to achieving area 4 of the 11th Malaysia Plan, which pertains to pursuing green growth for sustainability and resilience. This will be achieved by enhancing awareness among investors and homebuyers regarding the importance of green residential buildings in contributing to the environment, the economy and society. Originality/value The regression model for housing attributes and green certification on house price developed in this study could offer valuable benefits to support and advance Malaysia in realising its medium and long-term goals for green technology. ",13.0,,False,JournalArticle,,2024-01-18,5e83261e49e7715cfa061ba4d497b1dab4327631,assessingtheeffectofhousingattributesandgreencertificationonmalaysianhouseprice,chuweni,10.1108/ijhma-10-2023-0145,assessingtheeffectofhousingattributesandgreencertificationonmalaysianhouseprice|2024,1,3343,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,4,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",55736d315d7ddbddf68a2b9c919fa123ed40b87a,10.1016/j.jtrangeo.2021.103029,,Geographically weighted regression models of residential property transactions: Walkability and value uplift,2021.0,Journal of Transport Geography,Edmund J. Zolnik,"Abstract Value uplift refers to the increase in property value attributable to proximate transportation infrastructure. The empirical literature is generally supportive of the argument to capture a fraction of such additional value to operate public transportation systems. Unfortunately, attention to the relative value of different transportation infrastructure besides rail infrastructure is not evident in the literature. The gap in the literature is problematic because an accurate estimate of the total revenue potential from present transportation infrastructure investments is not realistic nor is an accurate estimate of the value uplift potential from future transportation infrastructure investments realistic. In order to fill the gap, adoption of a geographically-weighted approach expands the temporal scale of analysis to more than a decade and the spatial scale of analysis to an entire heavy rail system in order to properly contextualize the value uplift from pedestrian infrastructure and from rail infrastructure. Results advance the state of knowledge on value uplift to show the range of value uplift attributable to walkability in proximity to heavy rail stations. Overall, the results warrant further research to better understand why the estimate of the value uplift attributable to walkability is higher than the estimate in the empirical literature.",13.0,,False,,Business,2021-04-01,55736d315d7ddbddf68a2b9c919fa123ed40b87a,geographicallyweightedregressionmodelsofresidentialpropertytransactionswalkabilityandvalueuplift,zolnik,10.1016/j.jtrangeo.2021.103029,geographicallyweightedregressionmodelsofresidentialpropertytransactionswalkabilityandvalueuplift|2021,1,2376,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1029/2008wr007281,,Using hedonic property models to value public water bodies: An analysis of specification issues,2009.0,Water Resources Research,Nicholas Z. Muller,"The hedonic literature has established that public water bodies provide external benefits that are reflected in the value of nearby residential real estate. The literature has employed several approaches to quantify these nonmarket services. With a residential hedonic model, this paper tests whether model specification affects resource valuation using an actively managed reservoir in Indiana and a passively managed lake in Connecticut. The results indicate that valuation is quite sensitive to model specification and that omitting either the waterview or waterfront variables from the hedonic function likely results in a misspecified model. The findings from this study are important for researchers and public agencies charged with managing water resources to bear in mind as the external benefits from existing or proposed man‐made lakes and reservoirs are estimated. Therefore, while it requires considerably more effort to determine which properties are in waterfront locations and which properties have a view, the potential mispecification of “distance‐only” models likely justifies these extra research costs. Further, the findings in this analysis call into question results from distance‐only models in the literature.",13.0,,,journal-article,,,doi_10_1029_2008wr007281,usinghedonicpropertymodelstovaluepublicwaterbodiesananalysisofspecificationissues,muller,10.1029/2008wr007281,usinghedonicpropertymodelstovaluepublicwaterbodiesananalysisofspecificationissues|2009,1,2233,3,apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic property valuation,2,0,1,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5a3d0caa40a6f1c0c02b9c2b731c3caceb87d1e2,10.1016/j.apgeog.2024.103479,,"Urban terrain, mountain landscape, and housing price: A heterogeneous investigation of the amenity effects in a mountainous city (Guiyang) from the vertical dimension",2025.0,Applied Geography,Xingyin Wang; Haizhen Wen; Bin Gui; Zhenlong Liu; Liu Yang,,13.0,,False,JournalArticle,,2025-01-01,5a3d0caa40a6f1c0c02b9c2b731c3caceb87d1e2,urbanterrainmountainlandscapeandhousingpriceaheterogeneousinvestigationoftheamenityeffectsinamountainouscityguiyangfromtheverticaldimension,wang,10.1016/j.apgeog.2024.103479,urbanterrainmountainlandscapeandhousingpriceaheterogeneousinvestigationoftheamenityeffectsinamountainouscityguiyangfromtheverticaldimension|2025,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8668b7e56b1ea1030296fd1fcdeddbc1e7a91421,10.1016/0094-1190(81)90028-0,,Ridge estimation of house value determinants,1981.0,,John E. Anderson,,13.0,,False,,Mathematics,1981-05-01,8668b7e56b1ea1030296fd1fcdeddbc1e7a91421,ridgeestimationofhousevaluedeterminants,anderson,10.1016/0094-1190(81)90028-0,ridgeestimationofhousevaluedeterminants|1981,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,819ca6a5cbf0da74dd95823377e63ac65e0cd9f0,10.1016/j.scitotenv.2021.145547,,Bringing the vertical dimension into a planar multilevel autoregressive model: A city-level hedonic analysis of homebuyers' utilities and urban river attributes.,2021.0,Science of the Total Environment,Xun Li; Wendy Y. Chen; F. Cho; R. Lafortezza,"Throughout history, urban rivers have been regarded as valuable natural elements that satisfy various human needs and affect where people reside. With the increasing expansion of modern cities along the vertical dimension, how urban rivers affect housing values and homebuyers' purchasing decisions in a 3-D context has attracted a significant amount of attention from researchers, environmental practitioners, urban planners, and policymakers. In this paper, we attempt to estimate how homebuyers' utilities are affected by various river attributes and their interactions using the vibrant high-rise apartment housing market in Guangzhou (south China) as a case study. An appropriate 3-D weights matrix is identified using ex ante Monte Carlo simulation combined with ex post validation on the basis of information criteria. By using the identified 3-D spatial weights scheme in a multilevel autoregressive modelling framework, an intricate combination of multidimensional spatial heterogeneity and spatial dependence can be sufficiently accounted for. Our analytical results reveal that river view and riverfront location are considered as negative utilities by Guangzhou's homebuyers, showing the significant negative impacts of river pollution. Yet, the proximity to urban rivers is regarded as a positive utility, revealing that homebuyers enjoy a sense of being close to nature and an emotional bond with traditional water culture. The black-odorous river water itself devalues apartment prices and adds the negative utilities of river view and riverfront location. Riparian greening would command a price premium, as well as mitigate the negative utilities of river view and riverfront location. Although the availability of walking paths and sitting benches along river stretches is generally regarded as a positive utility, it may worsen the negative impact of river view, but enhance the positive impact of river proximity. These results provide deeper managerial insights into how different river attributes influence apartment buyers' utilities and thus help environmental managers (in collaboration with housing developers) design urban river restoration initiatives so as to create pleasant and attractive neighbourhoods for prospective homebuyers.",12.0,,False,JournalArticle,Medicine,2021-06-10,819ca6a5cbf0da74dd95823377e63ac65e0cd9f0,bringingtheverticaldimensionintoaplanarmultilevelautoregressivemodelacitylevelhedonicanalysisofhomebuyersutilitiesandurbanriverattributes,li,10.1016/j.scitotenv.2021.145547,bringingtheverticaldimensionintoaplanarmultilevelautoregressivemodelacitylevelhedonicanalysisofhomebuyersutilitiesandurbanriverattributes|2021,1,3262,3,apartment prices hedonic || hedonic housing prices || spatial hedonic model housing,2,3,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,3c8a3b51ca38b7df2afd41cab52518e0976de88e,10.1108/ijhma-02-2018-0016,,How cultural values are reflected on the housing market – direct effects and the cultural spillover,2019.0,International Journal of Housing Markets and Analysis,M. Andersson; F. Kopsch; Peter Palm," Purpose The purpose of this paper is to analyse two questions. First, is there, and if so, how large is the price premium paid for a building exhibiting a cultural value? Second, are there any spillover effects of buildings with cultural values on sales prices of neighbouring houses? Design/methodology/approach Using a unique database of all buildings in the region of Halland, Sweden, combined with transaction data, hedonic models can be estimated, with spatially lagged variables describing proximity to three classes of culturally classified building – A, B and C – corresponding to building of national interest, building of regional interest and building of local interest. In addition, the authors also estimate models with a spatial specification on the error term, in an attempt to control for omitted variables. Findings The results indicate that cultural classification plays a role in determining the price of a property, with large effects (ranging between 36 and 60% price premiums) for the highest classification. In addition, the authors find evidence of a cultural externality, houses in the vicinity of building with high cultural value sell at a small, but statistically significant premium of 1%. Originality/value The cultural externality may be overlooked when it comes to valuation of cultural values in society, and therefore, it is likely that warranted protection acts to preserve cultural values in buildings become less than the social optimum. This paper suggests a new measure to cultural values contrasting previous research that rely on cultural preservation. This approach should limit problems with measurement errors that may lead to biased results. ",12.0,,False,,Economics,2019-06-03,3c8a3b51ca38b7df2afd41cab52518e0976de88e,howculturalvaluesarereflectedonthehousingmarketdirecteffectsandtheculturalspillover,andersson,10.1108/ijhma-02-2018-0016,howculturalvaluesarereflectedonthehousingmarketdirecteffectsandtheculturalspillover|2019,1,2694,6,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || hedonic property valuation || house prices proximity hedonic || spatial hedonic model housing",2,1,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bf67083cd43ab7938fd0c0f48f112cfe29e9fa3c,10.1016/j.jenvman.2022.115672,,Assessing the impact of wildfires on property values in wildland-urban intermix and interface in Colorado: A hedonic approach.,2022.0,Journal of Environmental Management,Longzhong Shi; Bo Chen; Xuan Chen; Zhuo Chen,"In this article, we investigate the impact of wildfire on property values in wildland-urban intermix (WUIM) and wildland-urban interface (WUIF) in Colorado. We construct fire indices accounting for the nonlinear impact of wildfires, and evaluate the effects of different fire attributes-proximity, frequency, and scale-on property values in a hedonic price framework using housing transactions data and wildfire data in Colorado from 2000 to 2016. We find a substantial difference in the impact of wildfires on property values between WUIM and WUIF. Specifically, larger fires depreciate property values in WUIM, but increase property values in WUIF. In addition, we find that small fires lead to a negative impact on property values in both WUIM and WUIF, which is comparable with larger fires. Our findings provide policymakers novel information in making more efficient wildfire management plans.",12.0,,False,JournalArticle,Medicine,2022-07-14,bf67083cd43ab7938fd0c0f48f112cfe29e9fa3c,assessingtheimpactofwildfiresonpropertyvaluesinwildlandurbanintermixandinterfaceincoloradoahedonicapproach,shi,10.1016/j.jenvman.2022.115672,assessingtheimpactofwildfiresonpropertyvaluesinwildlandurbanintermixandinterfaceincoloradoahedonicapproach|2022,1,1899,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,2,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1017/s1355770x10000483,,The value of air quality and crime in Chile: a hedonic wage approach,2011.0,Environment and Development Economics,FELIPE VÁSQUEZ LAVÍN; JORGE DRESDNER; RENATO AGUILAR,"ABSTRACT We estimate the implicit prices of the crime rate and airborne pollution in Chile, using spatially compensating price differentials in the housing and labor markets. We evaluate empirically the impact of different estimation strategies for the wage and rent equations, on the economic value of these two amenities. The results show that increments in the crime rate or in air pollution have a negative impact on welfare and that the estimated welfare measures and their variances are sensitive to selection bias, endogenous amenities and clustering effects. In contrast, the welfare measures do not seem to be very sensitive to the simultaneity bias.",12.0,,,journal-article,,,doi_10_1017_s1355770x10000483,thevalueofairqualityandcrimeinchileahedonicwageapproach,lavín,10.1017/s1355770x10000483,thevalueofairqualityandcrimeinchileahedonicwageapproach|2011,1,1659,3,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || apartment rent hedonic || hedonic housing prices",2,1,1,3,2,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),f16dea09ef89e4fd19c7f74bcba148d48bebcbc2,10.1108/ijhma-02-2018-0010,,Wind power and real estate prices in Oklahoma,2018.0,International Journal of Housing Markets and Analysis,B. Castleberry; J. S. Greene," Purpose Oklahoma has seen rapid growth in the development of wind energy over the past decade. Residents are concerned about the negative impacts of turbines such as noise or their appearance. This has raised concerns about property values. Thus, this paper aims to examine and quantify the overall impact of wind turbines upon real estate prices in Western Oklahoma. Design/methodology/approach Sales prices and the history of approximately 23,000 residential real estate records for both platted and unplatted properties in five counties were examined prior to the announcement of construction, after announcement and after construction. A hedonic analysis was undertaken to examine the real estate prices of the properties near wind farms. Findings While there may be isolated instances of lower property values for homes near wind turbines, results show no significant decreases in property values over homes near wind farms in the study area. Similar results are found for the unplatted properties. Practical implications This paper highlights that in spite of mixed attitudes toward wind farms and misconceptions regarding the link between turbines and property values, Oklahoma’s growing wind industry can continue to thrive without negatively impacting nearby home and land values and prices. Originality/value Although there have been numerous studies examining the relationship between wind turbine locations and real estate prices, no study has combined the large quantity of records (over 23,000) as well as both platted and unplatted locations. ",12.0,,False,,Economics,2018-09-24,f16dea09ef89e4fd19c7f74bcba148d48bebcbc2,windpowerandrealestatepricesinoklahoma,castleberry,10.1108/ijhma-02-2018-0010,windpowerandrealestatepricesinoklahoma|2018,1,2567,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,3,3,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e92bb514e3258a302ceaf70b31697cccfb37fe95,10.1016/j.jhe.2021.101790,,"Neighbourhood, school zoning and the housing market: Evidence from New South Wales",2021.0,Journal of Housing Economics,Firmin Doko Tchatoka; Vanessa Varvaris,"Abstract This paper investigates the impact of primary school zoning on housing prices in Australia. Using the combination of boundary and regression discontinuity design techniques, along with an index of school academic quality and comprehensive data on both schools and housing transactions in New South Wales we find that the price of houses located in high-performing side of primary school zone boundaries is, on average, about 2.5% to 3.6% higher than that of similar houses located in low-performing side of these boundaries. This finding provides not only an insight into the price elasticity of demand for high quality education, but also has important policy implications as it highlights the need to address the potential educational inequalities associated with school zoning in Australia.",12.0,,False,,Geography,2021-08-04,e92bb514e3258a302ceaf70b31697cccfb37fe95,neighbourhoodschoolzoningandthehousingmarketevidencefromnewsouthwales,tchatoka,10.1016/j.jhe.2021.101790,neighbourhoodschoolzoningandthehousingmarketevidencefromnewsouthwales|2021,1,1802,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",df9bd6aab9c25a764585548d28c338ac1b397458,10.1061/(asce)up.1943-5444.0000795,,Influence of Accessibility to Urban Service Amenities on Housing Prices: Evidence from Beijing,2022.0,Journal of urban planning and development,Chao Zhang; Mimi Xiong; Xuehui Wei,,12.0,,False,,,,df9bd6aab9c25a764585548d28c338ac1b397458,influenceofaccessibilitytourbanserviceamenitiesonhousingpricesevidencefrombeijing,zhang,10.1061/(asce)up.1943-5444.0000795,influenceofaccessibilitytourbanserviceamenitiesonhousingpricesevidencefrombeijing|2022,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7b1ba7a767f823276c2dbc08652bcda2752d6b82,10.1016/j.regsciurbeco.2016.07.011,,Urban house price surfaces near a World Heritage Site: Modeling conditional price and spatial heterogeneity,2016.0,,Markus Fritsch; Harry Haupt; Pin T. Ng,,12.0,,False,,Economics,2016-09-01,7b1ba7a767f823276c2dbc08652bcda2752d6b82,urbanhousepricesurfacesnearaworldheritagesitemodelingconditionalpriceandspatialheterogeneity,fritsch,10.1016/j.regsciurbeco.2016.07.011,urbanhousepricesurfacesnearaworldheritagesitemodelingconditionalpriceandspatialheterogeneity|2016,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2796ce3bcafdb17fdfb1fb9ade9b84d23715f4d1,10.1016/j.jclepro.2022.132982,,Impact of proximity to thermal power plants on housing prices: Capitalizing the hidden costs of air pollution,2022.0,Journal of Cleaner Production,I. Tsai,,12.0,,False,,,2022-07-01,2796ce3bcafdb17fdfb1fb9ade9b84d23715f4d1,impactofproximitytothermalpowerplantsonhousingpricescapitalizingthehiddencostsofairpollution,tsai,10.1016/j.jclepro.2022.132982,impactofproximitytothermalpowerplantsonhousingpricescapitalizingthehiddencostsofairpollution|2022,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",26a86fd4dd9609b708b509ba87965698266d7b6d,10.1080/10835547.2015.12092093,,School Vouchers and Home Prices: Premiums in School Districts Lacking Public Schools,2015.0,,S. Cannon; Bartley R. Danielsen; David M. Harrison,,12.0,,False,,Economics,2015-07-31,26a86fd4dd9609b708b509ba87965698266d7b6d,schoolvouchersandhomepricespremiumsinschooldistrictslackingpublicschools,cannon,10.1080/10835547.2015.12092093,schoolvouchersandhomepricespremiumsinschooldistrictslackingpublicschools|2015,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,0a600c8337f12c953d7aab40b543dec7ee64afff,10.1108/ijhma-12-2018-0099,,"Analysis of housing prices in Petaling district, Malaysia using functional relationship model",2019.0,International Journal of Housing Markets and Analysis,Y. Chang; Wei Cheng Choong; S. Y. Looi; W. Pan; Hong Lip Goh," Purpose The purpose of this paper is to analyse and predict the housing prices in Petaling district, Malaysia and its six sub-regions with a set of housing attributes using functional relationship model. Design/methodology/approach A new multiple unreplicated linear functional relationship model with both the response and explanatory variables are subject to errors is proposed. A total of 41,750 housing transacted records from November 2008 to February 2016 were used in this study. These data were divided into 70% training and 30% testing sets for each of the selected sub-regions. Individual housing price was regressed on nine housing attributes. Findings The results showed the proposed model has better fitting ability and prediction accuracy as compared to the hedonic model or multiple linear regression. The proposed model achieved at least 20% and 40% of predictions that have less than 5% and 10% deviations from the actual transacted housing prices, respectively. House buyers in these sub-regions showed similar preferences on most of the housing attributes, except for residents in Shah Alam who preferred to stay far away from shopping malls, and leasehold houses in Sri Kembangan are more valuable. From the h-nearest houses indicator, it is concluded that the housing market in Sungai Buloh is the most volatile in Petaling District. Research limitations/implications As the data used are the actual housing transaction records in Petaling District, it represents only a segment of Malaysian urban population. The result will not be generalized to the entire Malaysian population. Practical implications This study is expected to provide insights to policymakers, property developers and investors to understand the volatility of the housing market and the influence of determinants in different sub-regions. The potential house buyers could also use the model to determine if a house is overpriced. Originality/value This study introduces measurement errors into the housing attributes to provide a more reliable analysis tool for the housing market. This study is the first housing research in Malaysia that used a large number of actual housing transaction records. Previous studies relied on small survey samples. ",11.0,,False,Review,Business,2019-10-07,0a600c8337f12c953d7aab40b543dec7ee64afff,analysisofhousingpricesinpetalingdistrictmalaysiausingfunctionalrelationshipmodel,chang,10.1108/ijhma-12-2018-0099,analysisofhousingpricesinpetalingdistrictmalaysiausingfunctionalrelationshipmodel|2019,1,3250,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices",2,2,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W3120566130,10.1108/ijhma-08-2020-0100,,Hedonic housing prices and environmental quality in Lebanon,2021.0,International Journal of Housing Markets and Analysis,Walid Marrouch; Nagham Sayour,"Purpose This study aims to examine the impact of local air pollution on housing prices in Lebanon. Design/methodology/approach The authors apply a hedonic pricing approach using a unique data set from Lebanon. To account for non-linearities in pricing, the authors use three different functional regression forms for the hedonic model approach. The authors also deal with potential omitted variable bias by estimating a hedonic frontier specification. Findings The authors find that, in all specifications, air pollution negatively and significantly affects housing prices. The estimated marginal willingness to pay for a one microgram per cubic meter change in particulate matter (PM10) concentration ranges between 2.88% and 3.18% of mean housing prices. The authors also provide evidence of a negative pricing gradient away from the city center, landing support for the monocentric urban development hypothesis. Research limitations/implications Given the lack of a data set linking household socioeconomic characteristics with housing data, the authors only consider the first-stage hedonic model. Practical implications The proposed hedonic pricing regression approximates a housing pricing equation that can be used by policymakers. Social implications The findings suggest that pollution is a significant factor in household behavior in Lebanon. Originality/value This paper adds to the scant literature studying the effects of air pollution on housing prices in developing countries. To the best of the authors’ knowledge, this is the first paper to study the impact of pollution on housing prices in a country in the Middle East and North Africa Region.",11.0,,False,article,,2021-01-11,https://openalex.org/W3120566130,hedonichousingpricesandenvironmentalqualityinlebanon,marrouch,10.1108/ijhma-08-2020-0100,hedonichousingpricesandenvironmentalqualityinlebanon|2021,1,2662,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || housing characteristics prices hedonic",2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9c989fd3c2e77418653894f62cb739b34103685d,10.1108/pm-04-2020-0022,,Hedonic valuation of apartments in Kenya,2020.0,,Raphael M. Kieti; Walter Ogolla,"This paper applies the hedonic pricing model (HPM) approach to identify critical determinants of apartment value and employs the technique to develop a valuation model that can accurately estimate the value of apartments.,The research employed a case study design that was limited to transaction sales and attribute data of apartments in Nyali estate, Mombasa County in Kenya. A sample of 120 sales of apartments obtained from registered real estate firms was analyzed using quantitative methods.,According to the study results, the hedonic valuation model developed comprises four critical determinants of apartment value, namely, number of parking lots, presence of swimming pool, age of apartment and provision of balcony. The hedonic model was tested and found to be accurate and reliable in estimating apartment value.,The model will improve accuracy, reliability and efficiency in valuation. The application of the model in the valuation of apartments is, however, limited to the case study area where the data are obtained. The scope of application of the model may be improved by increasing the sample size to include apartment sales data from other estates in Mombasa County.,Previous studies that have used the HPM technique in analysis of apartment values have focused on the “explanatory” and “contributory” power of attributes on apartment values, rather than the development and use of the model to measure value. The present study is the first to develop a HPM equation for property value estimation in the apartment real estate sector in Kenya.",11.0,,False,,Business,2020-09-11,9c989fd3c2e77418653894f62cb739b34103685d,hedonicvaluationofapartmentsinkenya,kieti,10.1108/pm-04-2020-0022,hedonicvaluationofapartmentsinkenya|2020,1,2560,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic property valuation",2,1,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c19ed129a24c047505aa3dddb7314d74b4bcf99a,10.1080/14445921.2021.1961182,,What is in a name? A modern interpretation from housing price in Hong Kong,2021.0,Pacific Rim Property Research Journal,Na Li; R. Li; Ruihui Pu,"ABSTRACT Hong Kong is a metropolitan city. Owing to its historical background as a former British colony, it is characterised by a mixture of Chinese and Western cultures. While most housing estates’ names are bilingual in Chinese and English, many recent large scale housing estates have an English name only. Do housing estates with an English name only associate with higher prices than bilingual ones? To the best of our knowledge, none have studied that before. We attempted to fill this research gap. Four large-scale residential projects in Yuen Long District were selected, two are bilingual and two have English names only. 253,605 data points, including macroeconomic variables and housing characteristics, were included in this study. The results of the hedonic price model showed that properties with English names only were associated with a higher price than bilingual ones. We also utilised a long short-term memory (LSTM) housing price prediction model to predict housing price. Our research results show that housing estates with English names only are associated with higher housing price. It provides a new perspective on real estate housing estates’ brand management and provide a reference for real estate buyers in the future.",11.0,,False,,,2021-01-02,c19ed129a24c047505aa3dddb7314d74b4bcf99a,whatisinanameamoderninterpretationfromhousingpriceinhongkong,li,10.1080/14445921.2021.1961182,whatisinanameamoderninterpretationfromhousingpriceinhongkong|2021,1,2248,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,676cfbcad902fd4f757e99e1b954f340855ba093,10.2139/ssrn.3671981,,Convergent Validity of Satellite and Secchi Disk Measures of Water Clarity in Hedonic Models,2021.0,Land Economics,David M. Wolf; Thomas Kemp,"Budget-constrained environmental agencies across the United States are limited in their ability to monitor water quality changes using traditional in situ sampling. Satellite-based monitoring systems represent a lower-cost alternative. It is unclear, however, how this transition will impact nonmarket valuation estimates of perceived water quality. To answer this question, we merge housing information with Secchi depth and satellite estimates of water clarity from over 100 lakes in Wisconsin. A comparison of hedonic price functions reveals satellite estimates of water clarity provide a stronger statistical fit of housing price and produce implicit prices up to 11% larger in magnitude.",11.0,,False,JournalArticle,Environmental Science,2021-02-01,676cfbcad902fd4f757e99e1b954f340855ba093,convergentvalidityofsatelliteandsecchidiskmeasuresofwaterclarityinhedonicmodels,wolf,10.2139/ssrn.3671981,convergentvalidityofsatelliteandsecchidiskmeasuresofwaterclarityinhedonicmodels|2021,1,1692,2,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic housing prices",2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/j.jhe.2010.04.004,,The political economy of housing prices: Hedonic pricing with regression discontinuity,2010.0,Journal of Housing Economics,Eddie D.W. Sue; Wei-Kang Wong,,11.0,,,journal-article,,,doi_10_1016_j_jhe_2010_04_004,thepoliticaleconomyofhousingpriceshedonicpricingwithregressiondiscontinuity,sue,10.1016/j.jhe.2010.04.004,thepoliticaleconomyofhousingpriceshedonicpricingwithregressiondiscontinuity|2010,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic house price || hedonic housing prices",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,9562a7b440fd126686a824fdd76f1d890164482f,10.1080/13504851.2020.1853665,,Estimating the impact of pollution on wages and housing prices using satellite imagery,2020.0,Applied Economics Letters,M. Goodwin; Matías Fontenla; Fidel Gonzalez,"ABSTRACT Air pollution is a global concern. However, research and policy addressing the problem remains hampered by a lack of ground-level air quality data. We estimate the value residents of Mexico City place on clean air using aerosol optical depth (AOD) from satellite imagery as a proxy for air pollution. We employ a unique dataset that combines actual household-level data with AOD and rainfall within a hedonic model focusing on people’s decisions regarding where to work and live in a developing nation. We address endogeneity and simultaneity concerns by using a 3SLS-IV estimation. Results suggest that residents place significant value on clean air, with a 10% increase in AOD leading to a 1.3% decrease in home prices and a 2.3% increase in wages.",11.0,,False,,Environmental Science,2020-11-30,9562a7b440fd126686a824fdd76f1d890164482f,estimatingtheimpactofpollutiononwagesandhousingpricesusingsatelliteimagery,goodwin,10.1080/13504851.2020.1853665,estimatingtheimpactofpollutiononwagesandhousingpricesusingsatelliteimagery|2020,1,1759,2,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || hedonic housing prices",2,2,3,0,1,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2195494019,10.1111/asej.12077,,Semiparametric Model of Hedonic Housing Prices in J apan,2015.0,Asian Economic Journal,Koji Karato; Oleksandr Movshuk; Chihiro Shimizu,"We propose a semiparametric hedonic model of housing prices with nonlinearity in age and cohort effects. The model avoids the simultaneity problem among age, cohort and year effects, which is a common problem in linear hedonic models. Applying the model to housing prices in Tokyo between 1990 and 2008 revealed significant nonlinearities in both the age and cohort effects, and significant interactions between these effects, with the shape of the age effect differing across housing cohorts. Estimates of the year effect indicated a declining trend in prices that was more pronounced compared with those of conventional linear hedonic models.",11.0,,False,article,,2015-12-01,https://openalex.org/W2195494019,semiparametricmodelofhedonichousingpricesinjapan,karato,10.1111/asej.12077,semiparametricmodelofhedonichousingpricesinjapan|2015,1,1644,2,cites:sirmans2006 || hedonic housing prices,2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,580b976304e7483e7fa6141851796e9cab03fc45,10.1177/0042098019895774,,What’s so special about character?,2020.0,,M. Fernandez; Shane L. Martin,"Special Character Areas (SCAs) in Auckland, New Zealand, are areas with distinctive aesthetic, physical and visual qualities. Preservation policies entail controls on the design and appearance of new buildings, and on demolition, additions and alterations to existing buildings. To promote densification of the city, the Auckland Unitary Plan (AUP) removed SCA preservation rules in certain areas. This article assesses the trade-off between SCA preservation and housing development. We employ hedonic prices models to about 85,000 sale transactions between 2012 and 2016 and find that in 2012, the SCA price premium was 11.4% whereas the premium on upzoned properties (those with increased development allowances under the AUP) was zero. Over time, the SCA premium decreases, and by 2016 it was down to about 4.3%. At the same time, upzoning premiums increased to about 5% in 2016. These results reveal a demand shift from the protections of SCA towards flexibility on the development options of land.",11.0,,False,JournalArticle,Geography,2020-01-29,580b976304e7483e7fa6141851796e9cab03fc45,whatssospecialaboutcharacter,fernandez,10.1177/0042098019895774,whatssospecialaboutcharacter|2020,1,2002,1,hedonic housing prices,2,0,1,1,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1007/978-1-4615-0909-7_9,,Neural Network vs. Hedonic Price Model: Appraisal of High-Density Condominiums,2002.0,Research Issues in Real Estate Real Estate Valuation Theory,K. C. Wong; Albert T. P. So; Y. C. Hung,,11.0,,,book-chapter,,,doi_10_1007_978_1_4615_0909_7_9,neuralnetworkvshedonicpricemodelappraisalofhighdensitycondominiums,wong,10.1007/978-1-4615-0909-7_9,neuralnetworkvshedonicpricemodelappraisalofhighdensitycondominiums|2002,1,1000,1,apartment rent hedonic,2,0,2,1,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),ffd4547e241e346df6023820aa0f9cd7e8694caa,10.22004/ag.econ.180098,,Housing Price Response to the Interaction of Positive Coastal Amenities and Negative Flood Risks,2014.0,,Ajita Atreya; J. Czajkowski,,11.0,,False,,Economics,,ffd4547e241e346df6023820aa0f9cd7e8694caa,housingpriceresponsetotheinteractionofpositivecoastalamenitiesandnegativefloodrisks,atreya,10.22004/ag.econ.180098,housingpriceresponsetotheinteractionofpositivecoastalamenitiesandnegativefloodrisks|2014,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5faee46c0833928b29cfb6aade41ed28d4527589,10.1016/j.econmod.2023.106252,,A new framework to disentangle the impact of changes in dwelling characteristics on house price indices,2023.0,Economic Modelling,P. Reusens; F. Vastmans; S. Damen,,11.0,,False,,,2023-03-01,5faee46c0833928b29cfb6aade41ed28d4527589,anewframeworktodisentangletheimpactofchangesindwellingcharacteristicsonhousepriceindices,reusens,10.1016/j.econmod.2023.106252,anewframeworktodisentangletheimpactofchangesindwellingcharacteristicsonhousepriceindices|2023,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0f3eeb38c327dd084869cc31f2dd3b2e5222ef42,10.1016/j.cities.2021.103304,,"Strong, weak, or reversed: The spatial heterogeneities in the effects of squatter settlements on house prices",2021.0,,A. F. Ogas-Mendez; Y. Isoda; T. Nakaya,,11.0,,False,,Geography,2021-10-01,0f3eeb38c327dd084869cc31f2dd3b2e5222ef42,strongweakorreversedthespatialheterogeneitiesintheeffectsofsquattersettlementsonhouseprices,ogas-mendez,10.1016/j.cities.2021.103304,strongweakorreversedthespatialheterogeneitiesintheeffectsofsquattersettlementsonhouseprices|2021,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7955ccf99ff8661d0cd58598160435f5bc3ffa1a,10.1080/12265934.2016.1273129,,The varying impact of land subsidence and earth fissures on residential property values in Maricopa County – a quantile regression approach,2017.0,,James J. Yoo; Thomas V. Frederick,,11.0,,False,,Environmental Science,2017-01-08,7955ccf99ff8661d0cd58598160435f5bc3ffa1a,thevaryingimpactoflandsubsidenceandearthfissuresonresidentialpropertyvaluesinmaricopacountyaquantileregressionapproach,yoo,10.1080/12265934.2016.1273129,thevaryingimpactoflandsubsidenceandearthfissuresonresidentialpropertyvaluesinmaricopacountyaquantileregressionapproach|2017,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ee5819b504637f53b33d66f1d83894d0524f96d5,10.1080/08965803.2021.1925498,,"House Prices, Open Space, and Household Characteristics",2021.0,,Tracy M. Turner; Youngme Seo,,11.0,,False,,Economics,2021-06-16,ee5819b504637f53b33d66f1d83894d0524f96d5,housepricesopenspaceandhouseholdcharacteristics,turner,10.1080/08965803.2021.1925498,housepricesopenspaceandhouseholdcharacteristics|2021,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",06eb1be36560a706e8a4c9154d6e9962cdbbec15,10.1080/10835547.2008.12091992,,The Impact of Inflation on Home Prices and the Valuation of Housing Characteristics Across the Price Distribution,2008.0,,J. Zietz; G. Sirmans; Greg T. Smersh,,11.0,,False,,Economics,,06eb1be36560a706e8a4c9154d6e9962cdbbec15,theimpactofinflationonhomepricesandthevaluationofhousingcharacteristicsacrossthepricedistribution,zietz,10.1080/10835547.2008.12091992,theimpactofinflationonhomepricesandthevaluationofhousingcharacteristicsacrossthepricedistribution|2008,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",382d09edfd87b2051617bd758166c9ccef2d0019,10.1016/j.apgeog.2024.103486,,Capturing the distance decay effect of amenities on housing price using explainable artificial intelligence,2025.0,Applied Geography,Hojun Lee; Hoon Han; Christopher J. Pettit,,11.0,,False,JournalArticle,,2025-01-01,382d09edfd87b2051617bd758166c9ccef2d0019,capturingthedistancedecayeffectofamenitiesonhousingpriceusingexplainableartificialintelligence,lee,10.1016/j.apgeog.2024.103486,capturingthedistancedecayeffectofamenitiesonhousingpriceusingexplainableartificialintelligence|2025,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,rent hedonic regression housing,af0839d7ffcae17d3f1555a0f9c6c80746ab0265,10.1108/ijhma-04-2019-0042,,Modeling of residential property rents in Somalia using two-stage modeling,2019.0,International Journal of Housing Markets and Analysis,M. Nor; T. A. Masron; Sharif Yusuf Gedi," Purpose Real estate is one of the fundamental growth engines for developing economies as it contributes urbanization and infrastructure development. In recent years, Somalia has witnessed massive real estate development in both housing and commercial buildings. The purpose of this study is twofold. First, the study examines the determinants of residential property rents. Second, it investigates whether residential property rents are fairly valued. Design/methodology/approach This study uses two-stage modeling. A hedonic regression model is used in the first stage, while an artificial neural network is applied in the second stage. Findings After analysis, this study established that size, location and security of a residential property have a significant influence on its monthly rents. Alternatively, the study identified that residential property rents are not fairly valued in Mogadishu and overvaluation is more frequent than undervaluation. Originality/value This implies that Somalia’s real estate industry is more speculative-driven than real demand-driven. Though Somali real estate is an infant industry with huge potentials in the long run, it may end up disastrously following the well-known bubble-then-burst behavior. To avoid such crisis, this study recommends formulating government policies that regulates, supervises and protects the infant real estate industry without undermining the needs of the poor and low-income citizens. ",10.0,,False,,Economics,2019-07-29,af0839d7ffcae17d3f1555a0f9c6c80746ab0265,modelingofresidentialpropertyrentsinsomaliausingtwostagemodeling,nor,10.1108/ijhma-04-2019-0042,modelingofresidentialpropertyrentsinsomaliausingtwostagemodeling|2019,1,2462,3,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression || rent hedonic regression housing,2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0c30a3a4592f6ad208346291b6dc026399d89ceb,10.1016/j.econmod.2021.03.010,,A Time-Varying Hedonic Approach to quantifying the effects of loss aversion on house prices,2021.0,,Ryan Greenaway-McGrevy; Kade Sorensen,"Abstract Loss aversion is a widely-recognized cognitive bias that can affect house price determination. Existing empirical approaches to quantify its effects require hedonic methods to predict what prices would be in the absence of loss aversion. However, conventional methods for making this counterfactual prediction unrealistically preclude time variation in the hedonic values of housing attributes, thereby exacerbating specification error and reducing the accuracy of point estimates. We show how time-varying hedonic regression (TVHR) can overcome this deficiency by allowing hedonic values to change over time. Applying the method to a dataset of residential transactions from Auckland, New Zealand, that spans 2009 to 2018, we find that loss aversion significantly inflates transaction prices. However, by comparison, existing methods yield imprecise estimates of this premium that are implausibly large and sensitive to the sample period selected. Our findings suggest that THVR is an attractive alternative to conventional approaches to quantifying loss aversion.",10.0,,False,,Economics,2021-03-27,0c30a3a4592f6ad208346291b6dc026399d89ceb,atimevaryinghedonicapproachtoquantifyingtheeffectsoflossaversiononhouseprices,greenaway-mcgrevy,10.1016/j.econmod.2021.03.010,atimevaryinghedonicapproachtoquantifyingtheeffectsoflossaversiononhouseprices|2021,1,2074,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices",2,2,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1080/0042098032000116103,,Estimating the Effect of Land Leases on Prices of Inner-city Apartment Buildings,2003.0,Urban Studies,Christian T. L. Janssen,"The effect of land leases as opposed to freehold tenure on selling price is investigated for income property in proximity to the city centre. Hedonic estimation with multiplicative and additive models in a cross-section analysis is used to estimate the relationship between property characteristics on the one hand and selling price on the other. If land leases are valued by the market as the present value of the lease payments independent of other property features, an additive lease component should be appropriate. If, on the other hand, the valuation of the land lease aspect is not independent of other property features, a multiplicative lease component should be appropriate. Theoretical arguments can be advanced for either point of view, making it interesting to study the issue empirically. A sub-issue of interest is the effect on value of distance to the city centre. The sample consists of major apartment buildings centrally located in downtown Stockholm sold during a selected three-year period.",10.0,,,journal-article,,,doi_10_1080_0042098032000116103,estimatingtheeffectoflandleasesonpricesofinnercityapartmentbuildings,janssen,10.1080/0042098032000116103,estimatingtheeffectoflandleasesonpricesofinnercityapartmentbuildings|2003,1,2013,3,apartment prices hedonic || apartment rent hedonic || hedonic property valuation,2,1,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/1540-6229.00432,,Economic Effects of Racial Integration: An Analysis of Hedonic Housing Prices and the Willingness to Pay,1987.0,Real Estate Economics,Stuart A. Gabriel,"This study evaluates household economic effects stemming from neighborhood racial integration in Oakland, California. To that end, housing market data are applied to estimate hedonic price and willingness‐to‐pay functions for neighborhood racial composition. Results of the analysis indicate the problematic nature of the constant willingness‐to‐pay assumption and suggest this standard method may underestimate the household economic effects of racial integration. The paper concludes with implications for neighborhood integration policy.",10.0,,,journal-article,,,doi_10_1111_1540_6229_00432,economiceffectsofracialintegrationananalysisofhedonichousingpricesandthewillingnesstopay,gabriel,10.1111/1540-6229.00432,economiceffectsofracialintegrationananalysisofhedonichousingpricesandthewillingnesstopay|1987,1,1540,4,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices",2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1080/00343404.2010.521147,,Spatial Transferability of Hedonic House Price Functions,2012.0,Regional Studies,David Philip McArthur; Liv Osland; Inge Thorsen,,10.0,,,journal-article,,,doi_10_1080_00343404_2010_521147,spatialtransferabilityofhedonichousepricefunctions,mcarthur,10.1080/00343404.2010.521147,spatialtransferabilityofhedonichousepricefunctions|2012,1,1000,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || cites:sirmans2006 || hedonic house price || house prices proximity hedonic || modelo de precios hedónicos vivienda || precios hedónicos vivienda || prix hédoniques logement",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),f447ec9285fdc368c15e5f8313529025bf30a601,10.1108/jerer-07-2017-0025,,Is there room for another hedonic model?,2018.0,Journal of European Real Estate Research,Marcelo Cajias," Purpose This paper aims to explore the in-sample explanatory and out-of-sample forecasting accuracy of the generalized additive model for location, scale and shape (GAMLSS) model in contrast to the GAM method in Munich’s residential market. Design/methodology/approach The paper explores the in-sample explanatory results via comparison of coefficients and a graphical analysis of non-linear effects. The out-of-sample forecasting accuracy focusses on 50 loops of three models excluding 10 per cent of the observations randomly. Afterwards, it obtains the predicted functional forms and predicts the remaining 10 per cent. The forecasting performance is measured via error variance, root mean squared error, mean absolute error and the mean percentage error. Findings The results show that the complexity of asking rents in Munich is more accurately captured by the GAMLSS approach than the GAM as shown by an outperformance in the in-sample explanatory accuracy. The results further show that the theoretical and empirical complexities do pay off in view of the increased out-of-sample forecasting power of the GAMLSS approach. Research limitations/implications The computational requirements necessary to estimate GAMLSS models in terms of number of cores and RAM are high and might constitute one of the limiting factors for (institutional) researchers. Moreover, large and detailed knowledge on statistical inference and programming is necessary. Practical implications The usage of the GAMLSS approach would lead policymakers to better understand the local factors affecting rents. Institutional researchers, instead, would clearly aim at calibrating the forecasting accuracy of the model to better forecast rents in investment strategies. Finally, future researchers are encouraged to exploit the large potential of the GAMLSS framework and its modelling flexibility. Originality/value The GAMLSS approach is widely recognised and used by international institutions such as the World Health Organisation, the International Monetary Fund and the European Commission. This is the first study to the best of the author’s knowledge to assess the properties of the GAMLSS approach in applied real estate research from a statistical asymptotic perspective by using a unique data basis with more than 38,000 observations. ",10.0,,False,,Economics,2018-07-24,f447ec9285fdc368c15e5f8313529025bf30a601,isthereroomforanotherhedonicmodel,cajias,10.1108/jerer-07-2017-0025,isthereroomforanotherhedonicmodel|2018,1,3332,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,527549ae967cd285e82137d9247b9adbcda78354,10.1016/j.cities.2020.103043,,Homevalue misestimation and household leverage: An empirical study of Chinese urban households,2020.0,,Ping-Han Liang; Nan Gao,"Abstract Households often make errors in assessing homevalue. By merging representative Chinese urban household survey data with web-scraped housing price data, we estimate a hedonic model to assess the real value of homes and calculate the extent of homevalue misestimation. We show that homevalue misestimation is significantly related to household leverage: a 10% increase in the extent of homevalue misestimation is associated with a 1.88 percentage increase in household leverage, in terms of debt-to-income ratios. Moreover, both formal and informal borrowing are increasing with respect to homevalue misestimation. A variety of robustness checks and heterogeneity analyses further support our main results. The correlation between misestimation and leverage is even larger for households who underestimate their homevalue. It's demonstrated that the lack of housing market information and endowment effect both contribute to misestimation. However, there is no evidence that overconfidence, risk attitude, or anchoring effect significantly affect misestimation. These findings have policy implications for the recent debate on property taxes, indicating the unexpected consequences of property taxes by providing more precise information on homevalue.",10.0,,False,Review,Economics,2020-12-04,527549ae967cd285e82137d9247b9adbcda78354,homevaluemisestimationandhouseholdleverageanempiricalstudyofchineseurbanhouseholds,liang,10.1016/j.cities.2020.103043,homevaluemisestimationandhouseholdleverageanempiricalstudyofchineseurbanhouseholds|2020,1,2258,1,hedonic housing prices,2,1,4,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,house prices proximity hedonic,f5f366b2d367f60b497b4077f135578116ab9799,10.1007/s10901-014-9403-8,,"Creating sustainable urban built environments: An application of hedonic house price models in Wuhan, China",2014.0,Journal of Housing and the Built Environment,Hao-Cheng Huang; Yin Li,,10.0,,False,,,2014-04-27,f5f366b2d367f60b497b4077f135578116ab9799,creatingsustainableurbanbuiltenvironmentsanapplicationofhedonichousepricemodelsinwuhanchina,huang,10.1007/s10901-014-9403-8,creatingsustainableurbanbuiltenvironmentsanapplicationofhedonichousepricemodelsinwuhanchina|2014,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices || house prices proximity hedonic || housing characteristics prices hedonic",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bb52b42d7df9da3149b3e4c587e390f1c6734244,10.1016/j.resourpol.2020.101821,,Valuing shale gas development in resource-dependent communities,2020.0,,Zachary T. Keeler; H. Stephens,"Abstract Technological advances in oil and gas drilling have enabled the productive extraction of natural gas in new regions. The benefits from employment and income opportunities can help stimulate economies and may be valued by local residents. At the same time, however, shale gas activity can alter natural landscapes and is associated with negative externalities, including potential groundwater contamination. While some previous research has examined the impact of shale development, our paper focuses on the local impacts in West Virginia, a state with a long history of resource-extraction and one whose economy has lagged the nation. Because of its history of resource extraction, communities in West Virginia who may have limited other economic prospects may value the activity differently. Additionally, most of the previous research used data during the initial boom, ignoring the slowdown that followed. Using the coarsened exact matching (CEM) technique, we match houses near producing wells with other similar houses, in order to examine how property values in West Virginia are affected by proximity to horizontal producing wells. This technique helps compensate for the relatively small number of housing transactions in West Virginia, ensuring we have a good counterfactual. After matching, we estimate the average capitalization effect of houses near producing shale wells. We find that the price of all houses (regardless of water source) decreases as the number of surrounding wells increases. However, we also find some evidence that this effect varies over time and that the negative capitalization effect attenuates over space.",10.0,,False,,Economics,2020-12-01,bb52b42d7df9da3149b3e4c587e390f1c6734244,valuingshalegasdevelopmentinresourcedependentcommunities,keeler,10.1016/j.resourpol.2020.101821,valuingshalegasdevelopmentinresourcedependentcommunities|2020,1,2652,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c78288c38ff4eb563cbe921cb2774a82731f52bd,10.1016/j.landusepol.2021.105443,,"Is it the flood, or the disclosure? An inquiry to the impact of flood risk on residential housing prices",2021.0,Land Use Policy,Lin-Han-Chiang Hsieh,"Abstract In 2015, the government of Taipei, Taiwan, published a series of flood risk maps based on simulation results. It sparked a broad public debate on flood risk information and the real estate market. This study therefore investigates the effect of making flood risk information public on residential housing prices and makes the following contributions. The first contribution concerns public policy. By analysing the relationship between the published flood risk information and more than 12,000 central Taipei sales records in 2015 and 2016, this study finds that the effect of flood risk on housing prices is significant and negative both before and after the maps were disclosed. Although some local variation in the effect of flood risk after the map disclosure was identified, there is no evidence that the disclosure altered the effect on housing prices. This finding suggests that the disclosure of the flood risk maps impacted the effect of the flood risk locally without influencing the market as a whole. Another contribution concerns perception. The result indicates that homebuyers’ negative attitude towards flood risk mainly focuses on the risk under low-intensity, high-frequency rainfall conditions. The final contribution of this study concerns its methodological perspective. By combining the spatial fixed-effect (SFE) model with geographically weighted regressions (GWRs), this study successfully mitigates the endogeneity problem while addressing the heterogeneity problem.",10.0,,False,,Business,2021-07-01,c78288c38ff4eb563cbe921cb2774a82731f52bd,isitthefloodorthedisclosureaninquirytotheimpactoffloodriskonresidentialhousingprices,hsieh,10.1016/j.landusepol.2021.105443,isitthefloodorthedisclosureaninquirytotheimpactoffloodriskonresidentialhousingprices|2021,1,2501,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b6dafdfd9f520fba28bba5b6d66881de6571b1e0,10.1177/0361198120911054,,Impact of Transit-Oriented Development on Residential Property Values around Urban Rail Stations,2020.0,,Yang Jiang; Pei-Qin Gu; Zhejing Cao; Yu-Lin Chen,"Few previous studies have linked transit-oriented development (TOD) patterns and property values at the station level in the Chinese context. Using Shanghai as a case study, we obtained 21,094 transaction records within the 800 m radius catchment areas of 280 urban rail stations from a local property agency. A performance-based TOD indicator framework was established to capture built-environment features for density, diversity, and design dimensions. Using open-source geospatial data, we generated a series of TOD indicators for each station catchment area and further developed a composite TOD index with information entropy weighting. The results of multilevel regression models showed that within urban rail station catchment areas, residential properties with higher TOD levels were positively associated with higher value. In addition, increasing pedestrian and bike accessibility and integrating diversified urban functions around stations could create more value for nearby residential properties.",10.0,,False,JournalArticle,Business,2020-03-10,b6dafdfd9f520fba28bba5b6d66881de6571b1e0,impactoftransitorienteddevelopmentonresidentialpropertyvaluesaroundurbanrailstations,jiang,10.1177/0361198120911054,impactoftransitorienteddevelopmentonresidentialpropertyvaluesaroundurbanrailstations|2020,1,2009,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",29950be0deecc8c7f4b21b18e5126a14bb3669a2,10.1080/00036846.2019.1683146,,The value of school in urban China: a spatial quantile regression with housing transactions in Beijing,2020.0,Applied Economics,Leiju Qiu; Dongmei Guo; Xuankai Zhao; Wenzhan Zhang,"ABSTRACT This study adopts microdata of individual housing transactions in Beijing to explore the value that households place on high-quality schools. It is among the first to match exactly the housing unit with its corresponding school in studies on Chinese housing markets. We find a significant school premium of 21.1% in Beijing’s housing market after controlling the unobservable neighbourhood effects using regressions with a spatial lag. We have further discovered different school premiums across quantiles with a spatial quantile regression. The result finding a larger school premium in the lower-priced housing units suggests that low-income households prefer high-quality schools over housing consumption. Our results indicate that public education influences households’ residential location choice and housing consumption decisions, which thus have important policy implications in public goods provision, housing development and urban planning.",10.0,,False,,Economics,2020-04-26,29950be0deecc8c7f4b21b18e5126a14bb3669a2,thevalueofschoolinurbanchinaaspatialquantileregressionwithhousingtransactionsinbeijing,qiu,10.1080/00036846.2019.1683146,thevalueofschoolinurbanchinaaspatialquantileregressionwithhousingtransactionsinbeijing|2020,1,1959,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,3,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1080/02673039008720685,,Valuing residential properties with the hedonic method: A comparison with the results of professional valuations,1990.0,Housing Studies,J. S. Dodgson; N. Topham,,10.0,,,journal-article,,,doi_10_1080_02673039008720685,valuingresidentialpropertieswiththehedonicmethodacomparisonwiththeresultsofprofessionalvaluations,dodgson,10.1080/02673039008720685,valuingresidentialpropertieswiththehedonicmethodacomparisonwiththeresultsofprofessionalvaluations|1990,1,1000,2,apartment rent hedonic || hedonic property valuation,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0255c95356ae80ad336281034f4c23d036ba8a9a,10.1080/02664763.2016.1254163,,Combined asymmetric spatial weights matrix with application to housing prices,2017.0,,Haiyong Zhang; Xin-Yu Wang,,10.0,,False,,Geography,2017-10-03,0255c95356ae80ad336281034f4c23d036ba8a9a,combinedasymmetricspatialweightsmatrixwithapplicationtohousingprices,zhang,10.1080/02664763.2016.1254163,combinedasymmetricspatialweightsmatrixwithapplicationtohousingprices|2017,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8c767bffc71b3ac653a80ca6d0a65b169e18bc20,10.1007/s12061-025-09638-6,,Assessing the Combinational Effects of Access to Urban Amenities on Housing Prices: A Perspective on the “15-Minute City”,2025.0,Applied spatial analysis and policy,Peiyu Tian; Weiye Xiao; Feng Yuan,,10.0,,False,JournalArticle,,2025-01-24,8c767bffc71b3ac653a80ca6d0a65b169e18bc20,assessingthecombinationaleffectsofaccesstourbanamenitiesonhousingpricesaperspectiveonthe15minutecity,tian,10.1007/s12061-025-09638-6,assessingthecombinationaleffectsofaccesstourbanamenitiesonhousingpricesaperspectiveonthe15minutecity|2025,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",771145e1d79e9d0399823a474ccbd76d57434104,10.1080/03610920701858404,,Bayesian Spatial Modeling of Housing Prices Subject to a Localized Externality,2008.0,,M. Ecker; V. D. de Oliveira,,10.0,,False,,Mathematics,2008-05-02,771145e1d79e9d0399823a474ccbd76d57434104,bayesianspatialmodelingofhousingpricessubjecttoalocalizedexternality,ecker,10.1080/03610920701858404,bayesianspatialmodelingofhousingpricessubjecttoalocalizedexternality|2008,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,06c511a7bb75fa76f1a3a719192b2c7b305678af,10.2307/136099,,Forecast Comparisons of Residential Housing Prices by Parametric and Semiparametric Regression,1996.0,,R. Gencay; Xian Yang,,10.0,,False,,Economics,1996-04-01,06c511a7bb75fa76f1a3a719192b2c7b305678af,forecastcomparisonsofresidentialhousingpricesbyparametricandsemiparametricregression,gencay,10.2307/136099,forecastcomparisonsofresidentialhousingpricesbyparametricandsemiparametricregression|1996,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,f30bc522dd315955f9336120dcac97e54765ed2b,10.1080/00036846.2020.1751050,,Major league soccer expansion and property values: do sports franchises generate amenities or disamenities?,2020.0,,Aakrit Joshi; Brady P. Horn; R. P. Berrens,"ABSTRACT While amenity effects generated by sports stadiums or facilities have been studied extensively for housing markets, there has been significantly less attention focused on team effects generated by sports franchises alone. The objective of this analysis is to estimate the impact of Major League Soccer (MLS) expansion on property values, using nearby condominium sales from 2003–2016 in Seattle, Washington. Econometric results from hedonic pricing method and repeat sales regression indicate that property values depreciated after the Seattle Sounders Football Club was promoted to the MLS in 2009. The distance-decaying depreciation in condominium values occurs within a mile of the facility.",9.0,,False,,Economics,2020-04-22,f30bc522dd315955f9336120dcac97e54765ed2b,majorleaguesoccerexpansionandpropertyvaluesdosportsfranchisesgenerateamenitiesordisamenities,joshi,10.1080/00036846.2020.1751050,majorleaguesoccerexpansionandpropertyvaluesdosportsfranchisesgenerateamenitiesordisamenities|2020,1,1703,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,1,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",572161e9059b07e805c820460ac1d693fd733798,10.1177/0042098018800576,,Unbundling negative and positive externalities of nature in cities: The influence of wild animals on housing prices,2018.0,Social Science Research Network,Dani Broitman; Vladimir Griskin; D. Czamanski,"Proximity to nature is highly valued by urbanites, who demonstrate higher willingness to pay for housing at locations near open and green spaces. However, nature in cities can generate negative externalities as well. In this article, we illustrate the complex relationship between cities and nature and suggest that their balance is time and location specific. The article presents estimates of positive and negative externalities based on data about encounters of humans with wild animals in the city of Haifa, Israel, and residential property values nearby. The data were analysed to uncover spatial regularities and basic statistical relationships. The results reveal the presence of dominant positive externalities when the human–wild animals interaction is low, driven by proximity to open and green areas. However, in certain areas and under certain circumstances, the nuisance generated by higher probabilities of encounters with wild animals near dwelling areas is correlated with lower property prices, overcoming the positive externalities of location near natural areas.",9.0,,False,JournalArticle,Economics,2018-12-11,572161e9059b07e805c820460ac1d693fd733798,unbundlingnegativeandpositiveexternalitiesofnatureincitiestheinfluenceofwildanimalsonhousingprices,broitman,10.1177/0042098018800576,unbundlingnegativeandpositiveexternalitiesofnatureincitiestheinfluenceofwildanimalsonhousingprices|2018,1,2081,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),245a75478429a9d8b7bf5c2791025797b5314f0f,10.1080/02815738708730140,,House prices in Sweden: The significance of attributes,1987.0,,R. Wigren,,9.0,,False,,Economics,,245a75478429a9d8b7bf5c2791025797b5314f0f,housepricesinswedenthesignificanceofattributes,wigren,10.1080/02815738708730140,housepricesinswedenthesignificanceofattributes|1987,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || real estate hedonic regression",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,3a9dfefd1638fdade59ba0ac74493b5a067a1dab,10.1108/ijhma-08-2022-0108,,Does work from home reshape the urban rental structure? Early evidence from a rental gradient analysis in Auckland,2023.0,International Journal of Housing Markets and Analysis,Chung Yim Edward Yiu; Ka-Shing Cheung; D. Wong," Purpose This study aims to identify the pandemic’s impact on house rents by applying a rental gradient analysis to compare the pre-and post-COVID-19 periods in Auckland. The micro-level household census data from the Integrated Data Infrastructure of Statistics New Zealand is also applied to scrutinise this WFH trend as a robustness check. Design/methodology/approach Since the outbreak of COVID-19, work-from-home (WFH) and e-commerce have become much more common in many cities. Many news reports have contended that households are leaving city centres and moving into bigger and better houses in the suburbs or rural areas. This emerging trend has been redefining the traditional theory of residential location choices. Proximity to central business district (CBD) is no longer the most critical consideration in choosing one’s residence. WFH and e-commerce flatten the traditional bid rent curve from the city centre. Findings The authors examined micro-level housing rental listings in 242 suburbs of the Auckland Region from January 2013 to December 2021 (108 months) and found that the hedonic price gradient models suggest that there has been a trend of rental gradient flattening and that its extent was almost doubled in 2021. Rents are also found to be increasing more in lower-density suburbs. Research limitations/implications The results imply that the pandemic has accelerated the trend of WFH and e-commerce. The authors further discuss whether the trend will be a transient phenomenon or a long-term shift. Practical implications Suppose an organisation is concerned about productivity and performance issues due to a companywide ability to WFH. In that case, some standard key performance indicators for management and employees could be implemented. Forward-thinking cities need to focus on attracting skilful workers by making WFH a possible solution, not by insisting on the primacy of antiquated nine-to-five office cultures. Social implications WFH has traditionally encountered resistance, but more and more companies are adopting WFH policies in this post-COVID era. The early rental gradient and the micro-level household data analysis all confirm that the WFH trend is emerging and will likely be a long-term shift. Instead of resisting the change, organisations should improve their remote work policies and capabilities for this WFH trend. Originality/value So far, empirical studies of post-COVID urban restructuring have been limited. This study aims to empirically test such an urban metamorphosis by identifying the spatial and temporal impacts of COVID on house rental gradients in the Auckland Region, New Zealand. The authors apply rental gradient analysis to test this urban restructuring hypothesis because the method considers the spatial-temporal differences, i.e. a difference-in-differences between pre-and post-pandemic period against the distance measured from the city centre. The method can control for the spatial difference and the endogeneity involved. ",8.0,,False,,Medicine,2023-02-14,3a9dfefd1638fdade59ba0ac74493b5a067a1dab,doesworkfromhomereshapetheurbanrentalstructureearlyevidencefromarentalgradientanalysisinauckland,yiu,10.1108/ijhma-08-2022-0108,doesworkfromhomereshapetheurbanrentalstructureearlyevidencefromarentalgradientanalysisinauckland|2023,1,4017,4,hedonic house price || hedonic housing prices || house prices proximity hedonic || spatial hedonic model housing,2,1,6,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,05fc4dde7646a2be9613d068bc573355c1d486c0,10.1108/ijhma-09-2017-0082,,"Landscape view, height preferences and apartment prices: evidence from major urban areas in Indonesia",2018.0,International Journal of Housing Markets and Analysis,Andi Nida'ul Hasanah; M. H. Yudhistira," Purpose Landscape view is a crucial factor in house-buying decisions. Landscape views provide an amenity to residents, and this can influence the house or apartment owners in their residence decisions. Yet, the relative value of different types of view potentially differs. Additionally, the value of each type of view may differ depending on an apartment’s elevation above the ground level. In this study, the authors aim to estimate the value of landscape views on apartment prices in major urban areas in Indonesia. Design/methodology/approach This paper evaluates to what extent various landscape views including mountain, sea, river, lake, street, urban village, garden and sports center views affect apartment prices in major urban areas in Indonesia. Two hedonic regression approaches are used: ordinary least squares and semiparametric regression. The latter is used to accommodate a possible non-linearity in the relationship between price and apartment characteristics. The model also incorporates housing and locational characteristics as control variables. Findings Using online apartment market data, the estimates in this paper show some degree of heterogeneity in the value of various views to the extent of providing negative externalities. Mountain, street and sports center views are associated with higher apartment prices. Sea, lake and garden views are statistically insignificant in explaining the prices. In contrast, the unappealing nature of the rivers and their surrounding creates a negative impact on prices. The estimates also suggest that an apartment’s floor height plays a significant role in the valuation of views. Originality/value There is little research on landscape view effects on apartment prices, especially in Indonesia. In addition, the relationship between the value of views and height preferences has seldom been analyzed. This paper provides the valuation of an extensive list of landscape views in urban areas in Indonesia. The estimation results also suggest that the value of views may differ depending on the floor on which an apartment lies. ",8.0,,False,,Economics,2018-06-07,05fc4dde7646a2be9613d068bc573355c1d486c0,landscapeviewheightpreferencesandapartmentpricesevidencefrommajorurbanareasinindonesia,hasanah,10.1108/ijhma-09-2017-0082,landscapeviewheightpreferencesandapartmentpricesevidencefrommajorurbanareasinindonesia|2018,1,3102,4,apartment prices hedonic || hedonic house price || hedonic housing prices || housing characteristics prices hedonic,2,2,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,479ef93706cda4540ad9e7de616a40abd3b26255,10.1108/ijhma-05-2020-0050,,Estimating the price of apartments in Tehran using extracted compound variables,2020.0,,Javad Koohpayma; M. Argany," Purpose Housing price is a barometer of a national economy. In recent years, Iran experienced high inflation in its economy, which affects everything, including housing. The purpose of this study is the estimation of the value of residential apartments of Tehran using ordinary least square (OLS) and geographically weighted regression (GWR) methods. Design/methodology/approach This paper proposed a method for determining the compound variables and used them to estimate and evaluate the prices in the district six of Tehran city. Also, this paper compared the GWR and OLS methods with different types of factors and their influences in house price estimations. Findings During the high inflation period of the study period, the age of buildings, inflation, parking, storage room and their locations are the most critical factors that affect the price of apartments in district six of Tehran. Besides, compound variables have the most influence on the prediction of the prices. Research limitations/implications The exact location of the apartments in the study area were unknown. Therefore, the positions are extracted from their addresses. The uncertainty of location forced us to ignore the neighborhood terms in the hedonic method. Practical implications The exact locations of the apartments in the study area were unknown. Therefore, the positions are extracted from their addresses. The uncertainty of location forced us to ignore the neighborhood terms in the hedonic method. Originality/value The originality of the proposed method is that it used a different approach to determine the valid variables of the apartment prices. Also, the evaluation of the method showed that the proposed variables are significantly useful. ",8.0,,False,,Economics,2020-10-19,479ef93706cda4540ad9e7de616a40abd3b26255,estimatingthepriceofapartmentsintehranusingextractedcompoundvariables,koohpayma,10.1108/ijhma-05-2020-0050,estimatingthepriceofapartmentsintehranusingextractedcompoundvariables|2020,1,2745,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment prices hedonic || hedonic house price || hedonic housing prices",2,4,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,house prices proximity hedonic,649664b7d83d315457a2a548f911f1657b4faaae,10.1177/00420980211024156,,"The effect of film production studios on housing prices in Atlanta, the Hollywood of the South",2021.0,Urban studies,Velma Zahirovic-Herbert; K. Gibler,"Governments compete to attract film productions to their communities, expecting them to provide economic benefits directly through local purchases and employment and indirectly through publicity and image building. Atlanta, Georgia, has become a large film production hub, partially because of a state tax credit programme. As a result, permanent production facilities have been established that may influence surrounding real estate prices. Convenience and a desire to live among other creatives may encourage actors, technical workers and support staff to live nearby, and the film industry cachet may attract additional residents, increasing house prices. However, if the facilities are perceived to be similar to other industrial operations, they may negatively affect surrounding house prices. We investigate film production studios’ effect on residential property values using housing sales data and information on film production studios’ location, size and year established. To mitigate bias from the potential endogeneity of studios’ location choices, we augment the standard hedonic pricing framework to incorporate proximity to a film production studio and construct treatment and comparison groups. Using a spatial difference-in-difference framework, we find that while houses located closest to film production studios tend to sell for lower prices than houses further away, the discount is not attributable to the studios. Instead, the establishment of a new studio, especially a large one, is a positive event in the housing market. These findings have important policy implications for local governments in making decisions about film industry incentives and facilities’ site selection as possible drivers of redevelopment.",8.0,,False,JournalArticle,Business,2021-07-29,649664b7d83d315457a2a548f911f1657b4faaae,theeffectoffilmproductionstudiosonhousingpricesinatlantathehollywoodofthesouth,zahirovic-herbert,10.1177/00420980211024156,theeffectoffilmproductionstudiosonhousingpricesinatlantathehollywoodofthesouth|2021,1,2739,6,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || house prices proximity hedonic",2,4,4,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,dc8fd709b4ebe9dac1a9ea061e5cc080645a0618,10.1093/ssjj/jyab029,,The Application of Machine Learning Approaches on Real-Time Apartment Prices in the Tokyo Metropolitan Area,2021.0,Social Science Japan Journal,Ti-Ching Peng; chun-chieh wang," The widely applied hedonic regression approach for the relationship between property prices and housing attributes is subject to assumptions and specifications of models as well as the availability and content of second-hand official data. In a cross-disciplinary spirit, this study employs machine learning techniques to examine hedonic apartment prices in the Tokyo Metropolitan Area of Japan based on online sales data extracted by web-parsing technology. With 14,579 apartment observations, two machine learning regressions—decision tree (DT) and random forest (RF)—are compared to conventional ordinary least squares regression (OLS) for hedonic modelling. Empirical results demonstrated that RF regressions led to the highest accuracy in model prediction performance, followed by DT and OLS. The comparison with results across models revealed that the housing features that have consistent influences on apartment prices tend to be those associated with living quality (including management funds, repair fund fees, floor size, located floor, total floor of the building, and location in Tokyo). Other commonly appreciated features, such as southward orientation or corner-lot location, did not demonstrate importance, possibly due to changes in residents’ preferences. In this big-data era, the adaptation of real-time data and machine learning approaches should add value to the variable selection process and model performance.",8.0,,False,,Computer Science,2021-08-24,dc8fd709b4ebe9dac1a9ea061e5cc080645a0618,theapplicationofmachinelearningapproachesonrealtimeapartmentpricesinthetokyometropolitanarea,peng,10.1093/ssjj/jyab029,theapplicationofmachinelearningapproachesonrealtimeapartmentpricesinthetokyometropolitanarea|2021,1,2438,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || apartment prices hedonic || hedonic housing prices || machine learning hedonic pricing housing",2,2,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 crossref,apartment rent hedonic,,10.1111/j.1465-7287.2009.00190.x,,THE ECONOMIC VALUE OF PROTECTING INVENTORIED ROADLESS AREAS: A SPATIAL HEDONIC PRICE STUDY IN NEW MEXICO,2010.0,Contemporary Economic Policy,GERMÁN M. IZÓN; MICHAEL S. HAND; MATIAS FONTENLA; ROBERT P. BERRENS,"The objective of this paper is to examine the off‐site benefits, as capitalized into housing values, of protecting 1.6 million acres of Inventoried Roadless Areas (IRAs) in the state of New Mexico, United States. In light of petitions filed by various U.S. states to maintain the status of IRAs as roadless lands, spatial hedonic price models are estimated and used to calculate the implicit value of IRAs in New Mexico. Findings show that a two‐stage least squares (2‐SLS), robust spatial‐lag model is the most appropriate econometric representation of the hedonic price function, and that IRA lands are a significant and positive determinant of house value. After controlling for the presence of Wilderness Areas (WAs) and other characteristics, results indicate that, on average, there is a 5.6% gain in the property value of a house from being located in, or adjacent to, a Census tract with IRAs. In the aggregate, this gain represents 3.5% of the value of all owner‐occupied units in New Mexico ($1.9 billion in capitalized value, or an annualized value in perpetuity of $95 million, assuming a 5% interest rate). ( JEL R22, H40, Q51, C21)",8.0,,,journal-article,,,doi_10_1111_j_1465_7287_2009_00190_x,theeconomicvalueofprotectinginventoriedroadlessareasaspatialhedonicpricestudyinnewmexico,izón,10.1111/j.1465-7287.2009.00190.x,theeconomicvalueofprotectinginventoriedroadlessareasaspatialhedonicpricestudyinnewmexico|2010,1,2145,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment rent hedonic || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,3,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,hedonic housing prices,,10.1108/01443589710156907,,Measuring Irish housing quality,1997.0,Journal of Economic Studies,Edward Shinnick,"Reviews the development of Irish housing policy from the eighteenth century to the present. Emphasizes the importance of measuring quality in housing, where housing may be described by a vector of measured characteristics. Undertakes a detailed price determination of these various characteristics using a hedonic model. Explains observed house prices in terms of the various housing characteristics using a hedonic model, from which a set of implicit or “hedonic” prices emerges. Reviews the theoretical basis of such an approach and develops a model specifically for aggregated data, as other studies in this area used disaggregated models. Obtains quantitative estimates of the qualitative and quantitative characteristics of the housing bundle before and after a series of model transformations. The results indicate the premium associated with certain housing characteristics. They emphasize, in particular, the importance of floor area and location in determining new Irish estate house prices.",8.0,,,journal-article,,,doi_10_1108_01443589710156907,measuringirishhousingquality,shinnick,10.1108/01443589710156907,measuringirishhousingquality|1997,1,2000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || implicit prices housing attributes",2,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c907712a0677442e279c0250af0baebe78de5b5d,10.1111/1759-3441.12259,,What Makes a Locality Attractive? Estimates of the Amenity Value of Parks for Victoria,2019.0,Economic Papers: A journal of applied economics and policy,Roberto Evangelio; Simon Hone; Moses Lee; David L. Prentice,"This paper provides the first estimates of the effects of parks on house prices within Victoria. We estimate hedonic regressions of house prices on the distance to six types of parks as well as a wide range of other amenities that may impact on house prices. We find that moving from the median to the first percentile of distances from a park is associated with increased property prices of up to $86,000. Parks are more likely to have a positive effect on house prices in regional Victoria than in Melbourne, where we speculate that for some types of parks, congestion or other types of negative externalities may be present. The current guidelines for cost-benefit analysis for transport projects do not include values for amenity effects. So the results of this work can be used to construct estimates of the amenity value of a park for a rapid cost benefit analysis.",8.0,,False,,Economics;Geography,2019-09-01,c907712a0677442e279c0250af0baebe78de5b5d,whatmakesalocalityattractiveestimatesoftheamenityvalueofparksforvictoria,evangelio,10.1111/1759-3441.12259,whatmakesalocalityattractiveestimatesoftheamenityvalueofparksforvictoria|2019,1,1871,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,3,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1a1bb3e008862bd1c0992dabbda8d9c30a372253,10.1007/s41685-023-00290-x,,"Spatial heterogeneity of marginal willingness to pay for air quality in PM2.5: analysis of buyers’ housing price in Beijing through hedonic price, spatial regression, and quantile regression models",2023.0,Asia-Pacific Journal of Regional Science,Chao Zhang; Mimi Xiong; Xuehui Wei; Zongmin Lan,,8.0,,False,JournalArticle,,2023-04-17,1a1bb3e008862bd1c0992dabbda8d9c30a372253,spatialheterogeneityofmarginalwillingnesstopayforairqualityinpm25analysisofbuyershousingpriceinbeijingthroughhedonicpricespatialregressionandquantileregressionmodels,zhang,10.1007/s41685-023-00290-x,spatialheterogeneityofmarginalwillingnesstopayforairqualityinpm25analysisofbuyershousingpriceinbeijingthroughhedonicpricespatialregressionandquantileregressionmodels|2023,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,1,1,3,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,312d3caae468127052510c1d6c4399fe8ba6e475,10.11821/yj2010070012,,Investigation of the capitalization of municipal government infrastructure investment on housing market: Hedonic model based on Guangzhou housing price data,2010.0,,Wen-Ying Fu,,8.0,,False,,Business,2010-07-20,312d3caae468127052510c1d6c4399fe8ba6e475,investigationofthecapitalizationofmunicipalgovernmentinfrastructureinvestmentonhousingmarkethedonicmodelbasedonguangzhouhousingpricedata,fu,10.11821/yj2010070012,investigationofthecapitalizationofmunicipalgovernmentinfrastructureinvestmentonhousingmarkethedonicmodelbasedonguangzhouhousingpricedata|2010,1,1000,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,1c6bf9be1758fab4d0d1723cb5c7fc8b1defd63a,10.1061/(asce)up.1943-5444.0000564,,Impacts of an Urban Environmental Event on Housing Prices: Evidence from the Hangzhou Pesticide Plant Incident,2020.0,,W. Yue; Chaoran Ni; Chuan-Hao Tian; Hai-Zhen Wen; Li Fang,Abstract Environmental pollution incidents affect urban residents and the natural environment. This article employs hedonic price models and a difference-in-difference (DID) approach to examine how...,8.0,,False,,Environmental Science,2020-06-01,1c6bf9be1758fab4d0d1723cb5c7fc8b1defd63a,impactsofanurbanenvironmentaleventonhousingpricesevidencefromthehangzhoupesticideplantincident,yue,10.1061/(asce)up.1943-5444.0000564,impactsofanurbanenvironmentaleventonhousingpricesevidencefromthehangzhoupesticideplantincident|2020,1,1200,1,hedonic housing prices,2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,3f2d10c5d6d3d089c494489695293c13e38d7090,10.1007/s00181-019-01804-3,,A hedonic approach to the valuation of the effect of criminal violence on housing prices in Acapulco City,2019.0,Empirical Economics,J. Delgado; Giovanni Wences,,8.0,,False,,Economics,2019-11-15,3f2d10c5d6d3d089c494489695293c13e38d7090,ahedonicapproachtothevaluationoftheeffectofcriminalviolenceonhousingpricesinacapulcocity,delgado,10.1007/s00181-019-01804-3,ahedonicapproachtothevaluationoftheeffectofcriminalviolenceonhousingpricesinacapulcocity|2019,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,71582f5038912c9996144f353323eb657c312604,10.1108/ijhma-02-2018-0017,,Leveling the playing field: out-of-town buyer premiums in US housing markets over time,2019.0,International Journal of Housing Markets and Analysis,Katrin Kandlbinder; N. Miller; Michael A. Sklarz," Purpose Historically, research shows that out-of-town buyers of real estate are informationally disadvantaged and therefore pay higher prices compared to in-town buyers. However, with the recent advent of online housing platforms, a plethora of information about the housing market is provided for free. The purpose of this paper is to examine whether out-of-town buyers do in fact pay a premium and why, and whether this premium has decreased because of better information availability. Design/methodology/approach A hedonic regression model over a ten-year window (2005, 2015) is developed to analyze condominium transactions in Miami-Dade County. The results are validated by various robustness checks and the propensity score matching algorithm to identify a comparable control sample for 2015 in terms of relevant housing characteristics. Findings The results support the hypothesis that out-of-town buyers pay higher prices for real estate, compared to their local counterparts, and that both search costs and anchoring cause a premium in both years, whereas wealth only plays a significant role in 2005. The premium because of search costs, and therefore, information availability has decreased slightly over time. Originality/value This is the first out-of-town paper that compares two points in time versus a single cross-section analysis. Besides the premium caused by information asymmetry/search costs measured by distance and the anchoring effect, the regression model is extended by the wealth effect. ",8.0,,False,,Economics,2019-01-09,71582f5038912c9996144f353323eb657c312604,levelingtheplayingfieldoutoftownbuyerpremiumsinushousingmarketsovertime,kandlbinder,10.1108/ijhma-02-2018-0017,levelingtheplayingfieldoutoftownbuyerpremiumsinushousingmarketsovertime|2019,1,2523,4,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic || real estate hedonic regression,2,0,3,3,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),e4c99cb0673f5701b5a8c54aef99b5b0305f4014,10.1111/rurd.12061,,DO PREFERENCES FOR AMENITIES DIFFER AMONG HOME BUYERS? A HEDONIC PRICE APPROACH,2017.0,,P. D. de Araujo; K. Cheng,,8.0,,False,,Business,2017-07-05,e4c99cb0673f5701b5a8c54aef99b5b0305f4014,dopreferencesforamenitiesdifferamonghomebuyersahedonicpriceapproach,araujo,10.1111/rurd.12061,dopreferencesforamenitiesdifferamonghomebuyersahedonicpriceapproach|2017,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices",2,0,1,1,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4aa212dc4d750f529d65e957b96ec2b893984391,10.1016/j.resourpol.2020.101777,,"Impacts of mining on property values in Kalgoorlie-Boulder, Western Australia",2020.0,,Chomba Kolala; M. Polyakov; James Fogarty,"Abstract Mining activities have a positive impact on local communities through providing formal employment opportunities in the local area. However, the air and noise pollution associated with surface mining represent a negative externality for residents in communities near mining activity. To support urban planning processes, accurate information on both the positive and negative impacts of surface mining is required. In this study, we estimate the dis-amenity of proximity to a large open-pit gold mine in Kalgoorlie, Western Australia, that has been capitalised into residential property values. To estimate the impact of proximity to the mine on property values, we use sales data for 21,850 residential properties sold in Kalgoorlie-Boulder between 1990 and 2018. We use distance to the mine as a measure of the impact. We control for spatial heterogeneity using spatial fixed effects. We found that residential properties located within two kilometres of the mine trade at a 20% to 30% discount to similar residential properties located at least six to seven kilometres from the mine. Our results can be used for planning appropriate buffer zones around mining activities.",8.0,,False,,Geography,2020-10-01,4aa212dc4d750f529d65e957b96ec2b893984391,impactsofminingonpropertyvaluesinkalgoorlieboulderwesternaustralia,kolala,10.1016/j.resourpol.2020.101777,impactsofminingonpropertyvaluesinkalgoorlieboulderwesternaustralia|2020,1,2182,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,5,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f26be5616b0eb9e906c42d88c81d8459ebcaabbb,10.1177/15270025221132221,,Sports Facilities as a Housing Amenity: Do Prices Follow Facilities?,2022.0,Journal of Sports Economics,Geoffrey Propheter,"The sports facility amenity theory predicts that when facility sites change, home prices nearer the prior site decline while home prices nearer the new site increase. Using home sales data in the vicinity of two proposed facility sites for the Golden State Warriors in San Francisco, the data are generally supportive of the amenity theory. The study also shows that Euclidean and walking distance can yield dissimilar treatment effect sizes, thus compelling researchers to justify their distance measurement choice. Finally, the data provide weak support that home buyers respond to changes in facility re-designs towards increased public benefits.",8.0,,False,JournalArticle,,2022-10-17,f26be5616b0eb9e906c42d88c81d8459ebcaabbb,sportsfacilitiesasahousingamenitydopricesfollowfacilities,propheter,10.1177/15270025221132221,sportsfacilitiesasahousingamenitydopricesfollowfacilities|2022,1,1649,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,b7236dafbd8ab8a4e6e6ba18bd114f4f46e2511a,10.1111/j.1467-940x.2009.00160.x,,SPATIALLY‐VARYING EFFECTS OF REZONING ON HOUSING PRICE,2009.0,,Seong-Hoon Cho; S. Kim; D. Lambert,,8.0,,False,,Economics,2009-03-01,b7236dafbd8ab8a4e6e6ba18bd114f4f46e2511a,spatiallyvaryingeffectsofrezoningonhousingprice,cho,10.1111/j.1467-940x.2009.00160.x,spatiallyvaryingeffectsofrezoningonhousingprice|2009,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || hedonic price model amenities residential || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",c02ca0c54d59f80228e7f3d36c848a26da1ed15d,10.1007/s10901-020-09805-w,,"City proximity, travel modes and house prices: the three cities in Sydney",2021.0,Journal of Housing and the Built Environment,S. Herath; A. S. Jayasekare,,8.0,,False,,Geography,2021-02-03,c02ca0c54d59f80228e7f3d36c848a26da1ed15d,cityproximitytravelmodesandhousepricesthethreecitiesinsydney,herath,10.1007/s10901-020-09805-w,cityproximitytravelmodesandhousepricesthethreecitiesinsydney|2021,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,74beea8fa6f43d6193c5a245bd33b399c7b29d33,10.1111/jors.12207,,Hedonic Pricing When Housing is Endogenous: The Value of Access to the Trans‐Israel Highway,2016.0,,M. Beenstock; Daniel Feldman; D. Felsenstein,,8.0,,False,,Economics,,74beea8fa6f43d6193c5a245bd33b399c7b29d33,hedonicpricingwhenhousingisendogenousthevalueofaccesstothetransisraelhighway,beenstock,10.1111/jors.12207,hedonicpricingwhenhousingisendogenousthevalueofaccesstothetransisraelhighway|2016,1,1000,2,hedonic house price || hedonic housing prices,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",14085b9809485e5d173c2551146e857360933205,10.1109/iccis.2013.125,,"Spatial Variation Analysis of the Housing Price in Multi-center City: A Case Study in Chongqing City, China",2013.0,International Conference Communication and Information Systems,Li-Juan Wang; Gui-Wen Liu,,8.0,,False,Conference,Geography,2013-06-21,14085b9809485e5d173c2551146e857360933205,spatialvariationanalysisofthehousingpriceinmulticentercityacasestudyinchongqingcitychina,wang,10.1109/iccis.2013.125,spatialvariationanalysisofthehousingpriceinmulticentercityacasestudyinchongqingcitychina|2013,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,cb93312e6abe610fac1f70478caf02ca6b79910a,10.1080/13504509.2023.2234332,,Understanding the effects of urban perceptions on housing rent using big data and machine learning,2023.0,International Journal of Sustainable Development & World Ecology,Yonglin Zhang; Li Wang; R. Dong; H. Deng; Xiao Fu; Bo Huang; Zheng Niu; Fang Chen,"ABSTRACT Urban perceptions are related to residents’ subjective well-being, activities, settlement choice, and physical and mental health, which are vital for promoting sustainable community development. Landsenses ecology proposes the concepts of physical and psychological perceptions to measure the interaction between humans and the urban environment. However, such perceptions and their quantitative relationships with housing rental prices (HRPs) are still not clear. In this study, a novel urban-perception-oriented hedonic price model (UP-HPM) is proposed based on the construction of a multisource geographic database of Beijing’s rental market with rich information on people-oriented perception metrics, neighborhood environment characteristics, and HRPs. Furthermore, we use advanced machine learning (ML) pipelines to measure residents’ multidimensional urban perceptions and investigate their impact on block-level HRPs and their spatial disparities. We also quantify and compare the contribution of different physical and psychological perceptual attributes to HRPs through UP-HPM. The results show that neighborhood perceived greenery and the use of fences tend to bring about positive psychological perceptions and further affect HRPs. By comparing different combinations of hedonic and perceptual features, the UP-HPM performs well in terms of fitting capacity, spatial description, and interpretability and can inspire humanistic thinking and landsenses planning guidelines for the regulation of the allocation of public rental housing and improvement in block-level physical settings and infrastructure services, thereby providing insights into how best to promote residents’ quality of life and well-being.",7.0,,False,JournalArticle,,2023-07-14,cb93312e6abe610fac1f70478caf02ca6b79910a,understandingtheeffectsofurbanperceptionsonhousingrentusingbigdataandmachinelearning,zhang,10.1080/13504509.2023.2234332,understandingtheeffectsofurbanperceptionsonhousingrentusingbigdataandmachinelearning|2023,1,2727,4,hedonic housing prices || housing characteristics prices hedonic || machine learning hedonic pricing housing || spatial hedonic model housing,2,2,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,apartment prices hedonic,003b880249e7306789750ef9f4bc2d36cb0c9cd6,10.1177/23998083211000343,,Estimating coastal premiums for apartment prices: Towards a new multilevel modelling approach,2021.0,Environment and Planning B Urban Analytics and City Science,Yuheng Ling,"This article attempts to assess ‘coastal premiums’ for apartment prices in Corsica, France. The coastal premium should consist of two parts, those related to the view of the Mediterranean Sea, and those associated with living close to beaches for access purposes. Views, being a qualitative and subjective variable, are difficult to measure and quantify. Further, apartments are located in neighbourhoods and then are situated in districts or at more aggregated levels. This induces complex spatial interactions. To deal with these, we first employ a viewshed analysis implemented in geographical information systems (GIS) to generate an objective, continuous measurement. We then develop a Bayesian spatial/spatiotemporal multilevel model, which integrates Leroux’s conditional autoregressive process with multilevel modelling for conducting a hedonic analysis in the presence of multiple-scale housing data. Since the developed model pertains to latent Gaussian models, estimation is carried out by integrated nested Laplace approximations. The results demonstrate that homebuyers have a higher marginal willingness to pay for larger views of the sea, and proximity to beaches produces positive impacts on apartment sale prices. Further, strong spatial spillovers emerge among high-level units, but heterogeneity dominates among apartments. Our findings illustrate the importance of coasts to homebuyers. Further, the estimated value permits planners and policymakers to assess the trade-off between developing and preserving land along the coast.",7.0,,False,JournalArticle,Geography,2021-03-22,003b880249e7306789750ef9f4bc2d36cb0c9cd6,estimatingcoastalpremiumsforapartmentpricestowardsanewmultilevelmodellingapproach,ling,10.1177/23998083211000343,estimatingcoastalpremiumsforapartmentpricestowardsanewmultilevelmodellingapproach|2021,1,2549,4,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment prices hedonic || hedonic housing prices || spatial hedonic model housing",2,2,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,62d36e81ccea4c8c563a0288bc2f59d12ef51ca5,10.1108/ijhma-03-2020-0025,,Neighborhood effects of housing program using Jeonse in Korea,2020.0,,S. Yun," Purpose This study aims to introduce the Youth Jeonse Rental Housing Program using the unique characteristics of the Korean housing market to explain its theoretical value in the current theoretical landscape of public housing and to identify the effect of the program on the regional housing price. Design/methodology/approach This study uses three hedonic price models on the basis of Korea’s housing market, namely, own, jeonse and rent models. Moreover, it uses the hierarchical linear model to include both house- and region-level variables. Findings Analysis shows that youth rental housing has no effect on falling prices in the region unlike long-term rental housing. Thus, the policies using regional tenure system are more effective in the social mix than existing public housing policies. Originality/value This study introduces the program using Korea’s unique tenure system called jeonse, arguing its advantages for the supplier, recipients and regional neighborhoods. Suppliers can easily provide affordable housing at a low economic and administrative cost, whereas recipients can easily mix socially, have broad housing choices and a fighting chance for a stable life. Additionally, this policy has a low negative impact on the region. Furthermore, this study theoretically presents the potential for mixed paths other than demand or supply policies. It introduces and analyzes special policy objectives for youth housing problems. ",7.0,,False,,Business,2020-06-30,62d36e81ccea4c8c563a0288bc2f59d12ef51ca5,neighborhoodeffectsofhousingprogramusingjeonseinkorea,yun,10.1108/ijhma-03-2020-0025,neighborhoodeffectsofhousingprogramusingjeonseinkorea|2020,1,2455,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,2,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,real estate hedonic regression,76a0c1254f7c6017ddaba2614f2618c5679059ea,10.1080/07352166.2022.2147077,,Past structural racism and present home prices,2023.0,Journal of Urban Affairs,Robert W. Wassmer,"ABSTRACT The widely acknowledged rating system of the Federal Homeowners’ Loan Corporation (HOLC) was a relative measure of pre-1940 racist practices in residential real estate activities in large United States central cities. It was not an assessment that guided HOLC or later Federal Housing Administration (FHA) mortgage lending practices. The research question examined here is whether a similar home in different HOLC-rated neighborhoods in the City of Sacramento (California) sold for a different price eight decades after receiving its HOLC grade. A hedonic regression result shows that homes in the two forms of lower-rated neighborhoods sold for about 13% less than those in the two forms of higher-rated neighborhoods. A Blinder-Oaxaca decomposition breaks down the average selling price difference of $195,000 between a home sold in HOLC green/blue compared to yellow/red rated neighborhoods. A third of this difference in home prices, which is not due to home characteristics, represents a starting measure of the legacy of discriminatory housing practices. The remaining two-thirds of the difference could be due to variations in current home characteristics also influenced by Pre-1940s discriminatory housing practices. These findings are relevant to understanding the contribution of past structural racism in the housing market to present inequalities.",7.0,,False,,,2023-01-18,76a0c1254f7c6017ddaba2614f2618c5679059ea,paststructuralracismandpresenthomeprices,wassmer,10.1080/07352166.2022.2147077,paststructuralracismandpresenthomeprices|2023,1,2369,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic || real estate hedonic regression",2,3,4,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,a719a56ff10dbde96aa64c738fede432288b47fc,10.1080/08965803.2024.2346413,,The Value of Sustainable Property Management in Real Estate: Evidence From Hong Kong,2024.0,Journal of Real Estate Research,Jianfu Shen; Kwok-Yuen Fan; E. Hui; Sik-Man Ho,": This study investigates whether sustainability in property management is valued by property buyers. Using a sample of 365,201 property transactions in Hong Kong from 2007 to 2021 and based on a hedonic pricing model, we find that the environmental, social and governance (ESG) performance of property management companies is positively associated with housing price; and housing price drops if property management companies incur ESG related risk incidents. The results are robust if the sample is restricted to transactions with repeated sales and a location-based matching approach is used. Further analysis suggests that housing prices increase with social and governance performance but not environmental performance of property management companies. The negative effect of ESG accidents on property prices is exaggerated if the accidents are reported by high reach media. Home buyers and non-local buyers are more willing to pay a premium for sustainability in property management than housing speculators and local buyers. Additional evidence suggests that ESG practices play a more significant role in determining property value through lower risk rather than higher rents. Taken together, this study provides evidence on the value of sustainable property management in real estate.",7.0,,False,JournalArticle,,2024-05-20,a719a56ff10dbde96aa64c738fede432288b47fc,thevalueofsustainablepropertymanagementinrealestateevidencefromhongkong,shen,10.1080/08965803.2024.2346413,thevalueofsustainablepropertymanagementinrealestateevidencefromhongkong|2024,1,2291,3,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices",2,4,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9eb5221bbb4629776a5e209d2cd7f7dc94d7ec4f,10.1111/1475-4932.12455,,How Do Crime Rates Affect Property Prices?,2018.0,The Economic Record,Melek Cigdem‐Bayram; David L. Prentice,"In this paper we take the first steps to providing parameters capturing the wider impacts of crime when performing cost benefit analysis of investments in justice infrastructure. Specifically, we provide the first set of estimates of the wider impacts of crime for metropolitan Melbourne and regional Victoria. We estimate the effects of two different types of crime on households applying a hedonic regression model to a three year dataset of house prices and characteristics, distances to local amenities and crime rates. We find while an increase in the per capita rate of crime against persons reduces property prices in regional Victoria, it has no such effect in Melbourne. And we find no significant relationship between crimes against property and property prices in either Melbourne or regional Victoria. This implies that when investing in justice infrastructure to deliver services in regional Victoria that are expected to reduce crime against persons, the impact on the broader community (as captured through effects on property prices) should be taken into account in a cost-benefit analysis.",7.0,,False,,Economics,2018-05-17,9eb5221bbb4629776a5e209d2cd7f7dc94d7ec4f,howdocrimeratesaffectpropertyprices,cigdem‐bayram,10.1111/1475-4932.12455,howdocrimeratesaffectpropertyprices|2018,1,2106,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,3,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,80f3ea38184dea1a46e7e9880b737707bcab8af4,10.1080/09599916.2018.1562490,,Analysis of spatial variance clustering in the hedonic modeling of housing prices,2019.0,Journal of Property Research,Sören Gröbel,"ABSTRACT This paper examines the spatial dependency exhibited by the error term variance of hedonic modeling based on German housing price data. To this end, it applies the spatial autoregressive conditional heteroscedasticity (SARCH) model previously discussed in housing literature, which allows for the consideration of spatial dependency when modeling the error variance of hedonic pricing. This model represents a spatialized version of the well-known ARCH-model used in time series analysis. Consistent with previous findings, this paper confirms the existence of spatial conditional heteroscedasticity, i.e. dependency in the error variance. However, this spatial dependency is not a global phenomenon, but can be ascribed to spatial concentrations of apartments with a relatively high variance in a small number of the same neighborhoods. The analysis of spatial heteroscedasticity helps to improve the estimation efficiency and prediction accuracy. In addition, spatial differences can be used to account for idiosyncratic risk when conducting mass appraisal.",7.0,,False,,Mathematics,2019-01-02,80f3ea38184dea1a46e7e9880b737707bcab8af4,analysisofspatialvarianceclusteringinthehedonicmodelingofhousingprices,gröbel,10.1080/09599916.2018.1562490,analysisofspatialvarianceclusteringinthehedonicmodelingofhousingprices|2019,1,2068,3,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || hedonic housing prices || spatial hedonic model housing",2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),5e29c89db542c6c2eac7f27b138b14759c353a92,10.1080/20421338.2020.1830542,,Waste Disposal Sites and Residential Rental Values Nexus: An Appraisal of Agogo Asante Akyem Dumps,2021.0,,Peres Ofori,"The study, using regression and ANOVA, assessed the extent to which waste disposal sites impact residential rental values. The research collected data from both secondary and primary sources using a mixed method cross sectional survey. The linear hedonic price model was adopted to suit the research objectives. A questionnaire survey, expert interviews and a field investigation were used to gather raw data. The study revealed that all the residential properties that were far away from the waste disposal sites had higher rental values between (GH¢1200–1680) than those that were close (GH¢600–720). Construction of an engineered landfill by the district assembly for the town was recommended. Also, real estate developers in the town should be more circumspect about the siting of their residential properties since most of the people were not willing to pay higher rent if buildings are close to a filthy area like a dump site, irrespective of the architectural designs.",7.0,,False,Review,,2021-02-23,5e29c89db542c6c2eac7f27b138b14759c353a92,wastedisposalsitesandresidentialrentalvaluesnexusanappraisalofagogoasanteakyemdumps,ofori,10.1080/20421338.2020.1830542,wastedisposalsitesandresidentialrentalvaluesnexusanappraisalofagogoasanteakyemdumps|2021,1,1975,3,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression",2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,0fcbfba2a7f1cd280a6254e66b7c0a49d859c7c5,10.2139/ssrn.3998795,,The Marginal Cost of Mortality Risk Reduction: Evidence from Housing Markets,2021.0,Social Science Research Network,Kelly C. Bishop; N. Kuminoff; Sophie M. Mathes; A. Murphy,"We provide the first evidence that spatial variation in all-cause mortality risk is capitalized into US housing prices. Using a hedonic framework, we recover the annual implicit housing cost of a 0.1 percentage-point reduction in mortality risk among older Americans and find that this figure is both relatively low and decreasing in age, from $ 3,486 for a 67 year old to $ 637 for an 87 year old. These estimates are far below comparable estimates found in the labor market, suggesting that the housing market provides an alternative, substantially cheaper channel to reducing mortality risk. heart disease, depression, osteoporosis, rheuma-toid arthritis and osteoarthritis, stroke or transient ischemic attack, breast cancer, colorectal cancer, prostate cancer, lung cancer, endometrial cancer, anemia, asthma, hyperlipidemia, hypertension, hyperplasia, hy-pothyroidism.",7.0,,False,JournalArticle,,2021-12-01,0fcbfba2a7f1cd280a6254e66b7c0a49d859c7c5,themarginalcostofmortalityriskreductionevidencefromhousingmarkets,bishop,10.2139/ssrn.3998795,themarginalcostofmortalityriskreductionevidencefromhousingmarkets|2021,1,1874,1,hedonic housing prices,2,1,1,1,1,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,7b6011b06cf4c6aadbfb1379c445bc71daf2939b,10.1016/j.tra.2019.09.011,,"How large are the non-travel time effects of urban highway tunneling? Evidence from Maastricht, the Netherlands",2019.0,,J. Tijm; T. Michielsen; R. van Maarseveen; P. Zwaneveld,"Abstract Infrastructure projects increasing aim at to improve the quality of life and public space, in particular in urban areas. We analyse a Dutch case in which an existing highway in an urban area was moved underground in order to improve intercity traffic flows and to reduce the negative effects associated with an open urban highway. Travel times within the city hardly changed, which allows for a relatively clean identification of the other benefits via a hedonic pricing model. We find that the non-travel time benefits of such integrated infrastructure are substantial relative to the construction costs. A halving of distance to the tunneled segment is associated with a 3.4% appreciation in house prices since the start of the project. Taken together, the non-travel time effects add up to a quarter the construction costs.",7.0,,False,,Business,2019-12-01,7b6011b06cf4c6aadbfb1379c445bc71daf2939b,howlargearethenontraveltimeeffectsofurbanhighwaytunnelingevidencefrommaastrichtthenetherlands,tijm,10.1016/j.tra.2019.09.011,howlargearethenontraveltimeeffectsofurbanhighwaytunnelingevidencefrommaastrichtthenetherlands|2019,1,1835,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,72ea48fcd0527987b61ecf1a6e024ea77e3e1a72,10.1177/0739456x221133022,,When and Where Do Home Values Increase in Response to Planned Light Rail Construction?,2023.0,Journal of planning education and research,Qiong Peng; G. Knaap,"This paper examines the effects of an investment in light rail transit on single-family property values in Montgomery County, Maryland, with a focus on when and where such capitalization takes place and with intent to inform efforts to promote equitable transit-oriented development. We employ both hedonic price and repeat-sales estimation techniques to assure the results are robust. The findings indicate that housing prices, after the light rail transit went into the engineering phase, rose with proximity to anticipated new rail stations but not with proximity to existing metro stations. For planners and policymakers and advocates in Montgomery County, the results provide critical information about where and when value capture and anti-displacement policies are most needed and potentially effective.",7.0,,False,JournalArticle,,2023-02-13,72ea48fcd0527987b61ecf1a6e024ea77e3e1a72,whenandwheredohomevaluesincreaseinresponsetoplannedlightrailconstruction,peng,10.1177/0739456x221133022,whenandwheredohomevaluesincreaseinresponsetoplannedlightrailconstruction|2023,1,1810,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,3,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",43f647146a4a9bd7bc80736e1c0bb91273518d7c,10.1016/j.landusepol.2018.06.027,,Enthusiasm curbed: Home value implications of curbside parking rights,2018.0,Land Use Policy,A. Maas; Philip Watson,"While property rights assignment can be more politically acceptable than limiting access to public resources through higher costs, assigning property rights via residential permitting transfers the value of those resources to particular groups or individuals. This study quantifies the home value increase associated with transferring public parking spaces to residential permitting using spatially explicit difference-in-difference and triple-difference hedonic price models. Results suggest that homes within walking distance of a destination location—a large state university—increase in value by $31,000 after the introduction of residential parking policies that limit the ability of other citizens to commute and park near campus.",7.0,,False,,Business,2018-09-01,43f647146a4a9bd7bc80736e1c0bb91273518d7c,enthusiasmcurbedhomevalueimplicationsofcurbsideparkingrights,maas,10.1016/j.landusepol.2018.06.027,enthusiasmcurbedhomevalueimplicationsofcurbsideparkingrights|2018,1,1736,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2,1,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2c777d53a0356c060ec452c1ee17fe4f762d5e7b,10.1007/978-3-642-37896-6_5,,Spatial Hedonic Modeling of Housing Prices Using Auxiliary Maps,2018.0,,B. Bajat; M. Kilibarda; Milutin Pejović; Mileva Samardžić Petrović,,7.0,,False,,Economics,,2c777d53a0356c060ec452c1ee17fe4f762d5e7b,spatialhedonicmodelingofhousingpricesusingauxiliarymaps,bajat,10.1007/978-3-642-37896-6_5,spatialhedonicmodelingofhousingpricesusingauxiliarymaps|2018,1,1000,4,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || spatial hedonic model housing",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f2e3227ca1f0bdfd1e71f86c886f933a73f15444,10.1007/978-3-319-54885-2_15,,Impacts of Urban Environmental Attributes on Residential Housing Prices in Warsaw (Poland): Spatial Hedonic Analysis of City Districts,2017.0,,Magdalena Ligus; Piotr Peternek,,7.0,,False,,Business,,f2e3227ca1f0bdfd1e71f86c886f933a73f15444,impactsofurbanenvironmentalattributesonresidentialhousingpricesinwarsawpolandspatialhedonicanalysisofcitydistricts,ligus,10.1007/978-3-319-54885-2_15,impactsofurbanenvironmentalattributesonresidentialhousingpricesinwarsawpolandspatialhedonicanalysisofcitydistricts|2017,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,1,2,2,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,5b7fdd77ea672cc550e45d2f7bbc171920de19ca,10.69554/yzcu7790,,Investigating the differences in the impacts of redevelopment and building rehabilitation on house prices: A Hong Kong case study,2012.0,"Journal of Building Survey, Appraisal & Valuation",Y. Yau,"While redevelopment is a common approach to housing improvement or urban renewal, increasing numbers of scholars, urban managers and sociologists started promoting the use of building rehabilitation, which is more sustainable. In Hong Kong, public administrators have been facing challenges in choosing between redevelopment and building rehabilitation when executing urban renewal projects. For more informed policymaking, it is necessary to know and compare the benefits generated from different housing improvement projects. The literature suggested that redevelopment and rehabilitation could add values to the nearby properties, yet it is still unclear whether there are any differences in the positive externalities posed by the two processes, for example in terms of magnitude of price change and affected distance. In this regard, this study aims to investigate the differences in the effects between redevelopment and building rehabilitation on house prices in Hong Kong. Two housing improvement projects in the old urban area are targeted for investigation, using hedonic price analysis. Yet no significant positive net externalities are identified from both redevelopment and rehabilitation. The findings of this study offer valuable insights to the public administrators into the policy formulation regarding urban renewal in the city.",7.0,,False,JournalArticle,Economics,2012-03-01,5b7fdd77ea672cc550e45d2f7bbc171920de19ca,investigatingthedifferencesintheimpactsofredevelopmentandbuildingrehabilitationonhousepricesahongkongcasestudy,yau,10.69554/yzcu7790,investigatingthedifferencesintheimpactsofredevelopmentandbuildingrehabilitationonhousepricesahongkongcasestudy|2012,1,2347,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices",2,2,2,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,348d2e2e800e2a8a4a7e1ea3e2be9cb323e49b95,10.1080/00036846.2019.1640858,,"Residential property price differentials of waste plants: evidence from Beijing, China",2019.0,Applied Economics,Ying-Dan Mei; Li Gao; Pei-Yuan Zhang,"ABSTRACT As a primary source of urban pollution, waste plants release toxic gases and polluted waste water that can cause great harm to human health and contaminate water resources. The adverse impact of waste plants on environmental threaten the quality of life of surrounding residents which will be reflected in residential property values. In this study, we develop a hedonic price model to estimate the environmental externalities of waste plants based on a panel dataset for real-estate transactions in Beijing from 2011 to 2015. We apply fixed effects and a Heckman selection model to control for omitted variable bias and sample selection bias and then construct price counterfactuals for properties. The price differentials between expected and counterfactual prices are then calculated. Our empirical results suggest that waste plants negatively influence residential property prices. Additionally, the influences of other factors, such as school district, traffic convenience, and average housing area, on property prices are consistent with common sense, though they are weakened for properties within three kilometers of waste plants.",7.0,,False,,Economics,2019-07-11,348d2e2e800e2a8a4a7e1ea3e2be9cb323e49b95,residentialpropertypricedifferentialsofwasteplantsevidencefrombeijingchina,mei,10.1080/00036846.2019.1640858,residentialpropertypricedifferentialsofwasteplantsevidencefrombeijingchina|2019,1,2147,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices,2,3,2,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",faf116e3bdb264262e2ca66f5fcdcf119569a05e,10.1007/s10901-022-09990-w,,Impact of different ecological landscapes on housing prices—empirical evidence from wuhan through the hedonic pricing model appraisal,2022.0,Journal of Housing and the Built Environment,Xiao-Ling Ke; Chang Yang; Wei Shi; A. Mougharbel; Haixiang Guo; Mo Zheng,,7.0,,False,,,2022-11-09,faf116e3bdb264262e2ca66f5fcdcf119569a05e,impactofdifferentecologicallandscapesonhousingpricesempiricalevidencefromwuhanthroughthehedonicpricingmodelappraisal,ke,10.1007/s10901-022-09990-w,impactofdifferentecologicallandscapesonhousingpricesempiricalevidencefromwuhanthroughthehedonicpricingmodelappraisal|2022,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,2,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bb585e3b1bd1ac160b05cf31245217236fd66b46,10.1080/08882746.2019.1601928,,"House price impacts of construction quality and level of maintenance on a regional housing market: Evidence from King County, Washington",2019.0,Housing and Society,S. Mathur,"ABSTRACT Accurately estimating the monetary value of the construction quality and the level of maintenance of a house is a significant gap in house price research. Such estimates would be valuable for various actors in the housing market. Homebuyers would benefit from this information during the bidding process. Homeowners would benefit from it during renovation—particularly when financing their homes with a home equity line of credit—and when deciding on the need for renovation before selling their homes. Property value assessors would be able to provide better estimates of property values, and developers might make better decisions regarding the quality of new construction projects. Finally, lending institutions and public-sector housing agencies would benefit when providing loans or grants for housing rehabilitation and renovation.This research employs spatial econometric regression modeling and finds that construction quality and level of maintenance significantly affect house prices. For example, a medium-quality house sells for approximately 25% more than a low-quality house, and a well-maintained house sells for approximately 5% more than a house that is not well-maintained. The research also reports on the interaction between the quality and maintenance variables and how these variables vary with house size and the number of bedrooms.",7.0,,False,,Economics,2019-04-17,bb585e3b1bd1ac160b05cf31245217236fd66b46,housepriceimpactsofconstructionqualityandlevelofmaintenanceonaregionalhousingmarketevidencefromkingcountywashington,mathur,10.1080/08882746.2019.1601928,housepriceimpactsofconstructionqualityandlevelofmaintenanceonaregionalhousingmarketevidencefromkingcountywashington|2019,1,2364,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,4,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",266fbf57e3f931ee12c0a2f189676d152e2db050,10.1177/0739456x20912829,,Higher Density Development for Lower Cost Housing? Understanding the Multifamily Housing Market and the Role of Density in Multifamily Home Prices,2020.0,Journal of planning education and research,Hong-Wei Dong,"This study documents the development and the characteristics of multifamily homes in the Portland metropolitan area and evaluate the roles of density and density-related factors in determining multifamily home prices. The study finds that medium-density multifamily homes sell at lower prices than other housing types in dollar per unit and dollar per square foot. The cost-saving effect of higher density development is weak or even negative, and dwelling size is the primary determinant of multifamily home prices. The effects of neighborhood density on home prices depend on housing types. Investors own almost a half of nonapartment multifamily homes and buy homes at lower prices.",7.0,,False,JournalArticle,Business,2020-03-23,266fbf57e3f931ee12c0a2f189676d152e2db050,higherdensitydevelopmentforlowercosthousingunderstandingthemultifamilyhousingmarketandtheroleofdensityinmultifamilyhomeprices,dong,10.1177/0739456x20912829,higherdensitydevelopmentforlowercosthousingunderstandingthemultifamilyhousingmarketandtheroleofdensityinmultifamilyhomeprices|2020,1,1685,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,6,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c33ba8238ad4b0bb55ca5075b23da0628531d732,10.1080/13504851.2019.1613488,,Determining the effect of abnormal temperatures on the housing market,2020.0,Applied Economics Letters,Mitchell R. Livy,"ABSTRACT Extreme weather events have increased in frequency and are expected to grow in intensity over time. To examine perceptions of this abnormal weather, I model the relationship between degree days and the housing market. The results provide evidence that abnormal increases in cooling and heating degree days negatively affect housing prices, with cooling degree days having a larger impact. Adding to these estimates, the housing price capitalizations of heating and cooling related housing attributes are shown to vary with recent abnormal temperatures.",7.0,,False,,Environmental Science,2020-02-06,c33ba8238ad4b0bb55ca5075b23da0628531d732,determiningtheeffectofabnormaltemperaturesonthehousingmarket,livy,10.1080/13504851.2019.1613488,determiningtheeffectofabnormaltemperaturesonthehousingmarket|2020,1,1561,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e408c0b40841065d0b2eef1d581bfec02823f049,10.1177/0739456x19898737,,"Impact of Heavy-rail-based Rapid Transit on House Prices: Evidence from the Fremont, CA, Warm Springs BART Extension Project",2020.0,Journal of planning education and research,S. Mathur,"This study estimates households’ willingness to pay for single-family houses and condominiums/townhouses located within 2 miles of Warm Springs (WS) BART Station in Fremont, CA. The study finds that, compared to the houses sold in the control distance band (2–5 miles away), an average-priced single-family house within 2 miles of the WS BART Station was higher in price by 9 to 15 percent. The total property value increment for the single-family houses is large enough to fund the $802 million Warm Springs BART Extension Project cost five times over.",7.0,,False,JournalArticle,Geography,2020-01-20,e408c0b40841065d0b2eef1d581bfec02823f049,impactofheavyrailbasedrapidtransitonhousepricesevidencefromthefremontcawarmspringsbartextensionproject,mathur,10.1177/0739456x19898737,impactofheavyrailbasedrapidtransitonhousepricesevidencefromthefremontcawarmspringsbartextensionproject|2020,1,1553,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,5,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",965f2234f38cd18c8f4d6363a914aef179db03b8,10.1016/j.infoecopol.2018.07.002,,Information technology in the property market,2018.0,Information Economics and Policy,Yong Suk Lee; Yuya Sasaki,"Information technology is increasingly being utilized in the property market. This paper examines how sensitive house transaction prices are to online price estimates using data collected from Zillow. We find that online property price estimates strongly predict transaction prices even when observable and unobservable house and neighborhood characteristics are controlled for. In addition, we find evidence that suggests that online price estimates may have a direct impact on transaction prices.",7.0,,False,JournalArticle,Economics;Computer Science,2018-09-01,965f2234f38cd18c8f4d6363a914aef179db03b8,informationtechnologyinthepropertymarket,lee,10.1016/j.infoecopol.2018.07.002,informationtechnologyinthepropertymarket|2018,1,1498,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,apartment prices hedonic,afd97faa83868b9dfa1076408fe014055df02a69,10.1007/s41324-023-00565-7,,"Examining the effect of apartment attributes on their sale prices in Riyadh, Saudi Arabia",2024.0,Spatial Information Research,Ali Chikhmous; Muhammad Tauhidur Rahman,,7.0,,False,JournalArticle,,2024-01-02,afd97faa83868b9dfa1076408fe014055df02a69,examiningtheeffectofapartmentattributesontheirsalepricesinriyadhsaudiarabia,chikhmous,10.1007/s41324-023-00565-7,examiningtheeffectofapartmentattributesontheirsalepricesinriyadhsaudiarabia|2024,1,1000,1,apartment prices hedonic,0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",8bb2d90259ecee2da847f548a2527133a00ef0f5,10.1108/ijhma-06-2018-0042,,Understanding real estate investments through big data goggles,2019.0,International Journal of Housing Markets and Analysis,Marcelo Cajias," Purpose This paper aims to develop a conceptual understanding and a methodological approach for calculating residential net initial yields for both a buy-to-hold and rental investment strategy from hedonic models. Design/methodology/approach The markets modelled comprehend of dwellings for rent and sell in Germany. For each of them, two regression models are estimated to extract implicit prices and rents for an artificial identical dwelling and estimate the willingness to pay for the same asset from both a buy-to-hold and rental investment strategy. Findings The 3,381 estimated net initial yields in the 161 German markets showed a spatial pattern with the biggest and most attractive cities showing the lowest yields and a self-adjusting process in the markets surrounding the top cities. The net initial yields over time show that prices have increased stronger than rents, leading to rock bottom yields for residential assets and a significant premium in comparison to government bond yields. The approach responds to the spatial hierarchy of markets in Germany, meaning that the level of the estimated yields is accurate and achievable from an investment perspective. Practical implications The investment case in residential markets is certainly unique as net initial yields are scarce, especially due to the relatively low number of investment comparables. The paper sheds light on this problem from a conceptual and methodological perspective and confirms that investment yields are deducible by making usage of hedonic models and big data. Originality/value In the era of digitalization and big data, residential assets are mostly brought to the market via digital multiple listing systems. Transparency is an essential barrier when assessing the pricing conditions of markets and deriving investment decisions. Although international brokers do provide detailed investment comparables on – mostly commercial – real estate markets, the residential sector remains a puzzle when it comes to investment yields. The paper sheds light on this problem. ",6.0,,False,,Economics,2019-08-05,8bb2d90259ecee2da847f548a2527133a00ef0f5,understandingrealestateinvestmentsthroughbigdatagoggles,cajias,10.1108/ijhma-06-2018-0042,understandingrealestateinvestmentsthroughbigdatagoggles|2019,1,3071,4,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression",2,1,3,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,581b08355bf0171fcd8fbc78ce3131f0b8ed8e92,10.1108/ijhma-12-2023-0173,,Does the market value energy efficiency within EPC-labels? An analysis of the residential real estate market in Flanders,2024.0,International Journal of Housing Markets and Analysis,Alesia Gerassimenko; L. De Moor; Laurens Defau," Purpose The current literature has not investigated the perceived value of energy efficiency by households, regardless of financial benefits. Furthermore, there is a severe lack of research that investigates the effectiveness of the current format of EPC-labels. Therefore, the purpose of this paper is twofold: to study how households value energy efficiency in the housing market, regardless of price effects. Design/methodology/approach This study uses multiple hedonic regression models to analyse 706,778 Flemish properties for sale or rent between 2019 and 2023. The data is provided by Immoweb – the largest online real estate platform in Belgium. Given that the selling market is driven by different mechanisms than the rental market, the data set was divided in sold (522,164 listings) and rented properties (184,614 listings). Findings The ambiguous results of the A-label in the selling market indicate that the “class evaluation effect” found in related markets which use labels (e.g. household appliances) is also present in the housing market. However, the results of the other (lower) labels clearly show that owners do value energy improvements within labels, and this effect becomes stronger as the EPC-label becomes better. The rental market shows the opposite results. Energy improvements are only valued if they translate into a financial benefit. Taking these findings into account, the second part of this research shows that rescaling the EPC-label creates an incentive for improvements within labels. Originality/value This paper provides novel insights by studying the perceived value of energy efficiency in the absence of financial benefits and critically studying the effectiveness of the EPC-labels in their current shape. By investigating both the sales and rental market, the authors are able to make a comparison which creates valuable insights for academia, governments and real estate professionals. ",6.0,,False,JournalArticle,,2024-02-15,581b08355bf0171fcd8fbc78ce3131f0b8ed8e92,doesthemarketvalueenergyefficiencywithinepclabelsananalysisoftheresidentialrealestatemarketinflanders,gerassimenko,10.1108/ijhma-12-2023-0173,doesthemarketvalueenergyefficiencywithinepclabelsananalysisoftheresidentialrealestatemarketinflanders|2024,1,2941,4,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression || rent hedonic regression housing,2,1,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,38179b5c7e3f351be7bae8fd62cdf31516c1ef88,10.1108/ijhma-08-2022-0112,,"The impacts of pandemic on urban housing prices: evidence from the outbreak of COVID-19 in Beijing, 2020",2023.0,International Journal of Housing Markets and Analysis,B. Qin; Yan-Yan Peng; Lu Feng," Purpose The COVID-19 pandemic has significantly raised economic risk and uncertainty worldwide. How does COVID-19 affect urban housing markets? Is there any difference when different areas encounter COVID-19? This study aims to investigate the impacts of the pandemic on housing prices by using Beijing’s housing markets data in 2020. Design/methodology/approach The authors use transaction-level data from April to September in 2020 to conduct a hedonic price analysis of the housing markets in Beijing. The data included 70,843 transactions scraped from a real estate agent’s website. The authors use the difference-in-differences approach to evaluate the impacts of the COVID-19 outbreak from the Beijing Xinfadi market (the largest and most important food wholesale market in Beijing) in 2020. Findings This outbreak of COVID-19 caused a 6.3% drop in housing prices in Beijing from April to September in 2020. However, the impacts of COVID-19 on housing prices in different urban neighbourhoods were spatially heterogeneous. Housing prices in neighbourhoods with industries that rely on face-to-face communication were more affected by the pandemic, while those that can work remotely were less affected. Originality/value By investigating the impacts of COVID-19 on housing prices in Beijing, this study illustrates that urban housing prices would be impacted by the pandemic, at least in the short term. While the rise and fall of housing prices were found spatially heterogeneous in Beijing, it suggests that urban neighbourhoods with specific socioeconomic characteristics and geographic locations would unfold different resilience when encountering pandemic. By using data scraping and rigorous statistical tools, the study is probably one of the first ones examining the consequences of COVID-19 in intra-urban housing markets. ",6.0,,False,,Medicine,2023-03-02,38179b5c7e3f351be7bae8fd62cdf31516c1ef88,theimpactsofpandemiconurbanhousingpricesevidencefromtheoutbreakofcovid19inbeijing2020,qin,10.1108/ijhma-08-2022-0112,theimpactsofpandemiconurbanhousingpricesevidencefromtheoutbreakofcovid19inbeijing2020|2023,1,2845,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,2,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,32069cd74e9248c2bc49e04917b58ca3eeac9d79,10.1108/ijhma-12-2018-0106,,Foreclosure discounts for German housing markets,2019.0,International Journal of Housing Markets and Analysis,Tobias Just; Michael Heinrich; Mark Maurin; T. Schreck," Purpose This paper aims to investigate the foreclosure discount for the German residential market in the years from 2008 to 2011. Design/methodology/approach The determinants of the foreclosure discount are estimated in a hedonic price model. The analysis is based on a unique data set compiled from three different data sources with 135,000 foreclosed properties. Findings The findings reveal that residential units in foreclosures are sold at a discount of 19 per cent compared to residential units with similar characteristics that are not in foreclosure. Second, a regional pattern can be observed, with discounts being negatively correlated to unemployment risk and liquidity. Third, the model with interaction terms shows that foreclosure discounts are linked to specific property characteristics. Fourth, these object-related risks are typically smaller than regional risks or locational risks. Research limitations/implications Given the highly fragmented system of Gutachterausschüsse in Germany, who are responsible for collecting transaction data, we were not able to directly analyze transaction data, but only a proxy for this price information. Practical implications The results can be important for financial institutions that are trying to assess the risk of lending for a specific object in a specific location. So far, banks primarily try to assess the default risk of private lenders by analyzing the debtor’s financial position and the quality of the property. The analysis provides insights into which characteristics of a property might imply additional risk, and in which region these risks are biggest. Originality/value To the best of the authors’ knowledge, this is the first attempt to analyze the foreclosure discount for the German housing market. ",6.0,,False,,Economics,2019-06-22,32069cd74e9248c2bc49e04917b58ca3eeac9d79,foreclosurediscountsforgermanhousingmarkets,just,10.1108/ijhma-12-2018-0106,foreclosurediscountsforgermanhousingmarkets|2019,1,2789,3,hedonic housing prices || housing characteristics prices hedonic || residential property price determinants,2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1108/17538271111137930,,Insight into apartment attributes and location with factors and principal components,2011.0,International Journal of Housing Markets and Analysis,Alain Bonnafous; Marko Kryvobokov,"Purpose The purpose of this paper is to better understand the spatial structure of the Lyon urban area focusing on real estate. For this, two aims are formulated. The first aim is to identify and geographically analyse latent structure underlying apartment variables and location. The second aim is to decrease a number of explanatory variables in a hedonic model of real estate prices applying latent constructs. Design/methodology/approach For the first aim of a parsimonious representation among measured variables, exploratory factor analysis is applied. For the second aim of data reduction, principal component analysis (PCA) is used. The exploited regression methodologies are global and geographically weighted ordinary least squares. Findings Four factors are extracted, of which two represent apartment attributes and other two – location attributes. Principal components provide better insight into location attributes dividing the service employment centres into two geographical groups. The inclusion of principal components in hedonic price equation instead of initial location variables decreases goodness of fit, but does not gradually change non‐location estimates and other parameters. Originality/value Differently from previous applications of factor analysis and PCA in the real estate domain, oblique rotation is applied, which allows the extracted factors or components to be correlated. The scores of factors and components are interpolated from points to raster maps creating a continuous geographical distribution. Hedonic models with and without principal components are compared in detail.",6.0,,,journal-article,,,doi_10_1108_17538271111137930,insightintoapartmentattributesandlocationwithfactorsandprincipalcomponents,bonnafous,10.1108/17538271111137930,insightintoapartmentattributesandlocationwithfactorsandprincipalcomponents|2011,1,2617,1,apartment rent hedonic,2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,68510a4da77b898ef3de006c8d2e33e5f06fa8bb,10.1016/j.apgeog.2020.102272,,The effect of farmers’ market access on residential property values,2020.0,,LaPorchia A. Collins,"Abstract Improving farmers' market accessibility is increasingly being used as a mechanism to promote healthy food consumption. Previous studies about farmers’ markets largely focused on describing either the demographics of market patrons or the impact of market participation on fruit and vegetable intake. Thus, little empirical research has examined how market accessibility impacts nearby communities. The goal of this study is to assess whether consumers value farmers' market accessibility by exploiting preferences revealed through housing purchases. Using residential sales data from 2007 to 2012 in five Central Maryland counties, we tested whether farmers' market accessibility is associated with housing prices by estimating hedonic models with temporal and neighborhood fixed effects. The results suggest an inverse association between housing prices and distance to the closest farmers’ market, a relationship that remains statistically significant when using matching and instrumental variables estimators. The results also suggest that the association between market accessibility and housing prices is stronger in areas with poor-quality food environments. The findings are important to policy makers and planners because they suggest that farmers' market accessibility is valued by consumers in nearby communities and that consumers with limited access to food stores are willing to pay a premium for houses with access to farmers’ markets. Assessing the mechanisms driving this association is an important area for future research.",6.0,,False,,Geography,2020-10-01,68510a4da77b898ef3de006c8d2e33e5f06fa8bb,theeffectoffarmersmarketaccessonresidentialpropertyvalues,collins,10.1016/j.apgeog.2020.102272,theeffectoffarmersmarketaccessonresidentialpropertyvalues|2020,1,2550,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices",2,2,4,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,ae3b5d4490a4b63206e2a0461c83ed634df63718,10.1108/ijhma-09-2022-0136,,The effect of proximity and spatial dependence on the house price index for Dar es Salaam,2023.0,International Journal of Housing Markets and Analysis,Frank Nyanda," Purpose This study aims to examine the effect of proximity and spatial dependence on the house price index for the nascent market Dar es Salaam, Tanzania. Despite the ongoing housing market transactions, there is no single house price index that takes into account proximity and spatial dependence. The proximity considerations in question are proximal to arterial roads, public hospitals, an airport and food markets. Previous studies on sub-Saharan Africa have focused on the ordinary least squares (OLS)-based hedonic model for the index and ignored spatial and proximity considerations. Design/methodology/approach Using the OLS and spatial econometric approach, the paper tests for the significance of the two effects – proximity and spatial dependence in the hedonic price model with year dummy variables from 2010 to 2019. The paper then compares the three indices in the following configurations: without the two effects, with proximity factors only, and with both effects, i.e. proximity and spatial dependence. Findings The inclusion of proximity factors and spatial dependence – spatial autocorrelation – seems to improve the hedonic price model but does not significantly improve the house price index. However, further research should be called for on account of the nascent nature of the market. Originality/value The paper brings new knowledge by demonstrating that it may not be necessary to take into account proximity factors and spatial dependence for the Dar es Salaam house price index. ",6.0,,False,,,2023-03-02,ae3b5d4490a4b63206e2a0461c83ed634df63718,theeffectofproximityandspatialdependenceonthehousepriceindexfordaressalaam,nyanda,10.1108/ijhma-09-2022-0136,theeffectofproximityandspatialdependenceonthehousepriceindexfordaressalaam|2023,1,2515,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || house prices proximity hedonic || spatial hedonic model housing",2,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,eacdaf5f66d60ef571a5c73905941b610aa3fe93,10.1108/ijhma-06-2022-0083,,House price forecasting using the multi-level modelling method in Sydney,2022.0,International Journal of Housing Markets and Analysis,X. Ge; V. Mangioni; S. Shi; S. Herath," Purpose This paper aims to develop a house price forecasting model to investigate the impact of neighbourhood effect on property value. Design/methodology/approach Multi-level modelling (MLM) method is used to develop the house price forecasting models. The neighbourhood effects, that is, socio-economic conditions that exist in various locations, are included in this study. Data from the local government area in Greater Sydney, Australia, has been collected to test the developed model. Findings Results show that the multi-level models can account for the neighbourhood effects and provide accurate forecasting results. Research limitations/implications It is believed that the impacts on specific households may be different because of the price differences in various geographic areas. The “neighbourhood” is an important consideration in housing purchase decisions. Practical implications While increasing housing supply provisions to match the housing demand, governments may consider improving the quality of neighbourhood conditions such as transportation, surrounding environment and public space security. Originality/value The demand and supply of housing in different locations have not behaved uniformly over time, that is, they demonstrate spatial heterogeneity. The use of MLM extends the standard hedonic model to incorporate physical characteristics and socio-economic variables to estimate dwelling prices. ",6.0,,False,,,2022-09-16,eacdaf5f66d60ef571a5c73905941b610aa3fe93,housepriceforecastingusingthemultilevelmodellingmethodinsydney,ge,10.1108/ijhma-06-2022-0083,housepriceforecastingusingthemultilevelmodellingmethodinsydney|2022,1,2439,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,4,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1108/02637470710723263,,Pricing inter‐neighbourhood variation,2007.0,Property Management,Sherif Roubi; Ashraf Ghazaly,"Purpose The purpose of this paper is to focus on inter‐neighbourhood variation in the rental apartment market in Greater Cairo, Egypt, and its potential influence on property prices and performance of hedonic pricing models. Design/methodology/approach The paper delves into the issue of whether comparables from different neighbourhoods are homogeneous enough to be aggregated in hedonic pricing models. This paper extends the research on rental‐property market segmentation by investigating the existence of apartment submarkets determined by neighbourhoods. Findings Results show that parameters are unstable across neighbourhoods and Chow test provides further support for utilising spatial hedonic pricing models. Originality/value This paper provides further support for spatial hedonic pricing models using empirical evidence from Greater Cairo, Egypt. The paper finds that explanatory and predictive powers of hedonic pricing models are improved when separate hedonic equations are estimated for each neighbourhood in Greater Cairo. The paper does not provide an elaborate solution for implementing spatial models in Greater Cairo but rather supports the notion that one has to be developed.",6.0,,,journal-article,,,doi_10_1108_02637470710723263,pricinginterneighbourhoodvariation,roubi,10.1108/02637470710723263,pricinginterneighbourhoodvariation|2007,1,2199,3,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment prices hedonic || apartment rent hedonic",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b23d650898011c941f1d8cc88538f01f64200f7d,10.1080/02673037.2024.2313606,,Are gated communities “safe havens”? Examining housing price dynamics of Chinese gated and non-gated communities during COVID-19 pandemic,2024.0,Housing Studies,Li-Rong Hu; Shen-Jing He,"Abstract Gated communities (GCs) and associated socio-economic implications are of global concern. How GCs and non-GCs responded to the external shock of the pandemic has not been comprehensively understood. Drawing on the information of infected cases during the first wave pandemic (January-March 2020) and longitudinal data of housing sales and rental prices for each community from July/2019-June/2020 in 11 Chinese cities, we employ a hedonic difference-in-differences approach accounting for heterogenous effects in stagger timing to discover the effects of COVID-19 on housing price differences between GCs and non-GCs. Moreover, the buffer zones of infected epicentres are delineated to explore the spillover effects. Compared with GCs, non-GCs with different distances from the pandemic epicentres experienced significantly greater reductions in both housing sales and rental prices at varying degrees. While the effect on housing sales prices was significant over time, it only lasted for one month for rental prices. This study enhances our understanding of housing price dynamics and the socio-spatial implications of GCs.",6.0,,False,JournalArticle,,2024-02-14,b23d650898011c941f1d8cc88538f01f64200f7d,aregatedcommunitiessafehavensexamininghousingpricedynamicsofchinesegatedandnongatedcommunitiesduringcovid19pandemic,hu,10.1080/02673037.2024.2313606,aregatedcommunitiessafehavensexamininghousingpricedynamicsofchinesegatedandnongatedcommunitiesduringcovid19pandemic|2024,1,2134,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,4,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,76c2b177b1919a324c0457db859a5dea7811a53a,10.1016/j.jhe.2019.04.001,,The impact of leasehold status on apartment price,2019.0,,Carl Caesar; H. Donner; F. Kopsch,"Based upon cooperative apartments sold in Stockholm, Sweden during the period of 2012 to mid-2014, we find that leasehold status (meaning that the cooperative does not own the land that the apartment building occupies) has a small but statistically significant impact on price of −2.3% when controlling for location and apartment characteristics in a hedonic model. At the time of renegotiation (i.e. a lease duration of 0), leasehold depreciates price with 4.2%. We also apply propensity score matching, which results in slightly larger negative impact on the price being estimated. As current lease payments are covered by monthly fees that are to be paid to the cooperative, a negative effect on price should mostly be attributed to an increased uncertainty of future levels of monthly fees. We therefore extend the existing literature by examining the impact on price by the remaining leasehold term whilst still controlling for monthly fees. Consistent with our hypothesis, apartment prices are found to increase with 0.22% for each additional year that remains until renegotiation of the lease contract.",6.0,,False,,Business,2019-12-01,76c2b177b1919a324c0457db859a5dea7811a53a,theimpactofleaseholdstatusonapartmentprice,caesar,10.1016/j.jhe.2019.04.001,theimpactofleaseholdstatusonapartmentprice|2019,1,2109,1,apartment prices hedonic,2,2,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,aa214ac3bb3dcebf011382a60decdaf92a0fe9eb,10.1111/1540-6229.12411,,The capitalization of metro rail access in urban housing markets,2022.0,Real Estate Economics,Zachary T. Keeler; H. Stephens,"Increasing access to public transportation (including metro rail) can help alleviate traffic congestion and address climate and environmental priorities. Living close to a metro line may be especially important in terms of providing improved commuting options. However, proximity to metro lines can also be associated with negative externalities, such as noise and crime, that may make living near a station less desirable. One way to assess the net value that residents place on metro rail access is to examine how proximity to metro lines is capitalized into house prices. Using a hedonic spatial difference‐in‐differences model, we analyze the impact of proximity to the stations on the Gold and Expo Lines in Los Angeles, California, on nearby house prices. Our findings suggest that the capitalization effect is heterogeneous. Some residents value living near new metro stations, while others do not. Overall, our results provide evidence that the value residents place on metro rail access varies based on their income levels and other demographics.",6.0,,False,,,2022-09-16,aa214ac3bb3dcebf011382a60decdaf92a0fe9eb,thecapitalizationofmetrorailaccessinurbanhousingmarkets,keeler,10.1111/1540-6229.12411,thecapitalizationofmetrorailaccessinurbanhousingmarkets|2022,1,2055,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices || house prices proximity hedonic || spatial hedonic model housing",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,5eb2e3296ccf561014e46902530d34ab40a9965f,10.1080/10871209.2021.1953196,,Measuring the value of public hunting land using a hedonic approach,2021.0,Human Dimensions of Wildlife,W. Casola; M. N. Peterson; Yu Wu; E. Sills; Brent S. Pease; K. Pacifici,"ABSTRACT Acquisition of public land is critical for wildlife conservation and can impact local tax bases and property values. Those impacts reflect the capitalized value of benefits (e.g., recreational opportunities) and costs (e.g., nuisance wildlife) of living near protected areas. We employed the hedonic price framework to determine how proximity and adjacency to public hunting land in North Carolina were capitalized into housing prices. We modeled sale price as the composite value of structural, neighborhood, and environmental characteristics. Proximity to public hunting land had positive effects on sale price in some locations, whereas adjacency had negative effects in some locations. These relationships were dependent on the sociocultural context of the public hunting land, including proximity to other forms of public land. This research may help facilitate negotiations among stakeholders impacted by protected areas, including land dedicated to wildlife-based recreation.",6.0,,False,,Business,2021-08-11,5eb2e3296ccf561014e46902530d34ab40a9965f,measuringthevalueofpublichuntinglandusingahedonicapproach,casola,10.1080/10871209.2021.1953196,measuringthevalueofpublichuntinglandusingahedonicapproach|2021,1,1991,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic",2,3,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",f2631ef144acaf76b8e8b167b341d3332c82f9b1,10.1080/17549175.2020.1726800,,"Do home buyers value the New Urbanist neighborhood? The case of Issaquah Highlands, WA",2020.0,,Jiny Y. Kim; C. Bae,"ABSTRACT This study compares Issaquah Highlands’ home prices with those of traditional suburban single-family homes in the city of Issaquah. Issaquah Highlands is a community that was developed using New Urbanism principles. The null hypothesis is that the sale prices of houses in Issaquah Highlands are not different from the conventional suburban neighborhood in the city of Issaquah. The principal database consists of US Census Washington State Geospatial Data Archive, and the King County Tax Assessments. The final dataset contains 1,780 single family homes over the seven-year period from 2012 to 2018 based on sale records throughout the city of Issaquah. This study uses the hedonic pricing technique to assess the impact of New Urbanism on the value of single-family residences. The findings suggest that people are willing to pay a $92,700–96,800 premium (approximately 7.1–12.0 percent of the sales prices) for houses in Issaquah Highlands.",6.0,,False,,Geography,2020-02-28,f2631ef144acaf76b8e8b167b341d3332c82f9b1,dohomebuyersvaluethenewurbanistneighborhoodthecaseofissaquahhighlandswa,kim,10.1080/17549175.2020.1726800,dohomebuyersvaluethenewurbanistneighborhoodthecaseofissaquahhighlandswa|2020,1,1953,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price",2,4,5,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,5602359f18abfbbca803794a365ddd850b5a5e94,10.1017/s1355770x20000194,,The spatial effect of estuaries pollution on the housing rental market: evidence from South America,2020.0,Environment and Development Economics,Manuel A. Zambrano-Monserrate; M. A. Ruano,"Abstract Estuaries are water bodies, many of which contribute to the sustainable development of cities. Many of them are considered natural landscapes, offering various recreational services to the population. However, estuary degradation is a common problem in developing countries. Ecuador, for example, has a significant problem with the pollution of its estuaries. Machala is one of the most affected cities due to the contamination of these water bodies. This issue has generated negative externalities in the housing market. Thus, our central hypothesis is that rental prices of houses are revalued as their distance from polluted estuaries increases. Using the hedonic pricing approach, our results confirm that the price of a residence increases by US$9.45 for every 100 m farther it is from the closest estuary. This amount constitutes approximately 5.25 per cent of the average rental value in Machala.",6.0,,False,,Geography,2020-06-10,5602359f18abfbbca803794a365ddd850b5a5e94,thespatialeffectofestuariespollutiononthehousingrentalmarketevidencefromsouthamerica,zambrano-monserrate,10.1017/s1355770x20000194,thespatialeffectofestuariespollutiononthehousingrentalmarketevidencefromsouthamerica|2020,1,1912,2,hedonic house price || hedonic housing prices,2,2,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2325155093,10.1080/14445921.2002.11104123,,An Hedonic Price Model of New Housing in Indonesia,2002.0,Pacific Rim Property Research Journal,Supardi Samapatti; Linda Tay,"This study investigates the prices of new housing in the Greater Jakarta Metropolitan Area, Indonesia. Based on the average sale prices of 149 new housing projects launched between January and June 1997, the ANOVA statistical tool is used to test whether different sizes of development adopt the same hedonic pricing model. Following this, multiple regression analyses are conducted to identify the hedonic factors and their impacts on the new house prices in small, medium and large developments. The study concludes that small, medium, and large-sized developments do not adopt the same hedonic pricing model. With the exception of small-sized developments hedonic model in which structural, locational and neighborhood characteristics are important determinants of prices, the medium and large-sized development hedonic models showed that only locational attributes have significant impact on house prices.",6.0,,False,article,,2002-01-01,https://openalex.org/W2325155093,anhedonicpricemodelofnewhousinginindonesia,samapatti,10.1080/14445921.2002.11104123,anhedonicpricemodelofnewhousinginindonesia|2002,1,1909,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,3,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2138716974,10.1109/icmse.2010.5720005,,Empirical analysis on the housing price in Harbin City based on hedonic model,2010.0,International Conference on Management Science and Engineering (ICMSE),Wang Wei; Tong Guang-ji; Hongrui Zhang,"This paper selects 9 housing characteristics as the independent variables for the market supply-demand equilibrium model to construct a housing hedonic model in a multiple linear functional form, and tests the model using sample data collected from more than 400 multi-storey housing buildings in Harbin City. Conclusion shows that the model can be used to analyze and explain the influence of housing characteristics on housing prices. It is found that the 9 housing characteristics have differing impacts on housing prices. Moreover, the paper also classifies and sorts different levels of influence of housing characteristics on the basis of hedonic prices.",6.0,,False,conference-paper,,2010-11-01,https://openalex.org/W2138716974,empiricalanalysisonthehousingpriceinharbincitybasedonhedonicmodel,wei,10.1109/icmse.2010.5720005,empiricalanalysisonthehousingpriceinharbincitybasedonhedonicmodel|2010,1,1660,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic housing prices || housing characteristics prices hedonic",2,2,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W3092430233,10.15301/jepa.2020.28.3.213,,Estimating Values of Urban Green Space in Seoul: Application of Hierarchical Linear Regression Model on Hedonic Housing Price Analysis,2020.0,환경정책,Hye Kyung Jung; Seong Do Yun; Tae-kyun Kim; Seung Gyu Kim,"도시민의 도시녹지 생태서비스에 대한 수요가 늘어남에 따라 이들에 대한 접근성이나 경관에 대한 가치 추정 또한 꾸준히 이어지고 있다. 본 연구는 서울의 이질적 자연 환경 즉, 한강, 일반산림, 북한산에 대한 가치를 추정하기 위하여 서울의 대표적 주거 형태인 아파트에 대한 실거래가격을 취득하여 주택특성가격분석을 시행하였다. 과정에서 집합건물의 특성상 동일한 위치 정보와 동일한 주택특성을 가진 다수의 거래자료로 인하여 거래가격에는 공간종속성, 설명변수에는 위계적 구조가 발생하였다. 이의 해결을 위해 공간시차 변수 추가, 동일 주택 특성별 데이터 그룹화, 위계선형모형의 적용이 고려되었고, 주택 특성별 그룹화 거래자료에 2수준 임의절편 선형모형을 적용할 때 주택특성가격모형 오차항의 공간종속성이 통계적으로 존재하지 않는 수준으로 감소함을 확인하였다. 따라서 집합주택 거래자료를 이용한 주택특성가격 모형의 추정에서 대용량 거래건수로 인해 공간계량모형의 적용이 불가할 경우 대안적으로 고려할 가치가 있는 것으로 보인다. 추정결과를 살펴보면 북한산 조망에 대해서는 양의 경관가치가 추정되었지만 일반 산림에 대해서는 나타나지 않았다. 한강에 대해서는 다른 도시녹지에 비해 상대적으로 큰 조망가치가 계측되었다.",6.0,,False,article,,2020-09-30,https://openalex.org/W3092430233,estimatingvaluesofurbangreenspaceinseoulapplicationofhierarchicallinearregressionmodelonhedonichousingpriceanalysis,jung,10.15301/jepa.2020.28.3.213,estimatingvaluesofurbangreenspaceinseoulapplicationofhierarchicallinearregressionmodelonhedonichousingpriceanalysis|2020,1,1615,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),64213566ff01a4482a5952304e4ff3e526cb24f1,10.1007/s12076-015-0142-6,,A spatial hedonic model application of variance function regression to residential property prices in Beijing,2016.0,,Yue Zhang; R. Cromley; Dean M. Hanink,,6.0,,False,,Economics,2016-03-01,64213566ff01a4482a5952304e4ff3e526cb24f1,aspatialhedonicmodelapplicationofvariancefunctionregressiontoresidentialpropertypricesinbeijing,zhang,10.1007/s12076-015-0142-6,aspatialhedonicmodelapplicationofvariancefunctionregressiontoresidentialpropertypricesinbeijing|2016,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression",2,1,1,2,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,prix hédoniques logement,,10.3917/reru.034.0597,,Caractéristiques hédoniques et changement environnemental : la couverture de l'autoroute A1,2003.0,Revue d’Économie Régionale & Urbaine,Francis Calcoen; Didier Cornuel; Hervé Leleu,"Résumé L’objectif de ce texte est d’identifier l’impact éventuel de la couverture de l’autoroute A1 à Saint-Denis sur les prix des logements. Cette couverture induit une réduction notable des nuisances et une amélioration manifeste du cadre urbain. La méthode utilisée est celle des caractéristiques hédoniques : elle consiste à quantifier la contribution des différentes caractéristiques d’un logement à la valeur de celui-ci. La couverture de l’A1 représente une nouvelle caractéristique des logements situés à proximité, et non la variation du volume d’une caractéristique existante. Pour tenir compte de cette caractéristique nouvelle et de son caractère commun à tous les logements de la zone, on a développé deux stratégies d’estimation différentes. La première considère l’impact global à l’aide de variables dichotomiques ; l’impact est alors chiffré à environ 16 % de la valeur moyenne des appartements dans la zone directement concernée. La seconde stratégie intègre la seule amélioration phonique mesurée par la réduction des décibels pour chaque appartement. Dans ce cas, la valorisation apportée est estimée entre 7 % et 8 % de la valeur d’un appartement directement situé sur l’autoroute couverte, et se réduit à 4 % de la valeur dans la proche périphérie de l’autoroute. L’impact est donc significatif, même s’il ne transforme pas radicalement le marché des logements existants.",6.0,,,journal-article,,,doi_10_3917_reru_034_0597,caracteristiqueshedoniquesetchangementenvironnementallacouverturedelautoroutea1,calcoen,10.3917/reru.034.0597,caracteristiqueshedoniquesetchangementenvironnementallacouverturedelautoroutea1|2003,1,2393,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || prix hédoniques logement",1,2,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",56bc5ebd8542dd52cd905ef715e1fbdd106f4b0a,10.1007/s10037-013-0074-9,,Hybrid multilevel STAR models for hedonic house prices,2013.0,,Wolfgang A. Brunauer; S. Lang; W. Feilmayr,,6.0,,False,,Economics,2013-02-15,56bc5ebd8542dd52cd905ef715e1fbdd106f4b0a,hybridmultilevelstarmodelsforhedonichouseprices,brunauer,10.1007/s10037-013-0074-9,hybridmultilevelstarmodelsforhedonichouseprices|2013,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1475-4932.1991.tb02549.x,,Risk and Information: a Hedonic Price Study in the New Zealand Housing Market*,1991.0,Economic Record,S. A. MAANI; S. B. KASK,"This paper employs the hedonic price approach developed by Rosen (1974) and recently extended to study the effects of potential natural hazards on consumer behaviour. Hedonic prices are used to estimate home buyers' willingness to pay to avoid a high‐pressure gas pipeline installed in a residential area Studies of risk and information (e.g. Brookshire et al., 1985, Smith and Johnson, 1988) suggest that the subjective probability of a hazard occurring differs from the objective probability. If we allow for this, and relax the assumption of full information on the part of home buyers, our theoretical analysis indicates that a single hedonic price is not sufficient because hedonic prices are likely to change with a change in information. Our empirical analysis of cross‐sectional data across time is in accord with the theoretical model, although not highly significant",6.0,,,journal-article,,,doi_10_1111_j_1475_4932_1991_tb02549_x,riskandinformationahedonicpricestudyinthenewzealandhousingmarket,maani,10.1111/j.1475-4932.1991.tb02549.x,riskandinformationahedonicpricestudyinthenewzealandhousingmarket|1991,1,1876,3,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment rent hedonic || hedonic housing prices",2,0,3,1,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",202b881ac676b4c978328328da5022a7b99378a8,10.2139/ssrn.3794796,,Do Property Rehabs Affect Neighboring Property Prices?,2024.0,Social Science Research Network,Rohan Ganduri; G. Maturana,"We examine the effect of distressed property rehabilitations on neighboring property prices using a recently developed nonparametric method that accounts for very local confounding house price trends. We find that house prices around a rehabilitated property increase between 2.3 and 4.0 percentage points following the rehabilitation. Moreover, we estimate that the average rehabilitation generates an aggregate welfare benefit that is 3.8 times greater than the amount invested. Rehabilitation externalities are stronger for longer rehabilitations and greater rehabilitation investments, and they are prevalent even in areas with high rates of foreclosures. The spillover effect of rehabilitations operates through their salience as opposed to through a reduction in the supply of distressed properties, through property appraisals, or through homebuyers with higher income moving into the neighborhood. Overall, our results suggest that investing in property rehabilitations can be a useful tool to stabilize distressed housing markets. JEL classification: G21, R31 keywords : property rehabilitation, house prices, externalities, foreclosures There is compelling evidence that foreclosed properties have negative spillover effects on neighboring property prices (e.g., Campbell et al. 2011; Anenberg and Kung 2014). Foreclosures generate negative externalities because foreclosed properties tend to be poorly maintained and because forced sales occur at price discounts. Since these spillover effects can be devastating for neighborhoods, they have been a major concern for academics and policymakers (e.g., Posner and Zingales 2009; Congressional Oversight Panel 2009). One way in which both federal and local governments have tried to deal with this concern is to incentivize the rehabilitation of foreclosed properties. These incentives not only mitigate damage to the foreclosed property itself; they also potentially generate positive price spillovers on neighboring properties, which in turn can mitigate the negative externalities of foreclosures and stabilize neighborhoods. In this paper, we examine whether property rehabilitations affect neighboring property prices. Evaluating the effect of property rehabilitations on neighboring property prices is challenging for multiple reasons. First, any potential property rehabilitation effect is likely to be very local. Thus, to measure this effect, one needs detailed property-level data on property rehabilitations and the transactions that involve neighboring properties. Second, properties for rehabilitation are not chosen randomly. Thus, the empirical methodology employed must account for the possibility of very local confounding house price trends. For example, if property rehabilitations are more likely to occur in distressed neighborhoods, then decreasing house price trends may make it challenging to detect any effects of property rehabilitations on neighboring property prices. Alternatively, if property rehabilitations are more likely to occur in thriving neighborhoods, then one may erroneously attribute the increase in neighboring property prices to the property rehabilitations themselves. We address the challenges in measuring the effect of property rehabilitations on neighboring property prices by exploiting a novel dataset of property rehabilitations and a nonparametric method that allows us to account for very local house price trends. This nonparametric method is a generalization of the ring method that compares the price changes of properties",6.0,,False,JournalArticle,,2024-09-01,202b881ac676b4c978328328da5022a7b99378a8,dopropertyrehabsaffectneighboringpropertyprices,ganduri,10.2139/ssrn.3794796,dopropertyrehabsaffectneighboringpropertyprices|2024,1,4537,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fbc07523e1163aba60b0610914815fae3e0fbe46,10.1108/pm-01-2021-0003,,Quantifying the value of high-quality property management,2021.0,Property Management,Jayantha Wadu Mesthrige,"Purpose“Ill-maintenance of common areas” in multi-owner residential properties is described as the “tragedy of anti-commons problem”. The problem can be addressed by outsourcing the management of the property to a company possessing quality property management (PMGT) certification. The PMGT certification is normally hailed as an effective means of reassuring occupiers and prospective property buyers of the good level of quality of the property management to be expected. This study investigated whether PMGT certification carries with it a price premium for residential properties.Design/methodology/approachA fixed-effects modelling approach was employed in relation to a large residential properties dataset in Hong Kong, in the period 2009–2018.FindingsThe results indicate, on average, that the PMGT certification commands a price premium of between 3.3 and 3.9%. This premium can be called, the “tragedy of anti-commons premium (TAC premium)”. The results further suggest that significant price premium differences relate to the four different types of certifications studied. The price of a residential unit managed by a property management company (PMC) with Q-mark certification is about 3.4% higher than a unit managed by a non-certified PMC, ceteris paribus. Likewise, corresponding price premiums for units certified related to ISO9001 and HKMAQA certifications are 3.5 and 2.4%, respectively.Originality/valueTo the best of author's knowledge, this is the first attempt to investigate if there is any relationship between the property price premium attributed to “tragedy of anti-commons” and PMGT certification.",6.0,,False,,Business,2021-06-15,fbc07523e1163aba60b0610914815fae3e0fbe46,quantifyingthevalueofhighqualitypropertymanagement,mesthrige,10.1108/pm-01-2021-0003,quantifyingthevalueofhighqualitypropertymanagement|2021,1,2628,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",77f3a1deb2c3b9e8703548840034d0920ab52994,10.1093/jeg/lbad034,,Are local retail services an amenity or a nuisance?,2023.0,Journal of Economic Geography,Pooya Ghorbani; Rachel Meltzer," We test whether local retail services are considered a nuisance or an amenity and how this distinction is capitalized into residential property values. Using a rich, micro-spatial dataset on property sales transactions and business activity in New York City, we estimate the impact of access to neighborhood retail services on residential sales prices. We construct two instruments to channel supply-side drivers of retail change and to address concerns of endogeneity between changes in retail activity and property values. Results show that retail services that are more frequently consumed and experiential, and are located in relatively more mixed-use neighborhoods are positively capitalized into property values. Residents also pay more to be closer to more diverse retail clusters, and relatively less to be closer to chains. This is true across smaller 1-to-4 family homes as well as larger condos/coops and multi-family rental buildings. The price effects from certain classifications of retail, like restaurants and personal services, are mixed depending on the kind of residential property and the local concentration of the retail. Therefore, the relative strength of the amenity or nuisance effect is very much conditioned on the type of service and the localized neighborhood context.",6.0,,False,JournalArticle,,2023-12-26,77f3a1deb2c3b9e8703548840034d0920ab52994,arelocalretailservicesanamenityoranuisance,ghorbani,10.1093/jeg/lbad034,arelocalretailservicesanamenityoranuisance|2023,1,2300,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,5,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7510ef6ee69a6e713f65993ee266878dd26c49e8,10.1177/0042098019872391,,"Private government, property rights and uncertain neighbourhood externalities: Evidence from gated communities",2020.0,Urban studies,G. Turnbull; Velma Zahirovic-Herbert,"Economists traditionally view public and private land use regulation as alternatives to each other. An alternative view argues that public and private regulation are not equally suited to accomplish the same outcomes. In particular, government regulation is easily changed while private regulation is not, making the latter better suited to control future externality risk. One implication of the alternative view is that more risk-averse households are drawn to gated neighbourhoods while their less risk-averse counterparts are not. This paper exploits exogenous differences in neighbourhood amenity uncertainty created by public school attendance zone changes to test this prediction of the alternative view. The results show that greater exogenous amenity uncertainty yields stronger house price capitalisation in gated subdivisions than in open neighbourhoods, a pattern consistent with the risk-aversion sorting hypothesis. The results are robust and are consistent with the key implication of the alternative view of private regulation.",6.0,,False,JournalArticle,Business,2020-03-01,7510ef6ee69a6e713f65993ee266878dd26c49e8,privategovernmentpropertyrightsanduncertainneighbourhoodexternalitiesevidencefromgatedcommunities,turnbull,10.1177/0042098019872391,privategovernmentpropertyrightsanduncertainneighbourhoodexternalitiesevidencefromgatedcommunities|2020,1,2043,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.1590/0103-6351/2441,,O mercado imobiliário residencial no município de São Paulo: uma abordagem de preços hedônicos espacial,2017.0,Nova Economia,Rodger Barros Antunes Campos,"Resumo: Um dos méritos da abordagem de preços hedônicos é poder estimar preços implícitos de características que não têm mercado específico. Por exemplo, podem-se estimar as características implícitas do complexo mercado imobiliário. O ponto de partida do artigo é o reconhecimento da importância dos microfundamentos do modelo AMM (Alonso-Muth-Mills) e do AMM com amenidades. Utilizando a modelagem de preços hedônicos conjuntamente a abordagem de econometria espacial, pretende-se estimar aspreferências do consumidor por características do imóvel e amenidades no mercado imobiliário residencial no município de São Paulo considerando os perfis dos consumidores.",6.0,,,journal-article,,,doi_10_1590_0103_6351_2441,omercadoimobiliarioresidencialnomunicipiodesaopauloumaabordagemdeprecoshedonicosespacial,campos,10.1590/0103-6351/2441,omercadoimobiliarioresidencialnomunicipiodesaopauloumaabordagemdeprecoshedonicosespacial|2017,1,1664,1,preços hedônicos imóveis,1,0,1,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",49748964576f4eaaa601b9c5592f1b09db8a9fa7,10.1016/j.enpol.2018.08.035,,Property values and tax rates near spent nuclear fuel storage,2018.0,Energy Policy,Kirstin Munro; G. Tolley,Abstract This paper examines potential property value impacts when a nuclear power plant closes and spent fuel remains on site. We confirm earlier research that fails to find an impact on property values of proximity to a nuclear power site. Another contribution of this paper is our finding that a one percentage point increase in property taxes is associated with a 4.31% decrease in the sale price of a home. These results provide evidence for a positive impact of operating nuclear facilities on surrounding communities in the form of reduced residential property taxes for a given level of public expenditures.,6.0,,False,,Business,2018-12-01,49748964576f4eaaa601b9c5592f1b09db8a9fa7,propertyvaluesandtaxratesnearspentnuclearfuelstorage,munro,10.1016/j.enpol.2018.08.035,propertyvaluesandtaxratesnearspentnuclearfuelstorage|2018,1,1615,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",a77f9dd2471b029cc23a89f601beb491dc5d90ba,10.1108/ijhma-04-2017-0041,,Housing demand in Indian metros: a hedonic approach,2018.0,,Debarpita Roy,,6.0,,False,Review,Economics,2018-05-10,a77f9dd2471b029cc23a89f601beb491dc5d90ba,housingdemandinindianmetrosahedonicapproach,roy,10.1108/ijhma-04-2017-0041,housingdemandinindianmetrosahedonicapproach|2018,1,1000,4,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9a88c0eef784fe1bd5a8c84513cd4963de8e4197,10.1007/978-3-642-31994-5_14,,Dealing with Data at Various Spatial Scales and Supports: An Application on Traffic Noise and Air Pollution Effects on Housing Prices with Multilevel Models,2012.0,,J. le Gallo; C. Chasco,,6.0,,False,,Environmental Science,,9a88c0eef784fe1bd5a8c84513cd4963de8e4197,dealingwithdataatvariousspatialscalesandsupportsanapplicationontrafficnoiseandairpollutioneffectsonhousingpriceswithmultilevelmodels,gallo,10.1007/978-3-642-31994-5_14,dealingwithdataatvariousspatialscalesandsupportsanapplicationontrafficnoiseandairpollutioneffectsonhousingpriceswithmultilevelmodels|2012,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e6f814bd1b9579d4b8b8bd837b804d6fc45b1591,10.1007/s00168-020-01032-z,,The impact of land use regulation across the conditional distribution of home prices: an application of quantile regression for group-level treatments,2020.0,The annals of regional science,Tammy Leonard; Xi Yang; Lei Zhang,,6.0,,False,,Economics,2020-11-21,e6f814bd1b9579d4b8b8bd837b804d6fc45b1591,theimpactoflanduseregulationacrosstheconditionaldistributionofhomepricesanapplicationofquantileregressionforgroupleveltreatments,leonard,10.1007/s00168-020-01032-z,theimpactoflanduseregulationacrosstheconditionaldistributionofhomepricesanapplicationofquantileregressionforgroupleveltreatments|2020,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,residential property price determinants,7c19f9988af184fb4878f1387e1716bdb8814cc5,10.1007/s12061-023-09530-1,,"Investigating the Determinants of Housing Rents in Hangzhou, China: A Spatial Multilevel Model Approach",2023.0,Applied spatial analysis and policy,D. Zhan; Chunxin Xie; Juan-Feng Zhang; Bin Meng,,6.0,,False,JournalArticle,,2023-07-21,7c19f9988af184fb4878f1387e1716bdb8814cc5,investigatingthedeterminantsofhousingrentsinhangzhouchinaaspatialmultilevelmodelapproach,zhan,10.1007/s12061-023-09530-1,investigatingthedeterminantsofhousingrentsinhangzhouchinaaspatialmultilevelmodelapproach|2023,1,1000,1,residential property price determinants,0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,316c30d2e4f2d221113acdec03718479dcb6fd54,10.1007/s12061-023-09514-1,,"Impact of Mixed Land Use on Housing Prices, Spatial Differentiation and Implications: Empirical Analysis Based on Qingdao",2023.0,Applied spatial analysis and policy,Yuan Gao; Chang-Chun Feng,,6.0,,False,JournalArticle,,2023-04-18,316c30d2e4f2d221113acdec03718479dcb6fd54,impactofmixedlanduseonhousingpricesspatialdifferentiationandimplicationsempiricalanalysisbasedonqingdao,gao,10.1007/s12061-023-09514-1,impactofmixedlanduseonhousingpricesspatialdifferentiationandimplicationsempiricalanalysisbasedonqingdao|2023,1,1000,2,hedonic housing prices || spatial hedonic model housing,0,2,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,1e4327e93f1787b12468bedf76325864b1996ff5,10.1080/02815739508730383,,Property values and health risks: The willingness to pay for reducing residential radon radiation,1995.0,,T. Söderqvist,,6.0,,False,,Economics,,1e4327e93f1787b12468bedf76325864b1996ff5,propertyvaluesandhealthrisksthewillingnesstopayforreducingresidentialradonradiation,söderqvist,10.1080/02815739508730383,propertyvaluesandhealthrisksthewillingnesstopayforreducingresidentialradonradiation|1995,1,1000,3,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,d7bafcfe00c8c1481a4adcfc99e041cbc2706a38,10.1108/pm-03-2022-0023,,"Modelling apartments values in the Nairobi metropolitan area, Kenya",2022.0,Property Management,E. K. Museleku,"PurposeThe purpose of this paper is to determine the most significant factors influencing apartment values in Nairobi Metropolitan Area (NMA), Kenya and develop a valuation model using the identified variables. The paper is thus promoting the adoption of advanced, more reliable and accurate valuation methods in developing economies such as Kenya. Therefore, the paper demonstrates the potential of the hedonic pricing method (HPM) in property valuation by making objective adjustments to comparable sales data.Design/methodology/approachThe paper utilizes a case study design by analysing recent (January 2020 to December 2021) apartment sales and apartment attributes in NMA. A sample size of 264 transactions was analysed using the HPM.FindingsThe study identified six significant factors affecting apartment values in the study area: apartment size, location, floor finishes, provision of shopping facilities, swimming pool and gym. A valuation model has been developed using the six variables.Research limitations/implicationsThe paper is contributing to the ongoing global debate on the accuracy, reliability and validity of property valuations with particular emphasis on the developing economies to adopt the more accurate, efficient and reliable valuation approaches.Practical implicationsThe findings are useful to property valuers and estate managers in determining the most statistically significant attributes affecting apartment values in the NMA hence enabling them to offer informed professional advice. However, the findings of the study are limited to the study area since real estate is heterogeneous.Social implicationsProperty investors, financiers and other stakeholders in the society will benefit from the findings of this study in their decision-making process. Additionally, wide adoption of the more reliable and accurate valuation approaches would foster public confidence in valuation and estate management professional services leading to enhanced consumption of these services by the society and promoting public care.Originality/valueThe paper is promoting valuation accuracy, efficiency and reliability in the developing economies by advocating for advanced valuation methods. It is the first attempt to develop a valuation model for apartments in the NMA, the capital city of Kenya and the business hub of East and Central Africa.",5.0,,False,,,2022-09-08,d7bafcfe00c8c1481a4adcfc99e041cbc2706a38,modellingapartmentsvaluesinthenairobimetropolitanareakenya,museleku,10.1108/pm-03-2022-0023,modellingapartmentsvaluesinthenairobimetropolitanareakenya|2022,1,3365,3,apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic property valuation,2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,abfbaa44b12551b6f57b5817d678cf6d56a7a872,10.1108/ijhma-09-2023-0121,,Housing price determinants in Ecuador: a spatial hedonic analysis,2024.0,International Journal of Housing Markets and Analysis,Felipe Miguel Valdez Gómez de la Torre; Xu-Wei Chen," Purpose This paper aims to compare the efficiency of spatial and nonspatial hedonic price models in capturing housing submarkets dynamics for cities in developing countries. This study expects to contribute to a better understanding of the housing price determinants from both nonspatial and spatial perspectives. In addition, this paper fills a gap in the literature on the study of housing prices from a spatial perspective in Latin American cities. Design/methodology/approach This study uses a comparative analysis between an ordinary least squares regression and a geographical weighted regression, GWR. The study also assesses the performance of two distinct data sources: the city’s cadastral records and a real estate sales web portal. Findings The results suggest that compared to the traditional regression model, the spatial regression models are more effective at capturing housing market variations on a fine scale. Moreover, they reveal interesting findings on the spatial varying, sometimes contradictory effects of some housing attributes on housing prices in different areas of the city, suggesting the potential impact from segregation. Research limitations/implications The availability of data on housing prices and characteristics in Latin American cities is fragmented and complex. The level of detail, granularity and coverage is not consistent over time. For this reason, this study combines and compares data sets from official and unofficial sources in an effort to close this gap. Likewise, the socioeconomic variables that come from the census must be carefully analyzed, knowing the historical context in which they were constructed, what they represent and their interpretation. Practical implications This paper suggests that despite the improvement on the spatial models, the selection of a specific one should always be based on the diagnosis of it as it highly depends on the data used and the objectives of the study. Originality/value This study enriches the limited body of literature on spatial hedonic price models of housing in Latin American cities. It also shed light on the importance of spatial approaches to identify complex housing submarkets. ",5.0,,False,JournalArticle,,2024-07-02,abfbaa44b12551b6f57b5817d678cf6d56a7a872,housingpricedeterminantsinecuadoraspatialhedonicanalysis,torre,10.1108/ijhma-09-2023-0121,housingpricedeterminantsinecuadoraspatialhedonicanalysis|2024,1,3201,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || cites:sirmans2006 || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic || real estate hedonic regression || spatial hedonic model housing",2,2,1,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,c6ce00542686f8005d7aa70875ff34aba6ec193a,10.1108/ijhma-04-2020-0032,,Does concentration of social housing influence house prices? Evidence from New Zealand,2020.0,,Vernon J. Sequeira; O. Filippova," Purpose Housing affordability in New Zealand is placing significant pressure on the country’s social housing sector with increased demand for public sector accommodation. A common belief suggests that social housing has a negative effect on nearby residential property values. This study aims to develop proximity and concentration measures of social housing to determine if their spatial distribution affects property values. Design/methodology/approach Using over 32,000 residential sales transactions from Auckland (New Zealand) during a three-year period (2014–2016), this study applies standard hedonic OLS framework with the addition of spatial autoregressive model and spatiotemporal autoregressive model to test if proximity and concentration of social housing influence residential property values. Findings The research found that private houses that share boundaries with public housing are discounted by 1.7%–3.3% depending on the socio-economic status of the submarket. The authors find that wealthier submarkets are better equipped to absorbing negative externalities attached to social housing. Proximity measures tend to peak at 250 m, with houses discounted up to 5% within that distance. Concentration levels of social housing had a greater influence on the private residential market. At low levels of concentration, houses in areas of high and low socio-economic levels were discounted by approximately 6.5%. The discount does not remain uniform and the gap between the two areas is apparent at medium and high concentration levels. The negative effect was the highest − 23% – in the neighbourhoods that were socially and economically deprived. Originality/value The study’s findings can assist policymakers in informing strategies on the future social housing initiatives. The findings suggest that a dispersed development strategy that incorporates a balanced mix of tenure and socio-economic groups should be preferred over a high-density social housing concentrated in already deprived neighbourhoods. ",5.0,,False,,Economics,2020-11-06,c6ce00542686f8005d7aa70875ff34aba6ec193a,doesconcentrationofsocialhousinginfluencehousepricesevidencefromnewzealand,sequeira,10.1108/ijhma-04-2020-0032,doesconcentrationofsocialhousinginfluencehousepricesevidencefromnewzealand|2020,1,3033,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || house prices proximity hedonic || spatial hedonic model housing",2,3,4,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4ffd3d082d1a00d1d5db19ea5af26d65b2c1edbe,10.1108/ijhma-05-2023-0066,,The effect of school quality on house prices in the global south: evidence from South Africa,2023.0,International Journal of Housing Markets and Analysis,Anthony Owusu-Ansah; S. Azasu; William Seremi," Purpose This paper aims to investigate the effects of school quality (SQ) on residential property prices in Johannesburg, South Africa. Previous studies have empirically examined the quality of private and public schools without a standard proxy that is accepted in the literature. As a result, this paper extends the literature to the global south by the effect that SQ has on residential property price changes in the local markets of the City of Johannesburg. Design/methodology/approach The research adopts the hedonic pricing model to evaluate and quantify the impact that the structural attributes such as erf size; number of bedrooms and bathrooms; and SQ measured by pass rates, sport rankings and quality of facilities have on house prices. A total of 2,763 property transactions covering the Kensington and Observatory areas of the City of Johannesburg over the period 2010 and 2020 were obtained from the deeds registry and used for the empirical analysis. Findings The study finds that SQ has a positive impact on house prices. When the average pass rate of the model school increases by 1%, all other things being equal, house prices also increase by 1.8%. This suggests that people who live closer to the model school are willing to pay more when the school performance improves. The 1.8% premium this study attributes to a 1% increase in school performance is however generally low when compared to some findings in the literature suggesting that there may be some other important factors that households consider when purchasing their home. Originality/value The main contribution is uncovering the relationship between the SQ and residential property prices in the local markets, using Kensington and Observatory in Johannesburg as sampled areas. Due to the presence of reliable and quality of data sets, such studies are not many in the global south and a study of this nature in South Africa is notably not existing in the literature. ",5.0,,False,JournalArticle,,2023-08-02,4ffd3d082d1a00d1d5db19ea5af26d65b2c1edbe,theeffectofschoolqualityonhousepricesintheglobalsouthevidencefromsouthafrica,owusu-ansah,10.1108/ijhma-05-2023-0066,theeffectofschoolqualityonhousepricesintheglobalsouthevidencefromsouthafrica|2023,1,2961,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price",2,2,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,2853af8fa0610dda89f0fcba063d8556708ed19c,10.1108/ijhma-03-2019-0038,,Shared amenities’ impacts on condominium resale values,2019.0,International Journal of Housing Markets and Analysis,K. Tajima," Purpose This study aims to analyze whether and how condominium shared utilities and facilities (e.g. community spaces), of which buyers assume a share of the ownership upon acquisition of a residential unit, affect the condominium unit price over time. Design/methodology/approach The transaction price of each unit reflects the quality of the residential unit and the properties of the shared facilities. Based on the hedonic pricing method, this study assesses the impact of shared amenities on unit resale prices, using an original data set on condominium unit resale transactions and the status of housing characteristics for both condominium units and condominium buildings. Findings Results show that holding other conditions constant, a meeting room and an external space that can host events increase the unit resale price by approximately 7 and 16 per cent, respectively. Some community amenities such as a meeting room may increase its impact later in the condominium’s lifetime. Research limitations/implications Because of the proprietary nature of data, the analysis focuses on high-end condominium properties in central Tokyo. Although it is difficult to single out the shared amenities’ effects on condominium resale prices from potentially confounding factors, this study partially overcomes this issue by including explanatory geographical variables (e.g. ground heights). Practical implications The results suggest that a shared facility that hosts social interactions among residents significantly affects the resale market value of housing units and that their magnitudes may change over time. Originality/value To the best of the author’s knowledge, this study provides the first empirical evidence of the impacts of shared structures on condominium unit sales using micro-level transaction data in Japan. ",5.0,,False,,Business,2019-07-29,2853af8fa0610dda89f0fcba063d8556708ed19c,sharedamenitiesimpactsoncondominiumresalevalues,tajima,10.1108/ijhma-03-2019-0038,sharedamenitiesimpactsoncondominiumresalevalues|2019,1,2838,2,hedonic housing prices || housing characteristics prices hedonic,2,2,4,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W4411991185,10.1016/j.mlwa.2025.100707,,Explaining drivers of housing prices with nonlinear hedonic regressions,2025.0,Machine Learning with Applications,Heng Wan; Pranab K. Roy Chowdhury; Jim Yoon; Parin Bhaduri; Vivek Srikrishnan; David R. Judi; Brent Daniel,"Housing markets play a critical role in shaping the spatial and demographic evolution of urban areas. Simulating housing price dynamics can enhance projections of future urban development outcomes. However, traditional hedonic regressions for housing prices, which neglect nonlinear interactions among explanatory variables, often exhibit limited predictive performance. While machine learning (ML) methods can provide a more flexible representation of the relationships between predictors, they are often regarded as “black boxes” due to their complexity and lack of transparency. Interpretable ML techniques provide a promising route by combining the flexibility of ML methods with approaches to analyze the relationships between inputs and outputs. In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16% more of housing price variance than a traditional linear regression model. The interpretable ML model also reveals more nuanced and realistic nonlinear relationships between housing sales price and predictors as well as interactive effects underlying Baltimore home price dynamics. For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.",5.0,,True,article,,2025-07-03,https://openalex.org/W4411991185,explainingdriversofhousingpriceswithnonlinearhedonicregressions,wan,10.1016/j.mlwa.2025.100707,explainingdriversofhousingpriceswithnonlinearhedonicregressions|2025,1,2758,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,3,2,7,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,rent hedonic regression housing,c939212633a7542afd72190505f7218f5bdf65b6,10.1108/ijhma-11-2020-0141,,"A hedonic assessment of the relative importance of structural, locational and neighbourhood factors on advertised rents in Malta",2021.0,,Brian W. Micallef; Reuben Ellul; N. DeBono,"Purpose: The private rental market in Malta has expanded significantly in recent years, but as at 2020, no official rent index is yet published This paper aims to construct such an index and explores the relative importance of structural, locational and neighbourhood factors to advertised rents Design/methodology/approach: The authors compile hedonic indices for advertised rents in Malta collected from publicly available sources using webscraping techniques The database comprises more than 25,000 listings with information on various property attributes Hedonic regressions are estimated using ordinary least squares and rent indices are computed using three alternative methods: the time dummy method, the rolling time dummy method and the average characteristics method For the latter, indices are computed using the Laspeyres, Paasche and Fisher methods Findings: The results from the hedonic indices indicate that the annual growth rate in advertised rents was slowing down during 2019, albeit still remaining relatively high, while in 2020, advertised rents contracted sharply, amplified by the effects of COVID-19 The findings also reveal that advertised rental prices are significantly influenced by various structural, locational and neighbourhood factors Originality/value: This paper introduces the first rent index in Malta that will be used to monitor developments in the rental segment of the housing market and for financial stability purposes given the share of buy-to-let properties It also provides various elasticities on the impact of property attributes on advertised rents in Malta Finally, the study contributes to the literature on the effect of foreign-born residents on advertised rents © 2021, Emerald Publishing Limited",5.0,,False,,Economics,2021-04-02,c939212633a7542afd72190505f7218f5bdf65b6,ahedonicassessmentoftherelativeimportanceofstructurallocationalandneighbourhoodfactorsonadvertisedrentsinmalta,micallef,10.1108/ijhma-11-2020-0141,ahedonicassessmentoftherelativeimportanceofstructurallocationalandneighbourhoodfactorsonadvertisedrentsinmalta|2021,1,2751,4,cites:sirmans2006 || hedonic housing prices || housing characteristics prices hedonic || rent hedonic regression housing,2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),0a59d5d819d469e24a6d9d4cb8216c6ffdf3de2d,10.1108/ijhma-04-2021-0041,,Distance in geographic and characteristics space for real estate pricing,2021.0,International Journal of Housing Markets and Analysis,E. Ozhegov; A. Ozhegova," PurposeA common approach to predicting the price of residential properties uses the hedonic price model and its spatial extensions. Within the hedonic approach, real estate prices are decomposed into internal characteristics of an apartment, apartment characteristics and external characteristics. To account for the unobserved quality of the surrounding environment, price models include spatial price correlation factors, where the distance is usually measured as the distance in geographic space. In determining the price, a seller focuses not only on the observed and unobserved factors of the apartment and its environment but also on the prices of similar marketed objects that can be selected both by geographic proximity and by characteristics similarity. The purpose of this study is to show the latter point empirically. Design/methodology/approachThis study uses an ensemble clustering approach to measure objects' proximity and test whether the proximity of objects in the property characteristics space along with spatial correlation explain the significant variation in prices. FindingsIn this paper, the pricing behaviour of sellers in a reselling market in Perm, Russia is studied. This study shows that the price transmission mechanism includes both geographic and characteristics spaces. Practical implicationsAfter testing on market data, the proposed framework for the distance construct could be used to obtain higher predictive power for price predictive models and construction of automated valuation services. Originality/valueThis study tests the higher explanatory power of the model that includes both the distance measured in geographic and property characteristics spaces. ",5.0,,False,,Computer Science,2021-08-17,0a59d5d819d469e24a6d9d4cb8216c6ffdf3de2d,distanceingeographicandcharacteristicsspaceforrealestatepricing,ozhegov,10.1108/ijhma-04-2021-0041,distanceingeographicandcharacteristicsspaceforrealestatepricing|2021,1,2711,5,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || apartment prices hedonic || automated valuation model residential || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic property valuation",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,cacce33824550f0ec562df8168cc2fd5a10f3db4,10.1080/14445921.2019.1693323,,The value of a tram station on local house prices: an hedonic modelling approach,2019.0,Pacific Rim Property Research Journal,D. Higgins; A. Rezaei; P. Wood,"ABSTRACT There are many drivers of house prices. In urban locations, public infrastructure is considered one of the leading determinants. To explore this relationship, an Hedonic house pricing model was applied to an established Birmingham, the UK working class residential area which is serviced by the Metro Midland tram line. The selected Wednesbury Great Western Street station provides ease of access for the local residents to Birmingham and Wolverhampton City Centres plus there has been recent sale evidence of 100 properties within close proximity to the station. Independent variables consisted of a range of typical physical, locational and neighbourhood attributes for the Hedonic house pricing model input data. Based on the preferred linear Hedonic model, the findings from the recent 100 Wednesbury house sales showed property type, bedrooms, floor area, ambience and distance to the tram station were key explanatory factors in relation to pricing levels. On the evidence provided, property price increased by £16,878 for every km closer to the tram stop. However, when examining properties within defined distance bands with smaller datasets, the location to the tram station was not a significant statistical driver. This suggests that proximity to a public transport access point alongside key housing characteristics should form part of the housing decision making process as they are significant predictors of local house prices.",5.0,,False,,Business,2019-09-02,cacce33824550f0ec562df8168cc2fd5a10f3db4,thevalueofatramstationonlocalhousepricesanhedonicmodellingapproach,higgins,10.1080/14445921.2019.1693323,thevalueofatramstationonlocalhousepricesanhedonicmodellingapproach|2019,1,2450,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || house prices proximity hedonic || housing characteristics prices hedonic || residential property price determinants",2,2,3,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,b8c394ccc08a68ca85188494555b6dfbef89e3f7,10.1108/ijhma-05-2020-0064,,"Close enough: housing price effects of urban parks, reserves and volcanic parks in Auckland, New Zealand",2020.0,International Journal of Housing Markets and Analysis,Lydia Cheung; M. Fernandez," Purpose This study aims to test whether the size of and distance to the nearest green space has any effects on residential property transaction prices in Auckland, New Zealand. Design/methodology/approach This paper divides all green spaces in Auckland into three categories, namely, urban parks, regional reserves and volcanic parks (a unique feature in Auckland, New Zealand). This study uses six years of residential property transaction data to estimate hedonic price regressions. For each property, this paper calculates the size of and distance to the nearest park in each category. Findings The logged sizes of the nearest regional reserve and volcanic park have positive effects on property prices. The logged distances to the nearest urban park and volcanic park are insignificant, while the logged distance to the nearest regional reserve is positively significant. In other words, homebuyers prefer larger regional reserves and volcanic parks and prefer to be further away. Originality/value Auckland is ranked as a top-five city in the world in terms of the proportion of public green space, trailing four European cities. However, because of Auckland’s much younger age, it presents a very different urban form. The study shows that the distribution of green space (not only its total amount) can bring negative capitalization on property prices. ",5.0,,False,,Economics,2020-12-28,b8c394ccc08a68ca85188494555b6dfbef89e3f7,closeenoughhousingpriceeffectsofurbanparksreservesandvolcanicparksinaucklandnewzealand,cheung,10.1108/ijhma-05-2020-0064,closeenoughhousingpriceeffectsofurbanparksreservesandvolcanicparksinaucklandnewzealand|2020,1,2367,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,4,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,hedonische Preise Wohnungen,,10.1007/s13147-017-0485-0,,Der Einfluss städtischer Grünflächen auf die Immobilienpreise: Eine hedonische Analyse für die Stadt Berlin,2017.0,Raumforschung und Raumordnung | Spatial Research and Planning,Henry Wüstemann; Jens Kolbe,"Urban green space represents a key aspect for the sustainable development of urban areas. The benefit of urban green space can also be reflected in higher property and land prices. Although a high number of international studies prove the impact of urban green on real estate prices, the empirical basis in Germany is still very limited. The overall purpose of this study is to investigate the impact of urban green space on real estate prices in Germany. The data set contains transaction data for single family homes (1996-2012) provided by the Committee of Valuation Experts (GAA, Gutachterausschuss für Grundstückswerte) for the city of Berlin and cross section geo-coded data for urban green drawn from the European Urban Atlas (EUA) of the European Environment Agency. In order to control for additional open space categories we further incorporated geo-coded data on water bodies, forest, farmland and fallow land. The analysis shows a positive impact of urban green on real estate prices. The results provide helpful information on the capitalization of urban green space in real estate prices.",5.0,,,journal-article,,,doi_10_1007_s13147_017_0485_0,dereinflussstadtischergrunflachenaufdieimmobilienpreiseeinehedonischeanalysefurdiestadtberlin,wüstemann,10.1007/s13147-017-0485-0,dereinflussstadtischergrunflachenaufdieimmobilienpreiseeinehedonischeanalysefurdiestadtberlin|2017,1,2102,1,hedonische Preise Wohnungen,1,2,2,1,1,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,78c8565dad17d0909801fbca0086a58af83c2ab3,10.1109/icwr.2019.8765250,,Using Web Mining in the Analysis of Housing Prices: A Case study of Tehran,2019.0,2019 5th International Conference on Web Research (ICWR),R. Annamoradnejad; Issa Annamoradnejad; T. Safarrad; J. Habibi,"There have been many previous works to determine the determinants of housing prices. All of these works relied on a relatively small set of data, mostly collected with the help of real estate agencies. In this work, we used web mining methods to generate a big, organized dataset from a popular national brokerage website. The dataset contains structural characteristics of more than 139,000 apartments, alongside their location and price. We provided our full dataset for the article, so that other researchers can reproduce our results or conduct further analyses. Using this dataset, we analyzed housing prices of Tehran in order to identify its major determinants. To this aim, we examine the dynamics of housing prices at the district levels of Tehran using Hedonic Price model. Our results highlight a number of points, including: Base area of an apartment is positively correlated with price per square meter (r=0.89), showing a two-folded impact on the overall price. Air quality is in a positive, and floor level is in negative correlation with housing prices.",5.0,,False,Conference,Business,2019-04-24,78c8565dad17d0909801fbca0086a58af83c2ab3,usingwebminingintheanalysisofhousingpricesacasestudyoftehran,annamoradnejad,10.1109/icwr.2019.8765250,usingwebminingintheanalysisofhousingpricesacasestudyoftehran|2019,1,2069,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,rent hedonic regression housing,99b7e936aed2e2ae92958948173e755e39615476,10.1177/15356841221123762,,Affordable Regulation: New York City Rent Stabilization as Housing Affordability Policy,2022.0,City & Community,Kasey Zapatka; Juliana de Castro Galvao,"The growing housing affordability crisis is at the center of conversations about U.S. inequality. This paper reconsiders the role of rent stabilization as one important affordability tool. We investigate who is most likely to benefit from rent stabilization, how much non-stabilized renters would save if their units were stabilized, and the extent to which stabilization would reduce rent burden among households. Using New York City Housing Vacancy Survey data and employing logistic and hedonic regression techniques, we show that Hispanic and foreign-born householders are more likely to live in rent-stabilized units and find evidence of both rent savings and rent burden reduction when comparing stabilized tenants with their non-stabilized counterparts. We argue that expanded rent stabilization could be paired with policies that stimulate new construction to simultaneously curb rent inflation, protect current populations from displacement, and increase housing supply.",5.0,,False,JournalArticle;Review,,2022-10-07,99b7e936aed2e2ae92958948173e755e39615476,affordableregulationnewyorkcityrentstabilizationashousingaffordabilitypolicy,zapatka,10.1177/15356841221123762,affordableregulationnewyorkcityrentstabilizationashousingaffordabilitypolicy|2022,1,1979,1,rent hedonic regression housing,2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1080/00420988520080761,,Resident Age and Housing Search: Evidence From Hedonic Residuals,1985.0,Urban Studies,Larry DeBoer,"Elderly people are likely to have higher housing market search costs than the population as a whole. Costly search increases the price paid by older persons for housing relative to its characteristics, and also increases the range of prices paid. To test this latter hypothesis, hedonic equations are estimated for six SMSAs using data from the Annual Housing Survey. The residuals of these hedonics represent variation in prices not explained by housing characteristics. It is found that the absolute variation of these residuals is positively related to resident age, which is evidence supporting the hypothesis that the elderly pay higher housing prices due to higher search costs. A possible policy response to this problem is to create a housing market information service directed at reducing the search costs of the elderly.",5.0,,,journal-article,,,doi_10_1080_00420988520080761,residentageandhousingsearchevidencefromhedonicresiduals,deboer,10.1080/00420988520080761,residentageandhousingsearchevidencefromhedonicresiduals|1985,1,1831,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic housing prices || housing characteristics prices hedonic",2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",37635a8033736018ebfc4e1103251a4a6a696cd7,10.1080/00396265.2023.2293366,,Model agnostic interpretable machine learning for residential property valuation,2023.0,Survey Review,Tugba Gunes,"Machine learning algorithms commonly outperform the traditional hedonic models in property valuation; however, it is hard to capture the inner workings of these complex models due to their black-box nature. To address the opaqueness of ML models, this study applies model-agnostic interpretability methods at the both global and local levels for a Random Forest model which provided better prediction accuracy than Support Vector Machines and eXtreme Gradient Boosting algorithms in predicting residential property prices. The results of this study suggest that interpretable ML methods can bring transparency to opaque ML models, and visualize feature effects and interactions in property valuation models.",5.0,,False,JournalArticle,,2023-12-18,37635a8033736018ebfc4e1103251a4a6a696cd7,modelagnosticinterpretablemachinelearningforresidentialpropertyvaluation,gunes,10.1080/00396265.2023.2293366,modelagnosticinterpretablemachinelearningforresidentialpropertyvaluation|2023,1,1707,2,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic property valuation",2,1,1,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 openalex,hedonic housing prices,https://openalex.org/W1993389335,10.2747/0272-3638.18.4.347,,SPATIOTEMPORAL CONTROLS IN HEDONIC HOUSING PRICE MODELS,1997.0,Urban Geography,Kyoun-Sup Chung; Paul A. Waddell; Brian J. L. Berry,An interactive spatiotemporal fixed-effect hedonic housing price model estimated by weighted least squares will control for most forms of spatial and ecological autocorrelation and produce efficient housing price estimates.,5.0,,False,article,,1997-05-01,https://openalex.org/W1993389335,spatiotemporalcontrolsinhedonichousingpricemodels,chung,10.2747/0272-3638.18.4.347,spatiotemporalcontrolsinhedonichousingpricemodels|1997,1,1223,3,apartment rent hedonic || hedonic housing prices || spatial hedonic model housing,2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",14d90baab3d6b08e6abbc686c43df7491db35636,10.1142/s2010007821500032,,ESTIMATING DEMAND FOR GOOD CLIMATE AND AIR QUALITY IN TAIWAN,2021.0,,D. Shaw; JU-CHIN Huang; CHUAN-YAO Lin; Tingting Hsu; Ming-Hugh Tsai,"We measure the amenity values of climate and air quality in Taiwan using the two-step hedonic price method (HPM). Separate hedonic housing price equations, one for each of three metropolitan areas ...",5.0,,False,,Environmental Science,2021-02-10,14d90baab3d6b08e6abbc686c43df7491db35636,estimatingdemandforgoodclimateandairqualityintaiwan,shaw,10.1142/s2010007821500032,estimatingdemandforgoodclimateandairqualityintaiwan|2021,1,1200,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",a4ed184162eb6d76ba7b279ed239872bd3452fca,10.1177/1087724x18757535,,"Pavement Condition and Residential Property Values: A Spatial Hedonic Price Model for Solano County, California",2018.0,,Kihwan Seo; D. Salon; F. Shilling; M. Kuby,,5.0,,False,JournalArticle,Economics,2018-02-13,a4ed184162eb6d76ba7b279ed239872bd3452fca,pavementconditionandresidentialpropertyvaluesaspatialhedonicpricemodelforsolanocountycalifornia,seo,10.1177/1087724x18757535,pavementconditionandresidentialpropertyvaluesaspatialhedonicpricemodelforsolanocountycalifornia|2018,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,7795a2c292ccad4719f593f78952039641270a5e,10.1142/9789814723237_0006,,IMPACT OF NATURAL DISASTERS ON RESIDENTIAL PROPERTY VALUES: EVIDENCE FROM AUSTRALIA,2018.0,Economics of Natural Disasters,W. Athukorala; W. Martin; P. Neelawala; D. Rajapaksa; J. Webb; C. Wilson,"One of the most prominent casualties of a natural disaster is the property market. The private and social costs from such events typically run into millions of dollars. In this chapter we use a dataset to examine the impact of natural disasters on residential house prices using a hedonic property (HP) values approach. Data before and after both a wildfire and floods, which affected Rockhampton in central coastal part of the state of Queensland, Australia are used. The data is unique because one of Rockhampton’s suburb was affected by wildfires and another by floods. For the analysis, three suburbs namely Frenchville, Park Avenue and Norman Gardens are used. Frenchville was significantly affected by wildfires in the latter part of 2009 and to a lesser extent in 2012, while Park Avenue was affected by floods at the end of 2010, January 2011, 2012 and 2013. Norman Gardens, which was relatively unaffected, is used as a control site. This enables us to examine the before and after effects on residential property values in the three suburbs. The results confirm that in the aftermath of a natural disaster property prices in affected areas decrease even though the large majority of individual houses remain unaffected. Furthermore, the results indicate that while prices in largely unaffected suburbs may gain immediately after a natural disaster, this gain may disappear if natural disasters continue to occur in the area/region due to a flood prone stigma being created. The results have several important policy decision and welfare implications which are briefly discussed in the chapter.",5.0,,False,,Geography,2018-12-01,7795a2c292ccad4719f593f78952039641270a5e,impactofnaturaldisastersonresidentialpropertyvaluesevidencefromaustralia,athukorala,10.1142/9789814723237_0006,impactofnaturaldisastersonresidentialpropertyvaluesevidencefromaustralia|2018,1,2603,1,hedonic house price,2,3,3,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,prix hédoniques logement,,10.3406/ecop.2010.8020,,Évaluation de l’impact des politiques « Quartiers verts » et « Quartiers tranquilles » sur les prix de l’immobilier à Paris,2010.0,Économie & prévision,Matthieu Glachant; Benjamin Bureau,This study uses the hedonic-price method to assess the impact on residential property values of two policies implemented by the Paris municipality : “ Quartiers Verts ” (“ Green Neighborhoods ”) and “ Quartiers Tranquilles ” (“ Quiet Neighborhoods ”). Both policies aim to reorganize targeted neighborhoods in order to promote non-motorized transportation modes. The neighborhoods cover 18 % of the total Paris area. We find that the “ Quartiers Verts ” and “ Quartiers Tranquilles ” policies caused property values to rise 3 % and 1.5 % respectively.,5.0,,,journal-article,,,doi_10_3406_ecop_2010_8020,evaluationdelimpactdespolitiquesquartiersvertsetquartierstranquillessurlesprixdelimmobilieraparis,glachant,10.3406/ecop.2010.8020,evaluationdelimpactdespolitiquesquartiersvertsetquartierstranquillessurlesprixdelimmobilieraparis|2010,1,1551,1,prix hédoniques logement,2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,488210c141acbeeb9555857923d916a80eb94e8d,10.1108/ohi-04-2022-0091,,"Housing demand of urban fringe residents and underlying causes: example of Ankara, Türkiye",2022.0,Open House International,Gizem Hayrullahoğlu; Yeşim Aliefendioğlu (Tanrivermis),"Purpose This study aims to explore the housing demand of urban fringe residents in southwest Ankara. Two subquestions were developed: What are the respondents’ perceptions of Ankara city center and which characteristics do they prioritize for living in the urban fringe? Data were collected through a face-to-face household survey, and a hedonic regression model was developed based on responses.Design/methodology/approach Increasing housing demand, lifestyle change and faulty housing policies in Ankara have triggered urban sprawl along fringe areas, which causes several urban problems. Considering that urban sprawl is related to housing demand, it is essential to examine the structure of housing demand and the preference to live near the urban fringe.Findings According to the survey results, security, crime, noise pollution, traffic congestion and parking problems that reduce the welfare of Ankara city center encouraged expansion toward the rural–urban fringe, in addition to low-quality or traditional housing attributes. The urban core became unattractive to the respondents for being insecure, chaotic and down-market. The hedonic model showed that seven variables, all related to housing characteristics, best explain the housing demand in the area. Socioeconomic status and lifestyle were found to be associated with the desire to live on the urban fringe, also indicating the snob effect.Originality/value The authors propose taking domain-specific housing demand patterns in the spatial planning assumptions and housing policies into consideration for a well-governed urban development in Ankara. Making the city center more appealing through rehabilitation should be preferable rather than limiting demand on the urban fringe with a strict intervention in housing supply.",5.0,,False,Review,,2022-09-27,488210c141acbeeb9555857923d916a80eb94e8d,housingdemandofurbanfringeresidentsandunderlyingcausesexampleofankaraturkiye,hayrullahoğlu,10.1108/ohi-04-2022-0091,housingdemandofurbanfringeresidentsandunderlyingcausesexampleofankaraturkiye|2022,1,2791,1,spatial hedonic model housing,2,0,2,4,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",16cc600420fba3a1da5111ab0d68bf2ffb371075,10.17208/jkpa.2019.11.54.6.80,,Comparing the Housing Implicit Prices of Restricted and Unrestricted Hedonic Price Models,2019.0,,Wonseok Seo,,5.0,,False,,Economics,2019-11-30,16cc600420fba3a1da5111ab0d68bf2ffb371075,comparingthehousingimplicitpricesofrestrictedandunrestrictedhedonicpricemodels,seo,10.17208/jkpa.2019.11.54.6.80,comparingthehousingimplicitpricesofrestrictedandunrestrictedhedonicpricemodels|2019,1,1000,2,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic housing prices",2,0,1,1,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",af796977cadee27143bd7a348cdfb93402980b7f,10.1080/15623599.2021.1975021,,"Predictive rental values model for low-income earners in slums: the case of Ijora, Nigeria",2021.0,International Journal of Construction Management,C. Iroham; S. Misra; Onyeka Emebo; H. Okagbue,"Abstract It is well known most often that values of properties tend to hike at the effluxion of time. This has necessitated the adoption of predictive models in interpreting outcomes in the property market in the future. Earlier studies have been oblivious of such models' outcomes as it affects any focal group, particularly the vulnerable. This present study focuses on the low-income earners found in the slum. The Ijora community in Lagos was the highlight of this study, particularly Ijora Badia and Ijora Oloye, regarded as slums according to the UNDP report. The entire fifty-two (52) local agents in the Ijora community were surveyed in cross-sectional survey research that entailed the questionnaire's issuance. The nexus of data collection, pre-processing, data analysis, algorithm application, and model evaluation resulted in retrieving rental values within the years 2010 and 2019 on two predominant residential property types of self-contain and one-bedroom flats found within the community. Three selected algorithms, Artificial Neural Network (ANN), Support Vector Machine, and Logistic Regression, were essentially used as classifiers but trained to predict the continuous values. These algorithms were implemented through the use of Python's SciKit-learn Library and RapidMiner. The findings revealed that though all three models gave accurate predictions, Logistic Regression was the highest with low error values. It was recommended that Logistic Regression be applied but with much data set of property values of low-income earners over much more period. This study will contribute to the Sustainable development goals(SDG) 11(Sustainable cities and communities) of the United Nations to benefit developing countries, especially in sub-Saharan Africa.",5.0,,False,Review,Economics,2021-09-09,af796977cadee27143bd7a348cdfb93402980b7f,predictiverentalvaluesmodelforlowincomeearnersinslumsthecaseofijoranigeria,iroham,10.1080/15623599.2021.1975021,predictiverentalvaluesmodelforlowincomeearnersinslumsthecaseofijoranigeria|2021,1,2772,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",848088b7f141ed2599f30e710a359c2b8c516233,10.1108/ijhma-04-2022-0053,,"Flood hazards and housing prices: a spatial regression analysis for Hat Yai, Songkhla, Thailand",2022.0,International Journal of Housing Markets and Analysis,Sukampon Chongwilaikasaem; T. Chalermyanont," Purpose Global warming exacerbates sea level rise and extreme weather events that cause severe flooding, resulting in lost productivity and property damage. To reduce the impact of flooding, residents are avoiding purchasing homes in high-risk areas. There are numerous studies on the relationship between flood hazards and housing prices in developed countries, but few in developing countries. Therefore, this study aims to investigate the relationship between flood hazards and housing prices in Hat Yai, Songkhla, Thailand. Design/methodology/approach This study uses spatial-lag, spatial error and spatial autoregressive lag and error (SARAR) models to analyze the effect of flood risk on property prices. The main analysis examines the degree of flood risk and housing rental prices from our survey of 380 residences. To test the robustness of the results, the authors examine a different data set of the same samples by using the official property valuation from the Ministry of Finance and the flood risk estimated by the Southern Natural Disaster Research Center. Findings The SARAR model was chosen for this study because of the occurrence of spatial dependence in both dependent variable and the error term. The authors find that flood risk has a negative impact on property prices in Hat Yai, which is consistent with both models. Originality/value This study is one of the first to use spatial econometrics to analyze the impact of flood risk on property prices in Thailand. The results of this study are valuable to policymakers for benefit assessment in cost–benefit analysis of flood risk avoidance or reduction strategies and to the insurance market for pricing flood risk insurance. ",5.0,,False,Review,,2022-07-29,848088b7f141ed2599f30e710a359c2b8c516233,floodhazardsandhousingpricesaspatialregressionanalysisforhatyaisongkhlathailand,chongwilaikasaem,10.1108/ijhma-04-2022-0053,floodhazardsandhousingpricesaspatialregressionanalysisforhatyaisongkhlathailand|2022,1,2708,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,4,4,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,residential property price determinants,3b47440810374382cb48ec73484a25f61ad3d2a3,10.2139/ssrn.3514560,,Determinants of Residential House Rental Prices in Accra Metropolis,2020.0,Social Science Research Network,Raymond Darfo-Oduro,"The study analysed the effect of neighbourhood characteristics and structural characteristics on residential rental prices in the Accra metropolis of Ghana. This is in response to the absence of scientific approach to determining rental prices and as a way to influence policies to deal with the arbitrarily fixing of rental prices. Multiple regression analysis was employed in analysing data sourced primarily from tenants of residential houses. The study specified four regression equations, linear, linear-log, log-linear and log-log. The different regression models were found to have different effect on the relationship between house rental prices and its influencing factors. However, the log-log mode and linear-log model revealed similar results, both models revealed that distance from church or mosque to a residential property were significant and positively related to house rental prices whilst all other variables were insignificant. The linear model and log-linear model also showed some similarities. In both models distance to central business district, distance to market, electricity supply, refuse dump, wooded area and urban effect were significant with the same signs in both models. However, single household was significant only in the linear model but not in the log-linear model whereas in the case of the log-linear model variables such as distance to church or mosque, park within fifty metres and uncovered drains were significant but insignificant in the linear model. The study concluded that there is a significant relation between house rental prices and neighbourhood characteristics as well as structural characteristics of a residential property.",5.0,,False,,Economics,2020-01-06,3b47440810374382cb48ec73484a25f61ad3d2a3,determinantsofresidentialhouserentalpricesinaccrametropolis,darfo-oduro,10.2139/ssrn.3514560,determinantsofresidentialhouserentalpricesinaccrametropolis|2020,1,2683,1,residential property price determinants,0,2,4,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",91eff0fd679efa3ad24377427cfda395ee843bf7,10.1108/pm-02-2022-0013,,"Measuring the impact of heavy rail transport infrastructure on house prices in Melbourne, Australia: a case study of Mernda rail extension project",2022.0,Property Management,Maojian Zhang; J. Shukla,"PurposeThe research examines the impact of heavy rail transport infrastructure on residential property prices in Melbourne at different stages of project development using the Mernda Rail Extension Project as a case study.Design/methodology/approachA difference-in-difference approach is used to quantitatively measure the magnitude of change in the house price at different stages of rail transport infrastructure project development.FindingsWhen controlling for a range of structural, neighbourhood, and locational attributes, the authors find that properties within 800 m from the proposed train station are 8.7% higher in value than those outside 800 m (but within 1,600 m). However, during the project's construction, the project's benefits in the form of house price appreciation are not fully realised. “Unrealised benefit” is attributed to the negative externalities of construction works and apprehensions associated with the project's shelving and time delays.Research limitations/implicationsDepending on the availability of data on residential property transactions in the future, a spatial analysis of rail infrastructure's radius of catchment effect is needed.Practical implicationsFindings from this research are beneficial for policymakers concerned with transport and land use planning, property valuation for taxation and mortgage purposes.Originality/valueThis research contributes to the knowledge of the impact of the rail project on house prices in Melbourne. While there are earlier studies on the topic, there is limited understanding of this prime Australian city attractive to domestic and foreign investors.",5.0,,False,,,2022-06-10,91eff0fd679efa3ad24377427cfda395ee843bf7,measuringtheimpactofheavyrailtransportinfrastructureonhousepricesinmelbourneaustraliaacasestudyofmerndarailextensionproject,zhang,10.1108/pm-02-2022-0013,measuringtheimpactofheavyrailtransportinfrastructureonhousepricesinmelbourneaustraliaacasestudyofmerndarailextensionproject|2022,1,2634,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",858db664fb39ee9a01d8a762083ba9f644466f84,10.1080/10511482.2022.2157219,,Yes or Not in My Backyard (YIMBY vs. NIMBY)? The Impact of New Social Housing Construction on Single-Family House Prices in Quebec City (Canada),2023.0,Housing Policy Debate,Jean Dubé; François Des Rosiers; N. Devaux,"Abstract The development of new social housing faces important resistance by local population, a phenomenon knows as the “not in my backyard” movement. One argument from residents to oppose such project is the idea that new construction will negatively impact property values. This is what this paper aims to investigate. The analysis is based on a complete recension of the new social housing projects built between 2000 and 2020 and on single-family house transactions that occurred between 2004 and 2020 in Quebec City (Canada). A repeated sales model integrating a difference-in-differences estimator is developed to isolate the net price premium related to the emergence of a new social housing building while accounting for the possible heterogeneity impact related to characteristics of the building, including the number of apartments and the type of clientele hosted as well as the local characteristics, such as the spatial concentration of social housing buildings and distance to the city center. The results show a complex net price premium rent function that leads to mixed conclusions and has important implications for the development of new social housing projects.",5.0,,False,,,2023-01-03,858db664fb39ee9a01d8a762083ba9f644466f84,yesornotinmybackyardyimbyvsnimbytheimpactofnewsocialhousingconstructiononsinglefamilyhousepricesinquebeccitycanada,dubé,10.1080/10511482.2022.2157219,yesornotinmybackyardyimbyvsnimbytheimpactofnewsocialhousingconstructiononsinglefamilyhousepricesinquebeccitycanada|2023,1,2182,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,4,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",2a092a5dbf502c2ee632dac65980d333fdd0cd2f,10.1109/geoinformatics.2011.5980723,,Spatial pattern and structural determinants of Shanghai's housing price: A GWR-based approach,2011.0,GEOINFORMATICS,Qi Tang; Wei Xu; Fu-Li Ai,,5.0,,False,Conference,Economics,2011-06-24,2a092a5dbf502c2ee632dac65980d333fdd0cd2f,spatialpatternandstructuraldeterminantsofshanghaishousingpriceagwrbasedapproach,tang,10.1109/geoinformatics.2011.5980723,spatialpatternandstructuraldeterminantsofshanghaishousingpriceagwrbasedapproach|2011,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",da1b32f0c7ded091ba309f27339297afe10d9387,10.1007/978-3-030-24302-9_46,,"Spatial Analysis of the Proximity Effects of Land Use Planning on Housing Prices (Case Study: Tehran, Iran)",2019.0,Communication Systems and Applications,Amin Safdari Molan; Ebrahim Farhadi,,5.0,,False,JournalArticle,Computer Science;Business,2019-07-01,da1b32f0c7ded091ba309f27339297afe10d9387,spatialanalysisoftheproximityeffectsoflanduseplanningonhousingpricescasestudytehraniran,molan,10.1007/978-3-030-24302-9_46,spatialanalysisoftheproximityeffectsoflanduseplanningonhousingpricescasestudytehraniran|2019,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8b63060d8fd395b1339b1b60075584e8633c4abc,10.1080/02815738508730065,,Housing prices: An empirical analysis of the determinants of the price level in the metropolitan area of Helsinki,1985.0,,V. Vihriala; Samuli Skurnik,,5.0,,False,,Economics,,8b63060d8fd395b1339b1b60075584e8633c4abc,housingpricesanempiricalanalysisofthedeterminantsofthepricelevelinthemetropolitanareaofhelsinki,vihriala,10.1080/02815738508730065,housingpricesanempiricalanalysisofthedeterminantsofthepricelevelinthemetropolitanareaofhelsinki|1985,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",266a65259d60178ac8bc559bdb9abfea6815d7ae,10.1002/aepp.13196,,Valuing impacts of proximity to Saguaro National Park on house prices,2021.0,Applied Economic Perspectives and Policy,Julie M. Mueller; J. Loomis; Leslie A. Richardson; Ryan A. Fitch,,5.0,,False,,Geography,2021-09-17,266a65259d60178ac8bc559bdb9abfea6815d7ae,valuingimpactsofproximitytosaguaronationalparkonhouseprices,mueller,10.1002/aepp.13196,valuingimpactsofproximitytosaguaronationalparkonhouseprices|2021,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",56f6c5c110638bb1bdf65edaeebdeeeb28a59f48,10.1108/ijhma-10-2018-0085,,Does the school input quality matter to nearby property prices in Taipei metropolis?,2019.0,International Journal of Housing Markets and Analysis,Ti-Ching Peng," Purpose This paper aims to analyse the spatial effect of school input – “student–teacher ratio” – on property prices in Taipei Metropolis, Taiwan. The falling fertility rate inevitably changes educational system since more less-experienced part-time teachers are hired for the sake of schools’ budget. Hence, in addition to full-time teachers, part-time teachers are included in measuring the student–teacher ratio to see if an increase in part-time teachers, indicating the possible plunge of school quality, may decrease the value of nearby properties. Design/methodology/approach Three types of spatial regressions (including spatial lag, spatial error and SARAR models), which incorporate different kinds of spatial dependencies into hedonic models, are applied to reveal the relationship between two measures of student–teacher ratios and property values. Findings Conventional variables, including housing attributes, demographics and local facilities, demonstrated their consistent and expected influence on property prices. More importantly, the significant “student–teacher ratio 2” (both full-time and part-time teachers) indicated that low-paid, less-experienced and overworked part-time teachers can hardly deliver quality instruction, which inevitably causes harm to school credit and potential buyers’ confidence in valuing neighbouring properties. Practical implications Facing the decrease in children and the shrinking budget, the solution to maintain teacher’s quality is to remove the unnecessary administrative chores from full-time teachers and let them do their jobs rather than hiring part-time teachers. Good school input quality should add value to nearby properties, which in return appeals more students to enrol in this school and further elevate schools’ financial burden. Originality/value This paper is one of the few studies that consider part-time teachers in capitalising school-input quality into property prices. The increase in part-time teachers, which may lead to an illusion that each student could have higher degree of individual attention from teachers, actually lowers the education quality distributed to all the students. It provides a different perspective in defining the importance of teaching quality to property values in Chinese culture. ",4.0,,False,,Economics,2019-06-11,56f6c5c110638bb1bdf65edaeebdeeeb28a59f48,doestheschoolinputqualitymattertonearbypropertypricesintaipeimetropolis,peng,10.1108/ijhma-10-2018-0085,doestheschoolinputqualitymattertonearbypropertypricesintaipeimetropolis|2019,1,3299,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,3,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d536c9af6b0a1a741a0b9a7b2e9651cf70a61fdf,10.1108/pm-09-2022-0070,,"Urban property markets and security risk: explaining how neighborhood security shapes housing rental prices in Ojo Lagos, Nigeria",2023.0,Property Management,I. R. Aliu,"PurposeWhile the declining rate of urban security and its potential effects have been globally acknowledged, the ways urban neighborhood security shapes real estate markets in African cities remain largely unexplained. The purpose of this paper therefore is to present the findings from a study of the nexus between urban neighborhood security and home rental prices in Lagos, Nigeria.Design/methodology/approachThis paper is based on the hedonic price theory, an objectively derived urban neighborhood security index (UNSI) and property rental price data in Ojo, Lagos, Nigeria. This is a quantitative cross-sectional study that employs multistage sampling survey procedure. Data are analyzed using descriptive statistics, nonparametric correlation and hedonic price function with ordinary least squares (OLS).FindingsResults show that nearly 50% of the study area is prone to insecurity and average rental values in Ojo, Lagos range from N151329.41 ($302.66) to N167333.33 ($334.67) per annum. Correlation analysis shows that home rental prices have high, positive and significant correlations (rs = 0.725 and p < 0) with UNSI. After controlling for neighborhood and structural factors, it is found that urban neighborhood security positively influences home rental values as a unit improvement in security leads to N81000.00 ($162.00) increase in rental value per annum.Practical implicationsUrban neighborhood security risk threatens residential property values, creates unintended residential mobility and destabilizes families. Findings from this study point to the facts that security is a key component of urban housing values and developers, and real estate investors must ensure that this component is well factored into property design, construction and valuation.Originality/valueThis is perhaps the first study that uses an objectively derived UNSI to study home rental price dynamics in Nigeria. The study extends knowledge on urban housing price determinants and contributes to literature on the crucial place of security in property management.",4.0,,False,Review,,2023-03-01,d536c9af6b0a1a741a0b9a7b2e9651cf70a61fdf,urbanpropertymarketsandsecurityriskexplaininghowneighborhoodsecurityshapeshousingrentalpricesinojolagosnigeria,aliu,10.1108/pm-09-2022-0070,urbanpropertymarketsandsecurityriskexplaininghowneighborhoodsecurityshapeshousingrentalpricesinojolagosnigeria|2023,1,3060,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || residential property price determinants",2,5,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1111/j.1538-4632.1995.tb00339.x,,Place‐to‐Place Rent Comparisons among Canadian Cities,1995.0,Geographical Analysis,John R. Miron,"This paper estimates average monthly gross rents for comparable rented private‐sector accommodation in Census Metropolitan Areas across Canada. By “gross rent” is meant the typical monthly payment in a CMA for a principal residence by a tenant of given traits under the conditions of a standard lease to a for‐profit landlord in competitive market equilibrium for premises used exclusively for accommodation, in an arm's length transaction, and includes heating and utility expenses where paid separately by the tenant. A single average gross rent is estimated for each city for dwellings and households, each with specified characteristics. This paper reviews the application of hedonic price models for this purpose, identifies shortcomings of current research in the field, and suggests a new form for such models. This new model is estimated using microlevel data from a sample of renter households taken across the twenty‐three largest urban areas in Canada. A classical version of the model is fitted first to renter data for individual cities, and its limitations assessed. A measure of “relative supply” is introduced to help explain city to city variations in the hedonic prices. Then an all‐city new model is estimated which incorporates relative supplies. This all‐city model is then used to derive place‐to‐place rent comparisons.",4.0,,,journal-article,,,doi_10_1111_j_1538_4632_1995_tb00339_x,placetoplacerentcomparisonsamongcanadiancities,miron,10.1111/j.1538-4632.1995.tb00339.x,placetoplacerentcomparisonsamongcanadiancities|1995,1,2342,1,apartment rent hedonic,2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,0d896ad4da506c7ad6d29f952bc02cbf2078422f,10.1108/ijhma-02-2021-0016,,The influence of privately initiated rezoning on housing prices,2021.0,,J. Robert; Velma Zahirovic-Herbert," Purpose The purpose of this paper is to evaluate the parcel-level impacts of the zoning change. Design/methodology/approach Using hedonic regression and propensity score matching econometric techniques, this paper analyses single-family housing prices within Fulton County Georgia. This paper combines data on the parcel-level zoning changes with nearby housing sales transactions to study the potential externality effects because of rezoning induced by private parties. Findings The paper finds evidence of heterogeneous rezoning effects, depending upon the type of rezoning conducted. At a distance within 0.75 miles, housing prices appreciate by 8.31% when nearby privately initiated rezoning maintains the residential character of a neighbourhood. However, housing prices decline by 21.26% when residential housing zones are converted to non-residential housing zones. The negative influences of rezoning residential use to non-residential uses decline as distance increases. Originality/value The analysis provides quantitative information on the impact of rezoning on residential property prices. Planning officials and developers can use these results to assuage homeowner fears of potential negative housing price effects associated with rezoning. ",4.0,,False,,Business,2021-06-10,0d896ad4da506c7ad6d29f952bc02cbf2078422f,theinfluenceofprivatelyinitiatedrezoningonhousingprices,robert,10.1108/ijhma-02-2021-0016,theinfluenceofprivatelyinitiatedrezoningonhousingprices|2021,1,2264,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,2,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",28404b5b33c2442fe37f7c23dcedd9a617e649d0,10.1080/08941920.2023.2254730,,Recreation Area Characteristics and Their Impact on Property Values within Florida’s Wekiva River System,2023.0,Society & Natural Resources,Kotryna Klizentyte; Andres Susaeta; Damian C. Adams; Taylor V. Stein,"Abstract Water-based recreation areas provide many benefits to society and the ecosystem services they provide are increasingly being considered in land planning and conservation decisions. We examine the value of the Wekiva River in central Florida using a hedonic valuation model of residential property values. We analyze how structural characteristics of single-family homes and recreation area characteristics, such as fee systems, size of the area, and activities provided, impact property values. We find that publicly owned recreation areas with both land and water-based recreation activities increased property values within 5 miles of the Wekiva River. Proximity to recreation areas marginally increased housing values, and the size of the recreation areas and ecosystem type had no impact on property values. We recommend that recreation managers and land use planners focus efforts on conserving more publicly owned conservation lands that increase recreation access to residents to promote contiguous natural areas.",4.0,,False,JournalArticle,,2023-09-22,28404b5b33c2442fe37f7c23dcedd9a617e649d0,recreationareacharacteristicsandtheirimpactonpropertyvalueswithinfloridaswekivariversystem,klizentyte,10.1080/08941920.2023.2254730,recreationareacharacteristicsandtheirimpactonpropertyvalueswithinfloridaswekivariversystem|2023,1,2029,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic property valuation",2,2,5,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),18be2c2cbd8cba146ad89528e652ddbafc384355,10.1080/09599916.2021.1888774,,Appraising residential property using hierarchical generalised additive models,2021.0,,Dane Bax; Temesgen T. Zewotir; D. North,"ABSTRACT Log linear hedonic models are ubiquitous in econometric real estate research even though functional form assumptions are often not satisfied and the nested structure of homes in suburbs is not captured adequately. This study focuses on appraising different residential property types located throughout South Africa, investigating a flexible approach which does not assume some explicit functional form. The objective of this paper was to fit and compare two hierarchical generalised additive models to 412 500 property listings from 2013 to 2017. A gamma hierarchical model with random intercepts for the suburb provided the best fit and generalisability, while accounting for the spatial dependency in the data. The results show that hierarchical generalised additive models capture complex shapes between listing prices and structural property characteristics, and further reveal that partial pooling is useful to capture between suburb variability.",4.0,,False,,Economics,2021-02-25,18be2c2cbd8cba146ad89528e652ddbafc384355,appraisingresidentialpropertyusinghierarchicalgeneralisedadditivemodels,bax,10.1080/09599916.2021.1888774,appraisingresidentialpropertyusinghierarchicalgeneralisedadditivemodels|2021,1,1961,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,2,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1017/s1355770x0300202,,Hedonic pricing in Windhoek townships,2003.0,Environment and Development Economics,Michael Nokokure Humavindu; Jesper Stage,"This study applies the hedonic pricing model to property sales in the township areas in Windhoek, the capital city of Namibia, where municipal authorities have pursued a programme of selling plots of land to settlers in order to encourage them into a formalized economic situation. We find that, apart from house quality, access to the central business district, access to marketplaces and access to transportation, environmental quality also has a large impact on property prices. Properties located close to a garbage dump sell at considerable discounts, while properties located close to a combined conservation and recreation area sell at premium prices. The results thus suggest that the hedonic pricing method can be useful for studying townships in developing countries, and that this can help to clarify the importance of environmental factors which are otherwise frequently neglected in town planning for township settlements.",4.0,,,journal-article,,,doi_10_1017_s1355770x0300202,hedonicpricinginwindhoektownships,humavindu,10.1017/s1355770x0300202,hedonicpricinginwindhoektownships|2003,1,1935,2,apartment rent hedonic || hedonic house price,2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d77c6f67d0521f20e3fb61913dc38e5348ef0f70,10.1080/00330124.2022.2048866,,Spatial Hedonic Regression Analysis of the Impact of Cell Towers on Las Vegas Real Estate Market,2022.0,Professional Geographer,Awanti Acharya; S. Basu; Dean M. Hanink,"Cell towers are considered a disamenity due to apprehensions about health issues and visual pollution. This study examines the impact of cell towers on house prices in Las Vegas (2014–2017) using three spatial hedonic regression models. The distance-based model shows that house prices appreciate by approximately 2.02 percent with a 1-km increase in distance from the tower. The inverse distance–based model is used to determine the nonlinear effect of distance from a tower on house prices. Finally, the impact of visibility on house prices is considered but is not statistically significant. A negative impact of tower height on house prices is also observed. Furthermore, it is identified that camouflaged tree towers have a relatively less negative impact on house prices compared to conventional cell towers.",4.0,,False,,,2022-04-25,d77c6f67d0521f20e3fb61913dc38e5348ef0f70,spatialhedonicregressionanalysisoftheimpactofcelltowersonlasvegasrealestatemarket,acharya,10.1080/00330124.2022.2048866,spatialhedonicregressionanalysisoftheimpactofcelltowersonlasvegasrealestatemarket|2022,1,1814,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || real estate hedonic regression",2,1,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ac3ac03b9b7b5a1369098534befd900449d1ea14,10.3368/le.95.4.515,,The Implicit Price of Food Access in an Urban Area: Evidence from Milwaukee Property Markets,2019.0,Land Economics,Phillip Warsaw; D. Phaneuf,"An ongoing debate in the food security literature focuses on a definition of food security that is operational and generalizable. In this paper, we suggest that food security is related to the implicit prices paid by households in urban residential markets for food access. Using the hedonic property value model, we examine the distribution of implicit prices across socioeconomic characteristics for access to local food sources. Using land use and residential transaction data from Milwaukee, Wisconsin, we find evidence of higher implicit access prices in neighborhoods with a larger proportion of African American and Latino American households. (JEL Q18, R21)",4.0,,False,,Business,2019-09-30,ac3ac03b9b7b5a1369098534befd900449d1ea14,theimplicitpriceoffoodaccessinanurbanareaevidencefrommilwaukeepropertymarkets,warsaw,10.3368/le.95.4.515,theimplicitpriceoffoodaccessinanurbanareaevidencefrommilwaukeepropertymarkets|2019,1,1665,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",2,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",a40c543486e003823667bfd2166dc015a8c5d26a,10.1007/s12076-021-00298-x,,"A spatial hedonic approach for modeling the relationship between quality of urban life and housing prices, case study: Alexandria city, Egypt",2022.0,Letters in spatial and resource sciences,Mohamed A. A. Abdrabo; M. Hassaan; Rofida G. Abdelwahab,,4.0,,False,,,2022-01-17,a40c543486e003823667bfd2166dc015a8c5d26a,aspatialhedonicapproachformodelingtherelationshipbetweenqualityofurbanlifeandhousingpricescasestudyalexandriacityegypt,abdrabo,10.1007/s12076-021-00298-x,aspatialhedonicapproachformodelingtherelationshipbetweenqualityofurbanlifeandhousingpricescasestudyalexandriacityegypt|2022,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",3f35556a906b3e58f87a26e3a08bbb83c7f99fa4,10.1016/j.apgeog.2024.103483,,"Measuring the impact of the urban microclimate on housing price using the spatial hedonic pricing method: The case study of Mueller, Austin, TX",2025.0,Applied Geography,Se Woong Kim; Robert D. Brown,,4.0,,False,JournalArticle,,2025-01-01,3f35556a906b3e58f87a26e3a08bbb83c7f99fa4,measuringtheimpactoftheurbanmicroclimateonhousingpriceusingthespatialhedonicpricingmethodthecasestudyofmuelleraustintx,kim,10.1016/j.apgeog.2024.103483,measuringtheimpactoftheurbanmicroclimateonhousingpriceusingthespatialhedonicpricingmethodthecasestudyofmuelleraustintx|2025,1,1000,2,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,807335511e2617caa1df375cccf38a79763acb45,10.24957/hsr.2020.28.2.113,,A Study on Gender Difference of Housing Costs: Focusing on Single-Person Household,2020.0,Korean Association for Housing Policy Studies,Mihwa Lim,"In terms of housing selection, women prefer to live in a house that is comfortable, convenient, and safe from crimes despite their lower income compared to men. Considering this, the housing cost of single-woman households may be higher than that of single-man households. This study estimated and compared the user costs and rental fees as the self-resident cost, using the hedonic price model for housing in single-woman and single-man households. As a result, it was found that the user cost of single-woman households is approximately 20,000 won higher than that of single-man households while the user cost per area (m2) of single-woman households is approximately 3,000 won higher than that of single-man households. Based on the age and gender analysis of the user cost per area (m2), the housing cost ratio of single-woman households is higher than that of single-man households. In the case of rental fee per area (m2), the housing cost ratio of single-woman households is 1.0 or higher than that of single-man households in the 20s, 30s, and 40s age groups. In conclusion, the housing cost ratio of single-woman households is 1.0 or higher than that of single-man households in terms of user cost and rental fee. As a result, single-woman households pay a higher housing cost than single-man households, but this ratio decreases as the age increases.",4.0,,False,,Psychology,2020-05-30,807335511e2617caa1df375cccf38a79763acb45,astudyongenderdifferenceofhousingcostsfocusingonsinglepersonhousehold,lim,10.24957/hsr.2020.28.2.113,astudyongenderdifferenceofhousingcostsfocusingonsinglepersonhousehold|2020,1,2360,2,hedonic house price || hedonic housing prices,2,1,3,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,a27a2e4f9f6ad1d9d05ad188b0a5e20f75a15ebf,10.1093/oxfordhb/9780190279707.013.30,,Does Crime Impact Real Estate Prices?,2018.0,,V. Ceccato; Mats Wilhelmsson,"This chapter examines the impact of crime and accessibility on housing prices. In particular, it assesses the effect of residential burglary on apartment prices in Stockholm, building on previous research that indicated residential burglary—not violence or vandalism—had the greatest effect on apartment prices in the Swedish capital. A review of the literature on hedonic modeling covering more than three decades shows that, despite different modeling strategies, these studies consistently find evidence that crime affects housing prices. Two measures of accessibility are used: a “global” distance decay from Stockholm city center and a “local” measure of accessibility to work expressed as travel costs. Results for all of Stockholm County confirm previous findings once limited to Stockholm municipality only, that residential burglary reduces apartment prices. However, such an effect varies across space and levels of accessibility.",4.0,,False,Review,Economics,2018-09-14,a27a2e4f9f6ad1d9d05ad188b0a5e20f75a15ebf,doescrimeimpactrealestateprices,ceccato,10.1093/oxfordhb/9780190279707.013.30,doescrimeimpactrealestateprices|2018,1,1940,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices",2,4,3,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic property valuation,117ece8d9f6f27eb3420fceece83b4d2ea4c9e8c,10.1111/jors.12381,,Economic Valuation of a Rise in Ambient Radiation Risks: Hedonic Pricing Evidence from the Accident in Fukushima,2018.0,Social Science Research Network,A. Munro,"Using a combined data set of radiation levels and property prices from Fukushima and Miyagi prefectures that runs from 2009 to early 2017, the economic impact of radiation from the Fukushima nuclear accident is assessed. A 1 percent rise in radiation is associated with approximately 0.051 percentage drop in housing prices and though the level of radiation falls substantially after 2011, the estimated elasticity is broadly stable. The associated estimate of VSL (value of a statistical life) is in the region of US$4.5–6.4 million, which suggests no strong element of dread or overestimation of risks is present.",4.0,,False,,Economics,2018-03-08,117ece8d9f6f27eb3420fceece83b4d2ea4c9e8c,economicvaluationofariseinambientradiationriskshedonicpricingevidencefromtheaccidentinfukushima,munro,10.1111/jors.12381,economicvaluationofariseinambientradiationriskshedonicpricingevidencefromtheaccidentinfukushima|2018,1,1615,2,hedonic housing prices || hedonic property valuation,2,2,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5d2c053894e7bd8f9d2bd969084ad2f043953e14,10.4028/www.scientific.net/amm.587-589.2285,,The Analysis of Second-Hand Housing Price Influencing Factors Based on Hedonic Model and WEB Information,2014.0,,Hui-Rong Li; Yu-Mei Sun,,4.0,,False,,Economics,2014-07-01,5d2c053894e7bd8f9d2bd969084ad2f043953e14,theanalysisofsecondhandhousingpriceinfluencingfactorsbasedonhedonicmodelandwebinformation,li,10.4028/www.scientific.net/amm.587-589.2285,theanalysisofsecondhandhousingpriceinfluencingfactorsbasedonhedonicmodelandwebinformation|2014,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",b68ffa9feb59bc3834a29453f69f666e5fe24968,10.22004/ag.econ.205664,,Validating Spatial Hedonic Modeling with a Behavioral Approach: Measuring the Impact of Water Quality Degradation on Coastal Housing Markets,2015.0,,R. Mahesh,,4.0,,False,,Economics,,b68ffa9feb59bc3834a29453f69f666e5fe24968,validatingspatialhedonicmodelingwithabehavioralapproachmeasuringtheimpactofwaterqualitydegradationoncoastalhousingmarkets,mahesh,10.22004/ag.econ.205664,validatingspatialhedonicmodelingwithabehavioralapproachmeasuringtheimpactofwaterqualitydegradationoncoastalhousingmarkets|2015,1,1000,4,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,0,1,1,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,b97fa02fdc8b48e3425f91802b06711e1a40bb8c,10.5075/epfl-thesis-4186,,Assessing environmental amenities and testing for discrimination in housing markets using hedonic price models,2008.0,,C. Schaerer,,4.0,,False,,Economics,,b97fa02fdc8b48e3425f91802b06711e1a40bb8c,assessingenvironmentalamenitiesandtestingfordiscriminationinhousingmarketsusinghedonicpricemodels,schaerer,10.5075/epfl-thesis-4186,assessingenvironmentalamenitiesandtestingfordiscriminationinhousingmarketsusinghedonicpricemodels|2008,1,1000,1,hedonic housing prices,2,0,1,1,0,5,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cdb92e2c78cfeb4d2505dc1e427f50fa693febdc,10.1080/23754931.2022.2100225,,"On the Geographic Balance of Risk and Amenity: Changes in Evacuation Zones and Their Ripples in Housing Prices in Pinellas County, FL",2022.0,Papers in applied geography,John D. Morgan; Thomas Douthat; Li-Hsiang Lin,"Abstract This study considers the balance of amenities and coastal hazards on housing prices between 2000 and 2015. We test whether risk communication via hurricane evacuation zone (a proxy for storm-surge risk and coastal flooding) affects sales price while controlling for relevant property characteristics, beach frontage, flood zones, and proximity to the coast. Additionally, we employ a difference-in-difference technique to test whether two highly publicized updates to evacuation zone maps, which changed the status of many homes in the county, led to price penalties for homes. We focus our study on areas of the new evacuation zone maps that reclassified parcels into zones associated with higher coastal surge exposure. This approach allows us to isolate the effects of risk communication about a property independent of changes to its insurance premiums or actual hazard exposure. We find that amenities are associated with significant bonuses and hazard variables correlated with price penalties, and homes in areas reclassified “up” in hazard zone classification have lost value. Our findings shed light on how information changes about hazards may affect coastal real estate markets as concerns about coastal hazards and exposure to storm-related surges increase along with climate change.",4.0,,False,,,2022-07-10,cdb92e2c78cfeb4d2505dc1e427f50fa693febdc,onthegeographicbalanceofriskandamenitychangesinevacuationzonesandtheirripplesinhousingpricesinpinellascountyfl,morgan,10.1080/23754931.2022.2100225,onthegeographicbalanceofriskandamenitychangesinevacuationzonesandtheirripplesinhousingpricesinpinellascountyfl|2022,1,2304,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",7e43914d96cf593ea92c851c78640c4208f1f3e5,10.3233/jifs-210917,,Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model,2021.0,Journal of Intelligent & Fuzzy Systems,Chao‐nan Liu; Jie Lu; Weijin Fu; Zhuoyi Zhou,"How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.",4.0,,False,JournalArticle,Computer Science,2021-10-11,7e43914d96cf593ea92c851c78640c4208f1f3e5,secondhandhousingbatchevaluationmodelofzhengzhoucitybasedonbigdataandmgwrmodel,liu,10.3233/jifs-210917,secondhandhousingbatchevaluationmodelofzhengzhoucitybasedonbigdataandmgwrmodel|2021,1,2161,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bd496e6b12ae4d851731821ee3fdf3218880e6ef,10.1080/10511482.2020.1821747,,"Effects of Proximity to Multifamily Housing on Property Values in Little Rock, Arkansas, 2000–2016",2020.0,Housing Policy Debate,Michael J. Craw,"ABSTRACT A common form of not-in-my-backyard (NIMBY) activism is resistance to multifamily housing. Although NIMBY activism often targets both market-rate and subsidized multifamily development, studies of the effects of multifamily housing primarily focus on subsidized rental apartments. This study addresses this gap by analyzing the effects of condominiums and market-rate apartments as well as subsidized rental housing. Taking Little Rock, Arkansas, as a case study, this research uses a difference-in-differences approach to measure the effects of five types of multifamily housing on nearby single-family home sales prices: condominiums, market-rate rental apartments, subsidized rental apartments, senior and special needs apartments, and other multifamily housing (such as dormitories). The results suggest that most forms of multifamily housing have either no effect or a positive effect on sales prices for single-family homes within 2,000 feet of a new multifamily housing development.",4.0,,False,,Business,2020-10-21,bd496e6b12ae4d851731821ee3fdf3218880e6ef,effectsofproximitytomultifamilyhousingonpropertyvaluesinlittlerockarkansas20002016,craw,10.1080/10511482.2020.1821747,effectsofproximitytomultifamilyhousingonpropertyvaluesinlittlerockarkansas20002016|2020,1,1998,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,9,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,residential property price determinants,882ebab87d193b9d70daabc4167fe747b61ea37d,10.2139/ssrn.3435126,,Il mercato degli affitti nelle città italiane: un'analisi basata sugli annunci online (The Rental Market in Italian Cities),2019.0,Social Science Research Network,Michele Loberto,"We analyze the most recent trends in the residential rental market within the Italian provincial capitals based on a new database of rental listings. First, we assess the consistency of online listing data with the main official statistical sources. Then, we examine the characteristics of the listed dwellings, the main market dynamics and the determinants of rental prices. Finally, we show that the physical characteristics of a dwelling affect the probability of it being listed in the rental market or in the property sales market, examining the consequences for the estimation of the rate of return on housing.",4.0,,False,,Business,,882ebab87d193b9d70daabc4167fe747b61ea37d,ilmercatodegliaffittinellecittaitalianeunanalisibasatasugliannuncionlinetherentalmarketinitaliancities,loberto,10.2139/ssrn.3435126,ilmercatodegliaffittinellecittaitalianeunanalisibasatasugliannuncionlinetherentalmarketinitaliancities|2019,1,1616,1,residential property price determinants,0,2,4,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",43a2b10abcf6c2779b386ab058830b95d073f6e6,10.1080/08965803.2020.1810524,,The Effects of Government-Announced Soil Liquefaction Potential on Housing Prices in Reported Areas: A Two-Stage Spatial Quantile Regression Analysis,2020.0,,Chih-Min Liang; Chun-Chang Lee; Jia-Wei Lee; Zheng Yu,"This study examines the effects of a government announcement of soil liquefaction potential on housing prices in the reported areas, and explores the rate at which these prices changed after the an...",4.0,,False,,Economics,2020-09-23,43a2b10abcf6c2779b386ab058830b95d073f6e6,theeffectsofgovernmentannouncedsoilliquefactionpotentialonhousingpricesinreportedareasatwostagespatialquantileregressionanalysis,liang,10.1080/08965803.2020.1810524,theeffectsofgovernmentannouncedsoilliquefactionpotentialonhousingpricesinreportedareasatwostagespatialquantileregressionanalysis|2020,1,1200,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b3902e82ba9f679ee0c8dd3886e15951dfd7a920,10.22300/1949-8276.11.1.2,,The Impact of Shuttered Golf Courses on Property Values,2019.0,The Journal of Sustainable Real Estate,Stephanie R. Yates; Lary B. Cowart,"We measure the impact of a golf course as a residential amenity on surrounding home values in several communities in Shelby County, Alabama. We compare the values of homes in golf course communitie...",4.0,,False,,Geography,2019-01-01,b3902e82ba9f679ee0c8dd3886e15951dfd7a920,theimpactofshutteredgolfcoursesonpropertyvalues,yates,10.22300/1949-8276.11.1.2,theimpactofshutteredgolfcoursesonpropertyvalues|2019,1,1200,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.3923/sscience.2010.520.524,,Hedonic Analysis of Residential Housing Market in a Third World City: A Preliminary Investigation,2010.0,The Social Sciences,B.K. Ajide; I.O. Kareem,,4.0,,,journal-article,,,doi_10_3923_sscience_2010_520_524,hedonicanalysisofresidentialhousingmarketinathirdworldcityapreliminaryinvestigation,ajide,10.3923/sscience.2010.520.524,hedonicanalysisofresidentialhousingmarketinathirdworldcityapreliminaryinvestigation|2010,1,1000,1,apartment rent hedonic,2,0,2,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1023/a:1003158231050,,The Auction Price of Apartments in Moscow: Hedonic Estimation in Disequilibrium1,1998.0,Economics of Planning,Yasushi Toda; Nadezhda Nozdrina; G.S. Maddala,,4.0,,,journal-article,,,doi_10_1023_a_1003158231050,theauctionpriceofapartmentsinmoscowhedonicestimationindisequilibrium1,toda,10.1023/a:1003158231050,theauctionpriceofapartmentsinmoscowhedonicestimationindisequilibrium1|1998,1,1000,3,apartment rent hedonic || hedonic housing prices || housing characteristics prices hedonic,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",032506256464cb057d099eda9047dfcd294dd6a4,10.1007/s12061-019-09301-x,,Analyzing Spatial Heterogeneity of Housing Prices Using Large Datasets,2019.0,Applied spatial analysis and policy,Yangyi Wu; Y. Wei; Han-Duo Li,,4.0,,False,,,2019-05-21,032506256464cb057d099eda9047dfcd294dd6a4,analyzingspatialheterogeneityofhousingpricesusinglargedatasets,wu,10.1007/s12061-019-09301-x,analyzingspatialheterogeneityofhousingpricesusinglargedatasets|2019,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_citations,cites:sirmans2006,878656516d92bfdfae8f5fc2f249bfe52ff2eaa7,10.1016/j.jnc.2025.126916,,Measuring the premium effect of marine ecological restoration on housing prices: A quasi-experimental design based on real estate transaction data,2025.0,Journal for Nature Conservation,Yufei Zhou; Jing-Mei Li; Shuqin Li; Qi Chen,,4.0,,False,JournalArticle,,2025-03-01,878656516d92bfdfae8f5fc2f249bfe52ff2eaa7,measuringthepremiumeffectofmarineecologicalrestorationonhousingpricesaquasiexperimentaldesignbasedonrealestatetransactiondata,zhou,10.1016/j.jnc.2025.126916,measuringthepremiumeffectofmarineecologicalrestorationonhousingpricesaquasiexperimentaldesignbasedonrealestatetransactiondata|2025,1,1000,1,cites:sirmans2006,0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",bc0d3b9b1319300b1c1c5d89ea22e63a1415e646,10.1007/s11769-025-1565-7,,How Does Urban Public Transit Accessibility Affect Housing Prices? A Comprehensive Analysis with Geographical Detector Combined and Geographically Weighted Regression,2025.0,Chinese Geographical Science,Jingjing Tang; Hui-Ran Han; Cheng-Feng Yang; Lingyi Xu; Hui Geng; Lei Li,,4.0,,False,JournalArticle,,2025-09-19,bc0d3b9b1319300b1c1c5d89ea22e63a1415e646,howdoesurbanpublictransitaccessibilityaffecthousingpricesacomprehensiveanalysiswithgeographicaldetectorcombinedandgeographicallyweightedregression,tang,10.1007/s11769-025-1565-7,howdoesurbanpublictransitaccessibilityaffecthousingpricesacomprehensiveanalysiswithgeographicaldetectorcombinedandgeographicallyweightedregression|2025,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",cb18deb200f1bb47549ae34dea1616e9a0e4c82a,10.1353/sgo.0.0058,,"Neighborhood Characteristics and Housing Values within the San Juan, MSA, Puerto Rico",2009.0,,José R. Díaz-Garayúa,,4.0,,False,,Geography,2009-12-04,cb18deb200f1bb47549ae34dea1616e9a0e4c82a,neighborhoodcharacteristicsandhousingvalueswithinthesanjuanmsapuertorico,díaz-garayúa,10.1353/sgo.0.0058,neighborhoodcharacteristicsandhousingvalueswithinthesanjuanmsapuertorico|2009,1,1000,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,adf7d808dcf743b7c5f42b9115bc4dd74faf398a,10.1080/10835547.2018.12092140,,Housing Depreciation Revisited: Hedonic Price Modeling Versus Assessor Estimates,2018.0,,S. Shultz,,4.0,,False,,Economics,2018-12-06,adf7d808dcf743b7c5f42b9115bc4dd74faf398a,housingdepreciationrevisitedhedonicpricemodelingversusassessorestimates,shultz,10.1080/10835547.2018.12092140,housingdepreciationrevisitedhedonicpricemodelingversusassessorestimates|2018,1,1000,1,hedonic housing prices,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",df82821de51ea5db43e76c5cff0d2647c8370a74,10.1108/ijhma-03-2025-0054,,Air pollution and housing market valuation: a spatial hedonic pricing approach to welfare loss estimation,2025.0,International Journal of Housing Markets and Analysis,Warawut Ruankham," Purpose This study aims to explore three key questions: How does air pollution affect real estate prices across markets, considering income, demographics and urban density? How do spatial econometrics improve air pollution impact estimation compared to traditional hedonic pricing models (HPM)? What are the welfare implications of air pollution, particularly in terms of compensating surplus (CS), and how can a multistage HPM framework enhance economic loss assessment? By addressing these, the research aims to refine property valuation, inform urban policy and support sustainable housing and environmental regulations. Design/methodology/approach This study uses a multistage HPM combined with spatial econometric techniques (Spatial Lag Model [SLM] and Spatial Error Model [SEM]) to estimate the impact of air pollution on housing prices in Thailand. To address endogeneity, a two-stage least squares (2SLS) approach is implemented using distance to factory and rainfall as instrumental variables. The marginal implicit price (MIP) and CS are derived to quantify economic losses. The Hausman and Hansen J-Tests validate model robustness. This integrated framework provides a spatially aware valuation of air pollution’s economic burden, offering policy-relevant insights for sustainable urban and housing market management. Findings This study confirms that air pollution significantly reduces housing prices, with ordinary least squares overestimating the MIP due to ignored spatial dependencies. Spatial econometric models (SLM and SEM) and 2SLS provide more precise estimates, addressing endogeneity and valuation biases. The average MIP of air pollution per house is 39,838 THB ($1,138), while the CS averages 568,078 THB ($16,578) per household, highlighting substantial economic losses. The Hausman and Hansen J-Tests validate the robustness of the instrumental variables. These findings emphasize the importance of spatially aware valuation techniques in real estate pricing and environmental policymaking. Research limitations/implications This study is limited by data availability and potential measurement errors in air pollution and housing price records, which may introduce estimation biases in Thailand. While spatial econometric models (SLM and SEM) and 2SLS help address spatial dependence and endogeneity, some unobserved market dynamics could still affect results. In addition, findings are specific to Thailand, limiting broader applicability. Future research should incorporate higher-resolution pollution data, alternative instruments, and longitudinal analysis to improve accuracy. Practical implications Policymakers should encourage real estate developers to adopt green building standards and integrate air filtration systems into urban housing projects. Localized interventions, such as pollution taxation, congestion pricing and differentiated property tax schemes based on environmental quality, can help internalize the external costs of pollution and promote more sustainable urban development. Social implications Integrate environmental justice principles into urban development to protect low-income communities that often reside in high-pollution zones. Incentivize development of sustainable housing projects with better air quality and urban greenery. Originality/value This study contributes to the literature by integrating spatial econometric models (SLM and SEM) and 2SLS to address spatial dependence and endogeneity in air pollution valuation. Unlike traditional HPMs, this approach provides less bias estimates of the MIP and CS, quantifying the economic burden of air pollution on housing markets. The findings offer policy-relevant insights for urban planning, environmental regulation and real estate valuation. ",3.0,,False,JournalArticle,,2025-04-25,df82821de51ea5db43e76c5cff0d2647c8370a74,airpollutionandhousingmarketvaluationaspatialhedonicpricingapproachtowelfarelossestimation,ruankham,10.1108/ijhma-03-2025-0054,airpollutionandhousingmarketvaluationaspatialhedonicpricingapproachtowelfarelossestimation|2025,1,4778,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || hedonic property valuation || spatial hedonic model housing",2,3,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",139a479d89e00ab1b758c098bc0ce8ec92d8f75d,10.1108/ijhma-09-2023-0124,,Housing affordability and housing demand assessment for urban poor in India using the hedonic model,2023.0,International Journal of Housing Markets and Analysis,P. Rao; Arindam Biswas," Purpose This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing affordability by considering various housing and household-related variables. This study focuses on the impoverished urban population, as they experience the most severe housing scarcity. This study’s primary objective is to understand the demand dynamics within the market comprehensively. An understanding of housing demand can be achieved through an examination of its characteristics and components. Individuals consider the implicit values associated with various components when deciding to purchase or rent a home. The components and characteristics have been obtained from variables relating to housing and households. Design/methodology/approach A socioeconomic survey was conducted for 450 households from slums in Lucknow city. Two-stage regression models were developed for this research paper. A hedonic price index was prepared for the first model to understand the relationship between housing expenditure and various housing characteristics. The housing characteristics considered for the hedonic model are dwelling unit size, typology, condition, amenities and infrastructure. In the second stage, a regression model is created between household characteristics. The household characteristics considered for the demand estimation model are household size, age, education, social category, income, nonhousing expenditure, migration and overcrowding. Findings Based on the findings of regression model results, it is evident that the hedonic model is an effective tool for the estimation of housing affordability and housing demand for urban poor. Various housing and household-related variables affect housing expenditure positively or negatively. The two-stage hedonic regression model can define willingness to pay for a particular set of housing with various attributes of a particular household. The results show the significance of dwelling unit size, quality and amenities (R2 > 0.9, p < 0.05) for rent/imputed rent. The demand function shows that income has a direct effect, whereas other variables have mixed effects. Research limitations/implications This study is case-specific and uses a data set generated from a primary survey. Although household surveys for a large sample size are resource-intensive exercises, they provide an opportunity to exploit microdata for a better understanding of the complex housing situation in slums. Practical implications All the stakeholders can use the findings to create an effective housing policy. The variables that are statistically significant and have a positive relationship with housing costs should be deliberated upon to provide the basic standard of living for the urban poor. The formulation of policies should duly include the housing preferences of the economically disadvantaged population residing in slum areas. Originality/value This paper uses primary survey data (collected by the authors) to assess housing affordability for the urban poor of Lucknow city. It makes the results of the study credible and useful for further applications. ",3.0,,False,JournalArticle;Review,,2023-12-29,139a479d89e00ab1b758c098bc0ce8ec92d8f75d,housingaffordabilityandhousingdemandassessmentforurbanpoorinindiausingthehedonicmodel,rao,10.1108/ijhma-09-2023-0124,housingaffordabilityandhousingdemandassessmentforurbanpoorinindiausingthehedonicmodel|2023,1,4210,6,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || housing characteristics prices hedonic || implicit prices housing attributes || rent hedonic regression housing || willingness to pay housing attributes hedonic",2,1,4,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,dd6360aad0a34ea8efeb25ad432bafeb3bda79e5,10.15396/eres2019_151,,The impact of an urban toll-ring on housing prices,2019.0,26th Annual European Real Estate Society Conference,A. Emblem; Theis Theisen,"Over the last decades, there has been considerable interest in the use of tolls on cars driving into towns. Not only has this been a hot political topic; this issue has also attracted substantial attention from economists. The focus in most of the economics literature has been on how tolls impact traffic volumes, and on how tolls may be used to reduce negative externalities of car use. Less attention has been devoted to how property values and house prices inside and outside toll rings may be affected. From the urban economics literature, it is however well known, that local commuters’ cost of accessing centrally located amenities and work-places will be discounted into house prices. The magnitude of this effect will be contingent on the extent to which tolls also reduce congestion, external effects related to noise, etc. Moreover, since the imposition of a toll ring will alter the long-run spatial equilibrium of a town and the surrounding area, it will set into motion processes of sorting and re-location of households, and possibly also a re-location of some work-places. Consequently, the effects of tolls on house prices may be quite different in the short-run and long-run.In this paper we aim to determine the long-run impact of a toll ring on house prices. In order to assess how much a household initially residing outside the toll ring may gain from relocating to a dwelling within the toll ring we construct a simple household model for choice of location. In the empirical part of the paper we exploit a sample of about 15000 dwellings sold during an eight-year period in the Norwegian town of Kristiansand. We estimate various hedonic house price functions that account for not only whether the house lies within the toll ring or not, but also the precise location of houses and the distance from houses to important amenities. Accounting for other factors beside that of toll, that may impact the sales price of a house, is important in order to identify the impact of the toll ring on house prices. Our findings indicate that introducing a toll ring increases the price of houses located inside this ring with 3 percent. We expect that our findings will be useful in the discussion on establishing toll rings around towns, and on road pricing in general. These issues are particularly relevant for the Norwegian context, where tolls recently have become increasingly more important, not only as means to reduce congestion and other negative externalities, but also as a part of financing the construction of new roads.",3.0,,False,Conference,Business,,dd6360aad0a34ea8efeb25ad432bafeb3bda79e5,theimpactofanurbantollringonhousingprices,emblem,10.15396/eres2019_151,theimpactofanurbantollringonhousingprices|2019,1,3547,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || spatial hedonic model housing",2,5,4,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,a84f7a323a0ecff8d18c7eec8b3d892b7f33fd14,10.1108/ijhma-03-2022-0042,,Hedonic housing values in a transition economy: the case of Tashkent,2022.0,International Journal of Housing Markets and Analysis,Onur Ozsoy; Muzaffarjon Ahunov," Purpose This paper aims to scrutinize the value of houses explained by their location in Central Asia’s largest city – Tashkent, the capital of Uzbekistan. The city was completely rebuilt after a devastating earthquake of 1961 and since the country was under the centrally planned regime, houses followed a common standard and style. This allows minimizing the error from omitting possible intrinsic characteristics in a hedonic model, while measuring the impact of location on housing values. Design/methodology/approach This study uses unique data on housing values and characteristics harvested from classified adverts. This study deliberately focused on data between 2013 and 2016, when Uzbekistan was characterized as hesitant in economic reforms. Therefore, residential buildings constructed during the Former Soviet Union dominated all parts of the city in the analysis period. The results are based on the hedonic pricing model. Findings The results suggest that locational characteristics are relatively more important than intrinsic characteristics. This study finds that proximity to the city center and access to subway stations significantly increase housing values. The opposite is true for the case of proximity to a wastewater treatment plant. Surprisingly, on the contrary to the authors’ expectations access to a park has no economically significant impact on housing values. The results, therefore, call for an integrated approach for city planning. Originality/value The first and most important contribution of this study to the field of housing economics is that it is the first study done for Uzbekistan, the most populated Central Asian country, and it is a case study for other Central Asian countries. The second contribution of the study is that, after a long and meticulous study, the determinants of housing prices were analyzed by taking into account all possible intrinsic and extrinsic properties of houses with the similar characteristics, over a unique data set created from approximately 18,000 observations from classified pages of a major newspaper published every Thursday in Tashkent City. The final contribution of the study is that it offers new perspectives and policy recommendations to the housing market actors in Uzbekistan. ",3.0,,False,,,2022-06-02,a84f7a323a0ecff8d18c7eec8b3d892b7f33fd14,hedonichousingvaluesinatransitioneconomythecaseoftashkent,ozsoy,10.1108/ijhma-03-2022-0042,hedonichousingvaluesinatransitioneconomythecaseoftashkent|2022,1,3278,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || house prices proximity hedonic || housing characteristics prices hedonic || residential property price determinants",2,2,4,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,b34f2af5cff586af9757e0b66b7191bb692373e4,10.55463/hkjss.issn.1021-3619.64.29,,"Determinants of New Condominium Prices near MRT Orange Line Stations: Case study of Estimating Housing Affordability in Bangkok, Thailand",2025.0,Hong Kong Journal of Social Sciences,Pornraht Pongprasert,"This research aims to analyze the factors influencing the pricing of condominium projects along the Mass Rapid Transit (MRT) Orange Line, an electric train expected to start operations in 2025. As the main heavy rail line connecting the Eastern Bangkok suburbs with the city’s New Central Business District (New CBD), the Orange Line has significant implications for urban development. To promote land development opportunities, Bangkok’s city plan has undergone changes along the Orange Line corridor, particularly on the Ramkhamhaeng Road, which is anticipated to drive up land prices.This study examines how condominium prices may fluctuate when various factors are considered, such as proximity to MRT stations, main roads, expressway access points, private facilities, operator reliability, and the cost of common areas. The research equips real estate developers with a comprehensive understanding of these determinants to guide project development and pricing strategies aligned with market conditions. Condominium buyers can leverage accurate pricing information to make informed decisions, while real estate investors can incorporate these insights into their investment strategies. Additionally, the Revenue Department could use the data to inform land tax policies. The hedonic price model was used to determine the factors influencing the prices of condominiums along the Orange Line. The research samples are 28 brand-new condominium properties that are located only one kilometer away from the MRT stations of the Orange Line, and most of them are new condominiums that were constructed in less than 5 years. A total of 17 factors, grouped into three categories—locational, physical, and neighborhood characteristics—were evaluated using regression analysis. The findings reveal that three key factors significantly impact the prices of condominium units: floor level, distance from the train station, and distance from department stores.",3.0,,False,JournalArticle,,2025-02-06,b34f2af5cff586af9757e0b66b7191bb692373e4,determinantsofnewcondominiumpricesnearmrtorangelinestationscasestudyofestimatinghousingaffordabilityinbangkokthailand,pongprasert,10.55463/hkjss.issn.1021-3619.64.29,determinantsofnewcondominiumpricesnearmrtorangelinestationscasestudyofestimatinghousingaffordabilityinbangkokthailand|2025,1,2952,4,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic || real estate hedonic regression,2,1,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2001190078,10.1109/icmse.2006.314131,,"Impacts of Land Supply Factors on Housing Price: An Hedonic Price Model on Chengdu, China",2006.0,,Zheng Li; Bing Yu,"The relationship between housing price and land price is one of most important issues in Mainland of China, which is concerned by developers, consumers and government. However, this issue is often discussed on the theoretical level. This paper suggests an alternative and meaningful model on testing the relationship between housing price and land supply factors. The study is based on land parcels' information and relevant housing developments information of Chengdu, the capital of Sicuan Province, from 1999 to 2004. This paper choose an basic angle to study the relationship between housing price and land factors that are included in parcel area, land lease mode, land price etc. The Hedonic price model is applied in this paper to measure the implicit price of different factors of land. The empirical results present that: (1) If other factors keep stable, the elasticity of land price to housing price is very small. This means that housing price is not impacted by land cost very much; (2) Land supply mode has very significant impact on housing price. Paper compares three kinds of mode. They are agreement supply (mode 1), ownership transfer mode (mode 2) and public auction (mode 3). Average housing price of mode 2 is 8.7% higher than that of mode 1. And, average housing price of mode 3 is 15.8% higher than that of mode 1; (3) Lot area has positive impact on housing price. If other factors keep stable, For example, the average housing price on the land over 100,000 m2is 13.4% higher than that on the land below 10,000 m2. Followed by discussion of the findings of empirical study on the impacts of the land supply factors on housing price in Chengdu, some proposals are given as follow. The conclusions and proposals from this paper should also be applicable to other places of Mainland China",3.0,,False,conference-paper,,2006-01-01,https://openalex.org/W2001190078,impactsoflandsupplyfactorsonhousingpriceanhedonicpricemodelonchengduchina,li,10.1109/icmse.2006.314131,impactsoflandsupplyfactorsonhousingpriceanhedonicpricemodelonchengduchina|2006,1,2811,3,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || apartment rent hedonic || hedonic housing prices",2,2,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,real estate hedonic regression,48eac7bf57c93dd9c75ad616d67bde640db7b527,10.1080/16184742.2025.2494829,,A monetary valuation of hosting a sport mega-event via residential real estate markets in small host communities: evidence from post-event analysis,2025.0,European Sport Management Quarterly,Chan Hyeon Hur; Nicholas M. Watanabe,"ABSTRACT Research question This study explores the monetary value of non-market goods, such as hosting a sport mega-event, by analyzing housing prices in small host communities, particularly those influenced by the PyeongChang 2018 Olympics. The research anticipates that small communities might experience unique effects from social overhead capital, including new rail lines and sport facilities upgraded for the Games. Research methods The study analyzes apartment sales data before and after the Games. It employs Two-Way Fixed Effects (TWFE) regressions, grounded in the hedonic pricing (HP) model and propensity score matching (PSM), to estimate housing price changes in host regions and determine if these changes correlate with the Olympics. Geographic Information System (GIS) network analysis assesses realistic proximity effects, measuring driving time to sport facilities. Results and findings Results indicate the Games had no significant influence on housing prices in host communities during or immediately after the event. However, about a year later, an effect on prices emerged, suggesting a delayed influence. Although GIS analysis suggests potential influences within a 20-minute driving distance from the facilities, no statistically significant effect on housing prices near these facilities was observed. Implications The findings suggest that negative externalities from the Games might have been reflected in the housing market, indicating that residents may prefer sport facilities in their regions but not near their homes. This study introduces a novel, generalizable approach using driving time measures, addressing inconsistent findings on proximity effects across different research contexts and contributing to the literature on monetary valuation in Asia.",3.0,,False,JournalArticle,,2025-05-24,48eac7bf57c93dd9c75ad616d67bde640db7b527,amonetaryvaluationofhostingasportmegaeventviaresidentialrealestatemarketsinsmallhostcommunitiesevidencefromposteventanalysis,hur,10.1080/16184742.2025.2494829,amonetaryvaluationofhostingasportmegaeventviaresidentialrealestatemarketsinsmallhostcommunitiesevidencefromposteventanalysis|2025,1,2788,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || cites:sirmans2006 || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression",2,2,5,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,e5c57008db05022564030cd217e49180e5791cd6,10.1142/s146433322250003x,,Effects of Environmental Quality on Urban Housing Prices: A Hedonic Multiple Linear Regression Model Approach,2022.0,Journal of Environmental Assessment Policy and Management,Mukarati Julius; Abel Sanderson; P. le Roux; Matsvai Simion; Mahlangu Zenzile,"While studies on the valuation of urban environmental amenities have been limited in developing countries, their scarcity is even amplified in the Sub-Saharan Africa and Zimbabwe is no exception. For a city such as Gweru, this particular study is among the few done so far. The aim of this research is to investigate the effects of environmental conditions in a given area on the residential location. We randomly sampled 800 households and the Hedonic Pricing Regression technique was applied to analyse whether environmental quality does have a significant effect on housing prices in the city of Gweru. The set of considered variables was carried out from a location choice survey and Hedonic regression estimators are presented to verify to what extent a relationship between the accessibility conditions, environmental context and the dwelling market values does exist. Based on the estimated model, the results revealed that vegetation, sewage system, water supply and distance from the central business district (CBD) are environmental characteristics that have a bearing on property prices with sewer availability being the most significant variable that affects the price. Residents are willing to trade-off clean air (air pollution) for proximity to the CBD. The findings suggest that properties (houses) are composite goods whose demand is greatly influenced by the health-related attributes they offer. On this basis, recommendations are for the Municipalities to ensure affordable housing that also meets the minimum health standards while relevant departments and land developers should include such amenities to assist and generate the highest value on a parcel of land or house.",3.0,,False,Review,,2022-03-31,e5c57008db05022564030cd217e49180e5791cd6,effectsofenvironmentalqualityonurbanhousingpricesahedonicmultiplelinearregressionmodelapproach,julius,10.1142/s146433322250003x,effectsofenvironmentalqualityonurbanhousingpricesahedonicmultiplelinearregressionmodelapproach|2022,1,2694,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || hedonic price model amenities residential || hedonic property valuation || house prices proximity hedonic || housing characteristics prices hedonic",2,2,5,5,1,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,bd743a3f41d84dd0f683bba87dd78264f1d19462,10.1016/j.jenvman.2023.118064,,"Capitalisation of multiple ecosystems on property prices in Auckland, New Zealand.",2023.0,Journal of Environmental Management,M. Fernandez,"Ecosystems and their associated services have become essential in policymaking as they often have implications for the welfare of those who live nearby. Coupled with the shift toward integrating green infrastructure, ecosystem services, and nature-based solutions in urban and environmental planning, ecosystems may capitalise on property prices. But a potential phenomenon in applications of hedonic prices models is the offsetting between amenities and disamenities values, which may result in insignificant marginal effects. Some strategies to mitigate this phenomenon involve introducing a greater categorisation of open spaces and housing submarkets typology. Hence, this paper explores the capitalisation patterns of 14 terrestrial and aquatic ecosystems across the housing market segments of Auckland, New Zealand's largest city. We used a dataset of 274 thousand sales transactions between January 2011 and December 2019. Models are estimated via OLS and unconditional quantile regressions (UQR) to account for heterogeneity, outliers and heavy-tailed distributions. UQR produce more accurate estimates of the implicit prices and lead to more effective policy recommendations. We find multiple capitalisation patterns across the prices distribution and confirm offsetting effects when ecosystems are not controlled as separate variables. This paper informs land-use decisions involving the preservation or creation of natural spaces in residential areas and improves our understanding of the interaction between their economic valuation and regional diversity.",3.0,,False,JournalArticle,Medicine,2023-05-09,bd743a3f41d84dd0f683bba87dd78264f1d19462,capitalisationofmultipleecosystemsonpropertypricesinaucklandnewzealand,fernandez,10.1016/j.jenvman.2023.118064,capitalisationofmultipleecosystemsonpropertypricesinaucklandnewzealand|2023,1,2568,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || hedonic price model amenities residential || hedonic property valuation",2,1,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f4c2de64517b144279be7b2447709dd6e88ff04d,10.2139/ssrn.3851745,,Are Move-in Ready Properties More Expensive?,2021.0,Social Science Research Network,Iimb Submitter; Arnab Dutta; Richard K. Green; Venkatesh Panchapagesan; M. Venkataraman,"A large share of residential properties on sale in developing countries is under construction. This is partly due to lengthy periods of construction. But purchasing under-construction properties can be costly and risky because of construction delays and the possibility of stalled construction. Hence, property sellers would expect buyers to pay higher prices for completed or move-in ready properties relative to under-construction ones. Prolonged construction times can also lead to higher resale prices. Buyers with an appetite for risk might purchase new under-construction properties at lower costs and resell these at higher prices, either when construction is complete, or when market prices increase, or both. In this paper, we use residential property listings data from the six largest urban agglomerations (UAs) in India - Bangalore, Chennai, Hyderabad, Kolkata, Mumbai, and Delhi - between 2010-2012 and estimate the sellers’ expected premium for move-in ready properties in each UA. Hedonic regressions of listed prices on property attributes reveal that the expected move-in ready premium is statistically significant in Bangalore, Kolkata, Mumbai, and Delhi, and its magnitude varies between 3-14%. At unconditional average property prices, the move-in ready premium is 23-150% of an average household’s income in these UAs. We also find that the expected resale premium is significant and varies between 2-16% in three UAs. The resale premium is partly explained by a higher move-in ready premium among resale properties and possible speculative behavior.",3.0,,False,,,,f4c2de64517b144279be7b2447709dd6e88ff04d,aremoveinreadypropertiesmoreexpensive,submitter,10.2139/ssrn.3851745,aremoveinreadypropertiesmoreexpensive|2021,1,2568,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,287cc849d7afd6569e4e6f1f33325f9c6193a013,10.1177/03611981221084700,,"Effects of Urban Expressways on Housing Prices: A Case Study of Qiushi Highway, Hangzhou, China",2022.0,Transportation Research Record,Ling Zhang; Rui-Guang Shen; Tian-Qi Li; Qing-Feng Zhou,"This paper discusses the effects of the construction of urban expressway on accessibility and further investigates the relationship between changes in accessibility and house prices caused by expressway construction. Taking the Qiushi Highway Phase III project in China as an example, a hedonic price model and difference-in-difference method are used to analyze the impact. The main innovation of the study is how it considers the improvement of the overall traffic conditions brought about by the construction of the expressway. In addition, three distance impedance parameters—namely Euclidean distance, road network distance, and actual transit time—are used to estimate the value of traffic accessibility during the process. The main research conclusions are as follows: (1) The opening of the Qiushi Highway Phase III project can significantly improve the accessibility of the surrounding areas to the city center. Additionally, this improvement can be extended to further sites through the urban expressway network; (2) The results of the difference-in-difference method show that the premium effect of the city expressway on house prices will not be directly generated by the expressway’s geographical proximity to the project. In fact, the expressway will increase accessibility in some related areas and will also have a further positive impact on housing prices; (3) Among the three types of traffic impedance parameters, accessibility calculated on the basis of actual transit time has the strongest connection to house prices.",3.0,,False,JournalArticle,,2022-03-26,287cc849d7afd6569e4e6f1f33325f9c6193a013,effectsofurbanexpresswaysonhousingpricesacasestudyofqiushihighwayhangzhouchina,zhang,10.1177/03611981221084700,effectsofurbanexpresswaysonhousingpricesacasestudyofqiushihighwayhangzhouchina|2022,1,2539,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || house prices proximity hedonic",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",68ff8f5a3696963aea58f292eea5d4fcc901d3df,10.1108/jerer-08-2024-0062,,AI-powered floor plan analysis for feature extraction in automated valuation models,2025.0,Journal of European Real Estate Research,Tor Kringeland; U. J. F. Aarsnes; A. Oust,"PurposeThe purpose of this article is to test whether floor plan image segmentation can be used to improve automated valuation model (AVM) accuracy and whether image segmentation provides the opportunity to assess single aspects of property floor plans.Design/methodology/approachUsing a dataset comprising floor plans of 5,498 apartments sold in Oslo, we estimate balcony sizes by image-segmenting the rooms in floor plans using our machine learning model FloorPlanNet and extracting the size of the balcony from the segmentation. We also extract balcony size using text recognition. Then we utilize two models for AVM estimation – hedonic regression (linear OLS) and the non-linear XGBoost model – before measuring feature importance using SHAP.FindingsOur experiments show that including balcony size as a feature in AVMs enhances model performance. We also find that balcony size has a positive but diminishing impact on property price.Research limitations/implicationsDemonstrating that image segmentation can be used for valuation in AVMs opens up the possibility to value numerous other aspects of dwelling floor plans.Practical implicationsThe use of floor plans in AVMs can provide a more objective valuation of single apartment floor plan aspects, giving architects and developers better insights into how homes should be designed.Originality/valueTo our knowledge, this is the first attempt to extract features for use in AVMs from floor plans.",3.0,,False,JournalArticle,,2025-01-23,68ff8f5a3696963aea58f292eea5d4fcc901d3df,aipoweredfloorplananalysisforfeatureextractioninautomatedvaluationmodels,kringeland,10.1108/jerer-08-2024-0062,aipoweredfloorplananalysisforfeatureextractioninautomatedvaluationmodels|2025,1,2455,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || hedonic property valuation",2,1,4,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",a0d72d354bbbcd8ff18f7c1df1a7890061a4a7ca,10.1080/13504851.2024.2308586,,Too close to the sun: solar farms’ impact on housing prices at subtropical latitudes,2024.0,Applied Economics Letters,Will Georgic; Goran Skosples; David Wolf; Robert J. Gitter,"ABSTRACT While the transition from fossil fuels to renewable energy will benefit many constituencies, recent work suggests that newly activated solar panels may negatively impact nearby housing prices. Although a single mechanism driving these effects has not been causally identified, alternative explanations posit that homes near solar farms lose value either due to glare or the loss of open space amenities and associated rural character. We supplement this literature with an analysis distinguished by a unique sample with the most equatorial location to date and the largest average solar farm (26 MW), allowing for a careful investigation of the role of size and glare in the capitalization of solar farm proximity. Using hedonic analysis, manually traced solar farm footprints, and difference-in-differences identification, we find a 6.86% negative capitalization of solar farm proximity that does not appear to be attributable to glare and is driven by the impacts of very large solar farms. The results are robust to concerns of negative weights associated with bad controls. To limit economic losses associated with the renewable energy transition, solar farms should be strategically located to minimize the number of nearby homes regardless of whether glare is likely to be a concern.",3.0,,False,JournalArticle,,2024-01-31,a0d72d354bbbcd8ff18f7c1df1a7890061a4a7ca,tooclosetothesunsolarfarmsimpactonhousingpricesatsubtropicallatitudes,georgic,10.1080/13504851.2024.2308586,tooclosetothesunsolarfarmsimpactonhousingpricesatsubtropicallatitudes|2024,1,2298,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,1,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",694f758f1694e5b76b38f514f371ca026dda205d,10.1177/27541223241236749,,"Tenure inequality, public facilities and housing prices: Evidence from Shenzhen, China",2024.0,Transactions in Planning and Urban Research,De Tong; Yue Shen; Chuan-Hao Tian; Chen-Yuh Wu,"Public facilities capitalization of formal housing has been widely discussed. However, how residents of informal housing value facilities and how they differ from formal housing remain unclear. We employ hedonic model and the gradient boosting decision trees approach to address the gap by identifying the differences between small property rights housing (SPRH) and commercial housing in Shenzhen, China. Empirical modeling results reveal that the capitalization effects of public facilities in SPRH are significantly lower than in commercial housing. SPRH buyers are significantly less willing than those commercial housing buyers to pay for public facilities, and the gap in willingness to pay between these two types of housing buyers is greater for public middle schools with access related to housing tenure than for metro stations and hospitals. After accounting for how the poor public facility provision of SPRH affects capitalization effects, we further find that informal tenure weakens SPRH capitalization effects. These findings help planners and policymakers better understand the housing market so they can take reasonable initiatives to make urban public facilities more accessible and urban housing systems more affordable, further improving the sustainability of urban areas.",3.0,,False,JournalArticle,,2024-03-01,694f758f1694e5b76b38f514f371ca026dda205d,tenureinequalitypublicfacilitiesandhousingpricesevidencefromshenzhenchina,tong,10.1177/27541223241236749,tenureinequalitypublicfacilitiesandhousingpricesevidencefromshenzhenchina|2024,1,2293,3,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e800e7ce33cb203ba4e10b690cf882b258b31a0e,10.1111/jors.12706,,311 calls and neighborhood attributes: A panel study of housing prices,2024.0,Journal of Regional Science,Sumei Zhang; Yanmei Li; E. Boamah,"This article aims to incorporate social relations into panel hedonic price modeling analyses, examining how the social conditions of neighborhoods are integrated into local housing markets. To achieve this, we utilize Jefferson County, Kentucky, as a case study and decompose publicly available 311 data, which consist of nonemergency concerns and requests reported by residents, into subcategories to proxy the diverse aspects of social relations. Our findings reveal several key insights: (1) 311 data are characterized by their complexity and encompass a wide range of inputs from residents, necessitating careful interpretation; (2) the overall number of 311 reports may offer limited utility in disclosing social relations; (3) specific categories of 311 reports pertaining to public domain issues hold potential as indicators of social relations. Reports on incivilities, for instance, can serve as proxies for conflicting social relations and exhibit negative impacts on housing prices. Conversely, reports on natural deterioration, environmental concerns, and planning issues demonstrate positive impacts on housing prices, suggesting their utility as indicators for social capital within housing studies.",3.0,,False,JournalArticle,,2024-05-08,e800e7ce33cb203ba4e10b690cf882b258b31a0e,311callsandneighborhoodattributesapanelstudyofhousingprices,zhang,10.1111/jors.12706,311callsandneighborhoodattributesapanelstudyofhousingprices|2024,1,2213,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,23f79c90f905cfa296fb72fd34315cc599bd3daa,10.24957/hsr.2020.28.2.37,,Influence of Accessibilities of Multiple Urban Neighborhood Parks and their Interaction Effect on Apartment Prices : Focusing on Dongtan 1 New Town,2020.0,Korean Association for Housing Policy Studies,Han-Ming Gu; Kabsung Kim,"This study empirically analyzed accessibilities of multiple urban neighborhood parks and their interaction effect on apartment prices by using the Hedonic Price Model (HPM) and hierarchical multiple regression. The results are as follows. First, it was shown that the closer the urban neighborhood park was, the higher the apartment prices were. This was consistent with findings of previous researches. Second, there was a significant interaction effect between accessibilities of multiple urban neighborhood parks, and the interaction effect worked in a way that inhibited the main effect on apartment prices. In other words, the farther away from one park an apartment was, the lower the price was, but the farther away from another park, the smaller the decrease was. This suggests that the two parks function as substitute and complementary goods. Therefore, to maximize impact on the welfare of urban residents, it is desirable to set up multiple urban neighborhood parks at locations that are symmetrical in the urban structure, considering the interaction effect of accessibilities to the parks.",3.0,,False,,Economics,2020-05-30,23f79c90f905cfa296fb72fd34315cc599bd3daa,influenceofaccessibilitiesofmultipleurbanneighborhoodparksandtheirinteractioneffectonapartmentpricesfocusingondongtan1newtown,gu,10.24957/hsr.2020.28.2.37,influenceofaccessibilitiesofmultipleurbanneighborhoodparksandtheirinteractioneffectonapartmentpricesfocusingondongtan1newtown|2020,1,2103,1,apartment prices hedonic,2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4b8dce22e1c4393eb7a0d08aac818d5ecd5c5cc4,10.1080/08920753.2021.1967564,,The Effect of Salt Marsh on Residential Property Values,2021.0,Coastal Management,G. Gardner,"Abstract Salt marshes provide a myriad of critical ecosystem services yet have been threatened by urban expansion, sea level rise, and the armoring of coastal property. This study uses a hedonic property price method to evaluate the effects of salt marshes on residential property values in the Eastern Shore of Virginia. Contrary to findings from the wetland literature, results show an insignificant relationship between proximity to salt marsh and residential property values. In contrast, results show a statistically significant increase in property values with increasing proportions of salt marsh within 250 to 500 meters of the parcel. Furthermore, the increase in value is found to be larger for proportions of preserved relative to non-preserved salt marsh in this proximity of the home. The findings of this study can contribute to understanding the spatial patterns for housing premiums stemming from salt marshes. The information can be used to guide coastal management agencies on how to conserve salt marshes in a way that promotes residential property values.",3.0,,False,,Environmental Science,2021-10-01,4b8dce22e1c4393eb7a0d08aac818d5ecd5c5cc4,theeffectofsaltmarshonresidentialpropertyvalues,gardner,10.1080/08920753.2021.1967564,theeffectofsaltmarshonresidentialpropertyvalues|2021,1,2075,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,2,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",69b22c5fccce5fb15438db89f202197ce6ddb546,10.1016/j.jhe.2018.06.009,,The value of community: Evidence from the CARES program,2018.0,Journal of Housing Economics,A. Hanson; Zackary B. Hawley; G. Turnbull,"Renters of multi-unit housing structures report weaker ties to their community than other renters and owner occupants. In response to the lack of a sense of community in multi-unit rental structures, owners and managers of these properties have implemented planned programs to establish stronger community ties for residents. We examine the effect of one such program, the CARES (Community Activities and REsident Services) program, on the rental price of apartments using both a standard hedonic approach as well as matching techniques designed to limit unobservable differences between treated and comparison units. Our results using propensity score matching to identify comparison units, suggest that monthly rents are between 5.7 and 9.3 percent higher for apartment units that offer the CARES program. We also find the effect of the CARES program to be stronger in larger apartment complexes, suggesting that renters are willing to pay a premium for a sense of community rather than just the increased services from the program.",3.0,,False,,Business,2018-09-01,69b22c5fccce5fb15438db89f202197ce6ddb546,thevalueofcommunityevidencefromthecaresprogram,hanson,10.1016/j.jhe.2018.06.009,thevalueofcommunityevidencefromthecaresprogram|2018,1,2034,3,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment prices hedonic || hedonic housing prices",2,2,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),b23321cc4ed934fd7892a99edc64415a9b2d4d98,10.1080/03610918.2019.1704418,,A non–stationary non–Gaussian hedonic spatial model for house selling prices,2019.0,Communications in statistics. Simulation and computation,V. D. de Oliveira; M. Ecker,"Abstract This work proposes a hedonic random field model to describe house selling prices from 2000 to 2005 in Cedar Falls, Iowa. This real estate market presents two distinctive features that are not well described by traditional stationary Gaussian random field models: (a) the city has, on its periphery, a hoglot that acts as an externality, affecting both the mean and variance of the selling prices, and (b) the distribution of house selling prices display heavy tails, even after the distance to the hoglot and house–specific covariates are accounted for in the mean structure of the model. A non–stationary and non–Gaussian random field model is constructed by multiplying two independent Gaussian random fields tailored to model the probabilistic features displayed by the Cedar Falls dataset. A Markov chain Monte Carlo algorithm that uses data augmentation is employed to fit the proposed model.",3.0,,False,JournalArticle,Computer Science;Mathematics,2019-12-23,b23321cc4ed934fd7892a99edc64415a9b2d4d98,anonstationarynongaussianhedonicspatialmodelforhousesellingprices,oliveira,10.1080/03610918.2019.1704418,anonstationarynongaussianhedonicspatialmodelforhousesellingprices|2019,1,1906,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price,2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2020614446,10.1109/icmss.2010.5577066,,Hedonic Price Study on Urban Housing: The Case of Shijiazhuang City,2010.0,International Conference on Management and Service Science (MASS),Zhao Yan; Xiaojun Liu,"Housing is a kind of heterogeneous product; every housing unit includes different characteristic aggregation. Hedonic price theory is an effective method to study the quantitative relation between housing characteristic and price. This paper applies hedonic price theory to the housing market in Shijiazhuang. After collecting and trimming 2075 housing price and characteristic samples in Shijiazhuang city, the paper selects housing structure, location and neighborhood characteristics as explanatory variables, and implements OLS regression to estimate the parameters of the model, then analysises main characteristics that affect residential housing price and attempts to explain the quantitative relationship between housing price and housing characteristics. Based on that, we establish the hedonic model of Shijiazhuang housing market and validate the applicability of hedonic price model.",3.0,,False,conference-paper,,2010-08-01,https://openalex.org/W2020614446,hedonicpricestudyonurbanhousingthecaseofshijiazhuangcity,yan,10.1109/icmss.2010.5577066,hedonicpricestudyonurbanhousingthecaseofshijiazhuangcity|2010,1,1895,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic housing prices || housing characteristics prices hedonic",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,a874c896126583fea118e828a5f59ff9dcb02b98,10.1142/s1464333218500060,,Quantifying Welfare Impacts of Air Pollution in Seoul: A Two-Stage Hedonic Price Approach,2018.0,Journal of Environmental Assessment Policy and Management,M. Jun,"This study aims to measure welfare loss due to the high pollution levels of PM10 and NO2in the Seoul metropolitan area (SMA), Korea, which do not comply with the international standards, using spatial hedonic price models with apartment sales and air pollution data in 2010. The major findings were as follow: First, spatial hedonic model estimation reveals statistically significant negative signs for air pollution index for all submarkets, empirically presenting a trade-off between air pollution level and housing values. Second, the own-good quantity variable (air pollution index) has a statistically significant negative coefficient, indicating a diminishing marginal willingness to pay (MWTP) for additional level of the air pollution. Third, a worsening air quality brings about a substantial amount of welfare loss ($26.3 billion) among households residing in the SMA.",3.0,,False,,Environmental Science,2018-06-01,a874c896126583fea118e828a5f59ff9dcb02b98,quantifyingwelfareimpactsofairpollutioninseoulatwostagehedonicpriceapproach,jun,10.1142/s1464333218500060,quantifyingwelfareimpactsofairpollutioninseoulatwostagehedonicpriceapproach|2018,1,1878,5,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment prices hedonic || hedonic housing prices || spatial hedonic model housing",2,1,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,5366be2d650d591647c7d1e89d25fe46c4a2023c,10.3368/wple.97.3.090119-0126r1,,Data-Driven Estimation of Treatment Buffers in Hedonic Analysis: An Examination of Surface Coal Mines,2021.0,Land Economics,Luke G. Fitzpatrick; Christopher F. Parmeter,"In hedonic studies of how environmental disamenities influence house prices, the homes that are treated are typically unknown. We propose a new method for defining treatment buffers and apply our technique to disamenities that have thus far received little attention: surface coal mines. Our leave-one-out cross-validation approach identifies an optimal buffer of 2,300 m in two Appalachian counties at which effects dissipate. Hedonic regressions indicate that treated homes sell for 15.5% less than untreated homes. We supplement these results with nearest-neighbor covariate matching models and recover average treatment effects on the treated of 14.7% price reductions.",3.0,,False,JournalArticle,,2021-08-01,5366be2d650d591647c7d1e89d25fe46c4a2023c,datadrivenestimationoftreatmentbuffersinhedonicanalysisanexaminationofsurfacecoalmines,fitzpatrick,10.3368/wple.97.3.090119-0126r1,datadrivenestimationoftreatmentbuffersinhedonicanalysisanexaminationofsurfacecoalmines|2021,1,1673,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,1,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),9ce8cb298438ae064be6a168a446ca4827b9cfb4,10.1007/978-3-319-11469-9_5,,Hedonic House Price Modeling Based on Multilevel Structured Additive Regression,2015.0,,Alexander Razen; Wolfgang A. Brunauer; Nadja Klein; S. Lang; Nikolaus Umlauf,,3.0,,False,Review,Economics,,9ce8cb298438ae064be6a168a446ca4827b9cfb4,hedonichousepricemodelingbasedonmultilevelstructuredadditiveregression,razen,10.1007/978-3-319-11469-9_5,hedonichousepricemodelingbasedonmultilevelstructuredadditiveregression|2015,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || real estate hedonic regression",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",dba4a79632ce16ef969c1186a72550e41ec13165,10.1111/asej.12169,,Monetarizing Spillover Effects of Soil and Groundwater Contaminated Sites in Taiwan: How Much More Will People Pay for Housing to Avoid Contamination?,2019.0,Asian Economic Journal,Je-Liang Liou; A. Randall; Pei-Ing Wu; Hanhui Chen,"This study monetizes spillover effects of soil and groundwater contaminated sites using a quantile hedonic price model for different site types in Taoyuan City in Taiwan, which has 1664 sites, the highest number for any city in Taiwan. The results show that except for those living in the cheapest housing, most residents are willing to pay a higher price to live farther away from contaminated sites. Farmland makes up the largest share of the total contaminated site area and has the highest per square monetary spillover effects in Taoyuan City; remediation of farmland is one of the most urgent missions for the city. Overall, a NIMBY effect is found to exist for soil‐ and groundwater‐contamination in this city.",3.0,,False,,Economics,2019-03-01,dba4a79632ce16ef969c1186a72550e41ec13165,monetarizingspillovereffectsofsoilandgroundwatercontaminatedsitesintaiwanhowmuchmorewillpeoplepayforhousingtoavoidcontamination,liou,10.1111/asej.12169,monetarizingspillovereffectsofsoilandgroundwatercontaminatedsitesintaiwanhowmuchmorewillpeoplepayforhousingtoavoidcontamination|2019,1,1717,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,1,1,0,1,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,precios hedónicos vivienda,,10.19053/22565779.2446,,Valoración hedónica de la vivienda. Una aplicación con variables ambientales,2014.0,APUNTES DEL CENES,Duván Emilio Ramirez Ospina; Lázaro Valencia Giraldo,"Mediante la utilización de técnicas de econometría espacial se estimó el efecto que tienen algunas variables ambientales en el precio de la vivienda urbana de la ciudad de Manizales, teniendo como fuente la base de datos predial con uso vivienda, resultante de la actualización realizada por el IGAC en el año 2010. Se encontró en promedio que las viviendas cercanas a las áreas de riesgo reducen su precio hasta un 11%, las ubicadas próximas a una ladera en un 37% y el encontrarse en zona de tratamiento geotécnico hasta un 53%.",3.0,,,journal-article,,,doi_10_19053_22565779_2446,valoracionhedonicadelaviviendaunaaplicacionconvariablesambientales,ospina,10.19053/22565779.2446,valoracionhedonicadelaviviendaunaaplicacionconvariablesambientales|2014,1,1530,1,precios hedónicos vivienda,1,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2cf2801938ea2e1ebf1ead64a1ab662dfe9802d8,10.4028/www.scientific.net/amm.507.642,,Study on the Affecting Factors of Housing Prices along Rail Transit Based on Hedonic Price Model - Case Study of Beijing Subway Line 5,2014.0,,Jun Wei; Yu-Guang Wei; Kun Jiang,,3.0,,False,,Business,2014-01-16,2cf2801938ea2e1ebf1ead64a1ab662dfe9802d8,studyontheaffectingfactorsofhousingpricesalongrailtransitbasedonhedonicpricemodelcasestudyofbeijingsubwayline5,wei,10.4028/www.scientific.net/amm.507.642,studyontheaffectingfactorsofhousingpricesalongrailtransitbasedonhedonicpricemodelcasestudyofbeijingsubwayline5|2014,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1080/00420989120080921,,Hedonic Indices and Housing Programme Benefits,1991.0,Urban Studies,Isaac F. Megbolugbe,"The availability of reliable measures of housing programme benefits is important for decisions that planners make about appropriate housing policies. This paper advocates the use of hedonic price estimation techniques for the direct measurement of housing programme benefits in developing countries without restrictive characterisations of housing demand. A market-weighted economic welfare index is used to measure the housing benefits. The benefits are the difference between estimated hedonic price equations before and after the implementation of a programme summed across all households and then multiplied by the cash equivalent of a unit of hedonic value. The cash value of a unit of hedonic value is determined by dividing the observed aggregate market value of houses in an analysis sample by the estimated aggregate hedonic value for that sample. Measuring housing programme benefits from direct market valuation of programme-induced changes in housing attributes does not imply that such programmes are necessarily efficient means of delivering services to residents. Nevertheless, the hedonic approach enables policy-makers to consider the opportunity cost for participants through robust analysis.",3.0,,,journal-article,,,doi_10_1080_00420989120080921,hedonicindicesandhousingprogrammebenefits,megbolugbe,10.1080/00420989120080921,hedonicindicesandhousingprogrammebenefits|1991,1,2210,3,apartment rent hedonic || hedonic house price || hedonic housing prices,2,0,3,1,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",de57c81acbdeb7fd07e4da7bd82e0d35695886fd,10.63642/1357-1419.1169,,Improving assessment equity in mass appraisal models,2014.0,Journal of Property Tax Assessment & Administration,Carmela Quintos,"53 Assessment equity is a primary concern for municipalities that levy ad valorem taxes. Property taxes are a popular source of revenue for funding local services, requiring that assessment levels be fair for properties across price levels (vertical equity) and across properties with similar characteristics (horizontal equity). The standard measures for horizontal and vertical equity published by the International Association of Assessing Officers (IAAO) are the coefficient of dispersion (COD) and the price-related differential (PRD), respectively. Modeling heterogeneity is essential to achieving a COD and a PRD that conform to IAAO standards. Failure to model the variation in price by correctly specifying the regression equation with relevant property characteristics can be reflected in residuals that are heteroskedastic. Heteroskedasticity, which means that the variance of the residuals (or pricing error) is not constant across certain property characteristics, is by definition a failure of horizontal equity and indicative of a high COD. Similarly, the PRD is known to be affected by unexplained heterogeneity (i.e., heteroskedastic residuals). Regression methods, which average effects, tend toward regressive assessments when heterogeneity is not modeled. Denne (2011), Gloudemans (2011), Jensen (2009), and Hodge et al. (2013) discuss the relation of heteroskedasticity and the regressive bias of the PRD. (The IAAO range for the COD is 5–15 percent for single-family homes, and the range for the PRD is 0.98– 1.03. To test whether these ranges are satisfied at a significance level, bootstrap methods are used to construct confidence intervals. When heteroskedasticity is present, the standard bootstrap method fails (see Cribari-Neto and Zarkos [2004] for an alternative approach). Several methods for dealing with heterogeneity are geographic information systems (GIS) methods, which allow coefficients to vary across location",3.0,,False,JournalArticle,Economics,2014-12-01,de57c81acbdeb7fd07e4da7bd82e0d35695886fd,improvingassessmentequityinmassappraisalmodels,quintos,10.63642/1357-1419.1169,improvingassessmentequityinmassappraisalmodels|2014,1,2950,1,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",54ac58cfe359a71f171d608bdf8a44e4f46643d0,10.1016/j.econedurev.2018.12.001,,"Which New Yorkers vote with their wallets? The impact of teacher quality data on household sorting, and residential and school demographics",2019.0,Economics of Education Review,E. R. Rivera Rodas,"This paper examines the relationship between the February 2012 New York City teacher quality data release, the racial and ethnic composition of elementary and middle public schools and their neighborhoods, and housing prices. A unique dataset that links teacher quality to school characteristics, housing prices and characteristics, and Census data is used to estimate a difference-in-difference model to estimate the impact of the teacher quality release on housing prices. These results are then used to estimate the impact of the data release on residential and school demographics. The results in this study provide the first evidence of the effects of teacher quality measures on the housing market in New York City and the demographic shifts in residential and school mobility patterns as a result. The data suggests that the housing market responds significantly to the new information that was provided by the release of the teacher quality information, even when taking into consideration the school grades and other variables that may influence teacher quality measures. The magnitude of how much housing prices increase is greatly impacted by certain neighborhood demographics and the results show that Hispanic neighborhoods with a high proportion of free and reduced price lunch students are among the neighborhoods that had the highest increase in housing prices due to the teacher quality release. Not only that, but the areas that had the highest increase in housing prices due to the teacher quality release have experienced increases in the proportion of white students and the neighborhoods are becoming more racially diverse.",3.0,,False,,Geography,2019-02-01,54ac58cfe359a71f171d608bdf8a44e4f46643d0,whichnewyorkersvotewiththeirwalletstheimpactofteacherqualitydataonhouseholdsortingandresidentialandschooldemographics,rodas,10.1016/j.econedurev.2018.12.001,whichnewyorkersvotewiththeirwalletstheimpactofteacherqualitydataonhouseholdsortingandresidentialandschooldemographics|2019,1,2645,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1437031d5059caa3fbb16381437fc279d49f30a7,10.1108/ijhma-08-2020-0104,,The external impacts of historic landmarks and buildings on townhouse prices in Vietnam,2021.0,,D. Nguyen; Thu Thi Hoai Nguyen,"PurposeThis paper aims to explore the external spillover effects of landmarks and buildings with historic preservation designation in Vietnam, a country marked with a unique property right regime and market transparency. The study contributes to the existing debate over the impact of distance to historic preservation sites and landmarks and property prices.Design/methodology/approachThe study examines property data of 274 attached townhouses in Ho Chi Minh City, Vietnam and estimates the spillover effects of historic preservation on property prices collected during 2018–2019. The authors test for spatial autocorrelation by using the Global Moran’s I and Lagrange Multiplier diagnostics and deploy different spatial regression models including SAR, SEM and SDM.FindingsThe authors find that there is a premium on the prices of townhouses near formally designated landmarks and buildings. This premium decreases monotonically away from the historic sites. However, this paper also demonstrates that there is a non-linear (U-shape) relationship between housing premium and the distance to the nearest historic building.Originality/valueThis study is the first to take advantage of the surveyed property data to study the external impacts of historic preservation designation on housing prices in Vietnam. The study also contributes to the ongoing scholarly debate over the direction of the impacts. The study suggests that similar to other amenities, the price effect of designation tends to fade away after a certain distance.",3.0,,False,Review,Geography,2021-02-09,1437031d5059caa3fbb16381437fc279d49f30a7,theexternalimpactsofhistoriclandmarksandbuildingsontownhousepricesinvietnam,nguyen,10.1108/ijhma-08-2020-0104,theexternalimpactsofhistoriclandmarksandbuildingsontownhousepricesinvietnam|2021,1,2532,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,3,5,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",72dabbf1c736ec8ec54bbdc63fc1bece8be83d4a,10.1080/12265934.2024.2346149,,"Still prefer, but not that much: the premium of subway access on house prices during the COVID-19 pandemic period",2024.0,International Journal of Urban Sciences,Dongwoo Hyun,"ABSTRACT Rail access has traditionally been considered a factor that enhances the amenity value of urban areas, resulting in a price premium for properties located near these stations. However, the Covid-19 pandemic has exerted a profound influence on people's mobility patterns and travel behavior within urban areas. One of the most conspicuous changes is a substantial decrease in subway ridership. This study investigates whether the decline in public transit ridership has led to shifts in housing location preferences driven by proximity to rail access during the Covid-19 pandemic. This study focuses on assessing the impact of the pandemic on the premium associated with rail access in relation to house prices. Using approximately 180,000 apartment transaction records from Seoul, South Korea, the empirical findings, based on a difference-in-differences approach, reveal an immediate and significant depreciation effect resulting from this exogenous shock during the 25-month pandemic period. The premium decreases significantly within a 1.2-kilometer radius from a subway station, and this reduction exhibits a distance-decay pattern within the designated area. Highlights Traditional amenity value of rail access challenged by Covid-19. Profound decline in subway ridership during pandemic. Investigation into pandemic's impact on housing location preferences. Assessment of pandemic's effect on rail access premium. Immediate and significant depreciation effect within 1.2 km from subway stations.",3.0,,False,JournalArticle,,2024-04-24,72dabbf1c736ec8ec54bbdc63fc1bece8be83d4a,stillpreferbutnotthatmuchthepremiumofsubwayaccessonhousepricesduringthecovid19pandemicperiod,hyun,10.1080/12265934.2024.2346149,stillpreferbutnotthatmuchthepremiumofsubwayaccessonhousepricesduringthecovid19pandemicperiod|2024,1,2510,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b5fd8a575c9603df68f30dd35dc0f60e6ca76035,10.22616/rrd.25.2019.022,,Analysis of different approaches to real estate appraisal,2019.0,,Vladimir Surgelas; I. Arhipova; V. Puķīte,"The traditional valuation of real estate in the field of civil engineering did not include the uncertainty of human behaviour, which cannot be explained by the traditional approach. There are different valuation methods for real estate appraisal, which are basically classified into three groups as a classic, statistical and advanced. In this article, we estimated the different housing price models using the sample of 37 residential apartments in Riga, Latvia, October 2018. In order to evaluate if there is a possible association between the variables involved in relation to the property price, the analytical data were analysed by correlation analysis, analysis of variance (ANOVA), regression analysis, covariance analysis (ANCOVA), principal component analysis (PCA) and cluster analysis. The models estimation results show that using ANCOVA models for the prices forecasting the model fitting to data is less than 58%. The preliminary results of this study suggest that the estimated properties can be distributed in 4 groups, depending on number of rooms, area and age. In addition, the decision tree was created based on algorithms (J48) and a preliminary definition of the best rules was made. The decision tree presents an accuracy of 84% with 31 accepted instances for a total of 37 currently classified instances.",3.0,,False,,Business,2019-12-12,b5fd8a575c9603df68f30dd35dc0f60e6ca76035,analysisofdifferentapproachestorealestateappraisal,surgelas,10.22616/rrd.25.2019.022,analysisofdifferentapproachestorealestateappraisal|2019,1,2328,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c50373c97b45aaefb98c0bbccbd9b045551af8a0,10.1080/21606544.2023.2287190,,The externality cost of environmental (dis)amenities in the urban housing market: an emerging evidence from Pakistan,2023.0,Journal of Environmental Economics and Policy,Tanzila Akmal; Faisal Jamil; Muhammad Haseeb Raza; Cosimo Magazzino,"ABSTRACT Rising urbanization on abiotic environmental conditions in housing units is one of the most severe difficulties that city municipality authorities face, all over the world. Waste production has increased as a result of households failing to implement waste management strategies that ensure sustainability. Thus, property values in the housing market have regularly deteriorated as a result of environmental (dis)amenities. The study examines the impact of a waste dump site on proximate property values in Pakistan’s twin cities. Using a systematic random sampling technique, questionnaires were distributed to 849 households. 35 dump sites were chosen from 100 metres to 500 metres. The dump sites were chosen based on their size and proximity to residential homes in the study area. The empirical results show that the distance between a residential property and a waste dump site significantly impacts rental values. Moreover, rents exhibit a negative relationship with increasing distance from the dump sites. Based on applied findings, more proactive enforcement of sanitary laws and regulations, such as removing all irregular dump sites from residential areas, is recommended.",3.0,,False,JournalArticle,,2023-11-29,c50373c97b45aaefb98c0bbccbd9b045551af8a0,theexternalitycostofenvironmentaldisamenitiesintheurbanhousingmarketanemergingevidencefrompakistan,akmal,10.1080/21606544.2023.2287190,theexternalitycostofenvironmentaldisamenitiesintheurbanhousingmarketanemergingevidencefrompakistan|2023,1,2193,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,6,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0fadb75139f324336e4a8a9203e1a9908c576878,10.17208/jkpa.2018.10.53.5.135,,Differential effects of public housing by type on nearby housing prices : Focused on the SHift and National rental housing in Seoul,2018.0,Journal of Korea Planning Association,Y. Jeong; Je-Sun Han; Chang-Moo Lee,"Due to the stereotypes that Public Rental Housing could affect negatively in housing prices, local residents often oppose the construction of public rental housing. Meanwhile, the government has recently diversified the types of public rental housing, such as SHIFT(Long-term rental housing) and National rental housing, to embrace the demands of single-person households, young people and newlyweds. In this situation, we have focused on the possibility that public rental housing may have different effects on nearby housing prices depending on the types and characteristics of public rental housing. To analyze the effect of public rental housing supply, we choose the case study sites through serial processes, and applied the Modified Repeat Sales Model with the concept of Difference-In-Difference method. The results show that the supply of public rental housing affects housing price decline and the magnitude of this effect varies by the type of public rental housing.",3.0,,False,,Business,2018-10-31,0fadb75139f324336e4a8a9203e1a9908c576878,differentialeffectsofpublichousingbytypeonnearbyhousingpricesfocusedontheshiftandnationalrentalhousinginseoul,jeong,10.17208/jkpa.2018.10.53.5.135,differentialeffectsofpublichousingbytypeonnearbyhousingpricesfocusedontheshiftandnationalrentalhousinginseoul|2018,1,1977,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bc9e4acf4a53e0562ac6aed2849e9700d8c1e5aa,10.1080/03081060.2023.2252397,,Train stations and house prices: a local perspective,2023.0,Transportation planning and technology (Print),Changro Lee,"ABSTRACT Opening a new train station is considered a way to generate amenity in a neighborhood. However, as train lines extend from a central city to suburbs and remote places, train stations may generate disamenity depending on the local context. This study examines the externality of stations from a house valuation perspective. A mixed-effects model is employed to capture the varying relationship between house prices and distance to a nearby station. The results show that this relationship significantly varies by county, which leads to a house price premium in some counties and price discount in others. This study attributes the price discount (disamenity) to low ridership, seasonality of ridership, passenger traits, and long distance from a central city. The study results are expected to provide policymakers with balanced insights on establishing a new station so that train stations can serve as a local amenity, not a harmful facility in the neighborhood.",3.0,,False,JournalArticle,,2023-08-28,bc9e4acf4a53e0562ac6aed2849e9700d8c1e5aa,trainstationsandhousepricesalocalperspective,lee,10.1080/03081060.2023.2252397,trainstationsandhousepricesalocalperspective|2023,1,1972,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,5,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_citations,cites:sirmans2006,7598cb6436340e69d3f178a41fad1b449837e10b,10.1080/02673037.2022.2074969,,"Does housing market segmentation ensure families a rental price benefit? The example of Frankfurt, Germany",2022.0,Housing Studies,Andreas Hartung,"Abstract Housing market segments are commonly differentiated based on the structural and spatial attributes of their dwellings. A sociological perspective highlights the fact that housing market segments can also arise along socio-economic characteristics of demanders. This can explain why and how particular groups of tenants are disadvantaged or favoured in terms of the rental prices they pay for comparable rental properties. This article illustrates this idea using the example of families with minor children. Exceptionally rich data collected in Frankfurt am Main, Germany, provides detailed measurements for housing conditions. The findings indicate that families in Frankfurt are beneficiaries of segmentation, with respect to the rental prices they pay. However, the extent of the benefit varies between local and specific structural submarkets.",3.0,,False,,,2022-05-16,7598cb6436340e69d3f178a41fad1b449837e10b,doeshousingmarketsegmentationensurefamiliesarentalpricebenefittheexampleoffrankfurtgermany,hartung,10.1080/02673037.2022.2074969,doeshousingmarketsegmentationensurefamiliesarentalpricebenefittheexampleoffrankfurtgermany|2022,1,1856,1,cites:sirmans2006,0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,house prices proximity hedonic,b72ade09ac2e54b80b64172cba43ede3c675da2d,10.2139/ssrn.2071199,,Spatial Weights Configuration and Impact of Environmental Externalities on Housing Prices,2013.0,,Masha Maslianskaia-Pautrel; C. Baumont,,3.0,,False,,Economics,2013-04-04,b72ade09ac2e54b80b64172cba43ede3c675da2d,spatialweightsconfigurationandimpactofenvironmentalexternalitiesonhousingprices,maslianskaia-pautrel,10.2139/ssrn.2071199,spatialweightsconfigurationandimpactofenvironmentalexternalitiesonhousingprices|2013,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || house prices proximity hedonic || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,e5b6b64d27473ca8546d56f8e32fbc8649576af3,10.1504/gber.2011.040729,,"Urban amenities and dwelling house prices in Sao Paulo, Brazil: a hierarchical modelling approach",2011.0,,L. Fávero,,3.0,,False,,Economics,2011-06-21,e5b6b64d27473ca8546d56f8e32fbc8649576af3,urbanamenitiesanddwellinghousepricesinsaopaulobrazilahierarchicalmodellingapproach,fávero,10.1504/gber.2011.040729,urbanamenitiesanddwellinghousepricesinsaopaulobrazilahierarchicalmodellingapproach|2011,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic price model amenities residential",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,eaa614b709f0d80a81abcdbb400c5a08f9277daf,10.1007/978-3-319-14938-7_9,,"The economic value of cyclonic storm-surge risks: A hedonic case study of residential property in Exmouth, Western Australia",2015.0,,R. Rebecca; L. Beckley; M. Tull,,3.0,,False,,Geography,,eaa614b709f0d80a81abcdbb400c5a08f9277daf,theeconomicvalueofcyclonicstormsurgerisksahedoniccasestudyofresidentialpropertyinexmouthwesternaustralia,rebecca,10.1007/978-3-319-14938-7_9,theeconomicvalueofcyclonicstormsurgerisksahedoniccasestudyofresidentialpropertyinexmouthwesternaustralia|2015,1,1000,1,hedonic price model amenities residential,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",61f14b23c6f19a1cfe319d36f6c29076c3667da5,10.1016/j.pacfin.2024.102661,,Exploring the value of green: The impact factors on China's second-hand green housing prices based on geographically weighted Lasso regressions,2025.0,Pacific-Basin Finance Journal,Qian-Wen Li; Ting-Yu Qian; Hui Wang; Chuanwang Sun,,3.0,,False,JournalArticle,,2025-01-01,61f14b23c6f19a1cfe319d36f6c29076c3667da5,exploringthevalueofgreentheimpactfactorsonchinassecondhandgreenhousingpricesbasedongeographicallyweightedlassoregressions,li,10.1016/j.pacfin.2024.102661,exploringthevalueofgreentheimpactfactorsonchinassecondhandgreenhousingpricesbasedongeographicallyweightedlassoregressions|2025,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5a3b4fbc9165f703719b7567196be18a0756f4a0,10.2747/0272-3638.15.1.9,,"Housing Prices in Master- Planned Communities: Are There Premiums? The Evidence for Collin Country, Texas, 1980-1991.",1994.0,,E. Alexandrakis; B. Berry,,3.0,,False,,Economics,,5a3b4fbc9165f703719b7567196be18a0756f4a0,housingpricesinmasterplannedcommunitiesaretherepremiumstheevidenceforcollincountrytexas19801991,alexandrakis,10.2747/0272-3638.15.1.9,housingpricesinmasterplannedcommunitiesaretherepremiumstheevidenceforcollincountrytexas19801991|1994,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",3ad37800defcd1676bc92485752dd8fbd940b6cf,10.1007/978-3-031-10542-5_15,,Spatial Econometric Analysis of Multi-family Housing Prices in Turin: The Heterogeneity of Preferences for Energy Efficiency,2022.0,Communication Systems and Applications,Federico Dell’Anna,,3.0,,False,JournalArticle,Computer Science,,3ad37800defcd1676bc92485752dd8fbd940b6cf,spatialeconometricanalysisofmultifamilyhousingpricesinturintheheterogeneityofpreferencesforenergyefficiency,dell’anna,10.1007/978-3-031-10542-5_15,spatialeconometricanalysisofmultifamilyhousingpricesinturintheheterogeneityofpreferencesforenergyefficiency|2022,1,1000,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",28da8306284d8d3d576bf60c09cca54850c34d9f,10.1007/s10901-025-10244-8,,"Identifying influencers of housing prices: spatial heterogeneity and pattern analysis using machine learning, spatial regression, and GIS",2025.0,Journal of Housing and the Built Environment,Nihal Genç; H. E. Colak,,3.0,,False,JournalArticle,,2025-11-04,28da8306284d8d3d576bf60c09cca54850c34d9f,identifyinginfluencersofhousingpricesspatialheterogeneityandpatternanalysisusingmachinelearningspatialregressionandgis,genç,10.1007/s10901-025-10244-8,identifyinginfluencersofhousingpricesspatialheterogeneityandpatternanalysisusingmachinelearningspatialregressionandgis|2025,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",53b6dec666a6943dd94855e9c9aed85e6ff8e321,10.1080/12265934.2025.2606073,,Exploring the spatial heterogeneity and nonlinear impacts of TOD on housing prices using osp-GNNW-XGBoost model,2025.0,International Journal of Urban Sciences,Chenxu Liu; Xiaodong Xu; Ke Liu; Abudureheman Abuduwayiti,,3.0,,False,JournalArticle,,2025-12-25,53b6dec666a6943dd94855e9c9aed85e6ff8e321,exploringthespatialheterogeneityandnonlinearimpactsoftodonhousingpricesusingospgnnwxgboostmodel,liu,10.1080/12265934.2025.2606073,exploringthespatialheterogeneityandnonlinearimpactsoftodonhousingpricesusingospgnnwxgboostmodel|2025,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),15d1d5c8aefe2b6d20676707fa88061b380c1f1a,10.1108/ijhma-09-2025-0213,,Understanding the determinants of house prices in Moroccan cities,2025.0,International Journal of Housing Markets and Analysis,Onur Ozsoy; C. Ozsoy; C. Ozsoy," This study aims to analyze the main factors affecting house prices in various major Moroccan cities. This study uses a hedonic pricing model with robust standard errors to figure out the relationship between the structural, amenity and locational characteristics and house prices. This study draws on data from Avito.ma, a leading Moroccan online real estate platform, filling a major gap in understanding housing markets across Morocco and the wider MENA region while providing valuable insights for policymakers, developers and investors. This study uses an Ordinary Least Squares model with robust standard errors. The dependent variable is the natural logarithm of housing prices. The analysis is based on 1,088 verified housing listings collected from Avito.ma between February and May 2025. The independent variables consist of property size, floor level and a range of amenities, including parking, air conditioning, sports facilities and security. To capture regional differences, city fixed effects were added to the model. This approach makes it possible to eliminate local price dynamics and account for unobserved spatial variations. A Chow-type test was conducted to establish whether the factors influencing housing prices differ from one city to another. Separate regressions were estimated separately for the cities included in the data set, and the results were compared using joint Wald tests. The null hypothesis of equal parameters across cities was rejected at the 5% level, showing that housing price determinants vary significantly between regions. Additional diagnostics confirmed that the model performs reliably and is statistically sound. The findings of this study indicate that property size is the predominant factor influencing home costs, while the advantage of additional space diminishes after a specific threshold is attained. Amenities significantly influence property value; homes equipped with security systems command approximately 7.8% higher prices, while those with parking spaces sell for roughly 5.9% more. Conversely, properties equipped with heating systems or in regions exhibiting indicators of social hardship generally possess lower value. Properties with ocean views command a significant premium of approximately 26%. The inclusion of city fixed effects clarifies the variations in property markets across Morocco. Prices in Rabat and Casablanca are significantly higher compared to neighboring cities, although El Jadida (−26%), Azemmour (−33%) and Kenitra (−11%) exhibit substantial reductions. The findings confirm that Morocco’s urban housing market operates within a polycentric spatial framework instead of a purely monocentric one. Although the Web sources provide valuable insights into housing market dynamics in major urban centers, they lack sufficient information about informal settlements and rural areas, leading to selection bias. The four-month observation window also makes it difficult to examine seasonal and cyclical dynamics. The field should move away from static, cross-sectional analyses and toward dynamic frameworks that can show how housing factors change over time in response to changes in the economy, policy and population. The use of a mixed method will help us better understand the factors affecting housing values. The findings highlight the need for geographically differentiated housing policies. To promote balanced regional development, policymakers should prioritize increasing infrastructure, safety and urban amenities in undervalued cities, while developers are encouraged to include security and parking facilities as value-enhancing investments. In high-demand areas like Rabat and Casablanca, supply-side initiatives such as inclusionary zoning can help with affordability, whereas periphery cities would benefit more from basic infrastructure and service upgrades. The recommendations based on the results help make cities more livable and welcoming to everyone. Better neighborhoods and better connections between cities can help people move up in society, lower inequality between areas and encourage balanced growth in cities. Making things safer, easier to get to and less expensive makes not only life better for people who live there but also the community healthier by making living spaces safer, more connected and more cohesive. To the best of the authors’ knowledge, this study is one of the first rigorous applications of hedonic pricing model to Morocco’s housing market, using high-frequency internet data. This study contributes to the empirical literature by merging modern amenities, spatial fixed effects and robust estimating methodologies, which improve methodological precision and policy relevance. The findings serve as an analytical foundation for evidence-based urban planning and housing policy in MENA’s growing economies. ",2.0,,False,JournalArticle,,2025-12-02,15d1d5c8aefe2b6d20676707fa88061b380c1f1a,understandingthedeterminantsofhousepricesinmoroccancities,ozsoy,10.1108/ijhma-09-2025-0213,understandingthedeterminantsofhousepricesinmoroccancities|2025,1,5895,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || real estate hedonic regression || spatial hedonic model housing",2,5,4,6,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic property valuation,e3ecb0f9b472aff4c9973a4b791ef947ba16e2fe,10.1108/ijhma-02-2025-0032,,The influence of buyers’ aesthetic preferences for building facade colors on the pricing of high-rise residential properties: a hedonic pricing model analysis,2025.0,International Journal of Housing Markets and Analysis,A. MohdAini; A. Adediran; Yeong Re Seng,"Purpose This study aims to investigate the impact of consumers’ aesthetic preferences for building facade colors on the price of high-rise residential properties. It explores how differences in color qualities influence transaction pricing, providing insights on market demand. Design/methodology/approach This study examines transaction data for apartments and condominiums in Petaling from 2023 to the first quarter of 2024, obtained from the National Property Information Centre. A hedonic pricing model is used to investigate the relationship between price components and facade color qualities, whereas quartile regression is used to determine how preferences fluctuate across income levels. Findings The results indicate that lower chroma and saturation levels are associated with higher transaction prices, while blue-green and red-purple hues are the most favored. In addition, income level influences aesthetic preferences, with higher income buyers demonstrating more selective tastes. Research limitations/implications Future studies could expand by incorporating additional variables, refining color measurement techniques and exploring cross-cultural differences in facade color preferences. Practical implications The findings offer valuable insights for property developers, architects and urban planners in aligning facade designs with buyer preferences, ultimately enhancing marketability and property value. Originality/value This study contributes to the limited body of research on facade color preferences in local housing markets. By integrating color attributes into hedonic pricing analysis, it provides empirical evidence on the impact of aesthetic factors on property valuation. ",2.0,,False,JournalArticle,,2025-04-29,e3ecb0f9b472aff4c9973a4b791ef947ba16e2fe,theinfluenceofbuyersaestheticpreferencesforbuildingfacadecolorsonthepricingofhighriseresidentialpropertiesahedonicpricingmodelanalysis,mohdaini,10.1108/ijhma-02-2025-0032,theinfluenceofbuyersaestheticpreferencesforbuildingfacadecolorsonthepricingofhighriseresidentialpropertiesahedonicpricingmodelanalysis|2025,1,2710,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || hedonic property valuation",2,2,5,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f61b328070efeea37eed3a7e0b623eae1f073c37,10.1108/ijhma-12-2023-0167,,"Impact of proximity to cell phone tower base stations on residential property prices in the City of Johannesburg, South Africa",2024.0,International Journal of Housing Markets and Analysis,Koech Cheruiyot; Nosipho Mavundla; Mncedisi J. Siteleki; Ezekiel Lengaram,"Purpose With revolutions in the telecommunication sector having led to wide unprecedented consequences in all facets of human life, this paper aims to examine the relationship between cell phone tower base stations (CPTBSs) and residential property prices within the City of Johannesburg (CoJ), South Africa. Design/methodology/approach The authors align their work with global literature and assess how the impact of CPTBSs influences residential property values in South Africa. The authors use a semi-log hedonic pricing model to test the hypothesis that proximity of CPTBSs to residential properties does not account for any variation in residential property prices. Findings The results show a significant impact that proximity of CPTBS has on residential property sale prices. However, the impact of CTPBSs’ proximity on residential property prices depends on their distance from the residential properties. The closer a residential property is to the CTPBS, the greater the impact that the CTPBS will have on the selling price of the residential property. Originality/value With international studies offering mixed findings on the impact of CPTBSs on residential property values, there is limited research on their impact in South Africa. The findings of this study offer crucial insights for the real estate practitioners, property owners, telecommunications companies and the public, providing a nuanced understanding of the relationship between CPTBSs and property values. This research helps property owners understand the effects of CPTBSs on their properties, and it assists property valuers in gauging the impact of CPTBSs on property values. ",2.0,,False,JournalArticle,,2024-05-01,f61b328070efeea37eed3a7e0b623eae1f073c37,impactofproximitytocellphonetowerbasestationsonresidentialpropertypricesinthecityofjohannesburgsouthafrica,cheruiyot,10.1108/ijhma-12-2023-0167,impactofproximitytocellphonetowerbasestationsonresidentialpropertypricesinthecityofjohannesburgsouthafrica|2024,1,2661,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate)",2,4,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",724aae65b0b64a5f69972a43498d31c2ec162203,10.1108/ijhma-12-2024-0191,,Spatial examination of house prices via cluster analysis,2025.0,International Journal of Housing Markets and Analysis,Leyla Alkan-Gökler," In Ankara, house prices exhibit significant variation across different locations, a trend closely tied to speculative behaviors in the Turkish housing market. Many neighborhoods remain unaffordable, even for households within the highest income quintile. This raises critical questions: To what extent are these pronounced price differences attributable to the specific attributes of the housing stock? Can Ankara’s housing market be segmented into homogeneous clusters based on housing characteristics and price levels? What are the primary factors influencing housing prices, and what role does speculation play in shaping these dynamics? This study aims to address these questions through the application of various statistical analyses. This study examines housing market differentiation in Ankara by applying cluster analysis, discriminant analysis and hedonic price analysis, using data from 12,460 dwelling units across 325 neighborhoods within eight central districts of the city. The findings of this study indicate that speculation can be a significant determinant of house prices, as evidenced by the considerable price variations observed even among properties with similar physical characteristics. Cluster analysis, a commonly used statistical method in the segmentation of housing submarkets, plays a central role in this research. However, unlike prior studies, this paper tries to explore the existence of homogeneous clusters within Ankara’s housing markets in the context of housing affordability and housing speculation via different statistical techniques. ",2.0,,False,JournalArticle,,2025-04-09,724aae65b0b64a5f69972a43498d31c2ec162203,spatialexaminationofhousepricesviaclusteranalysis,alkan-gökler,10.1108/ijhma-12-2024-0191,spatialexaminationofhousepricesviaclusteranalysis|2025,1,2589,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,3,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,c21f19e67ea8c7d7e20d55b40a6bf57357d83d42,10.1177/23998083261420306,,Quantity or structure? Asset or burden? Causal evidence of street greenery’s impact on housing prices from sequential data,2026.0,Environment and Planning B Urban Analytics and City Science,Zhao-Cheng Bai; Rui Ji; Feng Gao; Yu-Heng Mao; Song Liu,"Street greenery, as critical green infrastructure, provides extensive ecosystem services, and accurately evaluating its value benefits urban greening strategies and funding decisions. Previous studies relying on cross-sectional data and hedonic regression models are limited by confounding factors, thus failing to accurately measure the net effects of street greenery characteristics on housing prices. To address this limitation, hedonic regression, propensity score matching and difference-in-differences methodologies were employed based on housing price and built environment data from 2017 to 2022 to analyze how street greenery quantity and vegetation structure affect housing prices. Results indicated that street greenery impacts differ significantly between the central area and the expanded central area, revealing a negative price effect defined as the “Green Burden Effect” in expanded central area. Additionally, greenery quantity changes exerted stronger and more stable price impacts compared to vegetation structure. The DID analysis further clarified temporal changes in these relationships. Employing multiple econometric models revealed the complex spatial-temporal relationships between street greenery and housing prices, emphasizing the importance of spatially targeted greening strategies to optimize residential values and promote equitable urban development.",2.0,,False,JournalArticle,,2026-01-20,c21f19e67ea8c7d7e20d55b40a6bf57357d83d42,quantityorstructureassetorburdencausalevidenceofstreetgreenerysimpactonhousingpricesfromsequentialdata,bai,10.1177/23998083261420306,quantityorstructureassetorburdencausalevidenceofstreetgreenerysimpactonhousingpricesfromsequentialdata|2026,1,2384,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic housing prices,c921bca7961ace0c621e4e3cd8c150038e095dd8,10.24957/hsr.2023.31.1.5,,Influencing Academic Achievement and Regional Mediating Effects of General High Schools on Local Apartment Prices Based on Spatial Hedonic Price Model,2023.0,Korean Association for Housing Policy Studies,Jiyoung Oh; Wonseok Seo,"This study empirically analyzed the effect on housing prices according to the academic achievement of general high schools and the regional mediating effect using the spatial hedonic model. The main empirical analysis results are as follows. First, accessibility to general high schools had a positive effect on local apartment prices regardless of the type. It was found that the preferred high school type, which had the highest college entrance rate, had a greater influence. Second, a prestigious high school was more preferred when it comes to the type of high school closest to the apartment. This result seems to be due to the recognition that consumers in the local housing market place high value on the status of local prestigious high schools, and that there are many students with excellent grades in prestigious high schools. Consequently, there may be less incidence of juvenile delinquency, creating a positive effect on the residential environment around the school. Third, it was found that in an area where the regional effect on housing price is high, the closer an apartment is to a prestigious high school, the higher the apartment price can get. Through these results, this study confirmed that the characteristics of general high school academic achievement have a clear influence on apartment prices and that these influences appear differentially by region.",2.0,,False,,,2023-02-28,c921bca7961ace0c621e4e3cd8c150038e095dd8,influencingacademicachievementandregionalmediatingeffectsofgeneralhighschoolsonlocalapartmentpricesbasedonspatialhedonicpricemodel,oh,10.24957/hsr.2023.31.1.5,influencingacademicachievementandregionalmediatingeffectsofgeneralhighschoolsonlocalapartmentpricesbasedonspatialhedonicpricemodel|2023,1,2382,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment prices hedonic || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,3,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",2aa7fd38898d91520a1e835f9c23698e9be3c219,10.1108/ijhma-06-2024-0079,,Unveiling housing preferences amidst flood risks: an empirical study in an emerging market,2024.0,International Journal of Housing Markets and Analysis,H. Nguyen; H. Truong," Purpose This study investigates the interplay between flood risk, financial constraints and buyer behavior in the housing market. It aims to discern how flood experience influences housing choices, particularly among financially constrained buyers. Design/methodology/approach Using hedonic pricing models and probit analysis, this research examines the implicit prices of house characteristics and evaluates the determinants of housing choices, considering factors such as flood risk, financial status and past flood experience. Findings The study underscores that lower house prices incentivize buyers to opt for homes in flood-prone areas, thereby increasing their vulnerability to flood risks. Consequently, financial constraints exacerbate this situation, compelling low-income buyers to accept higher flood risks. However, flood experience fosters risk awareness among buyers, encouraging them to steer clear of flood-prone areas, albeit with lesser efficacy among financially constrained individuals. Originality/value This research contributes to the understanding of how flood risk and financial limitations shape housing decisions. It highlights the role of flood experience in influencing buyer behavior and emphasizes the need for targeted policies to mitigate flood-related risks in the housing market, particularly among economically vulnerable populations. ",2.0,,False,JournalArticle,,2024-11-12,2aa7fd38898d91520a1e835f9c23698e9be3c219,unveilinghousingpreferencesamidstfloodrisksanempiricalstudyinanemergingmarket,nguyen,10.1108/ijhma-06-2024-0079,unveilinghousingpreferencesamidstfloodrisksanempiricalstudyinanemergingmarket|2024,1,2379,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,1,4,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",73a99ac2da1279767fc4c587efa3df827085007e,10.48550/arxiv.2403.19915,2403.19915,Using Images as Covariates: Measuring Curb Appeal with Deep Learning,2024.0,arXiv.org,Ardyn Nordstrom; Morgan Nordstrom; M. Webb,"This paper details an innovative methodology to integrate image data into traditional econometric models. Motivated by forecasting sales prices for residential real estate, we harness the power of deep learning to add""information""contained in images as covariates. Specifically, images of homes were categorized and encoded using an ensemble of image classifiers (ResNet-50, VGG16, MobileNet, and Inception V3). Unique features presented within each image were further encoded through panoptic segmentation. Forecasts from a neural network trained on the encoded data results in improved out-of-sample predictive power. We also combine these image-based forecasts with standard hedonic real estate property and location characteristics, resulting in a unified dataset. We show that image-based forecasts increase the accuracy of hedonic forecasts when encoded features are regarded as additional covariates. We also attempt to""explain""which covariates the image-based forecasts are most highly correlated with. The study exemplifies the benefits of interdisciplinary methodologies, merging machine learning and econometrics to harness untapped data sources for more accurate forecasting.",2.0,,False,JournalArticle,Economics;Computer Science,2024-03-29,73a99ac2da1279767fc4c587efa3df827085007e,usingimagesascovariatesmeasuringcurbappealwithdeeplearning,nordstrom,10.48550/arxiv.2403.19915,usingimagesascovariatesmeasuringcurbappealwithdeeplearning|2024,1,2187,2,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate)",2,1,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),986d015372d1678cec83049efff0ba17173cd181,10.1080/14445921.2022.2110370,,Feng Shui and superstition in Hong Kong’s residential housing market,2021.0,Pacific Rim Property Research Journal,Kwok-Chiu Lam,"ABSTRACT Many studies are undertaken in Hong Kong or elsewhere to explore the impact of Feng Shui on house values. However, most of them seemingly mix it up with numerical superstition and seldom consider genuine Feng Shui factors except water view. Others simply treat those elements as environmental attributes and miss the most essential ingredient underlying Feng Shui’s Form School approach – orientation of dwellings. Moreover, under Compass School approach, there are entirely different meaning of the digits 1 to 9. Using a local private residential estate, we collect 622 sales transactions over 2018Q1 – 2021Q3 for analysis. We encompass diverse Feng Shui variables in a log-linear hedonic model and assess their price impacts. We find that southwest is the best facing direction of houses under Period 8. Whereas sea view can fetch a price premium of 2.46%, a great discount of 6.45% has to be offered for a residence facing Sha Qi. As Sha Qi diminishes with higher floors, homebuyers are willing to pay extra 0.1% for each upper floor. Hong Kong residents appear not numerological superstitious, but they do favor certain environmental attributes in accord with Feng Shui.",2.0,,False,,,2021-09-02,986d015372d1678cec83049efff0ba17173cd181,fengshuiandsuperstitioninhongkongsresidentialhousingmarket,lam,10.1080/14445921.2022.2110370,fengshuiandsuperstitioninhongkongsresidentialhousingmarket|2021,1,2184,5,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || willingness to pay housing attributes hedonic",2,2,6,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,house prices proximity hedonic,5bfe734e48a860fc23458401eeacf64ff966a420,10.1080/14445921.2019.1610595,,A comparative study on the value of scenic views between an inland and a coastal city in Korea,2019.0,Pacific Rim Property Research Journal,Tae-yun Jeong; Sae-Won Park; Sunhae Lee,"ABSTRACT This study estimates hedonic house price models for an inland city of Seoul and a coastal city of Pusan using spatial regression with successful bidding price data of court auction during the period from January 2006 to December 2014. Among hedonic attributes such as floor area, building age, total floor level, living floor level, direction of the window of the living room, proximity to natural environment, scenic views and others employed in this study, our main focus is on scenic views. As a result of our empirical analysis, the most preferred view turns out to be a water-related view such as a broad river view in Seoul or an ocean view in Pusan. A mountain view, affecting negatively in a coastal city like Pusan which is adjacent to the beautiful ocean, has positive influence in Seoul, since it is an inland city with no ocean in sight with only a river running across the heart of the city which is surrounded by mountains serving as its boundary. While a building view in Seoul has negative effect on the price, it affects positively in Pusan.",2.0,,False,,Geography,2019-04-29,5bfe734e48a860fc23458401eeacf64ff966a420,acomparativestudyonthevalueofscenicviewsbetweenaninlandandacoastalcityinkorea,jeong,10.1080/14445921.2019.1610595,acomparativestudyonthevalueofscenicviewsbetweenaninlandandacoastalcityinkorea|2019,1,2067,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || house prices proximity hedonic",2,1,1,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,4ae67269a70f22a9391910ccbe20e38eaf057620,10.2139/ssrn.3334469,,How Does Subway and Ground Transit Proximity Affect Rental Prices?,2019.0,Social Science Research Network,K. Kholodilin; M. Maksimova,"This study focuses on the ground transportation system and its impact on the rents in 30 of Russia’s largest cities. It also compares the effect with subway transit networks. The data set includes rent information from an all-Russia online advertisement website Avito and various measures of proximity to the public transit network stops (including subways for cities with them). The analysis is conducted using linear hedonic models. The results show that the ground transportation proximity is important for housing rent formation in both cities with and without subways, although the effect for subway stations is greater in comparison. Nevertheless, the benefits of a denser ground transportation system are high and stable, whereas the distance to the closest bus stop and the number within the walking distance are important solely for cities with a subway system and without it, respectively.",2.0,,False,,Business,,4ae67269a70f22a9391910ccbe20e38eaf057620,howdoessubwayandgroundtransitproximityaffectrentalprices,kholodilin,10.2139/ssrn.3334469,howdoessubwayandgroundtransitproximityaffectrentalprices|2019,1,1899,1,hedonic housing prices,2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,d637f4858470df91a33dd8b0d6d39d133a55e954,10.1080/13504851.2024.2331660,,Housing prices and environmental hazards: the effects of industrial park openings in Korea,2024.0,Applied Economics Letters,J. Park; Youngseok Park,"ABSTRACT We explore an empirical examination on the effects of industrial park openings on housing prices in Korea under under hedonic framework. First, the availability of multiple transactions within apartment complexes enables fixed-effects estimation and addresses unobserved heterogeneity across apartment complexes. Second, we trace the effect on housing values from the announcement stage to actual operation stage of industrial parks. Our results indicate housing prices positively respond to an announcement of industrial park opening, but less in areas close to the industrial park. Furthermore, housing prices in close proximity to industrial parks decline when industrial parks begin operation.",2.0,,False,JournalArticle,,2024-03-23,d637f4858470df91a33dd8b0d6d39d133a55e954,housingpricesandenvironmentalhazardstheeffectsofindustrialparkopeningsinkorea,park,10.1080/13504851.2024.2331660,housingpricesandenvironmentalhazardstheeffectsofindustrialparkopeningsinkorea|2024,1,1706,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic housing prices",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,residential property price determinants,0d23cd7a92ccfe63ffe85f31c85c690778a52ee9,10.1007/s10901-025-10202-4,,Assessing the impact of maintenance condition on multifamily rents: an integrated approach of machine learning and hedonic modelling,2025.0,Journal of Housing and the Built Environment,Qiong Peng; Grace Zhao; Xinyue Ye,,2.0,,False,JournalArticle;Review,,2025-04-09,0d23cd7a92ccfe63ffe85f31c85c690778a52ee9,assessingtheimpactofmaintenanceconditiononmultifamilyrentsanintegratedapproachofmachinelearningandhedonicmodelling,peng,10.1007/s10901-025-10202-4,assessingtheimpactofmaintenanceconditiononmultifamilyrentsanintegratedapproachofmachinelearningandhedonicmodelling|2025,1,1000,3,hedonic housing prices || machine learning hedonic pricing housing || residential property price determinants,2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic house price,83ad512586a8602964d721ee086178a111e4b4e3,10.3828/jtep.1996.30.1.15,,The Impact of a Light Rail System on the Structure of House Prices: A Hedonic Longitudinal Study,1996.0,Journal of Transport Economics and Policy,D. Forrest; J. Glen; R. Ward,Two conventional railway lines in Greater Manchester were replaced by a new light rail system. This paper uses hedonic price methodology to examine whether any of the claimed benefits were capitalised in house prices. No discernible effect was found. This finding contrasts with claims made for the urban transit schemes in other countries. Reasons for the differences and methodological problems with the current literature are discussed.,2.0,,False,JournalArticle,,1996-01-01,83ad512586a8602964d721ee086178a111e4b4e3,theimpactofalightrailsystemonthestructureofhousepricesahedoniclongitudinalstudy,forrest,10.3828/jtep.1996.30.1.15,theimpactofalightrailsystemonthestructureofhousepricesahedoniclongitudinalstudy|1996,1,1439,1,hedonic house price,2,2,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,dc8e95a42e4f28c5d406942223769fbfcaa06f54,10.4324/9780203799833-24,,The impacts ofland use on nearby property values: estimates from a hedonic house price model,2004.0,,J. Bergstrom; S. Goetz; J. Shortle,,2.0,,False,,Economics,2004-08-02,dc8e95a42e4f28c5d406942223769fbfcaa06f54,theimpactsoflanduseonnearbypropertyvaluesestimatesfromahedonichousepricemodel,bergstrom,10.4324/9780203799833-24,theimpactsoflanduseonnearbypropertyvaluesestimatesfromahedonichousepricemodel|2004,1,1000,1,hedonic house price,2,2,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,babc83ff8ba37ccca49ba0e923a16f989bf653cd,10.15396/eres2013_233,,Measuring Dwelling Price Changes in Poland with the Application of the Hedonic Methods,2013.0,,R. Trojanek,,2.0,,False,,Economics,,babc83ff8ba37ccca49ba0e923a16f989bf653cd,measuringdwellingpricechangesinpolandwiththeapplicationofthehedonicmethods,trojanek,10.15396/eres2013_233,measuringdwellingpricechangesinpolandwiththeapplicationofthehedonicmethods|2013,1,1000,2,hedonic house price || hedonic housing prices,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c1d21684ae344f91787d890ea9f2ee65e1c2a777,10.1080/12265934.2025.2517097,,Housing price dynamics within an integrated urban model: testing the spatial and temporal transferability,2025.0,International Journal of Urban Sciences,M. Orvin; M. Fatmi; Ifratul Hoque,"ABSTRACT This study evaluates spatio-temporal transferability of housing price models within an integrated urban modelling framework. Multi-year housing sales data for the city of Vancouver is used to develop models in pre-, during-, and post-pandemic periods. The models adopt a random parameter autoregressive method to accommodate autocorrelation and random parameters to capture unobserved heterogeneity. Results confirm the influence of temporal, dwelling, land use, neighbourhood and accessibility attributes on housing prices. For instance, in the case of land use attributes, prices are more likely to be higher in diverse land mix areas pre-pandemic. Results also confirm several differences among three phases of the pandemic and variations in effects across observations. For example, prices are less likely to be higher in diverse land mix areas during and post-pandemic for a certain number of observations. Results further reveal that housing prices tend to be influenced by nearby dwelling prices. For spatial transferability, the Vancouver model, developed using pre-pandemic data, was applied to another city within the same province – Kelowna, which features different geographic and population characteristics. The performance measures reveal that the model can be adapted to the Kelowna context with careful calibration and minimal adjustments, despite differences in parameter estimates. Additionally, the temporal transferability of the Vancouver model is assessed by applying the pre-pandemic model to both pandemic and postpandemic contexts. Results suggest that while the pre-pandemic model is not effectively transferable to the pandemic period, it demonstrates reasonable transferability to the post-pandemic context with minor adjustments. Overall, the findings highlight the importance of local and temporal dynamics in the housing market, particularly when adapting models across different regions or during periods of socio-economic change. This study will assist in enhancing the ability of the model to predict housing market behaviours over the long-term in response to disruptive socio-economic conditions across different regions. Highlights Tests the spatial and temporal transferability of housing price models. Utilizes multi-year housing sales data for Vancouver City to develop models. Adopts random parameter autoregressive modelling method. Develops pre-, during, and post-pandemic models to assess temporal transferability. Vancouver model was transferred to Kelowna city to evaluate spatial transferability. Developed models will be implemented in an integrated urban model.",2.0,,False,JournalArticle,,2025-06-15,c1d21684ae344f91787d890ea9f2ee65e1c2a777,housingpricedynamicswithinanintegratedurbanmodeltestingthespatialandtemporaltransferability,orvin,10.1080/12265934.2025.2517097,housingpricedynamicswithinanintegratedurbanmodeltestingthespatialandtemporaltransferability|2025,1,3619,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",62b24ec957881d4d472471680cc3cadd3aff5709,10.1108/ijhma-12-2024-0189,,Determinants of single-family residential property values in Ethiopia: a comparative analysis of willingness to pay or receive and real transaction data,2025.0,International Journal of Housing Markets and Analysis,Masresha Belete Asnakew; Melkam Ayalew Gebru; Wuditu Belete; Takele Abebe; Yeshareg Baye Simegn,"Purpose This study aims to identify determinants of single-family residential property values and fill the gap by analyzing respondents’ willingness to pay/receive data alongside real transaction data. Ordinal logistic regression and ordinal least square regression were used. Design/methodology/approach Ordinal logistic regression effectively analyzes willingness-to-pay/receive data, accommodating the ordered nature of property value responses while incorporating multiple influencing factors. Ordinal least square regression quantifies the impact of continuous and categorical predictors on real transaction data. Findings Findings revealed strong associations between property values and several variables. Analysis of willingness-to-pay/accept data from 232 respondents showed significant impacts of factors such as the number of rooms, site area, construction material, property orientation, property age and proximity to bus stations and the central business district (p < 0.05). Similarly, ordinal least square regression analysis of transaction data confirmed the significance of most of these factors, except for property orientation, which indicates the difference of preference in the local market or reporting inconsistencies, demand further investigation. Variables such as views, proximity to wetlands, roads, green areas, religious institutions and schools were statistically insignificant across both data sets (p > 0.05). Practical implications It provides a robust basis for housing and urban development strategies. The stakeholders such as real estate developers, urban planners and policymakers are encouraged to incorporate these findings into housing policies, land value capture initiatives and urban planning frameworks to enhance residential property value and align with sustainable urban development goals. Originality/value This study contributes original insights into single-family residential property valuation by integrating willingness-to-pay and transaction data, substantiating the determinants of property value. ",2.0,,False,JournalArticle,,2025-03-11,62b24ec957881d4d472471680cc3cadd3aff5709,determinantsofsinglefamilyresidentialpropertyvaluesinethiopiaacomparativeanalysisofwillingnesstopayorreceiveandrealtransactiondata,asnakew,10.1108/ijhma-12-2024-0189,determinantsofsinglefamilyresidentialpropertyvaluesinethiopiaacomparativeanalysisofwillingnesstopayorreceiveandrealtransactiondata|2025,1,3058,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bf1b5947ca826931e3e0455435b5797d306e57fb,10.1108/ijhma-07-2023-0095,,How the single-family residence housing market capitalizes green property upgraded features: evidence from city of Austin,2023.0,International Journal of Housing Markets and Analysis,Jun-Feng Jiao; Xiaohan Wu; Ye-Fu Chen; A. Farahi," Purpose By comparing regression models, this study aims to analyze the added home value of green sustainability features and green efficiency characteristics, rather than green certifications, in the city of Austin. Design/methodology/approach The adoption of home green energy efficiency upgrades has emerged as a new trend in the real estate industry, offering several benefits to builders and home buyers. These include tax reductions, health improvements and energy savings. Previous studies have shown that energy-certified single-family homes command a premium in the marketplace. However, the literature is limited in its analysis of the effects of green upgrades and certification on different types of single-family homes. To address this gap, this research collected data from 21,292 multiple listing services (MLS) closed home-selling listings in Austin, Texas, over a period of 35 months. Findings The analysis results showed that green efficiency features could generally increase single-family housing prices by 11.9%, whereas green sustainability upgrades can potentially bring a 11.7% higher selling price. Although green housing certification did not have significant effects on most housing groups, it did increase closing prices by 13.2% for single-family residences sold at the medium price range, which is higher than the impacts from simply listing the green features on MLS. Originality/value The study contributes to the body of knowledge by examining the market value of broadly defined energy efficiency and sustainability features in the residential housing market. The findings can help policymakers, brokerage firms, home builders and owners adjust their policies and strategies related to single-family home sales and mortgage approvals. The research also highlights the potential benefits of capitalizing on green housing features other than certifications. ",2.0,,False,JournalArticle,,2023-10-30,bf1b5947ca826931e3e0455435b5797d306e57fb,howthesinglefamilyresidencehousingmarketcapitalizesgreenpropertyupgradedfeaturesevidencefromcityofaustin,jiao,10.1108/ijhma-07-2023-0095,howthesinglefamilyresidencehousingmarketcapitalizesgreenpropertyupgradedfeaturesevidencefromcityofaustin|2023,1,2896,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,5,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",787ba5a84faf66a799bc0e0eb2cd65dde00952df,10.1108/ijhma-05-2025-0115,,Factors affecting housing prices in Kadiköy (Istanbul): integration of MGWR and Space Syntax analysis,2025.0,International Journal of Housing Markets and Analysis,Mustafa Kahraman; Furkan Taha Çobanli; Emre Akadal," The purpose of this study is to examine how street network morphology, measured by spatial configuration metrics, affects housing prices in Istanbul, beyond traditional factors such as structure and location. While numerous studies have investigated the determinants of property values, the relationship between street geometry and property prices remains to be studied. The data set was collected through web crawling. The area where for sale advertisements were concentrated and varied was selected as the study area. In addition to building data obtained through web scraping, environmental and road data were also used. Environmental data (distance to public transportation stops, parks, coastlines and business and commercial areas) were obtained from relevant municipalities and institutions. Street network data was obtained from OpenStreetMap, and Space Syntax metrics such as connectivity, choice, integration and total depth were calculated. Multiscale geographically weighted regression was used to investigate the relationship between these variables and housing prices. The results show that, in addition to common structure and location characteristics, street network characteristics also influence housing prices. Among street characteristics, “selection” is the most influential. Higher values of selection, connectivity and integration positively affect prices. Total depth showed a negative correlation. This study integrates Space Syntax-based street measurements with advanced spatial modeling techniques and offers a new perspective. It highlights the importance of urban form in shaping housing markets and provides valuable insights for planners, urban designers and real estate analysts. ",2.0,,False,JournalArticle,,2025-10-15,787ba5a84faf66a799bc0e0eb2cd65dde00952df,factorsaffectinghousingpricesinkadikoyistanbulintegrationofmgwrandspacesyntaxanalysis,kahraman,10.1108/ijhma-05-2025-0115,factorsaffectinghousingpricesinkadikoyistanbulintegrationofmgwrandspacesyntaxanalysis|2025,1,2738,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,3,1,5,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b6c5ee38b07ef993439806dc524301bc79a6888d,10.1080/10549811.2025.2513228,,Natural Attraction and Driving Forces: Analyzing the Impact of Parkland on Residential Prices and Typological Difference*,2025.0,Journal of Sustainable Forestry,Ruo-Nan Han; Siyu Wu; Fang He; Qiao-Yun Sun,"ABSTRACT Exploring the driving mechanism of parks and green spaces on the spatial heterogeneity of housing prices helps to rationalize the allocation pattern of social and environmental resources and the housing economic market, and to optimize the spatial pattern of green facilities in human habitat. The study integrates Shenzhen housing samples and their price transaction data, park green space and other related facilities data, and by using Ordinary Least Squares (OLS) Geographically Weighted Regression Model (GWR), compares and analyses the sequence of effects of architectural characteristics, location characteristics, neighborhood characteristics and green space characteristics on housing prices, and analyses the relationship between park green space proximity and housing prices, and the types of differences in their effect mechanisms. The results show that the driving effect of overall park green space proximity on housing prices shows the concentration effect of high-level development areas; the proximity of different types of park green space drives housing prices as urban parks > natural parks > community parks, and urban parks have higher economic value. The results of this study suggest that future land resource planning should pay more attention to the economic drive of ecological space and effectively improve the efficiency of social resource services.",2.0,,False,JournalArticle,,2025-05-28,b6c5ee38b07ef993439806dc524301bc79a6888d,naturalattractionanddrivingforcesanalyzingtheimpactofparklandonresidentialpricesandtypologicaldifference,han,10.1080/10549811.2025.2513228,naturalattractionanddrivingforcesanalyzingtheimpactofparklandonresidentialpricesandtypologicaldifference|2025,1,2387,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",821975318999a19182bad9230a88ef0b43d45980,10.35136/krer.32.3.1,,Analysis of the Relationship between Housing Prices and Regional Characteristics Using Machine Learning and XAI,2022.0,Korea Real Estate Institute,Geonhee Yang; Hae-jung Chun,"This study aims to explain the relationship between variables that were not explained in the existing machine learning methodology by interpreting the relationship between apartment prices and regional characteristic variables using the XAI methodology. Furthermore, the study's goal is to collect various regional variables (population, occupation, income/consumption) in the basic area unit (postcode unit) that could not be addressed in previous domestic studies due to data collection difficulties. In terms of the independent variable, a dataset was created for each facility, population, occupation, income/consumption, and the dependent variable. The relationship between independent and dependent variables was inferred using a random forest among XGBoost and Bagging models and SHAP models among XAI methodologies on the constructed dataset. The spatial scope of the analysis was determined by dividing Seoul Metropolitan Government into national basic zone units, the content range was apartment among houses, and the period was empirically analyzed in 2021. Results reveal that the proportion of high income, the proportion of rental income, and the number of apartments positively affect apartment prices, whereas the proportion of low income, bus stop, and the number of multi-family houses negatively affect apartment prices.",2.0,,False,,,2022-09-30,821975318999a19182bad9230a88ef0b43d45980,analysisoftherelationshipbetweenhousingpricesandregionalcharacteristicsusingmachinelearningandxai,yang,10.35136/krer.32.3.1,analysisoftherelationshipbetweenhousingpricesandregionalcharacteristicsusingmachinelearningandxai|2022,1,2339,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,5,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7d61981b386e99be54d31f175da6b9213f60f5c7,10.1080/10527001.2022.2111749,,Adjacency Neighborhood Effects and Owner-Assessed Home Values,2022.0,Journal of Housing Research,J. Freybote; Lauren Simon; Akhila Veldandi,"Adjacency neighborhood effects such as neighbor interactions or perceived neighborhood quality have been neglected in the housing literature due to a lack of data. We investigate the impact of these effects on the housing wealth perceived by homeowners. Using a national longitudinal survey, we find that perceived neighborhood quality positively impacts owner-assessed home values. However, this effect is driven by homeowners ’ assessment of their neighborhoods ’ quality relative to other neighborhoods rather than their neighborhoods ’ perceived cleanliness or upkeep. Depending on the financial characteristics of homeowners, we also find neighbor interactions and perceived neighborhood safety to have a significant relation with self-assessed home values. Trust and support among neighbors and joint feel of the home and neighborhood on the other hand have no relation with estimated values. While neighborhood quality consistently explains owner-assessed values, home quality only has a relation with value estimates for homeowners with certain financial characteristics. Considering the importance of perceived housing wealth for household financial decisions as well as transaction prices, our findings have implications for policy makers and developers.",2.0,,False,Review,,2022-09-14,7d61981b386e99be54d31f175da6b9213f60f5c7,adjacencyneighborhoodeffectsandownerassessedhomevalues,freybote,10.1080/10527001.2022.2111749,adjacencyneighborhoodeffectsandownerassessedhomevalues|2022,1,2264,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",64bcada8e378388c92bdc0ab3eb7a59366fa1b0a,10.1111/1540-6229.70033,,Natural disasters and housing prices: What can we learn from tornadoes?,2026.0,Real Estate Economics,Jeffrey P. Cohen; Violeta A. Gutkowski,"The impact of tornadoes on housing prices has not been extensively explored in a causal analysis framework. We estimate the effects of damage from a major tornado in Little Rock, Arkansas, on prices of nearby undamaged homes. We study how a typical home's proximity to damaged properties might have led to a discount in its price due to severe blight in the neighborhood. We focus on homes that sold between January 2022 and August 2024 and compare the effects of the March 31, 2023, tornado on sale prices for homes near versus far from damaged properties. For all home sales within 250 m of at least one tornado‐damaged property, our difference‐in‐differences estimates imply an average discount of approximately 20% relative to home sales further away. These effects disappear with greater distance from the nearest damaged property. Second, homes in lower income census block groups did not incur price effects that were significantly different from the effects for other homes. Finally, we show that the impact was short‐lived on average, as we find no significant price discount on homes sold near or far from damaged properties 9 months after the tornado.",2.0,,False,JournalArticle,,2026-01-29,64bcada8e378388c92bdc0ab3eb7a59366fa1b0a,naturaldisastersandhousingpriceswhatcanwelearnfromtornadoes,cohen,10.1111/1540-6229.70033,naturaldisastersandhousingpriceswhatcanwelearnfromtornadoes|2026,1,2162,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,precios hedónicos vivienda,,10.29105/vtga7.1-139,,Análisis de la valoración hedónica de la vivienda del Área Metropolitana de Monterrey (2021),2021.0,Vinculatégica,Rubí Martínez Moreno; Héctor Sotelo Ahumada,"El objetivo de esta investigación es analizar el mecanismo de valoración hedónica de las áreas verdes en la vivienda en el área metropolitana de Monterrey (AMM). La hipótesis sostiene que, si se vincula un valor económico a las áreas verdes, se generaran a su vez, incentivos para impactar positivamente al entorno natural y social de la zona metropolitana de Monterrey. Se utilizan los resultados de la aplicación de una encuesta semiestructurada de corte transversal para el primer trimestre de 2021, se efectúa un análisis descriptivo y de frecuencia, con la finalidad de reflejar la importancia que la sociedad brinda a las áreas verdes dentro de sus propiedades. El principal resultado muestra que la población que no tiene áreas verdes accedería a crear un espacio ajardinado o arborizar dentro de su predio si la estimación porcentual de su valor del área verde es igual o superior al 10%. La primera sección del trabajo presenta la introducción, la segunda revisa el marco teórico; en el tercer apartado se analiza la metodología; en seguida se presentan los resultados y finalmente, se concluye.",2.0,,,journal-article,,,doi_10_29105_vtga7_1_139,analisisdelavaloracionhedonicadelaviviendadelareametropolitanademonterrey2021,moreno,10.29105/vtga7.1-139,analisisdelavaloracionhedonicadelaviviendadelareametropolitanademonterrey2021|2021,1,2104,1,precios hedónicos vivienda,1,0,1,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",db331c0f24629fe48021dad0bdeb8a42113ac2ce,10.1177/0739456x241253188,,Spillover Effect of Urban Reconstruction from Announcement to Completion on Residential Prices: Application of Two-Stage Spatial Quantile Regression,2024.0,Journal of planning education and research,Chun-Chang Lee; Zhi Lin; Chih-Min Liang; Zheng Yu,"This study analyzed four urban reconstruction projects in Taipei City (Zhufeng Tianmu, Huaku Sky Garden, Leqin Sanxi, and Yongde Yanyan) that were completed in 2016 based on data released by the Urban Regeneration R&D Foundation. The housing transaction data spanned from January 2008 to December 2017. The difference-in-difference method was used in conjunction with quantile regression to analyze the spillover effects of urban reconstruction projects on neighboring areas during the various phases of these projects. The empirical results showed that, during the planning and announcement phase, the effect on housing prices was negative and statistically significant for mid-to-low-priced units but positive and statistically significant for high-priced units. During the construction and post-completion phases, the effect was negative and statistically significant for low-priced, mid-to-low-priced, and mid-priced units, with low-priced units being the most affected group.",2.0,,False,JournalArticle,,2024-05-21,db331c0f24629fe48021dad0bdeb8a42113ac2ce,spillovereffectofurbanreconstructionfromannouncementtocompletiononresidentialpricesapplicationoftwostagespatialquantileregression,lee,10.1177/0739456x241253188,spillovereffectofurbanreconstructionfromannouncementtocompletiononresidentialpricesapplicationoftwostagespatialquantileregression|2024,1,1980,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",8346464039bf4731ec2d12feb1b59cd8e813ac95,10.1145/3490700.3490705,,Research on the Influence of Transportation Network Centrality on Housing Price Based on Big Data,2021.0,International Conference on Advancements in Computational Sciences,Feng Gao; Zhi-Bin Zhang,"Based on the big data of transportation network and housing price, geographic weighted regression is used to measure the degree of difference in the impact of urban road network and public transportation network centrality on housing prices. The results show that the degree of influence of the public transport network on housing prices is generally higher than that of the road network; the betweenness of the public transport network has the greatest impact on housing prices, followed by the direct centrality of the road network, the proximity centrality of the public transport network, the proximity centrality of the road network, and the public transport network. Network direct centrality, road network between centrality. Lanzhou's urban housing prices have strong traffic-oriented characteristics, and differentiated traffic improvement plans should be formulated according to the characteristics of the traffic network of different land parcels.",2.0,,False,JournalArticle;Book,Computer Science,2021-09-24,8346464039bf4731ec2d12feb1b59cd8e813ac95,researchontheinfluenceoftransportationnetworkcentralityonhousingpricebasedonbigdata,gao,10.1145/3490700.3490705,researchontheinfluenceoftransportationnetworkcentralityonhousingpricebasedonbigdata|2021,1,1958,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",409193be85a6addfb8b9ea5cec8954a488a67c26,10.1111/1540-6229.12465,,Down payment requirements and house prices: Quasi‐experiment evidence from Shanghai,2023.0,Real Estate Economics,Hao Shen; Hang Zhou; Zhengyi Zhou,"Using the regression discontinuity design, a quasi‐experiment approach, this paper establishes a causal relationship between the down payment requirement and house prices by exploiting a unique institutional background in Shanghai. In the unique setting, the required minimal down payment ratio jumps at the Inner Ring, a circular elevated highway, from 50% to 70% for a large group of buyers. With transaction level data from the largest real estate broker in Shanghai, we find that a lower required down payment ratio increases the apartment price by 138.8 thousand RMB, around 3.71% of the average transaction price.",2.0,,False,JournalArticle,,2023-11-20,409193be85a6addfb8b9ea5cec8954a488a67c26,downpaymentrequirementsandhousepricesquasiexperimentevidencefromshanghai,shen,10.1111/1540-6229.12465,downpaymentrequirementsandhousepricesquasiexperimentevidencefromshanghai|2023,1,1619,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c37b3936fa038b50286214eff27342b60fb3168e,10.24191/myse.v7i2.10264,,DETERMINING SIGNIFICANT FACTORS AFFECTING GREEN BUILDING PRICE USING MULTIPLE REGRESSION ANALYSIS,2020.0,,Nur Syafiqah Jamil; T. Mohd; S. Masrom,"The green building (GB) can be defined as the method of increasing the efficiency of the building and site. However, the spillover effect of GB price in Malaysia has not widely been discovered as there are limited case studies on this issue. This paper attempts to determine the significant factors that influence the GB price. The empirical experiment has been conducted to test the Multiple Regression Analysis (MRA) model on a real dataset of house prices in the area of Klang Valley, Selangor with GB specifications. The result showed that factors related to tenure have significant contributions to the GB price",2.0,,False,,Mathematics,2020-08-04,c37b3936fa038b50286214eff27342b60fb3168e,determiningsignificantfactorsaffectinggreenbuildingpriceusingmultipleregressionanalysis,jamil,10.24191/myse.v7i2.10264,determiningsignificantfactorsaffectinggreenbuildingpriceusingmultipleregressionanalysis|2020,1,1616,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,hedonic housing prices,,10.1108/03068299810193650,,Privatization in Russia: some micro‐evidence based on housing markets,1998.0,International Journal of Social Economics,G.S. Maddala; Yasushi Toda; Nadezhda Nozdrina,"Examines the importance of the role of economic incentives in privatization using data collected at the micro level from auctions of state‐owned apartments in Moscow. Describes the nature of the auctions, the data collected and the estimated hedonic price functions. Concludes that the auction prices are very well explained by economic factors, particularly after the price liberalization that happened in 1992.",2.0,,,journal-article,,,doi_10_1108_03068299810193650,privatizationinrussiasomemicroevidencebasedonhousingmarkets,maddala,10.1108/03068299810193650,privatizationinrussiasomemicroevidencebasedonhousingmarkets|1998,1,1412,2,hedonic housing prices || implicit prices housing attributes,2,0,2,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",47c116f45a4345b1c8ea851eea5f1e4de19c4dd0,10.2139/ssrn.3289359,,Is It a Curse or a Blessing to Live Near Rich Neighbors? Spatial Analysis and Spillover Effects of House Prices in Beijing,2018.0,,K. Vergos; Zhi Hui,,2.0,,False,,Economics,2018-11-22,47c116f45a4345b1c8ea851eea5f1e4de19c4dd0,isitacurseorablessingtolivenearrichneighborsspatialanalysisandspillovereffectsofhousepricesinbeijing,vergos,10.2139/ssrn.3289359,isitacurseorablessingtolivenearrichneighborsspatialanalysisandspillovereffectsofhousepricesinbeijing|2018,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",461baa2ed46843009e81369c0cc2cdaa5f0edf1c,10.1007/s10708-025-11284-x,,"Return of house price premiums based on proximity to subway stations in the post-pandemic period in Seoul, South Korea",2025.0,GeoJournal,Dongwoo Hyun,,2.0,,False,JournalArticle,,2025-01-28,461baa2ed46843009e81369c0cc2cdaa5f0edf1c,returnofhousepricepremiumsbasedonproximitytosubwaystationsinthepostpandemicperiodinseoulsouthkorea,hyun,10.1007/s10708-025-11284-x,returnofhousepricepremiumsbasedonproximitytosubwaystationsinthepostpandemicperiodinseoulsouthkorea|2025,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",34a156ce3d3bde0a16bd869fc392677f973c3578,10.1007/978-3-642-35548-6_122,,Spatial Analysis and Spatial House Price Index Construction: Evidence from Chengdu Housing Market,2014.0,,L. Xin; Si-Qi Zheng,,2.0,,False,,Business,,34a156ce3d3bde0a16bd869fc392677f973c3578,spatialanalysisandspatialhousepriceindexconstructionevidencefromchengduhousingmarket,xin,10.1007/978-3-642-35548-6_122,spatialanalysisandspatialhousepriceindexconstructionevidencefromchengduhousingmarket|2014,1,1000,4,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || spatial hedonic model housing",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cdb76d714a8ac7bcf6615085a1fcaa242f2b0ccc,10.23843/as.17.3.1,,Nonlinear Effects of Physical Characteristics of Apartment on Housing Prices: Focusing on Multivariate Adaptive Regression Spline,2018.0,,Jihun Oh; Kim Jeongseob,,2.0,,False,,Mathematics,2018-12-30,cdb76d714a8ac7bcf6615085a1fcaa242f2b0ccc,nonlineareffectsofphysicalcharacteristicsofapartmentonhousingpricesfocusingonmultivariateadaptiveregressionspline,oh,10.23843/as.17.3.1,nonlineareffectsofphysicalcharacteristicsofapartmentonhousingpricesfocusingonmultivariateadaptiveregressionspline|2018,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8f782d8d97a57ebaaf57344a6d78709df63e185e,10.1061/(asce)up.1943-5444.0000757,,"Expressway Proximity Effects on Property Prices in Hangzhou, China: Multidimensional Housing Submarket Approach",2022.0,Journal of urban planning and development,Hai-Zhen Wen; Zaiyuan Gui; Chuan-Hao Tian; Yan Song; Ganghua Zhou,,2.0,,False,,,2022-03-01,8f782d8d97a57ebaaf57344a6d78709df63e185e,expresswayproximityeffectsonpropertypricesinhangzhouchinamultidimensionalhousingsubmarketapproach,wen,10.1061/(asce)up.1943-5444.0000757,expresswayproximityeffectsonpropertypricesinhangzhouchinamultidimensionalhousingsubmarketapproach|2022,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bf9865681ac40358fef441eb2d7310c8fa652b99,10.25436/e2201t,,Specifying multi-scale spatial heterogeneity in the rental housing market: The case of the Tokyo metropolitan area,2021.0,,Peng Zhan; R. Inoue,,2.0,,False,,Geography,2021-09-01,bf9865681ac40358fef441eb2d7310c8fa652b99,specifyingmultiscalespatialheterogeneityintherentalhousingmarketthecaseofthetokyometropolitanarea,zhan,10.25436/e2201t,specifyingmultiscalespatialheterogeneityintherentalhousingmarketthecaseofthetokyometropolitanarea|2021,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",388a8de2981377977b1e070b9c73f6a7e843718f,10.2139/ssrn.4407202,,Using Machine Learning to Estimate the Heterogeneous Impact of Airbnb on Housing Prices: Evidence from Corsica,2023.0,Social Science Research Network,D. Brunstein; G. Casamatta; S. Giannoni,,2.0,,False,JournalArticle,,,388a8de2981377977b1e070b9c73f6a7e843718f,usingmachinelearningtoestimatetheheterogeneousimpactofairbnbonhousingpricesevidencefromcorsica,brunstein,10.2139/ssrn.4407202,usingmachinelearningtoestimatetheheterogeneousimpactofairbnbonhousingpricesevidencefromcorsica|2023,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1522a79b9f036f8b5a70f9dbb6709a506d1f9e63,10.1007/s12061-025-09667-1,,Multidimensional Neighborhood Amenity Characteristics and Housing Prices in Urban China,2025.0,Applied spatial analysis and policy,Zihan Qu; Yang-Yi Wu; Meitong Liu,,2.0,,False,JournalArticle,,2025-06-01,1522a79b9f036f8b5a70f9dbb6709a506d1f9e63,multidimensionalneighborhoodamenitycharacteristicsandhousingpricesinurbanchina,qu,10.1007/s12061-025-09667-1,multidimensionalneighborhoodamenitycharacteristicsandhousingpricesinurbanchina|2025,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,6eb3df88f004d3b76d01d44c917cfde37a94f9b5,10.62206/sajm.31.1.2024.60-82,,Factors Affecting House-prices: A Study of House-owners in Bhubaneswar Using the Hedonic Pricing Model,2024.0,South Asian Journal of Management,Abhiraj Malia; Biswajit Das; Aurodeep Kamal; B. Bisoyi; I. Satpathy,,2.0,,False,JournalArticle,,2024-06-25,6eb3df88f004d3b76d01d44c917cfde37a94f9b5,factorsaffectinghousepricesastudyofhouseownersinbhubaneswarusingthehedonicpricingmodel,malia,10.62206/sajm.31.1.2024.60-82,factorsaffectinghousepricesastudyofhouseownersinbhubaneswarusingthehedonicpricingmodel|2024,1,1000,1,hedonic house price,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,54892b44fa14ba39e0048010e0534ed7f970190e,10.1049/cp.2011.0288,,Novel methods to demarcate urban house submarket - Cluster analysis with spatially varying relationships between house value and attributes,2011.0,,Danlin Yu; Jing-Yuan Yin; Fei-Yue Ye,,2.0,,False,,Geography,2011-07-06,54892b44fa14ba39e0048010e0534ed7f970190e,novelmethodstodemarcateurbanhousesubmarketclusteranalysiswithspatiallyvaryingrelationshipsbetweenhousevalueandattributes,yu,10.1049/cp.2011.0288,novelmethodstodemarcateurbanhousesubmarketclusteranalysiswithspatiallyvaryingrelationshipsbetweenhousevalueandattributes|2011,1,1000,1,spatial hedonic model housing,0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,real estate hedonic regression,4891d48de205d7d2d2555303eef3e46f4ecd27c0,10.66884/kesj.2025.xrbblz6e,,"SMART CITY TECHNOLOGIES AND REAL ESTATE PRICINGS IN ABUJA, NIGERIA: A HEDONIC PRICING APPROACH",2025.0,Kaduna State University Environmental Sciences Journal,J. Sakariyau; Oluwatunmise S. Moradeyo; Taiwo Q. Yusuff; C. Dibi; R. O. Alao,"Purpose: Limited empirical evidence exists on the effects of smart technologies on property values in African cities. This study investigates how smart city initiatives influence real estate development and property pricing in Abuja, Nigeria’s Federal Capital Territory, using a hedonic pricing regression model.
Design/methodology/approach: To achieve this aim, using a hedonic pricing regression model. The analysis utilized 1,247 property transactions and associated market data from 2020–2024. Primary data were obtained from consultations with Abuja Geographic Information Systems (AGIS) officials and real estate developers, while secondary data were sourced from major Nigerian property portals. Key smart city indicators including smart building features, digital connectivity, and proximity to technology-driven urban projects were incorporated into the model.
Findings: Findings show that smart city attributes affect property values: properties with smart features command higher prices, areas with stronger digital connectivity appreciate faster, and proximity to major smart city projects is linked to increased market values. The findings highlight how smart city technologies enhance property values and urban competitiveness. Improvements in Abuja’s global smart city ranking aligned with positive shifts in residential pricing.
Research limitations/Implications: The study is mostly based on official property transaction data that may not capture Abuja's informal or off-market dealings, resulting in potential market representation bias. The smart city indicators employed were limited to what was locally published between 2020 and 2024, which excluded dimensions like IoT systems and environmental sensors. study is one of the first empirical evaluations of how smart city projects affect property values in Abuja and Nigeria as a whole. It provides data-driven evidence that links advances in smart city development to demonstrable changes in residential property prices.
Practical implications: The developers should add energy-efficient, secure, and interconnected smart technologies to residential and commercial projects for real value premiums. Consider grouping developments near technologically advanced facilities to increase sales appeal and urban efficiency.
Originality/value: The study provides empirical evidence on how smart city efforts effect real estate development and property values in an African context, specifically Abuja, Nigeria.
",1.0,,False,JournalArticle,,2025-12-01,4891d48de205d7d2d2555303eef3e46f4ecd27c0,smartcitytechnologiesandrealestatepricingsinabujanigeriaahedonicpricingapproach,sakariyau,10.66884/kesj.2025.xrbblz6e,smartcitytechnologiesandrealestatepricingsinabujanigeriaahedonicpricingapproach|2025,1,3654,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression",2,2,1,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,e4592b36820f6060fbb36cbe5025a3125af4afe1,10.1108/ijhma-07-2024-0092,,Pricing strategies in the Silicon Valley housing market: an update on TOM and recent events,2024.0,International Journal of Housing Markets and Analysis,Wan-Hsiu Cheng; Shih-Chieh Chiu; Chia-Yueh Yen; Fu-Chang Yeh," Purpose This study aims to explore the relationship between house prices and time-on-market (TOM) in Silicon Valley. Previous findings have been inconclusive due to variations in property characteristics. This paper highlights the discrepancy between listing and selling prices and identifies differences among housing types such as condominiums, detached houses and townhouses based on housing orientations and customer groups. Additionally, this study considers the impact of the COVID-19 pandemic and the Fed’s interest rate policies on the housing market. Design/methodology/approach The authors analyze 63,853 transactions from the Bay East Board of Realtors’ Multiple Listing Service during 2018 to 2022. The study uses a multiple-stage methodology, including a nonlinear hedonic pricing model, search theory and two-stage least squares method to address concerns relating to endogeneity. Findings The Silicon Valley housing market shows resilience, with low-end properties giving buyers more bargaining power without significant price drops. High-end properties, on the other hand, attract more attention over time, leading to aggressive bidding and higher final sale prices. The pandemic, despite reducing housing supply, did not dampen demand, leading to price surges. Post-COVID, price correlations with TOM changed, indicating a more cautious buyer approach toward high premiums. The Fed’s stringent monetary policies post-2022 intensified these effects, with longer listing times leading to greater price disparities due to financial pressures on buyers and shifting dynamics in buyer interest. Practical implications Results reveal a nonlinear positive correlation between TOM and the price formation process, indicating that the longer a listed property is on the market, the greater the price changes. For low-end properties, TOM becomes significantly negative, while for high-end properties, the coefficient becomes significantly positive, with effects and magnitudes varying by type of dwelling. Moreover, external environmental factors, especially those leading to financial strain, can significantly impact the housing market. Originality/value The experience of Silicon Valley is valuable for cities using it as a development model. The demand for talent in the tech industry will stimulate the housing market, especially as the housing supply will not improve in the short term. It is important for government entities to plan for this proactively. ",1.0,,False,JournalArticle,,2024-09-20,e4592b36820f6060fbb36cbe5025a3125af4afe1,pricingstrategiesinthesiliconvalleyhousingmarketanupdateontomandrecentevents,cheng,10.1108/ijhma-07-2024-0092,pricingstrategiesinthesiliconvalleyhousingmarketanupdateontomandrecentevents|2024,1,3479,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,3,6,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,a0d0a07828e6a5525911b1d1ebfbca6c5b1d3a0f,10.22313/reik.2022.20.4.85,,The Effect of AI Smart Housing Construction on Apartment Transaction Prices in Seoul,2022.0,Residential Environment Institute Of Korea,Yong-Kyung Cho; Young-Ho Yoon,"The AI smart housing market is expected to grow, but there is no study on the effect of the installation of AI smart housing on housing prices in terms of activation. It was attempted to demonstrate whether there is a housing price as a characteristic of a house through the hedonic model, which is most commonly used. The empirical data was used by combining the K-apt apartment management fee disclosure data and the apartment price and apartment characteristics data provided by Real Estate 114 Reps. According to the apartment management cost information provided by K-apt, 2,144 (12.15%) of 17,638 apartment complexes nationwide for which the AI Smart Housing maintenance fee is charged as an AI Smart Housing were installed. In the case of Seoul, an AI Smart Housing was installed in 213 (8.59%) of the total 2,479 apartment complexes. As a result of attaching price information to the management cost information, a total of 12,073 apartment transaction price data were used, and 1,598 pieces of AI Smart Housing installation complexes were used for the analysis. As a result of the empirical analysis, as in the general hedonic model, the house sale price per unit area was higher as the transaction level increased, the number of years elapsed, the total number of households, and the common management cost per household increased. In the case of common management fees, in the case of apartments that are recently supplied, the common management fees are high according to the trend of supplying various resident convenience facilities such as swimming pools and golf driving ranges in the complex, as well as large lobby spaces, and this can be seen as a result of this part being reflected in the house sale price. . In addition, the fact that the smaller the exclusive area is, the higher the price per unit area is due to the fact that the preference for small flats due to the downsizing of the household size, such as the increase in one- or two-person households, is reflected. Lastly, it was found that the apartment price per unit area was higher when the AI Smart Housing, the subject of this study's interest, was installed, compared to the case where the AI Smart Housing was not installed.",1.0,,False,,,2022-12-31,a0d0a07828e6a5525911b1d1ebfbca6c5b1d3a0f,theeffectofaismarthousingconstructiononapartmenttransactionpricesinseoul,cho,10.22313/reik.2022.20.4.85,theeffectofaismarthousingconstructiononapartmenttransactionpricesinseoul|2022,1,3215,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,4,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,cd76ab2433f7053a4f62722b0b2d4747c865e092,10.1080/12265934.2019.1592696,,Impact of one-person households on detached housing sales prices,2020.0,International Journal of Urban Sciences,Y. Oh; Hyun Seok Lee,"ABSTRACT A considerable number of single-person households in Korea belong to the vulnerable social group, and as of 2015, more than 50% of them are showing a characteristic of being concentrated in a dilapidated detached dwelling which was subdivided into a small unit; in this context, the movements of the detached house market, and its development potential are directly connected to the residential stability problem of a single-person household. The purpose of this study is to diagnose the residential problem of a single-person household trough the empirical analysis of the relationship between detached housings sales prices in housing sales market and one-person households. After analysing the detached housing sales data in Seoul, it was verified that the SPAR (Sales Price Appraisal Ratio) of the area heavily resided by one-person households was statistically significantly high compared to other areas. In order to verify whether this positive correlation is established in case of total age groups of one-person households, or even in case of a transactions motive for reconstruction, the multiple regression analysis by using the hedonic model was implemented. As a result, the positive (+) relationship exists in the areas heavily resided by young one-person households, and there is a concern of relatively increased future residential cost burden of the young generation. Also, in case when the transactions are intended for reconstruction purposes, there was a negative (–) impact by the one-person households on the sales prices. From developers’ point of view, it seems that the concentration of one-person households can lower the possibility of repayment of loans borrowed for the reconstruction, the possibility of housing sales, and the rent profit. Therefore, it is difficult to expect the improvement in deteriorated areas heavily populated by one-person households to function as a market; hereupon, the government’s aggressive intervention seems to be inevitable.",1.0,,False,,Business,2020-01-02,cd76ab2433f7053a4f62722b0b2d4747c865e092,impactofonepersonhouseholdsondetachedhousingsalesprices,oh,10.1080/12265934.2019.1592696,impactofonepersonhouseholdsondetachedhousingsalesprices|2020,1,2995,4,hedonic house price || hedonic housing prices || housing characteristics prices hedonic || rent hedonic regression housing,2,2,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1108/14714170810867023,,"Hedonic models, internet-based technologies, and the provision of online property appraisal",2008.0,Construction Innovation,Stephen W.K. Mak; Lennon H.T. Choy; Winky K.O. Ho,"Purpose The objective of this paper is to propose the use of transaction data, hedonic model and internet-based technologies to provide potential home buyers and sellers with instant and online property appraisal services by real estate-related firms. Design/methodology/approach Hedonic price model is performed to estimate the price index of individual housing estate. The values of the coefficients of the attributes may change in response to a changing environment. A professional judgment should be given by appraisers on time intervals to re-run the hedonic price models, if needed. Once the algorithms have been updated, authenticated users can connect to the system via a secure socket layer. They can browse and search the valuation reports by entering search criteria of premises into the system that filters the results instantly according to the request of users, and a detailed asset valuation report will be displayed in the browser. Findings This sort of online services is believed to save consumers' time, by offering timely property appraisal services that facilitate property transactions. Originality/value Currently, local large property developers, agencies and surveying firms are still relying on manual processes of providing selected transaction data via the web to their prospective customers. The system proposed and developed as described in this paper is an automatic and self-learning system that produces reliable and authentic results based on large quantity of historical transaction records. The professional judgments of appraisers add flavor to the final valuation of properties. As well as providing information to individual buyers/sellers, the system is most suitable to investors who own a large portfolio of properties as the total capitalization of the portfolio can be computed easily and quickly. REITs are examples of these portfolio owners.",1.0,,,journal-article,,,doi_10_1108_14714170810867023,hedonicmodelsinternetbasedtechnologiesandtheprovisionofonlinepropertyappraisal,mak,10.1108/14714170810867023,hedonicmodelsinternetbasedtechnologiesandtheprovisionofonlinepropertyappraisal|2008,1,2887,4,apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic property valuation,2,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic property valuation,30337b8193b6beddb917cb1fbd796d62bcc95e46,10.1108/jpif-03-2024-0037,,"Multilevel modelling, location and property valuations: an application to the Italian residential market",2024.0,Journal of Property Investment & Finance,Gaetano Lisi; Erika Ghiraldo; David Nardelli,"PurposeThe use of statistical methods in the field of real estate appraisals presents a trade-off between the efficiency of the estimates (that would require the use of sophisticate econometric models) and the ease of the economic interpretation of the outcomes (that characterises the hedonic pricing models). This paper shows that multilevel modelling (MLM) can represent a suitable solution to this trade-off.Design/methodology/approachThis paper uses the so-called “multilevel modelling” (henceforth MLM). MLM can represent a further step forward in the use of statistical methods in property appraisals. MLM is easy to implement and the MLM estimates have a clear economic meaning. Furthermore, MLM provides more efficient estimates of hedonic prices (the prices of housing attributes) with respect to standard hedonic pricing models. Finally, MLM is particularly suitable for the housing market analysis, where the feature “location” plays a key role.FindingsFor the Italian context, characterised by many “benchmark locations” (small municipalities that share similar geographic, historic, and socioeconomic characteristics), the paper finds that multilevel modelling (MLM) is needed to correctly estimate the hedonic prices also in a micro-area.Practical implicationsMLM allows to further enhance the key role of “location”. Location is indeed used as the “grouping variable” in MLM, instead of being treated as a generic housing attribute in hedonic pricing models. When the benchmark locations are many, therefore, MLM represents a very effective compromise between the estimates’ efficiency and the ease of outcomes’ economic interpretation.Originality/valueUnlike the related literature that, basically, use MLM to investigate what are the main determinants (levels) of housing prices, this paper uses MLM to make more efficient the estimation of hedonic prices.",1.0,,False,JournalArticle,,2024-06-25,30337b8193b6beddb917cb1fbd796d62bcc95e46,multilevelmodellinglocationandpropertyvaluationsanapplicationtotheitalianresidentialmarket,lisi,10.1108/jpif-03-2024-0037,multilevelmodellinglocationandpropertyvaluationsanapplicationtotheitalianresidentialmarket|2024,1,2874,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic || residential property price determinants",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,a91dae120a602a96b6eee24365e149533ab40908,10.1108/pm-09-2025-0130,,Determinants of apartment rental prices in Nairobi metropolitan area: a hedonic pricing analysis of an emerging urban market,2026.0,Property Management,E. K. Museleku," This study aims to identify the key factors affecting apartment rental prices in emerging urban markets such as the Nairobi metropolitan area (NMA), to facilitate improved investment, portfolio management decisions and policy development. The paper uses a case study approach to analyse recent apartment lettings and apartment features in the NMA. A sample of 262 transactions was analysed using the hedonic pricing model. The data were collected between April and July 2025. The study identified apartment size, location, provision of a gymnasium, availability of an electricity backup generator, number of bedrooms and the age of the apartment as key determinants of apartment market rent in the emerging urban housing market of the Nairobi metropolis. The findings of this paper can be utilised by property investors, developers, valuers, managers, architects, urban planners and policymakers to inform their decisions. This information supports investment decisions by developers and landlords, guides rent-setting and valuation practices for property managers and informs planning and infrastructure decisions by policymakers. However, the results are specific and based on data collected from an emerging urban housing market, the Nairobi metropolis. There is limited literature on emerging rental housing markets in Sub-Saharan Africa. This study provides evidence-based insights from the NMA, drawing on location-specific structural, locational and amenity attributes that reflect the city's distinctive socioeconomic structure, level of market formalisation and urban spatial configuration. While these findings offer valuable lessons for comparable emerging cities, they may not be fully generalisable to countries where housing markets, income structures and institutional arrangements differ substantially. ",1.0,,False,JournalArticle,,2026-02-27,a91dae120a602a96b6eee24365e149533ab40908,determinantsofapartmentrentalpricesinnairobimetropolitanareaahedonicpricinganalysisofanemergingurbanmarket,museleku,10.1108/pm-09-2025-0130,determinantsofapartmentrentalpricesinnairobimetropolitanareaahedonicpricinganalysisofanemergingurbanmarket|2026,1,2851,4,apartment prices hedonic || hedonic housing prices || hedonic property valuation || spatial hedonic model housing,2,2,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,c62b767b279fcecc2db8dbd75bd87d923bd01b26,10.1177/0361198119857954,,Local Influences on Zonal Rents in Alberta: Implications for Integrated Modeling,2019.0,Transportation Research Record,J. Hunt; A. Silva; J. Abraham,"A hedonic price model was estimated for residential developments in the province of Alberta in Canada. The model is designed to be used with the Production, Exchange and Consumption Allocation System (PECAS) land use modeling framework, which forecasts future development patterns on each parcel based on developer return-on-investment functions, allocates households and jobs, and calculates economic benefit measures. In PECAS, base year rents by land use zone (LUZ) are a calibration target, while future rents by LUZ are calculated in a bid-rent allocation, so the estimation separates out local effects from neighbourhood (LUZ) effects. Local effects include proximity to busy collectors, light rail transit (LRT) stations, schools, and amenities such as parks, as well as local road and off-road access to the nearest network link. A non-linear treatment was required to separate out the value of land from the value of space, especially on large parcels at low intensities of development. A Bayesian approach was used to 1) incorporate prior knowledge from previous PECAS work in the U.S., and 2) to provide a model that can forecast future prices on any parcel in Alberta, even in areas where certain types of development are not currently sufficient in quantity to confidentally estimate parameters. The estimation quantifies the current price landscape in Alberta by LUZ, showing higher prices (and a tendency for future development, where allowed) in major cities, major oil industry locations, and national parks. It also quantifies the positive impacts of local road access, LRT station proximity, schools, rivers, and natural areas; the negative (nuisance) impact of being too near to busy roads and schools; and the depreciation of older buildings.",1.0,,False,JournalArticle,Engineering,2019-06-27,c62b767b279fcecc2db8dbd75bd87d923bd01b26,localinfluencesonzonalrentsinalbertaimplicationsforintegratedmodeling,hunt,10.1177/0361198119857954,localinfluencesonzonalrentsinalbertaimplicationsforintegratedmodeling|2019,1,2763,1,hedonic price model amenities residential,2,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,9bc03621995f587a66cc1c9d17f23d3b4c116999,10.1177/03611981251393236,,Capitalization Effects of Transport Infrastructure on Public Housing Prices during the Recession: Evidence from Hong Kong,2025.0,Transportation Research Record,Lin Deng,"This study aims to examine the capitalization effects of transportation infrastructure on public housing prices during economic downturns through a case study of Hong Kong’s 2017–2019 recession. The transaction records of public housing before and after the recession are collected and comparatively analyzed. Transportation accessibility is measured in two ways: continuous variables (distance) and dummy variables (whether within a distance threshold or not). The hedonic pricing models are estimated using both ordinary least squares (OLS) for initial estimation and two spatial econometric models, that is, the spatial error model (SEM) and the spatial lag model (SLM), for addressing spatial dependencies. Results show that proximity to transportation infrastructure significantly increases public housing prices. (Public housing, unlike private housing, is typically designed for low-income homebuyers.) Metro stations, bus terminals, and high-speed rail stations are preferred, while airports have a negative impact on prices because of noise and remoteness. The positive capitalization effect of metro stations decreases during the recession, while the positive impact of bus terminuses increases. The percentage decline was greater for metro stations at 300–600 m than within 300 m. For metro stations within 300 m, OLS showed the largest drop (8.46%) and SEM the smallest (0.89%), with SLM close to OLS (7.66%). For metro stations between 300 and 500 m, the decrease was 28.42% (OLS), 20.96% (SEM), and 30.86% (SLM). These findings benefit public housing providers, housing policymakers, and transport planners in stabilizing the public housing market and maintaining the capitalization effects of transportation infrastructure.",1.0,,False,JournalArticle,,2025-11-24,9bc03621995f587a66cc1c9d17f23d3b4c116999,capitalizationeffectsoftransportinfrastructureonpublichousingpricesduringtherecessionevidencefromhongkong,deng,10.1177/03611981251393236,capitalizationeffectsoftransportinfrastructureonpublichousingpricesduringtherecessionevidencefromhongkong|2025,1,2738,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,26dcb8068acfb3de3bfbaa369d249b0cd3e98d18,10.1111/jors.70044,,Housing Prices and Segregation: The Case of Social Frontiers,2025.0,Journal of Regional Science,Alexander J. W. Myatt; Urban Lindgren; Liv Osland," Residential segregation has become an increasingly prevalent feature of many European cities. This paper studies a special type of residential segregation—known as a social frontier —which arises from abrupt spatial divisions between the population composition of different neighboring groups. Although related “borders models” have been previously studied, both theoretically and empirically, the current work presents the first attempt to quantify the capitalization of social frontiers into housing prices by using the hedonic approach. While prior studies have used arbitrary measures and limits to distinguish between neighborhoods, the current study combines a Bayesian spatial conditional autoregressive model with Norwegian register data to identify ethnic social frontiers in the Oslo travel‐to‐work area, before applying hedonic modeling to individual house sale transaction data in the region. In this way, social frontiers have been identified in the study area, and results show that these frontiers are locally capitalized in housing prices. The paper's main result comes in the identification of price gradients that manifest themselves asymmetrically as one moves towards (or away from) the frontier itself. On the native‐dense side of the frontier, prices were lowest in the immediate vicinity of the frontier and rose nonlinearly with increased distance, whereas the opposite was true on the immigrant‐dense side of the frontier. Given the importance of housing prices in determining the spatial distribution and residential mobility patterns of peoples, these findings support the notion that a social frontiers framework may help to further our knowledge of the complex landscape of residential segregation. ",1.0,,False,JournalArticle,,2025-12-30,26dcb8068acfb3de3bfbaa369d249b0cd3e98d18,housingpricesandsegregationthecaseofsocialfrontiers,myatt,10.1111/jors.70044,housingpricesandsegregationthecaseofsocialfrontiers|2025,1,2734,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices || spatial hedonic model housing",2,2,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,94ac6b90bde1be98448bce61dfdec2880ca916ed,10.1111/grow.70043,,"Can the Disclosure of Enrollment Warning Information Really Reduce the Price of School District Housing? Evidence From a Natural Experiment in Hangzhou, China",2025.0,Growth and Change,Yue Xiao; Yun-Qin He; Haizhen Wen,"In the context of the nearby enrollment policy in China, parents’ pursuit of good educational resources has driven up the prices of school district housing. Concerning this situation, the Education Department of Zhejiang Province released the enrollment warning information for primary schools in October 2018. This information disclosure aims to guide parents to purchase housing rationally by publishing the lists of schools exceeding their enrollment capacity. Despite the policy’s practical meaning, its impact remains underexplored. Using Hangzhou, China as the study area, the current study quantitatively analyzes the effectiveness of enrollment warning disclosure through the housing market. With housing transaction data from 2018 to 2019, the study constructs the hedonic price model, difference‐in‐differences model, and quantile regression model to identify both average and heterogeneous effects. The results indicate that school quality is significantly capitalized into housing prices and the disclosure of enrollment warning information intensifies price differentiation between warning and non‐warning primary school districts. In particular, the high‐priced housing market is more sensitive to information disclosure with stronger responses observed among upper quantiles. Rather than mitigating demand, the release of the information reinforces homebuyers’ preference for high‐quality schools. The empirical results and implications of this study are helpful to the continuation and optimization of warning information policy, contributing to education equity and sustainable urban development.",1.0,,False,JournalArticle,,2025-08-02,94ac6b90bde1be98448bce61dfdec2880ca916ed,canthedisclosureofenrollmentwarninginformationreallyreducethepriceofschooldistricthousingevidencefromanaturalexperimentinhangzhouchina,xiao,10.1111/grow.70043,canthedisclosureofenrollmentwarninginformationreallyreducethepriceofschooldistricthousingevidencefromanaturalexperimentinhangzhouchina|2025,1,2613,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",cc8efd15018d45a844f6eaa3384dc9161878cea1,10.7202/1115222ar,,"Landslide Hazards, Public Intervention and Property Values – The Case of Saguenay, Quebec, Canada",2024.0,Canadian Journal of Regional Science,François Des Rosiers; Bienvenu Tossou,"Since the beginning of the 21st century, over 100 major landslides have been reported worldwide, which resulted in both human casualties and massive damages to property. While the academic and technical literature on landslide hazard risk assessment is quite abundant, relatively few studies address the issue of the impact of landslide exposure on property values. In this research, we look at the impact that public intervention aimed at compensating homeowners affected by landslide hazard constraint zones had on residential values in La Baie, an arrondissement of the City of Saguenay (formerly Chicoutimi), in the province of Quebec, Canada. The area was most affected by the Saguenay flood that hit the region on July 19 and 20, 1996 and which was followed by a series of landslides. A difference-in-differences (DiD) spatial hedonic price model is estimated using a representative sample of 813 single-family sales transacted in La Baie between 2009 and 2016. Findings suggest that, caeteris paribus, and nearly two decades after the 1996 events, the compensation scheme implemented at the local level in December 2012 did not manage to completely offset the disadvantages of building constraints on high-risk sites induced by the new regulations and translated into a statistically significant price discount of 3.4% for affected properties as opposed to unaffected ones. They also suggest that a panoramic view on the Saguenay fjord adds some 8.6% to a property’s value.",1.0,,False,JournalArticle,,,cc8efd15018d45a844f6eaa3384dc9161878cea1,landslidehazardspublicinterventionandpropertyvaluesthecaseofsaguenayquebeccanada,rosiers,10.7202/1115222ar,landslidehazardspublicinterventionandpropertyvaluesthecaseofsaguenayquebeccanada|2024,1,2480,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",2,1,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,7d74147f51c1a546b6e95b966d0c47644d825a0a,10.1177/10780874261442184,,Evaluating the Housing Market Effects of a Large Anchor Institution: The Case of the Obama Presidential Center,2026.0,Urban Affairs Review,Matthew D. Wilson; Elham Pilegar; A. El-Khattabi; Sofia Dermisi,"This study examines the housing market impacts of the Obama Presidential Center (OPC) on Chicago's South Side during the pre-opening period (2013–2024). Using spatial hedonic difference-in-differences models, we analyze property sale prices across distance buffers surrounding Jackson Park to assess whether proximity to this high-profile anchor institution generated anticipatory price effects. Despite conditions typically associated with speculative appreciation, including extensive media coverage, documented investor activity, and expectations of neighborhood transformation, we find no statistically significant price increases in areas closest to the OPC (within 0–1.5 miles). Instead, significant appreciation emerges only at 1.5–2 miles from the site, where prices rose approximately 12 percent above market trends in 2023–2024. This counterintuitive spatial pattern challenges conventional proximity-based models of anchor institution effects. We explore potential explanations, including construction-related disruption, uneven policy coverage, forward-looking investor behavior, and pandemic-era market dynamics. Overall, the findings demonstrate that anticipatory price effects are conditional on local context rather than automatic responses to major developments.",1.0,,False,JournalArticle,,2026-04-17,7d74147f51c1a546b6e95b966d0c47644d825a0a,evaluatingthehousingmarketeffectsofalargeanchorinstitutionthecaseoftheobamapresidentialcenter,wilson,10.1177/10780874261442184,evaluatingthehousingmarketeffectsofalargeanchorinstitutionthecaseoftheobamapresidentialcenter|2026,1,2279,3,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),73e121c361e5f5f62c862ab7e871ac4360ea1089,10.1108/jerer-07-2021-0039,,Hijacking the auction – seller's or buyer's curse?,2022.0,Journal of European Real Estate Research,Marte Flått; J. Olaussen; A. Oust; O. Sønstebø,"PurposeThis paper aims to investigate the likelihood and price effects of auction hijacking – transactions conducted before a planned auction – in the residential real estate market.Design/methodology/approachUsing a sample of 84,203 residential properties in Oslo, Norway for the period 2007–2017, the authors employ a probit sales choice model to study the likelihood of auction hijacking and hedonic models, fixed effects models and propensity score matching to investigate the price effects.FindingsThe authors find that auction hijacking is more likely to occur in periods of higher market activity and that hijacked auction properties sell at a premium of approximately 4% compared with properties sold at regularly conducted auctions. One possible explanation for the premium could be that risk aversion among buyers leads to a higher likelihood of hijacking offers and higher prices due to the risk reduction premium that sellers can extract.Originality/valueFor most households, buying a home is an investment of great economic significance, and understanding the different aspects of the auction is paramount for both buyers and sellers. Policymakers need to be aware of the market effects from auction hijacking and determine whether restrictions should be introduced.",1.0,,False,,,2022-03-21,73e121c361e5f5f62c862ab7e871ac4360ea1089,hijackingtheauctionsellersorbuyerscurse,flått,10.1108/jerer-07-2021-0039,hijackingtheauctionsellersorbuyerscurse|2022,1,2279,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,1,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic property valuation,29c0373c207f8d1505773566a7ff7f8b35beb10c,10.1108/ijhma-01-2025-0018,,An empirical case study on the effect of Al Aweer sewage plant odours on housing prices in Dubai’s International City,2025.0,International Journal of Housing Markets and Analysis,Nada Karim Abou-Gad; M. Myers; E. Aidoo,"Purpose This study aims to investigate the impact of proximity to the Al Aweer sewage treatment plant (STP) on residential property values in Dubai’s International City. It hypothesises that properties closer to the plant experience greater value diminution due to odour emissions. Design/methodology/approach The research uses traditional hedonic pricing models to analyse the impact of odour emissions on property values. Advanced machine learning techniques and spatial econometric models were considered but deemed less appropriate due to the small, homogeneous data set. The study uses a comprehensive data set of residential flats within International City. Findings The results indicate a significant negative impact of odour emissions on property values. Properties closer to the STP show a marked decrease in value, highlighting the importance of environmental factors in real estate valuation. Originality/value This study is unique in the United Arab Emirates, focusing on the environmental issue of odour emissions in a stigmatised area. It provides valuable insights for policymakers and real estate professionals, emphasising the need for accurate environmental impact assessments in property valuation. ",1.0,,False,JournalArticle,,2025-05-06,29c0373c207f8d1505773566a7ff7f8b35beb10c,anempiricalcasestudyontheeffectofalaweersewageplantodoursonhousingpricesindubaisinternationalcity,abou-gad,10.1108/ijhma-01-2025-0018,anempiricalcasestudyontheeffectofalaweersewageplantodoursonhousingpricesindubaisinternationalcity|2025,1,2221,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic property valuation || machine learning hedonic pricing housing || spatial hedonic model housing",2,2,2,3,1,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,fc923f75a19df80d6b540aac44da12602ce6592f,10.1080/10402381.2025.2546885,,Valuing shoreland development and environmental disturbances: a hedonic analysis of lakefront properties in Wisconsin,2025.0,Lake and reservoir management,S. Borchardt; T. Boyer; James I. Price,"Abstract Borchardt S, Boyer TA, Price JI. 2025. Valuing shoreland development and environmental disturbances: a hedonic analysis of lakefront properties in Wisconsin. Lake Reserv Manage. 41:297–312. Shoreland development, including boat lifts, manicured lawns, artificial beaches, and shoreline armoring, can offer considerable benefits to lakefront property owners. Drawing on data from the Wisconsin Department of Natural Resources, this study uses a hedonic property model to estimate the value of specific shoreland features on Wisconsin’s inland lakes. Results indicate that certain features—such as seawalls, riprap, and littoral zone structures—are positively associated with property sale price after controlling for housing characteristics and lake-specific factors. By estimating homeowners’ willingness to pay for these features, the analysis quantifies the economic benefits associated with shoreland development. These findings offer insight to guide shoreland management policies, which must weigh the private benefits to homeowners against the environmental costs of disrupting natural landscapes and increasing sediment and pollutant loading that degrade aesthetics, recreational use, and aquatic habitat.",1.0,,False,JournalArticle,,2025-09-23,fc923f75a19df80d6b540aac44da12602ce6592f,valuingshorelanddevelopmentandenvironmentaldisturbancesahedonicanalysisoflakefrontpropertiesinwisconsin,borchardt,10.1080/10402381.2025.2546885,valuingshorelanddevelopmentandenvironmentaldisturbancesahedonicanalysisoflakefrontpropertiesinwisconsin|2025,1,2221,3,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || housing characteristics prices hedonic",2,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,310f8a3e68666eaad2f9906d49200c046114ada1,10.1145/3664968.3664994,,The Implications of the Legacy of the 19th Asian Games on Housing Price Dynamics: Using Transaction Big Data for Hedonic Price Model-Based Empirical Analysis,2024.0,2024 6th International Conference on Management Science and Industrial Engineering,Fangziyi Huang; Chao Zhou; Fengjiao Tang; Haizhen Wen,"Hosting mega sporting events is of great significance to urban development. Quantitative research on the legacies of mega sporting events through the real estate market has attracted extensive attention among scholars. Taking the 19th Asian Games as a case study, we conducted an empirical analysis of one of the newly constructed sports facilities associated with the Asian Games. With the in-depth application of big data, the study utilized data from 18,016 housing transactions in Hangzhou, China from April 2019 to August 2023. The hedonic price model, covering building, neighborhood and location characteristics, was constructed. The results indicate that linear distance close to the Asian Games Park has an impact on housing prices, varying across different distance ranges. Housing prices increase by 5.9% in the range of 3-6km away from the Asian Games facility, with a larger growth observed within a 3km radius at 17.0%. However, there is a slight decrease within 6-9km. This implies that the legacy of the Asian Games exhibits different externalities across varying distances.",1.0,,False,Book;Conference,,2024-04-24,310f8a3e68666eaad2f9906d49200c046114ada1,theimplicationsofthelegacyofthe19thasiangamesonhousingpricedynamicsusingtransactionbigdataforhedonicpricemodelbasedempiricalanalysis,huang,10.1145/3664968.3664994,theimplicationsofthelegacyofthe19thasiangamesonhousingpricedynamicsusingtransactionbigdataforhedonicpricemodelbasedempiricalanalysis|2024,1,2090,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,2,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,8130eea96a036800ec13c5126c33993cdef2c27d,10.1145/3460824.3460828,,Hedonic Modeling for Predicting House Prices during CoVid19 Pandemic in the Philippines,2021.0,MSIE,Janice A. Abellana; M. Devaraj,"House projects kept changing in the Philippines, because of the changing needs and demands of the market valuation. Predicting the housing prices will be the focus of the study especially during the CoVid-19 pandemic. As the Consumer Price Index (CPI in the Philippines increased to 123.40 points in September from 123.30 points in August of 2020 [8]. The researcher will use Hedonic Regression Model techniques for analyzing its statistical technique for investigating and modeling the relationship between variables that will be applied in the study. In the data collection, the regression analysis will be the basis for collecting the data such as its historical data, observational study, and design experiment of housing prices before the CoVid19 pandemic came in the Philippines. The results and conclusion of the different machine algorithms will be used showing that regression analysis is the best technique for prediction of prices for housing in the Philippines during the Pandemic. The study will apply parameters that can calculate house valuations within Metro Manila.",1.0,,False,JournalArticle;Book,Medicine,2021-04-02,8130eea96a036800ec13c5126c33993cdef2c27d,hedonicmodelingforpredictinghousepricesduringcovid19pandemicinthephilippines,abellana,10.1145/3460824.3460828,hedonicmodelingforpredictinghousepricesduringcovid19pandemicinthephilippines|2021,1,2082,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices",2,3,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic property valuation,288ccd7e6b94d5a64902298ba37985b0f21d77d1,10.1177/00420980251410866,,The valuation of energy efficiency labels in the French housing market,2026.0,Urban studies,Sylvain Chareyron,"This study assesses the effect of energy efficiency labels on the private housing market using information on energy efficiency assessments of housing and property transactions in France between 2016 and 2021. We take into consideration two energy labels assigned to dwellings, one that calculates energy efficiency based on energy consumption and the other on greenhouse gas emissions. The results of the hedonic regressions show that having a higher efficiency rating has a significantly positive effect on housing prices. We also show that this effect increases with the number of annual heating degree days of the locality of the dwelling, indicating the importance of the energy-saving aspect in the market valuation of energy-efficient housing. Finally, using regression discontinuity estimates, we differentiate between the effect of the cognitive perception of labels and of the real energy efficiency gain on housing prices. Our findings reveal that the cognitive effect is predominantly observed in the least efficient dwellings.",1.0,,False,JournalArticle,,2026-02-12,288ccd7e6b94d5a64902298ba37985b0f21d77d1,thevaluationofenergyefficiencylabelsinthefrenchhousingmarket,chareyron,10.1177/00420980251410866,thevaluationofenergyefficiencylabelsinthefrenchhousingmarket|2026,1,2039,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || hedonic property valuation",2,2,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,2cfe9982c37f888ca8ffdb5c783b7d6cc430dd23,10.24957/hsr.2025.33.4.117,,The Impact of Urban Rail Station Characteristics on Apartment Prices,2025.0,Korean Association for Housing Policy Studies,Yuri-Jung Lee; Seunghoon Park,"This study analyzes the impact of urban rail station characteristics on apartment price formation in Seoul using 273,883 actual transactions from 2018 to 2022. A semi-log hedonic price model and a random forest ensemble were estimated, and SHAP (Shapley Additive Explanations) was applied to interpret variable contributions. Results show that structural and accessibility factors—such as distance to the Han River and CBD, number of units, building age, and floor area—mainly drive price variation, while station-related features including exits, nearby commercial land ratio, distance to universities, and daily ridership act as secondary but meaningful influences. Station characteristics are highly policy-relevant as they can be improved in the short term through public intervention, and the random forest model demonstrated superior predictive accuracy over the hedonic model. The findings emphasize the need for precise housing and transit-oriented policies and provide empirical evidence for land-use and transportation planning.",1.0,,False,JournalArticle,,2025-11-30,2cfe9982c37f888ca8ffdb5c783b7d6cc430dd23,theimpactofurbanrailstationcharacteristicsonapartmentprices,lee,10.24957/hsr.2025.33.4.117,theimpactofurbanrailstationcharacteristicsonapartmentprices|2025,1,2038,3,apartment prices hedonic || hedonic housing prices || housing characteristics prices hedonic,2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,spatial hedonic model housing,f19cd3fc0b31c535f962ac625c17f78fb1bd6d00,10.5814/j.issn.1674-764x.2024.02.008,,"The Spatial Impact of the Accessibility of Urban Green Infrastructure on Housing Prices in Nanjing, China",2024.0,Journal of Resources and Ecology,Zhoubing Gao; Junjun Zhu; Ligang Lv; Yong-Le Li; Junxiao Wang,"Abstract: The reasonable allocation of green infrastructure (GI) can improve the environmental quality of human settlements and urban residents' happiness. We used hedonic price and quantile regression models to quantitatively examine the impact of GI accessibility on housing prices, and the heterogeneity of this impact across different housing prices. The results showed that: (1) GI accessibility significantly affected housing prices. In addition, every 1% increase in GI area increased housing prices by approximately 0.3%. (2) GI accessibility had different effects on different housing prices; that is, different housing prices had different sensitivities to GI accessibility. This was especially true in the 25%–75% range of housing prices, where housing prices were negatively correlated with the time to the nearest GI. During urban development, reasonable planning and construction of urban GI should be undertaken to meet urban residents' needs for GI and promote sustainable urban development.",1.0,,False,JournalArticle,,2024-03-26,f19cd3fc0b31c535f962ac625c17f78fb1bd6d00,thespatialimpactoftheaccessibilityofurbangreeninfrastructureonhousingpricesinnanjingchina,gao,10.5814/j.issn.1674-764x.2024.02.008,thespatialimpactoftheaccessibilityofurbangreeninfrastructureonhousingpricesinnanjingchina|2024,1,2007,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || spatial hedonic model housing",2,2,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,721a07f055fdf8f7cd8969977253a43c77d88923,10.1080/13504851.2018.1486971,,Floor-level premiums in private housing: the case of condominiums in Singapore,2018.0,Applied Economics Letters,Danny Nam Chien Khiew; Chew-Ging Lee,"ABSTRACT Using a sample consists of 3367 actual condominium sales transacted in resale market from May 2012 to April 2015 from the Singapore Urban and Redevelopment Authority, a hedonic pricing model is used to investigate for the presence of floor-level premiums in the private housing market, condominium, in District 23 of Singapore. Unit size, types of land tenure and the distance to the nearest mass rapid transit (MRT) station are also used as other independent variables. The estimated coefficient of each of these independent variables is statistically significant. Each of these independent variables also has positive estimated coefficient, suggesting that they have positive effects on condominium resale prices. Main findings are (a) there is a presence of floor-level premium; (b) the condominiums with a distance more than 400 m to the MRT station get higher resale prices; and (c) there is an increasing effect of floor level on condominium price.",1.0,,False,,Business,2018-06-22,721a07f055fdf8f7cd8969977253a43c77d88923,floorlevelpremiumsinprivatehousingthecaseofcondominiumsinsingapore,khiew,10.1080/13504851.2018.1486971,floorlevelpremiumsinprivatehousingthecaseofcondominiumsinsingapore|2018,1,1963,1,hedonic housing prices,2,2,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,machine learning hedonic pricing housing,9f81693cc104896adddac5b634fb375eb3612b5c,10.24957/hsr.2023.31.2.133,,Exploring the Nonlinear Relationship between Development Density and Housing Price: Focusing on eXplainable AI (XAI),2023.0,Korean Association for Housing Policy Studies,J. Seo; Mihwa Lim; Sanguk Han; S. U. Purwaningati; Jeongseob Kim,"This study aimed to investigate the association between development density and housing prices in Seoul, utilizing a machine learning driven hedonic price model. The model employed actual transaction data of high-rise condominiums (apartments) spanning from January 2019 to December 2021. The study employed CatBoost algorithm and eXplainable AI techniques (SHAP) to explore the non-linear relationships between floor area ratio(FAR) and housing prices. The findings revealed that overall FAR had a negative relationship with housing prices for both general apartments and mixed-use development. However, the negative impact of FAR was relatively small in large-scale development. Furthermore, the negative impact of FAR may not be significant in some high-end mixed-use development. These findings imply the importance of proposing appropriate site plans to ensure a desirable residential environment when high-density development is necessary.",1.0,,False,JournalArticle,,2023-05-30,9f81693cc104896adddac5b634fb375eb3612b5c,exploringthenonlinearrelationshipbetweendevelopmentdensityandhousingpricefocusingonexplainableaixai,seo,10.24957/hsr.2023.31.2.133,exploringthenonlinearrelationshipbetweendevelopmentdensityandhousingpricefocusingonexplainableaixai|2023,1,1945,3,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices || machine learning hedonic pricing housing",2,1,5,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a6b86d514a6b47e859ca3e882ad916fa36ffd168,10.1504/ijewm.2019.10025338,,An optimal solid waste collection fee based on area characteristics using hedonic pricing approach,2019.0,International Journal of Environment and Waste Management,Komwit Siritorn; C. K. Permpoonwiwat,"To extract an optimal willingness to pay for a waste collection service from an expected house price was the goal of this paper. The data were collected from households in Songkhla and Hatyai cities representing cultural and commercial characteristic areas, respectively. They were analysed by hedonic pricing method (HPM) in a form of Box-Cox model framework based on area characteristics. The findings indicated that the marginal willingness to pay (MWTP) for a waste collection service is about 45 baht/month for Songkhla and is about 55 baht/month for Hatyai. Moreover, an inverse demand for the service showed that people living in commercial area would tend to pay less than those in the cultural area even though they gain more benefits from a higher house price in the commercial area. This could reflect an important role of area characteristics on determining willingness to pay of residents.",1.0,,False,,Business,2019-11-15,a6b86d514a6b47e859ca3e882ad916fa36ffd168,anoptimalsolidwastecollectionfeebasedonareacharacteristicsusinghedonicpricingapproach,siritorn,10.1504/ijewm.2019.10025338,anoptimalsolidwastecollectionfeebasedonareacharacteristicsusinghedonicpricingapproach|2019,1,1902,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price",2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2781558266,10.12677/mm.2017.76058,,Influencing Factors of Housing Prices in Chengdu Based on Hedonic Pricing Theory,2017.0,Modern Management,霜 周,"本文应用特征价格理论分析成都市住宅价格的影响因素，首先根据文献回顾梳理出成都市的住宅特征变量，然后基于搜集到的成都市的住宅样本数据建立成都市住宅市场的特征价格模型。本文定量地研究了多个成都市住宅特征对住宅价格的影响程度，结果表明有9大住宅特征显著影响着成都市的住宅价格。 In this paper, the hedonic price model is used to explore the key influencing factors of residential prices in Chengdu. Based on the extensive literature review and the availability of actual data, we selected 22 residential characteristic variables in Chengdu and 101 valid research samples. Then, we use hedonic price model to establish regression relationship between the various features of residential housing prices, with changes in the quantity of housing characteristics to explain the price of residential. The results show that the 9 major residential features have a significant impact on the residential prices in Chengdu.",1.0,,False,article,,2017-01-01,https://openalex.org/W2781558266,influencingfactorsofhousingpricesinchengdubasedonhedonicpricingtheory,周,10.12677/mm.2017.76058,influencingfactorsofhousingpricesinchengdubasedonhedonicpricingtheory|2017,1,1770,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic",3,1,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ece5e30d4b2224a261f47e5a8f795539d534576b,10.1177/0739456x19844814,,The Value of Proximity to a Vacation Home Rental in a Resort Community,2018.0,Journal of planning education and research,Robert W. Wassmer,"Based upon a hedonic regression analysis of home sales in the City of South Lake Tahoe (CA), a vacation home rental (VHR) sells for more than a similar non-VHR. In addition, the presence of VHRs within a quarter-mile radius of a home sale also raises its value. However, the overall effect on the value of all home sale values from the presence of VHRs in South Lake Tahoe is negative. VHRs benefit owners and neighbors near them, but overall reduce the city’s residential property values. These findings offer a logical basis for the heated discussions often observed over planning activities that restrict the presence of VHRs and/or attempt to mitigate their neighborhood effects. The paper concludes with implications for planning policy directed at VHRs.",1.0,,False,JournalArticle,Business,2018-07-20,ece5e30d4b2224a261f47e5a8f795539d534576b,thevalueofproximitytoavacationhomerentalinaresortcommunity,wassmer,10.1177/0739456x19844814,thevalueofproximitytoavacationhomerentalinaresortcommunity|2018,1,1759,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2,2,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",dc74f1b4275e385e5aa3626f19f26a825ebcc1b3,10.3828/jtep.2025.59.1.40,,"The Spatio-temporal Variations in the Impact of Rail Transit on Residential Property Value: Evidence from Tianjin, China",2025.0,Journal of Transport Economics and Policy,Yu-Ning Wang; Duanfang Lu; Shen-Nan Wang; Zhi-Qing Fan,"The hedonic price model and the quantile regression method are employed to estimate the capitalisation influence of rail transit on residential property in relation to the proximity of stations and different phases. The value lift effect occurred after the formal planning was announced and increased gradually, with the highest premium found during the operation phase. A rail transit proximity premium is lowest in high-priced segments, and highest in low-priced segments where public transportation matters most for residents. The study contributes an enhanced understanding of the spatio-temporal variations in the proximity premium of rail transit in a developing country setting.",1.0,,False,JournalArticle,,2025-01-01,dc74f1b4275e385e5aa3626f19f26a825ebcc1b3,thespatiotemporalvariationsintheimpactofrailtransitonresidentialpropertyvalueevidencefromtianjinchina,wang,10.3828/jtep.2025.59.1.40,thespatiotemporalvariationsintheimpactofrailtransitonresidentialpropertyvalueevidencefromtianjinchina|2025,1,1685,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,2,1,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,73b8fd06d670b527402041baeb57afad39bedf42,10.1080/13504851.2020.1782330,,Do home sellers know their market? Evidence from neighbourhood sober-living houses,2020.0,Applied Economics Letters,Velma Zahirovic-Herbert; G. Turnbull; B. Waller,ABSTRACT We use a listing price hedonic model coupled with a standard selling price model to identify seller beliefs about how the housing market responds to refurbished formerly substandard properties serving as group homes. Occupied houses benefit the most and vacant houses the least in the long run from a nearby group home. The listing price model indicates that sellers of owner-occupied and rental houses anticipate these price effects whereas sellers of vacant properties appear to systematically underestimate the discount associated with vacant houses per se but correctly anticipate the smaller long run group home premia for their properties.,1.0,,False,,Business,2020-06-17,73b8fd06d670b527402041baeb57afad39bedf42,dohomesellersknowtheirmarketevidencefromneighbourhoodsoberlivinghouses,zahirovic-herbert,10.1080/13504851.2020.1782330,dohomesellersknowtheirmarketevidencefromneighbourhoodsoberlivinghouses|2020,1,1654,2,hedonic house price || hedonic housing prices,2,3,6,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e3c715ec04729686c6afec38bccc0052a3463b62,10.1061/9780784413135.132,,Discovering the Urban Structure Using a Spatial Hedonic House Price Model,2013.0,,S. Herath,,1.0,,False,,Geography,2013-11-12,e3c715ec04729686c6afec38bccc0052a3463b62,discoveringtheurbanstructureusingaspatialhedonichousepricemodel,herath,10.1061/9780784413135.132,discoveringtheurbanstructureusingaspatialhedonichousepricemodel|2013,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || spatial hedonic model housing",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,precios hedónicos vivienda,,10.20430/ete.v86i342.774,,Crimen y configuración urbana: una evaluación de precios de propiedades y bienestar en equilibrio general para la ciudad de Buenos Aires,2019.0,El Trimestre Económico,Omar O. Chisari; María Priscila Ramos; Sonia León,"Antecedentes: la inseguridad es uno de los flagelos que mayormente preocupan a los habitantes de Latinoamérica, una de las regiones más inseguras del mundo, debido a la violencia y a la falta de seguridad. Buenos Aires (Argentina) no es ajena a este fenómeno, dado que sus estadísticas recientes muestran una tendencia creciente de delitos contra las personas. La pobreza puede provocar una mayor virulencia del crimen; sin embargo, este último también acarrea costos sociales y económicos que impactan negativamente sobre el bienestar y la pobreza. Este trabajo aborda esa segunda relación a partir del estudio de los cambios en la configuración urbana y su consecuente impacto sobre el bienestar de los hogares y las firmas debido al incremento del crimen en la ciudad de Buenos Aries.Metodología: se desarrolló un modelo de equilibrio general computado (EGC) con características urbanas para Argentina en 2011, con el fin de medir los cambios de bienestar generados por el crimen y los costos que éste produce sobre el mercado inmobiliario. Se tomaron los precios de los inmuebles como el mecanismo de transmisión entre el crimen y el bienestar y, con base en esto, se estimaron elasticidades entre el crimen y el precio de la vivienda a partir de un modelo econométrico hedónico.Resultados: frente a un incremento de la tasa de crimen de 10%, los precios de los inmuebles en la zona norte de la ciudad, relativamente más rica, pueden caer hasta 4%, mientras que en la región sur caen menos de 1%. El efecto neto es una caída significativa del valor promedio de los inmuebles en la ciudad, dado que en el norte se concentra la mayor parte de los inmuebles y de mayor valor. Estos resultados asimétricos entre zonas de la ciudad también se observan en los resultados de bienestar de los hogares, se reduce 8% en el norte y 1% en el sur. Dichos resultados sobre los precios de los inmuebles y del bienestar de los hogares inducen la relocalización de los hogares hacia el sur y de las firmas hacia el norte, lo que altera la configuración urbana inicial. Los resultados coinciden con los hallazgos para otras ciudades de América del Sur.Conclusiones: un aumento en el crimen en la ciudad de Buenos Aires genera costos de bienestar no desdeñables para las familias, produce incentivos para la relocalización, no sólo de las familias sino también de las firmas. En consecuencia, el crimen no resulta neutral para la configuración urbana y regional, dadas las interacciones entre la ciudad de Buenos Aires y el resto del país. Tanto las conclusiones como los resultados cuantitativos resultan útiles para el diseño de políticas públicas relativas a la seguridad y su impacto sobre la configuración urbana.",1.0,,,journal-article,,,doi_10_20430_ete_v86i342_774,crimenyconfiguracionurbanaunaevaluaciondepreciosdepropiedadesybienestarenequilibriogeneralparalaciudaddebuenosaires,chisari,10.20430/ete.v86i342.774,crimenyconfiguracionurbanaunaevaluaciondepreciosdepropiedadesybienestarenequilibriogeneralparalaciudaddebuenosaires|2019,1,3703,1,precios hedónicos vivienda,1,2,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.12662/2359-618xregea.v9i2.p60-70.2020,,DETERMINANTES DE PREÇOS NO MERCADO IMOBILIÁRIO À LUZ DO MODELO HEDÔNICO,2020.0,Revista Gestão em Análise,Regileuza Rodriques Campelo Bezerra Paz; Liana Holanda Nepomuceno Nobre; Fábio Chaves Nobre,"O mercado de imóveis é um segmento que contribui para o crescimento e desenvolvimento de uma região, pois é um dos setores com maior capacidade de impulsionar a economia, dada sua capacidade de gerar empregos e renda. O presente trabalho teve como objetivo geral analisar a formação de preços do mercado imobiliário de Mossoró-RN e, visando alcançá-lo, traçaram-se os seguintes objetivos específicos: descrever as variá veis determinantes na formação do preço dos imóveis e compreender o impacto de cada variável sobre o preço dos imóveis. A amostra compôs-se de 478 imóveis da cidade de Mossoró-RN, utilizando-se dados baseados nos registros disponibilizados por seis sítios de imobiliárias da cidade. Para análise dos dados, utilizou-se, inicialmente, a estatística descritiva das variáveis e, em seguida, a regressão linear múltipla. Os resultados sugeriram que o valor do imóvel sofre influência diretamente da classificação do bairro, da área do imóvel (m²), do número de salas, do número de garagens, do número de dormitórios, do número de suítes, do número de cozinhas e do número de banheiros. As variáveis independentes anteriores também explicaram 66,1% da variação do preço do imóvel no município de Mossoró-RN.",1.0,,,journal-article,,,doi_10_12662_2359_618xregea_v9i2_p60_70_2020,determinantesdeprecosnomercadoimobiliarioaluzdomodelohedonico,paz,10.12662/2359-618xregea.v9i2.p60-70.2020,determinantesdeprecosnomercadoimobiliarioaluzdomodelohedonico|2020,1,2222,1,preços hedônicos imóveis,1,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,eef294febe6df66d6680721daf3b75ab6cacba65,10.1080/00036846.2024.2388308,,Estimation of tax incidence in Beijing housing market,2024.0,Applied Economics,Ge-Ran Tian; Chongqi Sun; Wei-Xing Wu; Xiao Bai,"ABSTRACT Capital gains tax (CGT) in China charges 20% of the profit from home resale, exempted only if the seller owns only one home and the seller has owned it for more than five years. This work estimates how CGT is shared between seller and buyer. By calculating the price differential between similar CGT-accrued housing units and CGT-exempt housing units and estimating CGT accrued, we find that approximately 50% of CGT is in fact transferred to the buyer. It should be noted that 50% is a lower bound of actual buyer incidence due to data limitations. Appreciating, liquid, quality housing units are associated with higher tax incidence for buyers. To address non-hedonic motives to sell, it is also shown that the price differential between CGT- accrued and CGT-exempt housing units is not a result of demand for liquidity or out of market conditions. This work suggests that regulators should be prudent in utilizing such tax as tools to deter speculation in the housing market.",1.0,,False,JournalArticle,,2024-08-11,eef294febe6df66d6680721daf3b75ab6cacba65,estimationoftaxincidenceinbeijinghousingmarket,tian,10.1080/00036846.2024.2388308,estimationoftaxincidenceinbeijinghousingmarket|2024,1,1987,1,hedonic housing prices,2,1,2,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,27ef0dbd5725e37ff03a7aad4ac9f3ffc2e04b2b,10.22423/kreus.2023.15.2.23,,"The Impact of New Town Development Plan on Apartment Prices: Focusing on the Case of Changneung District, Goyang-si",2023.0,Konkuk Research Institute of Real Estate and Urban Studies,Y. Jung; J. Jeon,"This study analyzed the effect of the announcement of new town development in Goyang Changneung district on the prices of surrounding apartments using the difference in difference model based on the Hedonic model. Research that affected the existing development of new cities in the Seoul metropolitan area was limited to the land market. Therefore, for the first time, this study analyzes the Changneung district in Goyang to analyze the impact of the new city on the surrounding apartment market. it was analyzed that the announcement had a negative effect on the price increase in the area near the Changneung district in Goyang, Seoul. On the contrary, it was analyzed that apartments located in Goyang-si had a positive effect of increasing prices unlike the Seoul influence area. Through the analysis results, it was suggested that the influence of the presentation had different effects on apartment sales prices in Seoul and Goyang, respectively.",1.0,,False,,,2023-02-28,27ef0dbd5725e37ff03a7aad4ac9f3ffc2e04b2b,theimpactofnewtowndevelopmentplanonapartmentpricesfocusingonthecaseofchangneungdistrictgoyangsi,jung,10.22423/kreus.2023.15.2.23,theimpactofnewtowndevelopmentplanonapartmentpricesfocusingonthecaseofchangneungdistrictgoyangsi|2023,1,1954,1,apartment prices hedonic,2,3,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),eb59a3e0095394ea189d54583d1cb0b55cb9357d,10.15396/eres2014_35,,Modelling micro-location variables for hedonic house price models,2014.0,,Ronald S. Weberndorfer; Wolfgang A. Brunauer,,1.0,,False,Review,Geography,,eb59a3e0095394ea189d54583d1cb0b55cb9357d,modellingmicrolocationvariablesforhedonichousepricemodels,weberndorfer,10.15396/eres2014_35,modellingmicrolocationvariablesforhedonichousepricemodels|2014,1,1000,4,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || automated valuation model residential || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,840280365ad310c2e59263058ae245276553cc36,10.18170/dvn/dhz9vy,,Seattle House Prices Hedonic Model,2018.0,,戴 浩然,,1.0,,False,,Economics,2018-06-07,840280365ad310c2e59263058ae245276553cc36,seattlehousepriceshedonicmodel,浩然,10.18170/dvn/dhz9vy,seattlehousepriceshedonicmodel|2018,1,1000,1,hedonic house price,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,d64e03ae20c0baa03afff75285d892dc57b793a8,10.4324/9781315743035-11,,Negative environmental externalities and housing price: a Hedonic model approach,2016.0,,R. Li; Wing-Chau Kwong; Ming-Hon Li; Tat-Ho Leung,,1.0,,False,,Economics,2016-03-31,d64e03ae20c0baa03afff75285d892dc57b793a8,negativeenvironmentalexternalitiesandhousingpriceahedonicmodelapproach,li,10.4324/9781315743035-11,negativeenvironmentalexternalitiesandhousingpriceahedonicmodelapproach|2016,1,1000,1,hedonic housing prices,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2032055130,10.1109/icmt.2011.6003072,,The application of SPSS in empirical research of housing hedonic price,2011.0,,Xuyu Wang,"This paper introduces the application of SPSS in empirical research. Through estimate, test and regression analysis the housing hedonic price model of linear, logarithmic, half-logarithmic by SPSS, we obtained the housing hedonic price model of Shanghai.",1.0,,False,conference-paper,,2011-07-01,https://openalex.org/W2032055130,theapplicationofspssinempiricalresearchofhousinghedonicprice,wang,10.1109/icmt.2011.6003072,theapplicationofspssinempiricalresearchofhousinghedonicprice|2011,1,1254,1,hedonic housing prices,2,0,1,1,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",225d6eb968ddfd1d17a7fe26ac30d791e1188f79,10.2139/ssrn.3576073,,Fracking and Tracking: The Effects of Oil and Natural Gas Well Locations on the Housing Market.,2020.0,Social Science Research Network,B. Norwood,"The technique of hydraulic fracturing or ""fracking"", has made it possible to produce vast new quantities of oil and natural gas. States like Colorado, Texas, and Oklahoma have seen a dramatic increase in the number of wells for both oil and natural gas. In this study, the main source of exogenous variation to be explored is the location of injection well sites. With that I estimate the effect hydraulically fractured natural gas and oil injection sites have on both urban and rural residential home prices between years 2000 to 2018 in 11 different states. The data is coming from two rich data sets, one form the U.S. Department of Homeland Security that listed all oil and natural gas wells. The second comes from Zillow's ZTRAX data base and that contains home transaction and administrative data. ArcGis is used to combine the data and created varying buffer zones sizes around injection well sites to see how average home prices and the average number of transactions changed before and after a well is put in, and as distance from the well site increases. First, a zip code level fixed effects model, as well as a household level fixed effects model to see how only repeat sales are affected by new wells. Lastly, a spatial differences in differences (SDID) is used to see how homes prices change as homes that are already by wells see a new well come in that decreases the distance to the nearest well. My results show that homes that are within .5 mile of a well have a 2.9% increase in selling price and homes that are .5-1 mile from a well site see a 1.2% increase compared to homes that are more than 2 miles away. Lastly, the spatial model shows that home that initially start 1-2 miles from a well and a new well is drilled reducing the distance to .5-1 miles away see a 2.1% increase and homes that are now .5 miles or closer see a 5.1% increase.",1.0,,False,,Environmental Science,2020-04-14,225d6eb968ddfd1d17a7fe26ac30d791e1188f79,frackingandtrackingtheeffectsofoilandnaturalgaswelllocationsonthehousingmarket,norwood,10.2139/ssrn.3576073,frackingandtrackingtheeffectsofoilandnaturalgaswelllocationsonthehousingmarket|2020,1,2863,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,5,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d251156a74382116423042e77ed9a324244b3f33,10.22313/reik.2023.21.1.217,,"Effect of Microscopic Elementary School Commuting Environment on Housing Price: Focused on Mok-dong and Cheonggye-dong, Hwaseong-si, where Elementary School Students are Concentrated",2023.0,Residential Environment Institute Of Korea,Joo-Sung Kim; Sung-Joon Hong,"Recently, the issue of commuting safety for elementary school students is emerging, and research on this issue is necessary. However, research on the elementary school commuting environment has been mainly focused on accessibility through the straight-line distance between home and school, studies on the effect of microscopic commuting environment on housing prices are insufficient. Therefore, the purpose of this study is to analyze the effect of microscopic elementary school commuting environment on housing price. For this purpose, multiple regression analysis was used with the commuting environment as the independent variables and the housing price per unit area as the dependent variable. The subjects of the study were 324 apartment buildings in 27 apartment complexes in Mok-dong and Cheonggye-dong, Hwaseong-si, which have the highest ratio of elementary school students to the population. For the analysis data, 882 houses for sale registered in Naver real estate platform from July 16, 2021 to July 21, 2021 were used. The analysis results are summarized as follows. First, as the walking distance to the school increased, the housing price decreased. Second, if crossings were necessary for commuting to elementary school, housing prices decreased. Third, the wider the road to cross when commuting to school, the lower the housing price. Fourth, if a bicycle road with high bicycle speed exists in the commuter road, the housing price decreased. Summarizing these results, the microscopic elementary school commuting environment affects housing prices. In particular, variables related to traffic safety have a great influence on housing prices, and this should be considered in the site planning and apartment building arrangement process.",1.0,,False,JournalArticle,,2023-03-30,d251156a74382116423042e77ed9a324244b3f33,effectofmicroscopicelementaryschoolcommutingenvironmentonhousingpricefocusedonmokdongandcheonggyedonghwaseongsiwhereelementaryschoolstudentsareconcentrated,kim,10.22313/reik.2023.21.1.217,effectofmicroscopicelementaryschoolcommutingenvironmentonhousingpricefocusedonmokdongandcheonggyedonghwaseongsiwhereelementaryschoolstudentsareconcentrated|2023,1,2758,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,5,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2f61b315ee49e0c729d63ab20c92f944a341b7c8,10.1111/tgis.70239,,Constructing the Street Environment Composite Metric for Property Valuation: A Hybrid Approach Using Semantic Segmentation and Spatial Statistics,2026.0,Trans. GIS,Yi-Ni Meng; Jian Yin; Hui-Xiao Wang,"Quantifying the synergistic effects of urban environmental features remains a challenge in geocomputation and urban modeling. Traditional studies often treat the Green View Index (GVI) and Sky View Factor (SVF) as independent variables, overlooking their complex interaction in determining environmental quality. This study proposes a hybrid computational framework coupling semantic segmentation with spatial statistics to construct the Street Environment Composite Metric (SECM) for property valuation. Utilizing the DeepLabV3+ model, we processed massive unstructured Baidu Street View (BSV) imagery to extract high‐precision semantic features. To enhance spatial modeling granularity and mitigate the Modifiable Areal Unit Problem (MAUP), we implemented a fine‐grained segmentation algorithm for street vectors. These computed metrics were then integrated into a Multiscale Geographically Weighted Regression (MGWR) model to reveal the spatial non‐stationarity of housing price determinants in Shanghai's Xuhui District. Computational results demonstrate that the SECM provides a superior fit for capturing the non‐linear interactions between greenery and spatial openness compared to single‐variable models. Specifically, the analysis reveals a threshold effect: Sky View Factor (SVF) contributes to economic value only when supported by a sufficient ecological foundation. In low‐GVI zones, the SECM effectively identifies environmental deficits that suppress property values. This study presents a reproducible workflow for integrating deep learning‐based feature extraction with advanced spatial econometrics, offering a robust tool for fine‐scale urban environmental assessment.",1.0,,False,JournalArticle,Computer Science,2026-03-22,2f61b315ee49e0c729d63ab20c92f944a341b7c8,constructingthestreetenvironmentcompositemetricforpropertyvaluationahybridapproachusingsemanticsegmentationandspatialstatistics,meng,10.1111/tgis.70239,constructingthestreetenvironmentcompositemetricforpropertyvaluationahybridapproachusingsemanticsegmentationandspatialstatistics|2026,1,2687,4,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d0498e1331aede705a6bca174d284b6ac4e9ed72,10.1108/jerer-09-2020-0051,,Real estate development in the city of Athens during the financial crisis,2021.0,,Maria Nikitidou; F. Archontakis; Athanasios O. Tagkalakis," Purpose This study aims to determine how the prices of residential properties in the Greek real estate sector are affected by their structural characteristics and by the prevailing economic factors during recession. Design/methodology/approach Based on 13,835 valuation reports for the city of Athens, covering a period of 11 years (2006–2016), this study develops a series of econometric models, taking into account both structural characteristics of the property market and the macroeconomic relevant variables. Finally, the city of Athens is divided into sub-regions and the different effects of the structural factors in each area are investigated via spatial analysis confirming the validity of the baseline model. Findings Findings show that the size, age, level, parking and storage space can explain the property price movements. Moreover, the authors find evidence that it is primarily house demand variables (e.g. the annual average wage, the unemployment rate, the user cost of capital, financing constraints and expectations about the future course of the house market) that affect house prices in a statistically significant manner and with the correct sign. Finally, using a difference-in-differences approach, this study finds that an increase in house demand (on account of net migration) led to higher house prices in smaller and older than in larger and younger apartments in areas with high concentration of immigrants. Originality/value This study uses a novel data set to help entities, individuals and policy-makers to understand how the recent economic and financial crisis has affected the real estate market in Athens. ",1.0,,False,,Business,2021-06-08,d0498e1331aede705a6bca174d284b6ac4e9ed72,realestatedevelopmentinthecityofathensduringthefinancialcrisis,nikitidou,10.1108/jerer-09-2020-0051,realestatedevelopmentinthecityofathensduringthefinancialcrisis|2021,1,2651,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,3,2,1,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f61af9182b02e88d64f0b234e7e4dbdeea13a001,10.1371/journal.pone.0330932,,The spatial dynamics of urban vegetation and housing prices: Insights from pre- and post-pandemic Chicago using OLS and MGWR models,2025.0,PLoS ONE,D. Han; Chang Gyu Choi,"This study examines the spatial dynamics of urban vegetation and its impact on housing prices in Chicago, analyzing data from both pre- and post-COVID-19 periods. Employing Ordinary Least Squares (OLS) and Multiscale Geographically Weighted Regression (MGWR) models, we assess how the effects of green spaces on property values vary across different neighborhoods. The OLS model generally indicates a positive correlation between increased vegetation and housing prices. In contrast, the MGWR model reveals that the benefits of urban green spaces to property values are not uniformly distributed and exhibit significant variability. Notably, in some South Side areas of Chicago, increases in green space correlate with declines in property values, a sensitivity that intensified post-pandemic, leading to notable price declines. Conversely, the North Side, characterized as a higher-income area, shows greater resilience to the impacts of both increased green spaces and the COVID-19 pandemic, with less susceptibility to economic downturns. This research underscores the intricate interplay between urban green spaces and economic factors, highlighting how local socio-economic conditions and urban planning strategies can influence the economic benefits of vegetation. The findings provide essential insights for urban policymakers and planners striving to promote sustainable development and equitable economic growth in urban environments.",1.0,,False,JournalArticle,Medicine,2025-09-05,f61af9182b02e88d64f0b234e7e4dbdeea13a001,thespatialdynamicsofurbanvegetationandhousingpricesinsightsfrompreandpostpandemicchicagousingolsandmgwrmodels,han,10.1371/journal.pone.0330932,thespatialdynamicsofurbanvegetationandhousingpricesinsightsfrompreandpostpandemicchicagousingolsandmgwrmodels|2025,1,2443,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,3,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,prix hédoniques logement,,10.3917/reru.022.0239,,Différenciation spatiale des prix du logement à Monaco : une reformulation du concept de distance au centre,2002.0,Revue d’Économie Régionale & Urbaine,Bernard Fustier; Marc Vassallo,"Résumé En économie urbaine, le choix de localisation résidentielle résulte d’un arbitrage entre le prix du logement et le coût du transport vers le centre-ville. Le modèle ALONSO-MUTH décrit rigoureusement les termes de cet arbitrage et conclut à une décroissance des valeurs foncières en fonction de la distance au centre. Malgré les hypothèses très fortes sur lesquelles repose l’analyse, on souhaite confronter la « loi de décroissance des valeurs foncières » à la distribution spatiale des prix du logement (à la vente et à la location) dans la ville de Monaco. Malgré l’exiguïté du territoire de la Principauté, c’est la détermination du centre-ville qui a constitué la plus grande difficulté de l’étude. L’assimilation du centre de la ville à son noyau historique (facilement identifiable) n’est pas satisfaisante et on montre que la « loi » ne s’applique pas dans ce cas. Une analyse plus fine de la localisation des activités conduit à mettre l’accent sur le caractère plus ou moins central des différents quartiers. Une hiérarchie de centralité est construite dans le cadre des modèles de potentiels et une nouvelle mesure de la distance au centre est proposée (écart de centralité par rapport au sommet de la hiérarchie). En substituant cette dernière à la distance physique, on montre que les prix des logements diminuent d’une manière significative avec le degré de centralité du lieu de résidence.",1.0,,,journal-article,,,doi_10_3917_reru_022_0239,differenciationspatialedesprixdulogementamonacounereformulationduconceptdedistanceaucentre,fustier,10.3917/reru.022.0239,differenciationspatialedesprixdulogementamonacounereformulationduconceptdedistanceaucentre|2002,1,2410,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || prix hédoniques logement",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a5050719ab7d00f255f95d1baa5589b39118508d,10.32609/0042-8736-2022-12-118-136,,Real estate valuation based on big data,2022.0,Voprosy Ekonomiki,M. Mamedli; A. V. Umnov,"The paper considers the application of the web scrapping and machine learning algorithms for the assessment of the real estate price on the secondary housing market in Moscow. For this, we collect and process the data from the CIAN website and the data from “Reforma GKH”. To evaluate real estate objects, we consider such machine learning algorithms as Elastic Net, Random Forest and Gradient Boosting. We also apply Shapley vector-based approach to interpret the results of the black-box algorithms. The results suggest that the use of black-box algorithms in assessing the price of apartments on the Moscow secondary housing market allows to obtain more accurate price estimates both for different price segments and for the sample as a whole. At the same time, Gradient Boosting has demonstrated the best accuracy among other algorithms. Interpretation based on the Shapley vector shows that the total area, year of construction, ceiling height, renovation, as well as monolithic construction technology had a positive effect on the price. The price is negatively affected by the number of floors in the house, the possibility of mortgage and lack of repairs. Developed methodology can be applied in real estate insurance, mortgage, determination of cadastral value of real estate and others.",1.0,,False,,,2022-12-02,a5050719ab7d00f255f95d1baa5589b39118508d,realestatevaluationbasedonbigdata,mamedli,10.32609/0042-8736-2022-12-118-136,realestatevaluationbasedonbigdata|2022,1,2296,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",97f31e10003707306832d397074386a94f12ef39,10.1177/10575677261428020,,The Relationship Between Crime and Property Values at the Micro Geographic Level: Examining Diverging Developmental Trends,2026.0,International Criminal Justice Review,Sabo-Brants Hagit; W. David; Amram Shai; Shay Maor,"This study examines the relationship between crime trajectories and housing prices at the micro-geographic level, focusing on street segments in Tel-Aviv-Yafo. It combines two analytic strategies: a quasi-experimental comparison of matched segments using group-based trajectory modeling, and multivariate ordinary least squares regressions predicting logged housing prices. In the quasi-experimental analysis, matched pairs of street segments from a typology of nine crime trajectory groups began with similar crime levels but diverged over time. Segments that experienced steeper increases in crime tended to show slower housing price appreciation. In the multivariate models, a 10-crime increase between 2005 and 2014 was associated with approximately 3–4% decline in price per square meter (2013–2016). These effects remained robust after adjusting for demographic, land-use, and built environment characteristics, and controlling for spatial dependence. The findings advance the criminology of place by showing that crime is a dynamic and spatially concentrated force with measurable economic consequences. From a policy perspective, targeted crime prevention in high-trajectory areas may enhance both public safety and property values.",1.0,,False,JournalArticle,,2026-03-13,97f31e10003707306832d397074386a94f12ef39,therelationshipbetweencrimeandpropertyvaluesatthemicrogeographiclevelexaminingdivergingdevelopmentaltrends,hagit,10.1177/10575677261428020,therelationshipbetweencrimeandpropertyvaluesatthemicrogeographiclevelexaminingdivergingdevelopmentaltrends|2026,1,2240,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ffd8090aa7d0a4b6fc0ffd54b35386ae3a7f2c27,10.1080/00036846.2025.2504191,,Place-based scholarships: an updated assessment of property value impacts from the Kalamazoo promise,2025.0,Applied Economics,W. J. Beasley; Jahangir Alam,"ABSTRACT Place-based scholarships are a policy to increase college enrolment rates and stimulate local economic development. A growing body of research indicates a positive correlation between such scholarships, academic performance, and out-migration. However, gaps in the research preclude a firm understanding of property value impacts associated with these scholarships. In this study, we use the quasi-experimental launch of the Kalamazoo Promise, a place-based scholarship available to students in Kalamazoo, Michigan, to estimate the treatment effect on single-family residential properties. Using property transactions between 2005 and 2025, we find property values increased by 12.4 to 15.0 percent for Promise zone homes relative to areas with comparable schools and home construction. The treatment effects identified in this study are slightly larger than previously reported estimates, suggesting that differences in amenities and school quality between adjacent regions may obfuscate market impacts. These differences are likely to permeate most analyses of promise zones, given their policy mission to stimulate growth and innovation in the region.",1.0,,False,JournalArticle,,2025-05-14,ffd8090aa7d0a4b6fc0ffd54b35386ae3a7f2c27,placebasedscholarshipsanupdatedassessmentofpropertyvalueimpactsfromthekalamazoopromise,beasley,10.1080/00036846.2025.2504191,placebasedscholarshipsanupdatedassessmentofpropertyvalueimpactsfromthekalamazoopromise|2025,1,2163,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,4,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b2f53171261d88227971135a32f878e88c52de6a,10.48550/arxiv.2508.03156,2508.03156,Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions,2025.0,arXiv.org,Paul Gummer; Julian Rosenberger; Mathias Kraus; Patrick Zschech; Nico Hambauer,"House price valuation remains challenging due to localized market variations. Existing approaches often rely on black-box machine learning models, which lack interpretability, or simplistic methods like linear regression (LR), which fail to capture market heterogeneity. To address this, we propose a machine learning approach that applies two-stage clustering, first grouping properties based on minimal location-based features before incorporating additional features. Each cluster is then modeled using either LR or a generalized additive model (GAM), balancing predictive performance with interpretability. Constructing and evaluating our models on 43,309 German house property listings from 2023, we achieve a 36% improvement for the GAM and 58% for LR in mean absolute error compared to models without clustering. Additionally, graphical analyses unveil pattern shifts between clusters. These findings emphasize the importance of cluster-specific insights, enhancing interpretability and offering practical value for buyers, sellers, and real estate analysts seeking more reliable property valuations.",1.0,,False,JournalArticle,Computer Science,2025-08-05,b2f53171261d88227971135a32f878e88c52de6a,unveilinglocationspecificpricedriversatwostageclusteranalysisforinterpretablehousepricepredictions,gummer,10.48550/arxiv.2508.03156,unveilinglocationspecificpricedriversatwostageclusteranalysisforinterpretablehousepricepredictions|2025,1,2107,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",84cc4497992f80cf457a97f63d050c3843dd1ff9,10.24957/hsr.2020.28.4.109,,The Impact of Real Estate Regulation on Housing Prices: Focus on the Actual Transaction Price of Apartments at Suwon,2020.0,,Koangsung Choi; M. Noh,"This paper examines the impact of real estate regulation on housing prices. A quasi-experimental assignment of the regulation at Suwon will be utilized by adopting the regression discontinuity design (RDD) model. Furthermore, we are also combining the difference-in-differences (DID) to the regression discontinuity design (RDD) model to eliminate other influencing factors that affect house prices. The results reveal that the regulation reduced prices by 3.8~5.0%. We are unable to identify a negative effect on the price that is more than 300 million won, while the houses worth less than 300 million won decreased in price by 5.6~14.2% before the regulation was applied. Although the implementation of the regulation has a positive effect on the stabilization of housing prices, our findings indicate that regulation can intensify the polarization of housing prices, and reduce the supply of rental housing. Moreover, it is possible that a balloon effect of housing prices can occur.",1.0,,False,,Business,2020-11-30,84cc4497992f80cf457a97f63d050c3843dd1ff9,theimpactofrealestateregulationonhousingpricesfocusontheactualtransactionpriceofapartmentsatsuwon,choi,10.24957/hsr.2020.28.4.109,theimpactofrealestateregulationonhousingpricesfocusontheactualtransactionpriceofapartmentsatsuwon|2020,1,1987,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,5,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3c304f8406f95b0529b93977f78882d6777c8c62,10.48550/arxiv.2409.02130,2409.0213,From Predictive Importance to Causality: Which Machine Learning Model Reflects Reality?,2024.0,arXiv.org,Muhammad Arbab Arshad; Pallavi Kandanur; Saurabh Sonawani; Laiba Batool; M. U. Habib,"This study analyzes the Ames Housing Dataset using CatBoost and LightGBM models to explore feature importance and causal relationships in housing price prediction. We examine the correlation between SHAP values and EconML predictions, achieving high accuracy in price forecasting. Our analysis reveals a moderate Spearman rank correlation of 0.48 between SHAP-based feature importance and causally significant features, highlighting the complexity of aligning predictive modeling with causal understanding in housing market analysis. Through extensive causal analysis, including heterogeneity exploration and policy tree interpretation, we provide insights into how specific features like porches impact housing prices across various scenarios. This work underscores the need for integrated approaches that combine predictive power with causal insights in real estate valuation, offering valuable guidance for stakeholders in the industry.",1.0,,False,JournalArticle,Computer Science,2024-09-01,3c304f8406f95b0529b93977f78882d6777c8c62,frompredictiveimportancetocausalitywhichmachinelearningmodelreflectsreality,arshad,10.48550/arxiv.2409.02130,frompredictiveimportancetocausalitywhichmachinelearningmodelreflectsreality|2024,1,1939,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",518f84ffc2f5e7f4cdc678100c49e9f29ac8a661,10.4018/ijagr.2021040102,,The Impact of Natural Amenities on Home Values in Western Colorado,2021.0,,Nathan w. Perry; T. Casey; T. Parece; Cory J Castaneda,"This paper investigates the impact that proximity to natural amenities has on improving home values in Mesa County, Colorado. Controlling for standard home characteristics, the study investigates the value to homes of the proximity to trails, the Colorado National Monument, Bureau of Land Management Land, golf courses, the Colorado/Gunnison River, open space, and public parks, using ordinary least squares, fixed effects (controlling for time and zip codes), and a spatial error model. GIS is used to determine distances for the spatial econometric model. Each amenity is evaluated at 250, 500, and 1000 meters. The results show that homes located within 250 meters of a trail sell for 4.45% more, homes located within 500 meters of BLM land sell for 9.07% more, homes located within 250 meters of a golf course sell for 12.70% more, and homes located within 250 meters of the Colorado National Monument sell for 12.90% more.",1.0,,False,,Geography,2021-04-01,518f84ffc2f5e7f4cdc678100c49e9f29ac8a661,theimpactofnaturalamenitiesonhomevaluesinwesterncolorado,perry,10.4018/ijagr.2021040102,theimpactofnaturalamenitiesonhomevaluesinwesterncolorado|2021,1,1928,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.22456/2176-5456.10881,,MERCADO IMOBILIÁRIO E A IMPORTÂNCIA DAS CARACTERÍSTICAS LOCAIS: UMA ANÁLISE QUANTÍLICO-ESPACIAL DE PREÇOS HEDÔNICOS EM BELO HORIZONTE,2009.0,Análise Econômica,Bernardo Furtado,"A motivação principal desse artigo é identificar quão relevante é a localização de determinado imóvel na composição do seu valor de mercado. Isso, no intuito de compreender melhor como as relações econômicas se dão espacialmente no tecido urbano. Para tal utiliza-se de uma miríade de conceitos - oriundos de várias ciências, da geografia, à arquitetura, ao urbanismo, à economia -, e metodologias, da estatística, à econometria e ao geoprocessamento. A proposta segue a literatura de preços hedônicos, mas propõe que a síntese da percepção das amenidades urbanas seja especificada pelo elemento do bairro (Lynch, 1997). Dois modelos são apresentados, os testes e resultados comentados, sendo que o de melhor ajuste é o modelo de erro auto-regressivo espacial com especificação de bairros classificados por um índice de renda. A análise quantifica acrescenta informações relevantes no entendimento do modelo.",1.0,,,journal-article,,,doi_10_22456_2176_5456_10881,mercadoimobiliarioeaimportanciadascaracteristicaslocaisumaanalisequantilicoespacialdeprecoshedonicosembelohorizonte,furtado,10.22456/2176-5456.10881,mercadoimobiliarioeaimportanciadascaracteristicaslocaisumaanalisequantilicoespacialdeprecoshedonicosembelohorizonte|2009,1,1908,1,preços hedônicos imóveis,1,0,1,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",00880b62ef689980c93afe7995a3e5d3843a07ab,10.1353/jef.2017.a678329,,The Relationship Between Magnet Status and Neighborhood Home Values in Chicago,2017.0,Journal of Education Finance,C. Evans; J. Malin,"abstract: Research has demonstrated that families are consistently willing to pay more for homes situated in catchments with better quality schools. However, few have considered whether other school characteristics are capable of capitalizing home values in this way. Using data from the city of Chicago, we investigate whether traditional magnet schools may place upward economic pressure on adjoining neighborhood home prices. We also examine this possibility for Chicago’s “magnet clusters”—which are themed schools and connote magnet status, but utilize locally zoned boundaries. After controlling for neighborhood composition, only traditional magnet schools, with broader enrollment policies, were consistently associated with higher home values. We find, however, that this relationship operates mostly through systematic differences in school quality, not differences in school typology per se. ",1.0,,False,JournalArticle,Geography,2017-06-01,00880b62ef689980c93afe7995a3e5d3843a07ab,therelationshipbetweenmagnetstatusandneighborhoodhomevaluesinchicago,evans,10.1353/jef.2017.a678329,therelationshipbetweenmagnetstatusandneighborhoodhomevaluesinchicago|2017,1,1904,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8b017b4528a8b9fab27104a8af1538d95ab2ca8c,10.1109/urtc65039.2024.10937620,,An Explainable AI Framework with ML Model Stacking for House Price Prediction,2024.0,2024 IEEE MIT Undergraduate Research Technology Conference (URTC),S. Ganapathy; Tingyu Li,"The post-COVID-19 real estate market has presented challenges in finding affordable housing. Accurate house price prediction systems can aid in addressing this issue. This research develops a stacking regression model framework for improving predictive performance with the consideration of Explainable AI (XAI) of house price estimation. This new model was subsequently compared to Regression-based prediction models and a Neural Network to assess model performance. The Stacking Regressor’s strong $r^{2}$ value of 0.901 and its noticeably lower MAE and RMSE highlight strong precision. XAI techniques, particularly SHAP values, were employed to enhance model interpretability, offering insights into individual feature contributions.",1.0,,False,Conference,,2024-10-11,8b017b4528a8b9fab27104a8af1538d95ab2ca8c,anexplainableaiframeworkwithmlmodelstackingforhousepriceprediction,ganapathy,10.1109/urtc65039.2024.10937620,anexplainableaiframeworkwithmlmodelstackingforhousepriceprediction|2024,1,1736,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0525620d5bfc12c6d7f3f610e2c159110d0cfc03,10.1177/0739456x221082500,,Leveraging Main Street as a Real Estate Amenity: Downtown Revitalization and Residential Property Values,2022.0,Journal of planning education and research,A. V. Van Leuven,"As part of their efforts to reverse the effects of twentieth-century downtown disinvestment and automobile-oriented development, stakeholders in small towns across the United States look for viable means of restoring the vitality and character of their historic business districts. In this paper, I evaluate a widely adopted downtown revitalization strategy—the Main Street Program—by measuring its influence on the local housing market. I find that home sale prices are higher for residential properties sold in program-participating communities, and I observe an additional sale price premium for homes located in closer proximity to downtown districts with an active Main Street Program.",1.0,,False,JournalArticle,,2022-04-02,0525620d5bfc12c6d7f3f610e2c159110d0cfc03,leveragingmainstreetasarealestateamenitydowntownrevitalizationandresidentialpropertyvalues,leuven,10.1177/0739456x221082500,leveragingmainstreetasarealestateamenitydowntownrevitalizationandresidentialpropertyvalues|2022,1,1690,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,4,3,1,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5d797e7c3e5f84a3d32402072a64ec260ecfd1e1,10.64361/14445921.2024.212309,,"The impact of zoning reform on property prices within lower Hutt, Wellington",2025.0,Pacific Rim Property Research Journal,Ishan Parbhu; Hassan F. Gholipour; Sharon Yam; Kamel Taoum,"Zoning regulation can have a significant effect on property values by restricting types of development in a particular area. By focusing on the implementation of Hu> City Council’s District Plan Change 43, this study examines the effect of zoning changes on residential property prices in Lower Hu>, New Zealand. Using transaction records of 100 residential properties and employing multivariate regressions, the results show that properties affected by the zoning reforms experienced lower appreciation in value compared to unaffected properties.",1.0,,False,JournalArticle,,2025-03-18,5d797e7c3e5f84a3d32402072a64ec260ecfd1e1,theimpactofzoningreformonpropertypriceswithinlowerhuttwellington,parbhu,10.64361/14445921.2024.212309,theimpactofzoningreformonpropertypriceswithinlowerhuttwellington|2025,1,1547,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",3b17c175fc7f33e8c6315201e51fb05cca078290,10.1007/s11146-015-9513-4,,How Much Is a Good School Worth in Beijing? Identifying Price Premium with Paired Resale and Rental Data,2015.0,Journal of real estate finance and economics,Si-Qi Zheng; Wanyang Hu; Rui Wang,,1.0,,False,,,2015-07-05,3b17c175fc7f33e8c6315201e51fb05cca078290,howmuchisagoodschoolworthinbeijingidentifyingpricepremiumwithpairedresaleandrentaldata,zheng,10.1007/s11146-015-9513-4,howmuchisagoodschoolworthinbeijingidentifyingpricepremiumwithpairedresaleandrentaldata|2015,1,1000,1,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1109/icee.2010.729,,An Empirical Study of Residential Hedonic Prices in Nanjing,2010.0,2010 International Conference on E-Business and E-Government,Zhu Jian-jun; Li Qi-ming,,1.0,,,proceedings-article,,,doi_10_1109_icee_2010_729,anempiricalstudyofresidentialhedonicpricesinnanjing,jian-jun,10.1109/icee.2010.729,anempiricalstudyofresidentialhedonicpricesinnanjing|2010,1,1000,2,apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.1016/0094-1190(79)90010-x,,Hedonic price and depreciation indexes for residential housing again,1979.0,Journal of Urban Economics,Peter Chinloy,,1.0,,,journal-article,,,doi_10_1016_0094_1190_79_90010_x,hedonicpriceanddepreciationindexesforresidentialhousingagain,chinloy,10.1016/0094-1190(79)90010-x,hedonicpriceanddepreciationindexesforresidentialhousingagain|1979,1,1000,2,apartment rent hedonic || hedonic housing prices,2,0,2,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6b241feeb364dcce50a9f03065d861fc49ade0e3,10.22423/kreus.2017.9.2.151,,"The Influence of School District Characteristics on Housing Prices -Evidence from Beijing, China-",2017.0,,Kijung Kim; Yao Yao; Seonju Song,,1.0,,False,,Business,2017-02-28,6b241feeb364dcce50a9f03065d861fc49ade0e3,theinfluenceofschooldistrictcharacteristicsonhousingpricesevidencefrombeijingchina,kim,10.22423/kreus.2017.9.2.151,theinfluenceofschooldistrictcharacteristicsonhousingpricesevidencefrombeijingchina|2017,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6ef81330a7f0cacb5895b9044b9186752b8b6ca3,10.23843/as.21.2.5,,Multi-Level Effect of Public Development Characteristics of the New Towns in Korea on Housing Prices Using Hierarchical Linear Model,2022.0,Appraisal Studies,J. Oh; Wonho Seo,,1.0,,False,,,2022-08-31,6ef81330a7f0cacb5895b9044b9186752b8b6ca3,multileveleffectofpublicdevelopmentcharacteristicsofthenewtownsinkoreaonhousingpricesusinghierarchicallinearmodel,oh,10.23843/as.21.2.5,multileveleffectofpublicdevelopmentcharacteristicsofthenewtownsinkoreaonhousingpricesusinghierarchicallinearmodel|2022,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e47607abc037df7d0c313fc6915c93afe9ccdc35,10.1080/12265934.2026.2642349,,Economic value of neighbourhood perception on housing price: applications of unstructured multimodal big data and deep learning techniques,2026.0,International Journal of Urban Sciences,Hyebin Kim; Suhyun Lee; Juhyeon Lee; Junseo Lee; Sugie Lee; J. Han,,1.0,,False,JournalArticle,,2026-03-13,e47607abc037df7d0c313fc6915c93afe9ccdc35,economicvalueofneighbourhoodperceptiononhousingpriceapplicationsofunstructuredmultimodalbigdataanddeeplearningtechniques,kim,10.1080/12265934.2026.2642349,economicvalueofneighbourhoodperceptiononhousingpriceapplicationsofunstructuredmultimodalbigdataanddeeplearningtechniques|2026,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",8894dd807b2cfee518f061bb78e6e7efbe6ccae6,10.1007/s12061-025-09759-y,,"Spatially Heterogeneous Effects of Flood Hazards on Housing Prices: A Case Study of Nanjing, China",2025.0,Applied spatial analysis and policy,Yi Chen; Tao Liu; Bi-Feng Jiang; Yujun Qi; Yiiqun Zhang; Yuan Li; Yong Lei; Jing-Xian J. You; Yao Meng,,1.0,,False,JournalArticle,,2025-12-01,8894dd807b2cfee518f061bb78e6e7efbe6ccae6,spatiallyheterogeneouseffectsoffloodhazardsonhousingpricesacasestudyofnanjingchina,chen,10.1007/s12061-025-09759-y,spatiallyheterogeneouseffectsoffloodhazardsonhousingpricesacasestudyofnanjingchina|2025,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",29399980a79a3cc83a2d476a1ce2f95351454956,10.1007/s41685-024-00337-7,,"Measuring spatial heterogeneity of air quality on apartment transaction prices in Seoul, South Korea",2024.0,Asia-Pacific Journal of Regional Science,Dongwoo Hyun; H. Lee,,1.0,,False,JournalArticle,,2024-05-21,29399980a79a3cc83a2d476a1ce2f95351454956,measuringspatialheterogeneityofairqualityonapartmenttransactionpricesinseoulsouthkorea,hyun,10.1007/s41685-024-00337-7,measuringspatialheterogeneityofairqualityonapartmenttransactionpricesinseoulsouthkorea|2024,1,1000,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",52c8fa55a0ed0bb40c1e037ca9872f9c71cb03c7,10.17208/jkpa.2017.04.52.2.155,,"A Study on the Correlation between Characteristics of Crime and Space Syntax, Multifamily Housing Price",2017.0,,H. Kim; Yeol Choi,,1.0,,False,,Economics,2017-04-30,52c8fa55a0ed0bb40c1e037ca9872f9c71cb03c7,astudyonthecorrelationbetweencharacteristicsofcrimeandspacesyntaxmultifamilyhousingprice,kim,10.17208/jkpa.2017.04.52.2.155,astudyonthecorrelationbetweencharacteristicsofcrimeandspacesyntaxmultifamilyhousingprice|2017,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b811571347dea0a67edce3dba6a7e116324b0640,10.1142/s1793005708001021,,GP VERSUS GLS SPATIAL INDEX MODELS TO FORECAST SINGLE-FAMILY HOME PRICES,2008.0,,Mak Kaboudan,,1.0,,False,,Mathematics,2008-07-01,b811571347dea0a67edce3dba6a7e116324b0640,gpversusglsspatialindexmodelstoforecastsinglefamilyhomeprices,kaboudan,10.1142/s1793005708001021,gpversusglsspatialindexmodelstoforecastsinglefamilyhomeprices|2008,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8bdfaecc85eb64ed1feba397b68107ff52fef85f,10.22423/kreus.2020.12.2.63,,Effects of Urban Railway attributes on Housing Prices: Case of Apartments in Incheon Metropolitan City,2020.0,,S. Bae,,1.0,,False,,Geography,2020-02-28,8bdfaecc85eb64ed1feba397b68107ff52fef85f,effectsofurbanrailwayattributesonhousingpricescaseofapartmentsinincheonmetropolitancity,bae,10.22423/kreus.2020.12.2.63,effectsofurbanrailwayattributesonhousingpricescaseofapartmentsinincheonmetropolitancity|2020,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8b9d632fe59e5c3aa0ea9442aa831e15a6f50a4e,10.1007/s10901-020-09732-w,,"Impacts of online shopping convenience and physical retail proximity on housing prices in Shenzhen, 2016–2018",2020.0,Journal of Housing and the Built Environment,Lu Shen; Yi-Ming He; Ling-Hin Li; K. Chau,,1.0,,False,,,2020-02-10,8b9d632fe59e5c3aa0ea9442aa831e15a6f50a4e,impactsofonlineshoppingconvenienceandphysicalretailproximityonhousingpricesinshenzhen20162018,shen,10.1007/s10901-020-09732-w,impactsofonlineshoppingconvenienceandphysicalretailproximityonhousingpricesinshenzhen20162018|2020,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,bb77f5aca99326a7ee86cdf70f4ac704a82661dc,10.1108/ijhma-05-2026-0151,,"Spatio-temporal local price dependence in a metropolitan housing market: evidence from 64,529 apartment transactions in Busan, Korea",2026.0,International Journal of Housing Markets and Analysis,Kyoun-Sup Chung; Moon-bo Sim; Jung-Hwan Bae; Jong-Guk Kim," This study aims to examine spatio-temporal local-price dependence in a metropolitan apartment market. It investigates whether focal transaction prices are conditionally associated with nearby prices observed in past, same-quarter and subsequent temporal positions, while avoiding causal claims about market memory, peer influence or forward-looking anchoring. The analysis uses 64,529 geocoded apartment transactions in Busan, Korea. Sparse spatio-temporal weight matrices are constructed from projected coordinates and 14 quarterly periods. The empirical strategy compares a baseline hedonic model, a hedonic model with quarter and GU district fixed effects and a spatio-temporal SLX specification that includes past, same-quarter and subsequent local-price variables. Robustness checks examine zero-neighbour cases, influential observations, alternative spatial thresholds, residual Moran’s I and stronger inference procedures for the small number of quarter clusters. The spatio-temporal SLX model substantially improves model fit relative to the baseline and GU fixed-effects hedonic specifications. The past, same-quarter and subsequent local-price variables are all positive and statistically significant. The results are robust to data-quality correction, zero-neighbour sensitivity checks, spatial thresholds from 500 to 2,000 m, wild cluster bootstrap inference and leave-one-quarter-out diagnostics. Residual Moran’s I is reduced but not eliminated, indicating that the model captures substantial local spatial dependence while leaving some residual spatial clustering. The study has three limitations. Firstly, the estimated relationships are conditional associations rather than causal effects, and unobserved neighbourhood shocks may affect both focal and nearby prices. Secondly, the subsequent local-price variable is an ex post local-trend proxy, not direct evidence of expectations or asking-price anchoring. Future research could combine transaction, listing and asking-price data. Thirdly, the quarterly structure may miss finer micro-dynamics. Future work could estimate submarket-specific models, compare pre- and post-shock periods or use spatio-temporal local-price variables as interpretable features in valuation models. For appraisal practice, the results support a more temporally aware use of comparables. Nearby transactions should be assessed not only by geographic proximity but also by whether they occurred in past, same-quarter or subsequent periods. For market monitoring, the findings suggest that spatio-temporal local-price variables can help diagnose local price co-movement and persistent neighbourhood trends, although they should not be read as causal signals. For applied housing-market research, the spatio-temporal SLX approach offers an interpretable alternative to more complex simultaneous spatial models, especially when large transaction data sets make dense-matrix estimation impractical. The findings suggest that housing prices are embedded in temporally structured local market environments. Nearby prices observed in past, same-quarter and subsequent periods are conditionally associated with focal prices, but these associations should not be interpreted as causal behavioural mechanisms. For society and policy, the results show why local housing-market monitoring should consider neighbourhood-level price co-movement and persistent local trends, not only citywide averages. More spatially and temporally sensitive monitoring can help identify areas where price pressures are concentrated, supporting better-informed appraisal, public communication and housing-market oversight. This paper contributes to applied housing-market analysis by providing a scalable sparse-matrix framework for decomposing local price dependence by temporal position in a large transaction data set. The revised interpretation emphasizes conditional spatio-temporal local-price associations rather than distinct behavioural mechanisms. This provides a more conservative and empirically defensible basis for analysing local price formation in dense metropolitan housing markets. ",0.0,,False,JournalArticle,,2026-07-30,bb77f5aca99326a7ee86cdf70f4ac704a82661dc,spatiotemporallocalpricedependenceinametropolitanhousingmarketevidencefrom64529apartmenttransactionsinbusankorea,chung,10.1108/ijhma-05-2026-0151,spatiotemporallocalpricedependenceinametropolitanhousingmarketevidencefrom64529apartmenttransactionsinbusankorea|2026,1,5160,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic housing prices || spatial hedonic model housing",2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,b01d4098f11b25f2cdb64c308f46a073f4082e0f,10.1108/ijhma-10-2025-0231,,Buyer behavior under ambiguous price signals: socioeconomic segmentation in a transitional housing market,2025.0,International Journal of Housing Markets and Analysis,H. Nguyen; H. Truong," This paper explores how ambiguous price signals – specifically, suspiciously low asking prices – shape buyer behavior in an opaque housing market. This paper aims to explain how such signals influence transaction timing and how these effects differ by buyers’ income and experience, offering insight into how market opacity redistributes both value and liquidity across social groups. Using 430 transactions of self-built houses in Ho Chi Minh City (HCMC), Vietnam, the study applies a three-step empirical design. A hedonic regression first establishes market-consistent values to identify suspiciously underpriced listings. OLS models then estimate their static effects on final price and time-on-market. Finally, a sequence of monthly Cox proportional hazards models captures how sale likelihood evolves through the listing life-cycle, revealing time-varying stigma patterns and heterogeneous behavioral responses. The results show a clear three-phase dynamic: an early penalty for suspicious listings, a mid-cycle pause as buyers reassess risk and a later reactivation of stigma as unsold listings become publicly visible. The penalty is strongest for low-income and first-time buyers, whereas middle- and high-income groups use ambiguity as a strategic opportunity. Experience acts as a stabilizer rather than an accelerator, reflecting built-in diagnostic caution. The study focuses on 430 self-built house transactions in HCMC, which offers a consistent behavioral context but limits broader generalization. Ambiguity is measured through price deviations only, leaving out textual and visual cues that may also convey information quality. Although robustness checks support the main findings, some unobserved heterogeneity may remain. Future research could expand this approach to multi-city data and integrate seller behavior or richer listing information to capture the full dynamics of informational asymmetry in transitional housing markets. Enhancing listing transparency, standardizing disclosure and improving buyer education can reduce inequality in market participation without dampening efficient price signaling. Information asymmetry in transitional housing markets can deepen inequality in access to homeownership. Low-income and first-time buyers face slower transactions and higher exposure to market risks, whereas affluent or experienced groups convert ambiguity into advantage. Improving market transparency and consumer literacy therefore carries strong social value – it levels the playing field, strengthens buyer protection and enhances trust in local property markets. Reducing informational inequality can also contribute to more inclusive and sustainable urban housing systems in fast-growing cities like HCMC. This study integrates stigma theory and bounded rationality within a dynamic survival-analysis framework. It provides rare empirical evidence from a transitional housing market, showing how information asymmetry and buyer heterogeneity interact to produce unequal speed and opportunity in property transactions. ",0.0,,False,JournalArticle,,2025-12-03,b01d4098f11b25f2cdb64c308f46a073f4082e0f,buyerbehaviorunderambiguouspricesignalssocioeconomicsegmentationinatransitionalhousingmarket,nguyen,10.1108/ijhma-10-2025-0231,buyerbehaviorunderambiguouspricesignalssocioeconomicsegmentationinatransitionalhousingmarket|2025,1,4101,2,hedonic house price || hedonic housing prices,2,1,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4c9c43c418d6b1cc4d112d18aa4962b10948b800,10.5455/ey.24001,,Determination of Factors Affecting Housing Prices by Hedonic Price Model: The Case of Aydın City Center,2022.0,Ekonomik Yaklasim,Mehmet mu; H. Kaya,"The aim of this study is determining the meaningful variables and unlike the hedonic price model work will be established using actual sales prices Hedonic Price Model is to determine which would be the variable of meaningful will emerge and parameters magnitude at what level. unlike studies which were previously conducted on house prices y using hedonic price model in Turkey The aim of this study is the determination of the variables affecting the house prices by using the Hedonic Price Model on the houses where 759 numbers real purchase-sale data have been collected from seller or purchaser in Aydın city center in the last year. All the houses that were subject to sale in 2018 regarding the residences in eighteen neighborhoods in the city center of Aydın were taken into consideration and the transactions (real purchases and sales) of the parties in the deed by agreement. In this study, hedonic price model is used, lin-lin, log-lin and log-log model forms are analyzed and log-lin form is found to give better results. Ten dummy variables and thirteen continious variables totaly twenty three independent variables included in the model, and 13 of them are found statistically important. These variables are such as gross living area of the flat, number of rooms, number of floors, have more than one front, having two or more bathrooms, presence of elevator, presence of swimming pool, natural gas heating, apartment building on boulevard, distance to the bazaar, distance to the mall and, distance to the nearest school. The adjusted R2 of the equation is found 85.6 %. Bu çalışmanın amacı, daha önce Türkiye’de konut fiyatları üzerine yapılmış Hedonik Fiyat Modeli çalışmalarından farklı olarak, gerçek satış fiyatları kullanılarak kurulacak Hedonik Fiyat Modeli ile hangi değişkenlerin anlamlı çıkacağı ve parametre büyüklüklerinin ne düzeyde olacağının belirlenmesidir. Bu amaçla son bir yıl içinde Aydın kent merkezinde 759 adet bizzat konutu satın alan veya satan kişiler ile yapılan anket sonucu toplanan verilere Hedonik Fiyat Modeli uygulanmıştır. Aydın kent merkezinde toplam onsekiz mahalledeki konutlara ilişkin 2018 yılında satışa konu olmuş bütün konutlar dikkate alınmış, tarafların el sıkışıp anlaşarak tapuda yaptıkları işlemler (gerçek alım-satımlar) dikkate alınmıştır. Çalışmada doğrusal, yarı logaritmik ve tam logaritmik model formlarında çözümleme yapılmış, yarı logaritmik formda yapılan çözümlemenin daha iyi sonuç verdiği tespit edilmiştir. Modele 13’ü sürekli, 10’u kukla değişken olmak üzere toplam 23 adet bağımsız değişken dahil edilmiş, 13 bağımsız değişken istatistiki açıdan önemli bulunmuştur. Bu değişkenler; dairenin brüt kullanım alanı, dairedeki salon hariç oda sayısı, dairenin bulunduğu katın sayısı, bina yaşı, dairenin birden fazla cepheli olması, dairede iki ve daha fazla banyo olması, dairenin bulunduğu apartmanda asansör, yüzme havuzu, doğalgaz olması, dairenin bulunduğu apartmanın bulvar üzerinde olması, çarşıya uzaklık, AVM’ye uzaklık ve en yakın ilköğretim okuluna uzaklıktır. Denklemin düzeltilmiş R2’si % 85,6 olarak bulunmuştur",0.0,,False,,,,4c9c43c418d6b1cc4d112d18aa4962b10948b800,determinationoffactorsaffectinghousingpricesbyhedonicpricemodelthecaseofaydncitycenter,mu,10.5455/ey.24001,determinationoffactorsaffectinghousingpricesbyhedonicpricemodelthecaseofaydncitycenter|2022,1,4099,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic house price || hedonic housing prices || hedonik fiyat konut",3,3,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,real estate hedonic regression,0bb405826a305dd8418fa8a4e64a6fd058e2239a,10.1108/pm-09-2025-0133,,"Assessing the impact of various factors on residential apartment prices in Cazin, Bosnia and Herzegovina",2026.0,Property Management,M. Karamujic; Kerim Karamujic; Esma Skrgic," This paper aims to identify the key determinants influencing apartment prices in Cazin, the largest city in the Una-Sana Canton, one of the ten cantons within the Federation of Bosnia and Herzegovina, an entity of Bosnia and Herzegovina (BiH). The study focuses on apartment transactions recorded between 2018 and 2024. By analysing the most influential factors and understanding their interrelationships, this research seeks to provide valuable insights to a wide range of stakeholders, including government institutions, urban planners, real estate professionals, developers, educators and the general public, enabling them to make more informed decisions. Based on the reviewed literature on studies focusing on housing prices and their influence factors, the hedonic pricing model and multiple regression analysis in line with that promulgated by Rosen (1974), are the most commonly used approaches. Consequently, this research will also use the above specification to identify the relevance of selected factors in explaining variability of new apartment prices in Cazin. The results of this study indicate that the market value of apartments in Cazin is significantly influenced by six key variables: building quality, proximity to the main road, availability of green areas, floor level, parking access and distance from the city centre. Among these, building quality emerged as the most influential factor, highlighting buyers' strong preference for high-quality materials and modern construction standards. In contrast, the analysis revealed that apartment size did not have a statistically significant effect on market prices. The study has important implications for various stakeholders, including valuers, buyers and sellers, financial institutions, real estate agents, investors and government authorities. This research will help valuers to identify market value determination factors. Market participants are highly interested in understanding how different factors influence property values. Financial institutions, among other reasons, need to understand value determination process of collaterals, insured properties, etc., while real estate agencies and investors have an interest in enhancing their decision-making processes. Local and other authorities need this information to develop better policies related to, among other things, taxation, construction and housing. As far as the authors are aware, this study is the first quantitative research that utilises the hedonic pricing model and multiple regression analysis to identify the factors affecting apartment prices in B&H and their respective impact rates. It also highlights several specific factors affecting these prices that differ from those identified in prior research. ",0.0,,False,JournalArticle;Review,,2026-06-18,0bb405826a305dd8418fa8a4e64a6fd058e2239a,assessingtheimpactofvariousfactorsonresidentialapartmentpricesincazinbosniaandherzegovina,karamujic,10.1108/pm-09-2025-0133,assessingtheimpactofvariousfactorsonresidentialapartmentpricesincazinbosniaandherzegovina|2026,1,3795,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression || residential property price determinants",2,4,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,real estate hedonic regression,6f546db6e05156d793927bcc98c53fd6bee19feb,10.1108/ijhma-06-2026-0180,,AI-enabled smart home technologies and residential property price premiums in Malaysia: a hedonic pricing analysis,2026.0,International Journal of Housing Markets and Analysis,A. Adediran; S. Ajibade," This study investigates the influence of artificial intelligence (AI)-enabled smart-home technologies on residential property prices in Malaysia using an extended hedonic pricing framework. Specifically, it examines whether the integration of AI-based features, including smart security systems, automated energy management and intelligent home-control technologies, contributes to measurable housing price premiums. Using a cross-sectional dataset of 300 residential properties (229 in regression analysis), this study employs an extended hedonic pricing model and multiple regression analysis to evaluate the influence of structural, technological and locational attributes on residential property values. In addition, subgroup comparisons across smart-home technology levels are conducted to examine variations in the pricing effects of AI-enabled smart-home features across different housing submarkets. The results show that AI adoption is positively correlated with property prices (ß = 0.154, p = 0.002–0.003 for both regression models presented in Tables 12 and 13), indicating that smart-home technology is reflected in property prices in Malaysia. Depending on specification (Table 12: R2 = 0.310; Table 13: R2 = 0.212), the models explain between 21.2% and 31.0% of the variation in property prices. Further results indicate that AI security systems do command a measurable price premium for homes, and that size and other structural attributes remain key determinants of property prices. The negative relationship between distance to the central business district and property price remains significant, indicating the importance of location. The study is limited by the cross-sectional design, the focus on a subset of AI-enabled technologies, and the restriction to the Malaysian market. Future research should investigate this relationship further by using longitudinal data, broader measures of technology, and cross-country comparisons. The findings show that investors, developers, and financial institutions value digital housing features, encouraging the use of smart technology in residential property investment and valuation in Malaysia and elsewhere. This study offers empirical evidence from an evolving market on the pricing/value effects of AI-enabled housing attributes while contributing to the growing literature on technology-driven value creation in real estate markets. ",0.0,,False,JournalArticle,,2026-09-16,6f546db6e05156d793927bcc98c53fd6bee19feb,aienabledsmarthometechnologiesandresidentialpropertypricepremiumsinmalaysiaahedonicpricinganalysis,adediran,10.1108/ijhma-06-2026-0180,aienabledsmarthometechnologiesandresidentialpropertypricepremiumsinmalaysiaahedonicpricinganalysis|2026,1,3442,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || automated valuation model residential || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic property valuation || real estate hedonic regression || residential property price determinants",2,4,4,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,automated valuation model residential,08f08f6725b0a13c22a30e36ecbdb2411e99d8fd,10.1108/ijhma-07-2026-0273,,Capitalizing consumer school ratings into residential listing prices: evidence from Greater Houston,2026.0,International Journal of Housing Markets and Analysis,Arun Narayanasamy; Humnath Panta; M. Devkota," This paper aims to estimate how much of a consumer-facing school rating is capitalized into the price at which a home is offered for sale and to test whether that rating displaces the official state accountability score in a large metropolitan market that has received little direct attention in the capitalization literature and is unusual in having no conventional zoning. Log-linear hedonic price regressions are estimated on 2,332 active detached single-family listings collected from Realtor.com in November 2025 and matched to assigned public high schools. Because the GreatSchools rating is invariant within each school-assignment group, the identifying variation is across 27 groups. Standard errors are clustered by school-assignment group; significance is verified by wild-cluster bootstrap, and a 27-group aggregated regression is reported as a conservative benchmark. A one-point increase in the GreatSchools rating is associated with a 4.57% higher listing price, about US$15,733 at the sample median of US$344,082, rising to 4.65% with school-district fixed effects. The aggregated 27-group estimate is 3.59% per point. Entered jointly, the rating retains its full association while the Texas Education Agency score is indistinguishable from zero. The rating–price relationship is linear, with no evidence of convexity. The dependent variable is an asking price in a single cross section, and parcel coordinates are unavailable, so spatial dependence, flood exposure and attendance-boundary sorting cannot be tested. The estimates are capitalization measures conditional on observed controls rather than causal willingness to pay. The listing-level estimate may overstate causal willingness to pay, while the 27-group estimate provides a more conservative benchmark. Valuation and automated appraisal models may benefit from including the consumer-facing rating alongside official accountability measures. Because the rating co-varies with student composition and neighborhood characteristics, its capitalization also raises affordability, residential sorting and equity questions. The paper provides a metropolitan-scale capitalization estimate for Greater Houston, prices the rating households encounter during search rather than the official accountability measure and studies the listing-price margin, which has received far less attention than the transaction price. ",0.0,,False,JournalArticle,,2026-10-07,08f08f6725b0a13c22a30e36ecbdb2411e99d8fd,capitalizingconsumerschoolratingsintoresidentiallistingpricesevidencefromgreaterhouston,narayanasamy,10.1108/ijhma-07-2026-0273,capitalizingconsumerschoolratingsintoresidentiallistingpricesevidencefromgreaterhouston|2026,1,3425,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || automated valuation model residential",2,3,4,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,machine learning hedonic pricing housing,3458b9af86f05d847fef47a983987204e36f5ce2,10.1108/ijhma-03-2026-0065,,Comparing hedonic and machine learning models in residential property valuation: evidence from sub-market analysis in Italy,2026.0,International Journal of Housing Markets and Analysis,G. Maselli; Laura Gabrielli; Antonio Nesticò," This study aims to compare the predictive performance, robustness and interpretability of traditional hedonic regression and machine learning (ML) models in residential property valuation. While regression-based approaches estimate marginal prices directly, they may be limited in capturing non-linearities and multicollinearity. Conversely, artificial intelligence (AI) models provide greater flexibility but are more prone to overfitting and often lack transparency. Explainable AI (XAI) is integrated to improve the interpretation of price-formation mechanisms. An exploratory case study compares multiple linear regression (MLR), artificial neural networks (ANN), random forest (RF) and a hybrid stacking model combining ANN and RF. The analysis includes variable definition, data collection and preprocessing, model specification and implementation and validation through training and k-fold cross-validation. To enhance interpretability, permutation feature importance and SHapley Additive exPlanations values are applied. The empirical application focuses on two Osservatorio del Mercato Immobiliare zones (B1 and B2) of Padua (Italy). Model performance is strongly influenced by sample size, data heterogeneity and validation strategy. Stacking provides a favourable balance between predictive accuracy and robustness, particularly in the larger and more heterogeneous B2 sample, whereas ANN and RF are more prone to overfitting in smaller and less heterogeneous data sets (B1). MLR proves more stable across training and cross-validation phases. XAI confirms the central role of floor area in price determination, together with maintenance condition, construction period and service availability. Model performance should be interpreted as a trade-off between accuracy, stability and transparency. This study provides an integrated comparison of econometric, ML and XAI-based approaches within a homogeneous housing market. It jointly evaluates predictive accuracy, robustness and interpretability, allowing different modelling strategies to be evaluated not only in terms of forecasting performance but also in relation to their transparency and stability. The empirical application to the Padua residential market provides useful evidence for researchers, valuers and decision makers interested in the use of advanced predictive tools for property value forecasting and market analysis. ",0.0,,False,JournalArticle,,2026-08-24,3458b9af86f05d847fef47a983987204e36f5ce2,comparinghedonicandmachinelearningmodelsinresidentialpropertyvaluationevidencefromsubmarketanalysisinitaly,maselli,10.1108/ijhma-03-2026-0065,comparinghedonicandmachinelearningmodelsinresidentialpropertyvaluationevidencefromsubmarketanalysisinitaly|2026,1,3423,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices || hedonic property valuation || machine learning hedonic pricing housing",2,2,2,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic housing prices,568782a2bf2877b3eb5fd02c4f88fafa3f8d9664,10.1108/jfm-10-2024-0133,,The relationship between land consolidation and neighboring housing prices – application of quantile regression,2026.0,Journal of Facilities Management,Chun-Chang Lee; Zhong-Liang Lee; Pei-Syuan Lin; Chun-Chao Chen," This study aims to use the hedonic price method to explore the relationship between land consolidation and neighboring housing prices. Beginning in the 1980s, with the goal of expediting the city’s development, the Kaohsiung City Government began the expropriation of two zones covering the Kaohsiung Museum of Fine Arts and the Aozihdi Park areas in the Gushan District, which are termed the 44th (KMFA) Redevelopment Zone and the Nong-16 Zone, respectively. Land consolidation and zone expropriation are both, in essence, policy measures to promote appropriate land use. Both measures provide for land-use zoning and planning of parks and green spaces and are key determinants of housing prices within these zones. This study uses the hedonic price method to explore the relationship between land consolidation and neighboring housing prices. The ordinary least squares estimation results indicate that housing prices decrease by 0.1% for every additional meter in the distance to the KMFA or Nong-16 zones. The mean housing price in the KMFA zone is 0.2% higher than that in the Nong-16 zone. The quantile regression results show no significant differences in the relationship between the distance to the KMFA or Nong-16 zones and the price distributions for the high-, medium- and low-price housing markets. As the housing-price level increases, the difference in prices between the KMFA or Nong-16 zones and typical zones gradually diminishes. Additionally, as the price level increases, there is an increase in the difference in the distribution of high housing prices between the KMFA and Nong-16 zones. Overall, the findings suggest that land consolidation improves residents’ living environments and increases neighboring housing prices. These results provide valuable insights for government agencies when evaluating the benefits and investment returns of urban renewal, land development and zone expropriation policies. They also support the planning of public infrastructure and recreational spaces. Unlike previous studies that predominantly examined the impacts of land use control on housing prices (see Hannah, Kim and Mills, 1993; Pollakowski and Wachter, 1990; Ihlanfeldt, 2007; and Bramley, 1993), this study focused on the seldom-discussed topic of land consolidation. ",0.0,,False,JournalArticle,,2026-10-08,568782a2bf2877b3eb5fd02c4f88fafa3f8d9664,therelationshipbetweenlandconsolidationandneighboringhousingpricesapplicationofquantileregression,lee,10.1108/jfm-10-2024-0133,therelationshipbetweenlandconsolidationandneighboringhousingpricesapplicationofquantileregression|2026,1,3321,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,1,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,0dd914b51d9272cc84b31b94b3bbf3133c3ba903,10.1353/jda.2022.0005,,Investigating the Hedonic Housing Price Model for Lagos Residential Market,2021.0,Journal of Developing Areas,Olalekan Dimeji Bamiteko; Funso Sunday Ayadi; D. Oke,"ABSTRACT:The conundrum on what exactly consumers are willing to pay for when demanding for housing remains a concern in the developing world. This study investigates the hedonic housing price for Lagos residential market with a view to determine the willingness to pay for housing quality traits. The hedonic pricing model is one way through which willingness to pay is being measured. The data employed was obtained from adult residents in the urban centers of the state (Lagos Metropolitan city). Specifically, Lagos State has 20 Local Government Areas (LGAs), out of which 16 constitute the Lagos Metropolitan City. Multistage sampling technique was used to select a sample of 677 housing consumers from the population. The Ridge Regression (RR) was used to estimate the multifactorial model that formed the hedonic housing price model. This method overcomes the problem of multicollinearity in the model and produce unbiased estimates. The findings show that house type has a higher impact on the willingness to pay for a house. Flooring condition of a house is more considered by the consumers than its roofing and walling. Besides, availability of clean water is the most important variable that consumers consider in the basic attribute as relates to life essentials. The next to it is the availability of fence in the apartment. In essence, security of life and property remains a concern to living in Lagos and Nigeria by extension. This underscores the need for Nigerian government to sincerely tackle insecurity in the nation. Essentially, government must strengthen its intelligent mechanisms so as to unravel and deal with internal and external sponsors of insecurity in the country. Closeness to bank and commercial centres stands to be the variable with the greatest impact on willingness to pay among the positive environmental externalities. Meanwhile, a unit increase in travel distance to hospital, market and water facility decrease willingness to pay by 6.1 percent, 3.4 percent and 0.6 percent, respectively. In all, house consumers in the city are willing to pay different prices for different housing quality traits.",0.0,,False,,,2021-11-04,0dd914b51d9272cc84b31b94b3bbf3133c3ba903,investigatingthehedonichousingpricemodelforlagosresidentialmarket,bamiteko,10.1353/jda.2022.0005,investigatingthehedonichousingpricemodelforlagosresidentialmarket|2021,1,3140,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment prices hedonic || hedonic house price || hedonic housing prices || willingness to pay housing attributes hedonic",2,2,4,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic property valuation,87ddaebbb6eed74d30fe2116bc7301990e0fb060,10.1108/pm-04-2026-0073,,Valuation of short-term rentals as income-producing residential assets: a hybrid-hedonic framework,2026.0,Property Management,Marcos Medeiros; Nan Hua; Peng Liu; Jorge R. Ridderstaat; M. Rivera," This study examines whether financial performance mediates the relationship between conventional hedonic property attributes and short-term rental value, and whether short-term rentals should therefore be underwritten as hybrid income-producing residential assets rather than as ordinary housing alone. AirDNA listing-performance data were matched with Stellar MLS property and transaction records to create a property-level panel for a major destination market in Central Florida. Panel diagnostics were conducted for cross-sectional dependence, stationarity, serial correlation, heteroskedasticity and model selection, with conservative inference based on Driscoll-Kraay standard errors. Durable tangible attributes, including configuration, accessibility and leisure amenities, are associated with both revenue and sale price. Platform-facing and management-related intangible variables strongly influence financial performance, but most lose direct significance once revenue enters the value equation. A 1% increase in revenue is associated with an approximately 0.349% increase in sale price. The results, therefore, indicate partial mediation for tangible attributes and a predominantly income-based transmission channel for most intangibles. Institutional property managers, lenders and appraisers should apply dual-lens underwriting that combines residential collateral analysis with stabilized cash-flow analysis. Verified operating history should inform NOI normalization, repayment-capacity testing and destination-level concentration-risk assessment. The paper develops a hybrid hedonic-income framework for valuing short-term rentals and positions the asset class as a form of operating real estate exposure embedded in residential property. It contributes by distinguishing durable collateral features from operator-contingent platform signals and by showing that most intangible effects are capitalized indirectly through financial performance rather than directly into transaction price. ",0.0,,False,JournalArticle,,2026-09-21,87ddaebbb6eed74d30fe2116bc7301990e0fb060,valuationofshorttermrentalsasincomeproducingresidentialassetsahybridhedonicframework,medeiros,10.1108/pm-04-2026-0073,valuationofshorttermrentalsasincomeproducingresidentialassetsahybridhedonicframework|2026,1,3036,4,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic price model amenities residential || hedonic property valuation,2,3,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),5d29469aa07e316fd484c5cb6eae321dabbe618c,10.22423/kreus.2025.17.2.87,,"A Study on the Influence of Service Area on Apartment Prices: Focusing on National Housing-sized Units in Gangnam, Seocho, Songpa, and the Second-phase New Towns in 2023",2025.0,Konkuk Research Institute of Real Estate and Urban Studies,Minhyuk Choi; Seung-Han Ro; Jong-Hyun Park,"In the domestic apartment market, additional free space has long been a factor that has increased consumer preference. Since the emergence of the national housing size (with an area of exclusive use space of 85m or less) in the 1970s, consumers have overcome the limitations 2 of the area of small and medium-sized housing through additional free space. In addition, since the legalization of balcony expansion in 2005, additional free space has become larger and appeared in various forms. additional free space has various issues that cover the institutional and industrial aspects of the domestic apartment market, but there is a lack of research linking additional free space to real estate value. Therefore, this study conducted an empirical analysis of the impact of additional free space on apartment prices. We selected 12 apartment complexes in the three of Gangnam districts and the second phase of new towns that were supplied after 2005, and After securing floor plans of an apartment with an area of exclusive use space of 85m or less, the additional free space was calculated 2 using the AutoCAD program. Linear regression analysis was conducted using Hedonic price models for 790 transactions between April and October 2023. The analysis showed that the additional free space ratio and the dependent variable, price per area, were positively related, and that the smaller the apartment, the higher the utility of the additional free space. It was also confirmed that the additional free space ratio of apartments in the three of Gangnam region was lower than that of apartments in the second-phase New Town region. This study is significant in that it calculated the additional free space to an accurate approximation and used it for analysis. However, there are some issues with the calculation of the area size, which was calculated based on some arbitrary judgments, and thus there are limitations in data. We hope to conduct follow-up research by refining the data in the future.",0.0,,False,JournalArticle,,2025-02-28,5d29469aa07e316fd484c5cb6eae321dabbe618c,astudyontheinfluenceofserviceareaonapartmentpricesfocusingonnationalhousingsizedunitsingangnamseochosongpaandthesecondphasenewtownsin2023,choi,10.22423/kreus.2025.17.2.87,astudyontheinfluenceofserviceareaonapartmentpricesfocusingonnationalhousingsizedunitsingangnamseochosongpaandthesecondphasenewtownsin2023|2025,1,3001,4,apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression,2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic property valuation,8c496f55f91e146b2962537e0a46d9ea83e20970,10.1108/ijhma-09-2022-0142,,Development risk and unit size within the UK property market,2022.0,International Journal of Housing Markets and Analysis,Cassandra Moore," Purpose This paper aims to explore the relationship between market pricing and design quality within the development industry. Currently, there is a lack of research that examines real estate at the property level. Development quality is widely believed to have diminished over the past decades, while many investors seem uninterested in the design process. The study aims to address these issues through a pricing model that integrates design attributes. It is hoped that empirical findings will invite broader stakeholder interest in the design process. Design/methodology/approach The research establishes a framework for assessing spatial compliance across residential developments within London. Compliance is assessed across ten boroughs, with technical space guidelines used as a proxy for design quality. Transaction prices and spatial assessments are aligned within a hedonic pricing model. Empirical findings are used to establish whether undermining spatial standards presents a significant development risk. Findings Findings suggest a relationship between sale time and unit size, with “compliant” units typically transacting earlier than “non-compliant” units. Almost half of the 1,600 apartments surveyed appear to undermine technical guidelines. Research limitations/implications It is suggested that an array of design attributes be explored that extend beyond unit size. Additionally, future studies may consider the long-term implications of design quality via secondary transaction prices. Practical implications Practical implications include the development of a more scientific approach to design valuation. This may enhance the position of product design management within the development industry and architectural services. Social implications Social implications may include improvement in residential design. Originality/value An innovative approach combines a thorough understanding of both design and economic principles. ",0.0,,False,Review,,2022-12-14,8c496f55f91e146b2962537e0a46d9ea83e20970,developmentriskandunitsizewithintheukpropertymarket,moore,10.1108/ijhma-09-2022-0142,developmentriskandunitsizewithintheukpropertymarket|2022,1,2965,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic property valuation,2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic property valuation,7ae8515da5ca78a174cf983ae81cb942344a6e00,10.1108/pm-05-2025-0075,,Valuation of the economic impact of a disaster event,2025.0,Property Management,M. Razali; K. Achu," This study investigates the impact of environmental disasters on residential property values, focusing on the Putra Heights gas pipeline explosion and the Sungai Kim Kim River industrial waste pollution in Malaysia. It aims to develop and test a spatially explicit valuation framework that captures both tangible and perception-driven effects, enabling more accurate post-disaster property assessments. An extended hedonic pricing model, integrated with spatial econometric techniques – the spatial lag model (SLM) and spatial error model (SEM) – was applied to property transaction data before and after the disasters. Variables included structural and locational characteristics as well as disaster-specific factors such as hazard proximity, insurance accessibility, public perception indices and media coverage intensity. Geographic Information Systems (GIS) were used to quantify spatial spillover effects. Environmental disasters caused both direct and indirect devaluation of properties, with average losses per property ranging from RM 13,500 to RM 60,500, and total market losses in high-impact zones reaching RM 12–18 m. Spillover effects extended up to 800 m from hazard sites. Elevation and structural resilience attracted value premiums, while proximity to hazards, constrained insurance access and negative media coverage contributed to persistent value suppression. Buyer preferences shifted towards safer, more resilient housing designs. By integrating spatial econometric modelling with perception-based variables such as media coverage intensity, this research extends conventional valuation models. It bridges international valuation standards with Malaysian market conditions, offering practical tools for valuers, policymakers, insurers and developers to improve risk-adjusted pricing, compensation and recovery planning in disaster-prone property markets. ",0.0,,False,JournalArticle,,2025-09-12,7ae8515da5ca78a174cf983ae81cb942344a6e00,valuationoftheeconomicimpactofadisasterevent,razali,10.1108/pm-05-2025-0075,valuationoftheeconomicimpactofadisasterevent|2025,1,2902,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic || spatial hedonic model housing",2,1,2,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,fd9e2d589e4cf3d85a4769aff0adb4062a87cf53,10.57199/jgcr.2026.5.2.439,,"Analysis of Apartment Price Determinants Based on Differences in Characteristics of Regional Submarkets : Focusing on Haeundae-gu and Saha-gu, Busan",2026.0,Global Convergence Research Academy,Cheon-Woo Lee; Seongsoo Seo,"This study identified Haeundae-gu and Saha-gu in Busan Metropolitan City, which are expected to differ in regional characteristics, conducted a comprehensive survey of iapartment transaction data in 2025, and compared and analyzed price determinants using a hedonic price model. The key analysis results are summarized as follows. First, based on prior research, a total of 20 variables were included as explanatory variables. As a result, for Haeundae-gu, 18 variables were significant, excluding the number of rooms and the shortest distance to large marts, and the explanatory power was also high at R²=0.889. For Saha-gu, the significance of 15 variables was confirmed, excluding the number of rooms, entrance (mixed type), middle school accessibility, shortest distance to large hospitals, and gentle slope, and the explanatory power was also high at R²=0.869. In particular, as a result of testing using squared terms to verify the non-linear effects of the area and elapsed years variables, The estimated turning points were 224.6㎡ and 30.02 years in Haeundae-gu, and 163.2㎡ and 32.3 years in Saha-gu. In addition, significant differences were found in the degree of influence of brand value and slope by region. Based on the results of this study, the following policy and practical implications are presented. First, the government and local governments should consider differentiated application based on regional characteristics when formulating or implementing housing policies. Second, related companies, such as construction firms, should strive to supply customized housing that takes into account changing consumer utility values and regional characteristics, from the design stage to marketing. It is hoped that the empirical analysis results of this study will provide rational assistance to consumers in their apartment selection process across different regions.",0.0,,False,JournalArticle;Review,,2026-06-30,fd9e2d589e4cf3d85a4769aff0adb4062a87cf53,analysisofapartmentpricedeterminantsbasedondifferencesincharacteristicsofregionalsubmarketsfocusingonhaeundaeguandsahagubusan,lee,10.57199/jgcr.2026.5.2.439,analysisofapartmentpricedeterminantsbasedondifferencesincharacteristicsofregionalsubmarketsfocusingonhaeundaeguandsahagubusan|2026,1,2882,3,apartment prices hedonic || hedonic housing prices || housing characteristics prices hedonic,2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,house prices proximity hedonic,972e3c33bc16dcaeb5855c3af2beca214d69afa4,10.1108/ijhma-05-2025-0110,,The avian influence: examining the impact of birds on house prices,2025.0,International Journal of Housing Markets and Analysis,Lei Zhang; Shan Zhang," The purpose of this paper is to disentangle the often-overlooked relationship between biodiversity, measured by bird abundance and species richness, and residential property values. While conventional determinants such as location, amenities and economic conditions typically shape house prices, the ecological characteristics of a neighborhood – including its biological abundance and species richness – can also exert a significant influence. By integrating novel citizen science eBird data with housing sales records from the Hampton Roads region of Virginia, the authors employed a hedonic pricing model and a quantile regression approach to examine the impact of biodiversity on house prices. The analysis reveals a statistically significant positive association between bird biodiversity and house prices, with the strongest effects observed in close proximity to the properties. While avian presence at greater distances still influences property values, its impact diminishes with distance. The quantile regression results suggest that bird abundance has a more pronounced effect on higher-priced residences compared to lower-priced ones. It was also found that the relationship between bird biodiversity and house prices is heterogeneous across several dimensions, including bird species type, housing type and neighborhood characteristics. This research sheds light on the role of ecological amenities in shaping housing market dynamics. To the best of the authors’ knowledge, it is the first comprehensive study to investigate the impact of biodiversity, measured by bird abundance and species richness, on house prices. The findings contribute to a deeper understanding of how ecological services provided by birds, along with associated human leisure activities, interact with economic factors in the housing market. ",0.0,,False,JournalArticle,,2025-10-07,972e3c33bc16dcaeb5855c3af2beca214d69afa4,theavianinfluenceexaminingtheimpactofbirdsonhouseprices,zhang,10.1108/ijhma-05-2025-0110,theavianinfluenceexaminingtheimpactofbirdsonhouseprices|2025,1,2855,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || hedonic price model amenities residential || house prices proximity hedonic || housing characteristics prices hedonic || residential property price determinants",2,2,3,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,0b73ae4efcbfc1f6fb6c4aede6220c03499c9aef,10.15396/eres2019_306,,Residential rent indices in a long-term trend for the french case: An application of the hedonic method,2019.0,26th Annual European Real Estate Society Conference,Alexis Pourcelot,"The purpose of this paper is to understand the housing rents formation and their dynamics over almost half century (1970-2013) by sector (private/public) and location (Paris region, big, medium and small urban areas). We underline the role played by physical and locational characteristics in the formation of rents. Dynamics and cycles in rents over the period are tackled using macro-economic factors as well as market variables and housing policy. Likewise, we are able to show the impact of specific housing policies such as APL on rents. This stratified approach, will give a relevant overview of the French market for international investors. Likewise, valuation of asset prices is relevant and useful for practitioners (appraisers, brokers, developers, investors, fund managers, analysts and market researchers) but also policy-makers. In order to conduct this analysis we will use the French Housing Survey from the French Statistical and Economic Study Institute (INSEE). This survey describes the French housing stock as well as the household tenure choice and their housing consumption. Physical characteristics and dwelling quality such as size, sanitary convenience, heating, location, environment and security are topics addressed by the survey. The physical and contractual variables used in the empirical strategy got a high power of explanation of our stratified model. Regarding time variation, we expect a positive change of rents over the period 1970-2013. Nevertheless, when we focus on the real rent variation, we find that variation is spatially heterogeneous across urban areas. We also find several cycles in the rental market with a strong decrease in real rent in all urban areas over the period 1970-1984. Likewise, we highlight the inflationary effect of APL (housing subsidy) on rents.",0.0,,False,Conference;Review,Economics,,0b73ae4efcbfc1f6fb6c4aede6220c03499c9aef,residentialrentindicesinalongtermtrendforthefrenchcaseanapplicationofthehedonicmethod,pourcelot,10.15396/eres2019_306,residentialrentindicesinalongtermtrendforthefrenchcaseanapplicationofthehedonicmethod|2019,1,2815,3,hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing,2,1,5,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,9cf172e9f98dd8e4763e5e90a4b1cec2fc594c55,10.1177/23998083261460686,,Scaling-informed natural submarkets: A bottom-up approach to segment housing markets in the Yangtze River Delta,2026.0,Environment and Planning B Urban Analytics and City Science,Zihan Xu; Yunjie Feng; Qimeng Song; Yao Shen,"Urban housing markets exhibit pronounced spatial heterogeneity shaped by multiple spatial factors, resulting in scaling characteristics and hierarchical structures typical of complex systems. These properties provide an alternative perspective for delineating housing market areas and interpreting inter-market linkages. This study introduces the concept of natural submarkets, defined as spatially contiguous housing clusters exhibiting both hierarchical scaling in price patterns and consistent hedonic price determinants. We implement this concept through a bottom-up segmentation framework that combines an improved head/tail breaks with hedonic model fit, enabling data-driven delineation of market boundaries and comparative regional analysis. Applied to 26 cities in the Yangtze River Delta (YRD), China, the proposed method yields a more objective and interpretable representation of price differentiation and scaling properties than conventional classifications. The results indicate that economically advanced cities may exhibit either multi-level submarket structures with well-defined hedonic relationships or more complex hierarchical structures with weakly defined pricing mechanisms. Further submarkets clustering by shared pricing mechanism reveals two dominant spatial patterns, comprising interconnected development corridors and isolated market structures. These morphologies are generally driven by network centrality and accessibility to amenities. As a result, the YRD housing price distribution is characterized by a polycentric structure and ongoing cross-boundary integration. The proposed framework complements traditional regional analyses by revealing structural patterns of mechanical similarity and supporting differentiated policy interventions aligned with housing market dynamics.",0.0,,False,JournalArticle,,2026-06-11,9cf172e9f98dd8e4763e5e90a4b1cec2fc594c55,scalinginformednaturalsubmarketsabottomupapproachtosegmenthousingmarketsintheyangtzeriverdelta,xu,10.1177/23998083261460686,scalinginformednaturalsubmarketsabottomupapproachtosegmenthousingmarketsintheyangtzeriverdelta|2026,1,2813,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,2,1,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,97fda1ba42ae86574d254847d7472d9d602c0931,10.1108/ijhma-07-2026-0233,,Housing price formation after the 2023 Türkiye earthquakes: hedonic and spatial evidence from the Iskenderun housing market,2026.0,International Journal of Housing Markets and Analysis,İsmet Demirağ; Murat Sağır; Mehmet Duruel," This study aims to examine how the 2023 Türkiye earthquakes reshaped housing price formation and residential valuation patterns within the Iskenderun housing micro-market. This research uses publicly available micro-market listing data. This study applies hedonic pricing models combined with a Generalized Method of Moments Spatial Error Model to control for unobserved geographical spillovers. Post-disaster properties command a 16.7% price premium. A non-linear, U-shaped vertical price gradient emerged, where both lower and upper floors command premiums relative to middle floors. Additionally, the price advantage associated with centrally located properties declines as building age increases, indicating a horizontal revaluation of residential location after the earthquake. This research relies on public listing prices rather than final transaction data because of limited registry transparency, and it is geographically limited to a single micro-market. The findings of this study provide critical insights for urban planners, appraisers and policymakers to understand post-disaster market dynamics, support post-disaster housing policy and enhance market resilience in seismically active regions. To the best of the authors’ knowledge, this study provides one of the first spatially explicit analyses of post-earthquake housing price formation in the Türkiye context. This study extends hedonic pricing theory by demonstrating how post-disaster risk pricing simultaneously reshapes both vertical and horizontal valuation mechanisms. Specifically, this paper identifies a robust U-shaped floor-level pricing pattern and demonstrates that these effects remain stable across alternative spatial specifications. ",0.0,,False,JournalArticle,,2026-09-14,97fda1ba42ae86574d254847d7472d9d602c0931,housingpriceformationafterthe2023turkiyeearthquakeshedonicandspatialevidencefromtheiskenderunhousingmarket,demirağ,10.1108/ijhma-07-2026-0233,housingpriceformationafterthe2023turkiyeearthquakeshedonicandspatialevidencefromtheiskenderunhousingmarket|2026,1,2759,4,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || hedonic property valuation || spatial hedonic model housing",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),ee602fb41f88743093210eafd60c8269e980bf07,10.37407/kres.2022.40.2.79,,A Study on the Determinants of Housing Price in China: Focused on Qingdao City,2022.0,Korea Real Estate Society,Qi-Hui Wu; Hyung Keun Kim,"Qingdao City is main city of Shandong Province along with Jinan City, one of Shandong Province's major cities. As the interest of the housing market is increasing, studies on Chinese real estate are also being actively conducted. However, the reserch on the housing price in Qingdao is relatively insufficient. The purpose of this study is to analyze the housing market in Qingdao and to analyze the formation factors for housing price in Qingdao. Hedonic price model was used as a model to analyze the housing price formation factors in Qingdao City, and data were collected from China's major housing trading site, Soupang Mang. The subjects of the study were six districts: Shinan, Shibei, Licang, Laoshan, Chengyang and Huangdao District. and the time range is for houses traded from 2014 to 2016. Housing characteristics affecting Qingdao housing prices are divided into regional characteristics, complex characteristics, household characteristics, and location characteristics. The main analysis results are as follows. First, in the regional characteristics, the prices of houses located in Shinan, Shibei, Licang, and Laoshan district were higher than those in other regions. Second, in the characteristics of the complex, the more the total number of households in the complex, the shorter the elapsed years, the more famous the contractor who built the house, the house has a higher price. Third, in the characteristics of the household, the larger the supply area, the higher the price when the household's scent is south or south. Lastly, in terms of location characteristics, it was found that the distance from the park or bus stop is short, or the prices of houses located close to the subway are high. ",0.0,,False,,,2022-06-30,ee602fb41f88743093210eafd60c8269e980bf07,astudyonthedeterminantsofhousingpriceinchinafocusedonqingdaocity,wu,10.37407/kres.2022.40.2.79,astudyonthedeterminantsofhousingpriceinchinafocusedonqingdaocity|2022,1,2719,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,1,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",64b2ef04150450815c46f32ceb5898434d4eaa4b,10.37407/kres.2023.41.3.173,,Determinants of Housing Price by Region of Seoul,2023.0,Korea Real Estate Society,U.-Hui Lee; Hae Jung Chun,"This study conducted empirical analysis using the hedonic price model to examine regional differences in the determinants of housing prices among the representative examples of decoupling phenomena in inter-regional housing price movements, namely, “Gangnam 3-gu”, “Nodogang”, and “Mayongseong”, from 2017 to 2022. In the analysis results, the Gangnam 3-gu emerged as the region with the highest housing prices. It was observed that the longer the elapsed years, the more the anticipation of reconstruction was reflected in housing prices for older apartments. Additionally, a strong preference for convenient living facilities and an excellent educational environment was evident, indicating a dual focus on residential convenience and investment purposes. On the other hand, the Nodogang region had the lowest housing prices. The elapsed years were found to be significant only beyond a certain generational threshold. Preference for living convenience facilities showed contrasting results compared to the Gangnam 3-gu. Furthermore, the importance of proximity to subway stations was highlighted, indicating anticipation for reconstruction alongside the significance of convenient public transportation in this region. The Mayongseong region fell into the category of higher housing prices. It consisted relatively of newly constructed apartments, and the preference leaned more towards a pleasant residential environment rather than anticipation for reconstruction. This area exhibited a lower connectivity to subway stations, and there was a preference for a close proximity to local markets. In terms of pursuing residential convenience, it displayed similarities to the Gangnam 3-gu.",0.0,,False,JournalArticle,,2023-09-30,64b2ef04150450815c46f32ceb5898434d4eaa4b,determinantsofhousingpricebyregionofseoul,lee,10.37407/kres.2023.41.3.173,determinantsofhousingpricebyregionofseoul|2023,1,2693,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,1,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,1d3763b55f8ce1c6dbde59ab64d50acc1032eb4d,10.35136/ker.36.2.4,,Impact of the Closure of a Major Retail Facility on Housing Prices : Evidence from the Shutdown of the Lotte Department Store Masan Branch,2026.0,Korea Real Estate Institute,Nam-Chul Choi; Sang Youb Lee,"This study examines the effect of the closure of a large retail facility on the transaction prices of nearby apartments . It uses the 2024 closure of the Masan branch of Lotte Department Store as a quasi-natural experiment to analyze this effect. Based on apartment transaction data from Masanhappo-gu and Masanhoewon-gu in Changwon Special City (January 2021–December 2025), the study uses a two-way fixed effects model that combines a hedonic price framework with a difference-in-differences approach. The April 2024 closure announcement and the actual July 2024 shutdown are treated as separate events, and the identification strategy is examined through event-study evidence, placebo tests, and Wild Cluster Bootstrap-t inference. The estimated post-closure average treatment effect is approximately 2.2%; however, it does not reach statistical significance. The post-period joint F-test in the event-study specification is also insignificant, indicating no clear evidence of an average post-closure price adjustment in the full sample. Supplementary distance-band and submarket analyses show relatively large negative announcement-period coefficients in the 500–1,000-m band and selected submarkets. However, these patterns warrant caution because they may partly reflect sample composition, peripheral locations, older housing characteristics, and longer-term structural decline in Masan area. This study complements the literature by providing a conservative empirical assessment of how a department store closure may affect housing values in a declining regional city and by highlighting the need to consider spatial and temporal heterogeneity.",0.0,,False,JournalArticle,,2026-06-30,1d3763b55f8ce1c6dbde59ab64d50acc1032eb4d,impactoftheclosureofamajorretailfacilityonhousingpricesevidencefromtheshutdownofthelottedepartmentstoremasanbranch,choi,10.35136/ker.36.2.4,impactoftheclosureofamajorretailfacilityonhousingpricesevidencefromtheshutdownofthelottedepartmentstoremasanbranch|2026,1,2653,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,3,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,79f96b8c44ab08a687f21986600628b7cd6b1857,10.1108/ijhma-05-2025-0106,,Is climate gentrification happening? Evidence from the effect of elevation and flooding risks on housing price dynamics in South Florida,2025.0,International Journal of Housing Markets and Analysis,Yanmei Li; Sumei Zhang; Serena Hoermann," Using housing price and price appreciation as a proxy for gentrification, the purpose of this study is to explore whether climate gentrification exists in South Florida when new or redevelopment projects target areas with higher elevation and/or lower flooding risks. Existing literature lacks longitudinal comparisons to isolate the effect of climate change on the property market. This study uses spatial mapping and six hedonic pricing models to measure how elevation and flooding risks are associated with housing prices and price appreciation in 2016 and 2021, controlling for housing attributes, neighborhood demographic characteristics, changes and the interaction terms between elevation/flooding and neighborhood variables. The findings of this study reveal that elevation and flooding risks influence housing transaction prices differently in different periods, indicating the potential impact of the presence versus absence of hurricanes on short-term market corrections. When exploring the repeat sales data, the overall finding is that climate gentrification, manifested as climate retreat, may happen because higher elevation areas have seen higher housing price appreciation. Flooding risks have a more profound impact because of their more complex confounding effects with insurance policies and demographic and property characteristics. Addressing a research gap and a relatively new concept (climate gentrification), the findings from this study will offer valuable insights for policymakers to develop targeted strategies for climate adaptation, housing stabilization and equitable urban development. ",0.0,,False,JournalArticle,,2025-08-26,79f96b8c44ab08a687f21986600628b7cd6b1857,isclimategentrificationhappeningevidencefromtheeffectofelevationandfloodingrisksonhousingpricedynamicsinsouthflorida,li,10.1108/ijhma-05-2025-0106,isclimategentrificationhappeningevidencefromtheeffectofelevationandfloodingrisksonhousingpricedynamicsinsouthflorida|2025,1,2645,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,3,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),daa4f09de06676b87492130242694cd896541b5a,10.1108/ijhma-12-2025-0320,,Unveiling neighborhood housing preferences in Istanbul: a hedonic pricing perspective on urban livability,2026.0,International Journal of Housing Markets and Analysis,Tuba Kaya; N. Atakul," This paper aims to propose a guiding hedonic model for urban transformation and planning efforts by presenting an approach that can be used to examine the changing demands of users and investors following disasters such as pandemics and earthquakes, and their effects on housing prices, through a case study at the neighborhood scale. The housing data and model were developed by synthesizing systematic literature review results and area analysis. Data from major real estate platforms were analyzed using hedonic pricing, with coefficients log-transformed for percentage-based interpretation. Correlation, regression and significance tests were conducted via SPSS 27. The model identified eight statistically significant housing features affecting prices. In the study area, user preferences showed thresholds at buildings over ten floors and older than 15 years. Each additional floor reduced price by 0.17%, and each year of age by 0.9%, while a one-square-meter increase in area raised price by 0.6%. These numerical differences emphasize the contextual nature of housing preferences, suggesting that localized analysis yields more accurate insights for urban renewal and new developments. The mathematical findings are limited to the research area and the period during which the data were collected; however, this limitation does not undermine the overall applicability of the model. In contrast to other studies using similar methods, this research focuses on the smaller scale of the neighborhood, linking user preferences to the characteristics of the study area through their willingness to pay. ",0.0,,False,JournalArticle;Review,,2026-03-04,daa4f09de06676b87492130242694cd896541b5a,unveilingneighborhoodhousingpreferencesinistanbulahedonicpricingperspectiveonurbanlivability,kaya,10.1108/ijhma-12-2025-0320,unveilingneighborhoodhousingpreferencesinistanbulahedonicpricingperspectiveonurbanlivability|2026,1,2639,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic || real estate hedonic regression",2,1,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1ac5b712289799cd09b97505833d9082df152954,10.1108/pm-06-2024-0058,,"Customizing a mass appraisal model for residential property tax assessment: a hedonic regression approach in Bahir Dar city, Ethiopia",2024.0,Property Management,Melkam Ayalew Gebru; Tadesse Amsalu; Worku Nega,"PurposeThe paper aims to estimate the house rental values for the purpose of customizing mass appraisals in Bahir Dar City, Ethiopia. It seeks to identify the critical factors affecting the rental values of residential properties and customize a mass appraisal model for such properties. The study focuses on identifying attributes that significantly affect house rental values.Design/methodology/approachThe paper adopted a survey research design, utilizing a survey questionnaire, expert group discussion and document analysis. The data were analyzed using thematic, descriptive and inferential statistical analysis, including correlation and hedonic regression analysis.FindingsAmong the variables included in the model, the number of rooms, availability of schools, land value grading, type of nearest road, housing typology, built-up area, plot area, walling material, traveling cost and fencing materials were the most significant factors for predicting the annual rental value of residential properties in the city.Research limitations/implicationsThe findings of this study will provide valuable insights to tax assessors, property owners and local government authorities, including municipalities, concerning the key determinants of the rental values of residential properties. Besides, these findings will serve as a useful tool for valuers and researchers in the field of property value modeling.Originality/valueThis study represents the first attempt to develop a framework for mass appraisal of residential properties using annual rental values in the Ethiopian context.",0.0,,False,JournalArticle;Review,,2024-11-08,1ac5b712289799cd09b97505833d9082df152954,customizingamassappraisalmodelforresidentialpropertytaxassessmentahedonicregressionapproachinbahirdarcityethiopia,gebru,10.1108/pm-06-2024-0058,customizingamassappraisalmodelforresidentialpropertytaxassessmentahedonicregressionapproachinbahirdarcityethiopia|2024,1,2584,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2,2,4,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3197c905065b4dc447853fe08d80877fce0c8c7e,10.54091/krepa.2024.25.3.22,,Explainable Machine Learning for Housing Price Determinants: Focusing on Regional and Temporal Variation,2024.0,Korea Real Estate Policy Association,Jeongsik Park; Jihun Oh; Jaewoong Won,"The hedonic price model has long been a key method for predicting and explaining housing prices. Despite its widespread application, the model is limited by assumptions such as the normality of error terms and challenges like multicollinearity, which constrain its flexibility and predictive accuracy. Recently, machine learning techniques have emerged as powerful alternatives, demonstrating significant predictive capabilities while overcoming the limitations of traditional parametric models. Moreover, the advent of explainable machine learning methods has broadened their applicability across various domains, including housing price analysis. However, most existing studies on housing prices using machine learning have mainly focused on improving predictive performance with a limited set of variables, primarily emphasizing physical housing characteristics while overlooking the complex impact of locational factors. This study addresses this gap by leveraging actual transaction prices of apartments in Seoul between 2016 and 2018 to develop explainable machine learning models, specifically Random Forest and XGBoost. These models incorporate an extensive set of variables, including neighborhood urban environmental factors to provide a more comprehensive understanding of housing price determinants. Furthermore, this study examines how the importance of these variables and their influence on housing prices vary across different regions and time periods in Seoul, suggesting valuable insights into the dynamic and local context nature of housing markets",0.0,,False,JournalArticle,,2024-12-31,3197c905065b4dc447853fe08d80877fce0c8c7e,explainablemachinelearningforhousingpricedeterminantsfocusingonregionalandtemporalvariation,park,10.54091/krepa.2024.25.3.22,explainablemachinelearningforhousingpricedeterminantsfocusingonregionalandtemporalvariation|2024,1,2567,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices || housing characteristics prices hedonic || machine learning hedonic pricing housing",2,3,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",c186da01373d7305a1c7d1f43c3368e5f6a12afe,10.35136/krer.36.1.3,,A Spatial Hedonic Analysis of the Impact of Lake Parks on Residential Property Values,2026.0,Korea Real Estate Institute,Sang-Jun Lee; Sang-Gun Lee; Wanhee Park,"This study empirically examines the impact of pedestrian accessibility to lake parks on apartment transaction prices in four major new towns (Gwanggyo, Dongtan, Unjeong, and Cheongna) in the Seoul metropolitan area. Using 1,180 actual transaction records aggregated into 80 apartment complexes, a hedonic price model or ordinary least squares (OLS) was initially estimated. Moran's Index test was then conducted to assess spatial autocorrelation in the residuals. The results indicated significant spatial clustering only in Gwanggyo, characterized by its high-density linear urban structure, leading to the application of the spatial error model (SEM). Conversely, the OLS model was found to be statistically appropriate for the other three cities. The findings confirm that apartment prices significantly decline as walking distance to lake parks increases, indicating that walking access to parks is capitalized into housing prices in the form of price premium. In Gwanggyo, the application of the SEM significantly improved model performance, as indicated by lower Akaike Information Criterion values, implying that proximity to lake parks and subway stations, as well as apartment age (newer buildings), acts as key determinants of housing prices. This study highlights that the spillover effects of park-related premiums and the suitability of analytical models can vary depending on urban spatial structure, suggesting that a balanced distribution and improved accessibility of green infrastructure are essential for future urban planning.",0.0,,False,JournalArticle,,2026-03-31,c186da01373d7305a1c7d1f43c3368e5f6a12afe,aspatialhedonicanalysisoftheimpactoflakeparksonresidentialpropertyvalues,lee,10.35136/krer.36.1.3,aspatialhedonicanalysisoftheimpactoflakeparksonresidentialpropertyvalues|2026,1,2547,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment prices hedonic || hedonic housing prices || residential property price determinants || spatial hedonic model housing",2,4,3,5,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,b4a3796c49b63309644969e4f8cbb671392e85ec,10.37407/kres.2026.44.3.369,,The Effects of Green and Blue Amenities on Apartment Prices in Songdo International City : Focusing on Accessibility and Views,2026.0,Korea Real Estate Society,Jae In Lee; B.-S. Byun,"As multi-unit housing is increasingly recognized as a complex good that combines various attributes, there is growing interest in the phenomenon whereby the economic value of environmental amenities is capitalized into housing prices. In particular, Songdo International City within the Incheon Free Economic Zone (IFEZ) is a prime example of a planned waterfront city that features a concentration of green and blue amenities, such as Central Park and the waterfront. This study aims to empirically analyze the impact of accessibility to and views of “parks (Green Space)” and “waterfront spaces (Blue Space)”— factors constituting environmental amenities—on the actual transaction price per square meter of multi-family housing. To this end, a hedonic price model was constructed, and multiple regression analysis was conducted using physical, locational, and environmental characteristics as explanatory variables. The results showed that access to and views of waterfront areas have a statistically significant impact on apartment prices; in particular, views were found to have a statistically significant positive effect on apartment prices after controlling for physical and locational characteristics. This study is significant in that it empirically examines the impact of environmental amenities in planned cities on apartment prices, and it provides policy and practical implications for future urban planning, housing policy, the formulation of apartment development strategies, and consumers’ rational housing choices.",0.0,,False,JournalArticle,,2026-09-30,b4a3796c49b63309644969e4f8cbb671392e85ec,theeffectsofgreenandblueamenitiesonapartmentpricesinsongdointernationalcityfocusingonaccessibilityandviews,lee,10.37407/kres.2026.44.3.369,theeffectsofgreenandblueamenitiesonapartmentpricesinsongdointernationalcityfocusingonaccessibilityandviews|2026,1,2531,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic housing prices || housing characteristics prices hedonic",2,3,2,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,257c937dbbf7f50cb8e416c822adc6d6dc825b02,10.15396/eres2019_12,,Is access to public services valued differently in low- and high-end housing submarkets?,2019.0,26th Annual European Real Estate Society Conference,Wing-Chau Kwong; Lin-Chuan Yang; Zhu Yuan,"Previous research suggests that access to public services, generally, has a positive impact on housing prices. However, most, if not all, studies are silent on whether the effect varies across different housing submarkets. For example, wealthy households are likely to own cars and thus have high transportation mobility. Thus, they may not value access to public services in the same way as low-income households do. We use 22,586 second-hand housing data in Xiamen, China to test the differential impact of access to public services on housing prices in the high- and low-end submarkets. Low- and high-end housing submarkets are defined according to price. Results from estimating the hedonic pricing and spatial econometric models confirm several findings: (1) the benefits of access to most public services are capitalized in housing prices; (2) access to transit, commercial facility, education, and sports/cultural center has significantly positive impacts on house prices in the low-end housing submarket. Yet, this finding is less applicable to the high-end counterpart; (3) the effect of access to public services on housing prices varies across the two submarkets: typically, access to public services has lower positive impact for high-end properties than for the low-end ones; and (4) interestingly, access to shopping center has a larger positive impact on housing prices in the high-end housing submarket than in the low-end counterpart. Practical implications of our findings are also discussed.",0.0,,False,Conference,Business,,257c937dbbf7f50cb8e416c822adc6d6dc825b02,isaccesstopublicservicesvalueddifferentlyinlowandhighendhousingsubmarkets,kwong,10.15396/eres2019_12,isaccesstopublicservicesvalueddifferentlyinlowandhighendhousingsubmarkets|2019,1,2510,4,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || spatial hedonic model housing",2,3,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2346908174,10.5958/2231-3915.2015.00012.7,,Hedonic Pricing Model With Respect to Gated Housing Attributes in Hyderabad,2015.0,International Journal of Technology,Akshay Bhanage; Abhimanyu Brahmankar; Ganesh Bagal; B. Ravinder,"The fostering IT sector in Hyderabad has developed a demand for new residential accommodation in the city of Hyderabad. The suburban and urban areas of the city have been witnessing a rise in number of new housing projects. This has lead to the change in the dynamics of the valuation of the housing units in the city. In this study, an attempt has been made to value the housing units based on its intrinsic and extrinsic characteristics. To do so, the Hedonic Pricing concept has been used which is based on the multiple regression techniques to regress the pricing function and to study the individual as well as the overall effect of the characteristics on the value of the property. Factors that determine the house prices in Hyderabad are analyzed in this study using data collected from various sources such as the property expo, site sales office, sample flat visit and company's website. Data collected for 20 gated housing units in different areas of Hyderabad has been used to generate the model. The results showed that the following variables were significant: number of stories, proximity to bus stand and railway station, number of rooms and whether the house is ready to move or not. They accounted for 88.44% of the variations. The Hedonic Pricing concept is thus used to model the relationship between the sale price of a house and the housing attributes which will prove as a basis for any real estate transaction for a project developer, a house buyer, loan granting banks and insurers.",0.0,,False,article,,2015-01-01,https://openalex.org/W2346908174,hedonicpricingmodelwithrespecttogatedhousingattributesinhyderabad,bhanage,10.5958/2231-3915.2015.00012.7,hedonicpricingmodelwithrespecttogatedhousingattributesinhyderabad|2015,1,2506,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || hedonic property valuation || house prices proximity hedonic || housing characteristics prices hedonic || real estate hedonic regression",2,3,3,4,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9abeb80f56e08c3e8e71b17f0ba39158f1e8083d,10.1080/09640568.2024.2322538,,"Hedonic analysis of willingness to pay for dam removal: evidence from Plymouth, Massachusetts",2024.0,Journal of Environmental Planning and Management,M. Cahill; Nikolay Anguelov,"This study investigates how the removal of small dams, and subsequent river restoration investments, affect residential housing prices in Plymouth, Massachusetts, selected for its unusually high density of dam removals. From 2002 to 2019, 5 dams within 1.05 miles of each other have been removed. Following removal, riverine restoration was completed, leading to improved public safety for downstream infrastructure, fish passage, and better water quality. Yet, contention exists in local government decision-making from fear of loss of property values of homes that are no longer classified as “waterfront.” To examine that charge, we evaluate sale prices of 9,710 single-family homes from 1997 to 2021, in a comparative estimation of a hedonic regression and a difference in differences model. The results indicate that the price of homes sold in Plymouth after dam removal increased, with no impact on property values of waterfront and properties within 1 mile of riverine restoration locations. For properties within a 5-mile radius of a former dam, we observe comparative difference in price of a fraction of a percent for lots around 2 of the dams, in relation to all home lots in Plymouth. We explain that this difference is due to property-specific features, rather than the impact of dam removal. The implications are that dam removal would not negatively impact waterfront property values, while it may translate into an overall environmental price premium for the community at large.",0.0,,False,JournalArticle,,2024-03-20,9abeb80f56e08c3e8e71b17f0ba39158f1e8083d,hedonicanalysisofwillingnesstopayfordamremovalevidencefromplymouthmassachusetts,cahill,10.1080/09640568.2024.2322538,hedonicanalysisofwillingnesstopayfordamremovalevidencefromplymouthmassachusetts|2024,1,2494,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,3,5,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,ca7e7f948506ab23ecfa3be793e35d7ce16f2a1d,10.64361/14445921.2023.212298,,The Impact of the Lagos Metropolitan Development and Governance Project on Residential Property Values,2025.0,Pacific Rim Property Research Journal,E. Onyekwelu; J. Ogbuefi; O. Nnamani,"This paper aimed at empirically investigating the impact of a ‘moderately unsatisfactory’ urban regeneration scheme on the surrounding housing rental values of units of flats in some selected slum areas of Lagos state, Nigeria. The study employed the hedonic pricing model to assess the rental values of 385 units of flats, and the survey method is used to elicit information from 107 stakeholders. The t-test was used to assess the impact of the Lagos Metropolitan Development and Governance Project (LMDGP) on the rental values of units of flats; while stakeholders’ responses were analysed using mean and standard deviation. The t-test results show that the residential housing rental value was significantly higher in the post-urban regeneration period than the pre-urban regeneration period (t = 28.252, p < 0.001). This is also confirmed by the hedonic pricing model test (t = 26.289, p < 0.001). The stakeholders’ survey findings revealed that the LMDGPs have positive impact on the rental values of units of flats within the study areas. These results attest to the huge potential of using urban regeneration schemes to address urban problems by improving the physical, social, economic, and environmental conditions through reduction of spatial social inequalities, and enhancement of inhabitants’ overall quality of life through actions on building and the urban environment, improvement of housing conditions and dysfunctional real estate markets.",0.0,,False,JournalArticle;Review,,2025-06-19,ca7e7f948506ab23ecfa3be793e35d7ce16f2a1d,theimpactofthelagosmetropolitandevelopmentandgovernanceprojectonresidentialpropertyvalues,onyekwelu,10.64361/14445921.2023.212298,theimpactofthelagosmetropolitandevelopmentandgovernanceprojectonresidentialpropertyvalues|2025,1,2458,4,cites:sirmans2006 || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || spatial hedonic model housing,2,2,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic property valuation,f329415dedcfa87542be3a35868f12bbfc6d4ad5,10.1177/00420980261448667,,Impacts of flood zoning policies on housing markets: Evidence from the national flood insurance program,2026.0,Urban studies,Pin Sun; Douglas S. Noonan; Lilliard E. Richardson,"Floods pose significant threats in the United States, and the National Flood Insurance Program uses Special Flood Hazard Area (SFHA) designations to guide regulations intended to reduce flood damage. Yet the effect of SFHA regulation on housing markets is difficult to identify because floodplain status is correlated with flood risk and unobserved amenities. This study estimates SFHA policy effects on assessed housing values by combining parcel-level flood risk data, property-level housing information, and Federal Emergency Management Agency (FEMA) SFHA boundaries in Texas. Using a regression discontinuity (RD) design, we compare single-family homes just inside and just outside invariant SFHA boundaries to better separate local policy effects from flood risk and other confounding factors. Relative to a standard hedonic model, the RD analysis yields different estimates. The hedonic model suggests lower assessed values for coastal-county properties inside floodplains and no significant effect inland. In contrast, the RD estimates show no significant boundary effect in coastal counties and about a 2% assessed-value premium for homes inside SFHAs in inland counties. These findings suggest that assessed valuations do not consistently capitalize flood-zone designation and associated regulations, although they do not necessarily imply the same pattern for transaction prices.",0.0,,False,JournalArticle,,2026-05-27,f329415dedcfa87542be3a35868f12bbfc6d4ad5,impactsoffloodzoningpoliciesonhousingmarketsevidencefromthenationalfloodinsuranceprogram,sun,10.1177/00420980261448667,impactsoffloodzoningpoliciesonhousingmarketsevidencefromthenationalfloodinsuranceprogram|2026,1,2389,2,hedonic housing prices || hedonic property valuation,2,3,4,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e2ffb2f90fb1103bb936e2fe27f976d3cffc2a65,10.56409/kreis.2026.9.2.245,,Analysis of Heterogeneity by Quantum of Apartment Price Determinants in Haeundae-gu,2026.0,KOREA REAL ESTATE INDUSTRY SOCIETY,Seongsoo Seo,"This study analyzed the actual transaction prices of apartments in Haeundae-gu, Busan Metropolitan City in 2025 based on hedonic price theory after conducting a full survey to identify the heterogeneity of submarkets within the region and the price determinants by apartment price level. The analysis methods utilized were OLS, quantile regression, and Shapley decomposition. The results can be summarized as follows: First, Haeundae-gu is differentiated into a heterogeneous submarket structure characterized by significant price disparities between neighborhoods even within the same district. Second, variables such as the number of parking spaces per household, brand value, slope, and accessibility to elementary schools showed significant differences in significance or influence depending on the level of housing prices. Third, through Shapley decomposition, the high explanatory power of variables such as exclusive area, building dummy variables, number of parking spaces per household, highest floor, and years of age can be confirmed. Based on the results of this study, companies can utilize market segmentation within the same region to formulate development plans and marketing strategies. In particular, it is suggested that the government and local governments avoid uniform policies and respond by separating upper and lower regions within the area.",0.0,,False,JournalArticle;Review,,2026-06-30,e2ffb2f90fb1103bb936e2fe27f976d3cffc2a65,analysisofheterogeneitybyquantumofapartmentpricedeterminantsinhaeundaegu,seo,10.56409/kreis.2026.9.2.245,analysisofheterogeneitybyquantumofapartmentpricedeterminantsinhaeundaegu|2026,1,2366,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic housing prices",2,4,3,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",f8760f72ef0e86660f6413b70ff31c2d9ef67332,10.22313/reik.2022.20.4.21,,The Effect of the Establishment of a School for Students with Developmental Disabilities: Focusing on Seojin School,2022.0,Residential Environment Institute Of Korea,Sang Min Lee; Seo-Young Cho; S. Shin,"Special schools are essential facilities to guarantee the right to education for students with disabilities. Still, it is not easy to establish those schools due to strong opposition from local residents. The main reason for the residents' resistance is the negative effect of the community image and the resultant decline in housing prices. By studying the case of Seojin School, this study attempts to examine the special school's external economic impact and explore strategies to respond to the NIMBY phenomenon of local residents effectively. This study analyzes the sale price of apartments in nearby areas, focusing on Seojin School, which opened in 2020. 2017 was the year when the “Second Resident Hearing” was held, and the residents' NIMBY demonstration was covered in the national news. Before and after the debate, the sale price of apartments around Seojin School for five years(2015~2019) is analyzed using a hedonic price function model. The polygonal model was set in consideration of the spatial research area based on our judgment of the impact zone of Seojin school. First, the location of Seojin School has a negative effect on the apartment sale price. Second, these negative externalities still exist despite the passage of time. However, the negative external effect was very small, less than 10,000 won.",0.0,,False,,,2022-12-31,f8760f72ef0e86660f6413b70ff31c2d9ef67332,theeffectoftheestablishmentofaschoolforstudentswithdevelopmentaldisabilitiesfocusingonseojinschool,lee,10.22313/reik.2022.20.4.21,theeffectoftheestablishmentofaschoolforstudentswithdevelopmentaldisabilitiesfocusingonseojinschool|2022,1,2331,4,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || apartment prices hedonic || hedonic housing prices || spatial hedonic model housing",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,99a8ee7090d0d9110792636bc8c256959867cf89,10.1177/09731741261467139,,‘Untouchable’ Housing? Caste and House Prices in Rural Nepal,2026.0,Journal of South Asian Development,Binod Khanal; W. Chang; Sunil P. Dhoubhadel,"Caste-based discrimination is prohibited by law in Nepal. However, it remains deeply entrenched in Nepalese society, leading to widespread discrimination against lower castes, particularly Dalits, in various social and economic activities. This research aims to examine disparities in house prices between Dalits and non-Dalits using a hedonic price model. To accomplish this, we utilize demographic data, self-reported information on house attributes and house prices from the 2018 wave of the Household Risk and Vulnerability Survey (HRVS). The findings indicate that Dalit-owned houses are priced significantly lower than houses owned by Kshatriyas and Brahmins, even after accounting for all relevant house characteristics. Propensity score matching analysis reveals that this Dalit discount ranges from 11% to 16%, translating to a reduction of NRs 123,000 to NRs 174,000 for Dalit-owned houses compared to non-Dalit-owned houses. Furthermore, the analysis across regions suggests that house price differentials between Dalits and non-Dalits are less prevalent in the southern plains and urban areas than in mountainous and rural regions. One possible explanation for this housing price differential is the limited demand for Dalit-owned houses in the mountains and rural areas due to their smaller market size.",0.0,,False,JournalArticle;Review,,2026-07-20,99a8ee7090d0d9110792636bc8c256959867cf89,untouchablehousingcasteandhousepricesinruralnepal,khanal,10.1177/09731741261467139,untouchablehousingcasteandhousepricesinruralnepal|2026,1,2316,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,3,3,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic property valuation,5d97f7b57f1077093f12f7680af0d8d25a5b16bf,10.1080/08920753.2024.2431940,,A Hedonic Approach to Coastal Resilience in Post-Hurricane Maria’s Puerto Rico,2024.0,Coastal Management,L. Santiago; Kevián Pérez; Aurelio Castro; Maritza Barreto; J. García,"Abstract Hurricane Maria devastated Puerto Rico in 2017, resulting in reduced beach width and dune flattening in certain coastal areas. Economic development activity observed in coastal regions of Puerto Rico after Hurricane Maria has partly focused on commercial and housing construction projects. The following study seeks to estimate the implicit value attributed by coastal homeowners to natural infrastructure attributes that may serve not only for recreation, but also as protection from future extreme weather events. We estimated a hedonic valuation model to assess the implicit value attributed by coastal homeowners to key natural attributes, emphasizing beach width and property elevation. Property owners sufficiently close to the coastline (1,000 m or less) implicitly assigned a positive value to beach width at their closest coastal location. An unexpected result was that elevation, when interacted with beach and human-made coastal types, was negatively associated with property prices, an indication of the importance of closeness to sea level. Given the impact of extreme weather events in coastal regions, hedonic valuation data on natural coastal attributes such as beach width can provide valuable information not currently considered in coastline development permitting.",0.0,,False,JournalArticle,,2024-11-01,5d97f7b57f1077093f12f7680af0d8d25a5b16bf,ahedonicapproachtocoastalresilienceinposthurricanemariaspuertorico,santiago,10.1080/08920753.2024.2431940,ahedonicapproachtocoastalresilienceinposthurricanemariaspuertorico|2024,1,2293,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || hedonic property valuation || implicit prices housing attributes",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,6a495d8ab790e7b73fb54b9626ebd07aecde747c,10.24957/hsr.2023.31.1.79,,Price Effect Analysis by Improving the Qualitative Characteristics of Apartment Buildings by Period in Gangnam-gu,2023.0,Korean Association for Housing Policy Studies,Song-Jie Yu; Jae Hee Kim; Young-Sun Song; Chang-Moo Lee,"In the domestic apartment market, the quality enhancement and differentiated strategies of construction companies have been implemented since the 2000s. In many ways, the quality characteristics of apartment buildings that have been improved continuously provided a variety of choices in the criteria for housing buyers to purchase. This study empirically analyzed the effect of improving the quality characteristics of apartments by period among the determinants of housing prices on apartments in Gangnam-gu, Seoul through the Hedonic price model. The analysis showed that all the variables of qualitative characteristics, which are the core variables of this study, had a positive effect on apartment prices. It was confirmed that some of the variables corresponding to the qualitative characteristic factors showed higher estimation coefficient values than other variables, which had a greater impact on apartment price formation. In addition, it can be seen that a significant portion of the price effect due to aging, which has been measured as a quadratic function in many previous studies, is the result of overlapping effects due to the improvement of the qualitative characteristics of apartments.",0.0,,False,,,2023-02-28,6a495d8ab790e7b73fb54b9626ebd07aecde747c,priceeffectanalysisbyimprovingthequalitativecharacteristicsofapartmentbuildingsbyperiodingangnamgu,yu,10.24957/hsr.2023.31.1.79,priceeffectanalysisbyimprovingthequalitativecharacteristicsofapartmentbuildingsbyperiodingangnamgu|2023,1,2207,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic housing prices || housing characteristics prices hedonic",2,3,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,precios hedónicos vivienda,,10.2139/ssrn.1791067,,"Una Propuesta Metodológica Para Estimar Los Cambios Sobre El Valor De La Propiedad: Estudio De Caso Para Bogotá Aplicando Propensity Score Matching Y Precios Hedónicos Espaciales (A Methodological Proposal to Estimate Changes of Property Values: Case Study Developed in Bogota, using Propensity Score Matching and Spatial Hedonic Prices)",2011.0,,Jorge Andres Perdomo Calvo,"<b>Spanish Abstract:</b> Este estudio comparó los resultados obtenidos mediantes Propensity Score Matching (PSM, nombre y sigla en inglés) y un modelo de precios hedónico espaciales (PHE) para estimar el cambio en el valor de la propiedad en Bogotá, cuando un predio se encuentra ubicado cerca a una estación de TransMilenio (TM). Así, en 2008, las viviendas colindantes a una estación de TM en promedio obtienen un precio mayor entre $117.500 (PHE) y $115.403 (PSM) por metro cuadrado, cuando es contrastado con el valor por metro cuadrado de los predios alejados del área de influencia del sistema TM y que potencialmente hubiesen podido estar afectados directamente por el proyecto. <b>English Abstract:</b> This study uses Propensity Score Matching (PSM) and Spatial Hedonics Prices (SHP) to estimate the change value on residential proprieties in Bogotá (Colombia); as a consequence of the benefits of its Bus Rapid Transit (BRT-TransMilenio). The estimations made suggest that in 2008, the prices of properties located near to a BRT station were s higher on average US$59.76 (using SHP) and US$58.69 (using PSM) respectively, when compared to other properties",0.0,,,posted-content,,,doi_10_2139_ssrn_1791067,unapropuestametodologicaparaestimarloscambiossobreelvalordelapropiedadestudiodecasoparabogotaaplicandopropensityscorematchingyprecioshedonicosespacialesamethodologicalproposaltoestimatechangesofpropertyvaluescasestudydevelopedinbogotausingpropensityscorematchingandspatialhedonicprices,calvo,10.2139/ssrn.1791067,unapropuestametodologicaparaestimarloscambiossobreelvalordelapropiedadestudiodecasoparabogotaaplicandopropensityscorematchingyprecioshedonicosespacialesamethodologicalproposaltoestimatechangesofpropertyvaluescasestudydevelopedinbogotausingpropensityscorematchingandspatialhedonicprices|2011,1,2189,1,precios hedónicos vivienda,3,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,housing characteristics prices hedonic,1bcb6a357084b2c0a4d0bb2e2e3c22bd93fafef8,10.37407/kres.2026.44.2.275,,A Study on the Impact of Urban Rail Accessibility on Housing Prices: Focused on Gwangju Metro Line 1,2026.0,Korea Real Estate Society,I. Yun; K. Choi; Dae Ill Kim,"This study empirically examines the existence of a station-area premium and its station-level heterogeneity in apartment prices along Gwangju Metro Line 1. Apartments located within a 1 km radius of each station were selected, and areas within 500 m of a station were defined as station areas, while those beyond 500 m were classified as non-station areas. The results show that apartments in station areas have significantly higher housing prices than those in non-station areas on average. The hedonic price model, controlling for housing and spatial characteristics, also indicates that station-area location has a statistically significant positive effect on housing prices, corresponding to a price premium of approximately 12%. However, this premium is not uniform across all stations. After excluding overlapping station-area observations and estimating an interaction model for single-station catchment samples, the premium was found to be significant only at specific stations. These findings suggest that while urban rail accessibility is capitalized into housing prices on average, its effect varies depending on each station’s locational context and neighborhood functions.",0.0,,False,JournalArticle,,2026-06-30,1bcb6a357084b2c0a4d0bb2e2e3c22bd93fafef8,astudyontheimpactofurbanrailaccessibilityonhousingpricesfocusedongwangjumetroline1,yun,10.37407/kres.2026.44.2.275,astudyontheimpactofurbanrailaccessibilityonhousingpricesfocusedongwangjumetroline1|2026,1,2185,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,residential property price determinants,46ca9fdd13ccad0f4efeb349ea0fcd25702a960a,10.55463/hkjss.issn.1021-3619.64.12,,"Determinants Influencing the Price of Second-Hand Condominiums in Bangkok’s Central Business District, Thailand",2024.0,Hong Kong Journal of Social Sciences,Pornraht Pongprasert,": This study aims to analyze the factors affecting the prices of second-hand condominiums in Bangkok’s central business district (CBD). After the COVID pandemic, new residential properties in urban areas of Bangkok have become more complicated to sell because of declining purchasing power among customers, with many new buyers turning to second-hand properties. In 2023, the sales growth of second-hand houses and condominiums in Bangkok increased by up to 2.6 trillion baht compared to 2022. Notably, second-hand condominium sales in Bangkok grew by approximately 36.7%. Additionally, the younger generations prefer to live in urban areas near public transport and their workplaces. In this study, 400 second-hand condominiums in Bangkok's CBD opened between 2018 and 2023 were surveyed, and data were collected. Twenty-four independent variables related to pricing factors, including locational and structural attributes and neighborhood environments, were examined. The hedonic price model investigated the factors influencing second-hand condominium prices. This study aims to highlight the importance of various physical attributes that affect",0.0,,False,JournalArticle;Review,,,46ca9fdd13ccad0f4efeb349ea0fcd25702a960a,determinantsinfluencingthepriceofsecondhandcondominiumsinbangkokscentralbusinessdistrictthailand,pongprasert,10.55463/hkjss.issn.1021-3619.64.12,determinantsinfluencingthepriceofsecondhandcondominiumsinbangkokscentralbusinessdistrictthailand|2024,1,2149,2,hedonic house price || residential property price determinants,2,1,5,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,prix hédoniques logement,d5b166d93b85d7f563ac3aa3093228d596231330,10.3917/reru.255.0745,,Evaluation of local environmental factors using the hedonic price method: The case of Grand Tunis municipalities,2025.0,Revue d’Économie Régionale & Urbaine,Souha Temimi; Hanène B. Ouada Jamoussi; I. Ben Ammar,"Cette étude analyse les préférences résidentielles des ménages tunisiens à partir de modèles hédoniques intégrant les interdépendances spatiales. Elle s’appuie sur une base de données microéconomique géoréférencée couvrant 613 logements situés dans l’aire métropolitaine de Tunis. Les résultats soulignent l’importance des aménités locales dans la formation des prix immobiliers, en particulier la proximité des infrastructures scolaires, dont l’effet dépasse largement celui du littoral ou d’autres aménités naturelles ou structurelles. Les impacts indirects liés à l’accessibilité éducative s’avèrent plus déterminants que les effets directs dans les choix résidentiels. Par ailleurs, des facteurs socio-économiques comme le chômage et la pauvreté sont associés à une dévalorisation des biens immobiliers, notamment dans certains quartiers. Ces résultats plaident pour une meilleure coordination des politiques d’aménagement urbain et d’investissement social, afin de réduire les inégalités territoriales et renforcer l’attractivité résidentielle dans un contexte de croissance urbaine et de contraintes budgétaires.",0.0,,False,JournalArticle,,2025-12-09,d5b166d93b85d7f563ac3aa3093228d596231330,evaluationoflocalenvironmentalfactorsusingthehedonicpricemethodthecaseofgrandtunismunicipalities,temimi,10.3917/reru.255.0745,evaluationoflocalenvironmentalfactorsusingthehedonicpricemethodthecaseofgrandtunismunicipalities|2025,1,2118,2,prix hédonique immobilier résidentiel || prix hédoniques logement,3,2,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",df9e5695e7d28a9ca87ccc8774e4d5ce50e6238c,10.1080/02673037.2025.2552400,,Housing price spillovers after real estate agent fee reduction: evidence from the German housing market,2025.0,Housing Studies,Pekka Sagner,"Abstract This paper examines the impact of changes in the real estate agent fee (REAF) for homebuyers following a 2020 reform in Germany, which capped charges to non-hiring parties at the amount paid by the hiring party and required equal fee splitting when both sides hire an agent. Using a micro dataset of dwellings listed for sale, this study documents a significant decline in the average REAF paid by homebuyers. Findings from hedonic difference-in-differences regressions, with binary and continuous treatment approaches, supplemented by an event study design, suggest that the REAF reduction leads to higher dwelling prices, indicating a price pass-through effect. The effect appears stronger in high-demand housing markets and for apartments compared to single-family houses and less sought-after regions. Additionally, significant relative volume effects indicate shifts in transaction composition. These findings highlight the role of transaction costs in shaping housing prices and market structure, with implications for housing affordability and policy design.",0.0,,False,JournalArticle,,2025-08-31,df9e5695e7d28a9ca87ccc8774e4d5ce50e6238c,housingpricespilloversafterrealestateagentfeereductionevidencefromthegermanhousingmarket,sagner,10.1080/02673037.2025.2552400,housingpricespilloversafterrealestateagentfeereductionevidencefromthegermanhousingmarket|2025,1,2074,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || real estate hedonic regression",2,2,7,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,b48cbda72b0a9dd6c4e7460b11d81c5815466776,10.31303/krear.2022.87.163,,A Study on the Effect of Development Projects in Pyeongtaek City on Housing Price by Region,2022.0,Korea Real Estate Academy,Eum Jang Cho; Jung-Min Jang,"(1) RESEARCH OBJECTIVES The purpose of this study is to study how the real estate development project of Pyeongtaek City affects housing prices by subdividing the northern, southern, and western regions by region. (2) RESEARCH METHOD This study employed the Hedonic Price Model to achieve the major objectives of this study. (3) RESEARCH FINDINGS The implications of this study are as follows. First, through this study, it was found that although the start of development projects raised the housing price, it did not falls within the spatial scope of this study, was judged to be meaningful to examine the differences by region as a place where the differences by region are clearly visible. 2. RESULTS The results of this study are as follows. The influence of other characteristics did not differ significantly by region, but in terms of development characteristics, it was found that the SRT Jije Station start and Samsung Electronics start construction increase the apartment transaction price in the southern and western regions except for the northern region.",0.0,,False,,,2022-08-31,b48cbda72b0a9dd6c4e7460b11d81c5815466776,astudyontheeffectofdevelopmentprojectsinpyeongtaekcityonhousingpricebyregion,cho,10.31303/krear.2022.87.163,astudyontheeffectofdevelopmentprojectsinpyeongtaekcityonhousingpricebyregion|2022,1,2070,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,prix hédoniques logement,,10.3917/reru.125.0727,,Modélisation de la variabilité spatiale des prix des logements en Tunisie : une nouvelle approche d'estimation des indices de prix régionaux,2012.0,Revue d’Économie Régionale & Urbaine,Radhouane Filali,"Cet article se propose d’estimer les prix implicites des caractéristiques du logement et de dériver les indices de prix spatiaux en utilisant l’approche hédonique. Nous estimons en premier lieu le modèle hédonique standard sous différentes formes fonctionnelles. Nous présentons en deuxième lieu un modèle spatial qui tient compte de l’hétérogénéité des coefficients hédoniques. Les estimations sur des données tunisiennes fournissent des résultats qui font ressortir la supériorité du modèle spatial. Il se trouve que l’eau potable, une structure moderne du bâti et l’espace habitable sont les caractéristiques du logement les plus valorisées par les ménages tunisiens, d’une part, et que la mixité sociale semble caractériser la configuration urbaine en Tunisie, d’autre part. Le modèle spatial confirme l’hétérogénéité des valeurs marginales des caractéristiques du logement. Ces valeurs présentent des tendances spatiales régulières et résultent en des indices de prix spatiaux cohérents avec le poids économique et urbain des régions tunisiennes.",0.0,,,journal-article,,,doi_10_3917_reru_125_0727,modelisationdelavariabilitespatialedesprixdeslogementsentunisieunenouvelleapprochedestimationdesindicesdeprixregionaux,filali,10.3917/reru.125.0727,modelisationdelavariabilitespatialedesprixdeslogementsentunisieunenouvelleapprochedestimationdesindicesdeprixregionaux|2012,1,2051,5,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic housing prices || housing characteristics prices hedonic || prix hédoniques logement || spatial hedonic model housing",1,2,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),dd0192d4bd15e3955b858d67852cf8816655111f,10.22423/kreus.2025.18.1.25,,"Comparative Analysis of the Determinants of Sale and Rental Prices for High-End Officetels: Focused on Seoul, South Korea",2025.0,Konkuk Research Institute of Real Estate and Urban Studies,Y. Cho; Hyun-Seok Lee,"This study examines the determinants of sale and rental prices for high-end officetels in Seoul from 2020 to 2024, using a hedonic price model. The analysis reveals distinct factors influencing sale and rental prices, with some variables having opposite effects on each. Key findings show that factors such as parking spaces, occupancy ratio, floor level, and proximity to amenities generally positively impact both sale and rental prices. However, some features like concierge service and valet parking have differing effects on sale versus rental prices. The study also highlights the impact of the COVID-19 pandemic on the real estate market. Both sale and rental prices increased significantly from 2021 onwards compared to 2020, with varying patterns of growth and decline in subsequent years. This research provides valuable insights into the high-end officetel market in Seoul, offering data that can inform future development strategies and policies in this real estate segment.",0.0,,False,JournalArticle,,2025-08-31,dd0192d4bd15e3955b858d67852cf8816655111f,comparativeanalysisofthedeterminantsofsaleandrentalpricesforhighendofficetelsfocusedonseoulsouthkorea,cho,10.22423/kreus.2025.18.1.25,comparativeanalysisofthedeterminantsofsaleandrentalpricesforhighendofficetelsfocusedonseoulsouthkorea|2025,1,1989,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,269081eefa7196b7a90126f2af94582c41238eba,10.22313/reik.2023.21.1.91,,Effects of Direction of the Gaining Water and Draining Water on Apartment Sales Price,2023.0,Residential Environment Institute Of Korea,Gap Ju Son; Hae-jung Chun,"This study analyzed how the direction of the gaining water and draining water in the apartment along the Jeong-neung stream in Dongdaemun-gu affect the apartment sales price using the Hedonic Price model. In this study, the dependent variable was the actual apartment sales price. As independent variables, the direction of water extraction and draining water , elapsed years, the trading floor, the number of apartments building, the distance subway station, elementary school, and middle school were used. As a result of the analysis, it was analyzed that the direction of gaining water, the higher number of elapsed years, the higher number of apartments building and the higher the trading floor had a positive (+) effect on apartment sales prices. In addition, it was analyzed that direction of the draining water and the farther the distance to the subway station, elementary school, and middle school, the negative (-) effect on the apartment sales price.",0.0,,False,JournalArticle,,2023-03-30,269081eefa7196b7a90126f2af94582c41238eba,effectsofdirectionofthegainingwateranddrainingwateronapartmentsalesprice,son,10.22313/reik.2023.21.1.91,effectsofdirectionofthegainingwateranddrainingwateronapartmentsalesprice|2023,1,1962,1,apartment prices hedonic,2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,f3cd27354cf3a29a8ccb41a4aef3ae67a70ce98a,10.5604/01.3001.0054.8597,,Price-setting factors or revealed preferences? How to understand the results of hedonic models and hedonic indices of the housing rental market that base on listings data?,2024.0,Bank i Kredyt,Michał Hebdzyński,"The problematic access to housing rental market transactional data forces the use of alternative sources. Using the observations of transactions and listings of apartments for rent located in Poznań, it was checked whether the fruits of hedonic models based on listings data may be treated as a proxy of consumers’ revealed preferences. Moreover, the study answered whether listings and transactions- -based hedonic rent indices show the same dynamics. Observing listings and transactions of the same apartments, it was concluded that the estimated determinants of listed rents may be considered a proxy of revealed preferences. Similarly, the listings-based hedonic rent indices proved to reflect well the dynamics indicated by market transactions. However, it is not the difference between the height of the listed and transacted rents that proved to be problematic in hedonic modelling, but the inequality of the quality structure of the analysed data types.",0.0,,False,JournalArticle,,2024-08-31,f3cd27354cf3a29a8ccb41a4aef3ae67a70ce98a,pricesettingfactorsorrevealedpreferenceshowtounderstandtheresultsofhedonicmodelsandhedonicindicesofthehousingrentalmarketthatbaseonlistingsdata,hebdzyński,10.5604/01.3001.0054.8597,pricesettingfactorsorrevealedpreferenceshowtounderstandtheresultsofhedonicmodelsandhedonicindicesofthehousingrentalmarketthatbaseonlistingsdata|2024,1,1961,1,hedonic housing prices,2,1,3,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,7b3db83f17b60edf65e58e58daf2e4dc8f2af213,10.1108/ijhma-10-2020-0125,,"Is HOA capitalized in housing price? Evidence from Chongqing, China",2021.0,,F. Deng," Purpose International research has found that Homeowners Association (HOA) is capitalized in housing price in the West. Is that result applicable in Chinese cities? In China there is also widespread applause for HOA. Will that leave trail in the housing market? This paper aims to answers these questions by presenting empirical evidence from 113 private gated communities in Chongqing, China. Design/methodology/approach The data set comes from three different sources including a telephone survey. The research methodology includes hedonic models with an endogenous dummy variable of the presence of HOA in a community. Findings HOA is not capitalized in housing price. Research limitations/implications The empirical finding helps to explain why about 80% of private communities in big Chinese cities have not formed an HOA. Originality/value This is the first empirical study on HOA capitalization in housing price in China. ",0.0,,False,Review,Business,2021-02-15,7b3db83f17b60edf65e58e58daf2e4dc8f2af213,ishoacapitalizedinhousingpriceevidencefromchongqingchina,deng,10.1108/ijhma-10-2020-0125,ishoacapitalizedinhousingpriceevidencefromchongqingchina|2021,1,1938,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,9f07012b239b72b0712dbbae066b72fc7f6b02fe,10.2139/ssrn.3474304,,Residential Wealth and Its Interpersonal and Spatial Distribution in the Rio de Janeiro Metropolitan Region,2019.0,Social Science Research Network,D. Vetter; K. Beltrao; R. Massena,"October 22, 2019 Abstract: Given the importance of housing as a component of a household’s worth and of aggregate national wealth, we ask: What variables determine housing value in the Rio de Janeiro Metropolitan Region? What is Rio’s aggregate residential wealth (i.e., the sum of the values of all housing units) and its distribution among household income and tenure groups and in space? In other words, what generates residential wealth? How much residential wealth is there? Who holds it? Where is it located? To address these questions, we first calibrate a hedonic residential rent model with microdata from the 2010 Population Census. We then use this model to estimate the rents for homeowners and subsequently transform the actual and imputed rents into housing values. Finally, we analyze the distribution of residential wealth by household income and tenure and subregions and discuss the policy implications of our findings.",0.0,,False,,,,9f07012b239b72b0712dbbae066b72fc7f6b02fe,residentialwealthanditsinterpersonalandspatialdistributionintheriodejaneirometropolitanregion,vetter,10.2139/ssrn.3474304,residentialwealthanditsinterpersonalandspatialdistributionintheriodejaneirometropolitanregion|2019,1,1937,1,spatial hedonic model housing,2,2,4,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4220f418775d026d963f88a95b6987fc1d3c6721,10.1111/jors.70066,,Sweating Assets: The Effect of Extreme Heat on the Housing Market,2026.0,Journal of Regional Science,Jindong Pang; Jie Feng; Yuan Zhao," This paper studies the impact of extreme heat on the housing market in 26 Chinese cities by analyzing more than 1.5 million second‐hand housing transaction records. Empirical results from hedonic regressions show that extreme heat significantly decreases housing prices and transaction volumes. This negative effect is concentrated in warm seasons and is substantially stronger for high‐end apartments, top‐floor units, and units in high‐rise buildings. Green spaces and water bodies are effective in mitigating the negative effect of extreme heat on the housing market. Mechanism analysis demonstrates that extreme heat significantly decreases the number of on‐site property visits before a transaction. These findings underscore the important influence of climate change on the housing market.",0.0,,False,JournalArticle,,2026-04-24,4220f418775d026d963f88a95b6987fc1d3c6721,sweatingassetstheeffectofextremeheatonthehousingmarket,pang,10.1111/jors.70066,sweatingassetstheeffectofextremeheatonthehousingmarket|2026,1,1797,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,9dfdb887833a47e99f41dbf967dee634121437ef,10.1111/ehr.70096,,"Long‐run housing price indices and determinants: New data from Huizhou, China, 1570–1949",2026.0,The Economic History Review,Si-Yu Pan," Economic studies on historical housing markets have gained increased popularity in recent years, yet remain limited by data scarcity. In this paper, a novel housing transaction dataset with more than 1000 observations from the historic Huizhou prefecture in south China is collected. This study develops housing price indices spanning the late Ming, Qing, and Republic of China eras (1570–1949). Results from hedonic ordinary and quantile regressions reveal unique housing price determinants tied to the region's cultural background: property orientation, proximity to ancestral halls and Buddhist temples, and seller's gender. Sellers being in urgent financial situations is positively correlated with the sales price.",0.0,,False,JournalArticle,,2026-02-09,9dfdb887833a47e99f41dbf967dee634121437ef,longrunhousingpriceindicesanddeterminantsnewdatafromhuizhouchina15701949,pan,10.1111/ehr.70096,longrunhousingpriceindicesanddeterminantsnewdatafromhuizhouchina15701949|2026,1,1721,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,1,1,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,9caa3f75fb388144da165242da3e002177a38803,10.1111/irfi.70097,,The Impact of Homeless Encampments on Housing Prices,2026.0,International Review of Finance,J. Kallberg; Yoshikiyo Shimizu," A topic of intense and heated discussion in urban America is the impact of homelessness, yet the quantitative evidence is limited. This study examines how proximity to a homeless encampment influences housing prices. Analyzing home sale transaction data in Seattle from 2017 to 2020, our hedonic models reveal that houses within 0.75 miles of an encampment sell for about 2.0% less (equivalent to an average impact of $17,445). Quantile regressions show that this negative effect persists across most market segments and is most pronounced in 2018–2019, when Seattle recorded the highest number of encampment removals. Results remain generally robust after adjusting for spatial autocorrelation.",0.0,,False,JournalArticle,,2026-08-23,9caa3f75fb388144da165242da3e002177a38803,theimpactofhomelessencampmentsonhousingprices,kallberg,10.1111/irfi.70097,theimpactofhomelessencampmentsonhousingprices|2026,1,1697,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || house prices proximity hedonic || spatial hedonic model housing",2,1,4,3,0,7,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.34117/bjdv7n6-213,,Integração dos modelos de preço hedônicos e diferenças em diferenças espacial: uma análise do impacto do arco metropolitano do rio de janeiro sobre os preços dos imóveis / Integration of hedonic price models and spatial differences: an analysis of the impact of the metropolitan arc of rio de janeiro on real estate prices,2021.0,Brazilian Journal of Development,Joilson de Assis Cabral; Maria Viviana de Freitas Cabral; Isa Bárbara Ferreira Mendes; Paulo Victor dos Santos Lima; Matheus de Andrade Santos,,0.0,,,journal-article,,,doi_10_34117_bjdv7n6_213,integracaodosmodelosdeprecohedonicosediferencasemdiferencasespacialumaanalisedoimpactodoarcometropolitanodoriodejaneirosobreosprecosdosimoveisintegrationofhedonicpricemodelsandspatialdifferencesananalysisoftheimpactofthemetropolitanarcofriodejaneiroonrealestateprices,cabral,10.34117/bjdv7n6-213,integracaodosmodelosdeprecohedonicosediferencasemdiferencasespacialumaanalisedoimpactodoarcometropolitanodoriodejaneirosobreosprecosdosimoveisintegrationofhedonicpricemodelsandspatialdifferencesananalysisoftheimpactofthemetropolitanarcofriodejaneiroonrealestateprices|2021,1,1000,1,preços hedônicos imóveis,3,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",51045ea77fa3c3143d1f66ad6f1044e00438ff72,10.19172/kreaa.30.3.4,,The Effects of the Living-zone Park Types on the Housing Prices - Using Spatial Hedonic Model -,2024.0,Journal of the Korea Real Estate Analysts Association,Eunji Na; Dongwon Ko; Seung-Hoon Park,,0.0,,False,JournalArticle,,2024-09-30,51045ea77fa3c3143d1f66ad6f1044e00438ff72,theeffectsofthelivingzoneparktypesonthehousingpricesusingspatialhedonicmodel,na,10.19172/kreaa.30.3.4,theeffectsofthelivingzoneparktypesonthehousingpricesusingspatialhedonicmodel|2024,1,1000,3,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1bb2ec8fbb1cc849e055d2bc0799bb342e365f59,10.1007/978-3-319-39435-0_15,,Determinants of House Prices in Mauritius: A Hedonic Price Model à la Quantile Bootstrapped Approach,2017.0,,Indranarain Ramlall,,0.0,,False,Review,Economics,,1bb2ec8fbb1cc849e055d2bc0799bb342e365f59,determinantsofhousepricesinmauritiusahedonicpricemodelalaquantilebootstrappedapproach,ramlall,10.1007/978-3-319-39435-0_15,determinantsofhousepricesinmauritiusahedonicpricemodelalaquantilebootstrappedapproach|2017,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,1,1,1,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,spatial hedonic model housing,8079c987c374c2df518c5e529a25f614eb6a7291,10.2139/ssrn.4379221,,"Spatially Heterogeneous Effects of Location, Amenity and Built Environment on the Rental Housing Market: A Geographically Weighted Hedonic Model",2023.0,Social Science Research Network,Jiajie Liu; T. Liu; Guang-Zhong Cao,,0.0,,False,,,,8079c987c374c2df518c5e529a25f614eb6a7291,spatiallyheterogeneouseffectsoflocationamenityandbuiltenvironmentontherentalhousingmarketageographicallyweightedhedonicmodel,liu,10.2139/ssrn.4379221,spatiallyheterogeneouseffectsoflocationamenityandbuiltenvironmentontherentalhousingmarketageographicallyweightedhedonicmodel|2023,1,1000,1,spatial hedonic model housing,2,1,2,2,0,7,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,193bde438c5978e543681c37cff3e5ef339a093d,10.2139/ssrn.3467401,,Changes in Public Risk Perception after a Large Disaster: Evidence from the Housing Market,2019.0,Social Science Research Network,Shohei Yasuda; Michio Naoi; Norifumi Yukutake,"The unprecedented damage caused by the Great East Japan Earthquake and nuclear disaster greatly altered people’s awareness of risk not only in directly affected areas but also in unaffected areas as well. Changes in risk perception of future earthquakes can influence people’s location choice, which in turn can be capitalized into the real estate prices. This study focuses on the relationship between earthquake risk and real estate prices in unaffected areas before and after the earthquake in order to explore how large-scale natural disasters influence people’s perception toward disaster risk. We use large-scale property transaction data covering periods both before and after the earthquake, together with the earthquake risk indexes by the Tokyo Metropolitan Government, to estimate a hedonic model. Three features are worth noting about our data. First, our property data includes exact location of each property, so that the data can be matched with the geographical index of earthquake risk that is measured at the fairly detailed geographic level. Second, our data spans from 2008 to 2017, covering periods both before and after the 2011 earthquake. Third, given our very large sample sizes, we can explore price responses to potential hazard at disaggregated levels such as region or property types. All three of these features allow us to explore whether and to what extent the relationship between risk information and real estate prices is changed after a large disaster. Our empirical findings are summarized as follows. First, we found that after the 2011 earthquake * We would like to thank Yoshihisa Asada, Mototsugu Fukushige, Shinichiro Iwata, Mizuki Kawabata, Yasuo Kawawaki, Tatsuro Kuroda, Hiroaki Matsuura, Masayuki Nakagawa, Fumio Takuma, Iichiro Uesugi and Fukuju Yamazaki, and participants of the Japanese Economic Association 2018 Autumn Meeting, Applied Regional Science Conference 2018 Annual Meeting, and the 15th International Conference of the Western Economic Association for their valuable comments and suggestions. This study was supported by the JSPS KAKENHI, Grant Numbers 18H03639 (Yukutake) and 17H02072 (Naoi). Empirical analysis in this study used real estate property data provided by the Real Estate Information Network for East Japan. † Nibancho Sankyo Bldg. 5th floor. 6-3 Niban-cho, Chiyoda-ku, Tokyo 102-0084, Japan. Email: yasuda_s@hrf.or.jp ‡ 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan. Email: naoi@econ.keio.ac.jp § 1-3-2 Kanda Misaki-cho, Chiyoda-ku, Tokyo 101-8360, Japan. Email: yukutake.norifumi@nihon-u.ac.jp",0.0,,False,,,,193bde438c5978e543681c37cff3e5ef339a093d,changesinpublicriskperceptionafteralargedisasterevidencefromthehousingmarket,yasuda,10.2139/ssrn.3467401,changesinpublicriskperceptionafteralargedisasterevidencefromthehousingmarket|2019,1,3571,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices,2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,c646465e9f77eefafb4c78dcbe542c8db4feabd4,10.1108/ijhma-04-2023-0055,,"Impact of decentralized long-term rental apartments on the value of community housing--taking Tianjin, China, as an example",2023.0,International Journal of Housing Markets and Analysis,Guang-Ping Liu; Guo Zhang," Purpose This study aims to explore the impact of decentralized long-term rental apartments on the value of in-community housing from two perspectives of housing price and rent. Design/methodology/approach This study uses the hedonic model to identify the factors affecting the housing value, and the influence of distributed long-rented apartments on the housing value in the community is analyzed from two aspects of housing price and rent by using the ordinary least square method and propensity score matching method. Findings The primary finding indicates that decentralized long-term rental apartments increase housing prices while decreasing general rental housing rents in the community, with the average degree of increase ranging from 0.93% to 2.59% and the average degree of decrease ranging from 2.23% to 4.34%. According to additional research, the prices of houses within communities rise by 0.042% for every 1% increase in the share of decentralized long-term rentals, while the rents for other types of rental property fall by 0.162%. Practical implications The government can regulate the housing market by regulating the access and layout of distributed long-rent apartments. Originality/value The findings of this study indicate that the existence and share of distributed long-rent apartments have a heterogeneous impact on the housing price and rent in the community, respectively. ",0.0,,False,JournalArticle,,2023-06-09,c646465e9f77eefafb4c78dcbe542c8db4feabd4,impactofdecentralizedlongtermrentalapartmentsonthevalueofcommunityhousingtakingtianjinchinaasanexample,liu,10.1108/ijhma-04-2023-0055,impactofdecentralizedlongtermrentalapartmentsonthevalueofcommunityhousingtakingtianjinchinaasanexample|2023,1,2411,2,hedonic house price || hedonic housing prices,2,3,5,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.14295/vetor.v30i1.12879,,Modelo Hedônico para Estimação do Valor de Imóveis: Aplicação em Nova Friburgo-RJ,2021.0,VETOR - Revista de Ciências Exatas e Engenharias,Marco André Abud Kappel; Leonardo de Almeida Teodoro,"Com a expansão do mercado imobiliário na Região Serrana do estado do Rio de Janeiro, uma parcela cada vez maior da população precisa tratar com a compra e venda de imóveis. Porém, a avaliação justa de uma unidade imobiliária não é uma tarefa simples e pode ser influenciada por diferentes atributos da edificação. Com o propósito de auxiliar nessa incumbência, o presente trabalho tem como objetivo identificar as características mais importantes na avaliação de um imóvel nessa região e, em seguida, propor um modelo matemático simples capaz de estimar o seu valor de mercado. Para isso, informações sobre valores de comercialização e detalhes construtivos de casas e apartamentos à venda na cidade de Nova Friburgo foram extraídos de portais de anúncios online, formando uma base única de dados imobiliários, sobre a qual foram, posteriormente, aplicadas técnicas de seleção de variáveis e regressão linear múltipla para a obtenção do modelo pretendido. Os resultados obtidos revelaram que a característica de maior influência na determinação do preço de compra e venda de um imóvel na região é a sua área construída. Por outro lado, o modelo matemático construído foi capaz de estimar os preços de comercialização de uma propriedade com aproximadamente 25% de desvio percentual médio da base de testes.",0.0,,,journal-article,,,doi_10_14295_vetor_v30i1_12879,modelohedonicoparaestimacaodovalordeimoveisaplicacaoemnovafriburgorj,kappel,10.14295/vetor.v30i1.12879,modelohedonicoparaestimacaodovalordeimoveisaplicacaoemnovafriburgorj|2021,1,2305,1,preços hedônicos imóveis,1,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.36113/rec.v9idossie.4793,,Precificação dos Imóveis Urbanos em Itacaré-BA: uma análise de preços hedônicos,2025.0,Reflexões Econômicas,Pricila Pereira dos Santos; João Sotero Do Vale Junior; Andrea Da Silva Gomes; Mônica De Moura Pires; Leonardo Rodrigues Porto,"O crescimento populacional e a urbanização impulsionam transformações socioeconômicas que impactam diretamente o uso e ocupação do solo, influenciando, por sua vez, o preço da terra e o mercado imobiliário. Nesse contexto, este artigo objetiva identificar os fatores determinantes dos preços dos imóveis no município de Itacaré, Bahia, Brasil. Para tanto, empregou-se um modelo econométrico de Mínimos Quadrados Ordinários (MQO). A análise considerou o preço do imóvel como variável dependente, influenciado por características intrínsecas (como suíte, garagem e área construída) e extrínsecas (localização, tipo de terreno e proximidade a serviços e infraestrutura, como hospitais e escolas). Os dados foram coletados em sites de imobiliárias e corretoras locais, abrangendo o período de outubro a novembro de 2024. Os resultados revelam que as variáveis com maior poder explicativo para o preço dos imóveis em Itacaré são: a localização central, o metro quadrado construído e a presença de suíte. Ainda assim, uma maior quantidade de quartos mostrou-se inversamente relacionada ao preço. Estas especificidades do mercado imobiliário de Itacaré fornecem informações que podem aprimorar a eficiência do setor e orientar decisões de investimento para compradores, vendedores e investidores.",0.0,,,journal-article,,,doi_10_36113_rec_v9idossie_4793,precificacaodosimoveisurbanosemitacarebaumaanalisedeprecoshedonicos,santos,10.36113/rec.v9idossie.4793,precificacaodosimoveisurbanosemitacarebaumaanalisedeprecoshedonicos|2025,1,2289,1,preços hedônicos imóveis,1,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,5ad1c5878e340e91ed961ab6a35a6161756fbdd0,10.1108/ijhma-09-2024-0134,,The impact of the COVID-19 market shock on residential buyer preferences,2024.0,International Journal of Housing Markets and Analysis,Christopher N. Cain; Daniel Huerta; Norman Maynard; Bennie D. Waller," Purpose This paper aims to investigate the effect of the COVID-19 pandemic market shock on house pricing, time-on-market (TOM) and probability-of-sale functions using local multiple listing service data from Richmond, Virginia, USA. Design/methodology/approach The empirical analyses use a two-stage residual inclusion model to simultaneously address endogeneity and nonlinearity in modeling sales price and TOM, and a Heckman two-stage procedure to account for sample selection bias in estimating the probability-of-sale. Findings The pandemic shock not only directly impacted average home prices, TOM and probability-of-sale, but it also caused the coefficients of some of the factors that influence these metrics to change while others were stable to the exogenous shock of the pandemic. The authors find that coefficients in the hedonic pricing, TOM and probability-of-sale models did not shift instantaneously; instead, the impact evolved over several months at the beginning of the pandemic until stabilization. Originality/value The results should be of interest to buyers and sellers of residential properties, agents specializing in residential properties and researchers looking to better capture the impact of exogenous events on housing prices and buyer preferences. ",0.0,,False,JournalArticle,,2024-11-01,5ad1c5878e340e91ed961ab6a35a6161756fbdd0,theimpactofthecovid19marketshockonresidentialbuyerpreferences,cain,10.1108/ijhma-09-2024-0134,theimpactofthecovid19marketshockonresidentialbuyerpreferences|2024,1,2286,2,hedonic house price || hedonic housing prices,2,3,4,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.15446/rcdg.v34n1.104098,,Modelo de evaluación de impacto de las estaciones de buses de tránsito rápido (BRT) sobre los precios de los predios y su aplicación en Bogotá Impact of BRT stations on property prices in Bogotá Impacto das estações de BRT nos preços dos imóveis em Bogotá,2025.0,Cuadernos de Geografía: Revista Colombiana de Geografía,Hernando Arenas Castro,"Este estudio presenta un método para determinar el potencial tributario de los ingresos supletorios generados por las externalidades positivas asociadas a nuevos o mejorados bienes públicos locales (BPL). Para establecer ¿hasta dónde? se extienden los beneficios, se desarrolló un modelo de evaluación de impacto; y para establecer ¿cuánto? es la variación evidenciable en los precios del suelo y de los predios (PSyP), se desarrolló un modelo de precios hedónicos. Estos modelos se aplicaron a ocho troncales del sistema de buses de tránsito rápido “Transmilenio” (BRT-TM) en Bogotá. Del primer modelo, se determinó que los beneficios se extienden hasta una distancia x=0,5 km a partir de las estaciones BRT-TM. Del segundo modelo, se determinó que los PSyP se incrementan según su distancia a las estaciones BRT-TM: máximo 8,4 % (x=0 km) y decrecen continuamente hasta 0 % (x>0,5 km); en promedio, los PSyP se incrementan 3 %. Al aplicar estos modelos a las estaciones de la troncal BRT-TM Avenida 68, en etapa de construcción, se determinó que el potencial tributario de la troncal es de USD 520 millones. Estos modelos son aplicables a cualquier BPL, en cualquier ciudad, y constituyen una herramienta para los tomadores de decisiones de política pública urbana.",0.0,,,journal-article,,,doi_10_15446_rcdg_v34n1_104098,modelodeevaluaciondeimpactodelasestacionesdebusesdetransitorapidobrtsobrelospreciosdelosprediosysuaplicacionenbogotaimpactofbrtstationsonpropertypricesinbogotaimpactodasestacoesdebrtnosprecosdosimoveisembogota,castro,10.15446/rcdg.v34n1.104098,modelodeevaluaciondeimpactodelasestacionesdebusesdetransitorapidobrtsobrelospreciosdelosprediosysuaplicacionenbogotaimpactofbrtstationsonpropertypricesinbogotaimpactodasestacoesdebrtnosprecosdosimoveisembogota|2025,1,2269,1,preços hedônicos imóveis,1,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2ebd5e9c8103e3a286f1af905f91664128941fc7,10.24957/hsr.2024.32.4.57,,Analyzing the Announcement Effects of the First New Town Redevelopment on Apartment Property Values: Focusing on the Marginal Effects of House Building Age in Seongnam City,2024.0,Korean Association for Housing Policy Studies,Jaehyeon Kim; Ayoung Woo,"Interest in redevelopment and reconstruction has been increasing due to the legislation regarding the Special Act for the 1st New Town and commitments to expanding housing supply by accelerating reconstruction. While there has been a lot of discussion on the spillover effects of housing supply from the reconstruction of the 1st New Town on the real estate market, there has been little empirical research on the effects of policy announcements made prior to the implementation of reconstruction projects. Therefore, this study examines the impact of public announcements regarding the 1st New Town redevelopments on apartment prices in Seongnam City, Gyeonggi-do. Based on the Ministry of Land’s apartment transaction price data, which includes 7,492 units, we use a Difference-in-Difference-in-Differences (DDD) model to clarify the causal effects of public announcements and a hedonic price model with an interaction term between building age and announcement periods to account for the marginal effects of building age over time. The results of this study can be used as a basis for urban planners and policymakers to formulate policies on the maintenance and support of the 1st New Town.",0.0,,False,JournalArticle,,2024-11-30,2ebd5e9c8103e3a286f1af905f91664128941fc7,analyzingtheannouncementeffectsofthefirstnewtownredevelopmentonapartmentpropertyvaluesfocusingonthemarginaleffectsofhousebuildingageinseongnamcity,kim,10.24957/hsr.2024.32.4.57,analyzingtheannouncementeffectsofthefirstnewtownredevelopmentonapartmentpropertyvaluesfocusingonthemarginaleffectsofhousebuildingageinseongnamcity|2024,1,2193,4,apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices,2,3,3,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,dda7c152f667a493da84064be856365a57a8e8b9,10.53383/100370,,Aging City and House Prices: Impact of Aging Condominium Stock on the Housing Market in the Tokyo- Metropolitan Area,2023.0,International Real Estate Review,Masayuki Nakagawa; C. Shimizu,"Based on an analysis of the housing market in the Tokyo metropolitan area, this study focuses on the external diseconomies of aging condominiums. It is possible that the quality of the condominium stock deteriorates more rapidly than that of ordinary housing. This deterioration in quality leads to a reduction in the quality of housing services received by residents. Furthermore, this worsening of the residential environment may lead to external diseconomies. We run hedonic models to identify the external diseconomies of aging condominiums in the residential market of the Tokyo metropolitan area. The results of our estimated models indicate that such external diseconomies occur for detached housing in areas where detached houses and condominiums coexist. These external diseconomies exert downward pressure on prices. Specifically, detached housing prices are lowered by around 3.2% for each 1% increase in the proportion of total building floor area in neighborhoods where condominiums were built before 1990. In other words, aging condominiums begin to generate diseconomies in their vicinities approximately 20 years after they were built.",0.0,,False,JournalArticle,,2023-12-31,dda7c152f667a493da84064be856365a57a8e8b9,agingcityandhousepricesimpactofagingcondominiumstockonthehousingmarketinthetokyometropolitanarea,nakagawa,10.53383/100370,agingcityandhousepricesimpactofagingcondominiumstockonthehousingmarketinthetokyometropolitanarea|2023,1,2151,2,hedonic house price || hedonic housing prices,2,2,6,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.61673/ren.2022.1328,,LOCAÇÃO DE IMÓVEIS: UMA APLICAÇÃO DE PREÇOS HEDÔNICOS PARA A CIDADE DO RECIFE,2022.0,Revista Econômica do Nordeste,Matheus Gomes Coelho Ciarlini; Francisco de Sousa Ramos; Monaliza de Oliveira Ferreira,"Este trabalho situa-se entre os pioneiros em estimar o impacto de variáveis estruturais, amenidades urbanas e naturais na formação dos preços de aluguel das zonas norte e sul de Recife por meio da metodologia espacial de preços hedônicos. Com um banco de dados de 269 observações fornecidas pela maior imobiliária do Estado, foi possível constatar pelo teste I de Moran a existência de uma significativa interação entre os valores do aluguel na região. Em seguida, mostra-se que os modelos mais indicados para esta pesquisa são os modelos espaciais, em especial o modelo de erro espacial (SEM). Como resultado, verifica-se que todas as variáveis estruturais são importantes e significativas para a formação do preço de aluguel. Dentre as amenidades, o impacto positivo da variável de distância ao rio quando comparado às demais é significativa, indicando que, em Recife, os locatários preferem morar próximo ao Rio Capibaribe. Finalmente, conclui-se que o consumidor locador de imóvel apresenta interesses diferentes do consumidor comprador, principalmente quando se refere a área útil e mobília.",0.0,,,journal-article,,,doi_10_61673_ren_2022_1328,locacaodeimoveisumaaplicacaodeprecoshedonicosparaacidadedorecife,ciarlini,10.61673/ren.2022.1328,locacaodeimoveisumaaplicacaodeprecoshedonicosparaacidadedorecife|2022,1,2096,1,preços hedônicos imóveis,1,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,73ce45f1c8beb34fc288b6e7e99a78f64f4e05f7,10.37407/kres.2022.40.3.75,,The Effect of 2030 Housing Project for Young People on Nearby Housing Prices,2022.0,Korea Real Estate Society,Gi-hun Lee; Kang-Rae Ma,"It has been proven from the previous research that rising housing prices affect not only timing of the first marriage, but also family planning of young people. In response to housing shortage for young people, the Seoul Metropolitan Government came up with ‘the 2030 Housing Project for young people’ designed to supply social rental housing to young people in areas close to subway station. However, the project had been faced with the opposition from the nearby residents who are concerned with a decline of property value. The main purpose of this study is to analyze the impact of the 2030 Housing Project for young people on the nearby housing prices using a hedonic-based difference-in-difference analysis. The main finding of this research is that the supply of the 2030 Housing Project for young people has a negative effect on the nearby housing prices at first, and then turned into a positive effect over time. Such a result might be related to the fact that young people have a key role to play in fostering local economies and vitalizing commercial districts in the community.",0.0,,False,,,2022-09-30,73ce45f1c8beb34fc288b6e7e99a78f64f4e05f7,theeffectof2030housingprojectforyoungpeopleonnearbyhousingprices,lee,10.37407/kres.2022.40.3.75,theeffectof2030housingprojectforyoungpeopleonnearbyhousingprices|2022,1,2091,1,hedonic housing prices,2,3,2,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,31e4d7cc7597aa4c29e9b8b07db00cea4522bf07,10.22313/reik.2022.20.3.203,,A Study on the Factors of Rising in Apartment price: Focused on Jeju Special Self-Governing Province,2022.0,Residential Environment Institute Of Korea,Young-Jun Yang,"This study analyzed the factors of price rise in apartments traded in Jeju Special Self-Governing Province from 2019 to 2021 using the Hedonic price model. There are 486 apartments subject to analysis and 526 types based on area. In 2019, 2026 transaction cases were applicable, and in 2021, 2907 transactions were applicable. In summary, the high price increase rate is based on the size of the exclusively used area, whether large, medium or small. The larger the remaining building-to-land ratios and the longer the elapsed years, the better the return on reconstruction can be expected, indicating that the high price rise rate. The higher the number of households, the higher price rise rate. In addition, the price rising rate of entrust management apartments is higher than that of the autonomous management apartments. Compared to the Dong area of Jeju-si, the price increase rate of apartments in the Dong area of Seogwipo-si is the lowest, followed by Eup·Myeon area of Seogwipo-si. It was analyzed that the farther away from the market, the lower the price rise rate.",0.0,,False,,,2022-09-30,31e4d7cc7597aa4c29e9b8b07db00cea4522bf07,astudyonthefactorsofrisinginapartmentpricefocusedonjejuspecialselfgoverningprovince,yang,10.22313/reik.2022.20.3.203,astudyonthefactorsofrisinginapartmentpricefocusedonjejuspecialselfgoverningprovince|2022,1,2078,1,apartment prices hedonic,2,1,2,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,5ccbd6be6e707fff68fcc3a8c75830f909304004,10.54091/krepa.2025.26.2.158,,The Impact of Green Building Certification(G-SEED) of Apartments on Transaction Prices,2025.0,Korea Real Estate Policy Association,S.-H. Bae; Byungkyoo Jeon; Hyungkyoo Kim,"This study analyzes the actual transaction prices of apartments with and without green building certification located in the Seoul Metropolitan Area over a three-year period from 2021 to 2023. Employing an independent samples t-test and hedonic price models, we quantitatively assessed the effect of green building certification on apartment prices and examined annual variations. The results indicate that in general green building certification has a statistically significant positive impact, Our findings suggest that certification can influence how market participants perceive and evaluate property value, although its impact may be attenuated under conditions of heightened market uncertainty. This study offers empirical evidence of the market value of green building certification and underscores the need for policy measures to sustain its effectiveness across market cycles.",0.0,,False,JournalArticle,,2025-08-31,5ccbd6be6e707fff68fcc3a8c75830f909304004,theimpactofgreenbuildingcertificationgseedofapartmentsontransactionprices,bae,10.54091/krepa.2025.26.2.158,theimpactofgreenbuildingcertificationgseedofapartmentsontransactionprices|2025,1,1885,1,apartment prices hedonic,2,3,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,43c23442058a0195604af9e5f00d0be406723522,10.1111/ehr.70088,,"Measuring the interwar housing boom: House prices and rents in England, 1913–39",2026.0,The Economic History Review,Antoninus M. Samy," Existing statistics about England's interwar housing market are sparse and based on outdated sources and methods. This article seeks to improve upon them by drawing on mortgage data from a major London‐based building society and using the hedonic method of price index measurement to construct house price and rent indices for residential houses that are robust to intertemporal sampling differences. It is found that house prices and rents in England both rose significantly between 1919 and 1926 but moderated thereafter and declined throughout the 1930s housing boom. Further evidence indicates that there was excess supply in the housing market during the boom, driven by cheap money and a deliberate strategy by building societies of ‘making the market’ by fuelling private house construction.",0.0,,False,JournalArticle,,2026-01-29,43c23442058a0195604af9e5f00d0be406723522,measuringtheinterwarhousingboomhousepricesandrentsinengland191339,samy,10.1111/ehr.70088,measuringtheinterwarhousingboomhousepricesandrentsinengland191339|2026,1,1800,2,hedonic house price || hedonic housing prices,2,2,4,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W2485539241,10.1201/b18828-145,,An empirical analysis of housing price based on hedonic pricing theory: Evidence from Chengdu city,2015.0,CRC Press eBooks,M.S. Zhang H. Wang K. He,"Despite the Hedonic Pricing Model is widely used for housing valuation. However, the applications to Chinese cities are limited because of data availability, which constrains housing research in China. With the rapid development of the economy, the data are becoming accessible and more and more scholars are starting to devote themselves to hedonic housing price research in China. Housing studies of Chinese cities can offer useful policy implications, because China is in a unique transitional stage of economic development.",0.0,,False,book-chapter,,2015-08-18,https://openalex.org/W2485539241,anempiricalanalysisofhousingpricebasedonhedonicpricingtheoryevidencefromchengducity,he,10.1201/b18828-145,anempiricalanalysisofhousingpricebasedonhedonicpricingtheoryevidencefromchengducity|2015,1,1527,1,hedonic housing prices,2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W1553584142,10.1002/0470871334.ch5,,Mass Appraisal and Noise: The Use of Lifestyle Segmentation Profiles to Define Neighbourhoods for Hedonic Housing Price Mass Appraisal Models,2003.0,,Steven Laposa; Grant Ian Thrall,This chapter contains sections titled: Introduction Explanatory Variables and Lifestyle Segmentation Profiles Data and Methodology Analysis Conclusion References,0.0,,False,other,,2003-09-26,https://openalex.org/W1553584142,massappraisalandnoisetheuseoflifestylesegmentationprofilestodefineneighbourhoodsforhedonichousingpricemassappraisalmodels,laposa,10.1002/0470871334.ch5,massappraisalandnoisetheuseoflifestylesegmentationprofilestodefineneighbourhoodsforhedonichousingpricemassappraisalmodels|2003,1,1161,3,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || apartment rent hedonic || hedonic housing prices",2,1,1,0,0,6,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),74e91400f3d8f236457f752d6d6c524de3c8010d,10.1007/s40890-025-00243-3,,"The Impact of Public Transport Accessibility on Commercial and Residential Property Prices: A Hedonic Pricing Model Analysis in Mashhad, Iran",2025.0,Transportation in Developing Economies,E. Bozorgi; R. N. Esfahani; Babak Saffari,,0.0,,False,JournalArticle,,2025-04-14,74e91400f3d8f236457f752d6d6c524de3c8010d,theimpactofpublictransportaccessibilityoncommercialandresidentialpropertypricesahedonicpricingmodelanalysisinmashhadiran,bozorgi,10.1007/s40890-025-00243-3,theimpactofpublictransportaccessibilityoncommercialandresidentialpropertypricesahedonicpricingmodelanalysisinmashhadiran|2025,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate)",2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 openalex,hedonic housing prices,https://openalex.org/W3125030712,10.4324/9781315743035-10,,Superstition and Hong Kong housing prices: a Hedonic Pricing approach,2016.0,,Rita Yi Man Li; Kwong Wing Chau; Chui Yee Law; Tat Ho Leung,,0.0,,False,article,,2016-03-31,https://openalex.org/W3125030712,superstitionandhongkonghousingpricesahedonicpricingapproach,li,10.4324/9781315743035-10,superstitionandhongkonghousingpricesahedonicpricingapproach|2016,1,1000,1,hedonic housing prices,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,7c12a9637ff27f08d70bb302dbc921076dda88c4,10.19172/kreaa.24.1.4,,Spatio-temporal dependence in a hedonic house price model,2018.0,,Dongwoo Hyun,,0.0,,False,,Economics,2018-03-31,7c12a9637ff27f08d70bb302dbc921076dda88c4,spatiotemporaldependenceinahedonichousepricemodel,hyun,10.19172/kreaa.24.1.4,spatiotemporaldependenceinahedonichousepricemodel|2018,1,1000,1,hedonic house price,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,1937b683a495134a543a3557ef595ac54491948c,10.14445/22315373/ijmtt-v56p527,,Development of Mathematical Modeling using Hedonic Price Method to Establish Relation between Housing Price and Air Pollution,2018.0,,R. Yadav; Magan Grover,,0.0,,False,,Environmental Science,2018-04-25,1937b683a495134a543a3557ef595ac54491948c,developmentofmathematicalmodelingusinghedonicpricemethodtoestablishrelationbetweenhousingpriceandairpollution,yadav,10.14445/22315373/ijmtt-v56p527,developmentofmathematicalmodelingusinghedonicpricemethodtoestablishrelationbetweenhousingpriceandairpollution|2018,1,1000,1,hedonic housing prices,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,066ac27074c172c571a2e49aedd294be24f80b38,10.1007/978-981-16-3587-8_51,,"The Analysis of Second-Hand Housing Prices in Jiangmen City, Based on Semi-structured Interview and Hedonic Model",2021.0,Proceedings of the 25th International Symposium on Advancement of Construction Management and Real Estate,J. Liao,,0.0,,False,,,,066ac27074c172c571a2e49aedd294be24f80b38,theanalysisofsecondhandhousingpricesinjiangmencitybasedonsemistructuredinterviewandhedonicmodel,liao,10.1007/978-981-16-3587-8_51,theanalysisofsecondhandhousingpricesinjiangmencitybasedonsemistructuredinterviewandhedonicmodel|2021,1,1000,1,hedonic housing prices,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,559f5a0be29385181368bf25513d2969cd4a4397,10.26118/5792.2025.66.34.006,,Hedonic pricing as a tool for analyzing price differentiation in the secondary housing market (regional perspective),2026.0,"СБОРНИК МАТЕРИАЛОВ XXXXII МЕЖДУНАРОДНОЙ НАУЧНО-ПРАКТИЧЕСКОЙ КОНФЕРЕНЦИИ «Развитие науки и практики в глобально меняющемся мире в условиях рисков» (шифр –МКРНП) 16 декабря 2025г., г. Москва",Ю. Ю. Казанцев; A. Samuseva,,0.0,,False,,,2026-01-29,559f5a0be29385181368bf25513d2969cd4a4397,hedonicpricingasatoolforanalyzingpricedifferentiationinthesecondaryhousingmarketregionalperspective,казанцев,10.26118/5792.2025.66.34.006,hedonicpricingasatoolforanalyzingpricedifferentiationinthesecondaryhousingmarketregionalperspective|2026,1,1000,1,hedonic housing prices,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,3c72e72b55a4ca070873d9ffdd425dbb521c7db7,10.15396/eres2022_111,,The hedonic house price index for Poland in years 1996-2021,2022.0,28th Annual European Real Estate Society Conference,R. Trojanek,,0.0,,False,Conference,,,3c72e72b55a4ca070873d9ffdd425dbb521c7db7,thehedonichousepriceindexforpolandinyears19962021,trojanek,10.15396/eres2022_111,thehedonichousepriceindexforpolandinyears19962021|2022,1,1000,1,hedonic house price,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),1bff2b72d54a0306c424a28ef66b6eb1ba10cdb2,10.36871/ek.up.p.r.2026.09.03.012,,Housing class as a price signal in the capital and regional primary real estate markets,2026.0,"EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA",Alexander Sergeevich Ivonin,"Based on data on 274 lots in 103 residential complexes in Moscow, Moscow Oblast and Izhevsk, the paper estimates the independent price informativeness of the declared housing class. In a hedonic model controlling for objective characteristics, class effects in Izhevsk are monotonic and highly significant (from +15% to +124% relative to the standard class), whereas in Moscow the mass-market grades are devalued and only the upper segment of the scale is significant. It is concluded that, as market maturity grows, the discrete class signal is replaced by continuous mechanisms of price coordination.",0.0,,False,JournalArticle,,,1bff2b72d54a0306c424a28ef66b6eb1ba10cdb2,housingclassasapricesignalinthecapitalandregionalprimaryrealestatemarkets,ivonin,10.36871/ek.up.p.r.2026.09.03.012,housingclassasapricesignalinthecapitalandregionalprimaryrealestatemarkets|2026,1,1602,3,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic,2,0,2,1,0,5,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",45baf00f69a7c4fecd7894bc237e20ad5e93e9da,10.15396/eres2019_97,,The Impact of the Heathrow Northwest Runway Announcement on Residential Property Prices in Greater London,2019.0,26th Annual European Real Estate Society Conference,L. Papageorgiou,"As prior research has shown, airport expansions in densely populated urban areas affect homeowners in surrounding areas. The effect on prices is expected to be particularly pronounced in the London urban area due to the high absolute valuations. This study will examine the announcement effects of (a) the release of the planning report for Heathrow’s Northwest Runway on 01.07.15, (b) the acceptance of the plan by the Secretary for Transport on 14.12.2015, and (c) the official government approval for the initiation of Heathrow’s airport expansion on 25.10.2016. The research focus of this paper lies on the extent to which prices of single family, terraced and semi-detached homes in those areas not yet affected by Heathrow’s runway noise changed as a consequence of the announcements. In this context, it is of particular interest whether the observed changes in property prices match the noise contour proposed by the official planners’ report. The novelty of this study lies in the fact that we focus on the home price effects of the announcement of an airport expansion. For airports as opposed to other infrastructure projects, announcement effects have so far not been analyzed in the literature. This is understandable as the area affected by airport expansions tends to be not only far larger than other infrastructure projects, such as sports stadiums, but they are also more uncertain. In fact, the expected future effects can vary significantly with advancing technology and changing mobility patterns.Literature. The related literature reaches back to 1978, when Mieszkowski & Saper estimated the effects of airport noise on residential property values at Toronto’s Malton Airport. In 1990, Pennington et al. measured the same effect at Manchester’s airport. In a similar study in 1998, Tomkins, et. al. found that at Manchester’s airport the benefits of expansion extended beyond the local economy, while the costs were concentrated locally. In 2000, Espey & Lopez found a significant negative price discount for surrounding residential buildings near RenoSparks’ airport. In 2004, Nelson aggregated 20 studies covering 33 estimates of price discounts for 23 airports in Canada and the US to develop a model to explain the percentage drop per decibel increase in airport noise. The first airport expansion case in combination with noise discounts was analyzed by Mcmillen in 2004 on the basis of the 1997 Chicago O’Hare expansion. In 2009, Dekkers & Straaten found that noise discounts for residential property at Amsterdam’s airport surpassed railway and road noise discounts. In 2015, Suksmith & Nitivattananon showed that the noise of aircraft near Bangkok’s airport had an even higher impact on residential house prices than air pollution. The most similar analysis in terms of the data set and the focus on announcement effects is Kavetsos (2012), who measured the impact of the London Olympics announcement on residential prices. He found that properties in host boroughs sold at a 2.1%-3.3% premium after the announcement. Data. The data selected for this research are limited to Greater London due to its economic importance, a high international interest in changes of London property prices, and the amount of available public data. We use transactions data with price and date information retrieved from the UK Land Registry. Leasehold transactions and all but terraced and semi-detached building types are excluded. These 1-3 storey single family homes are very typical of London and allow for good comparability of sqqure meter prices. Single flats in multi-family homes, detached homes, and other types are excluded to avoid different square meter prices at the same address. The second data set used is the Ministry of Housing database, which provides detailed Energy Performance Certificates of the size and building material of each home. Since the data set is limited to Greater London and a large number of buildings are appraised in the metropolitan area of London, more than half of the addresses are matched from 1995 to 2018. The time window is reduced to 01.01.2015-31.12.2017 to cover all announcement dates tested. Methodology. We use a difference-in-difference approach, based on least squares, on the level of the individual property. For the treatment area, we compare the current Heathrow noise map and with the one predicted for 2030. The following control variables, which vary over time, capture demographic changes on the borough level: the percentage of elderly, young, and immigrants; population density, new unemployment rate, average taxpayer income. GIS is used to create control variables for distances from each address to the nearest amenity that could influence the value of a property: to the central business district, sub-centers, metro stations, rivers, kindergardens, schools, universities, and industrial areas. Time fixed effects capture changes that affect both the treated and the control areas.Preliminary Results. The preliminary results are summarized in Table 1. They imply that there is a full 2 month reporting-timelag due to the transaction duration. Contrary to popular opinion, the impact on prices occurred shortly after the government acceptance was published on 14.12.15 and not on the initial planning report release on 01.07.15. The results show that political announcements have a positive relationship with home prices. The magnitude of the effects increases as announcements become more official and airport expansion plans become more realistic. One fact to be highlighted is that the downward movement of prices was unexpectedly reversed with a public release by Tory and Labour members to vote against the airport expansion. Table 1: Table of Results of OLS Regression# Effect Date Price Discounts Announcement Date Announcement1 2,2016 -4.27%* 14.12.15 Gov. accepts expansion2 9,2016 -6.07%** 19.07.16 shareholders confirm runway funding3 12,2016 -4.14%* 25.10.16 Gov. approves expansion4 4,2017 -7.37%** 02.02.17 Gov. published its draft NPS on a third runway5 6,2017 -4.53%* 26.04.17 call for tender expansion & logistics 6 8,2017 -6.39%** 26.07.17 modified air quality plan7 11,2017 8.96%*** 24.08.17 Tory and Labor announce vote against the expansion",0.0,,False,Conference,Business,,45baf00f69a7c4fecd7894bc237e20ad5e93e9da,theimpactoftheheathrownorthwestrunwayannouncementonresidentialpropertypricesingreaterlondon,papageorgiou,10.15396/eres2019_97,theimpactoftheheathrownorthwestrunwayannouncementonresidentialpropertypricesingreaterlondon|2019,1,7245,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,5,5,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cef76f0068eb0c38d4e099a6478b8ff6a8b06339,10.1108/ijhma-05-2026-0152,,Urban infrastructure and its role in shaping Moscow’s residential real estate market,2026.0,International Journal of Housing Markets and Analysis,E. Semerikova; Anh Kiet Tran," This paper analyzes the apartment prices in Moscow using geographically weighted regression (GWR) to account for the spatial non-stationarity and local variations in the pricing of Moscow’s heterogeneous real estate market on the sample of 3,504 flats. The results suggest that the impact of infrastructure on property values varies considerably across neighborhoods, which is indicative of underlying socioeconomic trends and urban development patterns which are not captured by traditional global ordinary least squares (OLS) models. By empirically updating and extending the largely city-wide, transaction-era literature on Moscow housing with a post-pandemic, spatially disaggregated data set, and by contrasting global and locally varying estimates, this paper contributes to the urban economics literature on spatial heterogeneity in housing markets and offers policy-relevant, location-specific evidence for stakeholders engaged in urban development and residents’ investment decisions. The empirical strategy involves a comparative analysis. First, an OLS model is established to provide a baseline understanding of the average city-wide effects. Subsequently, the GWR model is implemented to uncover and map the spatial variation of these effects, offering a more nuanced and geographically contextualized interpretation. In particular, the analysis highlighted the significant role of proximity to schools, medical facilities, transportation facilities, cultural institutions and recreational areas in shaping housing prices. Proximity to schools consistently raised property values. However, the impact of medical facilities was mixed, with some districts showing price increases due to convenience, while others experienced price reductions, possibly due to noise or negative associations with hospitals and illnesses. Proximity to railway stations boosted prices in most areas, particularly in the southeast, while the effect of airports was more complex, with prices increasing at an optimal distance of around 5.5 km but decreasing if too close probably due to noise and pollution. Several limitations qualify the findings and should guide their interpretation. First, the price variable is constructed from asking prices posted on cian.ru rather than actual transaction prices; asking prices can diverge from final sale prices through negotiation, and this gap is unlikely to be constant across neighborhoods or property types, so estimated infrastructure premiums should be interpreted as premiums in listed rather than realized prices. Second, the cross-sectional design, based on a single month data (May 2024), rules out any causal interpretation of the estimated coefficients: proximity to infrastructure and housing prices are more plausibly jointly determined, since infrastructure is itself sited partly in response to existing or anticipated demand for housing (reverse causality), and neither the OLS nor the GWR/ multiscale GWR (MGWR) specification used here − unlike instrumental-variable or quasi-experimental designs based on the opening of new infrastructure − can separate the capitalization of infrastructure into prices from the endogenous location of infrastructure itself. Third, despite the inclusion of a broad set of structural, locational and infrastructure variables, omitted variable bias cannot be ruled out; unobserved factors such as building-specific renovation quality, precise floor plan or local air and noise pollution are likely correlated with both infrastructure proximity and price. Fourth, as discussed in the literature review, this paper focuses on GWR/MGWR because the research question concerns spatial heterogeneity in coefficients rather than global unbiasedness; it does not report a formal comparison against spatial lag or spatial error specifications, which would instead prioritize correcting spatial autocorrelation in a single global model, and such a comparison is left for future work. Finally, because the sample is restricted to the secondary housing market of a single, atypically large and monocentric post-Soviet city, the magnitude − though plausibly not the qualitative pattern − of the estimated infrastructure premiums may not generalize directly to other Russian cities or to primary-market housing. These findings can guide urban planning and infrastructure investment, suggesting that enhancing social infrastructure in underdeveloped areas could reduce price disparities and increase the overall attractiveness of residential areas. The GWR model’s ability to capture these spatial variations offers a valuable tool for stakeholders involved in urban development, enabling more precise and localized decision-making that reflects the diverse dynamics of Moscow’s real estate market. This approach effectively captured the spatially diverse relationships between the price per square meter of flats and various social infrastructure objects, offering more nuanced insights into the factors affecting real estate pricing across different areas of Moscow. ",0.0,,False,JournalArticle;Review,,2026-09-25,cef76f0068eb0c38d4e099a6478b8ff6a8b06339,urbaninfrastructureanditsroleinshapingmoscowsresidentialrealestatemarket,semerikova,10.1108/ijhma-05-2026-0152,urbaninfrastructureanditsroleinshapingmoscowsresidentialrealestatemarket|2026,1,6075,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,7,3,5,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1bb0dbe96c9863d2f870e60a2e7d663dc8ecc865,10.15372/gipr20250615,,ФАКТОРЫ ТЕРРИТОРИАЛЬНОЙ ДИФФЕРЕНЦИАЦИИ РЫНКА ЖИЛЬЯ В ИРКУТСКОЙ ГОРОДСКОЙ АГЛОМЕРАЦИИ,2025.0,География и природные ресурсы,Юлия Владимировна Антипина,"Оценены и систематизированы различные факторы, влияющие на рынок жилья в Иркутской городской агломерации, как для городских районов, так и для пригородной зоны. Материалами исследования послужили открытые данные сайта ЦИАН 2024 г. Оценена статистическая взаимосвязь цен на жилье и факторов плотности озеленения, плотности объектов социальной инфраструктуры, транспортной доступности и наличие факторов негативного и положительного соседства по пригородным и городским районам. Выявлено, что для крупных сибирских агломераций характерна центро-периферийная и береговая дифференциация цен. При этом Иркутскую агломерацию отличает более высокий прирост населения за счет пригорода и дисбаланс доступности жилья в городе: наибольший прирост населения зафиксирован не только в городских наиболее привлекательных районах с высокими ценами на жилье, но и в районах с низкими, а районы из среднего ценового сегмента растут заметно медленнее. Исследование показало принципиальные различия в дифференциации цен на жилье в городских и пригородных районах. Сделан вывод, что для городских районов наибольшее значение имеет плотность социальной инфраструктуры и транспортная доступность, криминальная обстановка и меньшее - экология и озеленение. В пригородной зоне наиболее важными факторами являются вид разрешенного использования земель (что в первую очередь связано с наличием инженерной инфраструктуры), транспортная доступность, природный и экологический фактор (близость объектов негативного соседства). Various factors affecting the housing market in the Irkutsk urban agglomeration, both for urban areas and for peri-urban area are evaluated and systematized. The research was based on the open data of the site CIAN 2024. Evaluated the statistical relationship between housing prices and factors of density of greenery, density of social infrastructure, transport accessibility and the presence of factors of negative and positive neighborhood in suburban and urban areas. It was found that for large Siberian agglomerations center-peripheral and coastal price differentiation is characteristic. Irkutsk agglomeration is characterized by higher population growth due to suburbia and the imbalance of housing affordability in the city: the largest population growth was recorded not only in the most attractive urban areas with high housing prices, but also in areas with low, middle-price areas are growing noticeably slower. The study showed fundamental differences in the differentiation of housing prices in urban and suburban areas. It was concluded that for urban areas the most important is the density of social infrastructure and transport accessibility, the criminal environment and the least - ecology and greening. In the peri-urban area, the most important factors are the type of permitted use of land (which is primarily related to the availability of engineering infrastructure), transport accessibility, natural and environmental factors (proximity of objects of negative neighborhood).",0.0,,False,JournalArticle,,2025-12-30,1bb0dbe96c9863d2f870e60a2e7d663dc8ecc865,,антипина,10.15372/gipr20250615,|2025,1,4000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7df8a4f2595bbe1e6ae88215fed9f1c2af7a574c,10.1108/pm-01-2025-0002,,"Factors impacting housing prices in National Capital Region (NCR), India: a quantile regression approach",2025.0,Property Management,Ashish Kumar," This study aims to empirically investigate the factors that impact housing prices in the National Capital Region (NCR), India, using ordinary least squares (OLS) and quantile regression estimation approaches. The data for the study were collected from various websites of various real estate intermediaries and through discussions with apartment owners’ associations in NCR. Quantile regression was used along with OLS to understand the impact of various factors on housing prices using a dataset of 529 residential properties. The study findings establish that size, age, number of bathrooms, floor level, security, parking, lift, power backup, waste disposal system and distances from hospital and shopping complexes have a positive impact on housing prices, while the number of rooms, number of balconies, distances from school, metro station and employment zone are established as negative factors for housing prices. The impact is not uniform across all quantiles for some factors. The impact of amenities was established to be the highest in deciding the housing prices in NCR, India. The research extends the ongoing empirical research on understanding “what impacts the housing prices?” by providing empirical evidence on the factors impacting housing prices in an emerging market (India) and a buoyant residential market (NCR). This study will help future researchers explore the other factors impacting housing prices in diverse residential settings in India and globally. The study findings provide significant insights to various stakeholders in housing development ecosystems like regulatory bodies, policymakers, builders, designers and homebuyers. The rising demand for houses driven by rural migration towards urban locations is a positive factor for real estate developers. Still, the increased competitive scenario makes it difficult to achieve competitive pricing to attract home buyers. The findings will help the real estate developers understand the factors impacting the housing prices and help them design and structure their house offerings accordingly. Governments also increasingly use public-private partnerships in the affordable housing segment. The study findings will help the planning bodies achieve the right design, amenities and pricing to attract buyers and make their housing societies citizen-friendly. As per the authors’ knowledge, this is the first comprehensive research endeavour to empirically investigate the major factors influencing housing prices in NCR, India. ",0.0,,False,JournalArticle,,2025-09-09,7df8a4f2595bbe1e6ae88215fed9f1c2af7a574c,factorsimpactinghousingpricesinnationalcapitalregionncrindiaaquantileregressionapproach,kumar,10.1108/pm-01-2025-0002,factorsimpactinghousingpricesinnationalcapitalregionncrindiaaquantileregressionapproach|2025,1,3550,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,6,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1f1bb58c9730f5443413b665e8af6f2a03aafb76,10.1108/ijhma-07-2025-0152,,Assessing the influence of cemetery proximity to rental values of residential properties in Calabar urban,2025.0,International Journal of Housing Markets and Analysis,Roseline Okoro; Ekerette Thomas," With the proliferation of cemeteries in residential neighborhoods in the Calabar metropolis and the fact that property investors invest majorly for profit, the study aims to investigate if cemetery proximity to residential properties significantly influence their rental values. This study adopted a cross-sectional case study approach using Big Qua Royal, Goldie (Etta Agbor) and Ikot Ansa cemeteries in the Calabar metropolis. For the purpose of comparison of the influence of cemetery proximity to rental values, this study adopted one-stage cluster sampling with four clusters (0–100, 101–200, 201–300 and 301–400) in the study area, with one cluster differing from the other by 100 m in distance to the cemetery. All properties within each cluster in each of the three cemeteries were studied, giving a total of 213 properties from which study data was obtained. One hypothesis was formulated to guide the study. Data was analyzed using frequency counts, simple percentages and four-point Likert analysis. The hypothesis was tested at the 0.05 significant level using a one-way ANOVA with cemetery proximity as factor and rental value as the dependent variable and Fisher’s least significant difference test for pairwise comparison of cluster rentals. The findings showed that cemetery proximity to residential properties significantly influences rental values, negatively. Also, rental values of properties located within 201–300 m distance to the cemetery were significantly different from those located at 0–100 m (mean difference [MD] = 30.486, p = 0.000), 101–200 m (MD = 797.419, p = 0.000) and 301–400 m (MD = 740.036, p = 0.000). Several studies have been conducted on the different externalities and their effect on property values in Nigeria and elsewhere, but cemeteries remain the least studied (Larson and Coleman, 2010). Literature is scanty on the effect of proximity of cemeteries to residential property values and the available ones are essentially foreign-based (Bolitzer and Netusil, 2000; Lutzenhiser and Netusil, 2000; Larson and Coleman, 2010; Nicholas and Crompton, 2005), with only a single study by Ibisola and Fateye (2021) on the subject in Lagos, but no enquiry has been made into this subject in CRS, Calabar, with particular reference to the study areas. ",0.0,,False,JournalArticle,,2025-11-14,1f1bb58c9730f5443413b665e8af6f2a03aafb76,assessingtheinfluenceofcemeteryproximitytorentalvaluesofresidentialpropertiesincalabarurban,okoro,10.1108/ijhma-07-2025-0152,assessingtheinfluenceofcemeteryproximitytorentalvaluesofresidentialpropertiesincalabarurban|2025,1,3316,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",419c959257365dddfb728bdf6a1a6424fc0a6b8f,10.1177/03611981261457410,2609.00375,Residential Price Modeling Using Spatially Validated Machine Learning Methods: A Comparison Across Geographical Contexts,2026.0,Transportation Research Record,Usman Ahmed; Jason Hawkins,"Land use (i.e., buildings) and transportation infrastructure are tightly coupled systems, such that residential property prices play a critical role in transportation planning. In a similar manner, transportation infrastructure influences property prices, such that accurate forecasts of both systems are related research problems. The objective of this study is to examine these interactions and evaluate the effectiveness of machine learning methods in modeling residential real estate prices across different urban contexts. Specifically, we first examine the impact of land and transportation infrastructure on residential real estate prices using Extreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning methods, and compare results between two cities representing diverse geographic and socioeconomic contexts. Second, we investigate the application of machine learning methods on spatial data and provide a comparison of non-spatial and spatial cross-validation on the performance of machine learning methods. We use SHapley Additive exPlanations (SHAP) values to study the impact of land use and transportation infrastructure on real estate prices. The models are applied, and results are compared between the Rawalpindi and Islamabad Metropolitan Area in Pakistan and the City of Toronto in Canada. We find that, despite differences in demographics and economic development, the two cities exhibit similarities in the effect of transportation infrastructure and local amenities on dwelling prices. Proximity to the major central city cores (i.e., downtowns) increases sale price. The effect of transportation infrastructure is differentiated, with high quality transit (e.g., subway and BRT) increasing and conventional bus stop proximity decreasing sale price, respectively. We confirm the previous finding in other fields that non-spatial cross-validation over-estimates the prediction accuracy of machine learning algorithms on spatially referenced datasets. We find that the XGBoost model has slightly higher performance than the RF model. We recommend careful use of machine learning methods in the case of spatial data specifically in modeling of land prices.",0.0,,False,JournalArticle,Economics,2026-07-03,419c959257365dddfb728bdf6a1a6424fc0a6b8f,residentialpricemodelingusingspatiallyvalidatedmachinelearningmethodsacomparisonacrossgeographicalcontexts,ahmed,10.1177/03611981261457410,residentialpricemodelingusingspatiallyvalidatedmachinelearningmethodsacomparisonacrossgeographicalcontexts|2026,1,3193,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,2,6,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0479a7f9dce822da6952d90dd415cf07722fcc05,10.1080/21606544.2026.2625954,,The effect of green space views and home elevation on assessed home values: evidence from a homogenous village,2026.0,Journal of Environmental Economics and Policy,M. Martins; Meaghan Roberts; K. Egan,"ABSTRACT This study investigates the interaction between greenspace views, elevation, and flood risk for assessed home values. We estimate the inferred willingness to pay for homes with unobstructed views of a protected green floodplain and examine whether this premium varies by home elevation. At higher elevations, the home has both an enhanced visual amenity and a reduced flood risk. We interact elevation and whether the home has a greenspace view. We find that, on average, homes with a greenspace view command a 14.3% premium. However, this effect is conditional on elevation: at lower elevations, the amenity value of the view is diminished, and homes near the floodplain at the lowest elevations even experience small discounts, suggesting a disamenity effect from flood risk. We find that each additional foot of elevation increases home value by 0.5% for homes with a view – equivalent to $1,633 in added value per foot. While no statistically significant discount is observed for homes located within the designated flood hazard area, our interaction term reveals that elevation plays a role in assessed value near flood-prone greenspaces. The findings offer insight for urban planners and policymakers seeking to balance environmental amenities with flood resilience under changing climate conditions. Key Policy Highlights This study quantifies the average premium associated with a view of the greenspace floodplain, controlling for structural and locational attributes. This study examines how this premium varies with elevation, testing whether the visual amenity is enhanced at higher elevations or perceived as a risk at lower elevations. Our findings have important implications for climate resilience, land use, and zoning policy. Nature-based solutions, such as preserving floodplain greenspaces, create real estate value. Municipalities might consider allowing higher-density or multi-level housing at elevated parcels adjacent to floodplain greenspaces to optimize both aesthetic amenities and stormwater management. In the face of accelerating climate risk, integrating elevation, amenity, and hazard into urban development decisions will be essential.",0.0,,False,JournalArticle,,2026-02-26,0479a7f9dce822da6952d90dd415cf07722fcc05,theeffectofgreenspaceviewsandhomeelevationonassessedhomevaluesevidencefromahomogenousvillage,martins,10.1080/21606544.2026.2625954,theeffectofgreenspaceviewsandhomeelevationonassessedhomevaluesevidencefromahomogenousvillage|2026,1,3178,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,5,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",9b9ecbeb8eddb13aaf5cfcc70cd2f036768b1f21,10.1108/ijhma-04-2025-0085,,Urban spatial analysis of the profitability of housing rental in Bogotá,2025.0,International Journal of Housing Markets and Analysis,J.S. Azcarate-Romero; J. Toloza-Delgado; N. A. Cruz Gutiérrez; P.A. Mahecha," This study aims to analyze the factors influencing rental rates in Bogotá, focusing on apartments and houses across different zones of the city. This study explores the relationship between physical property characteristics and spatial patterns affecting rental profitability. This research uses generalized additive models (GAMs) to estimate rental rates while accounting for spatial dependencies. Data is collected from rental advertisements across Bogotá, including physical attributes and geographical coordinates. A spatial interpolation is applied to visualize rental rate behavior across the city. The analysis reveals that the number of rooms and property age positively influence rental rates for both apartments and houses. Stratum (socioeconomic level) has an inverse relationship with profitability, especially for apartments. Built area has a positive effect for apartments but a negative one for houses. The spatial analysis shows higher rental rates on the periphery of the city, particularly in areas such as Usme and Bosa. In addition, property usage regulations in some areas affect profitability, with commercial properties having a different behavior from purely residential ones. A limitation of this study is that the GAM framework does not explicitly model variance or temporal dynamics. Transactions not governed by contracts, and generally not published on real estate portals or advertisements, were not taken into account. The results provide investors and urban planners with detailed spatial information to support better decision-making in Bogotá’s real estate market. The methodology can be adapted for market analysis in other cities or regions. The findings highlight potential risks of speculative investment, displacement and inequality, helping policymakers design more inclusive housing strategies and monitor urban equity. This research contributes to the spatial econometrics literature by applying GAM to rental market analysis, emphasizing the spatial heterogeneity of rental rates in Bogotá. ",0.0,,False,JournalArticle,,2025-08-18,9b9ecbeb8eddb13aaf5cfcc70cd2f036768b1f21,urbanspatialanalysisoftheprofitabilityofhousingrentalinbogota,azcarate-romero,10.1108/ijhma-04-2025-0085,urbanspatialanalysisoftheprofitabilityofhousingrentalinbogota|2025,1,3080,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,6,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",aa22e7ab448ac7e9d883345a8727b546b71652b8,10.37407/kres.2025.43.4.195,,Analyzing the Linkage Between Urban Landscape Elements and Property Value Formation in Residential Complexes,2025.0,Korea Real Estate Society,H. Yun,"In contemporary urban environments, landscaping has emerged as a core component that extends beyond aesthetic enhancement, contributing significantly to the improvement of residential quality, the physical and psychological well-being of residents, and the strengthening of local identity and urban competitiveness. Urban landscape resources—such as parks, green spaces, and view corridors—are closely associated with residential satisfaction and play a critical role in shaping real estate values. As demand for pleasant living conditions and environmental welfare increases in densely populated cities, the need for systematic research on the relationship between landscaping and real estate value has grown substantially. This study aims to empirically analyze the impact of urban landscaping factors on real estate values and to identify the key elements influencing value formation. To achieve this, the Analytic Hierarchy Process (AHP) was applied to evaluate the relative importance of landscaping attributes, based on survey responses from apartment residents, real estate practitioners, and landscape experts. During the analysis, landscaping factors were structured into a hierarchical framework consisting of upper- and lower-level categories, after which a targeted survey was conducted. Consistency verification was performed to ensure reliable weighting results. The findings reveal that green coverage within residential complexes and accessibility to parks—representing the overall quality of landscaping—are the most influential determinants of real estate value. These results confirm that landscaping contributes not only to visual satisfaction but also to substantial economic value creation, underscoring the importance of strategic planning and management of landscape environments. The study suggests the necessity of strengthening institutional requirements for minimum green coverage ratios in urban planning and emphasizes the need to consider park accessibility as a key locational factor in residential development.",0.0,,False,JournalArticle;Review,,2025-12-31,aa22e7ab448ac7e9d883345a8727b546b71652b8,analyzingthelinkagebetweenurbanlandscapeelementsandpropertyvalueformationinresidentialcomplexes,yun,10.37407/kres.2025.43.4.195,analyzingthelinkagebetweenurbanlandscapeelementsandpropertyvalueformationinresidentialcomplexes|2025,1,3046,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",721444f5149e0a7acb0af49f01833e82b405ae18,10.1108/jes-12-2024-0856,,Enhancing property value prediction: a regression model with fine-grid geographic segmentation,2025.0,Journal of Economic Studies,Hongjai Rhee,"PurposeWe propose a novel scalable regression model that incorporates two-dimensional location dummies based on longitudinal and latitudinal coordinates. This approach easily achieves comparable levels of explanatory power to ML models while avoiding the interpretability challenges inherent in non-parametric ML techniques.Design/methodology/approachRegression models have long been a fundamental and effective toolkit in economic analysis, facilitating the validation of theoretical predictions and the examination of policy effects. However, their explanatory power is often constrained by linearity constraints or limited information, which can, in turn, distort the estimated marginal effects of explanatory variables. While machine learning (ML) models can serve as an alternative, their improved fit often comes at the cost of increased complexity in interpretation and inference. This paper proposes that incorporating geocoded information into the regression models for property values can significantly enhance explanatory power to a level comparable to ML models while preserving the simplicity of regression approaches. As a case study to validate the proposed method, we examine the impact of a recent deregulation policy in Korea on the market prices of the corresponding properties.FindingsBy accounting for location-specific heterogeneity in its full magnitude, our model provides more accurate and reliable estimates for policy evaluation. As a case study, this paper examines the effect of deregulation on apartment redevelopment in Korea, demonstrating how the proposed methodology can be applied to real-world policy analysis.Originality/valueWe propose a novel regression model that incorporates grid dummies for the longitudinal and latitudinal coordinates of the properties. This approach easily achieves comparable levels of explanatory power to ML models while avoiding the interpretability challenges inherent in non-parametric ML techniques.",0.0,,False,JournalArticle,,2025-02-28,721444f5149e0a7acb0af49f01833e82b405ae18,enhancingpropertyvaluepredictionaregressionmodelwithfinegridgeographicsegmentation,rhee,10.1108/jes-12-2024-0856,enhancingpropertyvaluepredictionaregressionmodelwithfinegridgeographicsegmentation|2025,1,2968,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ba20c45aaae6b08ad9628c0fd3a3b1ee9357a8ce,10.1109/icaice54393.2021.00083,,Nonlinear fitting based on BP-neural network for prediction of commodity house prices in Beijing,2021.0,2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE),Diqin Jiang; Ran Biao,"This paper uses the price of commercial housing in Beijing as its focal point. It attempts to identify the factors influencing the price of commercial housing during the real estate development process, as well as to determine the extent to which these factors influence the price of commercial housing and make house price predictions based on this information. This study utilizes principal component analysis to discover the key influencing elements of home prices, find the weight connection between house prices and each primary influencing factor, and develop a house price prediction model using the BP neural network modeling technique using the principal component analysis technique. Using the principal component analysis method, we establish the main influencing factor indexes of house price determination by comparing the contribution rate of each index in the load matrix to the principal component and use multiple linear regression to get the functional relationship between the price of commodity house and its influencing factors for house price and regional prosperity, supporting facilities, transportation, housing design, education, property, and environment. Based on the compression of each commodity house price influencing factor by principal component analysis, the BP neural network algorithm was used to fit the commodity houses in different districts and counties of Beijing, located in different ring roads of Beijing, and the degree of conformity with the actual price was high. We use the BP neuron network model to approximate the distribution of commodity house sales prices and can get the prediction model of average commodity house sales prices more accurately. BP neural network and complete quadratic fitting have a better fitting effect on the original commodity house price data, which proves the effectiveness of nonlinear fitting based on BP neural network in commodity house price prediction.",0.0,,False,Conference,,2021-11-01,ba20c45aaae6b08ad9628c0fd3a3b1ee9357a8ce,nonlinearfittingbasedonbpneuralnetworkforpredictionofcommodityhousepricesinbeijing,jiang,10.1109/icaice54393.2021.00083,nonlinearfittingbasedonbpneuralnetworkforpredictionofcommodityhousepricesinbeijing|2021,1,2938,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,4,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e78e1ed0381cfce6524686274bc5e28c10d62dd1,10.1108/ijhma-04-2026-0100,,"Economic shocks and housing market dynamics: evidence from Shelby County, Tennessee",2026.0,International Journal of Housing Markets and Analysis,Fatemeh Kamkar; Jason De Freitas; Mark A. Sunderman," This study aims to examine housing market dynamics in Shelby County, Tennessee, during the COVID-19 period (2020–2022) to determine whether house price growth reflected speculative behavior or fundamental economic drivers. Using granular multiple listing service transaction data merged with macroeconomic indicators, including mortgage rates, inflation, unemployment and construction costs, the study uses fixed-effects regression models, propensity score weighting and nonlinear specifications to assess the relative importance of demand fundamentals, supply constraints and speculative activity. Market liquidity measures, including sales turnover and months’ supply, are incorporated to complement price-based analysis. House price appreciation during the pandemic was primarily driven by fundamental demand factors, particularly historically low mortgage rates, increased household liquidity and shifting housing preferences. Speculative activity, measured by non-owner-occupied and out-of-town purchases, played a limited role and did not amplify price growth. Liquidity indicators further show that price increases were closely associated with demand pressure rather than speculative excess. The analysis focuses on a single regional housing market, which may limit generalizability. However, the findings highlight the importance of regional heterogeneity in housing responses to macroeconomic shocks. Policymakers should account for local market fundamentals when designing housing and credit policies rather than relying solely on national narratives of speculative bubbles. This study contributes to the housing finance literature by integrating market liquidity measures with speculation indicators in a regional setting, demonstrating that speculative behavior does not uniformly influence housing markets across locations during periods of economic disruption. ",0.0,,False,JournalArticle,,2026-07-13,e78e1ed0381cfce6524686274bc5e28c10d62dd1,economicshocksandhousingmarketdynamicsevidencefromshelbycountytennessee,kamkar,10.1108/ijhma-04-2026-0100,economicshocksandhousingmarketdynamicsevidencefromshelbycountytennessee|2026,1,2914,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",4dce929b50af3a8b07f57e5120374b8cd886427d,10.1108/ijhma-01-2026-0006,,Integrating multi-source data and explainable AI for housing market analysis in Indonesia,2026.0,International Journal of Housing Markets and Analysis,Ivan Aldy Ganesen; Muhamad Farhan Syakir; Fawwaz Abrial Saffa; Ayu Purwarianti; N. Utama," This paper aims to address limitations in real estate valuation in Indonesia arising from regional heterogeneity, fragmented data sources and opaque automated models. Specifically, it seeks to develop a more accurate and transparent comparative market analysis framework by integrating multisource property data and explainable machine learning, thereby improving both valuation reliability and interpretability for heterogeneous market segments. The study integrates property listings from multiple major Indonesian online platforms into a unified dataset with over 70 spatial, structural, facility and pricing attributes. After extensive preprocessing and normalization, properties are grouped into low, medium and high price classes. Multiple regression-based machine learning models are evaluated, with hyperparameter optimization applied to tree-based models. Explainable analysis is used to examine feature contributions across price classes. Extreme Gradient Boosting demonstrates the strongest overall performance, achieving Mean Absolute Percentage Errors of 26.53% for medium-priced properties and 14.60% for high-priced properties. Explainable analysis indicates that spatial attributes consistently dominate price formation, while the influence of facility-related features varies by price segment, highlighting heterogeneous valuation drivers across the market. This paper contributes a multisource, large-scale Indonesian real estate dataset and an explainable automated valuation framework that moves beyond single-platform, black-box approaches. The study provides both predictive accuracy and interpretable insights into price formation, offering value to researchers, practitioners and policymakers seeking transparent and data-driven comparative market analysis in emerging real estate markets. ",0.0,,False,JournalArticle,,2026-04-09,4dce929b50af3a8b07f57e5120374b8cd886427d,integratingmultisourcedataandexplainableaiforhousingmarketanalysisinindonesia,ganesen,10.1108/ijhma-01-2026-0006,integratingmultisourcedataandexplainableaiforhousingmarketanalysisinindonesia|2026,1,2839,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,5,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",6fcf7ac157823343420ef213ef6aaa8747488570,10.1108/ijhma-09-2025-0200,,Power in the pitch: narratives and market value of solar PV homes in New Zealand,2026.0,International Journal of Housing Markets and Analysis,Y. Matthews; C. S. Harland," Residential solar photovoltaics (PV) have expanded rapidly in many countries, yet little is known about how solar features are communicated in property marketing or how they are capitalised in housing markets with low incentives and limited uptake. Existing international research primarily focuses on price effects in higher-penetration settings and has not included large-scale analysis of listing narratives. This study aims to address these gaps by examining how solar homes are described in real estate advertising and whether solar installations influence sale prices and days on market in Aotearoa New Zealand. The analysis draws on a four-year set of 79,944 matched listings and sales, combining nearest-neighbour propensity score matching, fixed-effects regression and topic modelling of listing descriptions. Results show that solar homes are marketed through lifestyle, rural and quality cues rather than explicit sustainability framing, suggesting that PV is bundled with broader amenity signals rather than positioned as a distinct attribute. PV-equipped homes command an average sale price premium of 1.34%, with no significant difference in days on market. Effects do not vary with solar irradiance or local solar penetration, indicating limited salience of site-specific energy benefits in buyer decision-making. The study contributes theoretically by integrating price capitalisation, behavioural economics and marketing narratives. It offers an explanation for why sustainability framing may be limited in a setting where financial incentives are weak. Practically, the findings highlight a need for clearer communication of PV performance and financial benefits in real estate markets, along with policy measures that strengthen the salience for prospective buyers. ",0.0,,False,JournalArticle,,2026-01-05,6fcf7ac157823343420ef213ef6aaa8747488570,powerinthepitchnarrativesandmarketvalueofsolarpvhomesinnewzealand,matthews,10.1108/ijhma-09-2025-0200,powerinthepitchnarrativesandmarketvalueofsolarpvhomesinnewzealand|2026,1,2810,1,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,1,4,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",093fbec598ea7e9c0fd12a48abae5c9a1345b8b4,10.1117/12.3123692,,Analysis of regional housing price fairness and public resource mismatch in Hangzhou based on K-Means++ and OLS regression,2026.0,"International Conference on Optics, Electronics, and Communication Engineering",Gao-Rui Zhang; Hui-Qin Sun; Can Li; Juan Li,"This study utilises an integrated analytical framework, incorporating K-Means++ spatial clustering, Gini-Theil indices, and OLS regression models, to investigate the impact mechanism and fairness of public resource allocation on Hangzhou's second-hand housing prices. Empirical research is conducted based on Hangzhou's second-hand housing transaction data and multidimensional POI data from 2020 to 2021. Through spatial clustering of housing prices and public resource characteristics, four typical zones are identified: ""high resources-high prices,"" ""low resources-low prices,"" and two intermediate patterns. The Gini coefficient was utilised to assess overall fairness, while the Theil index decomposition was employed to identify the primary sources of inequality. An OLS regression model was then constructed to quantify the marginal capitalisation effects of educational, medical, and transportation resources on housing prices. The findings indicate that transportation and healthcare resources are pivotal drivers of housing price differentiation, with unit impacts reaching ¥1,763/m² and ¥1,752/m², respectively, while education resources exert relatively limited influence. Despite the overall regional Gini coefficient remaining below the 0.4 warning threshold, significant resource misallocation and housing price disparities persist within certain areas. This study unveils the transmission mechanism linking ""resource inequality to housing price divergence,"" providing empirical evidence and spatial policy guidance for governments to optimise public resource allocation and promote residential equity in cities.",0.0,,False,Conference,Engineering,2026-09-08,093fbec598ea7e9c0fd12a48abae5c9a1345b8b4,analysisofregionalhousingpricefairnessandpublicresourcemismatchinhangzhoubasedonkmeansandolsregression,zhang,10.1117/12.3123692,analysisofregionalhousingpricefairnessandpublicresourcemismatchinhangzhoubasedonkmeansandolsregression|2026,1,2627,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9f976eb27a264362a24f0617056e6778161bf590,10.1109/agers67633.2025.11446448,,Geospatial Modeling of House Price Using MGWRSAR: A Case Study in DKI Jakarta,2025.0,"2025 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)",Yenro P Sagala; Sutikno; A. Choiruddin,"The dynamics of house prices in Jakarta are influenced by both spatial dependence and spatial heterogeneity. Conventional models, including the Spatial Autoregressive (SAR) and Geographically Weighted Regression (GWR), are only capable of partial capture of these effects, which often result in model misspecification. The present study employs the Mixed Geographically Weighted Regression-Spatial Autoregressive (MGWR-SAR) model to concurrently address the spatial dependence and heterogeneity in house price determinants. We used a dataset of houses listed in 2024, incorporating house and location characteristics. The construction of spatial dependence weight matrix was based on semi-variogram analysis, and optimal kernel bandwidths were selected using cross-validation method. The model is estimated using The Spatial Two-Stage Least Square (S2SLS) approach to address endogeneity in spatial lag component. The findings of the study indicate that the land and building area are the most significant price determinants, with spatial variation in their influence across Jakarta’s subregions. Jakarta Pusat exhibits stronger spatial spillovers, while Southern district demonstrates heightened sensitivity to building area. The MGWR-SAR model effectively eliminates residual spatial autocorrelation and captures location-specific variation, offering enhanced explanatory power for urban housing market. These findings underscore the importance of incorporating spatial structure in housing price modeling for more targeted policies and valuation strategies.",0.0,,False,Conference,,2025-12-17,9f976eb27a264362a24f0617056e6778161bf590,geospatialmodelingofhousepriceusingmgwrsaracasestudyindkijakarta,sagala,10.1109/agers67633.2025.11446448,geospatialmodelingofhousepriceusingmgwrsaracasestudyindkijakarta|2025,1,2560,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,3,3,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",27e232eeffc58b8e0a75c04f2ceb3aa6848d2746,10.1108/pm-06-2023-0048,,Exploring property orientation preferences and local variations,2023.0,Property Management,Changro Lee,"PurposeProperties with specific orientations are preferred in South Korea, depending on the real estate market. This preference is usually considered during property transactions and in designing buildings. Despite the importance of property orientation, the magnitude of preference for favored orientation has rarely been empirically estimated in the literature. This study attempts to estimate the value of favored orientation in a quantitative manner and interpret the results.Design/methodology/approachUsing a geographically weighted regression model, this study obtains nationwide property price data and estimates the strength of orientation preference, that is, the premium for favored orientation. Among the various property types, residential sites and forests were investigated because the orientation of these two property types is known to influence their sales prices in the Korean real estate market.FindingsThe results show that premiums for south-facing residential sites exist in the market, varying locally and ranging from zero to 13.2%, over residential sites with non-south orientations. The results for forests are mixed in that a south-facing forest commands a maximum of 33.1% premium in a certain region, over a forest with a non-south direction, while it also commands a maximum of 33.8% negative premium (discount) in another region, indicating significant local variations in premiums.Originality/valueThese findings are expected to be utilized in fields such as property valuation, house architecture and design.",0.0,,False,JournalArticle,,2023-09-28,27e232eeffc58b8e0a75c04f2ceb3aa6848d2746,exploringpropertyorientationpreferencesandlocalvariations,lee,10.1108/pm-06-2023-0048,exploringpropertyorientationpreferencesandlocalvariations|2023,1,2542,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4d69550518ff9b0f7ce57b5a5cf020f6d43b5a5d,10.22313/reik.2023.21.3.101,,A Study on the Determinants of Row House Sale price in Jeju-do,2023.0,Residential Environment Institute Of Korea,Young-Jun Yang; S. Lee,"In this study, the current status of houses in Jeju-do was examined, and the determinants of row house prices were analyzed using actual transaction price data of row houses traded from 2019 to 2022. To summarize the analysis results focusing on Jeju-do, the price of small sized row house is higher than that of large sized. The higher the number of floors, the higher the price, and the higher the land area, the higher the price. Over time, prices have been shown to fall and then rise. The gross floor area, the number of parking per household, and the remaining building to land ratio have a positive effect on the price, and the larger the width of the road facing the land, the higher the price. By region, prices are high in the order of Eup·Myeon area of Seogwipo-si, Dong area of Jeju-si, Eup·Myeon area of Jeju-si and Dong area of Seogwipo-si, and by special-purpose area, residential area, green area, and control area are in the order. The higher the distance between elementary schools and the market, the higher the price, and the lower the distance between middle schools and government offices. One of the purposes of this study, the further away the row housing complex from the apartment complex, the lower the price. In addition, in the empirical analysis that divided Jeju-do into four regions, it was confirmed that even the same variable could show different analysis results depending on the region.",0.0,,False,JournalArticle,,2023-09-30,4d69550518ff9b0f7ce57b5a5cf020f6d43b5a5d,astudyonthedeterminantsofrowhousesalepriceinjejudo,yang,10.22313/reik.2023.21.3.101,astudyonthedeterminantsofrowhousesalepriceinjejudo|2023,1,2425,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,5,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",76fb8d9ff6a4ec7822c172b3ef7ad53a19912562,10.22423/kreus.2024.16.2.25,,Study on the Impact of Social Infrastructure on Apartment Prices: Focusing on the Case of Seoripul Tunnel in Seoul,2024.0,Konkuk Research Institute of Real Estate and Urban Studies,Won-Joon Choi; Seong-won Lee; Yeon-Jae Lee,"This study aimed to understand the impact of social infrastructure investment on apartment prices. Using a difference-in-differences(DID) regression analysis, we empirically analyzed the impact of the new establishment of infrastructure on apartment prices in Bangbae-dong, Seoul by the commencement and completion of the Seoripul Tunnel, examining the effects step-by-step and by distance from the street using buffer analysis. Our findings revealed that the influence on apartment prices varied depending on the commencement and completion stages. For the commencement phase, a significant increase in actual transaction prices was observed within 500m when comparing 6 to 9 months before and after the construction started. On the other hand, during the completion phase, no significant influence was observed at the exact time of completion. However, when considering three months before completion, statistically significant positive changes were observed in both the subsequent 9 months and within distances of both 500m and 1km. This research provides an empirical evidence of the actual fluctuations in apartment prices due to large-scale social infrastructure investments in the area. The effects of social infrastructure on housing prices can manifest differently across time and distance from it. Such complexities suggest that related policy responses require much more careful research.",0.0,,False,JournalArticle,,2024-02-29,76fb8d9ff6a4ec7822c172b3ef7ad53a19912562,studyontheimpactofsocialinfrastructureonapartmentpricesfocusingonthecaseofseoripultunnelinseoul,choi,10.22423/kreus.2024.16.2.25,studyontheimpactofsocialinfrastructureonapartmentpricesfocusingonthecaseofseoripultunnelinseoul|2024,1,2399,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8595fe109eada7ded46f7be8f43ecff9898ed9ec,10.24957/hsr.2022.30.4.113,,A Study on the Effect of Resale Characteristics of Apartment Presale Rights on Apartment Sales Price,2022.0,Korean Association for Housing Policy Studies,J. Park; Han Sung Joo,"The government implemented a real estate policy regulating Finsens in units of each region by designating overheated speculation districts and areas to be adjusted according to the rise in housing prices. These regulated areas include consumer restraint policies. The purpose of this study was to examine the real estate policies related to the characteristics of pre-sale of apartments over a period of 6 years from January 2016 to December 2021, and to examine how these policies affected apartment sale prices by region. For the research method, the Hausmann fixed effect was used to examine the factors that have influence as well the changes according to the characteristics of real estate, macroeconomic characteristics, policy characteristics, and resale characteristics of real estate based on on nationwide and metropolitan area models, specifically the Daegu/Gyeongbuk, Booulgyeong, Chungcheong/Daejeon, and Gwangju/Jeonla models. For the statistical analysis, the model was assessed using the STATA17 version. Results showed it was possible to examine the policies affecting housing price stabilization among various government real estate policies, and to further examine the policies that manifested different results by region. These results should consider detailed customized policies for regions where the real estate market is overheated or where speculation is a concern.",0.0,,False,,,2022-11-30,8595fe109eada7ded46f7be8f43ecff9898ed9ec,astudyontheeffectofresalecharacteristicsofapartmentpresalerightsonapartmentsalesprice,park,10.24957/hsr.2022.30.4.113,astudyontheeffectofresalecharacteristicsofapartmentpresalerightsonapartmentsalesprice|2022,1,2390,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",18b92b0e188c00f5f9717ce170369158aafe285b,10.37407/kres.2022.40.4.63,,A Study on the Impact of the 1st New Town Reorganization Announcement on Apartment Prices in Ilsan New Town,2022.0,Korea Real Estate Society,Wang Bae; Choonwon Lee,"This study empirically analyzed the impact of the announcement of the 1st new town redevelopmentplan on the price of apartments subject to redevelopment in Ilsan New Town. The actual transaction ofapartments over 30 years ago was turned into data, and a trend analysis and multiple regression analysiswere conducted. First, the transaction volume of Ilsan New Town is similar to that of the entire city of Goyang, but theactual transaction price is higher than the average of the entire city of Goyang. It seems that thelocation conditions, such as the subway station, are relatively good, and there are expectations forreorganization. Second, as a result of multiple regression analysis, the variables that have a positive (+) effect on theapartment price are the number of households, exclusive area, number of floors, and distance to thesubway station. Consistent with previous studies, it was found that the closer the apartment is, the moreit has a positive effect on the apartment price. The results of this study suggest that the announcement of the 1st phase of new town reorganizationis not a continuous factor influencing apartment sales prices in the Ilsan area, but appears to have beenreflected in housing price fluctuations as a short-term shock. It is presumed to be the reason for thecalm of anxiety and the return to wait-and-see attitude.",0.0,,False,,,2022-12-31,18b92b0e188c00f5f9717ce170369158aafe285b,astudyontheimpactofthe1stnewtownreorganizationannouncementonapartmentpricesinilsannewtown,bae,10.37407/kres.2022.40.4.63,astudyontheimpactofthe1stnewtownreorganizationannouncementonapartmentpricesinilsannewtown|2022,1,2356,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9308ff2cf5a64c1b41ac0d9ab4919a135e3c93e8,10.35136/krer.32.3.4,,Effect of Redevelopment Project District Characteristics on Neighboring Housing Prices,2022.0,Korea Real Estate Institute,Jae Won Lee; S. Bae; Sang Youb Lee,"This study analyzed the impact of nearby redevelopment project characteristics on the apartment and single/multi-family house prices in all areas of Seoul. The impact of the distance to the redevelopment project area, the number of supply households, and the number of project areas were analyzed using the housing sale transaction price from 2006 to 2020, and all housing types show the same direction. Findings reveal that the closer the supply of households, the higher the transaction price. Moreover the number of project areas has a positive effect after a certain distance. This indicates a positive effect on factors such as price synchronization when new houses are supplied from nearby project areas, expectations as project candidates, and shares in the benefits of improving the residential environment. Redevelopment projects are likely to be used as large-scale housing supply assurance measures in the future. When designing an area, one must consider planning and progress by region so that the size, number of union members, and maintenance infrastructure ratio do not significantly impact the housing market. This study obtained general results for all regions in Seoul, yielding important policy implications and providing useful basic data to investors, public policymakers, and practitioners.",0.0,,False,,,2022-09-30,9308ff2cf5a64c1b41ac0d9ab4919a135e3c93e8,effectofredevelopmentprojectdistrictcharacteristicsonneighboringhousingprices,lee,10.35136/krer.32.3.4,effectofredevelopmentprojectdistrictcharacteristicsonneighboringhousingprices|2022,1,2313,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,5,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d36b509d603132645ed71fd666edd147059b9761,10.1109/uemcon62879.2024.10754766,,A Data-Centric Approach to Post-Pandemic Housing Market Analysis,2024.0,"Ubiquitous Computing, Electronics & Mobile Communication Conference",Matin Pirouz; Preston Mccullough,"The housing market in California has gone through some unexpected fluctuations during the COVID-19 pandemic. The housing inventory reached at all-time lows and prices reached highs not seen since before the economic crisis of mid2000s. This paper explores these trends using Central California as a case study. Home valuation on home sales since 2018 in Fresno County of Central California are modeled with geographically weighted regression, geographically and temporally weighted regression, as well as artificial neural networks. Moreover, autoregressive integrated moving average models are utilized to explore the trends of price and days on the market in Fresno County. Finally, statewide COVID-19 data and Blueprint for a Safer Economy are used to predict changes in year over year housing prices across various California counties. The geographically weighted regression model outperformed the other two valuation models, both of which incorporate the date a home was sold as a predictor. The time-series analysis on Fresno County trends demonstrates unpredictability during the pandemic. The neural network underperformed in predicting sales price in counties, indicating that different policy and outbreak status of certain counties may not have an effect on the increasing home prices in California.",0.0,,False,JournalArticle;Conference,Computer Science,2024-10-17,d36b509d603132645ed71fd666edd147059b9761,adatacentricapproachtopostpandemichousingmarketanalysis,pirouz,10.1109/uemcon62879.2024.10754766,adatacentricapproachtopostpandemichousingmarketanalysis|2024,1,2310,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,4,2,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d1e57dd75317f89c0f2bdafbbd0123f611146cdb,10.1108/jfmpc-04-2024-0031,,"Spatial justice and apartment prices in Prishtina, Kosovo: unveiling the predictors and implications",2025.0,Journal of Financial Management of Property and Construction,Visar Hoxha; B. Beqaj," The purpose of this study is to investigate the impact of spatial justice factors—such as access to amenities, green spaces, socio-economic status, transportation and environmental quality—on apartment prices in Prishtina, Kosovo, thereby contributing to the understanding of urban development dynamics in rapidly urbanizing cities. The present study used multiple regression analysis to investigate the relationship between apartment prices and various quantitative variables. Additionally, this study used logarithmic transformations and normalization to delve deeper into spatial predictors that affect housing prices. This study reveals that socio-economic status and access to emergency services are the most significant predictors of apartment prices. Furthermore, transportation and green spaces gained prominence after normalization, highlighting their context-sensitive roles in shaping housing prices. The originality of this study lies in its exploration of spatial justice variables in the underrepresented context of Prishtina, Kosovo, a rapidly urbanizing post-conflict city. By offering novel insights into urban housing markets and spatial justice, this research enhances the global understanding of equitable urban development in emerging urban landscapes. ",0.0,,False,JournalArticle,,2025-11-14,d1e57dd75317f89c0f2bdafbbd0123f611146cdb,spatialjusticeandapartmentpricesinprishtinakosovounveilingthepredictorsandimplications,hoxha,10.1108/jfmpc-04-2024-0031,spatialjusticeandapartmentpricesinprishtinakosovounveilingthepredictorsandimplications|2025,1,2299,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",dbd33f8beb3a03a0e37000aa26d189fcaabf2b0c,10.1108/ijhma-10-2020-0127,,Golf courses and home values in California’s Central valley,2021.0,International Journal of Housing Markets and Analysis,Nyakundi M. Michieka; D. Lacombe; Y. Ampatzidis," Purpose The purpose of this study is to examine the net effect of golf courses’ proximity on home sale prices in Kern County, California. Design/methodology/approach A spatial Durbin error model is used with sales price data for 1,693 homes sold in Kern County in the third quarter of 2018. This paper compares 90 different spatial econometric models using Bayesian techniques to produce posterior model probabilities which guided model selection and the number of neighbors to use. Findings The results show that significant spatial dependence exists in home values in Kern County. Point estimates indicate that homes abutting golf courses are valued at less than those which are not. This study also finds that the farther away from golf courses the average home is, the higher its value. Originality/value This study contributes to the existing literature in three dimensions. First, this paper analyzes whether proximity to golf courses impacts home values in Kern County where a study of this nature has not been conducted. Second, the analysis uses transaction data for 2018 which was a period when the sport’s popularity was fading and golf courses closing. Third, Bayesian model comparison techniques are used to select the appropriate model. ",0.0,,False,,,2021-07-20,dbd33f8beb3a03a0e37000aa26d189fcaabf2b0c,golfcoursesandhomevaluesincaliforniascentralvalley,michieka,10.1108/ijhma-10-2020-0127,golfcoursesandhomevaluesincaliforniascentralvalley|2021,1,2258,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7da8bdc306fd9690ce3d1e4097932659902b958f,10.1109/scisisis55246.2022.10002111,,Sequential Estimation of Prices of Newly Built Houses in Tokyo Using the Linear State Space Model,2022.0,2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS),Satoshi Yamada; Kazushi Okamoto; Atsushi Shibata,"In housing pricing, it is necessary to resolve information asymmetry and consider spatiotemporal heterogeneity. Conventional methods are based on regression models, which express heterogeneity by weighting using spatiotemporal distance or sampling by period while ensuring explanatory power through regression coefficients. These methods cannot capture changes from the previous regression coefficient. Therefore, the state-space model is used to update the regression coefficients sequentially, thereby making use of the information from the previous period. To verify the performance of this model, newly built house prices in central Tokyo from June 2016 to July 2017 were estimated and compared with the linear regression model and geographically weighted regression (GWR) in terms of mean absolute percentage error (MAPE) and calculation cost. The results suggest that interpolation can be used to estimate small sample sizes that cannot be estimated by the linear regression model and that the more efficient calculation since average prediction time is 338 times shorter than the linear regression model and 2234 times shorter than GWR. However, the MAPE median was 34.8% and 44.3% worse than that of the linear regression model and GWR, respectively.",0.0,,False,JournalArticle;Conference,Computer Science,2022-11-29,7da8bdc306fd9690ce3d1e4097932659902b958f,sequentialestimationofpricesofnewlybuilthousesintokyousingthelinearstatespacemodel,yamada,10.1109/scisisis55246.2022.10002111,sequentialestimationofpricesofnewlybuilthousesintokyousingthelinearstatespacemodel|2022,1,2258,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",d9fb17f32e98ce2eca7315eec0dd890bec9aba6c,10.1177/00302228241297726,,The Impact of Unnatural Death Stigmatization on Neighboring Property Prices: Propensity Score Matching and Difference-In-Differences Analysis,2024.0,Omega,Chun-Chang Lee; Wen-Chih Yeh; Pei-Syuan Lin; Jing Lee; Ting-Wei Dai,"This study primarily investigated the impacts of unnatural in-house deaths on nearby property prices. Propensity score matching was used to assess the homogeneity of experimental and control groups, and the difference-in-differences (DID) method was used for their comparison. Data for real estate transactions in Taipei City from 2012 to 2022 were retrieved from the Ministry of the Interior’s real estate actual transaction price registration query system. The DID interaction variable coefficient of 0.066 was significant at the 10% level. A comparison of the experimental and control groups showed a 6.8% property price difference following an unnatural in-house death. This suggests that an unnatural in-house death has a positive impact on property prices nearby. There were four unnatural deaths at the Xining Public Housing from 2003 to 2012 and four at the Jinxing Building from 1984 to 2012. The high incidence of events and the disclosure of information may have depreciated nearby property prices. The study results show that the stigmatization of unnatural in-house deaths reduces nearby property prices. The depreciation of implicit prices reverted and followed a U-shape pattern after several years.",0.0,,False,JournalArticle,Medicine,2024-11-05,d9fb17f32e98ce2eca7315eec0dd890bec9aba6c,theimpactofunnaturaldeathstigmatizationonneighboringpropertypricespropensityscorematchinganddifferenceindifferencesanalysis,lee,10.1177/00302228241297726,theimpactofunnaturaldeathstigmatizationonneighboringpropertypricespropensityscorematchinganddifferenceindifferencesanalysis|2024,1,2214,1,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",92b606bb098a0347861424cd53e378579ff05bad,10.1177/23998083251378324,,"The development differences of districts and the boundary effect of housing prices in Hangzhou, China",2025.0,Environment and Planning B Urban Analytics and City Science,Ling Zhang; Si-Jie Chen; Lifei Zhu,"This study investigates whether significant price differentials exist for otherwise comparable houses positioned on either side of district boundaries. A spatial regression discontinuity model is developed to explain the administrative boundary effect of housing prices through differences in development quality among various districts. The findings show that differences in the level of comprehensive economic and social development between districts are an important reason for differences in housing prices and the emergence of boundary effects. The spatial differentiation of housing prices on either side of a boundary is consistent with the comprehensive development quality of districts. Moreover, the difference in high-quality educational resources among different development factors holds the greatest explanatory effect on the administrative boundary effect of housing prices. The key remaining explanatory factors, in descending order of importance, are population density, employment density, the presence of first-class hospitals, and average fiscal revenue. These findings offer insights into potential strategies for balancing development in rapidly growing cities.",0.0,,False,JournalArticle,,2025-09-11,92b606bb098a0347861424cd53e378579ff05bad,thedevelopmentdifferencesofdistrictsandtheboundaryeffectofhousingpricesinhangzhouchina,zhang,10.1177/23998083251378324,thedevelopmentdifferencesofdistrictsandtheboundaryeffectofhousingpricesinhangzhouchina|2025,1,2183,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.4025/econrev.v31i1.64247,,ANÁLISE DE PREÇOS HEDÔNICOS NO SETOR IMOBILIÁRIO DE RESIDÊNCIAS NA CIDADE DE LONDRINA – PR,2023.0,A Economia em Revista - AERE,AMANDA THAIS ROCHA; THIAGO JOSE PINHEIRO,"O preço de imóvel é definido como preço hedônico, composto de características que avaliam, além das propriedades do próprio bem, como número de quartos, vagas na garagem, entre outros além das características de localização desse imóvel. Este trabalho tem como objetivo mensurar a valorização dos preços de casas a venda na cidade de Londrina-PR, baseado na Teoria dos Preços Hedônicos e métodos e técnicas da econometria espacial como a Análise Exploratória de Dados Espaciais (AEDE) e o modelo de regressão espacial SAR, a base de dados apresenta 655 anúncios de residências à venda, coletadas de janeiro a fevereiro de 2021, o modelo verifica quais atributos que mais impactam no preço final dos imóveis. Através da AEDE constatou-se uma dependência espacial com correlação positiva para o preço das casas na cidade de Londrina-PR, o modelo SAR constatou que a quantidade de quartos apresentou o maior valor no preço final dos imóveis sendo de 20,44%, seguidas de um aumento de 10,03% a cada unidade adicional de banheiro, 7,93% para a vagas de garagem e de 0,16% para cada m² adicional na residência, impactando todas num aumento do preço final.",0.0,,,journal-article,,,doi_10_4025_econrev_v31i1_64247,analisedeprecoshedonicosnosetorimobiliarioderesidenciasnacidadedelondrinapr,rocha,10.4025/econrev.v31i1.64247,analisedeprecoshedonicosnosetorimobiliarioderesidenciasnacidadedelondrinapr|2023,1,2149,1,preços hedônicos imóveis,1,0,1,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7c4dacb72d739297f840d937375d5317500d4937,10.47772/ijriss.2025.90300195,,Urban Regeneration and Housing Market Dynamics: Examining the Impact of Brownfield Sites in Malaysia,2025.0,International journal of research and innovation in social science,Aida Julia Aminuddin; Nurul Hana Adi Maimun; N. Zihannudin; S. Eni; Umussaa’dah Adam,"Urban regeneration efforts often face significant challenges due to the presence of brownfield sites, which pose financial, administrative, and policy issues. These sites can lead to negative environmental and social impacts, reducing the appeal and value of nearby properties. Due to the limited market evidence in Malaysia, this study investigates the impact of brownfield sites on house prices in Johor Bahru using a Multiple Regression Analysis (MRA) model. The research identifies key factors influencing house prices, including proximity to brownfield sites, and examines 691 housing observations over ten years. Results show no significant impact of an abandoned apartment building on house prices, suggesting that distance from certain brownfield sites may not deter buyers. These findings are valuable for property market stakeholders and urban planners, offering insights for future urban regeneration projects. Future research could explore other types of brownfield sites and employ more precise distance measures to understand their impact on the housing market further, supporting urban regeneration goals.",0.0,,False,JournalArticle,,,7c4dacb72d739297f840d937375d5317500d4937,urbanregenerationandhousingmarketdynamicsexaminingtheimpactofbrownfieldsitesinmalaysia,aminuddin,10.47772/ijriss.2025.90300195,urbanregenerationandhousingmarketdynamicsexaminingtheimpactofbrownfieldsitesinmalaysia|2025,1,2120,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",37c5cbfcb0500c5b1eab3b3a22beb8e88d00a500,10.1109/icares64249.2024.10768046,,House Price Modeling in DKI Jakarta: A Web-Based Comparative Study of Spatial Models,2024.0,2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES),Sevien; A. A. S. Gunawan; Margaretha Ohyver,"A house is a fundamental need for individuals. House sellers can easily determine the market price of their property using the NJOP formula, which is used in the calculation of Land and Building Tax (PBB). However, for buyers, determining a fair market price for a house can be challenging. As a result, it is common for buyers to purchase a house at a price higher than its fair market value. Therefore, this study aims to determine the attributes of a house that affect the price of a house but also considering the spatial value of the location of the district of the house by comparing Spatial Autoregressive Model with Spatial Error Model. We concluded that the SEM model, characterized by the highest $\mathbf{R}^{2}$ and the lowest AIC and BIC values, is identified as the best fit for the dataset. Furthermore, we embedded the house predicting models in a web application, making it easier for the public to determine the price of the house they want to purchase while also assisting property companies in planning future housing development strategies to generate maximum and sustainable profits.",0.0,,False,Conference,,2024-11-08,37c5cbfcb0500c5b1eab3b3a22beb8e88d00a500,housepricemodelingindkijakartaawebbasedcomparativestudyofspatialmodels,sevien,10.1109/icares64249.2024.10768046,housepricemodelingindkijakartaawebbasedcomparativestudyofspatialmodels|2024,1,2105,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",560cc2aad97f92ece603764092f3d9d1c5e7701a,10.22423/kreus.2024.17.1.61,,An Empirical Study on the Impact of Upzoning on House Prices: Focusing on the Naebang Station Intersection in Seoul,2024.0,Konkuk Research Institute of Real Estate and Urban Studies,Won-Joon Choi; Seong-won Lee,"This study uses the Differences-in-Differences (DID) method to examine the impact of upzoning announcements on house prices near Naebang Station. Data were analyzed using two models: apartments and townhouse & multi-family based on Chow Test result. We find that the upzoning announcement significantly increased house prices within 200 meters of Naebang Station upzoning area with statistically significant rises for townhouses and multi-family units, suggesting a positive externality. Apartments did not show significant effects, however we found significant price rise for townhouse and multi-family units due to substantial future development potential. Housing redevelopment projects also statistically significantly impacted house prices and there is much greater price increase in the redevelopment project. These findings highlight the need for policy-makers to anticipate varied market responses to rezoning initiatives and tailor policies to different housing types and neighborhood characteristics, contributing to a better understanding of upzoning policy impacts on housing markets.",0.0,,False,JournalArticle,,2024-08-31,560cc2aad97f92ece603764092f3d9d1c5e7701a,anempiricalstudyontheimpactofupzoningonhousepricesfocusingonthenaebangstationintersectioninseoul,choi,10.22423/kreus.2024.17.1.61,anempiricalstudyontheimpactofupzoningonhousepricesfocusingonthenaebangstationintersectioninseoul|2024,1,2096,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,4,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5169433946816c1edf68286657af8509d065a708,10.1002/ijfe.3115,,Do Mortgage Buy‐To‐Let Investors Pay More or Less for Properties? Empirical Evidence From the UK Residential Market,2025.0,International Journal of Finance & Economics,Panagiotis Petris; George Dotsis; P. Alexakis,"In this paper, we test whether mortgage buy‐to‐let investors buy houses at significantly different prices compared to other buyers. The distinctive characteristic of a mortgage buy‐to‐let transaction is that lenders require the price offered to be explicitly tied to the rental income that the property will be able to generate. To test the existence of a price differential between the two groups of buyers, we use house price data of more than 600,000 residential unit transactions that took place in England and Wales. Using data that span the period 2014–2021, our empirical findings show that mortgage buy‐to‐let investors buy houses at a significantly discounted price (discount of about 4.5%) compared to other buyers. We provide evidence that home price differentials among the two buyer groups are less pronounced in a highly competitive market, such as the London housing market. We also find that the mortgage buy‐to‐let discount was on average higher before the 3% rise in the stamp duty land tax that took place in 2016.",0.0,,False,JournalArticle,,2025-01-08,5169433946816c1edf68286657af8509d065a708,domortgagebuytoletinvestorspaymoreorlessforpropertiesempiricalevidencefromtheukresidentialmarket,petris,10.1002/ijfe.3115,domortgagebuytoletinvestorspaymoreorlessforpropertiesempiricalevidencefromtheukresidentialmarket|2025,1,2033,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,6,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",d0b3cf94e9762dba81ce02766c4c5a29ee5d1c74,10.25202/jakg.8.2.14,,Exploring Spatial Heterogeneity in Factors for Apartment Prices in Seoul Using Moran Eigenvector Spatial Filtering Based on Spatially Varying Coefficients,2019.0,Journal of the Association of Korean Geographers,Hyeongmo Koo,"Spatially varying coefficients models have been used as a tool for exploring spatial heterogeneity in regression coefficients. But, among various spatially varying coefficients models, only a geographically weighted regression (GWR) has been used for exploring spatial heterogeneity in factors for housing prices although it suffers from its low explanatory power and high multicollinearity between local coefficients. This study applies another type of a spatially varying coefficients model based on Moran eigenvector spatial filtering (MESF) to explore factors to apartment sale prices. The analysis results are compared to those of GWR and global MESF. The result shows MESF outperforms GWR in terms of explanatory power, spatial autocorrelation in model residuals, and multicollinearity between local coefficients. In additions, a variation in local coefficients against their global coefficients suggests that exploring spatial heterogeneity is necessary for a housing price estimation model.",0.0,,False,,Geography,2019-08-30,d0b3cf94e9762dba81ce02766c4c5a29ee5d1c74,exploringspatialheterogeneityinfactorsforapartmentpricesinseoulusingmoraneigenvectorspatialfilteringbasedonspatiallyvaryingcoefficients,koo,10.25202/jakg.8.2.14,exploringspatialheterogeneityinfactorsforapartmentpricesinseoulusingmoraneigenvectorspatialfilteringbasedonspatiallyvaryingcoefficients|2019,1,1998,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",34e69e5bc33931ed15d395f945460b3fbc513831,10.55186/2413046x_2025_10_6_155,,MATHEMATICAL MODELING AS A TOOL FOR ANALYZING VALUE-BASED CORRESPONDENCES IN THE HOUSING MARKET,2025.0,Moscow Economic Journal,German Pushkarev; Y. Ovchinnikov; Egor Anders,"This article analyzes the main factors influencing residential property prices in the city of Perm. To quantitatively assess the impact of various apartment characteristics on their market value, methods of multiple regression and variance analysis are applied. Econometric pricing models are developed, considering both vertical differentiation (market segmentation by housing class) and horizontal (territorial) stratification. The modeling results demonstrate that in elite market segments, qualitative parameters and housing size have a decisive impact on prices, whereas in mass segments, apartment compactness and infrastructure accessibility play a significant role. Thus, mathematical modeling serves as a tool for verifying value-based correspondences in pricing: price differences largely align with variations in property characteristics. The study results can be used for price forecasting and developing strategies for balanced urban environment development.",0.0,,False,JournalArticle,,2025-06-10,34e69e5bc33931ed15d395f945460b3fbc513831,mathematicalmodelingasatoolforanalyzingvaluebasedcorrespondencesinthehousingmarket,pushkarev,10.55186/2413046x_2025_10_6_155,mathematicalmodelingasatoolforanalyzingvaluebasedcorrespondencesinthehousingmarket|2025,1,1971,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.47328/revcch.v2i20.11013,,UMA ANÁLISE DO PREÇO DE ALUGUÉIS DE IMÓVEIS EM UMA CIDADE UNIVERSITÁRIA,2020.0,Revista de Ciências Humanas,Hugo Deleon de Freitas; Jader Fernandes Cirino,"O preço do aluguel imobiliário pode ser influenciado por diversos fatores, desde características de dentro do imóvel quanto do seu entorno, sendo elas tangíveis ou intangíveis. Portanto, este trabalho teve o objetivo de verificar a relação desse valor com as diversas características dos imóveis em uma cidade universitária. Ao analisar essa demanda para os estudantes, por meio do método hedônico pôde verificar que quanto mais próximos os imóveis estão do campus da universidade, maior tende a ser o valor do aluguel. Já a proximidade de bares de grande movimentação tende a diminuir o preço destes. Além disso, foi verificada a influência de diversos fatores externos no preço dos alugueis, como segurança e poluição sonora. Com isso, uma determinada desvalorização fora da normalidade em determinada região pode ser um indicativo de que uma localidade pode ter ficado menos segura ou mais barulhenta, sendo uma sinalização para ser investigado mais a fundo.",0.0,,,journal-article,,,doi_10_47328_revcch_v2i20_11013,umaanalisedoprecodealugueisdeimoveisemumacidadeuniversitaria,freitas,10.47328/revcch.v2i20.11013,umaanalisedoprecodealugueisdeimoveisemumacidadeuniversitaria|2020,1,1961,1,preços hedônicos imóveis,1,0,1,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d300b921d750a58d0305f218b301c6186beeb5aa,10.1111/1540-6229.70052,,From battlements to boom: The enduring influence of historical walled city on housing prices,2026.0,Real Estate Economics,Zhenyu Lin; E. Hui; Huan Yang," This study explores the long‐term economic effects of a unique form of historical architectural heritage—the city wall—on modern urban spatial structure. Using a Spatial Regression Discontinuity Design, the research compares housing prices on both sides of the Nanjing city wall, one of China's largest and best‐preserved urban fortifications. Results show a significant and consistent price premium for properties just inside the walls. Further analysis indicates that this premium is not solely due to the wall's heritage value, nor is it caused by a simple barrier effect or agglomeration dynamics on either side of the boundary. Instead, it stems from deep‐rooted path dependence within the city: early investments in the internal road network and the build‐up of local amenities, which together maintained a persistent urban structure and produced lasting capitalization effects within the wall.",0.0,,False,JournalArticle,,2026-04-25,d300b921d750a58d0305f218b301c6186beeb5aa,frombattlementstoboomtheenduringinfluenceofhistoricalwalledcityonhousingprices,lin,10.1111/1540-6229.70052,frombattlementstoboomtheenduringinfluenceofhistoricalwalledcityonhousingprices|2026,1,1902,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,5,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_citations,cites:sirmans2006,c7060b7364d2cf0dd30794b6b1ed2a7de38c8753,10.1080/09599916.2022.2119878,,The impact of cash financing on housing prices: a theoretical framework and empirical evidence from a volatile United States market cycle,2022.0,Journal of Property Research,R. Aroul; J. Hansz,"ABSTRACT This study examines cash financing transaction price implications over a 12-year United States (U.S.) housing market cycle centred around the 2008 Housing Crisis, a period of unprecedented transaction price volatility. We establish theoretical reasoning and empirical confirmation of the mortgage contingency pricing effect, operationalised as a cash financing discount. We document that cash discounts are associated with market conditions, price levels, and improvement sizes and conditions. Larger empirical cash discounts are related to greater market distress, lower price levels, smaller improvement sizes, and inferior improvement conditions. We conclude that a one-size-fits-all rule-of-thumb is not appropriate when estimating cash financing pricing impacts. Finally, additional research is encouraged across different market conditions and in non-U.S. markets.",0.0,,False,,,2022-09-13,c7060b7364d2cf0dd30794b6b1ed2a7de38c8753,theimpactofcashfinancingonhousingpricesatheoreticalframeworkandempiricalevidencefromavolatileunitedstatesmarketcycle,aroul,10.1080/09599916.2022.2119878,theimpactofcashfinancingonhousingpricesatheoreticalframeworkandempiricalevidencefromavolatileunitedstatesmarketcycle|2022,1,1879,1,cites:sirmans2006,0,3,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7b4aee2056fa837423b38e5b8acf04bb72c062a3,10.1109/icccbda69573.2026.11676545,,Exploring Factors Influencing Second-Hand Housing Market Prices Through Multi-Source Data Integration: A Case Study of Chongqing Liangjiang New Area,2026.0,2026 11th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA),Lin-Hui Yan; Liang-Ming Wen,"To reveal the price formation mechanism and structural characteristics of the secondary housing market following the administrative restructuring of Chongqing Liangjiang New Area, this study utilized 18,000 initial property listings from the Fang.com platform. After preprocessing, 16,015 valid samples were obtained and systematically analyzed using data mining methods. By constructing multiple linear regression and random forest regression models, we found that the random forest model achieved an $\mathbf{R}^{\mathbf{2}}$ of 0.7132 on the training set and 0.6535 on the test set, demonstrating significantly higher predictive accuracy than linear regression. Feature importance analysis further revealed that building area and distance to subway stations are the core variables influencing housing prices, collectively contributing nearly 70% of the importance weight.",0.0,,False,Conference,,2026-04-01,7b4aee2056fa837423b38e5b8acf04bb72c062a3,exploringfactorsinfluencingsecondhandhousingmarketpricesthroughmultisourcedataintegrationacasestudyofchongqingliangjiangnewarea,yan,10.1109/icccbda69573.2026.11676545,exploringfactorsinfluencingsecondhandhousingmarketpricesthroughmultisourcedataintegrationacasestudyofchongqingliangjiangnewarea|2026,1,1874,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d6e394b39690559fcf56412ae424ec49aa67e0ed,10.22423/kreus.2026.19.1.25,,The Spatial Impact of the GTX-A Partial Opening on Apartment Prices: The Case of Dongtan New Town,2026.0,Konkuk Research Institute of Real Estate and Urban Studies,J. Seo; Sang-Min Lee,"This study examines the effect of the partial opening of the Great Train Express-A (GTX-A) Suseo–Dongtan section on real apartment prices in Dongtan New Town. Using apartment transaction data from November 2022 to August 2025, a difference-in-differences model was estimated with the treatment group defined as areas within a 1km radius of Dongtan Station, and a distance-interval interaction model was applied to examine the spatial distribution of the opening effect. The results show a statistically significant increase in real apartment prices near the station following the partial opening, with the estimated coefficients declining as distance from the station increased. These results suggest that accessibility gains are capitalized into housing prices even before full-line completion, offering implications for station-area apartment price policy.",0.0,,False,JournalArticle,,2026-08-31,d6e394b39690559fcf56412ae424ec49aa67e0ed,thespatialimpactofthegtxapartialopeningonapartmentpricesthecaseofdongtannewtown,seo,10.22423/kreus.2026.19.1.25,thespatialimpactofthegtxapartialopeningonapartmentpricesthecaseofdongtannewtown|2026,1,1858,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d894812a17afa5a597e4f95bfc6698868e5eff78,10.36095/banxico/di.2024.18,,Housing Price Gradients in Mexico City During the COVID-19 Pandemic,2024.0,,D. Mayorga; Karla Hernández; Jorge Pérez Pérez,"We estimate the slope of the housing price gradient -the relationship between prices and distance to the center or employment density- in Mexico City, using administrative and survey data on housing prices in from 2019 to 2022. Our estimates rule out statistically significant changes in the slope of the housing price gradient after 2020 Q1 with respect to the pre-pandemic period. The Mexico City case contrasts with developed-country cities, where the housing price gradient flattened during the COVID-19 pandemic, with housing price increases in the suburbs relative to central areas. Possible mechanisms behind the lack of gradient change in Mexico city could include differences in pandemic restrictions relative to other countries, a reduced potential for remote work, lack of access to finance housing purchases, and housing supply shocks.",0.0,,False,CaseReport;Review,,2024-12-01,d894812a17afa5a597e4f95bfc6698868e5eff78,housingpricegradientsinmexicocityduringthecovid19pandemic,mayorga,10.36095/banxico/di.2024.18,housingpricegradientsinmexicocityduringthecovid19pandemic|2024,1,1847,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5e3046a4a44e5bb73de057b26db5200058055700,10.1109/icuems50872.2020.00042,,"How does Air Quality Affect Housing Price? – Evidence from Meishan, Sichuan Province",2020.0,2020 International Conference on Urban Engineering and Management Science (ICUEMS),Xue-Dong Liang; Yi Liu; Xing-Jiang Li,"Air pollution has become a major problem in China, and the impact of air quality on housing price has become more and more serious. By adopting the commodity house data of districts and counties in Meishan in 2016, this paper uses the “characteristic price model” to obtain the marginal willingness to pay (MWTP) of the consumer for buying houses under the improved air quality. The research shows that when the air quality index decreases drops by one unit, consumers are willing to pay an additional 494.9648 yuan for the improvement of air quality. Taking into consideration the different consumption levels of buyers, the marginal willingness payment level of buyers which corresponds to the price of each quantile house are obtained through the quantile regression model.",0.0,,False,Conference,Environmental Science,2020-04-01,5e3046a4a44e5bb73de057b26db5200058055700,howdoesairqualityaffecthousingpriceevidencefrommeishansichuanprovince,liang,10.1109/icuems50872.2020.00042,howdoesairqualityaffecthousingpriceevidencefrommeishansichuanprovince|2020,1,1776,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.36113/rec.v5i2.2659,,INFLUÊNCIA DAS AMENIDADES NOS PREÇOS DE IMÓVEIS,2020.0,Reflexões Econômicas,Luiz Pedro Couto Santos Silva; José Ricardo De Santana,"Esse estudo tem como objetivo investigar os principais aspectos responsáveis pela valoração de imóveis dos bairros Jardins e Grageru, localizados na zona sul de Aracaju, entre os anos de 2005 e 2015. Para uma análise empírica dessa formação de preços, foram utilizados anúncios de venda feitos para esses imóveis, através do jornal impresso Cinform, permitindo assim, um banco de dados capaz de gerar modelos econométricos, utilizando a técnica de preços hedônicos. Como resultado da análise, torna-se possível uma quantificação dos valores pagos por características estruturais dos mesmos, além da influência de fatores externos, como o comportamento do ambiente econômico do período e compreensão da influência do papel das amenidades locais na valorização dos imóveis.",0.0,,,journal-article,,,doi_10_36113_rec_v5i2_2659,influenciadasamenidadesnosprecosdeimoveis,silva,10.36113/rec.v5i2.2659,influenciadasamenidadesnosprecosdeimoveis|2020,1,1771,1,preços hedônicos imóveis,1,0,1,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",10bcfa0101f43980801fe9105a3b4f596fb5a6b0,10.1080/00036846.2025.2499744,,Stable expectations and housing prices: quasi-experimental evidence from the promulgation of China’s Civil Code,2025.0,Applied Economics,Zhen Xu; Xian Zheng; Xiaoyuan Zou,"ABSTRACT This study exploits the promulgation of China’s Civil Code as a quasi-experiment to examine how stable expectations influence housing prices. Using a regression discontinuity-in-time approach on detailed transaction data (May 2017–December 2022), we document a significant price appreciation of roughly 2.74% for residential properties post-policy. These findings hold under various robustness checks. We also observe a marked decline in time on the market, indicating enhanced liquidity. Further analyses reveal that older resale units benefit disproportionately from improved market confidence, mainly through reduced liquidity risk. Overall, the evidence highlights the crucial role of stable expectations and liquidity in shaping housing values.",0.0,,False,JournalArticle,,2025-04-30,10bcfa0101f43980801fe9105a3b4f596fb5a6b0,stableexpectationsandhousingpricesquasiexperimentalevidencefromthepromulgationofchinascivilcode,xu,10.1080/00036846.2025.2499744,stableexpectationsandhousingpricesquasiexperimentalevidencefromthepromulgationofchinascivilcode|2025,1,1758,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4fbee1cd63ad04297da344f6c6135fbe06cb9c88,10.1111/1540-6229.70011,,National Football League franchise departures and nearby home prices,2025.0,Real Estate Economics,Brad R. Humphreys; Geoffrey Propheter,"Research shows that US professional sports facilities positively impact nearby house prices. New facilities typically have existing teams, complicating the separate assessment of the impact of teams and facilities. Existing research analyzed departures of the National Basketball Association (NBA) teams, but arenas host many nonsporting events. These results may not generalize. We exploit the relocation of two National Football League (NFL) teams to investigate the impact of team departures on home sales. Difference‐in‐differences and repeat‐sales models indicate that proximity to one team is consistent with it being housing amenity while proximity to the other is not. Differences in urban form may be a key explanation for this heterogeneity.",0.0,,False,JournalArticle,,2025-09-28,4fbee1cd63ad04297da344f6c6135fbe06cb9c88,nationalfootballleaguefranchisedeparturesandnearbyhomeprices,humphreys,10.1111/1540-6229.70011,nationalfootballleaguefranchisedeparturesandnearbyhomeprices|2025,1,1751,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d85c0567b113878d0abab319e4fc0c1c654a301e,10.1111/1540-6229.70066,,Targeted tax relief and house prices: New evidence of capitalization,2026.0,Real Estate Economics,Patrick S. Smith; Mark Thibodeau," Florida's property tax system caps annual assessed value growth and allows homeowners to transfer accumulated tax benefits between homesteads. These features create cross‐sectional variation in buyers' tax liabilities, allowing us to test whether house prices capitalize property tax relief while holding local amenities, public goods, and neighborhood characteristics constant. Using three complementary measures of property tax relief, we find consistent evidence that expected tax savings from the accumulation and transferability of these benefits are capitalized into transaction prices. These results suggest that policies aimed at reducing ongoing ownership costs may unintentionally worsen housing affordability by inflating house prices.",0.0,,False,JournalArticle,,2026-08-20,d85c0567b113878d0abab319e4fc0c1c654a301e,targetedtaxreliefandhousepricesnewevidenceofcapitalization,smith,10.1111/1540-6229.70066,targetedtaxreliefandhousepricesnewevidenceofcapitalization|2026,1,1748,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,4,3,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.3895/rbpd.v11n2.13827,,Uma aplicação do método de preços hedônico no mercado imobiliário em duas zonas urbanas do oeste paranaense,2022.0,Revista Brasileira de Planejamento e Desenvolvimento,Diogo Lopes Cavalcante; Carlos Alberto Piacenti; Marcílio Lima de Oliveira,"Este artigo utiliza-se da Teoria dos Atributos proposta por Lancaster e do método de preço hedônico proposto por Rosen, a partir dos quais é possível analisar a importância relativa de grupo de atributos intrínsecos as residências anunciadas para venda no mercado imobiliário, em função de um bem ambiental em quatro regiões urbanas não contíguas. Para tanto, utilizou-se técnicas de estatística multivariada pelo método dos componentes principais cujos fatores extraídos permitem identificar e comparar a importância relativa de cada na formação dos preços dos imóveis. Os resultados demonstraram que as variáveis socioeconômicas, ambientais e localização, sobressaíram na elevação do preço das habitações.",0.0,,,journal-article,,,doi_10_3895_rbpd_v11n2_13827,umaaplicacaodometododeprecoshedoniconomercadoimobiliarioemduaszonasurbanasdooesteparanaense,cavalcante,10.3895/rbpd.v11n2.13827,umaaplicacaodometododeprecoshedoniconomercadoimobiliarioemduaszonasurbanasdooesteparanaense|2022,1,1707,1,preços hedônicos imóveis,1,0,1,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",70b7f297b80806d606892bccc590dd3b2cac2409,10.3368/le.102.4.040325-0023r1,,We Love Green,2026.0,Land Economics,Marine Coinon; Rachel Guillain; S. Legras,"Abstract Agricultural landscapes generate a complex bundle of positive and negative local externalities. This article estimates the impact of the type of farming (organic versus conventional) on residential property prices in France, using an exhaustive database of 2.9 million housing transactions merged with geolocalized land use data, and a dual identification strategy (buffer and distance-based). The results establish a clear hierarchy of preferences. At short distances, proximity to conventional agriculture is a significant baseline disamenity and organic farming is a mitigating factor. This premium increases with the extent of the organic landscape and is most pronounced in periurban areas.",0.0,,False,JournalArticle,,2026-04-13,70b7f297b80806d606892bccc590dd3b2cac2409,welovegreen,coinon,10.3368/le.102.4.040325-0023r1,welovegreen|2026,1,1704,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,3,1,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",aef3c35f56b14d8ef4f2a8231afce1eeaa36a403,10.2139/ssrn.4280776,,Housing Wealth and Overpayment: When Money Moves In,2022.0,Social Science Research Network,Darren Aiello; Jason D. Kotter; G. Schubert,"We construct a novel dataset tracking households across property purchases covering 25 years of moves within the U.S. We find that information frictions in residential real estate markets cause movers with larger exogenous housing wealth to overpay for their next house, relative to both time varying local prices as well as time invariant characteristics of the property itself. These housing wealth driven overpayments are associated with larger positive price impacts to the immediately surrounding neighborhood and are larger for local movers relative to non-local movers. The aggregate effect of housing wealth inflows is to increase commuting-zone-level house price growth.",0.0,,False,,,,aef3c35f56b14d8ef4f2a8231afce1eeaa36a403,housingwealthandoverpaymentwhenmoneymovesin,aiello,10.2139/ssrn.4280776,housingwealthandoverpaymentwhenmoneymovesin|2022,1,1679,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e40c154da2987eaf6612ca0abf0e9ff60c9af555,10.1109/iccrd54409.2022.9730362,,Factors Affect the House Price,2022.0,International Conference on Computer Research and Development,Minhong Shao,"The object of this paper is to evaluate whether house age and the distance between a house and the nearest metro station could affect housing price, within a given area. A sample of 414 houses in New Taipei City, Taiwan has been selected. Multiple linear regression has revealed a linear relationship between house price, house age, and distance to a metro station. However, scatter plots suggest that the linear regression model might not be the best model to explain the relationships between house price and the two independent variables because I use several scatter plots which show that quadratic models are much better to explain the relationships than linear models.",0.0,,False,Conference,,2022-01-07,e40c154da2987eaf6612ca0abf0e9ff60c9af555,factorsaffectthehouseprice,shao,10.1109/iccrd54409.2022.9730362,factorsaffectthehouseprice|2022,1,1674,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",826cbd53f328d497d0fcae54a0b290ebff89e9bf,10.3368/le.102.2.112724-0114r1,,Fires in the Built Environment and Residential Market Demand,2025.0,Land Economics,Jihwan Kim; Jayash Paudel; Hanbyul Ryu,"Abstract Using nationwide data on multifamily residential transactions in South Korea, we investigate how large-scale fires in the built environment affect housing markets. We make use of geospatial data and apply a difference-in-differences research design to show that fires in an apartment complex cause an approximately 9% decline in housing prices over a one- to two-month period. In contrast, fires that occur slightly farther away or in nonresidential buildings have no significant effect. Text mining analysis further suggests that greater media coverage—often associated with fires in nonresidential structures—does not necessarily result in larger price declines.",0.0,,False,JournalArticle,,2025-11-17,826cbd53f328d497d0fcae54a0b290ebff89e9bf,firesinthebuiltenvironmentandresidentialmarketdemand,kim,10.3368/le.102.2.112724-0114r1,firesinthebuiltenvironmentandresidentialmarketdemand|2025,1,1673,1,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,1,4,2,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9fefd0dc1e6bfd44d77914decc4a6c4fb82c639f,10.21005/pif.2025.64.c-05,,THE IMPACT OF CARDINAL AND ORDINAL DIRECTION EXPOSURE AND STRUCTURAL ATTRIBUTES ON PRICES IN THE PRIMARY HOUSING MARKET IN WARSAW,2025.0,Space&FORM,Hubert Horynek,"First aim of the research was to determine which structural attributes have the greatest impact on housing prices. The second was to determine to what extent the exposure of windows to specific cardinal and ordinal directions affects prices. Structural attributes clearly affect offer prices. The exposure of windows to specific directions is an additional, significant factor. The better the access to sunlight, the higher the prices per square meter. Two-sided flats provide little information as north exposure reduces the prices so much that south exposure correlates with relatively low prices. Corner flats analysis has much greater explanatory power.",0.0,,False,JournalArticle,,2025-12-03,9fefd0dc1e6bfd44d77914decc4a6c4fb82c639f,theimpactofcardinalandordinaldirectionexposureandstructuralattributesonpricesintheprimaryhousingmarketinwarsaw,horynek,10.21005/pif.2025.64.c-05,theimpactofcardinalandordinaldirectionexposureandstructuralattributesonpricesintheprimaryhousingmarketinwarsaw|2025,1,1657,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,preços hedônicos imóveis,,10.6008/cbpc2237-9290.2019.002.0004,,Externalidades positivas geradas pelo parque olhos d’água ao mercado imobiliário de Brasília (DF) por meio de índices hedônicos,2019.0,Natural Resources,Angelo de Sousa Santarlacci; Jorge Madeira Nogueira; Ildeu Soares Martins; Maísa Santos Joaquim; Ãlvaro Nogueira de Souza,"O estudo estimou os benefícios econômicos gerados ao mercado imobiliário em Brasília (DF) pela implementação do Parque Olhos D’Água, por meio de índices de preços hedônicos. Demonstrou-se a importância econômica de parques urbanos na valorização imobiliária e também a disposição do consumidor em pagar mais por imóveis situados próximos ao parque. Também foi observado que os benefícios econômicos gerados foram maiores que o custo de implantação do parque urbano, o que apoia a estruturação de mecanismos públicos para a criação, conservação, implantação e manutenção de áreas verdes urbanas.",0.0,,,journal-article,,,doi_10_6008_cbpc2237_9290_2019_002_0004,externalidadespositivasgeradaspeloparqueolhosdaguaaomercadoimobiliariodebrasiliadfpormeiodeindiceshedonicos,santarlacci,10.6008/cbpc2237-9290.2019.002.0004,externalidadespositivasgeradaspeloparqueolhosdaguaaomercadoimobiliariodebrasiliadfpormeiodeindiceshedonicos|2019,1,1594,1,preços hedônicos imóveis,1,0,1,0,0,4,True,candidate,False,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.3130/jaabe.9.71,,An Hedonic Valuation of Landmark Factors on Super High-Rise Residential Building Prices in Seoul (Republic of Korea),2010.0,Journal of Asian Architecture and Building Engineering,Woncheol Choy; Sanghyo Lee; Yoonsun Lee; Jaejun Kim,,0.0,,,journal-article,,,doi_10_3130_jaabe_9_71,anhedonicvaluationoflandmarkfactorsonsuperhighriseresidentialbuildingpricesinseoulrepublicofkorea,choy,10.3130/jaabe.9.71,anhedonicvaluationoflandmarkfactorsonsuperhighriseresidentialbuildingpricesinseoulrepublicofkorea|2010,1,1000,1,apartment rent hedonic,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,apartment rent hedonic,,10.4197/eco.2-1.3,,The Determinants of Dwelling Rent: A Case Study to the City of Jeddah التحليل الاقتصادي لإيجار الوحدات السكنية في مدينة جدة « دراسة ميدانية »,1989.0,Journal of King Abdulaziz University-Economics and Administration,ABDULAZIZ DIYAB,,0.0,,,journal-article,,,doi_10_4197_eco_2_1_3,thedeterminantsofdwellingrentacasestudytothecityofjeddah,diyab,10.4197/eco.2-1.3,thedeterminantsofdwellingrentacasestudytothecityofjeddah|1989,1,1000,1,apartment rent hedonic,0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 crossref,house prices proximity hedonic,,10.1061/(asce)0733-9488(1999)125:1(55),,Forecasting House Rental Levels: Analytical Rent Model versus Neural Network,1999.0,Journal of Urban Planning and Development,Arun Kumar; Ankur Sinha; Nitin Tomar; Atanu Adhikari,,0.0,,,journal-article,,,doi_10_1061__asce_0733_9488_1999_125_1_55_,forecastinghouserentallevelsanalyticalrentmodelversusneuralnetwork,kumar,10.1061/(asce)0733-9488(1999)125:1(55),forecastinghouserentallevelsanalyticalrentmodelversusneuralnetwork|1999,1,1000,2,apartment rent hedonic || house prices proximity hedonic,0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),e82c2ab651388045005bcb5235ffd2e84d85bb63,10.15396/eres2012_354,,The impact of earthquake risk on the spatial pattern of house prices: A multi-level analysis,2012.0,,Berna Keskin; Craig Watkins,,0.0,,False,,Geography,,e82c2ab651388045005bcb5235ffd2e84d85bb63,theimpactofearthquakeriskonthespatialpatternofhousepricesamultilevelanalysis,keskin,10.15396/eres2012_354,theimpactofearthquakeriskonthespatialpatternofhousepricesamultilevelanalysis|2012,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),d9c72b955f4aff856407f035e77977c713586448,10.15396/eres2010_399,,DO PEER EFFECTS SHAPE RESIDENTIAL VALUES? RECONCILING THE SALES COMPARISON APPROACH WITH HEDONIC PRICE MODELLING,2010.0,,F. D. Rosiers; Jean Dubé; M. Thériault,,0.0,,False,,Economics,,d9c72b955f4aff856407f035e77977c713586448,dopeereffectsshaperesidentialvaluesreconcilingthesalescomparisonapproachwithhedonicpricemodelling,rosiers,10.15396/eres2010_399,dopeereffectsshaperesidentialvaluesreconcilingthesalescomparisonapproachwithhedonicpricemodelling|2010,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || residential property price determinants || spatial hedonic model housing",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),b53531340f1f4ae81cd50baf5340de1c92eee9ea,10.15396/eres2015_289,,Estimation Of Hedonic House Pricing Model Through Non-Parametric Methods: Istanbul Real Estate Market,2015.0,,Sinem Guler Kangalli Uyar; N. Yayla; Bulent Guloglu,,0.0,,False,,Economics,2015-07-01,b53531340f1f4ae81cd50baf5340de1c92eee9ea,estimationofhedonichousepricingmodelthroughnonparametricmethodsistanbulrealestatemarket,uyar,10.15396/eres2015_289,estimationofhedonichousepricingmodelthroughnonparametricmethodsistanbulrealestatemarket|2015,1,1000,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || real estate hedonic regression || spatial hedonic model housing",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),11cb2667a75c059912f664fcf01a876a95d1b96b,10.1007/s11146-014-9456-1,,Determinants of Housing Prices in Hong Kong: A Box-Cox Quantile Regression Approach,2014.0,Journal of real estate finance and economics,Hyung-Gun Kim; K. Hung; Sung Y. Park,,0.0,,False,,,2014-03-05,11cb2667a75c059912f664fcf01a876a95d1b96b,determinantsofhousingpricesinhongkongaboxcoxquantileregressionapproach,kim,10.1007/s11146-014-9456-1,determinantsofhousingpricesinhongkongaboxcoxquantileregressionapproach|2014,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic || real estate hedonic regression",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),82ae71644210560096f2f0cf039cad6650423f44,10.1109/iceice.2012.1220,,Housing Project Hedonic Price Model of Qingdao Based on Principal Components Analysis,2012.0,,Lixin Zhang; Xin Li; Jie Wang,,0.0,,False,,Economics,2012-04-06,82ae71644210560096f2f0cf039cad6650423f44,housingprojecthedonicpricemodelofqingdaobasedonprincipalcomponentsanalysis,zhang,10.1109/iceice.2012.1220,housingprojecthedonicpricemodelofqingdaobasedonprincipalcomponentsanalysis|2012,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),6d0ef173a8d3d26b2a35fb7ef9a9adc927ce1bfd,10.4156/jcit.vol8.issue5.131,,Computer Assisted Mass Appraisal of Second-Hand Houses in China Based on Hedonic Price Model and Trading Cases,2013.0,,Dong-Jin Yu; Jianlei Xie; Wei Yang; Wei Wu,,0.0,,False,,Economics,2013-03-15,6d0ef173a8d3d26b2a35fb7ef9a9adc927ce1bfd,computerassistedmassappraisalofsecondhandhousesinchinabasedonhedonicpricemodelandtradingcases,yu,10.4156/jcit.vol8.issue5.131,computerassistedmassappraisalofsecondhandhousesinchinabasedonhedonicpricemodelandtradingcases|2013,1,1000,4,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic property valuation",2,0,2,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),362e4bb8a07e6bdf2e4aea7c58cfa515c5a53561,10.15396/eres2012_083,,The incidence of characteristics in housing prices and offer prices,2012.0,,E. Fregonara; P. Semeraro,,0.0,,False,,Economics,,362e4bb8a07e6bdf2e4aea7c58cfa515c5a53561,theincidenceofcharacteristicsinhousingpricesandofferprices,fregonara,10.15396/eres2012_083,theincidenceofcharacteristicsinhousingpricesandofferprices|2012,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,a812ae7903db53458b6a6770ab798efffe9f0b70,10.15396/eres2014_126,,Assessing the impact of public amenities on house prices in the Greek islands,2014.0,,Dimitra Kavarnou; Anupam Nanda,,0.0,,False,,Economics,,a812ae7903db53458b6a6770ab798efffe9f0b70,assessingtheimpactofpublicamenitiesonhousepricesinthegreekislands,kavarnou,10.15396/eres2014_126,assessingtheimpactofpublicamenitiesonhousepricesinthegreekislands|2014,1,1000,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || hedonic price model amenities residential || housing characteristics prices hedonic || residential property price determinants",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic price model amenities residential,581195405bf37ba6e1c94668e2456e9822faafd8,10.1177/0734242x251360560,,Living near dumpsites: A machine learning and econometric assessment of how environmental conditions shape rental prices,2025.0,Waste Management Research,Cosimo Magazzino; Tanzila Akmal; Tulia Gattone,,0.0,,False,JournalArticle,Medicine,2025-09-09,581195405bf37ba6e1c94668e2456e9822faafd8,livingneardumpsitesamachinelearningandeconometricassessmentofhowenvironmentalconditionsshaperentalprices,magazzino,10.1177/0734242x251360560,livingneardumpsitesamachinelearningandeconometricassessmentofhowenvironmentalconditionsshaperentalprices|2025,1,1000,3,hedonic housing prices || hedonic price model amenities residential || machine learning hedonic pricing housing,0,2,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,rent hedonic regression housing,dd86149e74ddbdb565232bf65020c9f1acc0b54a,10.15396/eres2017_373,,Determinants of rents in the French private rental housing sector,2017.0,,Camille Grivault; Ingrid Nappi-Choulet; J. le Gallo; Marie-Laure Breuillé; Kassoum Ayouba,,0.0,,False,,Business,2017-07-01,dd86149e74ddbdb565232bf65020c9f1acc0b54a,determinantsofrentsinthefrenchprivaterentalhousingsector,grivault,10.15396/eres2017_373,determinantsofrentsinthefrenchprivaterentalhousingsector|2017,1,1000,2,rent hedonic regression housing || spatial hedonic model housing,0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",cb7f14661805a7e83f0caf54a5354b084037e904,10.15396/eres2016_107,,Energy Capitalization in the Dutch Rental Market,2016.0,,Dorothee Hillrichs; E. Aydın; D. Brounen,,0.0,,False,,Business,,cb7f14661805a7e83f0caf54a5354b084037e904,energycapitalizationinthedutchrentalmarket,hillrichs,10.15396/eres2016_107,energycapitalizationinthedutchrentalmarket|2016,1,1000,2,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",613bb548ec32e59ff97057cc62a9f793acba62fd,10.1007/s10901-014-9421-6,,The influence of urban and natural amenities on second home prices,2014.0,Journal of Housing and the Built Environment,Pia Nilsson,,0.0,,False,,,2014-09-10,613bb548ec32e59ff97057cc62a9f793acba62fd,theinfluenceofurbanandnaturalamenitiesonsecondhomeprices,nilsson,10.1007/s10901-014-9421-6,theinfluenceofurbanandnaturalamenitiesonsecondhomeprices|2014,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",61f3be3ac1097e97c06dd8fc0da58b70f2bc40cc,10.22423/kreus.2021.14.1.89,,The Impact of Station Attributes on Housing Prices in Seoul Metropolitan Region,2021.0,Konkuk Research Institute of Real Estate and Urban Studies,S. Bae; Jae-won Lee,,0.0,,False,,Geography,2021-08-31,61f3be3ac1097e97c06dd8fc0da58b70f2bc40cc,theimpactofstationattributesonhousingpricesinseoulmetropolitanregion,bae,10.22423/kreus.2021.14.1.89,theimpactofstationattributesonhousingpricesinseoulmetropolitanregion|2021,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",700d36012270dbeae3c14f6a53db2c23dedeb41a,10.1061/9780784485910.128,,The Impact of Shopping Mall Attributes and Social Media Dynamics on Residential Property Prices in Shanghai,2025.0,ICCREM 2024,Yi-Ming He,,0.0,,False,,,2025-03-26,700d36012270dbeae3c14f6a53db2c23dedeb41a,theimpactofshoppingmallattributesandsocialmediadynamicsonresidentialpropertypricesinshanghai,he,10.1061/9780784485910.128,theimpactofshoppingmallattributesandsocialmediadynamicsonresidentialpropertypricesinshanghai|2025,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",7e2862e10e9483d76eea88d53849c36600d4d5cc,10.1007/s12061-026-10008-z,,Identifying Urban Centers and Modeling Nonlinear Housing Price Gradients in a Polycentric Metropolitan Region: Geographically Weighted Regression and Tree-Based Approaches in the Seoul Metropolitan Area,2026.0,Applied spatial analysis and policy,Changhyo Yi; Seongho Kim; Byung-hun Yun,,0.0,,False,JournalArticle,,2026-09-01,7e2862e10e9483d76eea88d53849c36600d4d5cc,identifyingurbancentersandmodelingnonlinearhousingpricegradientsinapolycentricmetropolitanregiongeographicallyweightedregressionandtreebasedapproachesintheseoulmetropolitanarea,yi,10.1007/s12061-026-10008-z,identifyingurbancentersandmodelingnonlinearhousingpricegradientsinapolycentricmetropolitanregiongeographicallyweightedregressionandtreebasedapproachesintheseoulmetropolitanarea|2026,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",48fe142597e1e95bb862d52f7402f47d07607407,10.15396/eres2024-202,,The Central Business District Proximity Effect on Housing Prices: a Bucharest Case Study,2024.0,30th Annual European Real Estate Society Conference,C. Ciora; E. Ionașcu; I. Anghel,,0.0,,False,Conference,,,48fe142597e1e95bb862d52f7402f47d07607407,thecentralbusinessdistrictproximityeffectonhousingpricesabucharestcasestudy,ciora,10.15396/eres2024-202,thecentralbusinessdistrictproximityeffectonhousingpricesabucharestcasestudy|2024,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",baa7e49f00a98b4f9127b7bfab6df0234a98601f,10.11648/j.ijebo.20160406.12,,Research on the Capitalization Effect of Local Public Goods in House Price and Its Spatial Heterogeneity in Zhengzhou,2017.0,,Xueying Zong; Ling-Yun Shi,,0.0,,False,,Business,2017-03-10,baa7e49f00a98b4f9127b7bfab6df0234a98601f,researchonthecapitalizationeffectoflocalpublicgoodsinhousepriceanditsspatialheterogeneityinzhengzhou,zong,10.11648/j.ijebo.20160406.12,researchonthecapitalizationeffectoflocalpublicgoodsinhousepriceanditsspatialheterogeneityinzhengzhou|2017,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",42319d65a54991a13578f82bb92daf6ba3940daf,10.15396/eres2018_108,,Public transport proximity impact on property value. Evidences from Bucharest residential market,2018.0,,S. V. Gomes; C. Ciora; I. Anghel,,0.0,,False,Review,Business,,42319d65a54991a13578f82bb92daf6ba3940daf,publictransportproximityimpactonpropertyvalueevidencesfrombucharestresidentialmarket,gomes,10.15396/eres2018_108,publictransportproximityimpactonpropertyvalueevidencesfrombucharestresidentialmarket|2018,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",c39dd07aae77bd11a2176291e209c989ea85ee55,10.1007/s12061-026-09990-1,,"The Impact of Urban Renewal on Neighborhood Housing Prices: A Spatial Effects and Differential Analysis in Nanjing City, China",2026.0,Applied spatial analysis and policy,Li-Fang Zhang; Jia-Jia Liao; Ying Li; Pin-Hua Jin; Shu-Qi Li; Ye-Ting Fan,,0.0,,False,JournalArticle,,2026-08-25,c39dd07aae77bd11a2176291e209c989ea85ee55,theimpactofurbanrenewalonneighborhoodhousingpricesaspatialeffectsanddifferentialanalysisinnanjingcitychina,zhang,10.1007/s12061-026-09990-1,theimpactofurbanrenewalonneighborhoodhousingpricesaspatialeffectsanddifferentialanalysisinnanjingcitychina|2026,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",0,2,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",059849a9595ee14ed145e6ac3f6b193ab874bdfe,10.15396/eres2012_254,,Building age as a factor affecting spatial autocorrelation in housing prices,2012.0,,D. Lo,,0.0,,False,,Economics,,059849a9595ee14ed145e6ac3f6b193ab874bdfe,buildingageasafactoraffectingspatialautocorrelationinhousingprices,lo,10.15396/eres2012_254,buildingageasafactoraffectingspatialautocorrelationinhousingprices|2012,1,1000,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",546d2806dd7a1be9c8f72a0e51edeaf9e59fab5a,10.19172/kreaa.31.1.3,,Housing Price Determinants and Forecasts: The Case of Jeonbuk State Apartments,2025.0,Journal of the Korea Real Estate Analysts Association,Seulki Lee; Young Min Kim; W. Chung,,0.0,,False,JournalArticle,,2025-03-31,546d2806dd7a1be9c8f72a0e51edeaf9e59fab5a,housingpricedeterminantsandforecaststhecaseofjeonbukstateapartments,lee,10.19172/kreaa.31.1.3,housingpricedeterminantsandforecaststhecaseofjeonbukstateapartments|2025,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",599145ce04ff673c626c1591f97036cf7ed24689,10.12677/mm.2022.128128,,A Study on the Characteristics and Influencing Factors of Housing Price Spatial Differentiation Based on POI Data—A Case Study of Chengdu City,2022.0,Modern Management,Yu-Run Fan,,0.0,,False,,,,599145ce04ff673c626c1591f97036cf7ed24689,astudyonthecharacteristicsandinfluencingfactorsofhousingpricespatialdifferentiationbasedonpoidataacasestudyofchengducity,fan,10.12677/mm.2022.128128,astudyonthecharacteristicsandinfluencingfactorsofhousingpricespatialdifferentiationbasedonpoidataacasestudyofchengducity|2022,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",3ba6cf1bc1886f1dff53cfa2287537ccca8e624b,10.15396/eres2015_246,,A study of English house price data with spatial dependence,2015.0,,I. Nase,,0.0,,False,,Geography,2015-07-01,3ba6cf1bc1886f1dff53cfa2287537ccca8e624b,astudyofenglishhousepricedatawithspatialdependence,nase,10.15396/eres2015_246,astudyofenglishhousepricedatawithspatialdependence|2015,1,1000,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",3d81da232b1ab1fef90241f7ad6ca7b2ce21eba9,10.1007/978-3-662-46994-1_72,,Distribution and Determinants of Housing Prices in Polycentric Mountainous Cities: The Case of Chongqing,2015.0,,Meng-Wei Wang; Yong Liu; Xiu-Hua Liu,,0.0,,False,,Geography,,3d81da232b1ab1fef90241f7ad6ca7b2ce21eba9,distributionanddeterminantsofhousingpricesinpolycentricmountainouscitiesthecaseofchongqing,wang,10.1007/978-3-662-46994-1_72,distributionanddeterminantsofhousingpricesinpolycentricmountainouscitiesthecaseofchongqing|2015,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",24d668bf44a16a357d92a9d091c726e3069fa2d3,10.5593/sgemsocial2017/52/s19.025,,PREDICTING HOUSE PRICES USING BOTH GEOGRAPHICALLY WEIGHTED REGRESSION AND KRIGING METHODS,2017.0,,A. Timofeeva,,0.0,,False,,Geography,2017-08-20,24d668bf44a16a357d92a9d091c726e3069fa2d3,predictinghousepricesusingbothgeographicallyweightedregressionandkrigingmethods,timofeeva,10.5593/sgemsocial2017/52/s19.025,predictinghousepricesusingbothgeographicallyweightedregressionandkrigingmethods|2017,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",2dff1bea50f2d99c79992fa392afb781fea68824,10.22645/udi.2020.6.30.033,,DETERMINANTS OF APARTMENT RENTS IN THE SEOUL METROPOLITAN REGION: SPATIAL ECONOMETRIC APPROACHES,2020.0,,Dong-Sung Lee,,0.0,,False,,Economics,,2dff1bea50f2d99c79992fa392afb781fea68824,determinantsofapartmentrentsintheseoulmetropolitanregionspatialeconometricapproaches,lee,10.22645/udi.2020.6.30.033,determinantsofapartmentrentsintheseoulmetropolitanregionspatialeconometricapproaches|2020,1,1000,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cd23d14ced11ec6963003d0c3964f936f8ca5104,10.21447/jup.2020.11.3.109,,Analysis of Determinants and Validation on Standard Official Housing Price in Seoul,2020.0,The Korean Association of Urban Policies,S. Jeong; Hoon-Gill Lee; Young-Min Lee; Joo-ah Lee,,0.0,,False,,,2020-12-31,cd23d14ced11ec6963003d0c3964f936f8ca5104,analysisofdeterminantsandvalidationonstandardofficialhousingpriceinseoul,jeong,10.21447/jup.2020.11.3.109,analysisofdeterminantsandvalidationonstandardofficialhousingpriceinseoul|2020,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cb3624e149a1715fdd002911814ec1d7028b48e4,10.36700/kruma.2020.3.33.1.93,,The Effect of Distance to Coastal Space by Type on Housing Price: Focused on Incheon,2020.0,,Jong-Woo Lee; Ah-Hyeon Bae; Sungjo Hong,,0.0,,False,,Geography,2020-03-31,cb3624e149a1715fdd002911814ec1d7028b48e4,theeffectofdistancetocoastalspacebytypeonhousingpricefocusedonincheon,lee,10.36700/kruma.2020.3.33.1.93,theeffectofdistancetocoastalspacebytypeonhousingpricefocusedonincheon|2020,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c49b3dbdfe0fb3be46d27f6b4e389aac3ceb4901,10.19172/kreaa.27.1.3,,Impact Analysis of Housing Price Determinants in Redevelopment Area,2021.0,,Jae-won Lee; Sangyoub Lee,,0.0,,False,,Economics,2021-03-31,c49b3dbdfe0fb3be46d27f6b4e389aac3ceb4901,impactanalysisofhousingpricedeterminantsinredevelopmentarea,lee,10.19172/kreaa.27.1.3,impactanalysisofhousingpricedeterminantsinredevelopmentarea|2021,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",76f5a7ef14702b2e333ffa2278732fcef815ebcd,10.1061/9780784482308.087,,Spatial Characteristics and Influencing Mechanism of Housing Price in Polycentric Mountainous City: A Case Study of Chongqing Central City,2019.0,ICCREM 2019,Chao Mao; Lin-Lin Zhang,,0.0,,False,,Geography,2019-08-29,76f5a7ef14702b2e333ffa2278732fcef815ebcd,spatialcharacteristicsandinfluencingmechanismofhousingpriceinpolycentricmountainouscityacasestudyofchongqingcentralcity,mao,10.1061/9780784482308.087,spatialcharacteristicsandinfluencingmechanismofhousingpriceinpolycentricmountainouscityacasestudyofchongqingcentralcity|2019,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7a63114e45c19f9ddb619d57022a27ff38405c50,10.22423/kreus.2022.14.2.83,,Variation of Apartment Housing Price Affected by the Line Attributes of Seoul Metropolitan Urban Railway System,2022.0,Konkuk Research Institute of Real Estate and Urban Studies,S. Bae; Jae-won Lee,,0.0,,False,,,2022-02-28,7a63114e45c19f9ddb619d57022a27ff38405c50,variationofapartmenthousingpriceaffectedbythelineattributesofseoulmetropolitanurbanrailwaysystem,bae,10.22423/kreus.2022.14.2.83,variationofapartmenthousingpriceaffectedbythelineattributesofseoulmetropolitanurbanrailwaysystem|2022,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e78632e0b0989b741040b49c199b7db131ff956d,10.15396/eres2013_62,,Measuring Determinants of House Prices: Listing Behaviour in Italian Real Estate Market,2013.0,,E. Fregonara; P. Semeraro,,0.0,,False,,Business,,e78632e0b0989b741040b49c199b7db131ff956d,measuringdeterminantsofhousepriceslistingbehaviourinitalianrealestatemarket,fregonara,10.15396/eres2013_62,measuringdeterminantsofhousepriceslistingbehaviourinitalianrealestatemarket|2013,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,apartment prices hedonic,ace0c9fd9b86b90269f9eb81e7d56b54d9e296ee,10.5659/jaik_pd.2014.30.11.159,,"The Effects of Apartment Complex Characteristics on Housing Value - Focused on Apartment complexes in Suseong-gu, Deagu City -",2014.0,,Keonggu Hong,,0.0,,False,,Business,2014-11-30,ace0c9fd9b86b90269f9eb81e7d56b54d9e296ee,theeffectsofapartmentcomplexcharacteristicsonhousingvaluefocusedonapartmentcomplexesinsuseonggudeagucity,hong,10.5659/jaik_pd.2014.30.11.159,theeffectsofapartmentcomplexcharacteristicsonhousingvaluefocusedonapartmentcomplexesinsuseonggudeagucity|2014,1,1000,3,apartment prices hedonic || hedonic housing prices || housing characteristics prices hedonic,0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,ed3434d792d9465e0c8695d6fb35a87d4f5d5c75,10.12815/kits.2023.22.1.39,,Estimation of Economic Value of Public Housing Parking Lot : Focusing on the Hedonic Price Approach in the Case of Hanam City,2023.0,The Journal of The Korea Institute of Intelligent Transport Systems,Eunnyeong Heo; Sung-Won Choi,,0.0,,False,,,2023-02-28,ed3434d792d9465e0c8695d6fb35a87d4f5d5c75,estimationofeconomicvalueofpublichousingparkinglotfocusingonthehedonicpriceapproachinthecaseofhanamcity,heo,10.12815/kits.2023.22.1.39,estimationofeconomicvalueofpublichousingparkinglotfocusingonthehedonicpriceapproachinthecaseofhanamcity|2023,1,1000,1,hedonic housing prices,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic house price,0ea3472086b77ecbffd3ff44e753ad4dfa356369,10.1007/s11146-016-9591-y,,Cash and Distressed House Sales Price Discounts: Dual Sample Selection Spatial Interdependence Approaches,2016.0,Journal of real estate finance and economics,Alan Tidwell; A. Jauregui; V. Sah; Andrew Narwold,,0.0,,False,,,2016-12-16,0ea3472086b77ecbffd3ff44e753ad4dfa356369,cashanddistressedhousesalespricediscountsdualsampleselectionspatialinterdependenceapproaches,tidwell,10.1007/s11146-016-9591-y,cashanddistressedhousesalespricediscountsdualsampleselectionspatialinterdependenceapproaches|2016,1,1000,1,hedonic house price,0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",301d475e67f5f2177460ddaf8c327e1a63273df6,10.15396/eres2024-099,,The Impact of Proximity to Enclosed Fresh Food Market on Housing Prices in Hong Kong,2024.0,30th Annual European Real Estate Society Conference,Wing-Chau Kwong; E. Liusman; Y. Wong,,0.0,,False,Conference,,,301d475e67f5f2177460ddaf8c327e1a63273df6,theimpactofproximitytoenclosedfreshfoodmarketonhousingpricesinhongkong,kwong,10.15396/eres2024-099,theimpactofproximitytoenclosedfreshfoodmarketonhousingpricesinhongkong|2024,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic housing prices,bb096e7f73d980ba8abc8d084d0ddf515236b708,10.2495/iceee140191,,The application of housing hedonic price model using kernel principal component analysis,2014.0,,He-Yong Wang; Jinjiong Lan,,0.0,,False,,Engineering,2014-06-01,bb096e7f73d980ba8abc8d084d0ddf515236b708,theapplicationofhousinghedonicpricemodelusingkernelprincipalcomponentanalysis,wang,10.2495/iceee140191,theapplicationofhousinghedonicpricemodelusingkernelprincipalcomponentanalysis|2014,1,1000,1,hedonic housing prices,2,0,1,0,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2d3d6c497876dfd9b8966e3f062a4b5ebbf8f65d,10.22423/kreus.2017.9.2.111,,"A Study on Determinants of Single-detached Housing Price -Focused on the Sales Price in Guro-gu, Seoul-",2017.0,,Kyutai Lee; H. Ha; Seon-Ju Song,,0.0,,False,,Business,2017-02-28,2d3d6c497876dfd9b8966e3f062a4b5ebbf8f65d,astudyondeterminantsofsingledetachedhousingpricefocusedonthesalespriceinguroguseoul,lee,10.22423/kreus.2017.9.2.111,astudyondeterminantsofsingledetachedhousingpricefocusedonthesalespriceinguroguseoul|2017,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,True,include,ELIGIBLE_HEDONIC,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),7fc5f156aaef4029a39151dba13011cc1c758b5e,10.1080/24694452.2022.2156318,,Climate Gentrification: Valuing Perceived Climate Risks in Property Prices,2023.0,Annals of the American Association of Geographers,Joshua J. Thompson; R. Wilby; J. Hillier; Richenda K. Connell; G. Saville,"There is growing evidence that physical climate hazards—such as floods and wildfires—affect property prices. Climate change scenarios suggest more frequent and severe physical climate hazards in the future, coinciding with greater exposure of populations to such threats. This raises concern because changes in property prices pose risks to homeowners’ financial status, as well as to the insurance and mortgage industries, bank portfolios, and thereby financial systems. We begin with a new definition of climate gentrification (CG) that captures links between physical climate hazards, perceptions of risk and resilience, and capital flows in property markets. This is followed by a structured assessment of the key drivers of CG, and an empirical case study of property data for a flood-prone UK city to demonstrate how CG depressed house price growth over the period from 2005 to 2018 by up to 50 percent in flood-exposed (relative to unexposed) locations. We then provide a discussion of ethical concerns around CG research, with suggested ways forward. Such price signals have potential ramifications for the long-term stability of real estate markets and raise policy implications for private and public sectors. We conclude with some priorities for further research into CG, recognizing key information and data gaps, and noting how existing knowledge and tools could contribute toward improved resilience to climate change. Key Words: climate gentrification, climate hazards, flood, hedonic model, property prices.",32.0,https://www.tandfonline.com/doi/pdf/10.1080/24694452.2022.2156318?download=true,True,,,2023-02-21,7fc5f156aaef4029a39151dba13011cc1c758b5e,climategentrificationvaluingperceivedclimaterisksinpropertyprices,thompson,10.1080/24694452.2022.2156318,climategentrificationvaluingperceivedclimaterisksinpropertyprices|2023,1,3023,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price,2,2,2,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",c637a826f8ccb62d18fe535adddef55c8e296b72,10.1080/17421772.2025.2603503,,Causality in spatial economic analysis: with reference to the London Green Belt and house prices,2026.0,Spatial Economic Analysis,B. Fingleton,"ABSTRACT This paper illustrates possibilities and pitfalls of two different approaches to causal analysis, the Rubin causal framework (RCF) and spatial econometrics, namely the spatial Durbin model (SDM). The two are directly opposing, for unlike the RCF, spatial interaction is embodied within the SDM. The main innovatory aspects are an attempt to reconcile RCF and SDM via ATIVE, meaning the average treatment instrumental variable effect, which parallels the average treatment effect (ATE) based on regression adjustment (RA). Additionally the paper illustrates the efficacy of using synthetic instrumental variables, providing estimates of the impact of London’s Green Belt on house prices.",2.0,https://www.tandfonline.com/doi/pdf/10.1080/17421772.2025.2603503?needAccess=true,True,JournalArticle,,2026-01-02,c637a826f8ccb62d18fe535adddef55c8e296b72,causalityinspatialeconomicanalysiswithreferencetothelondongreenbeltandhouseprices,fingleton,10.1080/17421772.2025.2603503,causalityinspatialeconomicanalysiswithreferencetothelondongreenbeltandhouseprices|2026,1,2195,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",758c13a836c699cd9d896f56b838bb21cbe69b43,10.1080/02673037.2023.2244908,,Demand for rent-regulated apartments in the Swedish housing market,2023.0,Housing Studies,Mats Wilhelmsson,"Abstract We are analysing Stockholm, Sweden’s regulated rental housing market, by analysing the distribution of rental apartments based on a person’s position in the housing agency queue. Our objective is to examine queue times and estimate the demand for rent-regulated apartments, as well as the market’s income elasticity. Using the hedonic two-step approach, we use empirical data from rental contracts provided by the Stockholm Housing Agency to investigate the relationship between individual waiting times and their characteristics, such as income, age, and family composition. The empirical analysis found a strong willingness to pay for rent-regulated apartments, especially for larger homes in better locations with lower regulated rent. The demand for rental apartments increases with increasing income, but this effect is less pronounced in the current distribution system. The findings will be valuable to real estate developers, property owners, and city planners to improve policies for the rental housing market.",1.0,https://www.tandfonline.com/doi/pdf/10.1080/02673037.2023.2244908?needAccess=true&role=button,True,JournalArticle,,2023-08-10,758c13a836c699cd9d896f56b838bb21cbe69b43,demandforrentregulatedapartmentsintheswedishhousingmarket,wilhelmsson,10.1080/02673037.2023.2244908,demandforrentregulatedapartmentsintheswedishhousingmarket|2023,1,2528,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate)",2,1,5,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b14194a49ebea349cdb50942c8975b34d2e5e19a,10.1080/02513625.2013.827510,,Mega-Retail-Led Regeneration and Housing Price,2013.0,,Jae Kwang Lee,"With the ongoing shift in the dynamics of retail development toward inner-city sites, mega-retail-led regeneration schemes have emerged as an important strategy for place marketing in the entrepreneurial city of London. While a number of studies have attempted to examine the effects of these schemes, the focus of previous studies has been limited to their wider economic and environmental effects. However, few studies have empirically investigated their impact on surrounding neighborhoods, particularly deprived neighborhoods. This paper investigates the effects of the Westfield London Shopping Centre on changes in housing price levels in the surrounding neighborhood of White City/Shepherd's Bush, in order to determine whether mega-retail-led regeneration schemes are a key determinant in the process of gentrification. A difference-in-differences analysis was used to assess differentials in the change rates of housing prices between control and treatment groups following the development of Westfield London. It was found that Westfield London development caused an increase in the change rate of housing prices of the treatment group relative to that of the control group. These findings indicate that mega-retail-led regeneration schemes may be a main cause of the pricing out of neighborhood residents who cannot afford the resulting increased rents and for altering the characteristics of neighborhoods and their social networks.",11.0,https://www.tandfonline.com/doi/pdf/10.1080/02513625.2013.827510?needAccess=true,True,,Sociology,2013-06-01,b14194a49ebea349cdb50942c8975b34d2e5e19a,megaretailledregenerationandhousingprice,lee,10.1080/02513625.2013.827510,megaretailledregenerationandhousingprice|2013,1,2944,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8896b8e1a8e37d8a7e07a91418ec772fcc9fa6ca,10.1080/21681376.2023.2186805,,"House prices, airport location proximity, air traffic volume and the COVID-19 effect",2023.0,"Regional Studies, Regional Science",T. Ngo; G. Squires; M. McCord; D. Lo,"ABSTRACT Although house prices and airports are influenced by distinct factors that shape their evolutions, they are also intrinsically connected through the natural and built environment. Standard theory suggests that air-traffic noise and proximity to key economic hubs such as airports are of prime importance to house prices and the housing market. This study contributes to understanding the link between the housing market, airport location proximity and air traffic. The research investigates this association across four key urban areas within New Zealand proximal to an international airport: Auckland, Wellington, Christchurch and Queenstown. Applying a generalized least squares (GLS) regression approach, the analysis reveals that house prices, air-traffic activity and proximity to airports within New Zealand demonstrate a statistically significant effect, and that air traffic volume has a positive effect on house prices. Moreover, the findings reveal a ‘U’-shape relationship between distance to the airport and house prices, suggesting that airport noise and pollution adversely affect house prices, with this effect diminishing with distance, indicating that economic influences and employment may also serve as a positive externality.",16.0,https://www.tandfonline.com/doi/epdf/10.1080/21681376.2023.2186805?needAccess=true&role=button,True,JournalArticle,Medicine,2023-04-12,8896b8e1a8e37d8a7e07a91418ec772fcc9fa6ca,housepricesairportlocationproximityairtrafficvolumeandthecovid19effect,ngo,10.1080/21681376.2023.2186805,housepricesairportlocationproximityairtrafficvolumeandthecovid19effect|2023,1,2754,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b9df5d29ea35f9d039b992207f5d74737b49f38b,10.1051/shsconf/202316301005,,Analyzing Local Dynamics in House Pricing During COVID-19,2023.0,SHS Web of Conferences,Rui-Jia Zhang,"Covid-19 has made it possible for people to work from home. As a result, there has been an increase in the demand for space. Since many positions now allow for the possibility of working online, people realize that having a larger space at home is necessary. This paper will use mathematical methods of data analysis and regression analysis in the context of the COVID-19 outbreak to discuss whether California house prices are suffering from an epidemic of infectious diseases.",0.0,https://www.shs-conferences.org/articles/shsconf/pdf/2023/12/shsconf_icssed2023_01005.pdf,True,JournalArticle;Conference,,,b9df5d29ea35f9d039b992207f5d74737b49f38b,analyzinglocaldynamicsinhousepricingduringcovid19,zhang,10.1051/shsconf/202316301005,analyzinglocaldynamicsinhousepricingduringcovid19|2023,1,1978,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",62f2f0cfd57683ec1543970a1d07b515988358aa,10.4236/me.2025.163016,,Economic and Social Attributes of Sold Homes Inside a House Price Bubble in Two Inner-City Neighbourhoods since 2020,2025.0,Modern Economy,A. Phipps,,0.0,https://www.scirp.org/pdf/me2025163_27203922.pdf,True,JournalArticle,,,62f2f0cfd57683ec1543970a1d07b515988358aa,economicandsocialattributesofsoldhomesinsideahousepricebubbleintwoinnercityneighbourhoodssince2020,phipps,10.4236/me.2025.163016,economicandsocialattributesofsoldhomesinsideahousepricebubbleintwoinnercityneighbourhoodssince2020|2025,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,residential property price determinants,f113e392ccaca6e040f151b80deca6aac4de7dab,10.1515/remav-2020-0020,,Italian Auction Market: Features of Discounted Forced Sale Price,2020.0,,P. Amoruso; M. Mariani; M. D'Amato; R. Didonato,"Abstract The purpose of the paper is to investigate the main causes of discount between list price and forced sale value in the Italian real estate auction segment, in favor of more comprehensive determination of the value of mortgage underlying guarantees; this would allow for an improvement of further valorization processes of assets intended to obtain a higher selling price. Starting from the results of existing literature, an ordinary least squares regression analysis was conducted in order to point out the main determinants of the discount between list price and forced sale value. It has been verified, when sampling 225 cases of forced residential property sales in the South of Italy, that the time constraint of the Italian regulatory framework is more relevant with reference to discount rather than the physical characteristics of assets. The influence of the duration of procedures is proof that even if recent government initiatives have led to positive results in terms of reducing the length of procedures, this is not yet satisfactory compared to other European contexts. This provides evidence of the need for further interventions aimed at improving the efficiency of the procedural system, also encouraging recourse to other options that lead to ease judicial burden.",5.0,https://www.sciendo.com/pdf/10.1515/remav-2020-0020,True,,Economics,2020-08-17,f113e392ccaca6e040f151b80deca6aac4de7dab,italianauctionmarketfeaturesofdiscountedforcedsaleprice,amoruso,10.1515/remav-2020-0020,italianauctionmarketfeaturesofdiscountedforcedsaleprice|2020,1,2792,1,residential property price determinants,0,2,1,4,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7a3e4b6f3e05917df9f1088ed874523003e34d7f,10.1016/j.sbspro.2013.07.211,,Attributes Influencing Housing Product Value and Price in Jakarta Metropolitan Region,2013.0,,R. A. Rahadi; S. K. Wiryono; Deddy P. Koesrindartoto; I. B. Syamwil,"Abstract This paper is a part of an ongoing dissertation research that tries to analyze the relationship between housing prices, Real Estate buyers’ preferences, Real Estate developers’ assumption of price, and external factors arising that influence the price. This paper acts as continuation study using the hypotheses from the author's previous research on the same topic to validate the hypotheses. The findings suggest that design, brand, facilities, reputation, reinvestment value, pricing policy, and speculative behavior play a significant part as internal and external attributes in price definition for housing price in Jakarta Metropolitan Region.",44.0,https://www.sciencedirect.com/science/article/pii/S187704281302106X/pdf,True,,Economics,2013-11-08,7a3e4b6f3e05917df9f1088ed874523003e34d7f,attributesinfluencinghousingproductvalueandpriceinjakartametropolitanregion,rahadi,10.1016/j.sbspro.2013.07.211,attributesinfluencinghousingproductvalueandpriceinjakartametropolitanregion|2013,1,2158,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",12db2a79faaef0e3b3972e1a3b9a7e8bac34850f,10.1016/j.sbspro.2012.09.423,,Critical Factors Affecting Double Storey Terrace Houses Prices in Bandar Baru Bangi,2012.0,,M. A. Khoiry; N. Tawil; N. Hamzah; A. I. Che Ani; S. Sood,"Abstract Everybody needs a house as shelter, either by renting or owning one. In order to have a good place called home, the location normally will be the priority in the list. Recently Bandar Baru Bangi which is recognized as Malay Elite Town has the significantly shown an increase in house prices starting from the mid 90's. Thus, this area is the main focused of the research. The increase in the prices for double storey terrace house in major areas in Malaysia has been a concern for property buyers, especially in developing areas. There are twenty one factors listed for the research and the factors are grouped into three main groups; internal, external and capitalist factors. The survey analysis by the developers who handles the double storey terrace house sales in Bandar Baru Bangi for past years indicates that that the capitalized factor such as supply and demand, market conditions, and access to public transport and distance to the city centre are the factors influencing the price of double storey terrace houses in Bandar Baru Bangi.",10.0,https://www.sciencedirect.com/science/article/pii/S1877042812038785/pdf,True,Review,Business,2012-10-17,12db2a79faaef0e3b3972e1a3b9a7e8bac34850f,criticalfactorsaffectingdoublestoreyterracehousespricesinbandarbarubangi,khoiry,10.1016/j.sbspro.2012.09.423,criticalfactorsaffectingdoublestoreyterracehousespricesinbandarbarubangi|2012,1,2554,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),80fa3d0527fab373e6bef3f7fa13049c4fe2bf11,10.1016/j.landusepol.2019.104458,,"Assessing economic instruments to steer urban residential sprawl, using a hedonic pricing simulation modelling approach",2020.0,,R. Mendonça; P. Roebeling; F. Martins; T. Fidélis; C. Teotónio; H. Alves; João Rocha,"Over the past centuries, cities have undergone major transformations that led to global urbanization. One of the phenomena emerging from urbanization is urban sprawl, defined as the uncontrolled spread of cities into undeveloped areas. The decrease in housing prices and commuting costs as well as the failure to internalize the real costs associated with natural land, led to households moving-out into the urban fringe – resulting in fragmented, low-density residential development patterns that has multiple negative impacts. Awareness for this problem has fed the need for the implementation of effective policies against urban residential sprawl – a subject that has received considerable attention in literature, albeit little attention has been given to economic incentive instruments. Hence, the objective of this paper is to assess the effectiveness of different economic instruments in steering urban residential sprawl – assessing property taxes, land taxes and public transport subsidies. To this end, the Sustainable Urbanizing Landscape Development (SULD) model is used and adapted, and a case study is provided for the medium-sized City of Aveiro in central Portugal. Results show that a flat property tax and a public transport subsidy for low and middle-income households are the most efficient instruments, leading to a decrease in urban residential area, living space and housing quantity as well as an increase in real estate values. On the other hand, a land tax results in insignificant changes in urban residential development patterns.",34.0,https://www.sciencedirect.com/science/article/pii/S0264837719305241/pdf,True,,Economics,2020-03-01,80fa3d0527fab373e6bef3f7fa13049c4fe2bf11,assessingeconomicinstrumentstosteerurbanresidentialsprawlusingahedonicpricingsimulationmodellingapproach,mendonça,10.1016/j.landusepol.2019.104458,assessingeconomicinstrumentstosteerurbanresidentialsprawlusingahedonicpricingsimulationmodellingapproach|2020,1,3059,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices,2,2,3,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d7fa75b3aa937ed9f56e61e7e373fdfd92935f56,10.1016/j.cities.2018.11.008,,"Quality of urban area, distance from city centre, and housing value. Case study on real estate values in Turin",2019.0,Cities,L. D'Acci,"Abstract A tremendous number of studies have investigated the relation between real estate value and characteristics of the area. This paper briefly shows more than one hundred empirical results from the literature, quantifying how much positional factors such as green, social context, pedestrian areas, pollution, aesthetics, views and accessibility influence the property value. We call Positional Value that part of the real estate value given from the characteristics of the area. As an empirical example, an analysis of the city of Turin shows how, changing the area of the city, the value of a house increases/decreases as much as 142% of its own value, ceteris paribus, even among areas quite close to each other. More specifically, the output of this study indicates that the real estate value decreases by 0.23% for each 1% increase of the distance from the city centre, but increases by 0.58% for each 1% increase of the quality of the site. The monetary costs of housing, time and transport, and the qualitative benefits received from the site's characteristics, play a main role within the households' decision processes when choosing among alternative dwellings. On the other way round, the quality of the area is capitalised by the real estate value which may then be seen an indicator of the former.",71.0,https://www.sciencedirect.com/science/article/pii/S0264275118308552,True,,Economics;Geography,2019-08-01,d7fa75b3aa937ed9f56e61e7e373fdfd92935f56,qualityofurbanareadistancefromcitycentreandhousingvaluecasestudyonrealestatevaluesinturin,d'acci,10.1016/j.cities.2018.11.008,qualityofurbanareadistancefromcitycentreandhousingvaluecasestudyonrealestatevaluesinturin|2019,1,2815,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fd67dcefff7f239109dad843c0d5d19749c00f23,10.1590/s0034-76122007000600004,,Proposta para construção de um índice local de preços de imóveis a partir dos lançamentos imobiliários de condomínios residenciais,2007.0,,Sérgio Rozenbaum; T. D. D. de Macedo-Soares,"It is very difficult to develop a national housing price index for a country with continental dimensions and the level of cultural and economic diversity such as Brazil. In addition, even at large metropolitan areas, there is a lack of information regarding the actual price of the real estate transactions and the characteristics of those units. The authors propose a model for developing an index of local markets based on information made available during the release of new residential real estate properties.",5.0,https://www.scielo.br/j/rap/a/NPZbPNfnP9GgsVtLFjjcZ3B/?lang=pt&format=pdf,True,,Economics,2007-12-01,fd67dcefff7f239109dad843c0d5d19749c00f23,propostaparaconstrucaodeumindicelocaldeprecosdeimoveisapartirdoslancamentosimobiliariosdecondominiosresidenciais,rozenbaum,10.1590/s0034-76122007000600004,propostaparaconstrucaodeumindicelocaldeprecosdeimoveisapartirdoslancamentosimobiliariosdecondominiosresidenciais|2007,1,2013,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || preços hedônicos imóveis",0,1,4,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",09c70e20c7c41d52ab52245bc7d7f39503bdb0bc,10.70121/001c.121732,,Deriving and Verifying a Community Vitality Index through a Machine Learning Model Based on House Price Data,2024.0,Scholarly Review Journal,Mark Li,"Recognizing the complexity and limitations of the approach to produce a traditional Multi-dimensional Community Index (MD-CVI) as well as high cost and time-intensive, the study explores another approach that establishes a machine-learning model to derive a community index by leveraging contemporary house prices. This method has several significant advantages over traditional methods, providing a more efficient and reliable way to assess community well-being. The model produces a dynamic Community Vitality Index (CVI) that quickly captures changes within a community and enhances the MD-CVI’s sophistication by selecting and combining any geographic regions generated by clustering algorithms, employing machine learning model and house sold price reduces human errors from questionnaires, and potentially improves accuracy. The study also exemplified and verified the model via house data from Minnesota, Texas, and Connecticut, spanning the last five years. Subsequently, comparing the house-price-based CVI with traditional MD-CVIs reveals a consistent alignment in their community index rankings. The conclusion is that the house-price-based CVI, as second research, is a viable proxy and a benchmark for a traditional MD-CVI, indicating an innovative approach for the stakeholders, including the government, education institutions, health and welfare organizations, etc. in assessing community well-being and making informed policy decisions regarding the flow of public funding of community-based projects. This research has the potential to significantly impact policy decisions, ensuring that public funding is directed to the communities that need it most.",0.0,https://www.scholarlyreview.org/article/121732.pdf,True,JournalArticle,,2024-06-15,09c70e20c7c41d52ab52245bc7d7f39503bdb0bc,derivingandverifyingacommunityvitalityindexthroughamachinelearningmodelbasedonhousepricedata,li,10.70121/001c.121732,derivingandverifyingacommunityvitalityindexthroughamachinelearningmodelbasedonhousepricedata|2024,1,3171,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cb13d7882e9f9411b86b2542821773fbb66399b5,10.21837/pm.v23i39.1897,,URBAN HOUSING AFFORDABILITY: ANALYZING THE IMPACT OF AFFORDABLE HOUSING SCHEMES ON HOUSING PRICES,2025.0,PLANNING MALAYSIA,Ahmad Fawwaz Saleh; Rohayu Ab. Majid; N. A. Ab Rahim; Khairul Afzan Lazi; Nuratiqah Aisyah Awang,"Housing affordability remains a critical issue in urban areas, where rising demand and limited supply drive property prices upward. Governments have introduced affordable housing schemes to address these challenges, but their effectiveness hinges on alignment with local market conditions and demand-side factors such as financial capability, government regulation on affordable housing, and location preferences. Despite their importance, few studies have examined the specific attributes of affordable housing schemes and their impact on housing prices. This study bridges this gap by investigating the relationship between affordable housing prices and housing demand, employing a mixed-methods approach and using correlation and regression analysis, an initial framework of 15 attributes was evaluated, categorized under financial approvals, government regulations, and location preferences. The results reveal that nine attributes significantly influence housing prices within affordable housing schemes. Key factors include credit history, down payment, loan margin, approval location, qualification requirements, and procedural complexities, all of which exhibit strong correlations with housing prices. These findings underscore the need for policymakers to refine regulatory mechanisms to better align with the financial capabilities of homebuyers. By addressing these factors, housing policies can be tailored to enhance affordability and promote sustainable housing development.",3.0,https://www.planningmalaysia.org/index.php/pmj/article/download/1897/1574,True,JournalArticle,,2025-12-28,cb13d7882e9f9411b86b2542821773fbb66399b5,urbanhousingaffordabilityanalyzingtheimpactofaffordablehousingschemesonhousingprices,saleh,10.21837/pm.v23i39.1897,urbanhousingaffordabilityanalyzingtheimpactofaffordablehousingschemesonhousingprices|2025,1,2989,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",2dbb6fabdb868ab58b639471ad3818c1c52cb77c,10.24302/drd.v15.5426,,Análise da relação entre criminalidade e valor por metro quadrado dos imóveis residenciais no município de Belo Horizonte,2025.0,DRd - Desenvolvimento Regional em debate,Iago Miranda Soares; Evandro Camargos Teixeira; I. S. Tupy,"The disorderly expansion of cities increases regional inequalities and contributes to the rise of crime in large urban centers. In view of this, this study investigated the relationship between the average homicide and robbery rates per 100,000 inhabitants and the value per square meter of residential property in Belo Horizonte in the period 2017-2021, using hierarchical models with spatial lag. As a result, the Exploratory Analysis of Spatial Data (AEDE) indicated distinct patterns of agglomeration for crime rates between the regions of the municipality of Belo Horizonte. In addition, the econometric results revealed that properties located in regions with higher homicide rates have lower square meter values in their built-up areas. On the other hand, the opposite is true for theft and robbery rates, which show a positive relationship with the square meter value of the properties. Keywords: crime; regional inequalities; hedonic prices; spatial hierarchical model; Belo Horizonte.",0.0,https://www.periodicos.unc.br/index.php/drd/article/download/5426/2598,True,JournalArticle,,2025-04-28,2dbb6fabdb868ab58b639471ad3818c1c52cb77c,analisedarelacaoentrecriminalidadeevalorpormetroquadradodosimoveisresidenciaisnomunicipiodebelohorizonte,soares,10.24302/drd.v15.5426,analisedarelacaoentrecriminalidadeevalorpormetroquadradodosimoveisresidenciaisnomunicipiodebelohorizonte|2025,1,2494,2,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || preços hedônicos imóveis",2,0,2,1,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ba2a9e6448271b83880e6cb2b007e142588aa58b,10.1016/j.jue.2008.09.001,,Why do households without children support local public schools? Linking house price capitalization to school spending,2002.0,,Christian A. L. Hilber; C. Mayer,,254.0,https://www.nber.org/papers/w10804.pdf,True,,Economics;Geography,2002-06-01,ba2a9e6448271b83880e6cb2b007e142588aa58b,whydohouseholdswithoutchildrensupportlocalpublicschoolslinkinghousepricecapitalizationtoschoolspending,hilber,10.1016/j.jue.2008.09.001,whydohouseholdswithoutchildrensupportlocalpublicschoolslinkinghousepricecapitalizationtoschoolspending|2002,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic housing prices,602877bc585b67c1c02101be64c2995a85447774,10.3390/realestate1010004,,Using Co-Ordinate Systems in Hedonic Housing Regressions,2024.0,Real Estate,S. Caudill; Neela D. Manage; F. Mixon,"Hedonic house price studies typically incorporate information about location by including either a set of dummy variables to represent individual locations called “neighborhoods” or by using a set of distance (or travel time) variables to characterize locations in terms of proximity to amenities and dis-amenities. As an alternative to these, relatively recent research advocates a latitude–longitude co-ordinate system for incorporating distance information into hedonic house price regressions. This study shows that many of the claims made in this research, particularly those referencing the elimination or diminution of “biases of coefficients of non-distance variables”, are given the particulars of the Monte Carlo experiments, not possible to investigate. We further show, both analytically and with our simulations, that there is no omitted variable bias present in their simulations because their randomly generated non-distance variable is uncorrelated with any of the other variables used in their regression models.",3.0,https://www.mdpi.com/2813-8090/1/1/4/pdf?version=1710244759,True,JournalArticle,,2024-03-12,602877bc585b67c1c02101be64c2995a85447774,usingcoordinatesystemsinhedonichousingregressions,caudill,10.3390/realestate1010004,usingcoordinatesystemsinhedonichousingregressions|2024,1,2529,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || house prices proximity hedonic",2,1,2,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",35cb3153162c677efd259e9e30fc2ca6aa291865,10.3390/geographies2010008,,"Spatial Modelling and Geovisualization of House Prices in the Greater Athens Region, Greece",2022.0,Geographies,Polixeni Iliopoulou; E. Feloni,"In this article, geovisualization is used for the presentation and interpretation of spatial analysis results concerning several house attributes. For that purpose, point data for houses in the region of Attica, Greece are analyzed. The data concern houses for sale and comprise structural characteristics, such as size, age and floor, as well as locational attributes. Geovisualization of house characteristics is performed employing spatial interpolation techniques, kriging techniques, in particular. Spatial autocorrelation in the data is examined through the calculation of the Moran’s I coefficient, while spatial clusters of houses with similar characteristics are identified using the Getis-Ord Gi* local spatial autocorrelation coefficient. Finally, a model is developed in order to predict house prices according to several structural and locational characteristics. In that respect, a classic hedonic pricing model is constructed, which is consequently developed as a geographically weighted regression (GWR) model in a GIS environment. The results of this model indicate that two characteristics, i.e., size and age, account for most of the variability in house prices in the study region. Since GWR is a local model producing different regression parameters for each observation, it is possible to obtain the spatial distribution of the regression parameters, which indicate the significance of the house characteristics for price determination in different locations in the study area.",5.0,https://www.mdpi.com/2673-7086/2/1/8/pdf?version=1645441695,True,,,2022-02-21,35cb3153162c677efd259e9e30fc2ca6aa291865,spatialmodellingandgeovisualizationofhousepricesinthegreaterathensregiongreece,iliopoulou,10.3390/geographies2010008,spatialmodellingandgeovisualizationofhousepricesinthegreaterathensregiongreece|2022,1,2999,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price",2,2,2,4,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",86da8ec33d3cf3a83d23cd582eec2741f6e08ad3,10.3390/ijgi9010002,,Towards Increasing Residential Market Transparency: Mapping Local Housing Prices and Dynamics,2019.0,ISPRS Int. J. Geo Inf.,R. Cellmer; R. Trojanek,"This article attempts to use spatial maps as a way of presenting additional information about the phenomena occurring in the housing market. In our opinion, spatial maps may facilitate understanding and provide more detailed information, which undoubtedly should increase the transparency of the housing market. The study used 12,219 transactions of apartments in Poznań in the years 2013–2017. General principles of price visualization activity and housing market dynamics were established in this study. The map of prices may reflect the location values determined by the quality of the urban infrastructure, distance from specific locations, and environmental factors. Market activity maps reveal areas where the market is dynamically developing, while information on trends in the number of transactions and price changes may demonstrate the growing or declining attractiveness of areas. The research is based on a model of hedonic regression in the form of ordinary least squares (OLS), quantile regression (QR), and geographically weighted regression (GWR). The maps presented should increase the transparency of the residential market (e.g., by providing more detailed information). However, one should bear in mind the limitations in the use of these methods resulting from a small number of transactions in a thin market.",22.0,https://www.mdpi.com/2220-9964/9/1/2/pdf?version=1577079565,True,JournalArticle,Business;Computer Science,2019-12-18,86da8ec33d3cf3a83d23cd582eec2741f6e08ad3,towardsincreasingresidentialmarkettransparencymappinglocalhousingpricesanddynamics,cellmer,10.3390/ijgi9010002,towardsincreasingresidentialmarkettransparencymappinglocalhousingpricesanddynamics|2019,1,2830,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,1,3,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",f20055c3edaa910192a90258f6367e59ecb3410d,10.3390/ijgi10060359,,Examining the Effect of Squatter Settlements in the Evolution of Spatial Fragmentation in the Housing Market of the City of Buenos Aires by Using Geographical Weighted Regression,2021.0,ISPRS Int. J. Geo Inf.,A. F. Ogas-Mendez; Y. Isoda,"The spatial fragmentation in the housing market and the growth of squatter settlements are characteristic for the metropolitan areas in developing countries. Over the years, in large cities, these phenomena have been promoting an increase in the spatial concentration of poverty. Therefore, this study examined the relationship between the squatter settlement growth and spatial fragmentation in the housing market of Buenos Aires. By performing a spatiotemporal analysis using geographically weighted regression in the house prices for the years 2001, 2010, and 2018, the results showed that while squatter settlements had a strong negative effect on house prices, the affected areas shifted over time. Our findings indicate that it is not the growth of the squatter settlement that causes spatial fragmentation, but rather the widening income disparities and further segregation of low-income households. However, squatter settlements determined the spatial demarcation of fragmented housing market by attracting low-income households to surrounding low house price areas.",11.0,https://www.mdpi.com/2220-9964/10/6/359/pdf?version=1621926111,True,JournalArticle,Geography;Computer Science,2021-05-23,f20055c3edaa910192a90258f6367e59ecb3410d,examiningtheeffectofsquattersettlementsintheevolutionofspatialfragmentationinthehousingmarketofthecityofbuenosairesbyusinggeographicalweightedregression,ogas-mendez,10.3390/ijgi10060359,examiningtheeffectofsquattersettlementsintheevolutionofspatialfragmentationinthehousingmarketofthecityofbuenosairesbyusinggeographicalweightedregression|2021,1,2574,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic house price,d715c36e9151f5e589c6db0ca5b9d34e21b2f0f7,10.3390/buildings14030824,,"Investigating the Effect of Transit-Oriented Development (TOD) on Social Equity—Examining the Displacement of Footscray, Melbourne",2024.0,Buildings,Xinyi Wang; Yixuan Xie; Li-Hui Xia; Jinquan He; Beiyu Lin,"As Melbourne faces exponential population growth, the necessity for resilient urban planning strategies becomes critical. These strategies include mixed land use, density, diversity, and sustainable transportation through transit-oriented development (TOD). While TOD promises to accommodate growing populations and address environmental concerns, it also raises issues regarding its unintended consequences on poverty and inequality, notably through residential displacement and gentrification. This study investigates the impacts of TOD construction on inequality in Footscray, employing spatial analysis techniques like the hedonic price model (HPM), robust regression analysis, and Pearson correlation analysis. It aims to understand how spatial factors influence housing prices and their correlations. Additionally, the study uses observational spatial analysis via Google Street View (GSV) to examine indices such as housing development type, traffic signage, sanitation facilities, and house beautification. This approach seeks to build an evaluation framework to assess the extent of TOD street reconstruction and its impact on gentrification and displacement. The research adapts existing knowledge to create a tool for reviewing past planning decisions and assessing the fairness of TOD planning implementation. By providing assessment and guidance to mitigate the potential adverse impacts of TOD, this study contributes to the advancement of urban-planning practices, offering insights into mixed land use and effective strategies to balance economic development and social equity, thereby enhancing community resilience. Ultimately, this research deepens our understanding of the impacts of TOD on urban inequality and offers practical tools and insights for more equitable and sustainable urban development.",5.0,https://www.mdpi.com/2075-5309/14/3/824/pdf?version=1710826849,True,JournalArticle;Review,,2024-03-19,d715c36e9151f5e589c6db0ca5b9d34e21b2f0f7,investigatingtheeffectoftransitorienteddevelopmenttodonsocialequityexaminingthedisplacementoffootscraymelbourne,wang,10.3390/buildings14030824,investigatingtheeffectoftransitorienteddevelopmenttodonsocialequityexaminingthedisplacementoffootscraymelbourne|2024,1,3321,5,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || spatial hedonic model housing",2,1,3,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,apartment prices hedonic,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,10.3390/land11060931,,Factors Affecting Spatial Autocorrelation in Residential Property Prices,2022.0,Land,D. Lo; Wing-Chau Kwong; S. Wong; M. McCord; M. Haran,"Within housing literature, the presence of spatial autocorrelation (S.A.) in housing prices is typically examined horizontally in a two-dimensional setting. However, in the context of apartment buildings, there is also a vertical component of S.A. for housing units located on different floor levels. This paper therefore explores the determinants of both horizontal and vertical S.A. within residential property prices. First, we posit that S.A. in housing prices is a consequence of the price discovery process of real estate, in which property traders acquire price information from recent market transactions (i.e., comparables) to value a subject property. Furthermore, we contend that the extent to which property traders rely on comparables to determine housing prices is governed by the liquidity and volatility conditions of the market, which in turn affects the magnitude of the S.A. By developing and testing several spatial autoregressive hedonic models using open market transaction data for the Hong Kong residential property market, we find that market liquidity tends to increase both vertical and horizontal S.A., whilst market volatility is more prone to increase vertical S.A. but depress horizontal S.A.",11.0,https://www.mdpi.com/2073-445X/11/6/931/pdf?version=1655461164,True,,,2022-06-17,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,factorsaffectingspatialautocorrelationinresidentialpropertyprices,lo,10.3390/land11060931,factorsaffectingspatialautocorrelationinresidentialpropertyprices|2022,1,2723,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || residential property price determinants || spatial hedonic model housing",2,3,3,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c827a3e790257dbb4cecb18e8d32dd8ef3b878bd,10.3390/land11060804,,The Impact of Brownfields on Residential Property Values in Post-Industrial Communities: A Study from the Eastern Part of the Czech Republic,2022.0,Land,Kamila Turečková; Stanislav Martinát; J. Nevima; František Varadzin,"Dilapidated, neglected, or abandoned sites and buildings (so-called brownfields) are frequently neither visually attractive nor aesthetically valuable. Indeed, neglected brownfields contribute to the emergence of both objective and subjective barriers to the social, economic, and environmental development of communities. We also know that the occurrence of decayed brownfields affect the prices of residential housing in their vicinity. In our paper, we seek to better understand the impact of brownfields on the perceived value of neighbouring properties. We are also keen to shed more light on the factors behind the allegedly undesirable impact of brownfields on property values. Our research is based primarily on data obtained from a survey of 1,152 respondents in ten municipalities situated at the eastern part of the Czech Republic, where neglected brownfields frequently occur. The findings are complemented by an analysis of sales of residential properties located in proximity to brownfield sites, to further confirm our preliminary survey results. The results confirmed that neglected brownfields tend to negatively affect the value of neighbouring residential properties. Our results also signal that the concentration of socially unacceptable behaviour is considered a key issue for distorting property prices around brownfields. Our analyses have confirmed that distance from a particular brownfield is one of the factors significantly influencing property prices. We claim that neglected brownfields create negative externalities that require public sector interventions and better planning. We urgently need more systematic support for brownfield regeneration to improve the quality of life of residents in affected communities.",9.0,https://www.mdpi.com/2073-445X/11/6/804/pdf?version=1654513868,True,Review,,2022-05-28,c827a3e790257dbb4cecb18e8d32dd8ef3b878bd,theimpactofbrownfieldsonresidentialpropertyvaluesinpostindustrialcommunitiesastudyfromtheeasternpartoftheczechrepublic,turečková,10.3390/land11060804,theimpactofbrownfieldsonresidentialpropertyvaluesinpostindustrialcommunitiesastudyfromtheeasternpartoftheczechrepublic|2022,1,3247,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic property valuation,529cf89c004b0b15709fa7a01fee2a8e3c44c1b8,10.3390/su162310612,,The Urban Park Green Spaces Landscape Premium Functional Value Accounting System: Construction and Application,2024.0,Sustainability,Lingling Duan; X. Niu; Bing Wang,"Urban park green spaces have the functions of improving the urban ecological environment and providing recreational services, and at the same time, they have a certain effect on the value of the surrounding residential property. To quantitatively assess the value of the landscape premium function of park green space, many scholars have carried out research exploration and adopted a variety of methods (such as the contingent valuation method (CVM), travel cost method (TCM) and hedonic price method (HPM)), which have developed from simple theoretical models with single factors to complex empirical models with multiple factors. Among them, the hedonic price method has become the mainstream research method, and in recent years, it has been widely adopted in combination with GIS technology. In terms of research objects, single park green space or multiple park green spaces in large cities are the main focus, while there are fewer studies on park green spaces in built-up areas of small and medium-sized cities. In terms of research content, there are more studies on the value-added coefficient of landscape premium and influence distance, and there are fewer studies on the total value of landscape premium. This article aims to calculate the total landscape premium value of all park green spaces in the built-up areas of small and medium-sized cities, proposing a complete and operable accounting system for the functional value of park green space landscape premiums by combining GIS with a hedonic pricing model and remote sensing image interpretation methods. For the first time, a method for interpreting the height of residential buildings within the benefit range of landscape premium through remote sensing images is proposed, and then the floor area ratio of residential plots is estimated, so as to estimate the total area of actual beneficial buildings. Therefore, this paper takes Chifeng City, a small and medium-sized city, as a case study, and empirically demonstrates the assessment of the landscape premium function of parks and green spaces in the built-up area of Chifeng City by using this accounting system. Research shows that this method has certain feasibility, not only calculating the total value of landscape premium but also addressing the issue in existing studies where all areas within the potential range of landscape premium function are counted as appreciated areas, leading to an overestimation of the premium. It further advances the accuracy of accounting for the value of landscape premium function of urban park green space and provides theoretical reference for the planning and construction of urban park green space.",4.0,https://www.mdpi.com/2071-1050/16/23/10612/pdf?version=1733240746,True,JournalArticle,,2024-12-03,529cf89c004b0b15709fa7a01fee2a8e3c44c1b8,theurbanparkgreenspaceslandscapepremiumfunctionalvalueaccountingsystemconstructionandapplication,duan,10.3390/su162310612,theurbanparkgreenspaceslandscapepremiumfunctionalvalueaccountingsystemconstructionandapplication|2024,1,4169,1,hedonic property valuation,2,0,1,1,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),59d56e278db106668f06fe0aab26281002526fca,10.3390/su15043731,,Improved Ecosystem Services and Environmental Gentrification after Rehabilitating Brazilian Urban Streams,2023.0,Sustainability,A. Golgher; M. Callisto; R. Hughes,"The high levels of environmental pollution observed in urban freshwater ecosystems result in losses of ecosystem goods and services, reducing the well-being of human populations in densely populated tropical cities. The Belo Horizonte Metropolitan Region (BHMR) resembles other megacities in the Global South, with inadequate collection and treatment of domestic sewage being an important source of environmental degradation. However, urban stream rehabilitation can improve ecosystem quality and the physical and mental well-being of local citizens. Therefore, the objective of this study was to assess whether the rehabilitation of three BHMR streams and the increased provision of ecosystem goods and services for local residents were associated with environmental gentrification and public health issues. To achieve this objective, we asked three questions. (i) Was there socioeconomic improvement in the households located near the rehabilitated streams? (ii) Did property values appreciate near the rehabilitated streams? (iii) Was the relative incidence of diseases decreased in the residents living near the rehabilitated streams? We tested three hypotheses. (H1) The socioeconomic profile of the households in the areas neighboring the rehabilitated streams improved. (H2) The property values of residences in the areas neighboring the rehabilitated streams increased. (H3) The incidence of waterborne and other types of diseases in the areas neighboring the rehabilitated streams decreased. To answer the first question and hypothesis, we compared 2000 to 2010 census tracts in the areas neighboring the rehabilitated streams with others in the municipality of Belo Horizonte (BH). We observed non-significant socioeconomic and demographic differences. To answer the second question and hypothesis, we used real estate transactions between 2009 and 2018 using hedonic models. After controlling for multiple interacting variables, we observed real estate appreciation after stream rehabilitation. To answer the third question and hypothesis, we analyzed the association between the prevalence of waterborne diseases, vector-borne diseases, and other diseases in the residents living near the rehabilitated streams. To do so, we analyzed hospitalization data from 2005 to 2016 for residents neighboring the streams versus BH. We observed a significant increase in the prevalence of waterborne and vector-transmitted diseases until two years after the stream rehabilitation, followed by a decrease until 2010. Trends for other types of diseases differed from these and mostly followed the general tendencies of BH, indicating different temporal variations. Our results highlight that ecosystem goods and services fostered by urban stream rehabilitation have potential contributions to the well-being of urban populations. The economic analyses applied in the paper have clear policy implications in support of urban stream rehabilitation. Although South American countries lack mandatory rehabilitation investments, Belo Horizonte’s example can inspire other interventions in tropical megacities.",13.0,https://www.mdpi.com/2071-1050/15/4/3731/pdf?version=1676635136,True,,,2023-02-17,59d56e278db106668f06fe0aab26281002526fca,improvedecosystemservicesandenvironmentalgentrificationafterrehabilitatingbrazilianurbanstreams,golgher,10.3390/su15043731,improvedecosystemservicesandenvironmentalgentrificationafterrehabilitatingbrazilianurbanstreams|2023,1,4603,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,2,1,0,1,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7aa0fe95a2fc31b97bdda4535535c59d199fc10c,10.3390/su152316495,,Do Public-Led Housing Site Development Projects Affect Local Housing Prices: A Proposal for a Comprehensive Policy Evaluation Methodology,2023.0,Sustainability,Jina E. Kim; S. Jee; Seunghan Ro,"As the population in urban areas increases, the importance of adequate public-led development policies for sustainable cities with affordable housing is becoming more highlighted. In this regard, this study aims to determine the effectiveness of public-led urban development policies for sustainable growth in urban areas, specifically measuring the effect of housing site development projects on housing prices. The geographical scope of the study is the project sites and their surrounding areas in South Korea, and the temporal background is from 2006 to 2023. The project sites were subdivided into four groups by using the Self-Organization Map (SOM), a machine-learning-based clustering analysis, to collect characteristics of each region. Then, the impact of the policy and the prediction of the real estate market of each cluster were analyzed by applying the DID and LSTM models, which have recently been proven to show a high validity. The results show that each cluster had different characteristics and effects from the development projects, depending not simply on the location, but on several characteristics, including the level of size, infrastructure installation, input cost, etc. Furthermore, it is expected for future studies that more detailed research should be conducted with larger datasets of the regional characteristics.",1.0,https://www.mdpi.com/2071-1050/15/23/16495/pdf?version=1701441118,True,JournalArticle,,2023-12-01,7aa0fe95a2fc31b97bdda4535535c59d199fc10c,dopublicledhousingsitedevelopmentprojectsaffectlocalhousingpricesaproposalforacomprehensivepolicyevaluationmethodology,kim,10.3390/su152316495,dopublicledhousingsitedevelopmentprojectsaffectlocalhousingpricesaproposalforacomprehensivepolicyevaluationmethodology|2023,1,2847,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",52c671b9aec7face27a100b76e8445629de70e8f,10.3390/su14127496,,Housing Sustainability: The Effects of Speculation and Property Taxes on House Prices within and beyond the Jurisdiction,2022.0,Sustainability,M. A. Rauf; O. Weber,"Housing plays an essential role in sustainable governance due to its socio-economic and environmental connection. However, the relationship between governance policies, market behavior, and socio-economic outcomes varies geographically and demographically. Therefore, segregated policies developed and implemented may fail to achieve their desired objectives because of the sensitivity of housing policies for their connection to human wellbeing. The effectiveness of housing policies in geographically connected regions is one of the areas that has received little attention in the Canadian context. The study follows a multi-step empirical method using a multiple linear regression model and a difference-in-difference approach to assessing the geographical variation of speculation and property taxes on housing markets. The study confirms that speculation taxes are not an effective tool in curbing house prices. Similarly, considering the role of property taxes in providing public services, delinking property taxes from a potential contributor to house prices would provide a better lens to develop local housing policies. Furthermore, the study also confirms that the housing market can be better assessed at a local scale, considering geographical influence in conjunction with investment trends.",9.0,https://www.mdpi.com/2071-1050/14/12/7496/pdf?version=1655721262,True,,,2022-06-20,52c671b9aec7face27a100b76e8445629de70e8f,housingsustainabilitytheeffectsofspeculationandpropertytaxesonhousepriceswithinandbeyondthejurisdiction,rauf,10.3390/su14127496,housingsustainabilitytheeffectsofspeculationandpropertytaxesonhousepriceswithinandbeyondthejurisdiction|2022,1,2805,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5f2571676195f5af48c3363aca86620764c5ea94,10.3390/su14116433,,"A Data Mining Study on House Price in Central Regions of Taiwan Using Education Categorical Data, Environmental Indicators, and House Features Data",2022.0,Sustainability,Min-Feng Lee; Guey-Shya Chen; Shaojun Lin; Wei-jie Wang,"This study takes the city of Taichung, Taiwan, as the research area, combines the survey results about the demand for residential houses for the next year, and uses relevant parameters and data of real price registration as the prediction results. In this study, eight types of school district features (such as teachers and students of secondary and elementary schools) and five types of air pollution features are selected and processed with a data mining method to discover the total transactions of real estate properties in various districts of Taichung. The results of K-means clustering and decision tree classification reveal that the four districts of the old Taichung City, namely, Beitun District, North District, Xitun District, and Nantun District, have houses meeting the conditions of egg yolk districts; houses in the old Taichung County have attributes of egg white districts. The results of decision tree classification show that the total price is the most important attribute influencing egg yolk and egg white districts.",3.0,https://www.mdpi.com/2071-1050/14/11/6433/pdf?version=1653569180,True,Review,,2022-05-24,5f2571676195f5af48c3363aca86620764c5ea94,adataminingstudyonhousepriceincentralregionsoftaiwanusingeducationcategoricaldataenvironmentalindicatorsandhousefeaturesdata,lee,10.3390/su14116433,adataminingstudyonhousepriceincentralregionsoftaiwanusingeducationcategoricaldataenvironmentalindicatorsandhousefeaturesdata|2022,1,2541,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,hedonic housing prices,c48d7e5562662aaad789c87de8f3d5958a2e40a3,10.3390/su14010284,,Urban Rail Transit in Bangkok: Chronological Development Review and Impact on Residential Property Value,2021.0,Sustainability,Varameth Vichiensan; Vasinee Wasuntarasook; Y. Hayashi; M. Kii; Titipakorn Prakayaphun,"Bangkok suffered from the world’s worst traffic congestion in the 1990s due to rapidly increasing car ownership, reflecting the economic growth and road-dependent transport policy beginning in the 1960s. Due to its monocentric but scattered urban structure, traffic congestion is severe, causing tremendous economic loss, deteriorating air quality, and badly affecting the quality of life. A historical review reveals that the urban and transport plan and development were not efficiently coordinated, resulting in unorganized suburbanization and progressively more severe traffic congestion. It is important to reveal the impact of the transportation project on the housing market in order to incorporate the policies for transportation and urban development. To define the impact, the OLS hedonic price model and the local multiscale geographically weighted regression (MGWR) model were estimated, along with the condominium sales data. The results revealed that the impact of rail transit on a rise in property value significantly varied across the study area. It was estimated that, for the area along the major rail transit corridor in the city center, a premium of a location 100-m closer to the station would be more than 200 USD per square meter. At the same time, the value would be less than 80 USD for the area along the rail corridor in the suburb. These findings provide policy insights for future urban and railway development, including the proper coordination of rail transit development and urban development with subcenters, transit-oriented development, and improved pedestrian flow around transit stations.",50.0,https://www.mdpi.com/2071-1050/14/1/284/pdf?version=1640681198,True,Review,,2021-12-28,c48d7e5562662aaad789c87de8f3d5958a2e40a3,urbanrailtransitinbangkokchronologicaldevelopmentreviewandimpactonresidentialpropertyvalue,vichiensan,10.3390/su14010284,urbanrailtransitinbangkokchronologicaldevelopmentreviewandimpactonresidentialpropertyvalue|2021,1,3126,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices",2,1,4,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d489781f49c7c107af16c21bd0279d71e4d41a4e,10.3390/su13042236,,The Impacts in Real Estate of Landscape Values: Evidence from Tuscany (Italy),2021.0,Sustainability,F. Riccioli; R. Fratini; Fabio Boncinelli,"Using spatial econometric techniques and local spatial statistics, this study explores the relationships between the real estate values in Tuscany with the individual perception of satisfaction by landscape types. The analysis includes the usual territorial variables such as proximity to urban centres and roads. The landscape values are measured through a sample of respondents who expressed their aesthetic-visual perceptions of different types of land use. Results from a multivariate local Geary highlight that house prices are not spatial independent and that between the variables included in the analysis there is mainly a positive correlation. Specifically, the findings demonstrate a significant spatial dependence in real estate prices. The aesthetic values influence the real estate price throughout more a spatial indirect effect rather than the direct effect. Practically, house prices in specific areas are more influenced by aspects such as proximity to essential services. The results seem to show to live close to highly aesthetic environments not in these environments. The results relating to the distance from the main roads, however, seem counterintuitive. This result probably depends on the evidence that these areas suffer from greater traffic jam or pollution or they are preferred for alternative uses such as for locating industrial plants or big shopping centres rather than residential use. Therefore, these effects decrease house prices.",18.0,https://www.mdpi.com/2071-1050/13/4/2236/pdf?version=1613809472,True,,Geography,2021-02-19,d489781f49c7c107af16c21bd0279d71e4d41a4e,theimpactsinrealestateoflandscapevaluesevidencefromtuscanyitaly,riccioli,10.3390/su13042236,theimpactsinrealestateoflandscapevaluesevidencefromtuscanyitaly|2021,1,2968,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,3,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",77edf6f3233b35bde823e71bdf993fb172c3fb34,10.3390/su12051844,,"The Spatial Optimization and Evaluation of the Economic, Ecological, and Social Value of Urban Green Space in Shenzhen",2020.0,Sustainability,Yu-Han Yu; Wenting Zhang; Peihong Fu; Wei Huang; Ke-Ke Li; K. Cao,"Urban green space (UGS) is important in urban systems, as it benefits economic development, ecological conservation, and living conditions. Many studies have evaluated the economic, ecological, and social value of UGS worldwide, and spatial optimization for UGS has been carried out to maximize its value. However, few studies have simultaneously examined these three values of UGS in one optimization system. To fill this gap, this study evaluated the economic value of UGS in terms of promoting housing prices, its ecological value through the relief of high land surface temperature (LST), and its social value through the provision of recreation spaces for residents within a 255 m distance. Subsequently, these three values were set as objectives in a genetic algorithm (GA)-based multi-objective optimization (MOP) system. Shenzhen was taken as the case study area. The results showed that the influencing distance of UGS in Shenzhen for house prices was 345 m, and the influencing distance of UGS for LST was 135 m. Using MOP, the Pareto solutions for increasing UGS were identified and presented. The results indicate that MOP can simultaneously optimize UGS’s economic, ecological, and social value.",29.0,https://www.mdpi.com/2071-1050/12/5/1844/pdf?version=1584010003,True,,,2020-03-01,77edf6f3233b35bde823e71bdf993fb172c3fb34,thespatialoptimizationandevaluationoftheeconomicecologicalandsocialvalueofurbangreenspaceinshenzhen,yu,10.3390/su12051844,thespatialoptimizationandevaluationoftheeconomicecologicalandsocialvalueofurbangreenspaceinshenzhen|2020,1,2708,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e214bad02ba010faf5d9c3edf93dffcb558da3f2,10.3390/su12187622,,Organization of the Building Space of Developments and Its Impact on Residential Housing Prices,2020.0,Sustainability,Edward Preweda; E. Jasińska,"This study has examined the process of urban space development in Krakow (southern Poland) using two developments as an example. The planning documents were analysed and spatial data obtained from the CAPAP (Centre for Spatial Analysis of Public Administration) project were processed. The data source for analysis comprised 2D outlines of certain buildings from the topographic object database (BDOT10k), elevation data from lidar aerial laser scanning at a density of 12 points per square meter and a numerical terrain model (NMT) with a one-meter mesh grid. In addition, existing models of the boundaries of the plots including their numbers and the ownership structure were used. On this basis, a digital mock-up of the analysed site was created. The issue surrounding the purchasing of these sites is interesting, since on the primary market, the apartments are bought long before the start of the first construction works. For this reason, the buyer is not necessarily aware of how the buildings surrounding his/her property will ultimately look, including whether or not they will obscure the sun’s rays. Until now, there has been no need to analyse the density of buildings, as there was no definitive impact of this feature on property prices; discussions were only held in relation to one of the developments built, as to how the sustainable development of the city should look. The positive actions applied during the implementation of the investment were detailed and the negative aspects of the resulting developments were identified. This allowed general analysis of land use development. The work used a model of regressive trees to determine the impact of certain attributes on the property prices.",6.0,https://www.mdpi.com/2071-1050/12/18/7622/pdf?version=1600245465,True,,Business,2020-09-16,e214bad02ba010faf5d9c3edf93dffcb558da3f2,organizationofthebuildingspaceofdevelopmentsanditsimpactonresidentialhousingprices,preweda,10.3390/su12187622,organizationofthebuildingspaceofdevelopmentsanditsimpactonresidentialhousingprices|2020,1,3214,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",63f18f50e72aa07849e0ef5b775fc0c39a740632,10.3390/su12125051,,Infrastructures and Sustainability: An Estimation Model for a New Highway Near Genoa,2020.0,Sustainability,P. Rosasco; Leopoldo Sdino,"The economic development of a territory is strongly correlated to its level of infrastructure (railway, roads, etc.); the complexity of this type of works requires careful planning and design that cannot be separated from the assessment of the impacts generated on citizenship affected by the new infrastructures. This study deals with the instrument defined by the Liguria Region for the implementation of infrastructures through the instruments called “Programmi Regionali di Intervento Strategico—P.R.I.S.” (Regional Strategic Intervention Programs) established by the Regional Law n. 39/2007. The aim of the P.R.I.S. is to guarantee the social protection of citizens that reside (as owners or tenants) or carry out economic activities in real estate units incompatible with the construction of the infrastructure, according to the main Italian law (Presidential Decree n. 327/2001) about the expropriation of private real estate for the construction of public works. In particular, the construction of a new link of the A7-A10-A12 motorway sections near the city of Genoa (called “Gronda”) is considered. The new infrastructure involves the expropriation of about 100 residential units and the relocation of about 50 production activities; the related P.R.I.S. defines the conditions that allow social cohesion through the recognition of indemnities for the expropriation of the real estate properties and the compensation of other expenses that the residents have to pay for their relocation. The valuation of the indemnities is developed through a multi-parameter model applicable for the estimation of real estate units (residential and productive) at a large-scale (mass appraisal); it is derived from the Market Comparison Approach and considers the most meaningful real estate characteristics. The aim is to develop a mass appraisal estimation model applicable in an easy way on real estate units with different destinations use. The model can be applied for the estimation of ordinary and special indemnities to be recognized for owners and tenants affected by the expropriation of their real estate units for the construction of public projects.",5.0,https://www.mdpi.com/2071-1050/12/12/5051/pdf?version=1592734276,True,,Business,2020-06-20,63f18f50e72aa07849e0ef5b775fc0c39a740632,infrastructuresandsustainabilityanestimationmodelforanewhighwayneargenoa,rosasco,10.3390/su12125051,infrastructuresandsustainabilityanestimationmodelforanewhighwayneargenoa|2020,1,3657,1,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2500a10eca56dc69f0c8e84c57bb76f725cf77b0,10.3390/su11092627,,"Perspectives of Socio-Spatial Differentiation from Soaring Housing Prices: A Case Study in Nanjing, China",2019.0,Sustainability,Chun-Hui Liu; Wei-Xuan Song,"Launched in 1998, the market-oriented reform of urban housing has given urban housing the dual attributes of residence and investment, and led to the rapid growth of housing prices as well as the intensification of its spatial differentiation within cities. However, the spatial patterns of the differentiation and its mechanism as well as socio-spatial effects are rarely touched. This paper studies 3963 urban residential districts in central Nanjing and explores the socio-spatial differentiation pattern and process of the urban housing prices and its growth in Nanjing based on the sample data of housing transactions over 30 quarters during the period of 2009–2017. The paper concludes that, by splitting the research duration into phases of six quarters each, the average housing prices in Nanjing alternates between “rapid growth” and “relatively stable” phases. At the same time, this paper finds that the spatial heterogeneity of housing prices in the city has been enhanced constantly, and the price gap in different types of residential housing has been clearly widened. In combination with the price level, location characteristics and architectural attributes of residential districts, this paper has categorized housing in Nanjing into nine typical types in a comprehensive manner. Based on the differences in their spatial attributes such as location, comfort and scarcity etc., different types of residences exhibit different pricing and price-to-rent ratio growth models. Based on those findings, we discussed the mechanism of the socio-spatial differentiation of housing prices in Nanjing from the housing reform and strategies of urban renewal and expansion. Beyond that, we discussed the role of urban housing consumption in the process of (re)production of urban classes, and its negative effects on urban young people, rural immigrants and other disadvantaged families. At the end of the paper, the policy suggestion about the supply-side reform of the housing market to promote socio-spatial equity and sustainable development is also presented.",21.0,https://www.mdpi.com/2071-1050/11/9/2627/pdf?version=1557226234,True,,Economics,2019-05-07,2500a10eca56dc69f0c8e84c57bb76f725cf77b0,perspectivesofsociospatialdifferentiationfromsoaringhousingpricesacasestudyinnanjingchina,liu,10.3390/su11092627,perspectivesofsociospatialdifferentiationfromsoaringhousingpricesacasestudyinnanjingchina|2019,1,3569,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,cfd672ad9855be9ac99c22cd8049d89019f9c793,10.3390/su11133707,,Urban Green Spaces and Housing Prices: An Alternative Perspective,2019.0,Sustainability,V. Liebelt; Stephan Bartke; N. Schwarz,"Urban green spaces (UGS) are essential components of sustainable cities that provide many benefits to urban residents, such as recreation or aesthetics. Urban residents may be willing to pay for some of these ecosystem services. Indeed, studies investigating the formation of housing prices through hedonic pricing analysis have shown that UGS can influence housing prices. Hedonic pricing analysis puts housing units at the center of analysis. In this study, we investigate whether an alternative perspective provides additional insights into the effects of UGS on pricing. The proposed approach puts UGS into the center of analysis by applying an analysis of buffer zones to housing prices, thus, linking approaches from urban economics and landscape ecology. Such an analysis could deepen our understanding of the effects of UGS on housing prices by analyzing price–distance slopes around UGS-thus supporting more informed decisions on efficient UGS management and urban planning. Our results using a case study on Leipzig, Germany, demonstrate that the size of UGS affects price–distance slopes around them. We conclude that further investigations should be fathomed to unleash the potential of applying the analysis of buffer zones around UGS as a method to inform sustainable UGS design in cities.",60.0,https://www.mdpi.com/2071-1050/11/13/3707/pdf?version=1562395888,True,,Business,2019-07-06,cfd672ad9855be9ac99c22cd8049d89019f9c793,urbangreenspacesandhousingpricesanalternativeperspective,liebelt,10.3390/su11133707,urbangreenspacesandhousingpricesanalternativeperspective|2019,1,2803,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,1,2,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,spatial hedonic model housing,bc0cfca7611d731f598cbe87c2e0ca9a878f54fb,10.3390/su10113879,,Assessing Neighborhood Walkability Based on Usage Characteristics of Amenities under Chinese Metropolises Context,2018.0,Sustainability,Zelong Xia; Hao Li; Yuehong Chen,"In the age of rapid motorization, walking, as both a green travelling mode and the most common form of daily physical activity, has been given increasing attention in Chinese metropolises. Walkability describes the extent to which a neighborhood environment is walking-friendly and recently has been regarded as a potential impetus for inflated housing prices. In this paper, we develop a walkability measurement model considering residents’ usage characteristics of the amenity, which incorporates three key factors: (1) amenity usage frequency; (2) amenity selection diversity; (3) the distance decay effect. Accordingly, we employ the proposed method to the case of Nanjing City and identify a clear spatial pattern of spatial heterogeneity in walkability among the 4143 dwelling areas within it. The experimental results suggest that the distribution of the residential walkability score varied greatly within Nanjing. It can be seen that dwelling areas with a high walkability score were clustered in the urban central regions and most dwelling areas showed a low walkability. Then, we utilized the hedonic price model to explore the correlation between neighborhood walkability and housing prices. The results show that the effects of community walkability on housing prices were statistically significant in Nanjing. Thus, we can infer that high walkability communities generally have concentrated rich amenity resources, and consequently have high property values.",23.0,https://www.mdpi.com/2071-1050/10/11/3879/pdf?version=1540460686,True,,Economics,2018-10-25,bc0cfca7611d731f598cbe87c2e0ca9a878f54fb,assessingneighborhoodwalkabilitybasedonusagecharacteristicsofamenitiesunderchinesemetropolisescontext,xia,10.3390/su10113879,assessingneighborhoodwalkabilitybasedonusagecharacteristicsofamenitiesunderchinesemetropolisescontext|2018,1,2972,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || hedonic price model amenities residential || housing characteristics prices hedonic || spatial hedonic model housing",2,2,3,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),7eadd470dbd0528a230b0d82610a65f8d5102be7,10.3390/jrfm16060291,,Delineating Housing Submarkets Using Space–Time House Sales Data: Spatially Constrained Data-Driven Approaches,2023.0,Journal of Risk and Financial Management,Meifang Chen; Y. Chun; D. Griffith,"With the increasing availability of large volumes of space–time house data, delineating space–time housing submarkets is of interest to real estate agents, homebuyers, urban policymakers, and spatial researchers, among others. Appropriately delineated housing submarkets can help nurture submarket monitoring and housing policy developments. Although submarkets are often expected to represent areas with similar houses, neighborhoods, and amenities characteristics, delineating spatially contiguous areas with virtually no fragmented small areas remains challenging. Furthermore, housing submarkets can potentially change over time along with concomitant urban transformations, such as urban sprawl, gentrification, and infrastructure improvements, even in large metropolitan areas, which can complicate delineating submarkets with data for lengthy time periods. This study proposes a new method for integrating a random effects model with spatially constrained data-driven approaches in order to identify stable and reliable space–time housing submarkets, instead of their dynamic changes. This random effects model specification is expected to capture time-invariant spatial patterns, which can help identify stable submarkets over time. It highlights two spatially constrained data-driven approaches, ClustGeo and REDCAP, which perform equally well and produce similar space–time housing submarket structures. This proposed method is utilized for a case study of Franklin County, Ohio, using 19 years of space–time private house transaction data (2001–2019). A comparative analysis using a hedonic model demonstrates that the resulting submarkets generated by the proposed method perform better than popular alternative submarket creators in terms of model performances and house price predictions. Enhanced space–time housing delineation can furnish a way to better understand the sophisticated housing market structures, and to help enhance their modeling and housing policy. This paper contributes to the literature on space–time housing submarket delineations with enhanced approaches to effectively generate spatially constrained housing submarkets using data-driven methods.",6.0,https://www.mdpi.com/1911-8074/16/6/291/pdf?version=1685703681,True,JournalArticle,,2023-06-02,7eadd470dbd0528a230b0d82610a65f8d5102be7,delineatinghousingsubmarketsusingspacetimehousesalesdataspatiallyconstraineddatadrivenapproaches,chen,10.3390/jrfm16060291,delineatinghousingsubmarketsusingspacetimehousesalesdataspatiallyconstraineddatadrivenapproaches|2023,1,3684,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,1,4,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",84aedeb2329b388be1bf74253e3c2cd2586be562,10.3390/jrfm16010046,,The Impact of the 2008 Financial Crisis on Lisbon’s Housing Prices,2023.0,Journal of Risk and Financial Management,João Fragoso Januário; C. Cruz,"Real estate markets are frequently affected by growth and contraction cycles. Given the social and economic impacts of changes on real estate prices, the understanding of these cycles is crucial from a socio-economic perspective, but also, and more importantly, from a public policy view. The literature has provided several contributions focusing on the deconstruction of the main determinants of housing prices. This research focuses on the analysis of housing prices variation with a particular emphasis on the analysis of the impacts of the 2008 financial crisis. Within the existing body of knowledge, few studies have focused on this particular issue, and even fewer have focused on countries where the financial crisis led to an external bailout, as was the case in Portugal. The analysis confirmed that the 2008 financial crisis had a negative impact on real estate prices, and the ex-post growth in GDP and low interest rates had a positive impact. The paper also provides a long-term analysis of housing price trends over the last decades.",13.0,https://www.mdpi.com/1911-8074/16/1/46/pdf?version=1674051376,True,,,2023-01-12,84aedeb2329b388be1bf74253e3c2cd2586be562,theimpactofthe2008financialcrisisonlisbonshousingprices,januário,10.3390/jrfm16010046,theimpactofthe2008financialcrisisonlisbonshousingprices|2023,1,2549,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",744649b4f02d97b30ff0e6ba7b373f746305e8b7,10.3390/s19143167,,"Potentiality of Using Luojia1-01 Night-Time Light Imagery to Estimate Urban Community Housing Price—A Case Study in Wuhan, China",2019.0,Italian National Conference on Sensors,Chang Li; Linqing Zou; Yi-Jin Wu; Hui-Min Xu,"The first professional night-time light remote sensing satellite in China, Luojia1-01, has raised the resolution of night-time light data to 130 m, which provides a possibility for the study of small-scale night-time light. This paper is the first research on spatial analysis and quantitative modeling between night-time light intensity (NTLI) and community housing price (CHP) on a small scale by using the Luojia1-01 night-time light imagery. This paper takes Wuhan as the research area, CHP data obtained by web-crawler technology as the research object, combines Luojia1-01 data, and carries out spatial correlation analysis and quantitative modeling on a small scale for them. The experimental results show that there is a strong linear positive correlation between the NTLI and CHP based on geographically weighted regression (GWR), and the CHP data in Wuhan have obvious spatial non-stationarity. Moreover, the coupling mechanism between the NTLI and CHP is also revealed. We can conclude that there is potential for estimating the CHP by using Luojia1-01 night-time light imagery.",21.0,https://www.mdpi.com/1424-8220/19/14/3167/pdf,True,JournalArticle,Medicine;Environmental Science;Computer Science,2019-07-01,744649b4f02d97b30ff0e6ba7b373f746305e8b7,potentialityofusingluojia101nighttimelightimagerytoestimateurbancommunityhousingpriceacasestudyinwuhanchina,li,10.3390/s19143167,potentialityofusingluojia101nighttimelightimagerytoestimateurbancommunityhousingpriceacasestudyinwuhanchina|2019,1,2589,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),38cb7c07e1fde762f81e69925a9125ec2746c338,10.11361/journalcpij.54.818,,Rent and Price Estimation of Real Estate with Different Data,2019.0,Journal of the City Planning Institute of Japan,Midori Maeda; Y. Sekimoto; T. Seto,"* 正会員 国土交通省 東北地方整備局 (Tohoku Regional Development Bureau, Ministry of Land, Infrastructure, Transport and Tourism) ** 正会員 東京大学 空間情報科学研究センター (Center for Spatial Information Science, The University of Tokyo) ***非会員 東京大学 生産技術研究所 (Institute of Industrial Science, The University of Tokyo) 1. 研究の背景と目的 近年都市部では民間企業主導の再開発事業が活発に 行われおり,一方地方部では地方活性化やコンパクトシティ の流れにより民間企業ならびに行政主導の地域開発が将 来的により一層促進され,今後多くの建築物が建設されるこ とが考えられる.そういった状況下で,建設する物件の価値 を知りたいという事業者のニーズがある.ここでの価値とは, 賃料や販売価格などといった,物件の値打ちを定量的に表 したものを意味する.また,開発計画策定にあたり地域の持 つ特徴を把握し,開発対象地の決定・開発計画の策定を行 いたいという事業者のニーズがある. 上記のニーズに対して, 全国をカバーし,可能な限りオープンで汎用的に入手可能 なデータを活用した件の賃料推定・地域指標分析システム を構築し,地域指標の分析のみならず開発物件の賃料の 推定ならびに開発による地域への影響の評価を可能にする ということを最終的な目的としている.前研究では,その初 段として,開発物件の賃料推定のための建物属性・地域属 性両方を加味したモデルの構築を行い,地域属性が賃料 に与える影響,すなわち建物が立地する地域が賃料に与え る影響を地域ポテンシャルと定義し,その可視化を行なった. 物件の価値を決定づける要因の分析には,従来ヘドニッ クアプローチが広く用いられてきた.一方で,近年オー プンデータの増加等に伴い,(Deep) Neural Network のような 学習手法も多く用いられるようになってきている .両者の 推定精度を比較する研究は数多く行われているものの,用 いるデータの量やデータの特性,パッケージの種類,被説 明変数と説明変数の関係が線形なのか否かによって推定 精度の優劣が変わるとされている 8), 9), 10), . 前研究では,At Home 社のデータ(不動産データライブラリー 戸データ 全 国データセット,以下 At Home データという)を用いて Deep Neural Network とヘドニックの比較を行い,ヘドニックよりも Deep Neural Network の推定精度が高いという結論に至った. しかしながら異なる特性を持つデータを用いて Neural Networkの優位性を検証することはしていない.そこで,異な るデータを用いて,ヘドニックと DNN で物件の価値推定モ デルを構築し,各々の特徴について比較・分析することが必 要である. 上記の課題に対して,本研究では性質の異なる2つのデー タを用いて比較・分析する.At Home データは個々の貸出 物件毎に登録されているデータであり,物件の価値を表す 項目としては物件の月額賃料がある.小規模物件から比較 的大規模な物件まで多岐に渡って登録されており,物件の サンプル数は多いが,建物用途のカテゴリーが少ない.また, 物件の所在地を表す住所は町字まで整理されている.他方, 不動産証券化協会(ARES)によって整備されている ARES JREIT Property Database(以下 ARES データという)は,J-REIT (日本の不動産法人)が保有する物件情報データベースを 指し,建物ごとに登録されているデータであり,物件の価値 を表す項目として建物の取得価格がデータに含まれている. 物件のサンプル数はAt Home の45 分の1程度であるが,用 途地域のカテゴリーが含まれており, さらに建物用途のカテ ゴリーが非常に細かく,建物の所在地を表す住所も番地ま で含んでいる.一般的に, サンプル数が多いほどパラメータ の推定が行いやすく, 推定結果の信頼性は増す. 一方で データ項目が多いほど, 物件のあらゆる特徴を加味した推 定を行うことができ, 推定精度が高くなることが期待できる. 異なるタイプの不動産データを用いた不動産価値推定モデルの研究 ヘドニックとDeep Neural Networkの比較 Rent and Price Estimation of Real Estate with Different Data Comparison of Hedonic Model and Deep Neural Network Model -",0.0,https://www.jstage.jst.go.jp/article/journalcpij/54/3/54_818/_pdf,True,,,2019-10-25,38cb7c07e1fde762f81e69925a9125ec2746c338,rentandpriceestimationofrealestatewithdifferentdata,maeda,10.11361/journalcpij.54.818,rentandpriceestimationofrealestatewithdifferentdata|2019,1,3565,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),3,1,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1d29800bb679afac1c4a58ef1db23572606eb1e5,10.14246/irspsd.2.2_64,,The Influence of Metro Station Development on Neighbourhood Quality,2014.0,,R. Kheyroddin; A. Taghvaee; Amir Forouhar,"Introducing urban public transportation facilities into a region often creates a significant opportunity to accelerate the process of development through affecting on the property values. Better access, more mobility options, and lower transportation costs are important factors that increase the land values of such regions, especially in blighted and poor neighbourhoods. This issue is important in Tehran Metropolis, because during the past decades an obvious kind of spatial segregation has been formed between the rich north (with high spatial quality) and the poor south (with low spatial quality) due to unequal distribution of opportunities and resources. On the other side, according to the population demand, Metro System is being developed rapidly in the city. Thus a comparative field study on the impacts of metro stations on property values in Tehran can represent not only the various impacts of metro station establishing on different urban textures, but also its consequences on reducing the spatial segregation in the city. We presume that the poor areas under discussion (southern regions) have received more benefits than the rich areas in terms of residential property values. So, establishing of metro stations in the poor and blighted regions of the city can improve the development potentials at these areas. Such improvement in spatial quality of the southern neighbourhoods of the city can reduce the spatial segregation in Tehran Metropolis. The hypothesis has been tested with a field study around Shari'ati Street Metro Station (in the northern part of Tehran) and Shohada Square Metro Station (in the southern part) with considering the quantitative (polynomial regression) and qualitative (personal interview) methods of impact assessment. The results show that establishing of metro stations has a consistently higher positive impact on the residential property values at the poor regions compared with the rich areas. Therefore, by means of acceleration in the values of the poor southern regions, Tehran's long-standing segregation will gradually diminish and subsequently the spatial urban integration will show itself, though in a rather long time.",15.0,https://www.jstage.jst.go.jp/article/irspsd/2/2/2_64/_pdf,True,,Geography,2014-04-15,1d29800bb679afac1c4a58ef1db23572606eb1e5,theinfluenceofmetrostationdevelopmentonneighbourhoodquality,kheyroddin,10.14246/irspsd.2.2_64,theinfluenceofmetrostationdevelopmentonneighbourhoodquality|2014,1,3684,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",1cb2244b68c3291edcf48ed0a573d6f0bb6e9245,10.3389/fams.2023.1133397,,The value of being the decisive voter: Exploratory evidence from a hedonic model,2023.0,Frontiers in Applied Mathematics and Statistics,E. Peters; Joshua C. Hall,"In the field of public economics, there is a literature on calculating the probability of being a decisive voter. The raison d'etre of this literature is to explain voter turnout. In this short empirical paper, we look at the question from a different angle. Heterogeneity in voting preferences means that some individuals vote rationally, others instrumentally, and some individuals are marginal and respond to changes in the probability of being decisive. Individuals with a preference for instrumental voting might be willing to pay more to live in an area with a greater probability of being a decisive voter. If this is the case, housing prices should reflect this higher willingness to pay. We test our theory using hedonic data from Columbus Ohio and find suggestive evidence that voters are willing to pay for a greater likelihood of being a decisive voter. Our results, however, cannot rule out other interpretations.",0.0,https://www.frontiersin.org/articles/10.3389/fams.2023.1133397/pdf,True,JournalArticle,Computer Science,2023-02-03,1cb2244b68c3291edcf48ed0a573d6f0bb6e9245,thevalueofbeingthedecisivevoterexploratoryevidencefromahedonicmodel,peters,10.3389/fams.2023.1133397,thevalueofbeingthedecisivevoterexploratoryevidencefromahedonicmodel|2023,1,2426,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,2,1,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1f0be637068f0974b2c62f28f9a662f03f0f2423,10.54254/2753-8818/51/2024ch0199,,Research on the Influencing Factors of House Prices based on Multiple Linear Regression Model,2024.0,Theoretical and Natural Science,Ruibin Li,"Abstract. House prices have risen rapidly, and finding the factors affecting house prices is significant for regulating house prices. Existing literature discusses the factors affecting house prices, but does not analyze the key factors affecting house prices and get a good fit with a multiple linear regression model. Therefore, this paper is of practical significance for the selection and fitting of key variables that explain the rise in house prices. In Spearman's correlation analysis, strong correlation and covariance diagnostics were found for gross product. After linear fitting, it is found that gross product, commercial real estate sales area will have an impact on house prices. The results of this paper on the impact of GDP and commercial real estate sales area on housing prices are conducive to the relevant agencies to take measures to adjust housing prices. At the same time, for people who have the need to buy a house, understanding the main influencing factors is conducive to consumers' initial judgment on the trend of house prices, and then make a decision.",0.0,https://www.ewadirect.com/proceedings/tns/article/view/16408/pdf,True,JournalArticle,,2024-11-01,1f0be637068f0974b2c62f28f9a662f03f0f2423,researchontheinfluencingfactorsofhousepricesbasedonmultiplelinearregressionmodel,li,10.54254/2753-8818/51/2024ch0199,researchontheinfluencingfactorsofhousepricesbasedonmultiplelinearregressionmodel|2024,1,2584,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,1,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cd19ddf7eaaf394a62a98d9d83e3a4c7d145761f,10.1108/jerer-06-2020-0037,,Do segregated housing markets have a spillover effect on housing prices in nearby residential areas?,2021.0,,Mohammad Ismail; Abukar Warsame; Mats Wilhelmsson,"From an international perspective, housing segregation in Sweden is considered relatively low. However, in recent years the issue has been raised and problematized. For example, some studies show that ethnic housing segregation increased in 199 of Sweden's 285 municipalities during the years 2005- 2015. The purpose of this project is to analyze the trends regarding housing segregation over the past 10-20 years, and whether housing segregation has a spillover effect on neighboring housing areas. Namely, does proximity to a specific type of segregated housing market has a negative impact, while another type of segregation has a positive impact, on nearby housing markets. The results indicate that segregation measured as income sorting has increased over time in some of the housing markets. Its effects on housing values in neighboring housing areas are significant and statistically significant.",7.0,https://www.emerald.com/insight/content/doi/10.1108/JERER-06-2020-0037/full/pdf?title=do-segregated-housing-markets-have-a-spillover-effect-on-housing-prices-in-nearby-residential-areas,True,,Economics;Business,2021-05-27,cd19ddf7eaaf394a62a98d9d83e3a4c7d145761f,dosegregatedhousingmarketshaveaspillovereffectonhousingpricesinnearbyresidentialareas,ismail,10.1108/jerer-06-2020-0037,dosegregatedhousingmarketshaveaspillovereffectonhousingpricesinnearbyresidentialareas|2021,1,2404,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,298786322ef938b6c42546d71394a6d94fd55f2f,10.1108/ijhma-04-2020-0034,,Brokers’ list price setting in an auction context,2021.0,,R. Hungria-Gunnelin; F. Kopsch; Carl Johan Enegren,"Purpose – The role of list price is often discussed in a narrative describing sellers’ preferences or sellers’ price expectations. This paper aims to investigate a set of list price strategies that real estate brokers have available to influence the outcome of the sale, whichmay be many times self-serving. Design/methodology/approach – By analyzing real estate brokers’ arguments on the choice of the list price level, a couple of hypotheses are formulated with regard to different expected outcomes that depend on the list price. This study empirically tests two hypotheses for the underlying incentives in the choice of list price from the real estate broker’s perspective: lower list price compared to market value leads to the higher sales price, lower list price compared to market value leads to a quicker sale. To investigate the two hypotheses, this paper adopts different methodological frameworks: H1 is tested by running a classical hedonic model, while H2 is tested through a duration model. This study further tests the hypotheses by splitting the full sample into two different price segments: above and below the median list price. Findings – The results show that H1 is rejected for the full sample and for the two sub-samples. That is, contrary to the common narrative among brokers that underpricing leads to a higher sales price, underpricing lower sales price. H2, however, receives support for the full sample and for the two sub-samples. The latter result points to that brokers may be tempted to recommend a list price significantly below the expected selling price to minimize their effort while showing a high turnover of apartments. Originality/value – Although there are a large number of previous studies analyzing list price strategies in the housing market, this paper is one of the few empirical studies that address the effect of list price choice level on auction outcomes of non-distressed housing sales.",4.0,https://www.emerald.com/insight/content/doi/10.1108/IJHMA-04-2020-0034/full/pdf?title=brokers-list-price-setting-in-an-auction-context,True,,Business,2021-04-09,298786322ef938b6c42546d71394a6d94fd55f2f,brokerslistpricesettinginanauctioncontext,hungria-gunnelin,10.1108/ijhma-04-2020-0034,brokerslistpricesettinginanauctioncontext|2021,1,3440,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices,2,3,2,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7536e063ce8f2b920b206deec418fa6cdba91cd3,10.5089/9781475552249.001,,How to Better Measure Hedonic Residential Property Price Indexes,2016.0,Social Science Research Network,M. Silver,"Hedonic regressions are used for property price index measurement to control for changes in the quality-mix of properties transacted. The paper consolidates the hedonic time dummy approach, characteristics approach, and imputation approaches. A practical hedonic methodology is proposed that (i) is weighted at a basic level; (ii) has a new (quasi-) superlative form and thus mitigates substitution bias; (iii) is suitable for sparse data in thin markets; and (iv) only requires the periodic estimation of hedonic regressions for reference periods and is not subject to the vagrancies of misspecification and estimation issues.",48.0,https://www.elibrary.imf.org/doc/IMF001/23813-9781475552249/23813-9781475552249/Other_formats/Source_PDF/23813-9781475555332.pdf,True,,Mathematics;Economics,2016-11-01,7536e063ce8f2b920b206deec418fa6cdba91cd3,howtobettermeasurehedonicresidentialpropertypriceindexes,silver,10.5089/9781475552249.001,howtobettermeasurehedonicresidentialpropertypriceindexes|2016,1,2127,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || cites:sirmans2006",2,1,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9be16c1062e365367a91bac0d9c9590c43121d35,10.1177/0042098015606595,,The spatial dimension of US house prices,2017.0,,Katharina Pijnenburg,,33.0,https://www.econstor.eu/bitstream/10419/215791/1/Pijnenburg-2015-The%20Spatial%20Dimension%20of%20US%20Ho%20-%20Kopie.pdf,True,JournalArticle,Economics,2017-02-01,9be16c1062e365367a91bac0d9c9590c43121d35,thespatialdimensionofushouseprices,pijnenburg,10.1177/0042098015606595,thespatialdimensionofushouseprices|2017,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",68e61ee03aa1fc5b3fb90df84383bb9ceb84edc6,10.1051/e3sconf/20199108049,,Residential real estate value assessment considering the influence of various factors,2019.0,E3S Web of Conferences,A. Minnullina,"Interest in modern methods of assessing the value of real estate in the housing market is fuelled by the increased business activity, increased competition and changes in the taxation system. In this regard, the article proposes a method for assessing the value of real estate based on the analysis of various factors influence degree. The proposed recommendations are based on correlation and regression analysis. They allow for an absolute and relative adjustment of the residential property value. In order to confirm its practical application, a possible adjustment of the property considering the influence of the “quality of repair” factor is provided.",3.0,https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/17/e3sconf_tpacee2019_08049.pdf,True,Conference,Business,,68e61ee03aa1fc5b3fb90df84383bb9ceb84edc6,residentialrealestatevalueassessmentconsideringtheinfluenceofvariousfactors,minnullina,10.1051/e3sconf/20199108049,residentialrealestatevalueassessmentconsideringtheinfluenceofvariousfactors|2019,1,2158,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",93146d3bddf257d7022e254036ddf165f5d00734,10.61173/wv98zg20,,Research on the Prediction of the Housing Pricing in China,2024.0,"Science and Technology of Engineering, Chemistry and Environmental Protection",Xin-Yi Hu,"As an important cornerstone of industrial development, real estate has been widely concerned by people. A house can not only provide a place of peace for people, but also provide a certain source of income for many people. A stable house price will give people confidence in their economy. After 2007, there have been obvious changes in house prices, which further increase the difficulty of forecasting and reduce the accuracy of forecasting. As a typical forecasting model, linear regression is favored by scholars all over the world for its accuracy and adaptability. This paper will use this model to forecast the house price in the next year. The conclusion is as follows: In the coming year, the overall trend will show a more obvious upward trend. Therefore, it is recommended to invest in real estate in the short term. At the same time, this paper believes that ensuring the stability of domestic housing prices can improve people’s quality of life and enhance the status of the country in the hearts of the people.",0.0,https://www.deanfrancispress.com/index.php/te/article/download/1715/TE003302.pdf,True,JournalArticle,,2024-09-15,93146d3bddf257d7022e254036ddf165f5d00734,researchonthepredictionofthehousingpricinginchina,hu,10.61173/wv98zg20,researchonthepredictionofthehousingpricinginchina|2024,1,2524,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,hedonic housing prices,5b85ac3400803502f29fd0eb3d3da85c444bf19e,10.2139/ssrn.3699240,,Redrawing of a Housing Market: Insurance Payouts and Housing Market Recovery in the Wake of the Christchurch Earthquake of 2011,2020.0,Social Science Research Network,Cuong Nguyen; Ilan Noy; D. E. Sommervoll; Fang Yao,"On the 22nd of February 2011, much of the residential housing stock in the city of Christchurch, New Zealand, was damaged by an unusually destructive earthquake. Almost all of the houses were insured. We ask whether insurance was able to mitigate the damage adequately, or whether the damage from the earthquake, and the associated insurance payments, led to a spatial re-ordering of the housing market in the city. We find a negative correlation between insurance pay-outs and house prices at the local level. We also uncover evidence that suggests that the mechanism behind this result is that in some cases houses were not fixed (i.e., owners having pocketed the payments) - indeed, insurance claims that were actively repaired (rather than paid directly) did not lead to any relative deterioration in prices. We use a genetic machine-learning algorithm which aims to improve on a standard hedonic model, and identify the dynamics of the housing market in the city, and three data sets: All housing market transactions, all earthquake insurance claims submitted to the public insurer, and all of the local authority’s building-consents data. Our results are important not only because the utility of catastrophe insurance is often questioned, but also because understanding what happens to property markets after disasters should be part of the overall assessment of the impact of the disaster itself. Without a quantification of these impacts, it is difficult to design policies that will optimally try to prevent or ameliorate disaster impacts.",5.0,https://www.cesifo.org/DocDL/cesifo1_wp8560.pdf,True,,Business,,5b85ac3400803502f29fd0eb3d3da85c444bf19e,redrawingofahousingmarketinsurancepayoutsandhousingmarketrecoveryinthewakeofthechristchurchearthquakeof2011,nguyen,10.2139/ssrn.3699240,redrawingofahousingmarketinsurancepayoutsandhousingmarketrecoveryinthewakeofthechristchurchearthquakeof2011|2020,1,3049,5,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic house price || hedonic housing prices || machine learning hedonic pricing housing || spatial hedonic model housing",2,1,4,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",59ccd188a58010d138a76e36cae65ffbd79a4645,10.4314/hpjsmt.v6i2.12,,Spatial impact of waste dumpsites on proximate housing values and household income in some selected metropolitan cities in North-western Nigeria,2025.0,"HAFED POLY Journal of Science, Management and Technology",A. A. Yakub; Aminu Mansir; S. D. Gimba; Z. M. Zaidu; Adebayo Falilat Folake,"This study investigates the consequence of waste dumpsites pollution on proximate housing property value on the one hand, and also its consequences on household earnings on the other hand in north-western Nigeria, thereby incorporating a 2-way relationship between pollution and real estate value, and pollution and income. Using relative distance from waste dumpsites, a survey of resident’s income level and an analysis of property data makes up the research technique. Thus, randomly selected houses situated around waste dumping sites within the selected survey area were administered structured questionnaires to elicit necessary information for the survey in order to ascertain the degree to which the neighborhood's residents' income level and the rent they pay for their houses are impacted by the proximity of a garbage dumpsite. Through a quantitative technique and cross sectional survey that includes questionnaire surveys and field research, the study gathered data, which was analysed to test the premise that neighborhoods that are exposed to high pollutants command lower property values, and that such neighbourhoods are often occupied by poorer residents than areas not exposed to pollution. The results indicated that across the three metropolitan cities that were adopted as case study, increase in residential property values cum household income were evident as distance away from the waste dumpsites increased indicating that properties in close proximity to waste dumpsites suffered value loss likewise such locations are dominated by low income earners. Hence, property value and household income depreciation relative to distance (in meters) within the concentric rings for all the dumpsites studied.",0.0,https://www.ajol.info/index.php/hpjsmt/article/download/309044/290601,True,JournalArticle;Review,,2025-10-28,59ccd188a58010d138a76e36cae65ffbd79a4645,spatialimpactofwastedumpsitesonproximatehousingvaluesandhouseholdincomeinsomeselectedmetropolitancitiesinnorthwesternnigeria,yakub,10.4314/hpjsmt.v6i2.12,spatialimpactofwastedumpsitesonproximatehousingvaluesandhouseholdincomeinsomeselectedmetropolitancitiesinnorthwesternnigeria|2025,1,3226,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,4,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,apartment prices hedonic,c561b6f62e3567245fe8a196863f9f40523be96b,10.5821/dissertation-2117-95032,,"Modelo para maximizar ingresos de nuevos proyectos inmobiliarios, por medio de la selección de la mejor combinación de atributos y características de las viviendas, para una determinada localización.",2013.0,,Roberto Schovelin Surhoff,"The thesis goal is to build a model that defines the characteristics that a Residential Real State Project and the houses in it must have in order to get the highest income possible at the moment of getting it into the market. The design model defines for each house type, according to the constructed surface, the land surface if it is a house; the amount of units and the unit price planned for each one. This depends on the combination of the location characteristics. These relations are obtained from hedonic projects carried out in apartments, detached houses and condominiums. The design model is an optimized model that maximizes the residential project¿s income allowing the variable characteristics to be adjusted and delimited according to physical, legal, architectural and economic restrictions. The design model was build at Concepcion City, Chile for three types of houses. The apartment model was build based on 37 projects and 201 house types. The price was calculated based on 34 variables and gives the Real State Developer 21 design characteristics. The condominium house model was build with 12 projects and 32 house types. The hedonic models were tested for 6 projects already started on apartment buildings, 7 in detached houses and 5 in condominium houses. In all cases the hedonic price was within the allowed error margin if it was bought at the price set by the developer. Finally the design model was successfully tested, when designing housing projects that maximize the income that can be obtained from it. El objetivo de esta tesis es construir un modelo para diseñar un proyecto de viviendas y las viviendas, en términos de atributos. El modelo entrega al gestor inmobiliario el diseño que maximiza el ingreso que se puede obtener por el proyecto de viviendas en una localización predeterminada, ajustando las variables superficie de las viviendas y precio proyectado, que se obtiene de la combinación de atributos que poseen los modelos de viviendas del proyecto. Las variables están acotadas según corresponda por restricciones físicas, legales, arquitectónicas y económicas. Para construir el modelo se combinaron un modelo hedónico con un modelo de optimización. El modelo se aplicó específicamente en la ciudad de Concepción en Chile y, para obtener mejores resultados, las viviendas se separaron en tres tipos. El modelo de departamentos entrega al gestor inmobiliario 21 variables, el de casas independientes 15 variables y el de casas en condominio 13 variables.",0.0,https://upcommons.upc.edu/bitstream/2117/95032/1/TRSS1de1.pdf,True,,Geography,2013-07-04,c561b6f62e3567245fe8a196863f9f40523be96b,modeloparamaximizaringresosdenuevosproyectosinmobiliariospormediodelaselecciondelamejorcombinaciondeatributosycaracteristicasdelasviviendasparaunadeterminadalocalizacion,surhoff,10.5821/dissertation-2117-95032,modeloparamaximizaringresosdenuevosproyectosinmobiliariospormediodelaselecciondelamejorcombinaciondeatributosycaracteristicasdelasviviendasparaunadeterminadalocalizacion|2013,1,4008,6,apartment prices hedonic || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || modelo de precios hedónicos vivienda || precios hedónicos vivienda,3,0,8,1,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,modelo de precios hedónicos vivienda,1db7cc73a8606e5ba21dfac1f5a0da6d535e3f89,10.5821/dissertation-2117-93437,,"Espacio ""privatizado"". El valor de privatizar un espacio público en zona urbana",2007.0,,José Refugio Rojas Lopez,"A través de la historia de la ciudad se vienen contemplando diferentes ideas, propuestas de solución al problema de la vivienda y su forma de ordenarla dentro del territorio urbano. En la actualidad una de las formas de presentarse estos modelos de urbanización son los fraccionamientos cerrados. Esta urbanización tiene como característica principal o singular el muro que delimita la zona de vivienda con el resto de la ciudad.El crecimiento en distintos países de la topología denominada en el presente trabajo como Ciudad Privatizada, llama a la reflexión sobre el tema y a conocer mas sobre el. Si bien, se establecen las características de estas topologías, se hecha en falta estudios que permitan conocer como afecta a los distintos sectores de la ciudad, así como los beneficios y obligaciones de las mismas.La Ciudad Privatizada, es una propuesta, cuyos inicios aparecen a principios del siglo XX, y evoluciona hasta llegar a los años 80 donde se presenta como la conocemos hasta hoy día. Actualmente se ha convertido en una alternativa importante para el desarrollador inmobiliario. Además, ha logrado tener una importante penetración en el urbanismo de países tan diversos como distintos.Este espacio privatizado se entiende como producto de la búsqueda de satisfacer la demanda del mercado actual, lo cual equivale a un mercado internacional, globalizado, que promueve la movilidad, el intercambio de capital humano de las diferentes compañías multinacionales y que conlleva la necesidad de satisfacer las necesidades básicas de la demanda y sus variables culturales, ideológicas y de formas de vida.Del incremento citado sobre la demanda de esta topología nace dar respuesta a una hipótesis: la privatización de espacios públicos genera un incremento del valor y precio de la propiedad.El objetivo principal del estudio es demostrar o aproximar, cuál es el valor que genera la ciudad privatizada al privatizar el espacio público.Para lograr el objetivo se utilizaron los Métodos de Precios Hedónicos y Valoración Contingente.",1.0,https://upcommons.upc.edu/bitstream/2117/93437/2/02JRrl02de10.pdf,True,,Art,2007-07-13,1db7cc73a8606e5ba21dfac1f5a0da6d535e3f89,espacioprivatizadoelvalordeprivatizarunespaciopublicoenzonaurbana,lopez,10.5821/dissertation-2117-93437,espacioprivatizadoelvalordeprivatizarunespaciopublicoenzonaurbana|2007,1,3538,2,modelo de precios hedónicos vivienda || precios hedónicos vivienda,1,1,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,precios hedónicos vivienda,ac5df38ff1193e29bc1ed35d6ad8cf4f1c037be5,10.5821/ctv.7988,,Rentabilidad inmobiliaria & calidad de zona,2014.0,"9° Congresso Città e Territorio Virtuale, Roma, 2, 3 e 4 ottobre 2013",Jesús Quintana; Josep Roca; A. Ojeda,"El presente trabajo tiene el propósito de estimar la influencia de los elementos de calidad de zona, en la rentabilidad inmobiliaria; entendiendo por zona, al espacio urbano diferenciado dentro de la ciudad denominado barrio; y por calidad de zona, aquellos elementos por los cuales los compradores de vivienda están dispuestos a incrementar su disposición a pagar. La tasa de rentabilidad inmobiliaria, como la capacidad del bien inmueble para generar renta; la medida de esta capacidad se estima a partir del Método de Capitalización Directa, el cual se fundamenta en el conocimiento del valor del inmueble y los ingresos periódicos que genera. La calidad de zona, se estimada con base en indicadores relativos a: la accesibilidad, el entorno urbano, la edificación y la jerarquía social. Esta calidad se integra con la maximización de la presencia de las características de calidad en la zona, bajo el supuesto de igualdad de importancia, para lo cual se utiliza un indicador sintético que contempla las variaciones de los elementos observados. El indicador sintético de calidad de zona, parte de la recolección de información asociada a la calidad de barrio, donde la recogida de estas medidas producen una gran cantidad de variables, mismas que son tratadas con técnicas de reducción de la información, como son los Componentes Principales y la Distancia Ponderada 2. La relación entre indicador de rentabilidad inmobiliaria y el indicador de calidad de zona, se establece a partir de modelos de precios hedónicos, resueltos con técnicas de regresión lineal múltiple. The present study aims to estimate the influence of the elements of quality of zone in the real estate profitability rate; understanding by zone, to the differentiated urban space within the city, called neighborhood, and quality of zone, those elements by which buyers are willing to increase their willingness to pay. The real estate profitability rate, as the ability of the property to generate income, the measure of this capacity is estimated from the Direct Capitalization Method, which is based on knowledge of the property value and the income generating periodic. The quality of zone, is estimated based on indicators related of: quality of the urban environment, the building, the accessibility and the social hierarchy. This quality is integrated with maximizing the presence of quality features in the area, under the assumption of equal importance, for which it uses a synthetic indicator, that provides the changes of the elements observed. The synthetic indicator of quality of zone, part of the collection of information associated with the quality of neighborhood, where the collection of these measures produce a large number of variables that are treated with reduction techniques of information, such as Components Principal and Distance Weighting 2. The relationship between real estate profitability rate and indicators of quality of zone, is set from hedonic pricing models, solved by multiple linear regression techniques.",2.0,https://upcommons.upc.edu/bitstream/2099/16467/1/1503_1526%20POLIT%20URB%20PROC%20TERRIT%20CIUDAD.pdf,True,,Biology;Art,2014-09-01,ac5df38ff1193e29bc1ed35d6ad8cf4f1c037be5,rentabilidadinmobiliariacalidaddezona,quintana,10.5821/ctv.7988,rentabilidadinmobiliariacalidaddezona|2014,1,4551,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || modelo de precios hedónicos vivienda || precios hedónicos vivienda || real estate hedonic regression",3,2,1,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1ce6b4a62036a2a4717dc7e5687e085dfd34c4d5,10.54254/2753-8818/52/2024ch0125,,Factors influencing housing prices: A case study of Shanghai,2024.0,Theoretical and Natural Science,X. Fang,"Housing is a major consumer good for residents, and housing prices affect people's quality of life and happiness. This paper analyzes the factors influencing housing prices based on the housing price dataset for Shanghai in 2023-2024. Comparison is made via multiple linear regression model to discover the difference between with and without considering interaction terms, assessing the importance and accuracy of the two models' results, and optimizing the model. According to the analysis, living area, decoration type, and building usage are the factors most positively correlated with housing prices. The number of bathrooms, living rooms, and bedrooms also shows a significant positive correlation with housing prices. Additionally, building type and the construction age of the house are negatively correlated with housing prices, and this correlation is very significant. Finally, the housing area weakens the positive impact of the number of bathrooms and bedrooms on housing prices.",0.0,https://tns.ewapub.com/article/view/15623.pdf,True,JournalArticle,,2024-09-27,1ce6b4a62036a2a4717dc7e5687e085dfd34c4d5,factorsinfluencinghousingpricesacasestudyofshanghai,fang,10.54254/2753-8818/52/2024ch0125,factorsinfluencinghousingpricesacasestudyofshanghai|2024,1,2492,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d38f630e7f22d16115f90355ad9950f0b3c0aac2,10.54254/2753-8818/38/20240561,,Research on housing prices prediction based on multiple linear regression,2024.0,Theoretical and Natural Science,Qi-Jiang Wang,"With the steady development of social economy, commercial housing, as an important real estate, occupies a large proportion in family assets. According to the China Household Wealth Survey Report (2018) compiled by the Social China Economic Trends Institute, household net worth accounts for 70% of household wealth, including housing prices in Beijing and Shanghai. In higher cities, the proportion is as high as 80%. This paper analyzes the transaction data of about 10,000 second-hand houses in Beijing, constructs a multiple regression model with SPSS software, and obtains the dependent variable (housing price per unit area). The dataset used in this paper is fetched from the Kaggle website (Housing Price in Beijing). The results show that the relationship between the elevator, the floor situation, the decoration method, the administrative division and other independent variables. Also, it is shown that the correlation between the two is significant, so the model can be used. This paper provides reference for the actual transaction of second-hand housing in Beijing.",0.0,https://tns.ewapub.com/article/view/13430.pdf,True,JournalArticle;Review,,2024-06-24,d38f630e7f22d16115f90355ad9950f0b3c0aac2,researchonhousingpricespredictionbasedonmultiplelinearregression,wang,10.54254/2753-8818/38/20240561,researchonhousingpricespredictionbasedonmultiplelinearregression|2024,1,2580,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,prix hédoniques logement,c5003629ff0bd51b59df134c629afdb9ce6c8fb2,10.70675/83eb908dz3eb5z4b0az84bfz2da3dbe7e506,,Three essays on housing markets and housing policies,2018.0,,Zhe-Jin Zhao," Cette thèse contient trois essais empiriques sur les marchés du logement et les politiques de logement. Dans le premier essai, nous étudions les effets du contrôle des loyers sur les loyers en utilisant des données historiques de panel sur une période de 78 ans à Lyon. Nous utilisons des régressions multiples avec des effets fixes comme principale forme d’analyse. Nos résultats montrent que l’effet causal du contrôle des loyers sur les loyers à Lyon est significativement négatif. Dans le deuxième essai, j’étudie l’influence de l’âge du chef de famille sur la demande de logements grâce à des données sur les ménages en Chine. Le modèle de prix hédonique en deux étapes, utilisé dans cet essai, me permet d’estimer l’effet de l’âge du chef de famille sur la demande de logements, en contrôlant la qualité du logement et les caractéristiques des autres ménages. Les résultats montrent que la disposition à payer, à qualité constante de la maison, diminue légèrement ou reste constante lorsque le chef de famille vieillit, une fois contrôlé le niveau d’instruction du chef de famille. En revanche, il diminue rapidement si le niveau d’instruction du chef de famille n’est pas contrôlé. Par conséquent, cet essai conclut que la demande totale de logements ne devrait pas diminuer avec le vieillissement de la population, parce que la génération actuelle est plus éduquée que la précédente. Enfin, dans le troisième essai, dans le cadre du modèle de Rosen-Roback, j’analyse l’impact des coûts du logement sur le ratio d’intensité des compétences (SIR), dans différentes villes chinoises. Pour éviter les problèmes d’endogénéité, j’utilise à la fois la part des terrains non disponibles et les prix des logements historiques comme instruments des prix actuels du logement. Les résultats montrent que les prix moyens des logements ont des effets positifs significatifs sur le SIR en 2010 lorsque la mobilité des travailleurs est assouplie, mais que les effets sont non significatifs sur le SIR en 2000 lorsque la mobilité des travailleurs était étroitement réglementée.",0.0,https://theses.hal.science/tel-02097496v1/file/These-ZHAO-Zhejin-2018.pdf,True,,Economics,2018-09-20,c5003629ff0bd51b59df134c629afdb9ce6c8fb2,threeessaysonhousingmarketsandhousingpolicies,zhao,10.70675/83eb908dz3eb5z4b0az84bfz2da3dbe7e506,threeessaysonhousingmarketsandhousingpolicies|2018,1,3570,1,prix hédoniques logement,1,2,2,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,prix hédoniques logement,b9c20c4429e1d016b294316bc39a3ed11f2625c3,10.70675/a99bdb73zb92fz4fbbz82f6z448ebf2f2c8f,,Densités urbaines et analyse économique des choix résidentiels,2016.0,,Marion Girard,"La thèse s’intéresse aux enjeux de maîtrise de la croissance urbaine au travers du modèle de Ville Compacte et de sa caractéristique spatiale clé : la densité. Ce travail de doctorat s’inscrit dans une démarche théorique, méthodologique et empirique. Premièrement, la thèse replace la densité dans les modèles de microéconomie urbaine relatifs aux choix de localisation des agents. Nous identifions ainsi les différents déterminants de la localisation résidentielle (centre d’emploi, aménités, service résidentiel) et analysons l’organisation spatiale – dense ou dispersée – qui en découle. De plus, la thèse ne considère pas uniquement une densité mais des densités urbaines telles des densités de contenu (population, emploi) et de contenant (bâti). Elle met ainsi en évidence divers enjeux méthodologiques associés au traitement et à l’analyse des densités urbaines et propose une mesure plus juste de l’intensité de l’occupation de l’espace, le ratio de densité net. Pour illustrer ces résultats, deux études empiriques portant sur le Grand Dijon sont réalisées. La première consiste en une typologie des quartiers dijonnais qui met en relation les déterminants théoriques de choix de localisation et les types et niveaux de densités urbaines. La seconde étude empirique s’intéresse aux conditions d’acceptabilité des densités urbaines par les individus. Elle consiste en une évaluation hédonique appliquée au prix du logement qui appréhende la valorisation de la densité perçue (bâti) et de la densité en tant que vecteur d’interactions sociales. La thèse apporte des éclairages sur les conditions de réalisation et d’acceptation des politiques d’aménagement actuelles visant la Ville Compacte.",1.0,https://theses.hal.science/tel-01557974v1/document,True,,Political Science,2016-11-15,b9c20c4429e1d016b294316bc39a3ed11f2625c3,densitesurbainesetanalyseeconomiquedeschoixresidentiels,girard,10.70675/a99bdb73zb92fz4fbbz82f6z448ebf2f2c8f,densitesurbainesetanalyseeconomiquedeschoixresidentiels|2016,1,3199,1,prix hédoniques logement,1,1,1,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",211fd7653280c12c2de6776972fbfe9e01089c03,10.70382/tijbees.v13i4.096,,ARCHITECTURAL AESTHETICS AND INDUSTRIAL DESIGN AS DETERMINANTS OF RESIDENTIAL PROPERTY VALUE IN NIGERIA,2026.0,International Journal of Built Environment and Earth Science,Elegbede Adebayo Michael; S. Ayodele,"Architectural aesthetics and industrial design are increasingly recognised as important attributes capable of influencing the attractiveness, functionality and market performance of residential properties. This study examined the impact of architectural aesthetics and industrial design on residential property values, focusing on external architectural features and internal design characteristics. A quantitative cross-sectional survey design was adopted. Data were collected through structured questionnaires administered to estate surveyors and valuers, property managers, landlords, tenants and property investors. Data were analysed using frequency and percentage, mean score, standard deviation, Pearson Product-Moment Correlation, factor analysis and multiple regression. The findings indicated that architectural aesthetics and industrial design were highly rated, with building façade, architectural form, external finishes, interior spatial functionality, kitchen design, furniture and fittings emerging as important attributes. Significant positive relationships were found between architectural aesthetics and residential property value and between industrial design and residential property value. Factor analysis identified distinct external, internal, environmental and quality-related design dimensions. Regression analysis further showed that architectural aesthetics and industrial design jointly explained a substantial proportion of variation in residential property value. The study concludes that design characteristics deserve greater consideration in residential property development, management and valuation. It recommends stronger collaboration among estate surveyors and valuers, architects and industrial designers to achieve aesthetically attractive, functional and economically valuable residential properties.",0.0,https://taapublications.com/tijbees/article/download/1264/1234,True,JournalArticle;Review,,2026-09-07,211fd7653280c12c2de6776972fbfe9e01089c03,architecturalaestheticsandindustrialdesignasdeterminantsofresidentialpropertyvalueinnigeria,michael,10.70382/tijbees.v13i4.096,architecturalaestheticsandindustrialdesignasdeterminantsofresidentialpropertyvalueinnigeria|2026,1,3342,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,5,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",323b30d53cb25830c8397eeb95e569a5dc5a3044,10.2478/jos-2022-0004,,Sub-National Spatial Price Indexes for Housing: Methodological Issues and Computation for Italy,2022.0,Journal of Official Statistics,Ilaria Benedetti; L. Biggeri; T. Laureti,"Abstract It is essential to measure within-country differences in housing costs in order to evaluate costs of living, assessing and comparing poverty levels, quantifying salaries and disposable income of families and finally for designing housing policies at local level. To the authors knowledge, no studies have yet been carried out on the computation of Space Price Indexes for Housing Rents (SPIHRs). In this article we computed preliminary estimates of sub-national SPIHRs by using hedonic regression model, which is an extension of the Country Product Dummy method, for all the Italian regions. The hedonic regression is generally used to obtain multilateral spatial indexes, thus allowing us to obtain multilateral SPIHRs for the Italian regions. The estimates have been done using 2017 data from the Real Estate Market Observatory which is a part of the Italian Agency of Revenue and Tax. This data source is the most comprehensive source of information on Italian houses price rents with a wide geographical coverage, including data for each Italian municipality. The obtained results show significant differences across the Italian regions, thus highlighting the importance of calculating SPIHR in Italy on a regular basis and the need to continue researches in this field.",1.0,https://sciendo.com/pdf/10.2478/jos-2022-0004,True,JournalArticle,,2022-03-01,323b30d53cb25830c8397eeb95e569a5dc5a3044,subnationalspatialpriceindexesforhousingmethodologicalissuesandcomputationforitaly,benedetti,10.2478/jos-2022-0004,subnationalspatialpriceindexesforhousingmethodologicalissuesandcomputationforitaly|2022,1,2783,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || real estate hedonic regression || rent hedonic regression housing || spatial hedonic model housing",2,1,3,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b5fdbb46fb9826180526e3bbc98d2c164d13da70,10.2478/geosc-2022-0011,,The Neighborhood Impact of Industrial Blight: A Path Analysis,2022.0,GeoScape,R. Banai; E. Momeni,"Abstract Historically, industry shaped the space-economy of the American city, a major source of employment opportunity for residents that selected housing nearby or within a convenient or affordable commuting distance. However, the contemporary American city is structurally characterized by abandoned, blighted, vacant industrial properties due to urban expansion, deindustrialization and the suburbanization of both jobs and population. The urban studies literature rarely documents the neighborhood impact of industrial blight akin to studies of residential blight. We determine the proximity-effect of industrial blight on the neighborhood thought of not as an isolated and closed entity, but as a connected and open entity within the city and the region. Unlike studies confined to the property value impact, we determine Pearson correlations of industrial blight and vacancy expansively with the socio-economic and physical characteristics of neighborhoods. We use path analysis to determine direct, indirect, and total neighborhood impact of industrial blight and vacancy. The census block group and parcel-level geographic information system (GIS) provide our principal sources of data. The block group geography contains the neighborhood as a fundamental spatial unit. We determine how the neighborhood impact varies with distance from the blighted, vacant industrial property.",3.0,https://sciendo.com/pdf/10.2478/geosc-2022-0011,True,,,2022-12-01,b5fdbb46fb9826180526e3bbc98d2c164d13da70,theneighborhoodimpactofindustrialblightapathanalysis,banai,10.2478/geosc-2022-0011,theneighborhoodimpactofindustrialblightapathanalysis|2022,1,2887,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",3f462deaffad7fe742db08cf3bb6e2c5576862af,10.1002/soej.12328,,Capitalization of Flood Insurance and Risk Perceptions in Housing Prices: An Empirical Agent‐Based Model Approach,2019.0,Southern Economic Journal,Koen Koning; T. Filatova; Okmyung Bin,"Federally regulated or insured lenders in the United States are mandated to require flood insurance on properties that are located in areas at high risk of flooding. Despite the existence of this mandatory flood insurance requirement, take‐up rates for flood insurance have been low, and the federal government's exposure to uninsured property losses from flooding remains substantial. Meanwhile, the value of capital at risk varies significantly with flood events and changing risk perceptions, which necessitates mechanisms that stabilize these dynamics. In this article we discuss how a scenario of complete insurance uptake, under various risk attitudes, affects the value of properties in the 100‐year and 500‐year flood zones. Our results indicate that an increase in flood insurance uptake may provide such a mechanism by lowering the value of capital at risk in the flood zone consistently, independent of homeowners' risk attitudes. We apply an empirical adaptive agent‐based model to examine the capitalization of insurance costs, risk premiums, and their interaction in housing prices. Our approach combines widely‐used empirical hedonic analysis with the computational economic framework. We highlight the usefulness of our method in capturing the marginal implicit price of homeowners' preferences that may change over time and separately assess the effect of various factors and policies on property values, illustrating the agent‐based modeling as a valuable complement to traditional hedonic analysis.",14.0,https://ris.utwente.nl/ws/files/100376393/Koning_et_al_2019_Southern_Economic_Journal.pdf,True,,Business,2019-03-08,3f462deaffad7fe742db08cf3bb6e2c5576862af,capitalizationoffloodinsuranceandriskperceptionsinhousingpricesanempiricalagentbasedmodelapproach,koning,10.1002/soej.12328,capitalizationoffloodinsuranceandriskperceptionsinhousingpricesanempiricalagentbasedmodelapproach|2019,1,3017,3,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,2,2,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5e6352e6979d5c3adcde40933a8af5b1d2dde147,10.21608/rijcs.2025.402309.1367,,"Evaluating the Impact of Resampling Methodologies on Multiple Regression Performance in Housing Price Analysis: A Comprehensive Empirical Study of Bootstrap, Stratified, and Systematic Sampling Techniques",2025.0,مجلة راية الدولية للعلوم التجارية,Dr.Heba Mahmoud Elsegai; Dr.Amr El Daba,,0.0,https://rijcs.journals.ekb.eg/article_441585_1b949c9dee0bdcad242157e3aeda39e8.pdf,True,JournalArticle,,2025-07-01,5e6352e6979d5c3adcde40933a8af5b1d2dde147,evaluatingtheimpactofresamplingmethodologiesonmultipleregressionperformanceinhousingpriceanalysisacomprehensiveempiricalstudyofbootstrapstratifiedandsystematicsamplingtechniques,elsegai,10.21608/rijcs.2025.402309.1367,evaluatingtheimpactofresamplingmethodologiesonmultipleregressionperformanceinhousingpriceanalysisacomprehensiveempiricalstudyofbootstrapstratifiedandsystematicsamplingtechniques|2025,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",aa68a60d562ad28ce94ac3c6c75b63657a44f202,10.33915/etd.7424,,Three Essays on Water Economics,2019.0,,Fahad Alzahrani,"Three Essays on Water Economics Fahad Alzahrani The reliable provision of safe drinking water is one of the most important health-related programs in the United States. Since enactment of the Safe Drinking Water Act in 1974, great progress has been made in drinking water quality and regulation in the United States. Despite this progress, drinking water outbreaks continue to occur nationwide and negatively affecting the welfare of the people. In particular, rural residents suffer from drinking water quality and reliability issues due to the aging water infrastructure and the lack of funding to the small water systems serving such areas. This dissertation consists of three essays that explore how changes in water supply quality and/or reliability affect health care expenditures, residential property prices, and residential water charges. The first essay explores the relationship between episodes of contaminated drinking water and health care expenditures in the United States. The analysis relies on panel data from the 48 contiguous states from 2000 to 2011. Using the population served by public water systems that violate health-based standards of the Safe Drinking Water Act as a proxy for contaminated drinking water. spatial and non-spatial models are estimated with controls for factors that may affect per capita health care expenditures including variables that reflect air quality violations along with ability to pay plus demand for and supply of health care services. The results from a Spatial Durbin Model indicate that a 1% decrease in the percentage of population exposed to drinking water quality violations is associated with reductions in in state and regional effects equal to 0.005% ($0.32) and 0.035% ($2.26) of per capita health care expenditures, respectively. Drinking water violations have a larger impact on expenditures than air quality violations (whose effects are not statistically different from zero). However, compared to other factors, such as health status and income, the impact of these violations on health care expenditures is relatively small. Regional health care expenditure impacts from drinking water violations are found to be substantially greater than in-state impacts. Thus, a regional approach is recommended to addressing drinking water quality improvements. In the second essay, spatial and non-spatial hedonic property price models are used to examine the problem of water supply reliability due to aging infrastructure of public water systems on residential property prices. Water supply unreliability is measured by issuance of boil water notices (BNWs) linked to home sale observations (e.g. number of notices affecting the house prior to the sale). The analysis is based on 1,985 housing transactions and 350 BWNs throughout Marion County, West Virginia between 2012-2017. Important control variables include home size and age, lot size, number of bedrooms, construction quality, physical condition, and presence of house amenities (air conditioning, fireplace, and fence). Mapping and distance tools in ArcGIS are used to measure neighborhood characteristics such as school quality and distance to the nearest highway interchange. Finally, the models include fixed effects for both time and tax district. Statistically significant, negative impacts are found for BWNs from the previous year before a house sale. Depending upon how water supply unreliability is measured – in either days, BWN episodes, or presence versus absence, BWNs can depreciate housing prices between 0.57% and 18%. A quantile regression approach is used to find that BWNs have a larger impact on low-price compared to high-price houses. Based upon quantile regressions, a reduction in one day under BWN per year will increase housing values at the lower end of the housing price distribution by $2,100 to $2,700, whereas houses at the mid-range of the distribution will increase by $850 to $1,000. However, houses at the higher end of the housing price distribution (0.7 quantile and above) show no statistically significant impact from this BWNs variable. Overall, water supply unreliability has a substantial impact on the value of residential properties in Marion County where the aggregate marginal willingness to pay for a one-day reduction of BWN is equal to $4.60 million. The third essay examines the impact of access to raw water sources on residential water charges by public service districts (PSDs) in West Virginia. This analysis determines the relative value of raw water from each source. Using regression analysis, we compare residential water charges per 4,500 gallons for PSDs that utilize different raw water sources including the surface water, purchased water, or multiple sources. We examine the difference in water charges between those PSDs that use NRCS’s flood control impoundments as a water source compared to those who use other sources. Our analysis is based on data for 110 PSDs throughout the state of West Virginia. Our results show that PSDs that use multiple sources have higher residential water charges by about 29% of the average water charges compared to PSDs who utilize only one raw water source. Therefore, policies that encourage public water utilities to utilize multiple sources, such as the Source Water Protection (Senate Bill 373) passed by the West Virginia legislature in 2014, may result in higher rates. No statistically significant difference is found between water charges of PSDs that use NRCS’s flood control impoundments and those who use conventional sources. Other factors, such as providing sewer plus water service in the same time result in lower residential water charges. Conversely, larger long-term debt and greater network length result in higher residential water charges.",0.0,https://researchrepository.wvu.edu/cgi/viewcontent.cgi?article=8471&context=etd,True,,Economics,,aa68a60d562ad28ce94ac3c6c75b63657a44f202,threeessaysonwatereconomics,alzahrani,10.33915/etd.7424,threeessaysonwatereconomics|2019,1,7283,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices || hedonic price model amenities residential || housing characteristics prices hedonic || spatial hedonic model housing",2,4,5,6,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",8efa8820cde88663ceca972f0a07bb12be64d216,10.33915/etd.4570,,Three essays on gay and lesbian rights legislation,2019.0,,David Christafore,"THREE ESSAYS ON GAY AND LESBIAN RIGHTS LEGISLATION David R. Christafore This dissertation consists of three essays that provide empirical evidence that informs the ongoing debate over the passage of gay and lesbian rights legislation. In Chapter 2 the difference-in-difference-in-difference methodology is used to examine the effect that the passage of a private employment anti-discrimination law has on the relative earnings and employment of gays and lesbians. The results of this analysis indicate that the passage of this law does not have an impact on either relative earnings or employment for lesbians, but it does have an impact on these labor market outcomes for gays. In particular, the passage of this anti-discrimination law increases the relative earnings for gays by 7.5% and reduces their relative probability of being employed by 2.4%. In Chapter 3 a gravity model of stateto-state gay and lesbian migration flows over the period 1995-2000 is estimated in order to analyze the revealed preferences of gays and lesbians for private and public employment anti-discrimination, hate crime, and domestic partnership laws. The results of this examination indicate that private employment anti-discrimination laws reduce in-migration, while public employment anti-discrimination and hate crime laws increase in-migration. Therefore, it appears that gays and lesbians prefer to live in places with public employment anti-discrimination and hate crime laws, and not in places with private employment antidiscrimination laws. In Chapter 4 a spatial hedonic autoregressive model is estimated for the Columbus, OH MSA to examine the impact that the percentage of gay and lesbian households in conservative and liberal neighborhoods has on the house prices in those neighborhoods. The results of the analysis indicate that a .1% point increase in the percentage of gay and lesbian households increases house prices in liberal neighborhoods by .81% and reduces house prices by .24% in conservative neighborhoods. Thus, evidence is provided that is consistent with gay and lesbian households being an amenity to liberal neighborhoods, but a disamenity, likely due to prejudice, to conservative neighborhoods.",0.0,https://researchrepository.wvu.edu/cgi/viewcontent.cgi?article=5607&context=etd,True,,,,8efa8820cde88663ceca972f0a07bb12be64d216,threeessaysongayandlesbianrightslegislation,christafore,10.33915/etd.4570,threeessaysongayandlesbianrightslegislation|2019,1,3710,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price",2,1,1,2,1,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",df92af5d7cf86c9b8af0286814f5ed9fd7262436,10.33915/etd.4133,,Three essays on the impacts of land -use regulations and land development,2020.0,,Gi-Don An,"Three Essays on the Impacts of Land-Use Regulations and Land Development Gi-Don An This dissertation is a collection of essays examining the effects of land-use regulations and redevelopment. The first chapter describes an overview of the Korean housing market. Chapter 2 employs the stock-flow model in order to incorporate land-use regulation into the analysis. Future land supply can not only constrain housing construction but also affect the people’s expectation. Land-use regulations can be incorporated into demand for housing as well as housing construction. Chapter 3 investigates the impact of land-use regulations on housing market in the case of South Korea. The South Korean rapid income and population growth have produced a sharp increase in housing demand. On the supply side, however, the government has played a crucial role in controlling the housing supply with various regulations in housing and land markets. Much of literature has argued that a shortage of land supply increases housing prices. This paper analyzes the mechanism by which landuse regulations affect housing prices and housing construction. The empirical examination provides that land-use regulations have no binding effects on housing production but raise housing prices by stimulating higher investment demand for housing. The expectation about a shortage of land supply leads to higher future housing prices which in turn spur the current demand. The resulting higher housing prices encourage more housing production by constructing high-rising housing buildings. Chapter 4 examines the anticipated impact on housing prices of a New Town Development in Seoul announced in 2002. Since the extensive land redevelopment project leads to significant change in residential neighborhoods, it generates spillover effects on the values of housing units located in close proximity. We employ a spatial hedonic housing price model to estimate and measure the spillover impacts of redevelopment with different mixed land uses on surrounding housing prices. The empirical results show that neighborhood spillovers of redevelopment depend on the type of mixed land uses. Housing prices within one kilometer of residential redevelopment mixed with open space were 17 percent higher than elsewhere while houses within one kilometer of residential redevelopment mainly mixed with commercial uses have a higher value of 35 percent compared to those farther away. The empirical results of a pre-post approach suggest that the change in housing prices takes place the year the announcement was made, which implies that housing markets anticipate the future effects of the completed project. The last chapter summarizes the major findings of the previous chapters and discusses areas of future research.",0.0,https://researchrepository.wvu.edu/cgi/viewcontent.cgi?article=5170&context=etd,True,Review,,,df92af5d7cf86c9b8af0286814f5ed9fd7262436,threeessaysontheimpactsoflanduseregulationsandlanddevelopment,an,10.33915/etd.4133,threeessaysontheimpactsoflanduseregulationsandlanddevelopment|2020,1,4275,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || house prices proximity hedonic || spatial hedonic model housing",2,2,4,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f665cafc2c3808b037de53e17cb1c1e29d8f0993,10.54822/qvhm1662,,The Utrecht Housing dataset: A housing appraisal dataset,2025.0,Computers and Society Research Journal,S. van Otterloo; Pavlo Burda,"This paper introduces a real-world dataset for analysing and predicting house prices. The dataset consists of actual data on the Dutch housing market collected in 2024 for a total of 153 houses in one city (Utrecht in The Netherlands). The dataset incorporates diverse variables on individual houses, includ- ing property characteristics (e.g., house type, build year, geolocation, area, energy label) and market metrics (e.g., asking price, final price). The data was collected from two public sources. The dataset has been created to help researchers and educators to demonstrate machine learning principles on several problem types. It can be used for classification (energy label and energy efficiency) and regression/ price estimation. There are ten original input features and one derived feature. The dataset can be freely used without restrictions under a Creative Commons license and is available via open data platform Kaggle.",0.0,https://research.vu.nl/en/publications/85957385-94ac-4204-ae69-ce2e2dd38571,True,JournalArticle,,,f665cafc2c3808b037de53e17cb1c1e29d8f0993,theutrechthousingdatasetahousingappraisaldataset,otterloo,10.54822/qvhm1662,theutrechthousingdatasetahousingappraisaldataset|2025,1,2436,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,precios hedónicos vivienda,e73f6a1e55ae17fd5454f734e83e9cd883b8cdcb,10.18235/0007174,,Medición de la calidad de vida en la ciudad de Guatemala y su importancia en el diseño de una política pública municipal,2014.0,,José Larios; Juan Robalino,"La ciudad de Guatemala ha crecido rápidamente y de forma desordenada, lo cual implica que no todas las zonas o los barrios de la ciudad reciben o se benefician de los servicios prestados por el gobierno municipal. A fin de determinar el impacto de la provisión o ausencia de servicios municipales en los hogares en diferentes zonas de la ciudad, estudiamos la calidad de vida en la ciudad de Guatemala de modo de ver cómo esta puede apoyar la definición de políticas públicas tanto en materia de inversión como de provisión de servicios municipales. Para ello, utilizamos dos métodos complementarios: precios hedónicos y preguntas de satisfacción. El método de precios hedónicos se basa en los precios o rentas de las viviendas para inferir la contribución de las características de los vecindarios y la ciudad sobre la calidad de vida. Para complementar este método se emplearon preguntas de satisfacción enfocadas en la vida, el vecindario y la ciudad. Al igual que en estudios previos, encontramos que la satisfacción en la vida aumenta significativamente con los ingresos, pero que el nivel de ingresos no afecta la satisfacción de vida, aun cuando la desigualdad de ingresos en la ciudad es alta. Con ambas metodologías se muestra que la provisión del servicio de recolección de basura aumenta la calidad de vida. Los daños en las viviendas a consecuencia de las precipitaciones reducen significativamente los niveles de satisfacción con la vida, la vivienda, el vecindario y la ciudad. Las tasas de criminalidad reducen significativamente las rentas de las viviendas pero no están asociadas con los niveles de satisfacción. Se concluye que para que esta metodología sea útil en la generación de políticas públicas novedosas y a la medida de las necesidades de los vecinos en los barrios que tienen espacio para mejorar su calidad de vida, la administración municipal debe contar con información adecuada de forma geo-referenciada, un plan de ordenamiento territorial actualizado y capacidad para ejecutarlo, catastros de propiedad y fiscal actualizados, una gestión financiera moderna, participación ciudadana, y capacidad de desplegar la administración municipal en el territorio.",0.0,https://publications.iadb.org/publications/spanish/document/Medición-de-la-calidad-de-vida-en-la-ciudad-de-Guatemala-y-su-importancia-en-el-diseño-de-una-política-pública-municipal.pdf,True,CaseReport,Political Science,2014-11-07,e73f6a1e55ae17fd5454f734e83e9cd883b8cdcb,mediciondelacalidaddevidaenlaciudaddeguatemalaysuimportanciaeneldisenodeunapoliticapublicamunicipal,larios,10.18235/0007174,mediciondelacalidaddevidaenlaciudaddeguatemalaysuimportanciaeneldisenodeunapoliticapublicamunicipal|2014,1,3687,1,precios hedónicos vivienda,1,1,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,spatial hedonic model housing,0dc9490a9305356558d8da54594b8ff216036083,10.1023/a:1007744706720,,Spatial Dependence and House Price Index Construction,1997.0,,Ay Se Can; I. Megbolugbe,,380.0,https://papers.ssrn.com/sol3/papers.cfm?abstract_id=9230,True,,Economics,1997-02-28,0dc9490a9305356558d8da54594b8ff216036083,spatialdependenceandhousepriceindexconstruction,can,10.1023/a:1007744706720,spatialdependenceandhousepriceindexconstruction|1997,1,1500,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",212a7de78f54a54bbaeb57b89da02d02f80ee713,10.18335/region.v9i1.360,,Municipal amalgamations and local housing prices,2022.0,Le Regioni,Vífill Karlsson; Grétar Thór Eythórsson,"In this paper, we examine the relationship between housing prices and municipal amalgamations. Due to consumer preference for access over amenity value, there is a spatial disparity of housing prices, reflecting the value of land in specific locations. According to \citet{tiebout1956} local amenity values will increase if the services of a municipality improve without a comparable rise in taxes or service fees, or if taxes or service fees decrease while the service level remains the same. The bid-rent curve is a framework frequently used to capture a presence of goods or bads such as amenities, disamenities, and externalities. Accordingly, the present study will be used to detect a possible social benefit or loss following amalgamations of municipalities. Macro panel data from Iceland will be used, representing several essential variables of the house market for 79 municipalities in Iceland during the period from 1992 through 2006. The research shows a significant positive relationship, ceteris paribus, between regional municipal amalgamations and local real prices of apartments.",2.0,https://openjournals.wu.ac.at/ojs/index.php/region/article/download/360/413,True,,,2022-04-25,212a7de78f54a54bbaeb57b89da02d02f80ee713,municipalamalgamationsandlocalhousingprices,karlsson,10.18335/region.v9i1.360,municipalamalgamationsandlocalhousingprices|2022,1,2596,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8d7a4fb8ea8b050402b49e0e1161aa7ab0c3767d,10.26686/wgtn.16552782.v1,,Risk from extreme precipitation and climate change on New Zealand’s residential property,2021.0,,Jacob Pastor Paz,"Three manuscripts form the basis of this dissertation exploring the effect of extreme precipitation and climate change on residential property in New Zealand. The first manuscript investigates the public insurer’s expected future liabilities, given future climate projections. Specifically, it examines the effect of extreme precipitation on direct property damage associated with rainfall-induced landslides, storms and floods. This study applies a fixed-effects panel regression model using claim data linked to extreme precipitation data over 2000-2017 and future climate change scenarios until 2100. The results show that liabilities will increase more if future greenhouse gasses emissions are higher. At the aggregate level, the percent change between past and future liabilities ranges between an increase of 7 to 8% higher in the next 20 years, and an increase between 9 to 25% increase by the end of the century, depending on the greenhouse gases emissions scenario. The second manuscript examines the risk of property damage from landslides associated with extreme precipitation. The focus is on the Nelson region as it displays the highest number of claims and pay-outs relative to its population and residential stock asset, and two thirds of the pay-outs come from a single event. The focus is on this event. This research combines past insurance claim data with geographic and sociodemographic data to estimate probability of damage, which is then combined with property replacement values and damage-ratio information to calculate the expected loses and map the spatial distribution of risk. The study integrates into the risk estimates the impact of climate change on precipitation based on an ‘attribution’ study. The analysis shows that slope and social deprivation play a significant role in the probability of damage. Furthermore, higher expected losses are associated with higher property values. The third manuscript studies the current and future risk of property damage from floods associated with extreme precipitation and climate change. The focus is on the most expensive event on record. This study applies a logistic cross-sectional regression model that exploits spatial variation of rainfall intensity-duration-frequency (with and without the effect of climate change), while controlling for other factors that might make a property more or less likely to experience damage. The expected monetary losses are calculated by factoring in the likelihood of flood damage derived from the regression model, property replacement values, and property vulnerability (based on flood-depth fragility functions). The results show that highest losses are associated with lowest annual exceedance probabilities (AEPs), still, sizeable losses are associated with higher AEPs. In this case, the effect of climate change for different emissions scenarios is too small to cause an economically meaningful increase in risk levels in the next 80 years (2100).",0.0,https://openaccess.wgtn.ac.nz/articles/thesis/Risk_from_extreme_precipitation_and_climate_change_on_New_Zealand_s_residential_property/16552782/1/files/30607218.pdf,True,,,2021-09-01,8d7a4fb8ea8b050402b49e0e1161aa7ab0c3767d,riskfromextremeprecipitationandclimatechangeonnewzealandsresidentialproperty,paz,10.26686/wgtn.16552782.v1,riskfromextremeprecipitationandclimatechangeonnewzealandsresidentialproperty|2021,1,4471,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",1d1b5c3e41712cd13ef61e1fd289406927fb1110,10.26686/wgtn.17139980.v1,,Economics of disaster recovery and earthquake insurance: Five essays on New Zealand,2019.0,,Cuong Nguyen,"The Canterbury earthquake sequence (2010-2011) was the most devastating catastrophe in New Zealand‘s modern history. Fortunately, in 2011 New Zealand had a high insurance penetration ratio, with more than 95% of residences being insured for these earthquakes. This dissertation sheds light on the functions of disaster insurance schemes and their role in economic recovery post-earthquakes. The first chapter describes the demand and supply for earthquake insurance and provides insights about different public-private partnership earthquake insurance schemes around the world. In the second chapter, we concentrate on three public earthquake insurance schemes in California, Japan, and New Zealand. The chapter examines what would have been the outcome had the system of insurance in Christchurch been different in the aftermath of the Canterbury earthquake sequence (CES). We focus on the California Earthquake Authority insurance program, and the Japanese Earthquake Reinsurance scheme. Overall, the aggregate cost of the earthquake to the New Zealand public insurer (the Earthquake Commission) was USD 6.2 billion. If a similar-sized disaster event had occurred in Japan and California, homeowners would have received only around USD 1.6 billion and USD 0.7 billion from the Japanese and Californian schemes, respectively. We further describe the spatial and distributive aspects of these scenarios and discuss some of the policy questions that emerge from this comparison. The third chapter measures the longer-term effect of the CES on the local economy, using night-time light intensity measured from space, and focus on the role of insurance payments for damaged residential property during the local recovery process. Uniquely for this event, more than 95% of residential housing units were covered by insurance and almost all incurred some damage. However, insurance payments were staggered over 5 years, enabling us to identify their local impact. We find that night-time luminosity can capture the process of recovery; and that insurance payments contributed significantly to the process of local economic recovery after the earthquake. Yet, delayed payments were less affective in assisting recovery and cash settlement of claims were more effective than insurance-managed repairs. After the Christchurch earthquakes, the government declared about 8000 houses as Red Zoned, prohibiting further developments in these properties, and offering the owners to buy them out. The government provided two options for owners: the first was full payment for both land and dwelling at the 2007 property evaluation, the second was payment for land, and the rest to be paid by the owner‘s insurance. Most people chose the second option. Using data from LINZ combined with data from Stats NZ, the fourth chapter empirically investigates what led people to choose this second option, and how peer effect influenced the homeowners‘ choices. Due to climate change, public disclosure of coastal hazard information through maps and property reports have been used more frequently by local government. This is expected to raise awareness about disaster risks in local community and help potential property owners to make informed locational decision. However, media outlets and business sector argue that public hazard disclosure will cause a negative effect on property value. Despite this opposition, some district councils in New Zealand have attempted to implement improved disclosure. Kapiti Coast district in the Wellington region serves as a case study for this research. In the fifth chapter, we utilize the residential property sale data and coastal hazard maps from the local district council. This study employs a difference-in-difference hedonic property price approach to examine the effect of hazard disclosure on coastal property values. We also apply spatial hedonic regression methods, controlling for coastal amenities, as our robustness check. Our findings suggest that hazard designation has a statistically and economically insignificant impact on property values. Overall, the risk perception about coastal hazards should be more emphasized in communities.",0.0,https://openaccess.wgtn.ac.nz/articles/thesis/Economics_of_disaster_recovery_and_earthquake_insurance_Five_essays_on_New_Zealand/17139980/1/files/31695422.pdf,True,,History,,1d1b5c3e41712cd13ef61e1fd289406927fb1110,economicsofdisasterrecoveryandearthquakeinsurancefiveessaysonnewzealand,nguyen,10.26686/wgtn.17139980.v1,economicsofdisasterrecoveryandearthquakeinsurancefiveessaysonnewzealand|2019,1,5676,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices",2,3,6,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",793cac9872835d7adc88e61ff90383f771a92b9a,10.54941/ahfe1007583,,"Machine Learning and Data Mining Insights into Monthly Housing Price Dynamics in Connecticut, USA",2026.0,AHFE International,Özerk Yavuz,"In today’s market place housing and housing associated goods and services constitutes important value for consumers. As indicated in the Maslow’s hierarcy of needs figure, sheltering, housing is one of the most basic needs of human beings. Later wants and demands take place, individuals engage in behaviours to afford their housing needs by either renting, buying a house or buying hotel, motel or other housing services. If demand of the consumers meet the available housing options and recently constructed housing projects, rent and selling prices of the housing options may be in a balance meaning a significant increase and decrease in the prices are unlikely to occur. If the demand passes the supply it would be on the spot to wait for increases in the housing market, whereas a lower demand in a normal supply scenario is likely to lower the prices in the marketplace for consumers. In this research average housing prices according to location considering date have been ananlysed using machine learning techniques available in data mining and computer science literature.",0.0,https://openaccess-api.cms-conferences.org/articles/download/978-1-964867-91-5_3,True,,,,793cac9872835d7adc88e61ff90383f771a92b9a,machinelearninganddatamininginsightsintomonthlyhousingpricedynamicsinconnecticutusa,yavuz,10.54941/ahfe1007583,machinelearninganddatamininginsightsintomonthlyhousingpricedynamicsinconnecticutusa|2026,1,2582,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,1,1,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,hedonic housing prices,95ca6c4ebd807bd140b9ac48994345fb67aceaf5,10.1111/tesg.12607,,Closing the Rent Index Gap – A Quantitative Approach to Rental‐Sector Gentrification,2023.0,Tijdschrift voor economische en sociale geografie,Selim Banabak,"This paper advocates for a greater emphasis on supply sided concepts such as the rent gap in the empirical operationalizations of gentrification. It provides a novel framework to identify rental‐sector gentrification areas through index construction by drawing on insights from the gentrification, rent gap, price index and hedonic regression literatures. The approach is highly adaptable to a variety of regulatory contexts and other housing market idiosyncrasies through the design of the underlying regression model. Drawing on data from the city of Vienna, local rent changes and their relationship to price‐effective transformations of the rental housing supply are quantified. After computing the respective indices, bivariate mapping is utilized to identify potential gentrification areas.",9.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/tesg.12607,True,JournalArticle,,2023-12-15,95ca6c4ebd807bd140b9ac48994345fb67aceaf5,closingtherentindexgapaquantitativeapproachtorentalsectorgentrification,banabak,10.1111/tesg.12607,closingtherentindexgapaquantitativeapproachtorentalsectorgentrification|2023,1,2296,2,hedonic housing prices || rent hedonic regression housing,2,1,2,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,rent hedonic regression housing,a9c2136ce5527532fc81a4fdfe185388198d68ad,10.1111/rsp3.12307,,Where is the consumer centre? A case of St. Petersburg,2020.0,Regional Science Policy & Practice,K. Kholodilin; I. Koroleva; Darya Kryutchenko,"In an urban economy, the distribution of people and real estate prices depends on the location of the central business district of a city. As distance from the city centre increases, both prices and population density diminish, for travel costs increase in terms of time and money. As manufacturing gradually leaves the cities, the importance of consumer amenities as attractors of population to the urban areas increases. The role of a business centre is being replaced by the consumer centre. In this paper, we identify the location of the consumer centre of St. Petersburg — the second largest city in Russia and its former capital. For this purpose using the data from open sources in the Internet regarding the location of many different types of urban amenities, the indices of their spatial density are computed. Using the weights based on coefficients of spatial variation and survey-based weights, the individual indices are aggregated to two general centrality indices. Their unique maxima correspond to the city centre of St. Petersburg, which is located on Nevsky prospekt, between Fontanka river and Liteinyi prospekt. En una economía urbana, la distribución de las personas y de los precios de los bienes inmuebles depende de la ubicación del distrito financiero central de una ciudad. A medida que aumenta la distancia al centro de la ciudad, tanto los precios como la densidad de población disminuyen, mientras que los costos de viaje aumentan en términos de tiempo y dinero. A medida que el sector de manufacturas abandona gradualmente las ciudades, la importancia de los servicios de consumo aumenta como un factor de atracción de la población a las zonas urbanas. El papel de un centro financiero está siendo reemplazado por el de un centro de consumo. En este artículo se identifica la ubicación del centro de consumo de San Petersburgo, la segunda ciudad más grande de Rusia y su antigua capital. Para ello se calcularon los índices de la densidad espacial de muchos tipos diferentes de servicios urbanos, utilizando datos sobre su ubicación de fuentes de Internet abiertas al público. Se utilizaron ponderaciones basadas en los coeficientes de variación espacial y ponderaciones basadas en un muestreo para poder agregar los índices individuales a dos índices generales de centralidad. Su máxima corresponde al centro de la ciudad de San Petersburgo, que se encuentra en la Avenida Nevsky, entre el río Fontanka y la Avenida Liteinyi. 都市経済では、人口分布及び土地価格はその都市の中心業務地区の立地に依存する。都市中心部からの距離が増加するにつれて、物価と人口密度は低下し、移動にかかる時間と交通費は増加する。製造業が徐々に都市部から離れるにつれて、都市地域への人口の誘因としての消費者アメニティの重要性が増している。経済の中心地としての役割は消費者センターに取って代わられようとしている。本稿では、ロシア第二の大都市であり、かつての首都であるサンクトペテルブルクの消費者センターの立地を検討する。この目的のために、インターネット上のオープンソースから得られる、様々なタイプの都市アメニティの立地に関するデータを用いて、アメニティの空間密度の指標を計算する。空間変動係数に基づく重みと調査に基づく重みを用いて、個々の指標を一般的な2つの中心性指標に集約した。サンクトペテルブルクの中心部に対応するユニークな極大値は、ネフスキー大通りの、フォンタンカ川とリチェイニ大通りに挟まれたところに位置する。 In the urban economy, the distribution of people and real estate prices depends on the location of the central business district of a city (Alonso, 1964; Mills, 1967; Muth, 1969). As distance from the city centre increases, prices and population density diminish, reflecting the increasing travel costs in terms of time and money. Glaeser, Kolko, and Saiz (2001) show that as manufacturing gradually leaves the cities, the importance of consumer amenities as an attractor of population to the urban areas increases. The role of a business centre is being replaced by the consumer centre. While cities once relied on jobs to attract people, urban amenities (restaurants, shops, education opportunities, museums, etc.) are becoming critical. Using a model in which the natural and urban amenities play a central role, Brueckner, Thisse, and Zenou (1999) explain the spatial distribution of different social classes across the surface of the city. If the city centre has plenty of these amenities, then ceteris paribus the rich will be concentrated in the centre, while poor will live in the periphery. Otherwise, the central part of the city will be populated by the low-income families, whereas the high-income households will live in the suburbs. Moreover, as Clark (2003) establishes, different types of amenities attract different groups of the population, whose differences are more nuanced than simply income level. For example, college graduates tend to live in the settlements with less natural and more urban amenities, while seniors favour more the natural amenities. Inventors are more likely to live in the places where both natural and urban amenities are in abundance (Clark, 2003). In the applied literature, the proximity to different natural and urban amenities is considered to be a factor determining the real estate values: Luttik (2000) (green areas, water, and open spaces); Bourassa, Hoesli, and Sun (2004) (view); Rietveld, Debrezion, and Pels (2007) and Brandt and Maennig (2012) (railway stations); Ahlfeldt and Maennig (2010) (stadiums). Relatively few studies apply the hedonic approach to Russian data. Most of them focus on the case of Moscow. Magnus and Peresetskiy (2010) analyse the determinants of the asking housing prices in Moscow. Two spatial variables are used: travel time to the next subway station and distance from the nearest subway station to the city centre. The authors set the centre of Moscow to be Red Square, based on the circular shape of the Russian capital city. Krasilnikov and Scherbakova (2011a) estimate hedonic model using data on the asking prices of dwellings in St. Petersburg. This study uses the same two spatial variables. The co-ordinates of the city centre are computed by averaging the co-ordinates of all dwellings in their sample. The estimated city centre is located in the Peter and Paul Fortress. Krasilnikov and Scherbakova (2011b) employ similar methodology in order to identify city centres in their study on four Russian metropolises (Moscow, Novosibirsk, St. Petersburg, and Yekaterinburg). Similarly, Katyshev and Khakimova (2012) use in their analysis of housing prices in Moscow the two variables of proximity to the centre and to the closest subway station. Interestingly, as the centre of Moscow they take the subway station Okhotnyi ryad, which is about 0.5 km from Red Square. In addition, since the focus of their study is on the environmental quality, they also consider the distance to the nearest factory. Tchugunov (2013) uses a much wider list of amenities to assess their impact on the housing prices in Moscow: (i) distance to the nearest subway station; (ii) distance to the secondary schools and their quality (measured by the performance of the pupils); (iii) number of parks; (iv) number of sports facilities; (v) number of health care institutions per 10,000 persons; and (vi) number of municipal police units per 10,000 persons. By contrast, this study does not employ any measure of proximity to the city centre, capturing the spatial heterogeneity of prices by the district dummies. Nosov and Tsypin (2015) investigate the determinants of asking prices for one-room apartments in a medium-size Russian city Orenburg. In order to capture spatial factors they take advantage of spatial clusters obtained by the k-means clustering technique and of the distance to the city centre, which is defined as the central post office of the city. In Russia, post offices are typically used as departure points to measure the distances. Kholodilin and Ulbricht (2015), who estimate hedonic regressions for 48 large European cities, including seven Russian cities (Kazan, Moscow, Nizniy Novgorod, Rostov on Don, Samara, St. Petersburg, and Yekaterinburg), capture spatial effects only by district dummies. As the amount and variety of information published on the Internet increase, the possibilities of exploiting it to measure the natural and urban amenities at the microlevel (individual parks, shops, restaurants, etc.) expand extraordinarily. For example, Ahlfeldt and Wendland (2016) suggest a method of computing the so-called potential spaces taking in account the geographical distribution of different natural and urban amenities objects. The aim of this paper is to develop a simple and easily applicable method of delineating the consumer city centre. As an example, it is used to identify the exact coordinates of the consumer centre of St. Petersburg, Russia, that is, the point with the highest density of consumer urban amenities in the whole city. This information can be used for different purposes. For example, it can be employed in the hedonic analysis of the housing prices and rents, where both the detailed evaluation of the impact of different urban amenities and a compact representation of all the relevant amenities by a single index are desirable. Moreover, when assessing accessibility, it is critically important to know where the city centre is located. In this case, the centre of St. Petersburg identified in this paper can be used to construct the isochrones (equal travel time curves, see, e.g., Kholodilin, 2016), which the co-ordinates of the city centre. the of population density an exact of the central business district If the centre co-ordinates are as and this can to an of the In this we the city centre as a point of an This is, of an the centre of the city be to This can be in it is much to travel to a single point than to an In the case of has to an to with to the of the to its the of a central can be much more for we to that will the central from the city centre as a point in the on hedonic where the is St. Petersburg is the second largest city in Russia and its former capital. the it many to and In as the capital of the Russian for two it a important city. St. Petersburg has its population it more than of its population both the Russian of and the the as a of and St. Petersburg the with is only the that the city to in terms of population, in Moreover, in the a to the centre of the city from the of the to the by about in the of Moscow. the of Russia the this the ",0.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/rsp3.12307,True,JournalArticle;Review,Geography,2020-10-09,a9c2136ce5527532fc81a4fdfe185388198d68ad,whereistheconsumercentreacaseofstpetersburg,kholodilin,10.1111/rsp3.12307,whereistheconsumercentreacaseofstpetersburg|2020,1,11500,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression || rent hedonic regression housing || spatial hedonic model housing",2,4,4,6,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",99e1e6314dcad04e7be39a5595dec7224ddf907c,10.1111/gean.12247,,Retail and Place Attractiveness: The Effects of Big‐Box Entry on Property Values,2020.0,Geographical Analysis,Sven-Olov Daunfeldt; Oana Mihaescu; Ö. Öner; N. Rudholm,"Opponents of big-box entry argue that large retail establishments generate noise and other types of pollution and a variety of negative externalities associated with traffic. Big-box advocates, on the other hand, argue that access to a large retail market delivers not only direct economic benefits but also a variety of positive spillover effects and therefore can be considered a consumer amenity that increases the attractiveness of the entry location. To test the validity of these competing arguments, we use the entry of IKEA in Sweden as a quasi-experiment and empirically investigate whether increased access to retail affects place attractiveness, which is proxied by residential property values. We find that IKEA entry increases the prices of the properties sold in the entry cities by, on average, 4.4% or 60,425 SEK (approximately 6,400 USD), but this effect is statistically insignificant for the properties in the immediate vicinity of the new IKEA retail trade area. In addition, we observe an attenuation of the effect with distance from the new IKEA store and the associated retail trade area, where the properties located 10 km away experience a 2% price increase. Our results are in line with some previous findings regarding the effects of entry by Walmart or supermarket stores in the US and show that large retailers have the potential to increase place attractiveness, but perhaps not in the immediate vicinity of the new establishment.",12.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/gean.12247,True,,Business,2020-07-03,99e1e6314dcad04e7be39a5595dec7224ddf907c,retailandplaceattractivenesstheeffectsofbigboxentryonpropertyvalues,daunfeldt,10.1111/gean.12247,retailandplaceattractivenesstheeffectsofbigboxentryonpropertyvalues|2020,1,2960,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",25d02f4b22954faacce875002c2eabd4644dc44d,10.1387/inecs.28108,,Crime and Perceived Insecurity as Determinants of Housing and Tourist Accommodation Prices: A Case Study of Barcelona,2026.0,International E-Journal of Criminal Sciences,Diego J. Maldonado Guzmán; Jonathan Torres-Tellez,"This study examines how crime rates and perceived insecurity influence the prices of Airbnb accommodations and the price per square meter of traditional housing in the city of Barcelona (Spain). We estimate two ordinary least squares (OLS) regression models and two geographically weighted regression models. All regression models include additional control variables grouped into three categories: insecurity, household or population structure, and the socio-spatial transformation of the neighborhood. The results indicate that crime is not associated with housing prices, whereas perceived insecurity, national heterogeneity, and the arrival of residents from high-income countries play a key role in both tourist accommodation prices and price per square meter. The influence of these factors on housing prices varies substantially across neighborhoods. These findings suggest that the effect of crime on housing prices may be mediated by other structural neighborhood factors. This underscores the need for a neighborhood-centered approach, rather than analyzing the effect of each individual variable in isolation.",0.0,https://ojs.ehu.eus/index.php/inecs/article/download/28108/26062,True,JournalArticle,,2026-02-09,25d02f4b22954faacce875002c2eabd4644dc44d,crimeandperceivedinsecurityasdeterminantsofhousingandtouristaccommodationpricesacasestudyofbarcelona,guzmán,10.1387/inecs.28108,crimeandperceivedinsecurityasdeterminantsofhousingandtouristaccommodationpricesacasestudyofbarcelona|2026,1,2621,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,2,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",99d4770c7afe60cc5020bc42784b9f2b8cd2b903,10.26689/pbes.v7i6.9103,,Analysis and Evaluation of Housing Price Factors Using Mathematical Modeling,2024.0,Proceedings of Business and Economic Studies,Xing Lyu,"In recent years, the real estate industry has achieved significant progress, driving the development of related sectors and playing a crucial role in economic growth. However, rapid real estate market expansion has led to challenges, particularly concerning housing prices, which have drawn widespread societal attention. This article explores the theories of housing prices, analyzes factors influencing them, and conducts an empirical investigation of the impact of representative factors on ordinary residential prices. Using regression analysis and the entropy weight method, a mathematical model was developed to examine how various factors affect housing prices.",0.0,https://ojs.bbwpublisher.com/index.php/PBES/article/download/9103/7925,True,JournalArticle,,2024-12-23,99d4770c7afe60cc5020bc42784b9f2b8cd2b903,analysisandevaluationofhousingpricefactorsusingmathematicalmodeling,lyu,10.26689/pbes.v7i6.9103,analysisandevaluationofhousingpricefactorsusingmathematicalmodeling|2024,1,2168,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),13a9f9d438c93631e37c42a70f18868aeb241c79,10.22610/jsds.v6i4.856,,“Environmental Economics for a Sustainable Development†Evaluation of Environmental and Recreational Values of the Area Lezhe –Shengjin using Indirect Approximate Method “Hedonic Price Methodâ€,2015.0,,C. E. Imeraj; .. Ing.ZefImeraj,"The area of the project Lezhe –Shengjin has a combination of recreational tourism and industry development. Promotion of tourism and environmental values of Lezha is a bold undertaking for a region with a diversity of values. Stable ecological development impacts directly in the development of the inhabited area and in the standards of it, while reflecting also in the home prices. Project aims to identify and to study the basic areas of the ecosystem and to present the impact that has the environment to the economy, by analyzing of the home prices of those, their diversity and changes. Methodology that the project will use is based on an indirect approximate method “Hedonic Price Method”. The base of these methods lies on questioners made face to face with the residents of the region and a considerable part of the data collection would be the collaboration with the commune and municipality administration offices of the region. Firstly, the project members will identify specifically services and environmental goods that the project will assess. Meanwhile, the meeting with the head officer of the commune would be planned and a contract of collaboration with his office would be signed. An important phase of the project will be the preparation of the questioners, on which behalf, the project members are selected. It is considered to prepare around 2000 Surveys for t h e respective method for areas of the targeted region, Lezhe and Shengjin. Getting the result of the indirect method that will be used, as mentioned, would need the data collection of the surveys and the data taken officially from the commune of Shengjin and Municipality of Lezha. During the data collection period the project members will consider also several meetings with real estate offices of the region and ecological NGOs. All data collected will be considered to be analyzed due to “Hedonic Price Method” and the expected results are considered to conclude in direct relations between the high ecosystem standards and relatively high competitive home prices. As well the result would be a good guide for the evaluation of the agency investments. Result of the project will firstly be presented to the interest group and then will be presented at scientific seminars and conferences within and outside the country and will serve as a starting point for further studies related to this field of study. Key results would provide good material to be firstly presented and published in scientific journals.",0.0,https://ojs.amhinternational.com/index.php/jsds/article/download/856/856,True,Review,Economics,2015-12-30,13a9f9d438c93631e37c42a70f18868aeb241c79,aenvironmentaleconomicsforasustainabledevelopmentaevaluationofenvironmentalandrecreationalvaluesofthearealezheashengjinusingindirectapproximatemethodahedonicpricemethoda,imeraj,10.22610/jsds.v6i4.856,aenvironmentaleconomicsforasustainabledevelopmentaevaluationofenvironmentalandrecreationalvaluesofthearealezheashengjinusingindirectapproximatemethodahedonicpricemethoda|2015,1,3997,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,1,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",730ff7c122de12ce15062db20663021c26be889b,10.13128/aestimum-12342,,Simulating housing prices in Lyon with UrbanSim: predictive capacity and sensitivity analysis,2013.0,Aestimum,Marko Kryvobokov; Aurelien Mercier; A. Bonnafous; D. Bouf,"Housing prices in the Lyon Urban Area are simulated with the land use framework UrbanSim interacting with transportation model. We focus on the Real Estate Price Model of the UrbanSim framework. This OLS regression model of housing prices is calibrated using a nine-year back-casting period. The calibrated model, applied in simulation, provides price dynamics similar to actual one in the centre of Lyon. Sensitivity analysis demonstrates the model’s ability to capture changes in employment accessibility on price dynamics. We conclude that the calibrated residential Real Estate Price Model from the UrbanSim application in Lyon is sensitive to changes in accessibility and provides good predictive capacity in the city centre, but underestimates prices in other areas.",1.0,https://oaj.fupress.net/index.php/ceset/article/download/5599/5599,True,JournalArticle,Economics,2013-02-21,730ff7c122de12ce15062db20663021c26be889b,simulatinghousingpricesinlyonwithurbansimpredictivecapacityandsensitivityanalysis,kryvobokov,10.13128/aestimum-12342,simulatinghousingpricesinlyonwithurbansimpredictivecapacityandsensitivityanalysis|2013,1,2272,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,800215511f8e27d7d9c3831e023d6dd1daa33b6e,10.13128/aestimum-3274,,Mixed environmental quality indexes for hedonic housing price models: an alternative with area-to-point kriging with external drift,2009.0,Aestimum,J. Montero; G. Fernández-Avilés,"(a) When environmental variables are included in hedonic house price models, the locations where a property transaction has been taken place are more than those equipped with an environmental monitoring station. (b) When environmental variables are numerous, an environmental quality index (EQI) is needed. The solution is to interpolate (kriging) environmental variables and, subsequently, elaborate an EQI, because of the lower prediction variance. (c) Environmental informations can be both objective and subjective. The potential mismatch between the spatial support for both the objective informations and the subjective ones is solved by using a kriging strategy (area-to-point kriging) that forces scarce objective environmental informations to be coherent with dense subjective ones. These options for elaborating EQIs are compared in Madrid City (Spain).",4.0,https://oaj.fupress.net/index.php/ceset/article/download/5561/5561,True,JournalArticle,Computer Science,2009-10-02,800215511f8e27d7d9c3831e023d6dd1daa33b6e,mixedenvironmentalqualityindexesforhedonichousingpricemodelsanalternativewithareatopointkrigingwithexternaldrift,montero,10.13128/aestimum-3274,mixedenvironmentalqualityindexesforhedonichousingpricemodelsanalternativewithareatopointkrigingwithexternaldrift|2009,1,2363,3,hedonic house price || hedonic housing prices || spatial hedonic model housing,2,3,2,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,spatial hedonic model housing,44aeecbddb5c52ee565cc4e86d1604b9df0541f1,10.1080/00396265.2015.1113029,,The application of spatial analysis to cadastral zoning of urban areas: an example in the city of Madrid,2016.0,,M. C. Morillo; F. Cepeda; S. Martínez-Cuevas,"Spatial Analysis has been used since the 1990s for hedonic cadastral models of urban areas in large cities. This research aims to apply spatial analysis to zoning a district in Madrid located in the central part of the city in order to estimate urban housing prices. In doing so, the necessary data have been gathered, both vector and raster, as found on the official government website and private company websites. This was done firstly to analyse the characteristics and then for the objectives of the study because it is helpful to use these types of analyses based on vector and raster data along with interpolation methods that obtain regular structures and attempt to avoid the subjective component inherent to traditional procedures. The essence of the work consisted of studying the minimum parameters that influence housing appraisals to resolve the problem. Normally, cadastral appraisal work fulfils the Official Technical Regulations in each country considering a greater number of parameters and corrective coefficients. The importance of this work consisted of lowering and prioritising the number of basic parameters necessary to set urban housing prices. The conclusions section confirms a high likelihood of successfully reducing the number of parameters to achieve an appraisal value that is similar to the one obtained using official methods.",7.0,https://oa.upm.es/40201/,True,,Geography,2016-04-15,44aeecbddb5c52ee565cc4e86d1604b9df0541f1,theapplicationofspatialanalysistocadastralzoningofurbanareasanexampleinthecityofmadrid,morillo,10.1080/00396265.2015.1113029,theapplicationofspatialanalysistocadastralzoningofurbanareasanexampleinthecityofmadrid|2016,1,2862,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,2,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ce1d044d0a0f93dad5321069c70f02f316b07dfa,10.1134/s2079970525600441,,Effects of Transport Accessibility on Residential Property Prices (A Case Study of Saratov),2025.0,Regional Research of Russia,P. O. Gonyukhov; A. Sheludkov,"Increasing spatial inequality is observed in Russia at many territorial levels, including within large cities. The most striking expression of the depth of social stratification in urban areas is differences in housing prices, which are often taken as an indicator of inequality due to the lack of other public data. In this article, we investigated the impact of transport accessibility and its characteristics on the socioeconomic status of urban areas, expressed through the average cost of residential real estate, with a case study of Saratov. For this, we aggregated data from 80 000 advertisements for the sale of apartments in the city in 2021, using network analysis methods to assess transport accessibility when traveling by vehicle and by public transport to the city center and with respect to the transport network of the entire city (integral accessibility). According to the results of the regression analysis, the characteristics of transport accessibility (together with the age of buildings) were able to statistically explain up to half of the differences between Saratov districts in the average listing price of residential real estate. Contrary to our expectations, the best predictor of apartment prices was accessibility by public transport to the city center. Every minute away from the historic city center by public transport reduced the average price of a residential property listing by RUB 455/m2. The public transport network in Saratov is highly centralized, which does not allow for the full use of the potential of the three transport hubs identified in the study. One of the possible steps towards mitigating territorial contrasts in transport accessibility in the city is the transformation of the bus network with the introduction of new main and feeder (to hubs outside the center) routes.",0.0,https://link.springer.com/content/pdf/10.1134/S2079970525600441.pdf,True,JournalArticle,,2025-09-01,ce1d044d0a0f93dad5321069c70f02f316b07dfa,effectsoftransportaccessibilityonresidentialpropertypricesacasestudyofsaratov,gonyukhov,10.1134/s2079970525600441,effectsoftransportaccessibilityonresidentialpropertypricesacasestudyofsaratov|2025,1,3328,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,3,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",85e991e5a5469b240d2036a499d834301b056b50,10.1007/s40300-021-00223-8,,A Bayesian piecewise linear model for the detection of breakpoints in housing prices,2021.0,Metron,J. Tomal; Hafizur Rahman,"Statistical thresholds occur when the changes in the relationships between a response and predictor variables are not linear but abrupt at some points of the predictor variable values. In this paper, we defined a piecewise-linear regression model which can detect two thresholds in the relationships via changes in slopes. We developed the corresponding Bayesian methodology for model estimation and inference by proposing prior distributions, deriving posterior distributions, and generating posterior values using Metropolis and Gibbs sampling algorithm. The parameters in our model are easy to understand, highly interpretable, and flexible to make inferences. The methodology has been applied to estimate threshold effects in housing market pricing data in two cities - Kamloops and Chilliwack - in British Columbia, Canada. Our findings revealed that the implementation of changes in the government property tax policies had threshold effects in the market price trend. The proposed model will be useful to detect threshold effects in other correlated time series data as well.",10.0,https://link.springer.com/content/pdf/10.1007/s40300-021-00223-8.pdf,True,JournalArticle,Medicine,2021-10-19,85e991e5a5469b240d2036a499d834301b056b50,abayesianpiecewiselinearmodelforthedetectionofbreakpointsinhousingprices,tomal,10.1007/s40300-021-00223-8,abayesianpiecewiselinearmodelforthedetectionofbreakpointsinhousingprices|2021,1,2582,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,spatial hedonic model housing,0f2b46f3c834649ff0e9b864745acffe2b620f12,10.1007/s12061-022-09500-z,,Assessment of Public Services Distribution in African Urban Areas: a Cost-Benefit Analysis Based on Bid Rents from Spatial Interaction Models,2023.0,Applied spatial analysis and policy,T. Dentinho; César Pakissi,"The paper proposes a cost-benefit analysis of alternative school networks that are otimized to cover the territory of the central part of Huambo Municipality in Angola. Benefits comes from increases in housing prices associated with changes in employment generated by schools, evaluated by variations in values of real estate estimated by the bid-rents of a spatial interaction model using hedonic regression methods. Costs are associated with construction and functioning of schools. The more advisable school networks alternatives are the ones that optimize the coverage of the region with education services while securing individual viability of each one of the schools.",0.0,https://link.springer.com/content/pdf/10.1007/s12061-022-09500-z.pdf,True,,,2023-01-30,0f2b46f3c834649ff0e9b864745acffe2b620f12,assessmentofpublicservicesdistributioninafricanurbanareasacostbenefitanalysisbasedonbidrentsfromspatialinteractionmodels,dentinho,10.1007/s12061-022-09500-z,assessmentofpublicservicesdistributioninafricanurbanareasacostbenefitanalysisbasedonbidrentsfromspatialinteractionmodels|2023,1,2174,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression || rent hedonic regression housing || spatial hedonic model housing",2,2,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,spatial hedonic model housing,78753eb78a8f8440566608f39b7bfb9e12910aae,10.1007/s12061-020-09367-y,,"Urban Housing Prices and Regional Integration: A Spatial Analysis in the City of Kaifeng, China",2020.0,Applied spatial analysis and policy,Yuanyuan Cai; Ying-Ming Zhu; Feng-Lin Yuan; Jin-Long Gao; M. Helbich,,7.0,https://link.springer.com/content/pdf/10.1007/s12061-020-09367-y.pdf,True,,Business,2020-11-23,78753eb78a8f8440566608f39b7bfb9e12910aae,urbanhousingpricesandregionalintegrationaspatialanalysisinthecityofkaifengchina,cai,10.1007/s12061-020-09367-y,urbanhousingpricesandregionalintegrationaspatialanalysisinthecityofkaifengchina|2020,1,1500,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),ee614ea3540dfee7e7f8f87032016a28f849c4b0,10.1007/s11146-025-10032-9,,Price Indices for Austrian municipalities - Hedonic models based on Microlevel Data,2025.0,Journal of real estate finance and economics,Sabrina-Sigrid Spiegel," Austrian municipalities must re-evaluate their real estate portfolios every year. The existing Austrian house price datasets (based on the Austrian land register) cannot fully fulfill these requirements due to a lack of descriptive variables. When constructing a hedonic model, it is vital to assemble a dataset as complete as possible to minimize the extent of the omitted variables problem. This paper shows how an existing micro-level dataset can be improved and extended to raise the data’s explanatory power. Then, these data are used to compile different temporal hedonic models for the nine regional capitals of Austria. The results show that the right choice of method is essential for smaller cities with fewer transactions. For bigger cities, with more transaction data, the choice of hedonic model is less important (with all suggested model formulations giving similar results). For smaller cities, the Average Characteristics and Rolling Time Dummy hedonic methods provide good results, but the Repricing method should be avoided. Thus, it is vital to consider the data structure and number of transactions when deciding on a hedonic method for small regions (cities).",1.0,https://link.springer.com/content/pdf/10.1007/s11146-025-10032-9.pdf,True,JournalArticle,,2025-10-01,ee614ea3540dfee7e7f8f87032016a28f849c4b0,priceindicesforaustrianmunicipalitieshedonicmodelsbasedonmicroleveldata,spiegel,10.1007/s11146-025-10032-9,priceindicesforaustrianmunicipalitieshedonicmodelsbasedonmicroleveldata|2025,1,2683,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price",2,2,1,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c668f47b665583eee00ab23aa66be21c40941906,10.1007/s11146-025-10012-z,,The Effect of Increased Police Spending on House Prices and Sales Volume: A Tale of Two Types of Cities,2025.0,Journal of real estate finance and economics,David M. Brasington,"We present the first capitalization study to look at crime fighting and house prices using the causal inference technique of regression discontinuity. It is also the first study on the link between police spending and housing sales volume. Voting to increase police taxes and spending by 15% is not linked to house prices or transaction volume overall. However, increasing spending causes 13% higher housing prices in low-income cities and at least a 14% decrease in house prices in high-income cities. The effects persist through all five years after the vote that we study. The sale price results are most consistent with overfunded police departments, rather than Tiebout sorting, neighborhood instability, or signaling. Our results suggest that the small or non-existent link between house prices and crime found by the literature really just reflects the sum of large but opposite moves in house prices in different market segments.",2.0,https://link.springer.com/content/pdf/10.1007/s11146-025-10012-z.pdf,True,JournalArticle,,2025-04-16,c668f47b665583eee00ab23aa66be21c40941906,theeffectofincreasedpolicespendingonhousepricesandsalesvolumeataleoftwotypesofcities,brasington,10.1007/s11146-025-10012-z,theeffectofincreasedpolicespendingonhousepricesandsalesvolumeataleoftwotypesofcities|2025,1,2437,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,automated valuation model residential,1165fd19265663d89986a4cf836d053c46a71996,10.1007/s11146-021-09836-2,,Ignoring Spatial and Spatiotemporal Dependence in the Disturbances Can Make Black Swans Appear Grey,2021.0,Journal of real estate finance and economics,R. Pace; R. Calabrese,"Automated valuation models (AVMs) are widely used by financial institutions to estimate the property value for a residential mortgage. The distribution of pricing errors obtained from AVMs generally show fat tails (Pender 2016; Demiroglu and James Management Science, 64(4), 1747–1760 2018). The extreme events on the tails are usually known as “black swans” (Taleb 2010) in finance and their existence complicates financial risk management, assessment, and regulation. We show via theory, Monte Carlo experiments, and an empirical example that a direct relation exists between non-normality of the pricing errors and goodness-of-fit of the house pricing models. Specifically, we provide an empirical example using US housing prices where we demonstrate an almost perfect linear relation between the estimated degrees-of-freedom for a Student’s t distribution and the goodness-of-fit of sophisticated evaluation models with spatial and spatialtemporal dependence.",1.0,https://link.springer.com/content/pdf/10.1007/s11146-021-09836-2.pdf,True,JournalArticle,Medicine,2021-03-31,1165fd19265663d89986a4cf836d053c46a71996,ignoringspatialandspatiotemporaldependenceinthedisturbancescanmakeblackswansappeargrey,pace,10.1007/s11146-021-09836-2,ignoringspatialandspatiotemporaldependenceinthedisturbancescanmakeblackswansappeargrey|2021,1,2463,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || automated valuation model residential",0,2,3,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,d5a685fc98ba2ca91101c515d0dbbe9830ac632e,10.1007/s10901-023-10033-1,,Do COVID-19 pandemic-related policy shocks flatten the bid-rent curve? Evidence from real estate markets in Shanghai,2023.0,Journal of Housing and the Built Environment,Yifu Ou; Zhi-Kang Bao; S. T. Ng; Jun Xu,"The COVID-19 pandemic has drastically affected the socioeconomic activities and peoples’ daily life, resulting in a change in locational preferences in the real estate markets. Although enormous efforts have been devoted to examining the housing price impacts of the COVID-19 pandemic, little is known about the responses of the real estate markets to the evolving pandemic control measures. This study investigates the price gradient effects of various pandemic-related policy shocks using a hedonic price model on the district-level property transaction data in Shanghai, China over a 48-month period from 2018 to 2021. We found that these shocks have significantly altered the bid-rent curves. The price gradient for residential property units decreased in absolute value to − 0.433 after Wuhan’s lockdown, demonstrating peoples’ preferences to avoid the high infection risks in districts closer to the city center. However, in the post-reopening and post-vaccine periods, the price gradient increased to − 0.463 and − 0.486, respectively, implying rational expectations of a recovering real estate market for the low infection and mortality rates. In addition, we discovered that Wuhan’s lockdown has steepened the price gradient for commercial property units, suggesting a decline in business volumes and an increase in operating costs in the low-density districts imposed by the strict pandemic control measures. This study contributes to the empirical literature on the price gradient effects of the COVID-19 pandemic by extending the study period to the post-vaccine era.",11.0,https://link.springer.com/content/pdf/10.1007/s10901-023-10033-1.pdf,True,JournalArticle,Medicine,2023-04-28,d5a685fc98ba2ca91101c515d0dbbe9830ac632e,docovid19pandemicrelatedpolicyshocksflattenthebidrentcurveevidencefromrealestatemarketsinshanghai,ou,10.1007/s10901-023-10033-1,docovid19pandemicrelatedpolicyshocksflattenthebidrentcurveevidencefromrealestatemarketsinshanghai|2023,1,3079,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices,2,2,2,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",54d5d54390ccc641a2ae74b805d72c1a07781973,10.1007/978-3-030-74544-8_4,,The Role of Migration Costs in Residential Sorting,2021.0,The Urban Book Series,Wenquan Liang; Ran-hee Song; Christopher Timmins,"Economistsgenerallyemploytwo ‘revealed preference’ approaches to measure households’ preferences for non-market amenities—the hedonic and equilibrium sorting models. The conventional hedonic model assumes free mobility across space. Violation of this assumption can bias the estimates of household willingness to pay for local amenities. Mobility constraints are more easily handled by the sorting framework. In this chapter, we examine the role of migration costs in household residential sorting and apply these two models to estimate the willingness to pay for clean air in the USAand China. Our results demonstrate that ignoring mobility costs in spatial sorting will underestimate the implicit value of non-market amenities in both countries. Such a downward bias is larger in developing countries, such as China, where migration costs are higher.",1.0,https://link.springer.com/content/pdf/10.1007%2F978-3-030-74544-8_4.pdf,True,,,,54d5d54390ccc641a2ae74b805d72c1a07781973,theroleofmigrationcostsinresidentialsorting,liang,10.1007/978-3-030-74544-8_4,theroleofmigrationcostsinresidentialsorting|2021,1,2352,1,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,0,2,3,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b3d62045b0df9370163d4c3582805e4dbafa60b9,10.57017/jorit.v3.1(5).05,,Predicting UK Housing Price using Machine Learning Algorithms,2024.0,"Journal of Research, Innovation and Technologies (JoRIT)",M. Ghafoor; Gbadebo A. Ogundeji; Dilkushi de Alwis Pitts,"The development of reliable predictive algorithm for house price as the housing market is a stand-out among the most involved regarding valuing the price and continues to fluctuate, is constantly a need for socio-economic advancement and welfare of citizen. In this paper, we develop machine learning algorithms for forecasting UK housing Price, and find an optimal algorithm that forecasts housing price accurately on the premises of the presence of many features or covariates. After applying correlation analysis to remove correlated variables in order to avoid multicollinearity, thereby increasing the statistical power, a novel method of using regression analysis to first of all understand and select statistically significant features for the various regions in England based on North South divide is adopted. These features are then used in the machine learning algorithm to further increase the statistical power of the algorithm, increase the level of accuracy for each of them and ultimately increase the predictive values for the algorithms.The model construction involves 3 stages: 1- correlation analysis to identify and remove correlated variables thereby avoiding multicollinearity and increasing the statistical power of the linear regression, 2 - using linear regression to determine variables that are statistically significant and 3 - building the machine learning algorithms based on the variables that are statistically significant from the linear regression. A comprehensive dataset of UK Paid housing Price from 2010 to 2019 was linked to a number of other datasets to generate a total 21 variables or features used for the models. Catboost, Gradient Boosting, Bagging, Random Forest, Extra Tree all achieved the excellent model’s performance result in all the regions considered. The comparison of the seven models showed that Extra Tree algorithm consistently achieved the best performance in term of level of accuracy in all the regions. K-Nearest Neighbours (KNN) is the only algorithm with less than 50% level of accuracy. Noticeably, the regions considered had varying or differing insignificant variables, implying that although many variables are common (statistically significant) to all the regions, there are regional differences and impact when modelling or predicting housing prices. This study validates the practicability of developing a machine learning methodology for the prediction of housing price. This research offers a reference for future house price prediction based on machine learning.",0.0,https://library.acadlore.com/JORIT/2024/3/1/JORIT_03.01_05.pdf,True,JournalArticle,,2024-06-01,b3d62045b0df9370163d4c3582805e4dbafa60b9,predictingukhousingpriceusingmachinelearningalgorithms,ghafoor,10.57017/jorit.v3.1(5).05,predictingukhousingpriceusingmachinelearningalgorithms|2024,1,4037,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",35023ddbc6507bea1f9631e44893a1f4e9a0367c,10.3368/le.91.2.386,,An Alternative to the Standard Spatial Econometric Approaches in Hedonic House Price Models,2015.0,Land Economics,Kathrine von Graevenitz; T. Panduro,,63.0,https://le.uwpress.org/content/wple/91/2/386.full.pdf,True,,Economics,2015-04-13,35023ddbc6507bea1f9631e44893a1f4e9a0367c,analternativetothestandardspatialeconometricapproachesinhedonichousepricemodels,graevenitz,10.3368/le.91.2.386,analternativetothestandardspatialeconometricapproachesinhedonichousepricemodels|2015,1,1500,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price",2,1,1,1,0,7,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",71f2cba94670cd3e414be2a6dedf7b181d8e4d0c,10.36787/jti.v19i1.1858,,Prediction of House Price using the Multivariate Adaptive Regression Spline Method,2025.0,Jurnal Tekno Insentif,Aysha Alia Iskandar; D. F. Al Husaeni; M. Z. I. Sahidin; L. Riza; W. Wahyudin,"Kekritisan lingkungan adalah kondisi suatu wilayah yang menunjukkan tingkat kerusakan atau kekritisan yang dapat memengaruhi kemampuan lingkungan dalam mendukung kehidupan manusia dan ekosistem. Faktor penyebabnya meliputi rendahnya vegetasi (NDVI), meningkatnya lahan terbangun (NDBI), suhu permukaan tinggi (LST), dan kepadatan penduduk. Tujuan penelitian ini menganalisis tingkat kekritisan lingkungan di Kota Bandar Lampung serta hubungan antara parameter tersebut terhadap kekritisan lingkungan. Metode yang digunakan adalah algoritma Environmental Critical Index (ECI) yaitu pendekatan integratif yang menggabungkan beberapa parameter lingkungan untuk menilai tingkat kekritisan wilayah secara spasial dan kuantitatif dengan data citra Landsat 8 (2014) dan Landsat 9 (2023). Hasil penelitian menunjukkan adanya peningkatan kekritisan cukup signifikasan sebesar 50% terjadi pada area sangat kritis seluas 1.447,74 ha. Sebaliknya terjadi penurunan pada area tidak kritis sebesar 35% atau seluas 1.029,16 ha dan pada area kritis sebesar 15% atau seluas 418,58 ha. Faktor yang paling berpengaruh terhadap kekritisan lingkungan adalah NDBI.",0.0,https://jurnal.lldikti4.or.id/index.php/jurnaltekno/article/download/1858/302,True,JournalArticle,,2025-07-05,71f2cba94670cd3e414be2a6dedf7b181d8e4d0c,predictionofhousepriceusingthemultivariateadaptiveregressionsplinemethod,iskandar,10.36787/jti.v19i1.1858,predictionofhousepriceusingthemultivariateadaptiveregressionsplinemethod|2025,1,2641,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,real estate hedonic regression,d8c5e068bdd181f5678fd59dbe595efa9c0046ec,10.21608/jur.2015.89792,,Economic Dimensions of Gated Communities in Egypt,2015.0,Journal of Urban Research,Ahmed Shalaby; Sarah A. Elariane,"The Gated communities represent a rather new development product on the housing market in Egypt. This type of development is spreading fast since the nineties of the last century. A large number of research has been conducted on the topic of gated communities in a number of different countries. However, in Egypt, this topic needs to be more discussed and researched. Therefore the topic of gated communities in Egypt will be further explored and analyzed, focusing on the economic dimensions and the impact of such developments on the Egyptian real estate market. This study specifically focuses on the city of El Sheikh Zayed where most of the gated communities are located. The objective of this research is to identify the main factors that can influence the housing prices in Egypt in order to know in what extent the gated communities affect positively or negatively the housing price especially for those units located inside. In this sense, a comparative study is applied between the prices of gated and non-gated housing units using simple regression analysis as well as Hedonic Pricing Model, in order to understand the influence of gated communities on the value of the housing units. This study analyzes the sales prices of middle class housing units in El Sheikh Zayed City.",1.0,https://jur.journals.ekb.eg/article_89792_f1491ec5cd5ca1efe8f659bc3ec4123d.pdf,True,,,2015-10-01,d8c5e068bdd181f5678fd59dbe595efa9c0046ec,economicdimensionsofgatedcommunitiesinegypt,shalaby,10.21608/jur.2015.89792,economicdimensionsofgatedcommunitiesinegypt|2015,1,2788,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression",2,3,1,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),c49249533069d801725af6064afc1523fa876dc1,10.5198/jtlu.2023.2297,,Access-based land value appreciation for assessing project benefits,2023.0,Journal of Transport and Land Use,Yadi Wang; D. Levinson,"The traditional mobility-oriented travel-time saving benefit assessment method has been repeatedly questioned for numerous intrinsic flaws, motivating the search for alternative benefit assessment approaches. Although a wealth of literature confirms the capitalization effect of access benefits induced by transport improvements to land or real estate value, the access-based land value uplift method hasn’t yet been widely recognized and employed as an official tool assisting transport decision-making. The present paper collects 136 empirical studies and aims to disentangle the obstacles hindering the promotion of the access-based assessment method by systematically reviewing methodological design, the access metrics used, and the target real estate sub-markets or land use types upon which access benefits are quantified. First, it was found that almost half of the sampled studies just investigated the general effects of transport operation on real estate prices without incorporating sufficient temporal and locational considerations, thereby failing to isolate project-specific incremental impacts. Second, while the hedonic pricing model remains the most popular model, a trend towards embracing more advanced modelling techniques such as spatial lag, spatial error, and Difference-in-Differences (DID) models to control for bias caused by spatial dependence has been observed. Third, Euclidean distance and distance buffer rings are the most widely used operational measures of access. Primal access measures covering the number of opportunities available at a destination and travel impedance are recommended. Last, over 86% of sampled empirical studies target the residential real estate market. The lack of non-residential land uses in the literature presents a significant research gap that should be addressed.",6.0,https://jtlu.org/index.php/jtlu/article/download/2297/1709,True,JournalArticle;Review,,2023-12-28,c49249533069d801725af6064afc1523fa876dc1,accessbasedlandvalueappreciationforassessingprojectbenefits,wang,10.5198/jtlu.2023.2297,accessbasedlandvalueappreciationforassessingprojectbenefits|2023,1,3329,3,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate)",2,1,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,housing characteristics prices hedonic,981bb7ef12a9d869b10b57e9f0c86b89fd48a9eb,10.68479/001c.7910,,The User Cost of Low-Income Homeownership,2013.0,Journal of Regional Analysis & Policy,Sarah F. Riley; Hong-Yu Ru; Qing Feng,"Empirical research examining whether owning a home is less costly than renting for low-income households is largely lacking. We use detailed property information provided by a set of low-income homeowners who participated in the Community Advantage Panel Survey, along with a matched sample of similar rental properties from the American Housing Survey, to determine whether low-income homeowners in the United States would have experienced lower housing costs by renting between 2003 and 2011. We calculate the homeowners’ user costs directly from the survey data, and we derive hedonic measures of equivalent rent for these homeowners via pooled regressions of house prices and rents on housing characteristics, from which we obtain capitalization rates. For the median homeowner in our sample, we find that owning was less costly than renting a comparable property between 2003 and 2011.",14.0,https://jrap.scholasticahq.com/article/7910.pdf,True,JournalArticle;Review,Economics,2013-06-22,981bb7ef12a9d869b10b57e9f0c86b89fd48a9eb,theusercostoflowincomehomeownership,riley,10.68479/001c.7910,theusercostoflowincomehomeownership|2013,1,2391,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || rent hedonic regression housing",2,2,4,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic house price,81418493337e8a52200257397b7d473cf2f8ab52,10.3846/1648715x.2013.822032,,Regional house price index construction -- the case of Sweden,2013.0,,L. Ericson; Han-Suck Song; Jakob Winstrand; Mats Wilhelmsson,"The academic literature on the construction of regional house price indexes usually uses geographic areas whose boundaries are administratively drawn. However such administrative regions might not be optimal for the construction of regional price indexes. When producing housing price indexes, we often encounter problems with insufficient number of observations. One way to remedy this problem is to estimate a quarterly index instead of a monthly index. Another possible way to mitigate the thin markets problem is to construct indexes for geographically aggregated regions. However, the literature that discusses methods of dealing with the problem of thin markets and especially geographical aggregation is very rare. The goal of this paper is to construct a housing price index for a major part of Sweden, and to construct price index series for a number of regions. The number of regions, and how their boundaries should be created in order to construct reliable regional price indexes, is however an open question. We apply traditional hedonic methodology in order to estimate house price indexes for both predefined regions whose boundaries are based on a division of labor markets in Sweden, as well as a division of regions based on statistical cluster analysis. The results from this study suggest that regions should be clustered together based on regional price levels and/or price development as clustering variables. If only geographical proximity is used as clustering variable, our computations show that there is a high risk that we end up with some clusters having large standard errors, which in turn might result in inaccurate indexes.",5.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/4201/3564,True,,Economics,2013-09-23,81418493337e8a52200257397b7d473cf2f8ab52,regionalhousepriceindexconstructionthecaseofsweden,ericson,10.3846/1648715x.2013.822032,regionalhousepriceindexconstructionthecaseofsweden|2013,1,3156,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || house prices proximity hedonic",2,2,2,1,1,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4b2e41c7138ea7ac90042f290d72f1122fbd0bfd,10.1080/13923730.2006.9636401,,Analysis of the dynamics of walking distances to public transport routes and its influence on housing prices,2006.0,,R. Ušpalytė-Vitkūnienė; M. Burinskienė,"Abstract Accessible public transport system is an essential indicator that guarantees equal travelling opportunities for all people. Scientific research has revealed that poor accessibility to public transport mostly affects low‐income people and the elderly. People from these groups have fewer opportunities to reach education institutions, working places, or health institutions. Access to public transport routes is among public transport indicators that are important for these social groups of people. An analysis reveals that during the last six years, from 1998 to 2004, access to public transport increased on average by 1,28 % in the district area. The analysed indicators of population density, working density, ratio of built up territories and green areas, accessibility of the public transport, density of routes in the area in question, and the distance to the centre revealed that the accessibility to public transport also has a significant influence on the price of housing, especifically for the above...",9.0,https://journals.vgtu.lt/index.php/JCEM/article/download/8811/7690,True,,Economics,2006-09-30,4b2e41c7138ea7ac90042f290d72f1122fbd0bfd,analysisofthedynamicsofwalkingdistancestopublictransportroutesanditsinfluenceonhousingprices,ušpalytė-vitkūnienė,10.1080/13923730.2006.9636401,analysisofthedynamicsofwalkingdistancestopublictransportroutesanditsinfluenceonhousingprices|2006,1,2523,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",42ade4696d73aa54dfaac36f19ef8d1f767181d6,10.29313/jrs.vi.905,,Pemodelan Faktor-Faktor yang Mempengaruhi Harga Rumah di Jabodetabek Menggunakan Metode Regresi Probit,2022.0,Jurnal Riset Statistika,Fifia Febyanti,"Abstract. In addition to clothes and food, houses are one of the basic needs. The lifestyle of dynamic modern society tends to require houses with various public and social facilities in one area. Property value will be formed through a factor of the completeness of the characteristics of the house and vehicle or facilities available. The price is a measuring device in the form of money, where the price can be obtained by a number of goods or services through the exchange process and obtain the rights and benefits of these goods or services. In this study want to know what factors affect house prices in Jabodetabek by using ordinal probit regression, where home prices consist of three categories, namely cheap, medium and expensive. The data used secondary data obtained from the site of the roof. MAI, namely the dataset of house prices in Indonesia. Based on the data analysis can be seen that the factors that influence the price of houses in Jabodetabek are the type of house, certificate, number of bedrooms, the number of bathrooms, and floors. The accuracy of the classification of 25.6% percent and the value of pseudo mcfadden of 57.57 percent. Abstrak. Selain pakaian dan makanan, rumah merupakan salah satu kebutuhan pokok. Gaya hidup masyarakat modern yang dinamis cenderung membutuhkan rumah dengan berbagai fasilitas umum dan sosial dalam satu kawasan. Nilai properti akan terbentuk melalui faktor dari kelengkapan karakteristik rumah dan sarana atau fasilitas yang tersedia. Harga merupakan alat pengukur dalam bentuk uang, dimana dengan harga tersebut dapat diperoleh sejumlah barang atau jasa melalui proses pertukaran dan mendapatkan hak serta manfaat atas barang atau jasa tersebut. Pada penelitian ini ingin mengetahui faktor apasaja yang mempengaruhi harga rumah di Jabodetabek dengan menggunakan regresi probit ordinal, dimana harga rumah terdiri dari tiga kategori yaitu murah, sedang dan mahal. Data yang digunakan adalah data sekunder yang diperoleh dari situs atapdata.ai yaitu dataset harga rumah di Indonesia. Berdasarkan analisis data dapat diketahui bahwa faktor-faktor yang mempengaruhi harga rumah di Jabodetabek adalah tipe rumah, sertifikat, jumlah kamar tidur, jumlah kamar mandi, dan lantai. Ketepatan klasifikasi sebesar 25,6% persen dan nilai Pseudo McFadden sebesar 57,57 persen.",8.0,https://journals.unisba.ac.id/index.php/JRS/article/download/905/675,True,,,2022-07-09,42ade4696d73aa54dfaac36f19ef8d1f767181d6,pemodelanfaktorfaktoryangmempengaruhihargarumahdijabodetabekmenggunakanmetoderegresiprobit,febyanti,10.29313/jrs.vi.905,pemodelanfaktorfaktoryangmempengaruhihargarumahdijabodetabekmenggunakanmetoderegresiprobit|2022,1,3830,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,3,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,cfad9a3d5976cdaea13d5b914838db02c66be9f0,10.37284/ijfa.5.1.4871,,How Do Psychological Expectations Influence the Relationship between Structural Attributes and Residential Housing Prices in Kenyan Cities?,2026.0,International Journal of Finance and Accounting,C. Owuor; F. Kalui; S. Onyuma,"The rise in residential housing prices in Kenya challenges affordability and stability, highlighting the need to understand how structural attributes and market dynamics interact. While research mainly focuses on microeconomic factors, the role of psychological expectations in buyer behaviour, especially in Africa, remains underexplored. This study examines how psychological expectations influence the link between structural attributes and housing prices in urban Kenya, using a quantitative approach with a hedonic pricing model and mediation analysis. Data from 242 real estate investors, sampled from 609 agents, show that structural attributes significantly impact prices. Psychological expectations enhance this relationship, increasing explanatory power by 8.3% (ΔR²= 0.083), and turning structural attributes from insignificant (β= -0.065, p= 0.184) to significant predictors (β= -0.224, p< 0.001). These findings highlight behavioural factors as key to Kenya’s housing crisis, challenging traditional views",0.0,https://journals.eanso.org/index.php/ijfa/article/download/4871/5289,True,JournalArticle,,2026-04-23,cfad9a3d5976cdaea13d5b914838db02c66be9f0,howdopsychologicalexpectationsinfluencetherelationshipbetweenstructuralattributesandresidentialhousingpricesinkenyancities,owuor,10.37284/ijfa.5.1.4871,howdopsychologicalexpectationsinfluencetherelationshipbetweenstructuralattributesandresidentialhousingpricesinkenyancities|2026,1,2518,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices",2,1,2,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",4b2f8e9c3bd35a6e6c8ac9a364e17931431bb092,10.1186/s40537-023-00786-5,,Online listing data and their interaction with market dynamics: evidence from Singapore during COVID-19,2023.0,Journal of Big Data,Jieun Lee; Kwan-Ok Lee,"With the emergence of Property Technology, online listing data have drawn increasing interest in the field of real estate–related big data research. Scraped from the online platforms for property search and marketing, these data reflect real-time information on housing supply and potential demand before actual transaction data are released. This paper analyzes the interactions between the keywords of online home listings and actual market dynamics. To do so, we link the listing data from the major online platform in Singapore with the universal transaction data of resale public housing. We consider the COVID-19 outbreak as a natural shock that brought a significant change to work modes and mobility and, in turn, consumer preference changes for home purchases. Using the Difference-in-Difference approach, we first find that housing units with a higher floor level and more rooms have experienced a significant increase in transaction prices while close proximity to public transportation and the central business district (CBD) led to a reduction in the price premium after COVID-19. Our text analysis results, using the natural language processing, suggest that the online listing keywords have consistently captured these trends and provide qualitative insights (e.g. view becoming increasingly popular) that could not be uncovered from the conventional database. Relevant keywords reveal trends earlier than transaction-based data, or at least in a timely manner. We demonstrate that big data analytics could effectively be applied to emerging social science research such as online listing research and provide useful information to forecast future market trends and household demand.",5.0,https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-023-00786-5,True,JournalArticle,Computer Science;Medicine,2023-06-11,4b2f8e9c3bd35a6e6c8ac9a364e17931431bb092,onlinelistingdataandtheirinteractionwithmarketdynamicsevidencefromsingaporeduringcovid19,lee,10.1186/s40537-023-00786-5,onlinelistingdataandtheirinteractionwithmarketdynamicsevidencefromsingaporeduringcovid19|2023,1,3198,1,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,1,3,3,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",934e5d482e8f5b306cf3472138c95e04d5d6a13d,10.1186/s40537-021-00476-0,,Predictive analytics using Big Data for the real estate market during the COVID-19 pandemic,2021.0,Journal of Big Data,Andrius Grybauskas; Vaida Pilinkienė; A. Stundžienė,"As the COVID-19 pandemic came unexpectedly, many real estate experts claimed that the property values would fall like the 2007 crash. However, this study raises the question of what attributes of an apartment are most likely to influence a price revision during the pandemic. The findings in prior studies have lacked consensus, especially regarding the time-on-the-market variable, which exhibits an omnidirectional effect. However, with the rise of Big Data, this study used a web-scraping algorithm and collected a total of 18,992 property listings in the city of Vilnius during the first wave of the COVID-19 pandemic. Afterwards, 15 different machine learning models were applied to forecast apartment revisions, and the SHAP values for interpretability were used. The findings in this study coincide with the previous literature results, affirming that real estate is quite resilient to pandemics, as the price drops were not as dramatic as first believed. Out of the 15 different models tested, extreme gradient boosting was the most accurate, although the difference was negligible. The retrieved SHAP values conclude that the time-on-the-market variable was by far the most dominant and consistent variable for price revision forecasting. Additionally, the time-on-the-market variable exhibited an inverse U-shaped behaviour.",64.0,https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-021-00476-0,True,JournalArticle,Medicine,2021-08-03,934e5d482e8f5b306cf3472138c95e04d5d6a13d,predictiveanalyticsusingbigdatafortherealestatemarketduringthecovid19pandemic,grybauskas,10.1186/s40537-021-00476-0,predictiveanalyticsusingbigdatafortherealestatemarketduringthecovid19pandemic|2021,1,2834,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,3,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",ccd54fa359a4327155cb94498d8f75de881dea42,10.29244/medkon.26.2.139-146,,THE ECONOMIC VALUE OF GREEN OPEN SPACE AREA IN MEDAN BASED ON TYPE OF LAND USE,2021.0,Media Konservasi,Mona Fhitri Srena; R. Hermawan; Bahruni,"Medan City which is the capital of North Sumatra Province has many environmental problems, so it requires the existence of green open space could ameliorate microclimate. Air temperature and humidity as part of the microclimate element affect the level of comfort that will ultimately affect quality the of society. It is necessary to quantitatively calculate the economic value of the urban forests so that their existence is considered a need for the city. This study aims to estimate the economic value of green open space based on biotic, physical, and social factors in residential areas, business and office areas, and industrial areas in Medan City. The research was conducted in Cadika, Beringin, and Maharani green open spaces. The research data was collected during December 2020-February 2021. The data analysis method is using multiple linear regression analysis. The result showed quantitatively through the green open space temperature humidity index value it falls into the uncomfortable category (THI>26) but qualitatively by perception visitors and communities that urban forests are comfortable. Total WTPs from green open space visitors with a visiting period which is valued at Rp. 29,268,000/year. Based on the hedonic price analysis, the closer 1 meter to the green space will increase the price of different properties in each green open space. Key words: economic value, green open space, Medan City, willingness to pay",1.0,https://journal.ipb.ac.id/index.php/konservasi/article/download/36943/22381,True,,Geography,2021-10-17,ccd54fa359a4327155cb94498d8f75de881dea42,theeconomicvalueofgreenopenspaceareainmedanbasedontypeoflanduse,srena,10.29244/medkon.26.2.139-146,theeconomicvalueofgreenopenspaceareainmedanbasedontypeoflanduse|2021,1,2945,1,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,1,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7b5fac902299872781620948c9eb739fc8f3997b,10.52783/jisem.v10i43s.8496,,The Effects of Zoning Regulations and Land Use Planning on Property Values in Sultanate of Oman,2025.0,Journal of Information Systems Engineering & Management,M. A. Al Amri,"This research seeks to analyse the impact of zoning regulations and land use planning on property values in the Sultanate of Oman. Zoning is a significant legal tool for regulating land use, building densities, and environmental standards. While zoning can boost house prices by limiting supply and curtailing the development of inappropriate land use, too rigid policies could hamper land use efficiency. This study is about how zoning affects property values by supply, amenity, scarcity, and external impacts. For example, the limitation of the population density increases prices because of the decline of the stock of housing and the increase in demand. Preserving green space and neighbourhood appeal and, thereby, property values is another area in which zoning can also be used. Real estate costs are compared across Muscat, Sohar, and Sur, all of which are characterized by different urban, industrial, and rural attributes, before examining how infrastructure development determines the zoning outcome. The objective of the study is to find out if zoning laws in three districts may decreasing real estate value. Solving these would attract investment, balance property prices, and promote good growth from an economic and financial perspective. The research is based on the use and analysis of academic literature and combines data collection, case studies, and interviews to identify how effective zoning strategies can be developed within Oman to promote urban comfort and investment potential in real estate development in line with Oman’s Vision 2040.",1.0,https://jisem-journal.com/index.php/journal/article/download/8496/3861,True,JournalArticle,,2025-05-07,7b5fac902299872781620948c9eb739fc8f3997b,theeffectsofzoningregulationsandlanduseplanningonpropertyvaluesinsultanateofoman,amri,10.52783/jisem.v10i43s.8496,theeffectsofzoningregulationsandlanduseplanningonpropertyvaluesinsultanateofoman|2025,1,3066,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",13524752d4c1c29c90ed3d605b2686f224e2fb4e,10.18297/etd/570,,"From vice to nice : A case study of Newport, Kentucky's HOPE VI program.",2009.0,,Hanka; M. Jude,"FROM VICE TO NICE: A CASE STUDY OF NEWPORT, KENTUCKY'S HOPE VI PROGRAM Matthew Jude Hanka April 3, 2009 This dissertation examines the outcomes of the Housing Opportunities for People Everywhere (HOPE VI) program in one American city, Newport, Kentucky. HOPE VI also improved the quality of life and satisfaction of former public housing residents who participated in the Community and Supportive Services (CSS) program, the case management program of HOPE VI. HOPE VI neighborhoods in Newport have experienced higher increases in property values. The site of the old public housing in Newport has been cleared to make way for an $800 million$1 billion investment over the next 10-15 years. The development will offer a first-class, mixed use development, including retail, office, condominiums, a hotel, and additional amenities in close proximity to downtown Newport. The reduction of HOPE VI funding has made this dissertation timely and important in documenting the impact of a federal program like HOPE VI on a small sized city. An increased commitment by the Obama administration and increased funding for HOPE VI is necessary to ensure that homeownership is the best way to ensure livable and sustainable communities and revitalize distressed communities throughout the United States.",5.0,https://ir.library.louisville.edu/cgi/viewcontent.cgi?article=1569&context=etd,True,,Sociology,,13524752d4c1c29c90ed3d605b2686f224e2fb4e,fromvicetoniceacasestudyofnewportkentuckyshopeviprogram,hanka,10.18297/etd/570,fromvicetoniceacasestudyofnewportkentuckyshopeviprogram|2009,1,2791,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,4,2,1,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),03bfeba917085bc65a0ab641e5a03916b687b20f,10.1088/2634-4505/abfe05,,Economic feasibility of wood-based structures—Improving urban carbon neutrality strategies,2021.0,,I. Talvitie; J. Vimpari; S. Junnila,"Countries and cities alike have set carbon neutrality goals to the near future. Urban areas are experiencing an increased demand for new housing developments, and buildings are responsible for one third of all greenhouse gas emissions. The relative share of construction emissions is increasing as energy production is de-carbonizing. Wooden structures have showcased potential in substantially decreasing these emissions. However, for the construction industry to utilize environmentally friendly products, the cost efficiency needs to be determined. Prior research does not present conclusive results regarding the construction costs or value of wooden buildings. This study aims to determine the economic feasibility of wood-based structures in housing. We approach this by estimating the effect of wood on Finnish dwelling prices through hedonic regression analyses. Dwelling prices are analysed with transaction data from the Helsinki metropolitan area. It is provided by the Central Federation of Finnish Real Estate Agencies. The data represents occurred transactions between 1999 and 2018. The study discovers that a wooden structure has a positive price effect in the Helsinki metropolitan region suggesting an economically feasible opportunity to create low carbon urban housing. In the city of Helsinki, the effect is statistically significant (+8.85%) whereas no significance is found in either Espoo or Vantaa. The results suggest that in Helsinki, a direct financial benefit exists for both the city and private developers. For other cities, the study encourages to increase the consumption of wood due to its environmental properties. The study implies that wooden construction is an economically feasible solution in mitigating climate change. As this may provoke an increased demand for wood in construction, further research should be conducted to analyse its effects on the economy and the environment.",7.0,https://iopscience.iop.org/article/10.1088/2634-4505/abfe05/pdf,True,,Business,2021-05-04,03bfeba917085bc65a0ab641e5a03916b687b20f,economicfeasibilityofwoodbasedstructuresimprovingurbancarbonneutralitystrategies,talvitie,10.1088/2634-4505/abfe05,economicfeasibilityofwoodbasedstructuresimprovingurbancarbonneutralitystrategies|2021,1,3421,4,cites:sirmans2006 || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression,2,1,3,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0f6dcae9dc8da736a128b35bd45eb0d06e704941,10.1088/1757-899x/829/1/012009,,Exploring the Mechanism of Residential Attractiveness in Compact Urban Areas – a Case Study of Hong Kong,2020.0,IOP Conference Series: Materials Science and Engineering,Yunxi Bai; Ju-Sheng Song; Shan-Shan Wu; Siu-Ming Lo; Gang Bai,"Compact cities adopt high density, mixed land use and transit-oriented development (TOD). For sustainable development, compact development reduces the use of automobile and lessens the overdevelopment of land resources. For solving urban sprawl and housing shortage, more and more metropolises have adopted compact development. Academics, planners and policy makers mainly focused on evaluating the impacts of neighborhood attributes on housing prices and residential movements. Few have examined the relationships between residents’ demand and residential attractiveness in compact cities. Therefore, this study develops Residential Attractiveness Evaluation (RAE) Model to evaluate the attractive degree of urban areas based on residents’ demand in compact cities. Hong Kong is chosen as the study area which is a typical compact city. The results show that districts near the CBD and both sides of Victoria Harbor have highest residential attractiveness than other areas. Community planners and policy makers should consider residential attractiveness based on residents’ demand in efforts to promote sustainable development. Residential attractiveness is the foundation of residential land use planning and policies. Planners in compact cities should adopt various planning strategies according to different residential attractiveness. Moreover, residential attractiveness is the benchmark of evaluating residents’ satisfaction of new residential planning and policies.",4.0,https://iopscience.iop.org/article/10.1088/1757-899X/829/1/012009/pdf,True,Conference,Physics;Geography,2020-05-19,0f6dcae9dc8da736a128b35bd45eb0d06e704941,exploringthemechanismofresidentialattractivenessincompacturbanareasacasestudyofhongkong,bai,10.1088/1757-899x/829/1/012009,exploringthemechanismofresidentialattractivenessincompacturbanareasacasestudyofhongkong|2020,1,2973,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,1,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d20a423948de6c8650f85bc0a123956446ce5458,10.11113/intrest.v14n1.140,,"Exploring Spatial Pattern of Residential Property Value around Cultural Heritage Sites in Ile-Ife, Nigeria",2020.0,International journal of real estate studies,O. B. Adebara; T. Adebara; A. Badiora; D. Ojo,"Cities in both developed and developing economies are characterised by various cultural heritage sites. Such sites can impact the value of residential properties around them either positively or negatively. In view of this, this study explores the spatial pattern of residential property values around cultural heritage sites in Ile-Ife, Nigeria. One (1) cultural heritage site each was purposively selected in the core and sub-urban areas of Ile-Ife. Furthermore, systematic sampling technique was used to select one of every twenty (20) buildings within different radii (1-300, 301-600 and 601-900 meters) of the selected cultural heritage sites. A total of two hundred and twenty-three (223) buildings were selected. Hence, 223 questionnaires were administered on the residents of the buildings. Interview was also conducted on ten (10) real estate practitioners in the study area to optimally provide answers rental values of residential properties around the cultural heritage sites. The extracted data were analysed using multiple regression and rent differential technique. Findings showed that as distance increases from the cultural heritage sites, the average rental value of residential properties was also on the increase in the core and sub-urban areas of Ile-Ife. It was therefore established that there exists a positive relationship between the distance from cultural heritage sites and rental values of residential properties. The study further revealed that cultural heritage sites had a negative impact on the rental values of proximate residential properties in the study area. The study however concluded that the rental values of residential properties reflect distance to cultural heritage sites. It is recommended that property investors should give adequate attention to location to make accurate and dependable decisions on the supply of residential properties, especially in cities where there are cultural heritage sites.",3.0,https://intrest.utm.my/index.php/intrest/article/download/140/87,True,JournalArticle,,2020-06-28,d20a423948de6c8650f85bc0a123956446ce5458,exploringspatialpatternofresidentialpropertyvaluearoundculturalheritagesitesinileifenigeria,adebara,10.11113/intrest.v14n1.140,exploringspatialpatternofresidentialpropertyvaluearoundculturalheritagesitesinileifenigeria|2020,1,3449,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d29def971aa3bd1b64672ff45e725f24b52a3b6a,10.5171/2021.146557,,"Comparing Residential Property Values in Border Towns of Lagos Megacity, Nigeria",2021.0,Journal of South African Business Research,O. Oloke; D. Jesusegun; Oluwole A. Alagbe; A. Akindele,"In this study, the rental values of residential properties in two adjacent communities within the emerging Lagos megacity were examined to find out if certain correlational attributes exist. The communities selected are located in different geopolitical zones (i.e. Lagos and Ogun States), although the Lagos megacity influence has crawled and absorbed parts of Ogun state, such that the physical boundary has virtually vanished. Residents in the two communities as well as estate surveyors and valuers were the respondent groups involved in the study. A total of 236 questionnaires were administered. Descriptive and inferential tools of percentage means and independent sample T-test statistics were employed for the analysis, while the Pearson coefficient of correlation was used to establish the relationship between rental values in the two communities. It was found, among others, that the level of infrastructure development, enforcement of physical planning regulations, administrative jurisdiction, population density and distance to the central business district are the factors responsible for the significant differences in the magnitude of rental values between the communities. The study, therefore, suggested the investment in the physical and socio-economic infrastructure in Ado-Odo/Ota local government area of the megacity as well as the creation of a joint development policy and programme by governments of Lagos and Ogun states so as to overcome the administrative jurisdiction barrier militating against the actualization of the megacity project.",1.0,https://ibimapublishing.com/articles/JSABR/2021/146557/146557.pdf,True,Review,Geography,2021-05-28,d29def971aa3bd1b64672ff45e725f24b52a3b6a,comparingresidentialpropertyvaluesinbordertownsoflagosmegacitynigeria,oloke,10.5171/2021.146557,comparingresidentialpropertyvaluesinbordertownsoflagosmegacitynigeria|2021,1,3069,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",802979f68fa791b2329ce531c56cdb3567bf6aee,10.1553/giscience2014s125,,"A Spatial Analysis of House Prices in the Kingdom of Fife, Scotland",2014.0,,Julia Zmölnig; M. Tomintz; S. Fotheringham,"The research presented in this paper focuses on the analysis of spatial variations in house price adjustments due to economic conditions, and to quantify and describe patterns in the variations of house prices in the study area of Fife, Scotland. To show the impacts of national and local economic conditions on house price levels, specific methods of spatial analysis, such as Interpolation and Geographically Weighted Regression, are needed. The data for this analysis comes from the Registers of Scotland. The Registers of Scotland is responsible for compiling and maintaining registers relating to property and other legal documents. Their main aim is to record and safeguard rights (e.g. respond to policies adopted by the Scottish Government), whilst providing open and efficient access to important information. Results show that there are spatial variations in house prices, whereby the house prices in the north are on the rise, whereas in the south the hotspots seem to disappear.",4.0,https://hw.oeaw.ac.at/0xc1aa5576_0x0030d417.pdf,True,,Geography,,802979f68fa791b2329ce531c56cdb3567bf6aee,aspatialanalysisofhousepricesinthekingdomoffifescotland,zmölnig,10.1553/giscience2014s125,aspatialanalysisofhousepricesinthekingdomoffifescotland|2014,1,2490,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),cbf33412e5793dcd323c80de240a7bc2645ab79e,10.32604/cmes.2022.018437,,Research on the Impact of Market Concern for Real Estate Policy on Housing Prices: Evidence from Internet Search and Hedonic Price Theory,2022.0,Computer Modeling in Engineering & Sciences,Wenwen Zhou; Mengyao Chen; Yang Gao; Ruilin Feng,,4.0,https://file.techscience.com/ueditor/files/cmes/TSP_CMES-131-3/TSP_CMES_18437/TSP_CMES_18437.pdf,True,,,,cbf33412e5793dcd323c80de240a7bc2645ab79e,researchontheimpactofmarketconcernforrealestatepolicyonhousingpricesevidencefrominternetsearchandhedonicpricetheory,zhou,10.32604/cmes.2022.018437,researchontheimpactofmarketconcernforrealestatepolicyonhousingpricesevidencefrominternetsearchandhedonicpricetheory|2022,1,1500,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices,2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",dbeff8a45649f69192b7d0f6d925e175605cb832,10.1080/24694452.2022.2092443,,Estimation and Inference of Special Types of the Coefficients in Geographically and Temporally Weighted Regression Models,2022.0,Annals of the American Association of Geographers,Zhi Zhang; Chang-Lin Mei; Hua-Yi Yu,"Geographically and temporally weighted regression (GTWR) models have been widely used to explore spatiotemporal nonstationarity where all the regression coefficients are assumed to be varying over both space and time. In reality, however, constant, only temporally varying, and only spatially varying coefficients might also be possible depending on the underlying effects of the explanatory variables on the response variable. Therefore, the development of inference and estimation methods for such special types of the coefficients is essential to the deep understanding of spatiotemporal characteristics of the regression relationship. In this article, an average-based approach, relying on a modified estimation of the conventional GTWR models, is proposed to calibrate the GTWR models with the special types of the coefficients, on which a statistical test is formulated to simultaneously infer constant, temporally varying, and spatially varying coefficients. The simulation study shows that the test method is of valid Type I error and satisfactory power and the average-based estimation method yields more accurate estimators for the special types of the coefficients. A real-life example based on Beijing house prices is given to demonstrate the applicability of the test and estimation methods as well as the extensibility of the test in model selection.",5.0,https://figshare.com/articles/journal_contribution/Estimation_and_Inference_of_Special_Types_of_the_Coefficients_in_Geographically_and_Temporally_Weighted_Regression_Models/20466300,True,,,2022-08-10,dbeff8a45649f69192b7d0f6d925e175605cb832,estimationandinferenceofspecialtypesofthecoefficientsingeographicallyandtemporallyweightedregressionmodels,zhang,10.1080/24694452.2022.2092443,estimationandinferenceofspecialtypesofthecoefficientsingeographicallyandtemporallyweightedregressionmodels|2022,1,2864,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",28f9e7960bb44faf3b17e4b3d979d4c04042231f,10.1080/00343404.2015.1135239,,Amenities and the attraction of Dutch cities,2017.0,,H. Garretsen; G. Marlet,"ABSTRACT Amenities and the attraction of Dutch cities. Regional Studies. Research on the relevance of amenities for urban wages, jobs or housing prices has mainly focused on the case of US cities. In many (European) countries, the labour and housing markets function, however, rather differently than in the case of the United States. As a result, amenities may affect urban labour or housing markets differently. In addition, in European countries the proximity of cities is such that spatial interdependencies between cities have to be taken into account as well. By using a very rich dataset, this paper analyses the relationship between urban amenities, agglomeration and housing prices for the case of a highly urbanized European country, the Netherlands. It finds that Dutch cities that combine a favourable job location with a variety of urban amenities have significantly higher housing prices. Relatively safe cities, offering a variety of history and cultural events, as well as good restaurants, also have significantly higher housing prices.",49.0,https://figshare.com/articles/journal_contribution/Amenities_and_the_attraction_of_Dutch_cities/3692052,True,,Economics,2017-05-04,28f9e7960bb44faf3b17e4b3d979d4c04042231f,amenitiesandtheattractionofdutchcities,garretsen,10.1080/00343404.2015.1135239,amenitiesandtheattractionofdutchcities|2017,1,2553,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,3,1,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",35b2e9b41a61855b661d26f5d0231fe4231d4694,10.1111/cico.12071,,"Changing Neighborhood Determinants of Housing Price Trends in Southern California, 1960–2009",2014.0,,John R. Hipp; Amrita Singh,,15.0,https://escholarship.org/content/qt1gt4f4cx/qt1gt4f4cx.pdf?t=nm2xvz,True,JournalArticle,Geography,2014-09-01,35b2e9b41a61855b661d26f5d0231fe4231d4694,changingneighborhooddeterminantsofhousingpricetrendsinsoutherncalifornia19602009,hipp,10.1111/cico.12071,changingneighborhooddeterminantsofhousingpricetrendsinsoutherncalifornia19602009|2014,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0b33dc01733e607fe24622cf750443b369694c71,10.1016/j.cities.2018.08.003,,"Does developers' ownership structure shape their market behavior? Evidence from state owned enterprises in Chengdu, Sichuan, 2004–2011",2019.0,Cities,Paavo Monkkonen; Guo-Ying Deng; Wanyang Hu,"Abstract Real estate development is a complex speculative endeavor and developer firms take a variety of forms throughout the world. State run development companies are relatively common and China is no exception. State Owned Enterprises (SOEs) have played a major role in China's transition to a market-based real estate sector, yet we know relatively little about the housing they build relative to private companies currently. To assess how their performance differs from private companies, we use a comprehensive set of georeferenced housing transactions, joined with remote sensing data and data on neighborhood amenities and transportation infrastructure, to analyze the dynamics of the Chengdu housing market from 2004 to 2011. We observe a drop in the variation in housing price and size, as well as a growing premium for larger units that we connect to changes in government regulations. Importantly, we find that units developed by SOE sell at a discount of roughly 7%. To explain this discount, we draw on literature and examine pricing strategy, difficult to measure quality elements, preferred treatment by local governments, and efforts to fulfill social goals related to housing provision. The study outlines directions for future research and we recommend the Chinese government formalize the relationship between SOEs in national housing policy to facilitate the production of lower-cost housing in a more consistent and equitable manner.",12.0,https://escholarship.org/content/qt11m170n4/qt11m170n4.pdf?t=plqaa6,True,,Business,2019-01-01,0b33dc01733e607fe24622cf750443b369694c71,doesdevelopersownershipstructureshapetheirmarketbehaviorevidencefromstateownedenterprisesinchengdusichuan20042011,monkkonen,10.1016/j.cities.2018.08.003,doesdevelopersownershipstructureshapetheirmarketbehaviorevidencefromstateownedenterprisesinchengdusichuan20042011|2019,1,2955,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic house price,9a9259d94766ecbba99acb6ad34c5c615bf58981,10.15396/eres2014_193,,Exploring the determinants of residential property values in a crisis: evidence from Greece,2014.0,,Theodoros Mitrakos; C. Akantziliotou; Vassiliki Vlachostergiou; Sotiris Tsolacos,,2.0,https://eres.architexturez.net/system/files/pdf/eres2014_193.content.pdf,True,,Economics,,9a9259d94766ecbba99acb6ad34c5c615bf58981,exploringthedeterminantsofresidentialpropertyvaluesinacrisisevidencefromgreece,mitrakos,10.15396/eres2014_193,exploringthedeterminantsofresidentialpropertyvaluesinacrisisevidencefromgreece|2014,1,1500,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || hedonic price model amenities residential || housing characteristics prices hedonic || residential property price determinants",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",df3b4adf69c8b83194ce00dc890b5f9535609557,10.15396/eres2013_44,,Unraveling the Inflated House Price in the New Housing Supply,2013.0,,R. Ismail,"There are a lot of concern both by public at large and the government on the inflated house price in the housing market, fearing that it could lead to property bubble and raises the issue of affordability. On a closer look at the property transactions data, it was observed that the main increase in the house price is often related to the primary housing market, i.e. new supply in the housing market whereas the sales price of existing supply, or the secondary market, remains stable or increase marginally. What is more baffling is that the two markets are sometimes in the same location, with same accessibility, with units available for sale but the sales price of houses in these market shows a marked difference. It is grounded in theory that price is a function of demand and supply. However, if supply is in abundance in the secondary market, what causes the price differentiation between these two markets. This research attempts to unravel the inflated house price in the primary market. First, the research investigates on market efficiency i.e. the extent of dissemination of sales information of both housing in the primary and secondary market. Second, it explores the level of complexity/ease in purchasing houses in the primary and secondary market. Third, it examines the costs of purchasing houses in the two markets to the purchasers. Fourth, it looks at what the housing in the primary and secondary market has to offer in terms of facilities, amenities, design and technology.This research involves looking at published data in the Annual Property Market report, interview with the Malaysian Association of Estate Agents, Residential Housing Developers Association, Auctioneers as well as survey using questionnaires to purchasers of new housing.At the end of this research, it will give an understanding why purchasers are willing to pay more for houses in the primary market, whilst similar housing in the same locality is available in the secondary housing market. The research will suggest what can be done to reduce the price gap between the primary and secondary market.",2.0,https://eres.architexturez.net/system/files/pdf/eres2013_44.content.00129.pdf,True,Review,Business,,df3b4adf69c8b83194ce00dc890b5f9535609557,unravelingtheinflatedhousepriceinthenewhousingsupply,ismail,10.15396/eres2013_44,unravelingtheinflatedhousepriceinthenewhousingsupply|2013,1,3598,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,4,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",daaf3309a526e7d04a6bb72f85bd00997c0ae1c9,10.15396/eres2001_300,,Transport Policy and Property Values,2001.0,,M. Wrigley,"Transport and environmental policy in the European Union and within the UK is increasingly concerned with the harmful effects of traffic. Stringent controls on car use in particular are being proposed and implemented. In the UK, for example, these controls include the exclusion of cars from certain streets and areas, road pricing and taxing of workplace parking. These restrictions must be implemented in parallel with investment in public transport in order to maintain levels of accessibility. Accessibility is a key determinant in the location decisions for a range of land uses. There is growing interest among policy-makers in how transport policy and the general rise in traffic congestion may affect land and property values by altering the pattern of accessibility in terms of transport mode, infrastructure provision and cost. In terms of transport influence on land and property values, new transport infrastructure may increase residential property values due to improved accessibility and increase commercial property values due to improved accessibility and possible agglomeration benefits. Previous research has suggested that lower transport costs will result in higher land and property values. This may be a reasonable hypothesis when considering an individual piece of infrastructure but may not be valid if the project has a significant impact on transport costs/accessibility in the city/region as a whole. Other research has shown that new transport infrastructure may shift values rather than increase aggregate land value. Therefore, the potential for redistribution of land and property values is a key issue. Furthermore, investment in public transport is being promoted as a positive means of tackling traffic congestion. Therefore, another key issue is whether the characteristics of the improved accessibility that will come from better public transport is equal or better to the accessibility that is lost due to increasing restraint on other modes as measured in terms of property values. Research at the University of the West of England, Bristol, UK is determining whether there is a relationship between investment in transport infrastructure and land and property values. Current research is establishing a methodology that will attempt to identify a relationship between accessibility and land and property value. In doing so it will be necessary to look for a relationship at the city/region level and at the local level. This will capture any relative shifts in values across the city/region as well as any absolute shifts in values near the transport improvement itself. This paper will present the findings of the literature review undertaken as part of the research project which examines academic literature, official publications from central and local government and published reports by transport consultants relating to infrastructure investment, property value and land use change. Specifically the review; / considers the emerging transport framework and its potential impact on the property market, / examines the classical theories of the relationship between the location of urban land uses and values, / provides a critique of empirical urban rent and price determination studies with an emphasis on studies of locational influences on property values and transport influences on property values in particular, and / examines techniques used to assess new public transport investment and identify any consideration of land and property values in these techniques. The paper will then outline the methodology of the study devised to link an urban rent determination model to an accessibility model. This will test whether there is a relationship between accessibility and land and property value and whether it is robust enough to handle significant changes in accessibility patterns.",11.0,https://eres.architexturez.net/system/files/pdf/eres2001_300.content.pdf,True,Review,Geography,2001-06-01,daaf3309a526e7d04a6bb72f85bd00997c0ae1c9,transportpolicyandpropertyvalues,wrigley,10.15396/eres2001_300,transportpolicyandpropertyvalues|2001,1,5336,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || residential property price determinants",0,2,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",00ce5283ca2d3f3452542fd5ac8e24bf14c05b3c,10.15396/eres2015_230,,House Price Determinants in Sydney,2015.0,,X. Ge; B. Williams,,6.0,https://eres.architexturez.net/system/files/eres2015_230.content.pdf,True,,Economics,2015-07-01,00ce5283ca2d3f3452542fd5ac8e24bf14c05b3c,housepricedeterminantsinsydney,ge,10.15396/eres2015_230,housepricedeterminantsinsydney|2015,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",14acc857ac2d69fb28ca0dc6e4cc291e46ec0882,10.15396/eres2023_42,,A methodology for local housing price index in France,2022.0,28th Annual European Real Estate Society Conference,Michel Baroni; Pierre Vidal; Carmelo Micciche,"Real estate accounts for 61% of France's national net wealth. Housing is the largest item of expenditure of French households. Indices that track real estate prices evolution are thus crucial instruments for decision makers of all kinds: households, investors, the scientific community, local governments, etc. Yet, the available public statistics fail to cope with the heterogeneity of the housing prices dynamics across the country. In France, Notaire-Insee indices are considered as the reference, especially because their methodology and indices are open source. Quarterly, the institute produces indices for apartments and houses in big agglomerates. With 9 indices for house prices in France, the division proposed by this methodology hides a lot of disparities. For instance, the “Province” house index includes more than 25000 cities as diverse as Toulouse (450k inhabitants) and Malroy (350 inhabitants), which represents 36% of the French housing stock. This indicator does not make it possible to highlight the differences in dynamics between cities geographically distinct and drived by different fundamentals due to different economic conditions. This work aims at producing a library of open data real estate price indices that track price evolution at fine geographical scale. To do so we develop a methodology for real estate price index computation, and then apply it on geographical clusters close to local markets. We want to be part of an open-source approach. Indeed, the methodology will be published, and all the indices will be made available for free to all. The proposed method is applied on the fiscal database of real estate transactions DV3F, containing all the transactions in France (except Alsace, Moselle and Mayotte) between 2010 and 2020. Our approach is based on classic hedonic price index methods. Each aspect and hypothesis of the hedonic method have been justified to produce precise indices. Producing indices close to local markets requires working in a low data environment, and increases the probability of encountering outliers. Hedonic methods being very sensitive to outliers, we tackle this issue by testing the impact of different dynamics filters methods. To reduce the heteroscedasticity and improve the precision of the model, different forms and combinations of the regression have been tested. This method is applied to 2 divisions of France: one for apartments, another for houses. In order to produce indices close to local markets, a clusterization of cities of France is computed as finely as possible and based on socio economic and local housing stock criteria. To preserve the quality of indexes, all clusters respect constraints of minimum transaction volumes. This division is based on a clusterization of urban areas thanks to Ascending Hierarchical Classification and Kohonen algorithms. This clusterization resulted in the computation of 350 apartments and 400 houses indices. The application of our approach on these geographical clusters reveals a great diversity of house price dynamics. For instance, the “Province” index produced by Notaire-Insee is divided into 220 clusters, with variations between 2015 and 2020 of 2% and 29% respectively for the first and ninth decile of these indices. By highlighting the plurality of real estate price dynamics in France and urban centers, our approach emphasizes the need for indices to be computed on a local scale to be useful.",0.0,https://eres.architexturez.net/system/files/P_20230228193744_5241.pdf,True,JournalArticle;Conference,,,14acc857ac2d69fb28ca0dc6e4cc291e46ec0882,amethodologyforlocalhousingpriceindexinfrance,baroni,10.15396/eres2023_42,amethodologyforlocalhousingpriceindexinfrance|2022,1,4941,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || real estate hedonic regression",2,4,4,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,residential property price determinants,349058fc6307665248a1e81921a0426eb197d24e,10.15396/eres2019_91,,Italian auction market: investigating forced sale value,2019.0,26th Annual European Real Estate Society Conference,M. Mariani; P. Amoruso,"The present work focuses on the considerable amount of real estate non-performing loans in Italy, not favored by the state of judicial proceedings. In this view various governmental initiatives intended to facilitate a more efficient liquidation of underlying real estate assets. In this regard, it could be useful better understand main causes of discount between market value and forced sale price with the primary aim of a more comprehensive determination of the value of mortgages ‘underlying guarantees and not least in order to improve further valorization processes of assets intended to get a higher selling price; starting from results of existing literature, an analysis of main determinants of the gap between the estimated listing price and forced sale value has been conducted, sampling 225 cases of forced residential property sales between 2014 and 2018 in the South of Italy.",0.0,https://eres.architexturez.net/system/files/P_20190718115348_3761.pdf,True,Conference,Business,,349058fc6307665248a1e81921a0426eb197d24e,italianauctionmarketinvestigatingforcedsalevalue,mariani,10.15396/eres2019_91,italianauctionmarketinvestigatingforcedsalevalue|2019,1,2391,1,residential property price determinants,0,3,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,spatial hedonic model housing,50e6c80c5cbd52ae27b5aa077dc5fa305fdca8e6,10.15396/eres2019_131,,Estimating the HARA land-use model for housing planning based on hedonic price analysis,2019.0,26th Annual European Real Estate Society Conference,I. Ossokina; Theo A. Arentze; Jian-Fei Li,"HARA is a land-use model that uses a search algorithm to find the optimal spatial allocation of new housing demands in an urban plan area. In the model, the plan area is represented as a grid of cells. A core element of the algorithm is a function that is used to evaluate the value of a cell for each possible land-use given its location. An optimum is found by stepwise improving an initial allocation based on the value function. In this paper we show that the value function can be specified as the net value of a (housing) development given the land costs, the construction costs and the market value of the development at a location. Specified in that way the solution generated represents an optimum as well as a market equilibrium (maximum net value for developers). A critical prerequisite for this is, however, that the value-function is specified such that it accurately represent buyers’ willingness-to-pay for dwelling and location characteristics in the housing market. In the paper, we show how the value function can be estimated using hedonic price analysis. The analysis is carried out based on a large housing transaction data set from The Netherlands. The trade-off between living in a green environment and having high-level urban facilities in the proximity of the residential location is of special interest for housing planning. This trade-off has become more significant even given the growing environmental concerns for creating climate-adapted as well as compact cities. We present the results of an application of the estimated model where we investigate the nature of this trade-off and the impact this has on the green-versus-compact character of urban development. We anticipate that the trade-off may change with the increasing importance of good climate performance (robustness for extreme weather conditions). Using the model as an experimentation tool, we consider what the impacts of changes in the trade-off will be for the spatial planning of housing.",0.0,https://eres.architexturez.net/system/files/P_20190710151755_8247.pdf,True,Conference,Economics;Computer Science,2019-07-03,50e6c80c5cbd52ae27b5aa077dc5fa305fdca8e6,estimatingtheharalandusemodelforhousingplanningbasedonhedonicpriceanalysis,ossokina,10.15396/eres2019_131,estimatingtheharalandusemodelforhousingplanningbasedonhedonicpriceanalysis|2019,1,3489,4,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,0,3,3,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",21933516b79decc88a6510dbe30380fd9042adc0,10.15396/eres2013_34,,Veblen Effect in the United States Housing Market: Spatial and Temporal Variation,2013.0,,Kwan-Ok Lee; M. Mori,"The finance literature suggests that the Veblen effect (i.e. luxury consumption behavior) could be translated into consumers' behavior with other goods and assets. This study is the first to examine the role that the Veblen effect plays in housing market dynamics, with a focus on spatial and temporal variation in this role. It uses a unique dataset that matches the consumers' appetite for non-housing luxury goods from Google Insights for Search to the premium that they pay for high-end houses in US Metropolitan Statistical Areas (MSAs) during 2004-2011. The results demonstrate that controlling for other MSA demographic and economic characteristics, the Veblen effect has a significant, positive relationship with a premium paid in the housing market. This suggests that high-end houses may have been purchased for the enjoyment of signaling wealth and status and this housing consumption behavior may have partly driven the large deviation of high-end house prices from the median. Housing consumption tends to be motivated more by the Veblen effect in the areas where people pay a steady, higher premium than in other areas with a more volatile, lower premium. In fact, the higher Veblen effect substantially contributes to maintaining the higher level of the premium in these areas even during the bust period.",4.0,https://eres.architexturez.net/system/files/34.pdf,True,,Economics,,21933516b79decc88a6510dbe30380fd9042adc0,vebleneffectintheunitedstateshousingmarketspatialandtemporalvariation,lee,10.15396/eres2013_34,vebleneffectintheunitedstateshousingmarketspatialandtemporalvariation|2013,1,2820,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic house price,d8c2ee90421e9aa6d7904bf832f53c58281d7fef,10.2139/ssrn.4532854,,Do Remote Workers Deter Neighborhood Crime? Evidence from the Rise of Working from Home,2026.0,Social Science Research Network,J. Matheson; Brendon Mcconnell; J. Rockey; Argyris Sakalis,"We examine the impact of the working from home (WFH) shift on neighborhood-level burglary rates, employing detailed street-level crime data and a neighborhood WFH measure. We find a one standard deviation increase in WFH (9.5pp) leads to a persistent 4% drop in burglaries. A spatial search model identifies two deterrence channels: occupancy, as burglars avoid occupied houses, and “eyes on the street”. We provide evidence supporting both channels. Despite crime displacement to low WFH areas offseting 30% of the burglary reduction, a hedonic pricing model reveals significant willingness to pay for high WFH areas, especially those with high ex-ante burglary risk. Keywords— Working From Home, Property Crime, Spatial Spillovers, Hedonic House Price Models JEL Codes— H75, K42, R20. We thank Mark Hoekstra, Agne Suziedelyte, Mariyana Zapryanova, Yves Zenou, Nicholas Zeibarth and audiences at the Essen Health Conference and Southampton for valuable comments. All errors are our own. Matheson: Sheffield, j.matheson@sheffield.ac.uk; McConnell: Southampton, brendon.mcconnell@gmail.com; Rockey: Birmingham, j.c.rockey@bham.ac.uk; Sakalis: Sheffield, a.sakalis@sheffield.ac.uk.",13.0,https://eprints.whiterose.ac.uk/202584/1/Binder2023020.pdf,True,JournalArticle;CaseReport,,2026-05-07,d8c2ee90421e9aa6d7904bf832f53c58281d7fef,doremoteworkersdeterneighborhoodcrimeevidencefromtheriseofworkingfromhome,matheson,10.2139/ssrn.4532854,doremoteworkersdeterneighborhoodcrimeevidencefromtheriseofworkingfromhome|2026,1,2679,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price",2,1,3,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic house price,1e477c1562d4467a99a2399d7841699999b36461,10.1177/2399808320924678,,A new toolkit for land value analysis and scenario planning,2020.0,,C. Pettit; Y. Shi; H. Han; M. Rittenbruch; M. Foth; S. Lieske; R. van den Nouwelant; P. Mitchell; S. Leao; B. Christensen; M. Jamal,"Abstract In the digital era of big data, data analytics and smart cities, a new generation of planning support systems is emerging. The Rapid Analytics Interactive Scenario Explorer is a novel planning support system developed to help planners and policy-makers determine the likely land value uplift associated with the provision of new city infrastructure. The Rapid Analytics Interactive Scenario Explorer toolkit was developed following a user-centred research approach including iterative design, prototyping and evaluation. Tool development was informed by user inputs obtained through a series of co-design workshops with two end-user groups: land valuers and urban planners. The paper outlines the underlying technical architecture of the toolkit, which has the ability to perform rapid calculations and visualise the results, for the end-users, through an online mapping interface. The toolkit incorporates an ensemble of hedonic pricing models to calculate and visualise value uplift and so enable the user to explore what if? scenarios. The toolkit has been validated through an iterative case study approach. Use cases were related to two policy areas: property and land valuation processes (for land taxation purposes) and value uplift scenarios (for value capture purposes). The cases tested were in Western Sydney, Australia. The paper reports on the results of the ordinary least square linear regressions – used to explore the impacts of hedonic attributes on property value at the global level – and geographically weighted regressions – developed to provide local estimates and explore the varying spatial relationships between attributes and house price across the study area. Building upon the hedonic modelling, the paper also reports the value uplift functionality of the Rapid Analytics Interactive Scenario Explorer toolkit that enables users to drag and drop new train stations and rapidly calculate expected property prices under a range of future transport scenarios. The Rapid Analytics Interactive Scenario Explorer toolkit is believed to be the first of its kind to provide this specific functionality. As it is problem and policy specific, it can be considered an example of the next generation of data-driven planning support system.",40.0,https://eprints.qut.edu.au/200099/1/Raise_Cupum_EnvPlanB_Anon_resubmission_III_FINAL.pdf,True,JournalArticle,Computer Science,2020-05-18,1e477c1562d4467a99a2399d7841699999b36461,anewtoolkitforlandvalueanalysisandscenarioplanning,pettit,10.1177/2399808320924678,anewtoolkitforlandvalueanalysisandscenarioplanning|2020,1,3766,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic property valuation",2,3,1,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",6f4507f62465ff464aadad09c2b8c343297df054,10.35241/emeraldopenres.15025.1,,Willingness to Accept and Willingness to Pay for Residential Properties: A Hedonic Model Approach,2023.0,Emerald Open Research,S. Mishi; R. Mwanyepedza,"The world over is becoming urbanized, and people are migrating to cities in large numbers in search of opportunities. The increased urbanization has posed challenges such as congestion, rising crime, and growing urban poverty. The governments respond by providing amenities such as schools, hospitals, and housing to meet to increase in demand for these facilities. However, there is a need for the provision of facilities that meets the expectations of the people, particularly on the proximity of amenities and bundles of utility-bearing housing characteristics. In an attempt to address the challenge mentioned, the study estimated the hedonic characteristics influencing the willingness to accept and willingness to pay for housing facilities in the Eastern Cape Province, South Africa. Using a multiple linear regression model and artificial neural network, the study found out that properties with a bathroom, garage and large floor size have a higher value compared to properties without these facilities. When making decisions to acquire a property, buyers consider the availability of discounts and the prevailing property price. Overall, willingness to pay and accept decisions are mainly determined by location and the price at which homogeneous neighborhood properties were sold. Therefore, the study recommends that urban town planners and other housing authorities prioritize the construction of properties with larger floor areas, parking bays, and bathrooms using a cost-effective mechanism that makes the properties affordable to residents.",2.0,https://emeraldopenresearch.com/articles/5-14/pdf,True,JournalArticle,,2023-05-04,6f4507f62465ff464aadad09c2b8c343297df054,willingnesstoacceptandwillingnesstopayforresidentialpropertiesahedonicmodelapproach,mishi,10.35241/emeraldopenres.15025.1,willingnesstoacceptandwillingnesstopayforresidentialpropertiesahedonicmodelapproach|2023,1,3058,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || hedonic price model amenities residential || housing characteristics prices hedonic",2,1,2,7,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e71ce2026d1fbc32e3d889d7afb917ace603f641,10.54097/wcqvdh81,,GIS-based Analysis of Distribution Characteristics and Driving Factors of House Prices in Nanjing's Central District,2024.0,"Frontiers in Business, Economics and Management",Xiao-Lin Gong,"This study aims to investigate the spatial distribution characteristics of house prices and their driving factors in the central area of Nanjing by using Geographic Information System (GIS) technology, combined with kernel density analysis, Moran index and geodetic detector method. The results show that: (1) The high values of house prices in central Nanjing have significant spatial aggregation characteristics, mainly concentrated in Qinhuai District, Gulou District and its border areas, and the house prices have significant spatial autocorrelation; (2) The results of the geo-detector show that the degree of traffic access and settlement characteristics have the greatest influence on house prices, while the influence of the construction of ancillary facilities is relatively weaker; (3) There are significant interactions between different factors, especially between transport accessibility and the construction of ancillary facilities, which play a crucial role in influencing house prices.",0.0,https://drpress.org/ojs/index.php/fbem/article/download/28229/27722,True,JournalArticle,,2024-12-24,e71ce2026d1fbc32e3d889d7afb917ace603f641,gisbasedanalysisofdistributioncharacteristicsanddrivingfactorsofhousepricesinnanjingscentraldistrict,gong,10.54097/wcqvdh81,gisbasedanalysisofdistributioncharacteristicsanddrivingfactorsofhousepricesinnanjingscentraldistrict|2024,1,2502,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1a641cb29064cb19e857e4c9e85ff21325a76aff,10.54097/m187nw17,,Predicting US Airbnb Listing Prices by Machine Learning Models,2024.0,"Highlights in Business, Economics and Management",Yuchen Yang,"This paper addresses the prediction of Airbnb property prices using 2023 open data through the application of machine learning methodologies. In the context of the flourishing sharing economy, accurate price prediction within the short-term rental market holds great significance for hosts and users alike. Drawing on the 2023 Airbnb open dataset, the study employs three distinct models – Linear Regression, Random Forest, and XGBoost. Rigorous training, testing, and evaluation of these models reveal insights into their predictive capabilities. The focus centers on assessing model fit using essential evaluation metrics including R-squared, Mean Squared Error, and Root Mean Squared Error. Results demonstrate that the XGBoost model outperforms both Linear Regression and Random Forest. After parameter tuning, the best parameter for XGBoost regressor exhibits the lowest prediction error and highest R-squared value, showcasing its ability to capture intricate patterns within the data. This outcome underscores the potency of advanced ensemble learning techniques for precise property price predictions. The study's implications are substantial, offering hosts and potential guests improved decision-making insights.",3.0,https://drpress.org/ojs/index.php/HBEM/article/download/16483/16009,True,JournalArticle,,2024-01-22,1a641cb29064cb19e857e4c9e85ff21325a76aff,predictingusairbnblistingpricesbymachinelearningmodels,yang,10.54097/m187nw17,predictingusairbnblistingpricesbymachinelearningmodels|2024,1,2722,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,3,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",a32da664bdfbb4e948cdcebe3a38b512ae4db40a,10.7251/emc2402408s,,APPLICATION OF THE CAMA ALGORITHM IN MASS PROPERTY VALUATION IN THE REPUBLIC OF SRPSKA,2024.0,APEIRON: a journal for ancient philosophy and science,Dragan Stanković,"The real estate market is an integral part of the economic development of every country and contributes to promoting investments, production, increasing income, as well as socio-economic stability. Every country strives to achieve stability in the real estate market. The most important factor in the stability of the real estate market is real estate prices. Real estate prices determine the general well-being of citizens. They also affect the operations of banks (through mortgages, sales, etc.). It is extremely important to stabilize real estate prices. Only in this way is it possible to implement policies and macroeconomic measures that will make the real estate market an efficient segment of the overall socio-economic system. For all these reasons, it is very important to accurately assess real estate prices. Mass appraisal or Computer-Assisted Mass Appraisal (CAMA) are terms usually applied for the valuation of real estate for property taxation purposes. The development of computational technologies enables computer-supported methods for valuing all properties. CAMA represents a computer algorithm for the mass and individual calculation of real estate values. The use of the term Computer-Assisted Mass Appraisal techniques is defined as the systematic assessment of the value of all properties on a given date using standardized procedures and statistical testing. The CAMA algorithm consists of several components: Model specification (mathematical form of the model or valuation model); Sales prices and descriptive property data; Income and expense data and descriptive property data; Model calibration software (usually multivariate statistical methods); Calibrated models (a function of input data with established model coefficients; a mathematical expression for calculating the estimated value); Attributes (characteristics required by the appraisal model in a computer-readable form); Valuation software (software for applying the calibrated model to the characteristics of input attributes to create value estimates; creating an estimate file and generating defined reports or producible); Value estimates (file or database of value estimates). The goal of this research is to estimate the value of real estate using the mass appraisal method - the CAMA algorithm. The specificity of forming the estimated value of real estate lies in defining the market value of real estate if the prices from sales contracts entered into the Real Estate Price Register (formed based on supply and demand for apartments) are known, forming value zones (location factor), value tables (relational tables and value levels), additional influence factors (apartment location factor within a building), and an equation for estimating real estate value. The research results show that deviations between notarial contract prices and estimated values correspond to the standards applied in mass real estate value appraisal in all countries that implement them.",0.0,https://doisrpska.nub.rs/index.php/economyandmarket/article/download/11412/10998,True,JournalArticle,,2024-10-21,a32da664bdfbb4e948cdcebe3a38b512ae4db40a,applicationofthecamaalgorithminmasspropertyvaluationintherepublicofsrpska,stanković,10.7251/emc2402408s,applicationofthecamaalgorithminmasspropertyvaluationintherepublicofsrpska|2024,1,4469,1,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",0,2,1,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a74dcde3d3f15d8253b6e915046fae427ae91082,10.5897/ajbm11.1626,,"How to affect housing values: Location, affordability and amenity",2012.0,,M. Guan,"During the last ten years, real house prices have increased in China at a rate faster than inflation and job compensation. It is now a conventional wisdom that location, affordability and amenity increase housing values in the neighborhoods and communities they inhabit. But, few existing studies deal with it. This paper aims to report on a study that contributes to the understanding of the roles of location, affordability and amenity on housing values in China. The key findings confirm the general theory and hypotheses. The analysis presents evidence that location, affordability and amenity are positively influenced by housing values. The correlation among location, affordability and amenity are also computed. This research contributes to the understanding of the determinants of housing values in emerging markets like China. Key words: Housing, affordability, location, amenity.",6.0,https://doi.org/10.5897/ajbm11.1626,True,,Economics,2012-04-25,a74dcde3d3f15d8253b6e915046fae427ae91082,howtoaffecthousingvalueslocationaffordabilityandamenity,guan,10.5897/ajbm11.1626,howtoaffecthousingvalueslocationaffordabilityandamenity|2012,1,2400,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,modelo de precios hedónicos vivienda,0b89e623e0b05b7c8f536f0cfe2f7928ae52db92,10.56712/latam.v5i6.3110,,"Determinación de la influencia de la contaminación acústica generada por el flujo vehicular, en la valoración económica de viviendas en el distrito de Arequipa, 2018",2024.0,LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades,Treyzy Fransheska Loza Osorio; Digmar Arturo Loza Alcalde; Yoana Alexandra Portugal Cano,"La presente investigación busca determinar la influencia de la contaminación acústica en la valoración económica de viviendas en el distrito de Arequipa generada por el flujo vehicular, el cual, El desarrollo de la investigación se realizó a través de un estudio empírico con 72 mediciones de ruido en diferentes puntos del distrito, mismas que fueron comparadas con los ECA (Estándares Nacionales de Calidad Ambiental) establecidos para ruido, dando como resultado que el 33.3% (24 mediciones) no superan los ECA; mientras que el 66.6% (48 mediciones) superan los ECA. Como resultado se obtuvo que el modelo econométrico de la función de precios hedónicos utilizados en el estudio es adecuado para medir la capacidad explicativa del modelo en su conjunto, donde el coeficiente de determinación R² dio un valor de 0,95. Se muestra que el ruido se convierte en una externalidad negativa en el valor comercial de las viviendas, puesto que a medida que se encuentran expuestos a altos niveles de ruido, su precio disminuye; es decir, su valor comercial se deprecia en un 0,96% por el incremento de 1 decibel (dB). Esta investigación se realizó con el fin de que sirva como instrumento para aplicar políticas de control, gestión y mitigación de la contaminación acústica por parte de la Municipalidad Provincial de Arequipa.",2.0,https://doi.org/10.56712/latam.v5i6.3110,True,JournalArticle,,2024-12-07,0b89e623e0b05b7c8f536f0cfe2f7928ae52db92,determinaciondelainfluenciadelacontaminacionacusticageneradaporelflujovehicularenlavaloracioneconomicadeviviendaseneldistritodearequipa2018,osorio,10.56712/latam.v5i6.3110,determinaciondelainfluenciadelacontaminacionacusticageneradaporelflujovehicularenlavaloracioneconomicadeviviendaseneldistritodearequipa2018|2024,1,2820,2,modelo de precios hedónicos vivienda || precios hedónicos vivienda,1,1,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,apartment prices hedonic,d5df9fadf6e2beaa12043f6c43dd4c994cf4e6c1,10.54694/stat.2022.46,,"Hedonic Price Methods and Real Estate Price Index: an Explanatory Study for Apartments Market in Belo Horizonte, Brazil, from 2004 to 2015",2023.0,Statistika: Statistics and Economy Journal,Luiz Andrés Ribeiro Paixão,"Brazil does not have an official real estate price index yet. A 2011 presidential decree stipulated for the Brazilian Institute of Geography and Statistics (IBGE) the assignment to create and spread a real estate price index for Brazil. In this paper, we test some different hedonic model methods to estimate quarterly price indices for apartments in Belo Horizonte, Brazil, from January 2005 to December 2015. Our goals are: i) to measure and compare the different hedonic methods; ii) to present some results that will contribute to the discussion about the development of an official real estate price index in Brazil. The empirical results corroborate the idea of intense apartment prices valuation in Belo Horizonte, mainly between 2007 and 2011, when the annual price index growth remained above 20%. These results cast light on the potential use of both hedonic methods and administrative data base to construct an official real estate price index for Brazil.",3.0,https://doi.org/10.54694/stat.2022.46,True,JournalArticle,,2023-06-16,d5df9fadf6e2beaa12043f6c43dd4c994cf4e6c1,hedonicpricemethodsandrealestatepriceindexanexplanatorystudyforapartmentsmarketinbelohorizontebrazilfrom2004to2015,paixão,10.54694/stat.2022.46,hedonicpricemethodsandrealestatepriceindexanexplanatorystudyforapartmentsmarketinbelohorizontebrazilfrom2004to2015|2023,1,2466,2,apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,3,2,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8dbff7a40283e1638b469220e9b2cb8d53734648,10.54254/2754-1169/97/20231551,,Analysis and Prediction of Influencing Factors of American Real Estate Price Based on Machine Learning,2024.0,"Advances in Economics, Management and Political Sciences",Ting He,"The American real estate market has always been a subject of widespread interest, with its price fluctuations impacting not only the lives of millions of American households but also exerting profound effects on the nation's overall economic health. This paper focuses on analyzing which factors are more significant in influencing the American real estate market in order to gain a better understanding of the factors influencing this market and to offer insightful information for stakeholders, policymakers, and investors navigating this dynamic market. We apply linear regression model, lasso regression model and random forest regression model to United States real estate data to explore variables that influence the U.S. housing price and find that PPI, M3, and population are the critical variables impacting the housing price most. By comparing the performance of three regression models, we identify the random forest regression model as the optimal model for predicting real estate prices.",0.0,https://doi.org/10.54254/2754-1169/97/20231551,True,JournalArticle,,2024-07-02,8dbff7a40283e1638b469220e9b2cb8d53734648,analysisandpredictionofinfluencingfactorsofamericanrealestatepricebasedonmachinelearning,he,10.54254/2754-1169/97/20231551,analysisandpredictionofinfluencingfactorsofamericanrealestatepricebasedonmachinelearning|2024,1,2500,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",141d117163eb2ec55ec09043eab599b7a0e1a1d7,10.53383/100065,,International Real Estate Review,2005.0,International Real Estate Review,Evren Ozus; V. Dokmeci,"This study investigates the effects of revitalization process in Beyoglu, Istanbul. Since the 1980s, spatial impacts of transformation have become apparent on Istanbul and these impacts have taken their most intense forms in the historic city centres, as Beyoglu. While Istanbul has grown through the restructuring of the urban economy, social and spatial reconstruction, and innovative transportation and communication technologies, Beyoglu has begun to regain its characteristics. Projects and investments to reconstruct Beyoglu have been successful in changing this process and have achieved their targets particularly in Beyoglu’s residential areas and the region has started to develop. In order to analyse the development process, this paper focused on the three revitalized neighbourhoods in Beyoglu, which have been popular residential areas in Istanbul. We use social, economic, and spatial indicators, such as population, property prices, and functional transformation. The revitalization process has significant effects on social, economic, and spatial structure of Beyoglu. For further studies, it will be useful to repeat this study in other historical residential areas of Istanbul in order to make comparative studies.lutes investment returns should be challenged. There is mounting evidence that RPI can be financially sound and socially beneficial. Leaders have emerged that are demonstrating its feasibility. Their activity should be considered as a basis for best practice guidelines. There is a need to develop metrics for comparing progress on RPI. We recommend: 1) establishing an RPI working group, 2) summarizing prior reports on urban issues, 3) identifying investment strategies that are profitable and responsive to the issues, 4) clarifying the financial effects of different responses and improving our means of measuring them, 5) identifying best practices, 6) adopting a rating system, 7) supporting RPI investment funds, and 8) recognizing leaders in the field.",4.0,https://doi.org/10.53383/100065,True,Review,,2005-06-30,141d117163eb2ec55ec09043eab599b7a0e1a1d7,internationalrealestatereview,ozus,10.53383/100065,internationalrealestatereview|2005,1,3493,17,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || apartment prices hedonic || automated valuation model residential || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic || implicit prices housing attributes || real estate hedonic regression || rent hedonic regression housing || residential property price determinants",0,2,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",777a06b6a772b70153b2268908089e7e39ce5250,10.5267/j.dsl.2022.6.002,,Determining the factors influencing residential property price: A comparative study between Indonesia and Malaysia,2022.0,Decision Science Letters,R. A. Rahadi; S. K. Wiryono; Y. Nainggolan; K. F. Afgani; R. Yaman; A. Azmi; F. Ismail; J. Saputra; D. Rahmawati; A. Moulyni,"The property is a unique product that cannot be contrasted with other commercial products due to pricing conditions. Property price determination is one of the crucial aspects of property development activities because of the profit margin made by the developer and the purchasing preferences. This study attempts to extend the literature that has largely focused on factors of housing prices in developed markets and provided recent evidence of housing price determinants in two countries (i.e., Indonesia and Malaysia). Thus, this study examines the factors affecting housing prices in Jakarta Metropolitan Region and Greater Kuala Lumpur. A quantitative approach was used involving two countries, namely Indonesia and Malaysia. The data was collected using a survey questionnaire through purposive sampling. A total of 100 respondents (Indonesia) and 134 respondents (Malaysia) participated in this study. The data was analyzed using descriptive (frequency) and inferential statistics (chi-square test and multinomial regression). The results indicated that housing location, property funding, and health have a significant effect on residential property prices in Indonesia. Besides that, the results displayed that housing physical design, home design and construction, developer and real estate products, development concepts, housing location, property funding, social status, health, law provisions, and external factors do not affect residential property price in Malaysia. Despite being neighbors, Indonesia and Malaysia have distinct economic and landscape characteristics. Furthermore, considering Indonesia has a higher number of Covid-19 cases than Malaysia, significant information on how the pandemic has affected the demand, cost, and pricing of residential housing in Jakarta and Kuala Lumpur will be provided. The findings of this study will provide recommendations to investors, buyers, and policy about the residential housing industry's prospects for growth in emerging nations following the pandemic.",10.0,https://doi.org/10.5267/j.dsl.2022.6.002,True,Review,Medicine,,777a06b6a772b70153b2268908089e7e39ce5250,determiningthefactorsinfluencingresidentialpropertypriceacomparativestudybetweenindonesiaandmalaysia,rahadi,10.5267/j.dsl.2022.6.002,determiningthefactorsinfluencingresidentialpropertypriceacomparativestudybetweenindonesiaandmalaysia|2022,1,3523,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || residential property price determinants",0,3,3,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",79f7c75eb746bd897d6f7938f2a6165dd60997a5,10.5220/0014879100005130,,"Exploring Determinants of Housing Prices: Take Beijing, Shanghai, Chongqing Housing Prices as Examples",2024.0,Proceedings of the 2nd International Conference on Financial Management and the Digital Economy,Qi An; Zhenglun Hu; Tian-Yi Ma,,0.0,https://doi.org/10.5220/0014879100005130,True,Conference,,,79f7c75eb746bd897d6f7938f2a6165dd60997a5,exploringdeterminantsofhousingpricestakebeijingshanghaichongqinghousingpricesasexamples,an,10.5220/0014879100005130,exploringdeterminantsofhousingpricestakebeijingshanghaichongqinghousingpricesasexamples|2024,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a77d4f15dc315a9a20152df49818fe723ad86be6,10.5220/0011754800003607,,A Research on the Relevance of the Crime and the Housing Price Based on the Linear Regression Model,2022.0,"Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology",Jia-Li Guo; Zitao Ying; Zhengqi Sun; Y. Zhu,": As the most expensive area to live in the United States, Manhattan's housing prices are influenced by many factors, with crime rates being one of the most important factors affecting Manhattan's housing prices. Under this situation, this paper will explore the relationship between house prices and crime rates in Manhattan through the house prices and crime rate data in 2016-2017, by using python and R to build a simple linear regression model to find the relationship between the average house price and crime rate. As for the conclusion section, this paper will give the results of the analysis of the data with the conclusion and the practical implications of this conclusion.",0.0,https://doi.org/10.5220/0011754800003607,True,Conference,,,a77d4f15dc315a9a20152df49818fe723ad86be6,aresearchontherelevanceofthecrimeandthehousingpricebasedonthelinearregressionmodel,guo,10.5220/0011754800003607,aresearchontherelevanceofthecrimeandthehousingpricebasedonthelinearregressionmodel|2022,1,2184,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9c2092fdcf05884505177de80451862874b1d9dc,10.4108/eai.5-8-2020.2301209,,Determinant Factors of House Price Using Regression Analysis,2020.0,,Zeni Rusmawati; Rieska Maharani; Dedy Surahman,,0.0,https://doi.org/10.4108/eai.5-8-2020.2301209,True,,Economics,2020-10-15,9c2092fdcf05884505177de80451862874b1d9dc,determinantfactorsofhousepriceusingregressionanalysis,rusmawati,10.4108/eai.5-8-2020.2301209,determinantfactorsofhousepriceusingregressionanalysis|2020,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic house price,30bb06cf60733bf6f9bbacbca2bbc1d2e8d63b85,10.4038/ss.v51i1.4729,,Construction of Residential Property Price Indices Using the Hedonic Approach: An Application to the Residential Real Estate Market in Sri Lanka,2021.0,Staff Studies,S. Gunawardhana,"Monitoring real estate property prices is crucial for policy makers all over the world, particularly for central banks due to their interconnections with the monetary and financial system stability of an economy. As a result, compiling property price indices has increasingly gained attention from policymakers. However, compiling property price indices is believed to be difficult due to the highly heterogeneous nature of properties, requiring reliable data sources and a methodological approach that is different to those used in compiling other price indices. Against this backdrop, this paper attempts to compile price indices for residential properties in Sri Lanka with a view to supporting policymakers to monitor the price movements in the real estate sector. Considering the relatively less heterogenous nature of condominiums and stages of the buying and selling process, price indices are first developed for advertised condominiums and new condominiums using the Hedonic Regression based Rolling Window Time Dummy method, which is identified as the most suited property price index compilation method in the Sri Lankan context. Further, internationally accepted model specification improvement techniques and index smoothing techniques are also used in the study. Upon successful compilation of price indices for condominiums, the compilation process has been extended to cover the house and land markets of Sri Lanka.",1.0,https://doi.org/10.4038/ss.v51i1.4729,True,JournalArticle,,2021-12-31,30bb06cf60733bf6f9bbacbca2bbc1d2e8d63b85,constructionofresidentialpropertypriceindicesusingthehedonicapproachanapplicationtotheresidentialrealestatemarketinsrilanka,gunawardhana,10.4038/ss.v51i1.4729,constructionofresidentialpropertypriceindicesusingthehedonicapproachanapplicationtotheresidentialrealestatemarketinsrilanka|2021,1,2931,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || real estate hedonic regression",2,2,4,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bf5f82c642c0b6ec62378894155e0a1d728267b1,10.4038/bhumi.v5i2.31,,A Study on Impact of Residential Property Value Based on Designated Heritage Property with Special Reference to the City of Kandy-Sri Lanka,2017.0,,Uditha Jayampath; P. Bandusena,"Real properties are fixed assets with a bundle of rights. Thus, the freehold property owners could possess, use, abuse and even destroy his real property. However, the rate of re-development and high demand has directly affected the increase of a clear site value of the property than the present capital value of the property. Further, the property owner always considers the highest and the best use of the property. In this situation, there is a less possibility for redevelopment of the property as there is reason for legal restraints of the heritage-designated property. Harvey & Jowsey (2004)) introduces methods of preserving a historic building, when a period of time the clear site value has been increasing than the present capital value of the property. In addition, after a period, ‘value as a historic house’ the heritage property value increases than the clear site value. However, heritage properties generally performs well and has a significant demand in the market. Few researchers had identified that there were less/no-observable impact on the property value of heritage-designated property. Therefore, this research focuses on the investigation of the impact of heritage designation on the residential property value in Kandy in Sri Lanka with reference to the methods introduced by Jack Harvey in 2004 on preserving a historic building through the given methods. This research was a case based study, which consists of a literal review, a detailed survey and analysis. The Kandy world heritage city was selected as the case study where 112 residential properties were designated as heritage property in 1987 in Kandy. These 112 heritage designated residential properties were selected as the sample and a detailed survey was carried out based on observations and interviews. Further, both quantitative and qualitative methods were applied for the analysis. The quantitative analysis was carried out by using a linear regression model. Both qualitative and quantitative analysis showed that the small-scale properties have less possibility to increase their present capital value of the property than the clear site value of the property. Further, when declaring heritage properties, it is essential to consider the scale of the property and the level of its contribution to the city image as a heritage property. Moreover, the government intervention is also essential to improve those heritage properties.",0.0,https://doi.org/10.4038/bhumi.v5i2.31,True,Review,Business,2017-12-31,bf5f82c642c0b6ec62378894155e0a1d728267b1,astudyonimpactofresidentialpropertyvaluebasedondesignatedheritagepropertywithspecialreferencetothecityofkandysrilanka,jayampath,10.4038/bhumi.v5i2.31,astudyonimpactofresidentialpropertyvaluebasedondesignatedheritagepropertywithspecialreferencetothecityofkandysrilanka|2017,1,3929,2,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2ca60d2f9386cb6db3d3ba979928484d1068046c,10.35414/akufemubid.933675,,Metro İstasyonları ve Metro Hatları Çevrelerinin Konut Alt Piyasasına Etkisi: İstanbul Kadıköy-Tavşantepe Metro Hattı,2021.0,Afyon Kocatepe University Journal of Sciences and Engineering,Birol Alas,"In this study, it is aimed to examine the sub-markets of real estate in terms of both metro station regions and regions between the metro stations in terms of both residential prices for sale and rental prices. Therefore; the answer to the question of whether there is a difference between the prices of the apartments (houses) close to metro stations and prices of the houses located in the same proximity to the metro lines. For this purpose, the prices of the houses on 26.5 k ilometers long Kadıköy - Tavşantepe Metro line on the Anatolian side of Istanbul were examined. The prices of the houses located around the metro stations and those between the two metro stations are mutually compared with all the data collected along the line for both sale and rental housing prices. Then the whole line is divided into a total of 17 regions as two stations and between these stations. In each of 17 zones, two station zones and the zone between these stations were compared for both sale and rental housing prices. As a result, in the comparison of all station region data and inter-station region data, no difference was found in both the prices for sale and rental prices. In the examination of two successive metro stations and each of regions between these stations, the price differences of 24% in prices of houses for sale and 18% in the prices of rental houses were observed. These differences occurred in regions where the prices for sale and rental housing are higher compared to neighboring stations and stations. It has been observed that metro station surroundings and subway lines in regions where the prices for sale and rental housing are higher than the surrounding regions can be considered as separate housing sub-markets.",2.0,https://doi.org/10.35414/akufemubid.933675,True,,,2021-08-31,2ca60d2f9386cb6db3d3ba979928484d1068046c,metroistasyonlarvemetrohatlarcevrelerininkonutaltpiyasasnaetkisiistanbulkadkoytavsantepemetrohatt,alas,10.35414/akufemubid.933675,metroistasyonlarvemetrohatlarcevrelerininkonutaltpiyasasnaetkisiistanbulkadkoytavsantepemetrohatt|2021,1,3239,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,6,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cad70754fe6402cfbd7c22bd2b1bbe6993bd0ca2,10.3390/su132413635,,Real Estate Values and Urban Quality: A Multiple Linear Regression Model for Defining an Urban Quality Index,2021.0,Sustainability,S. Carbonara; Marco Faustoferri; Davide Stefano,"Urban quality, real estate values and property taxation are different factors that participate in defining how a city is governed. Real estate values are largely determined by the characteristics of urban environments in which properties are located and, thus, by quality of the location. Beginning with these considerations, this paper explores the theme of urban quality through a study of property values that seeks to define all physical (and thus measurable) characteristics that participate in defining urban quality. For this purpose, a multiple linear regression model was developed for reading the residential real estate market in the city of Pescara (Italy). In addition to the intrinsic characteristics of a property (floor area, period of construction/renovation, level, building typology and presence of a garage), input also included extrinsic data represented by the Urban Quality Index. Scientific literature on this theme tells us that many independent variables influence real estate prices, although all are linked to a set of intrinsic characteristics (property-specific) and to a set of extrinsic characteristics (specific to the urban context in which the property is located) and, thus, to the quality of urban environments. The index developed was produced by the analytical and simultaneous reading of four macrosystems with the greatest impact on urban quality: environment, infrastructure, settlement and services (each with its own subsystems). The results obtained made it possible to redefine proportional ratios between various parts of the city of Pescara, based on a specific Urban Quality Index, and to recalculate market property values used to calculate taxes in an attempt to resolve the inequality that persists in this field.",15.0,https://doi.org/10.3390/su132413635,True,,,2021-12-09,cad70754fe6402cfbd7c22bd2b1bbe6993bd0ca2,realestatevaluesandurbanqualityamultiplelinearregressionmodelfordefininganurbanqualityindex,carbonara,10.3390/su132413635,realestatevaluesandurbanqualityamultiplelinearregressionmodelfordefininganurbanqualityindex|2021,1,3265,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",4f6fa8477e0f0d6c532fb2bf49e6f7a11e0860f0,10.3390/realestate3030008,,"Predicting a Housing Price Index: A Two-Stage Machine Learning Approach Using Linked Micro-, Socio- and Macroeconomic Data from Frankfurt am Main",2026.0,Real Estate,J. Schmid,"This study develops and evaluates a two-stage machine learning framework for forecasting the condominium price index of pre-pandemic market data of Frankfurt am Main, Germany, one quarter ahead. To the best of the author’s knowledge, it is the first study to combine German micro-level transaction and listing data, socioeconomic variables and macro-financial indicators in a single residential price-forecasting framework. Furthermore, it provides the first evidence on machine learning-based transaction price index forecasting in Germany. Methodologically, the framework links disaggregated and aggregate forecasting. In stage 1, prices per square metre are estimated for four market segments using ordinary least squares, random forest, extreme gradient boosting, and a stacked ensemble in a strictly out-of-sample expanding-window design. In stage 2, these predictions are combined with lagged index values and macro-financial indicators to forecast the city-wide index. The stage 2 model achieves a relative root mean squared error of 2.25% and a mean absolute percentage error of 1.85%, outperforming a naïve persistence benchmark by reducing root mean squared error by 23%. Model interpretation indicates that price persistence dominates stage 1, reflecting market inertia, while lagged macro-financial variables and location quality composition drive index forecasts, pointing to delayed financial market transmission and heterogeneous submarket dynamics.",1.0,https://doi.org/10.3390/realestate3030008,True,JournalArticle,,2026-07-02,4f6fa8477e0f0d6c532fb2bf49e6f7a11e0860f0,predictingahousingpriceindexatwostagemachinelearningapproachusinglinkedmicrosocioandmacroeconomicdatafromfrankfurtammain,schmid,10.3390/realestate3030008,predictingahousingpriceindexatwostagemachinelearningapproachusinglinkedmicrosocioandmacroeconomicdatafromfrankfurtammain|2026,1,2964,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,3,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d427d3752d0020389c462d7a79ada2d18579c1f8,10.3390/realestate2010002,,The Impact of Non-Market Attributes on the Property Value,2025.0,Real Estate,Julia Buszta; Iwona Kik; Kamil Maciuk,"In the realm of real estate, each property owns a unique set of characteristics that distinguish it from others. While each property has its own distinctive features, the appraisal process prioritises only those qualities that meaningfully affect the value in the given market context. However, in the dynamically evolving market situation, expectations of real estate buyers can also transform. This study aims to explore how the surrounding environment and micro-location aspects affect the property value, which can deliver valuable outcomes for real estate market participants and researchers. For that purpose, the authors selected nine factors, called non-market attributes, that may affect the estimated value: air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings. Moreover, apart from non-market attributes, the authors selected six market attributes usually used for the determination of residential real estate values according to the Polish regulations in this field. The detailed analysis of factors influencing the property value has been conducted based on the residential apartments in the district Zwięczyca in Rzeszów. Specifically, with the use of Pearson’s total correlation coefficients, authors explored market and non-market attributes and examined their relationships with unit transaction prices, attempting to answer the research question on whether non-market attributes can differentiate market values of residential apartments, when local real estate markets are considered. The results demonstrate that all selected market factors have a visible effect on analysed real estate prices and might be adopted for appraisal. Among nine non-market factors, only three of them have a pronounced effect on prices and might be used for the valuation of residential properties on the local market. The combined database of market and non-market factors reveals eight attributes (five market and three non-market) affecting prices of residential apartments.",3.0,https://doi.org/10.3390/realestate2010002,True,JournalArticle,,2025-02-06,d427d3752d0020389c462d7a79ada2d18579c1f8,theimpactofnonmarketattributesonthepropertyvalue,buszta,10.3390/realestate2010002,theimpactofnonmarketattributesonthepropertyvalue|2025,1,3604,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b6d7ebe69a7b26ea7465c0215b2e3a7970639e62,10.3390/land15081500,,Siting Versus Capitalization of Disamenity and Amenity Facilities in Housing and Land Prices Using Integrated Multi-Source Spatial Data in South Korea,2026.0,Land,Solhee Kim; Yunhee Park,"Disamenity and amenity facilities pull property values in opposite directions, yet prior studies have treated disamenities as a homogeneous category and have been constrained by data scattered across agencies and by mismatched spatial scales. This study integrates multi-source spatial data, comprising 3426 sub-districts (eup-myeon-dong) nationwide, a 500 m population grid of 376,668 cells, and the actual point locations of individual facilities, into a single spatial framework and applies a multi-method design that combines a spatial Durbin model (SDM), explainable machine learning (XGBoost–SHAP), an environmental-justice analysis, and a micro-siting analysis. Four findings emerge. First, not all disamenities are associated with lower prices: only pollution-emitting facilities with a clear emission signature, namely, sewage treatment plants and incinerators, capitalize robustly as cross-sectional associations, into both housing prices and officially assessed land values, with the effect extending into neighboring sub-districts rather than staying purely local. The other disamenities show no robust total effect on either price; their estimates are small or unstable across specifications. Second, capitalization of pollution-emitting facilities is pronounced in the urban sample and disappears in the rural one, and the coefficient structure differs overall between urban and rural areas (global Chow test, p < 10−21). Third, micro-siting analysis shows that where a facility is placed (siting) and what it leaves behind in prices (capitalization) are distinct layers, and that capitalization tracks the population a facility keeps nearby rather than its nuisance type: pollution-emitting facilities retain enough nearby housing for their burden to register, whereas funerary facilities sited far from people do not. Fourth, the environmental inequity of pollution exposure runs along income rather than age and concentrates in cities rather than in rural areas: the exposure rate among residents of low-income urban sub-districts (49%) is 2.5 times that of the urban population as a whole (19%), while the greater Seoul metropolitan area remains comparatively insulated. These layered findings were possible only once the dispersed spatial data had been integrated and standardized, and the study demonstrates that fusing and efficiently managing spatial data is a precondition for evidence-based land-use and environmental policy.",0.0,https://doi.org/10.3390/land15081500,True,JournalArticle,,2026-08-18,b6d7ebe69a7b26ea7465c0215b2e3a7970639e62,sitingversuscapitalizationofdisamenityandamenityfacilitiesinhousingandlandpricesusingintegratedmultisourcespatialdatainsouthkorea,kim,10.3390/land15081500,sitingversuscapitalizationofdisamenityandamenityfacilitiesinhousingandlandpricesusingintegratedmultisourcespatialdatainsouthkorea|2026,1,3949,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,4,1,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d7678592e66e90720a5e7fc4a8fc482ba1663f6a,10.3386/w28851,,Foreclosure Spillovers within broad Neighborhoods,2018.0,Social Science Research Network,Wei-Ran Huang; A. Nelson; Stephen L. Ross,"This paper tests for the spillover effects of foreclosure within broad neighborhoods. The best evidence that foreclosures have causal, spillover effects on housing prices and future foreclosures suggest highly localized spillover effects that are modest in magnitude, but these effects could multiply when the density of spillovers is high leading to larger aggregate effects in broader neighborhoods. We test this proposition by developing a proxy for the fraction of housing units/mortgages that are expected to be in negative equity during the crises. This proxy exploits the timing of purchases in each tract during the run up to the crisis, and we show that our source of identification, within tract variation in purchases over time, is not predicted the observed mortgage attributes. Our estimates suggest that 67 percent of the increase in the across tract dispersion in the recording of new foreclosure recordings can be explained by the spillover effects of the contemporaneous stock of foreclosures.",2.0,https://doi.org/10.3386/w28851,True,,Economics,2018-12-01,d7678592e66e90720a5e7fc4a8fc482ba1663f6a,foreclosurespilloverswithinbroadneighborhoods,huang,10.3386/w28851,foreclosurespilloverswithinbroadneighborhoods|2018,1,2510,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic house price,f52092003bc600d8361ec68cd8e077cda8410774,10.3386/w28252,,Why is Intermediating Houses so Difficult? Evidence from iBuyers,2020.0,Social Science Research Network,Greg Buchak; Gregor Matvos; T. Piskorski; Amit Seru,"We study the frictions in dealer-intermediation in residential real estate through the lens of “iBuyers,” technology entrants, who purchase and sell residential real estate through online platforms. iBuyers supply liquidity to households by allowing them to avoid a lengthy sale process. They sell houses quickly and earn a 5% spread. Their prices are well explained by a simple hedonic model, consistent with their use of algorithmic pricing. iBuyers choose to intermediate in markets that are liquid and in which automated valuation models have low pricing error. These facts suggest that iBuyers’ speedy offers come at the cost of information loss concerning house attributes that are difficult to capture in an algorithm, resulting in adverse selection. We calibrate a dynamic structural search model with adverse selection to understand the economic forces underlying the tradeoffs of dealer intermediation in this market. The model reveals the central tradeoff to intermediating in residential real estate. To provide valuable liquidity service, transactions must be closed quickly. Yet, the intermediary must also be able to price houses precisely to avoid adverse selection, which is difficult to accomplish quickly. Low underlying liquidity exacerbates adverse selection. Our analysis suggests that iBuyers’ technology provides a middle ground: they can transact quickly limiting information loss. Even with this technology, intermediation is only profitable in the most liquid and easy to value houses. Therefore, iBuyers’ technology allows them to supply liquidity, but only in pockets where it is least valuable. We also find limited scope for dealer intermediation even with improved pricing technology, suggesting that underlying liquidity will be an impediment for intermediation in the future.",8.0,https://doi.org/10.3386/w28252,True,JournalArticle,Business,2020-12-01,f52092003bc600d8361ec68cd8e077cda8410774,whyisintermediatinghousessodifficultevidencefromibuyers,buchak,10.3386/w28252,whyisintermediatinghousessodifficultevidencefromibuyers|2020,1,3309,4,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || automated valuation model residential || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price",2,0,3,2,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",550566cc82be356cc72d32045e0ef786c0599602,10.2139/ssrn.2062536,,Interjurisdictional Housing Prices and Spatial Amenities: Which Measures of Housing Prices Reflect Local Public Goods?,2013.0,,H. S. Banzhaf; Omar Al Farooque,,59.0,https://doi.org/10.3386/w17809,True,,Economics,2013-07-01,550566cc82be356cc72d32045e0ef786c0599602,interjurisdictionalhousingpricesandspatialamenitieswhichmeasuresofhousingpricesreflectlocalpublicgoods,banzhaf,10.2139/ssrn.2062536,interjurisdictionalhousingpricesandspatialamenitieswhichmeasuresofhousingpricesreflectlocalpublicgoods|2013,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8c0982f7a40d9c8b0824d4027f942ae36a32aeea,10.32468/be.1072,,Modelos hedónicos con efectos espaciales: una aproximación al cálculo de índices de precios de vivienda para Bogotá,2019.0,,Wilmar Alexander Cabrera-Rodríguez; Juan Sebastián Mariño-Montaña; Carlos Andrés Quicazán-Moreno,"En este documento se estima un indice de precios de vivienda nueva para la ciudad de Bogota empleando la base de datos de La Galeria Inmobiliaria, empresa dedicada a la recoleccion de informacion del mercado inmobiliario en Colombia. La metodologia de precios hedonicos aqui propuesta presenta tres ventajas con respecto a los indices de vivienda que se calculan en la actualidad para esta ciudad: i) considera que un inmueble es un bien diferenciado, cuyo precio esta definido en funcion de sus caracteristicas, ii) incorpora de manera parsimoniosa la ubicacion de las viviendas por medio de un modelo de econometria espacial, y iii) utiliza una ventana de tiempo para la estimacion del indice, haciendo que no sea susceptible a modificaciones ante incorporaciones de nuevos datos. Los resultados muestran que la bondad de ajuste del indice resultante es significativamente mayor a la de los que se producen en la actualidad, que los coeficientes presentan el comportamiento esperado y son estables en el tiempo, y que el indice es robusto a cambios en la participacion de inmuebles que se ubican en las colas de la distribucion de precios, gracias a la modelacion de vecindarios en la estimacion espacial. **** ABSTRACT: In this document a new housing price index is calculated for Bogota using data from La Galeria Inmobiliaria, company dedicated to the collection of information from the real estate market in Colombia. The hedonic price methodology proposed here presents three advantages over the housing price indexes which are currently produced for the city: i) it considers that a dwelling is a differentiated good, whose price is defined as a function of its characteristics, ii) incorporates in an parsimonious fashion the location of the residences through a spatial econometric model, and iii) it uses a rolling time window to produce the index, making it not susceptible to changes after including new data. The results show that the goodness of fit of the resulting index is significantly higher than the observed in the current indexes, the coefficients presents the expected behavior and are stable over time, and the results are robust to changes in the participation of dwellings located in the tails of the price distribution.",1.0,https://doi.org/10.32468/be.1072,True,,Geography,2019-05-01,8c0982f7a40d9c8b0824d4027f942ae36a32aeea,modeloshedonicosconefectosespacialesunaaproximacionalcalculodeindicesdepreciosdeviviendaparabogota,cabrera-rodríguez,10.32468/be.1072,modeloshedonicosconefectosespacialesunaaproximacionalcalculodeindicesdepreciosdeviviendaparabogota|2019,1,3747,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic || modelo de precios hedónicos vivienda || precios hedónicos vivienda || spatial hedonic model housing",3,3,3,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1770a9a7ab1f23b5565f55ac869ccc944ef090d7,10.25236/ajbm.2022.040315,,Analysis of Commodity Housing Price in Shanghai Based on Multiple Linear Regression Model,2022.0,Academic Journal of Business & Management,,,0.0,https://doi.org/10.25236/ajbm.2022.040315,True,,,,1770a9a7ab1f23b5565f55ac869ccc944ef090d7,analysisofcommodityhousingpriceinshanghaibasedonmultiplelinearregressionmodel,,10.25236/ajbm.2022.040315,analysisofcommodityhousingpriceinshanghaibasedonmultiplelinearregressionmodel|2022,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f77673ecd4b822ddfecae24584e31573fcdb7183,10.2478/cee-2025-0059,,Relationship Between Architectural Style and Property Value in the Czech Republic,2025.0,Civil and Environmental Engineering,Pavel Klika; David Brandejs; Karla Háva; Sofie Pokorná; Monika Doležalová; Vítězslava Hlavinková,"Abstract This study investigates the influence of architectural styles on the value of housing units in heritage-listed apartment buildings across four Czech cities: Brno, Olomouc, Pilsen, and Ceske Budejovice (Budweis). Utilizing data from the National Heritage Institute, the Czech Statistical Office and Czech office for Surveying, Mapping and Cadastre, the research evaluates the relationship between architectural styles and property values using statistical methods, including Analysis of Variance (ANOVA). The findings indicate no statistically significant influence of architectural style on property values, contrary to international studies suggesting a price premium for historical styles. However, a statistically significant effect of location on property values was identified, particularly in Brno. The study highlights cultural and geographic differences in architectural valuation and suggests further research to refine valuation methods for heritage-protected buildings, considering additional factors such as building condition, proximity to amenities, and public infrastructure.",0.0,https://doi.org/10.2478/cee-2025-0059,True,JournalArticle;Review,,2025-06-16,f77673ecd4b822ddfecae24584e31573fcdb7183,relationshipbetweenarchitecturalstyleandpropertyvalueintheczechrepublic,klika,10.2478/cee-2025-0059,relationshipbetweenarchitecturalstyleandpropertyvalueintheczechrepublic|2025,1,2599,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",89ca73e341fb243ec97c565b4db5c5b25941b66d,10.21837/pm.v19i17.988,,RESIDENTIAL BUILDING QUALITY MEASUREMENT AND THE RELATIONSHIP WITH HOUSE PRICES: A STUDY OF HOUSES IN KLANG,2021.0,PLANNING MALAYSIA,Muhamad Hilmi Mohamad@Masri; M. Sa’ad; Najma Azman; M. H. H. Aliasak,"This research focuses on the particular aspect of residential building quality and aims to measure the relationship that affects houses prices in Klang. The researcher through the problem statement and literature review has noted that respondents have knowledge in measuring the quality of their own house, but measurement must be conducted on an empirical basis with evidence. The main research objectives were to identify residential building quality and to measure the residential building quality effect on houses prices. The sampling of the research was conducted on fifty houses, and the measurement was conducted with the help of Regression Analysis. The results obtained show a significant relationship between higher quality of residential buildings and higher prices that can be commanded. The findings also could help to improve the estimation of value for new and older houses in the sampled areas.",0.0,https://doi.org/10.21837/pm.v19i17.988,True,Review,,2021-10-17,89ca73e341fb243ec97c565b4db5c5b25941b66d,residentialbuildingqualitymeasurementandtherelationshipwithhousepricesastudyofhousesinklang,mohamad@masri,10.21837/pm.v19i17.988,residentialbuildingqualitymeasurementandtherelationshipwithhousepricesastudyofhousesinklang|2021,1,2410,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0ea3c3fce5f230c6a53cbafac5bea9e7b12d3c49,10.21837/pm.v19i17.1015,,"CONSTRUCTING HOUSING PRICE INDEX FOR TERRACED PROPERTIES IN JOHOR BAHRU, MALAYSIA",2021.0,PLANNING MALAYSIA,W. Lim; B. Burhan; Mohd Lizam Mohd Diah,"Housing is a country’s biggest asset. Hence, the pattern of the housing price index (HPI) is an important topic to gain insight into the housing market while identifying the prevailing housing issues. The determinants of housing price vary for each city and state based on the different characteristics in each location. Accordingly, HPI should consider the property’s quality differences. Besides, national HPI is insufficient and restricted to the housing price at the state level. Thus, the study focused on constructing a specified HPI model for different cities, districts, and states. Effective HPI can give parties a better idea of the current property market situation and act as an analytical tool in managing the sector. Specifically, the study aims to examine the relationship between the heterogeneity housing attributes and housing prices of the terraced properties in Johor Bahru, Malaysia. Additionally, the study provides detailed information on the key determinants of the housing price variation in Johor Bahru. Hedonic price analysis is useful in constructing HPI, expressing housing price as a function of vector property characteristics. Furthermore, HPI is constructed based on the yearly indices and by pooling the data into certain periods. The results show the percentage of variance explained by the factors of HPI for the terraced properties in Johor Bahru. Correspondingly, the underlying correlation between the tested housing attributes with the housing price is explained through the analysis results.",0.0,https://doi.org/10.21837/pm.v19i17.1015,True,,,2021-10-17,0ea3c3fce5f230c6a53cbafac5bea9e7b12d3c49,constructinghousingpriceindexforterracedpropertiesinjohorbahrumalaysia,lim,10.21837/pm.v19i17.1015,constructinghousingpriceindexforterracedpropertiesinjohorbahrumalaysia|2021,1,3032,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic",2,2,1,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b0792843c775ae71af8a415310033a9291e111b8,10.1080/00036846.2011.636021,,Explaining the spatial variation in housing prices: an economic geography approach,2013.0,,K. De Bruyne; J. Van Hove,,65.0,https://doi.org/10.2139/ssrn.947671,True,,Economics,2013-05-01,b0792843c775ae71af8a415310033a9291e111b8,explainingthespatialvariationinhousingpricesaneconomicgeographyapproach,bruyne,10.1080/00036846.2011.636021,explainingthespatialvariationinhousingpricesaneconomicgeographyapproach|2013,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",db8a3faa3780e81eade4d76093121ed88d023484,10.1111/j.1540-6229.2009.00242.x,,House Price Changes and Idiosyncratic Risk: The Impact of Property Characteristics,2009.0,,Steven C. Bourassa; Donald R. Haurin; Jessica L. Haurin; M. Hoesli; Jian Sun,,98.0,https://doi.org/10.2139/ssrn.873451,True,,Economics,2009-06-01,db8a3faa3780e81eade4d76093121ed88d023484,housepricechangesandidiosyncraticrisktheimpactofpropertycharacteristics,bourassa,10.1111/j.1540-6229.2009.00242.x,housepricechangesandidiosyncraticrisktheimpactofpropertycharacteristics|2009,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",738bf39f1bf0152f03044b4f2c8c39aaa0cac91f,10.52903/wp2026359,,Threshold-based policies and distortions in housing markets,2026.0,Working papers,D. Karamanis; Christos Kotsogianis; Evangelia Papapetrou,"This paper examines how investment thresholds embedded in residence-by-investment programmes shape housing market outcomes. The analysis exploits a reform of Greece’s golden visa scheme that selectively doubled the minimum real estate investment requirement - from €250,000 to €500,000 - across municipalities. Using comprehensive administrative data on property transactions from 2017-2024, the study combines a bunching framework with a difference-in-differences design to identify behavioural responses to eligibility thresholds and their spatial equilibrium effects. Under the initial regime, pronounced bunching in transaction prices is observed around the €250,000 cutoff, indicating that the threshold was a salient determinant of pricing behaviour. Following the reform, this bunching disappears in municipalities subject to the higher threshold, while persisting in unaffected areas. Consistent with this pattern, treated municipalities experience a relative deceleration in price growth, alongside increased transaction activity in nearby markets that remain eligible under the lower threshold. The results demonstrate that residency-investment thresholds may operate as active policy instruments that distort housing price distributions and reallocate demand across space, highlighting the importance of threshold design for housing-market outcomes.",0.0,https://doi.org/10.2139/ssrn.6441680,True,,,2026-03-18,738bf39f1bf0152f03044b4f2c8c39aaa0cac91f,thresholdbasedpoliciesanddistortionsinhousingmarkets,karamanis,10.52903/wp2026359,thresholdbasedpoliciesanddistortionsinhousingmarkets|2026,1,2860,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",90b923037f1c4b5e6a9a71f19f2d15764f1ccbf6,10.2139/ssrn.6308901,,Substitutability In The Demand For Housing Over Small Distances,2026.0,Social Science Research Network,Salim B. Furth,"Some pairs of residential locations are better substitutes for one another than others, perhaps even within two hundred meters' distance. As a result, rents and home prices can behave in ways that depart from the predictions of the most common urban economics models. The imperfect and varying substitutability between locations may arise due to preferences, information, or moving costs. Literatures in economics and complementary disciplines offer indirect evidence on the spatial substitutability of housing, homebuyers' housing search processes, and the information accessed by housing searchers. The available evidence suggests that spatial substitutability varies from place to place, is non-monotonic across space, and is endogenous to market conditions. Housing searchers vary widely, and their diversity can produce market outcomes that differ substantially from those predicted by models where all searchers are the same. Spatial substitutability has implications for policy debates around gentrification, market power, and zoning.",0.0,https://doi.org/10.2139/ssrn.6308901,True,JournalArticle,,,90b923037f1c4b5e6a9a71f19f2d15764f1ccbf6,substitutabilityinthedemandforhousingoversmalldistances,furth,10.2139/ssrn.6308901,substitutabilityinthedemandforhousingoversmalldistances|2026,1,2541,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",76f161e1c15ab1d813cee869a310096fbaf4725f,10.2139/ssrn.5160602,,Impact of Institutional Owners on Housing Markets,2025.0,Social Science Research Network,Caitlin Gorback; Franklin Qian; Zi-Pei Zhu,"Since the Great Recession, the rise of single-family rental companies has changed the investor ownership landscape in the U.S. Using housing transaction data, we document the rise of Long Term Rental (LTR) companies by constructing a panel of national single-family housing portfolios. We show that LTR growth outstripped all other investor types and that these companies geographically concentrate their holdings, expanding their local market shares over time. We construct a novel instrument predicting LTR entry, leveraging differential revealed preferences in product characteristics across landlord types, interacting with a proxy for falling property management costs over time. We use this instrument for LTR market entry to estimate the causal impact of LTR market share on local house prices. We find that a 1-standard deviation above the mean increase in LTR share growth leads to an annual additional house price growth of 2.11pp. Finally, we discuss how the reallocation of homeownership across small and large landlords, as well as owner-occupants and investors, contribute to these price increases.",1.0,https://doi.org/10.2139/ssrn.5160602,True,JournalArticle,,,76f161e1c15ab1d813cee869a310096fbaf4725f,impactofinstitutionalownersonhousingmarkets,gorback,10.2139/ssrn.5160602,impactofinstitutionalownersonhousingmarkets|2025,1,2612,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,5,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0ac8fab28e8518c602e5bf5b3b7341a97fbf1053,10.1111/jors.70046,,"Hear Ye, Bear Ye: Housing Values, Noise Levels, and Noise Inequality",2026.0,Journal of Regional Science,Jeffrey P. Cohen; Cletus C. Coughlin; Felix Friedt," We explore how a 2017 federal government policy announcement and 2019 implementation requiring quieter engines in some new commercial aircraft impacted the relationships between home values, transportation noise, and demographics. We focus on aircraft noise in Census tracts across the contiguous U.S., as well as separating road and aircraft noise. Using a panel of tract‐level noise data for 3 years (2016, 2018, and 2020), along with Census data on demographics, house values, and property characteristics, we first demonstrate numerically and graphically the extent to which some residents experience disproportionate amounts of noise by constructing measures of inequality. Next, we rely on the policy announcement to test the hypothesis that this requirement is a cause of structural change in how aircraft noise affected house prices across demographic groups. We find overall the policy has a positive effect on house values in Census tracts with at least 45 dBA of noise, the federally designated cutoff for annoyance.",0.0,https://doi.org/10.2139/ssrn.5058147,True,JournalArticle,,2026-02-02,0ac8fab28e8518c602e5bf5b3b7341a97fbf1053,hearyebearyehousingvaluesnoiselevelsandnoiseinequality,cohen,10.1111/jors.70046,hearyebearyehousingvaluesnoiselevelsandnoiseinequality|2026,1,2529,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,3,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",624663dff1b3bf35814a9f24967c7be02207f074,10.2139/ssrn.4688022,,What Works and for Whom? Effectiveness and Efficiency of School Capital Investments Across the U.S,2024.0,Social Science Research Network,B. Biasi; Julien Lafortune; David Schönholzer," This paper identifies which investments in school facilities help students and which and are valued by homeowners. Using novel data on school district bonds, test scores, and house prices across 29 U.S. states and a research design based on narrowly decided elections with staggered timing, we find that increased capital spending in schools significantly improves test scores and is efficient on average. However, the effects vary widely depending on the type of project and the characteristics of the school district. Investments in essential infrastructure, such as HVAC systems or pollutant removal, yield notable improvements in student performance, while expenditures on athletic facilities show no measurable academic benefit. Socio-economically disadvantaged districts gain disproportionately from capital investments, even after accounting for project type, yet these districts typically underinvest in such projects.",17.0,https://doi.org/10.2139/ssrn.4688022,True,CaseReport;JournalArticle,,2024-01-01,624663dff1b3bf35814a9f24967c7be02207f074,whatworksandforwhomeffectivenessandefficiencyofschoolcapitalinvestmentsacrosstheus,biasi,10.2139/ssrn.4688022,whatworksandforwhomeffectivenessandefficiencyofschoolcapitalinvestmentsacrosstheus|2024,1,2428,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),a12ab3680acd1460637d1d820b06eff200f72a20,10.2139/ssrn.4636004,,Round-Number Effects in Real Estate Prices: Evidence from Germany,2023.0,Social Science Research Network,Florian Englmaier; Andreas Roider; Lars Schlereth; Steffen Sebastian,"Round numbers affect behavior in various domains, e.g., as prominent thresholds or focal points in bargaining. In line with earlier findings, residential real estate transactions in Germany cluster at round-number prices, but there are also interesting (presumably cultural) differences. We extend our analysis to the commercial real estate market, where stakes are even higher and market participants arguably more experienced. For the same type of object, professionals cluster significantly less on round-number prices compared to non-professionals. We employ machine learning and show that transactions of family homes and condominiums at round-number prices are 2–7% above their hedonic values.",1.0,https://doi.org/10.2139/ssrn.4636004,True,JournalArticle,,,a12ab3680acd1460637d1d820b06eff200f72a20,roundnumbereffectsinrealestatepricesevidencefromgermany,englmaier,10.2139/ssrn.4636004,roundnumbereffectsinrealestatepricesevidencefromgermany|2023,1,2199,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,1,5,1,1,7,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c454d39fa3bad62281f5c28185fa67a0c2e1b79d,10.2139/ssrn.4178207,,"Fiscal Monitoring, School District Outcomes, and Residential Choice",2022.0,Social Science Research Network,Travis St. Clair,"Under the canonical model of local political economy, taxpayers vote with their feet to find the community that provides the optimal bundle of taxes and public goods (Tiebout, 1956). One common objection to this model is that taxpayers lack perfect information about their choice sets, and consequently rely on heuristics or third-party rating agencies. This paper exploits New York state’s fiscal monitoring program to examine the effect that quality labels have on school district enrollment and outcomes. Taxpayers may be especially sensitive to labels providing information about the management of school districts, as studies have shown that parents of school-age children pay close attention to information on school quality. Using a regression discontinuity design, I examine the effect of fiscal stress and “environmental” stress labels on property values, school enrollment, demographics, and test scores. While fiscal stress labels have no effect, environmental stress labels – indicating social, economic, and demographic pressures, such as a high percentage of disadvantaged students – cause enrollment and property values to decline, especially in wealthier districts. Taxpayers are more sensitive to demographic and economic indicators of school quality than to fiscal indicators.",0.0,https://doi.org/10.2139/ssrn.4178207,True,,,,c454d39fa3bad62281f5c28185fa67a0c2e1b79d,fiscalmonitoringschooldistrictoutcomesandresidentialchoice,clair,10.2139/ssrn.4178207,fiscalmonitoringschooldistrictoutcomesandresidentialchoice|2022,1,2786,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5fa9d46d7189b4924cd83c4c0db71d5a2d72d43e,10.2139/ssrn.3465010,,The Assessment Gap: Racial Inequalities in Property Taxation,2020.0,Social Science Research Network,C. Avenancio-León; Troup Howard,"We use panel data covering 118 million homes in the United States, merged with geolocation detail for 75,000 taxing entities, to document a nationwide ""assessment gap"" which leads local governments to place a disproportionate fiscal burden on racial and ethnic minorities. We show that holding jurisdictions and property tax rates fixed, black and Hispanic residents nonetheless face a 10-13% higher tax burden for the same bundle of public services. This assessment gap arises through two channels. First, property assessments are less sensitive to neighborhood attributes than market prices are. This generates racially correlated spatial variation in tax burden within jurisdiction. Second, appeals behavior and appeals outcomes differ by race. This results in higher assessment growth rates for minority residents. We propose an alternate approach for constructing assessments based on small-geography home price indexes, and show that this reduces inequality by at least 55-70%.",57.0,https://doi.org/10.2139/ssrn.3465010,True,,Business,2020-07-06,5fa9d46d7189b4924cd83c4c0db71d5a2d72d43e,theassessmentgapracialinequalitiesinpropertytaxation,avenancio-león,10.2139/ssrn.3465010,theassessmentgapracialinequalitiesinpropertytaxation|2020,1,2483,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",76624f2ea27d7d339d99a81c0e14c57dc4eadcfc,10.2139/ssrn.3394549,,A Comprehensive Spatiotemporal Framework for Hedonic Pricing: Integrating the Comparable Sales Approach and Minimizing Spatial Omitted Variable Bias,2019.0,Social Science Research Network,Sotirios Thanos; Jean Dubé; Diègo Legros,"This paper develops a theoretical and methodological framework that integrates Hedonic Pricing (HP), grid comparable sales approach (CSA), and nearest neighbors into a general spatiotemporal specification. By explicitly providing a theoretical justification for introducing spatial (or spatiotemporal) econometrics to HP, this approach is not only relevant to house price forecasting and automated valuation models (AVM) but also to valuing environmental goods capitalized in housing and to all other fields employing house pricing models. The resulting econometric CSA and spatiotemporal Durbin models provide higher prediction accuracy and reliability to alternatives by reducing the spatially-delineated omitted variable bias (OVB) common in HP. Spatiotemporal autoregressive and error models are also derived, providing specific conditions, under which their application can be justified. Our analysis reinforces the common real estate practice of selecting a small number of comparables in grid CSA and challenges AVM approaches, in which hundreds or thousands of comparables are introduced.",0.0,https://doi.org/10.2139/ssrn.3394549,True,,Computer Science,2019-05-26,76624f2ea27d7d339d99a81c0e14c57dc4eadcfc,acomprehensivespatiotemporalframeworkforhedonicpricingintegratingthecomparablesalesapproachandminimizingspatialomittedvariablebias,thanos,10.2139/ssrn.3394549,acomprehensivespatiotemporalframeworkforhedonicpricingintegratingthecomparablesalesapproachandminimizingspatialomittedvariablebias|2019,1,2596,5,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || spatial hedonic model housing",2,1,2,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a4908625ccfda5cd40bfeb9400b0e333108faa0e,10.3386/w21729,,"Bright Minds, Big Rent: Gentrification and the Rising Returns to Skill",2015.0,Social Science Research Network,L. Edlund; Cecilia Machado; M. Sviatschi,"In 1980, housing prices in large US cities rose with distance from the city center. By 2010, that relationship had reversed. We propose that the inversion can be traced to more hours worked by the skilled. Scarce non-market time downgrades the importance of residential space and upgrades that of proximity to work, factors favoring the central-city location. Geo- coded census micro data covering the 27 largest US cities and the period 1980-2010 support our hypothesis: full-time skilled workers are more likely to locate in the city center and their growth can account for the observed price changes.",79.0,https://doi.org/10.2139/ssrn.2823672,True,,Geography;Economics,2015-11-13,a4908625ccfda5cd40bfeb9400b0e333108faa0e,brightmindsbigrentgentrificationandtherisingreturnstoskill,edlund,10.3386/w21729,brightmindsbigrentgentrificationandtherisingreturnstoskill|2015,1,2103,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,implicit prices housing attributes,e5d1f6c5a265d9c3f18309ea81709edd45609641,10.2139/ssrn.1985502,,Hedonic Model with Discrete Consumer Heterogeneity and Horizontal Differentiated Housing,2013.0,,Masha Maslianskaia-Pautrel,,0.0,https://doi.org/10.2139/ssrn.1985502,True,,Economics,2013-05-02,e5d1f6c5a265d9c3f18309ea81709edd45609641,hedonicmodelwithdiscreteconsumerheterogeneityandhorizontaldifferentiatedhousing,maslianskaia-pautrel,10.2139/ssrn.1985502,hedonicmodelwithdiscreteconsumerheterogeneityandhorizontaldifferentiatedhousing|2013,1,1500,3,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic housing prices || implicit prices housing attributes",2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic housing prices,19859aaff14033f9d01240e5e3e584aa8306d70a,10.2139/ssrn.1972111,,A New Way of Monitoring the Quality of Urban Life,2011.0,,Eduardo Lora; Andrew P. Powell,"This paper proposes a methodology to resolve the problems that result from using a combination of objective and subjective information in evaluating urban quality of life. The paper further suggests techniques to identify and rank issues of potential importance for urban dwellers. In order to combine objective and subjective information in a coherent manner and focus on the most relevant dimensions of the quality of life in a city or neighborhood, the paper attempts to exploit the complementary nature of two approaches: the ""hedonic"" approach, which employs market prices for housing, and the ""life satisfaction"" approach, which addresses subjective well-being. Results using both approaches in selected Latin American cities are discussed and compared. The paper concludes with a discussion of potential uses of the two-pronged methodology for policy analysis.",17.0,https://doi.org/10.2139/ssrn.1972111,True,CaseReport,Economics;Political Science,2011-11-01,19859aaff14033f9d01240e5e3e584aa8306d70a,anewwayofmonitoringthequalityofurbanlife,lora,10.2139/ssrn.1972111,anewwayofmonitoringthequalityofurbanlife|2011,1,2367,1,hedonic housing prices,2,1,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",d1ccbc3624629ca13730a12d343d3f7d4b589acf,10.2139/ssrn.1645156,,Spatial Autoregressive Models for House Price Dynamics in Italy,2010.0,,Tiziana Caliman; E. di Bella,,10.0,https://doi.org/10.2139/ssrn.1645156,True,,Economics,2010-07-19,d1ccbc3624629ca13730a12d343d3f7d4b589acf,spatialautoregressivemodelsforhousepricedynamicsinitaly,caliman,10.2139/ssrn.1645156,spatialautoregressivemodelsforhousepricedynamicsinitaly|2010,1,1500,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic housing prices,cda7954c0c7d899d0c8f7fca2eeeb857d414f95d,10.1016/j.regsciurbeco.2011.01.006,,Neighborhood Diversity and the Appreciation of Native- and Immigrant-Owned Homes,2011.0,Social Science Research Network,D. Cobb-Clark; Mathias Sinning,"This paper examines the effect of neighborhood diversity on the nativity gap in home-value appreciation in Australia. Specifically, immigrant homeowners experienced a 41.7% increase in median home values between 2001 and 2006, while the median value of housing owned by the native-born increased by 59.4% over the same period. We use a semi-parametric decomposition approach to assess the relative importance of the various determinants of home values in producing this gap. We find that the differential returns to housing wealth are not related to changes in the nature of the houses or the neighborhoods in which immigrants and native-born homeowners live. Rather, the gap stems from the fact that over time there were differential changes across groups in the hedonic prices (i.e., returns) associated with the underlying determinants of home values.",24.0,https://doi.org/10.2139/ssrn.1493186,True,,Economics,2011-05-01,cda7954c0c7d899d0c8f7fca2eeeb857d414f95d,neighborhooddiversityandtheappreciationofnativeandimmigrantownedhomes,cobb-clark,10.1016/j.regsciurbeco.2011.01.006,neighborhooddiversityandtheappreciationofnativeandimmigrantownedhomes|2011,1,2354,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices",2,2,5,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,housing characteristics prices hedonic,5be5e3ecc7dc58a071c4cdd27c791610b4a69d44,10.2139/ssrn.1313693,,Flexible Spatial and Temporal Hedonic Price Indexes for Housing in the Presence of Missing Data,2008.0,,R. Hill; Daniel Melser; Iqbal A. Syed,,16.0,https://doi.org/10.2139/ssrn.1313693,True,,Economics,2008-09-26,5be5e3ecc7dc58a071c4cdd27c791610b4a69d44,flexiblespatialandtemporalhedonicpriceindexesforhousinginthepresenceofmissingdata,hill,10.2139/ssrn.1313693,flexiblespatialandtemporalhedonicpriceindexesforhousinginthepresenceofmissingdata|2008,1,1500,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,0,1,1,0,5,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic house price,0700fce81747d166ee237b487b5b75e1d167496d,10.1007/bf03399266,,Constant-Quality House Price Indexes for Switzerland,2008.0,,Steven C. Bourassa; M. Hoesli; D. Scognamiglio; Philippe Sormani,"SummaryThe measurement of house price movements is a vital topic from both academic and practical perspectives and hence has been the focus of much research. There is almost unanimous consensus in the literature that house price indexes should control for the quality of properties; the most widely used methods to attain this aim are the hedonic and repeat sales approaches. The objective of this paper is to compare the Swiss house prices indexes published by the Swiss National Bank (SNB), which are constructed using medians of list prices as published in newspapers and on the internet, to hedonic indexes based on sale prices for the period 1985 to 2006. We find that the list price indexes exhibit quite a different price path than the hedonic indexes during the period. In particular, they appear to overstate price changes in housing markets. We attribute this, at least in part, to changes over time in the composition of the sample of properties on the market.",15.0,https://doi.org/10.2139/ssrn.1138641,True,,Economics,2008-05-08,0700fce81747d166ee237b487b5b75e1d167496d,constantqualityhousepriceindexesforswitzerland,bourassa,10.1007/bf03399266,constantqualityhousepriceindexesforswitzerland|2008,1,2471,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices",2,3,2,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,0aadab22e8e7afe59211c798afdd8945783c8065,10.15826/jtr.2021.7.1.087,,Individual property tax to fund public transport,2021.0,,E. Leontev; Y. Leontyeva,"Efficient and reliable public transport is of prime concern to city dwellers. To function efficiently, public transport generally needs subsidies from the state or local government. Our goal was to design and develop an alternative model of property tax that would provide financing for public transport. It was hypothesised that if the market value of real estate depends on the proximity of public transport, property tax can be a reliable source of financing for public transport. Based on the hedonic pricing theory, we used multiple regression to measure the impact of public transport proximity on the value of residential property. The data on the market value of property and property tax was taken from statistical tax reporting forms of the Federal Tax Service. The data on various public transport infrastructure facilities was used from the specialized open registers. We tested our alternative model of property tax, using the case of the Ekaterinburg Metro and the Tram and Trolleybus Company, through regression analysis of 7,685 objects of residential property in the City of Ekaterinburg. It was found that the efficiency of the underground service is higher than that of the city’s tram network. On the average, the proximity of underground stations increases the value of housing by over 6%. As predictive estimation of the amount of tax determined by the proximity of public transport showed, the alternative model of property tax is sufficient to cover capital expenditures of the city’s public transport operators and could, therefore, contribute to further expansion and modernization of the transport network. For citation Leontev E.V., Leontyeva Yu.V. Individual property tax to fund public transport. Journal of Tax Reform. 2021;7(1):6–19. DOI: 10.15826/jtr.2021.7.1.087 Article info Received January 10, 2021; Revised March 14, 2021; Accepted April 9, 2021",3.0,https://doi.org/10.15826/jtr.2021.7.1.087,True,,Business,2021-04-29,0aadab22e8e7afe59211c798afdd8945783c8065,individualpropertytaxtofundpublictransport,leontev,10.15826/jtr.2021.7.1.087,individualpropertytaxtofundpublictransport|2021,1,3383,3,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression,2,0,2,2,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,modelo de precios hedónicos vivienda,c2020efe69de94e5f2b73057150b6da505d6d03b,10.15446/bitacora.v32n1.87760,,Modelo de estratificación como generador de segregación en Bogotá,2021.0,Bitácora Urbano Territorial,Julián Alberto Gutiérrez López; Lina Fernanda Quenguan López; Héctor David Nieto Martínez,"Este trabajo evidencia que, en contra de los deseos de la política pública, el mercado inmobiliario, por medio de las rentas de segregación, hace que la mezcla socioeconómica resulte cada vez más difícil. Por medio de un modelo de precios hedónicos, en donde se toma como base el estrato socioeconómico y las variables urbanas más relevantes, encontradas en los modelos de precios urbanos que configuran los precios del suelo de la ciudad, se evidencian las rentas de segregación que algunos agentes dentro del mercado de vivienda deciden pagar por obtener los beneficios más altos (mejores localizaciones, mejores vecinos, mejores productos inmobiliarios). Se encontró que existe una relación significativa entre el valor del metro cuadrado de terreno y la distancia a estratos socioeconómicos considerados bajos (para Bogotá estratos 1 y 2), evidenciando que los agentes económicos de estratos altos pagan precios altos por localizaciones que se encuentren alejadas de los estratos bajos.",3.0,https://doi.org/10.15446/bitacora.v32n1.87760,True,,,2021-12-31,c2020efe69de94e5f2b73057150b6da505d6d03b,modelodeestratificacioncomogeneradordesegregacionenbogota,lópez,10.15446/bitacora.v32n1.87760,modelodeestratificacioncomogeneradordesegregacionenbogota|2021,1,2490,2,modelo de precios hedónicos vivienda || precios hedónicos vivienda,1,1,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bee52343ffa73aad23542914ec3ed1e1d03d6720,10.14455/isec.res.2016.122,,ANALYSIS OF ECONOMIC DETERMINANTS OF AFFORDABLE HOUSING PRICES,2016.0,,A. Olanrewaju; Lim-Tat Lee; Seong Yeow Tan; N. Mine; Z. Nizamani; A. Aziz,,12.0,https://doi.org/10.14455/isec.res.2016.122,True,Review,Business,2016-10-01,bee52343ffa73aad23542914ec3ed1e1d03d6720,analysisofeconomicdeterminantsofaffordablehousingprices,olanrewaju,10.14455/isec.res.2016.122,analysisofeconomicdeterminantsofaffordablehousingprices|2016,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic housing prices,4e103ef321687e5a9992a6355d4c3aec66d578de,10.1371/journal.pone.0215954,,Calibration and evaluation of Quigley’s hybrid housing price model in Microsoft Excel,2019.0,PLoS ONE,A. Phipps; Dingding Li,"Quigley derived his hybrid price model to improve the precision of predicted prices of sold homes by statistically merging data of resold homes in a repeat sales model with that of once-sold homes in a single sales hedonic price model. The literature has few applications of the hybrid model aside from those by Quigley and his collaborators. Two reasons for this underuse may be its computational intensiveness and its marginal empirical improvement in comparison with two other models. This paper first demystifies this computational intensiveness by calibrating models in Microsoft Excel with transferable procedures into other software. It second evaluates the hybrid price model’s empirical improvement as a reason for its underuse by predicting prices of 2,559 sold and resold homes observed in two inner-city neighbourhoods in Windsor, Ontario, during a 30-year period. The results as hypothesized are its lower standard errors of regression coefficients and higher simple R-squared than those of a single sales hedonic price model. Moreover, the hybrid model’s predictions have higher correlations than those of the single sales model with not only in-sample observed prices or changes in prices but also out-of-sample ones. The conclusion speculates in plans for future research about reasons for two models’ similar or dissimilar regression coefficients and standard errors predicting correspondingly similar or dissimilar sale prices of homes through time.",6.0,https://doi.org/10.1371/journal.pone.0215954,True,JournalArticle,Computer Science;Medicine,2019-04-25,4e103ef321687e5a9992a6355d4c3aec66d578de,calibrationandevaluationofquigleyshybridhousingpricemodelinmicrosoftexcel,phipps,10.1371/journal.pone.0215954,calibrationandevaluationofquigleyshybridhousingpricemodelinmicrosoftexcel|2019,1,2967,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,2,3,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),c30d7daeb064e3a1846a7f0fe32de25ea8c9a33e,10.1365/s41056-018-0023-3,,Wahrnehmung des Energieausweises in hypothetischen und realen Mietentscheidungen auf dem deutschen Wohnimmobilienmarkt,2018.0,Zeitschrift für Immobilienökonomie,Bertram I. Steininger; Claudia Nadler; M. Franke; C. Pommeranz,"ZusammenfassungDie Immobilienbranche nimmt eine Schlüsselfunktion in der Klimaschutz- und Energiepolitik Deutschlands ein, da durch sie große Einsparungspotenziale bei Energieverbrauch und CO2-Emissionen identifiziert werden. Der Energieausweis stellt ein wichtiges Informationsinstrument dar, um Aufschluss über die energetische Qualität einer Immobilie zu erlangen. In der vorliegenden Studie werden die Wahrnehmung und der Einfluss des Energieausweises anhand sich ergänzender Methoden untersucht: Experteninterviews, Choice-Based Conjoint-Analyse (CBCA) und hedonisches Preismodell. Neben der Analyse hypothetischer Mietentscheidungen bei Wohnimmobilien steht auch der Preiseffekt des Energieausweises in realen Mietentscheidungen im Fokus dieser Untersuchung. Die Ergebnisse zeigen zum einen eine vergleichsweise hohe Bedeutung der energetischen Qualität einer Immobilie bei der indirekten Ermittlung von Präferenzen im Rahmen der CBCA. Zum anderen ergibt sich aufgrund des vorhandenen Bewusstseins bei der realen Mietentscheidung ein positiver Preiseffekt für energetisch effiziente Wohnungen von bis zu 7,1 %. Eine Intensivierung der Preiseffekte nach Anpassung der gesetzlichen Rahmenbedingungen spricht für eine höhere Markttransparenz durch die Einführung der Vorlagepflicht des Energieausweises. Es bestehen jedoch weiterhin Defizite in der Umsetzung, sodass abschließend Empfehlungen für eine Weiterentwicklung des Informationsinstruments gegeben werden.AbstractThe real estate sector plays a key role in the German climate and energy policy due to its great potential for reducing the energy consumption and CO2 emissions. The Energy Performance Certificate (EPC) represents an important tool to provide information concerning the energy quality of a certain property. The introduction of the obligation to disclose the EPC when residential buildings are offered for sale or rent in 2014 should further strengthen the role of the information tool. This study investigates the perception and influence of the EPC using the following complementary methods: expert interviews, choice-based conjoint analysis and hedonic pricing model. In addition to the analysis of hypothetical rental decisions on residential properties, this study also focuses on the resulting price effect of EPCs. The results show on the one hand a relatively high importance of the energy quality when tenants’ preferences are determined indirectly. On the other hand, the price effect for energy-efficient apartments amounts up to 7.1%. The price effect also intensifies after the recast of the legal provisions indicating a higher transparency of the market through the introduction of the disclosure obligation. However, there are still obstacles, which require further development of the information tool in order to exploit the potential of the EPC.",0.0,https://doi.org/10.1365/s41056-018-0023-3,True,,Political Science,2018-11-01,c30d7daeb064e3a1846a7f0fe32de25ea8c9a33e,wahrnehmungdesenergieausweisesinhypothetischenundrealenmietentscheidungenaufdemdeutschenwohnimmobilienmarkt,steininger,10.1365/s41056-018-0023-3,wahrnehmungdesenergieausweisesinhypothetischenundrealenmietentscheidungenaufdemdeutschenwohnimmobilienmarkt|2018,1,4337,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonisches Preismodell Wohnungen,3,1,4,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",75a6485daf6c288200fc332c28b7abb1bebc0f01,10.12962/j2716179x.v13i1.5226,,Structural Equation Model For The Relationship between Property Value and Public Transport Accessibility,2018.0,Jurnal Penataan Ruang,S. Nurlaela,"Property value capitalization has been part of spatial-economic results of transportation infrastructure development. This paper presents a study to understand the relationship between property value and public transport accessibility. A structural equation model or SEM in terms of path analysis was developed and explained the backward-forward chain of sequential causality of property value, public transport accessibility and travel behavior. The backward chain explains the transportation influences land use in a sequential causality and the forward chain explains the relationship from land use to travel behavior. Path analysis reported the backward chain was only valid in a direct relationship from public transport accessibility to property value but failed to account for a sequential causality relationship from public transport accessibility, land use density/intensity and property value. The forward chain confirmed the significant relationship from accessibility to travel time and to the number of household trips through the influence of land use, however at low influence. Direct relationship from accessibility to travel behavior (car uses) mediating by travel distance was at moderate influence. Nevertheless, the paper reported the variances of property value explained by the overall LUTI relationship was only modes at 12%. Keywords : Property value, public transport accessibility, structural equation model (path analysis), land use development, travel behavior",1.0,https://doi.org/10.12962/j2716179x.v13i1.7062,True,,Economics,2018-05-15,75a6485daf6c288200fc332c28b7abb1bebc0f01,structuralequationmodelfortherelationshipbetweenpropertyvalueandpublictransportaccessibility,nurlaela,10.12962/j2716179x.v13i1.5226,structuralequationmodelfortherelationshipbetweenpropertyvalueandpublictransportaccessibility|2018,1,2988,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",3e8e59a2f4d09f7e08345cc75f9b15ef6aa22cde,10.1145/3630106.3659027,,"The Impact of iBuying is About More Than Just Racial Disparities: Evidence from Mecklenburg County, NC",2024.0,"Conference on Fairness, Accountability and Transparency",Isaac Slaughter; E. M. Brown; Nicholas Weber,"Instant buyers (iBuyers)—companies that buy and sell homes based on automated valuation models (AVMs)—now hold more than 5% market share in some USA cities. In this work, we investigate the fairness of iBuyers by constructing a dataset that links racial demographics from voter records with detailed property information on over 50,000 real estate transactions. Using Bayesian hierarchical modeling we find that: 1. iBuyers Decrease the Racial Sales Price Gap Between Black and White Home Sellers. Controlling for over 50 property features we find that iBuyers reduce the racial price gap that otherwise exists between homes sold by Black and White homeowners. This is not, however, a result of equity achieved through proprietary AVMs, but rather a result of both Black and White homeowners being similarly disadvantaged by iBuyers’ low purchase prices; and, 2. iBuyers Increase Property Conversion Rates from Individual to Institutional Ownership. We trace iBuyers’ purchases as well as their follow-on sales of homes in Mecklenburg County. In doing so, we show that iBuyers increase the rate at which properties are converted from being individually owned to institutionally owned. The eventual purchasers of iBuyer homes include national and international rental companies that have been tied to high eviction rates and poor property management. As with sale prices, we find that rather than reapportioning this social harm more equitably, iBuyers are simply increasing the rate at which homes bought from White homeowners are converted to institutional ownership. Ultimately, our analysis suggests that iBuyers are Equalizing Housing Outcomes by Extending Real Estate Harms Typically Isolated to Black Homeowners to White homeowners as Well.",0.0,https://doi.org/10.1145/3630106.3659027,True,JournalArticle;Book,Computer Science,2024-06-03,3e8e59a2f4d09f7e08345cc75f9b15ef6aa22cde,theimpactofibuyingisaboutmorethanjustracialdisparitiesevidencefrommecklenburgcountync,slaughter,10.1145/3630106.3659027,theimpactofibuyingisaboutmorethanjustracialdisparitiesevidencefrommecklenburgcountync|2024,1,3246,1,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",0,3,4,1,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",e4dbe798deeb7bb12347946d35d27dcf26003dd7,10.1111/jors.70027,,Decomposing Variations in Quality of Life Through Amenities: Evidence From Korean Cities,2025.0,Journal of Regional Science,Heepyung Cho," We estimate the quality of life (QOL) across Korean cities and decompose the cross‐sectional and temporal variations in QOL through amenities. Using administrative housing transaction data, a city's revealed‐preference QOL is estimated from its cost of living relative to its wage level. Seoul, the biggest city in Korea, had a significantly high QOL in 2019, and its QOL continuously increased from 2015 to 2019. Based on a hedonic framework, households are willing to pay more for denser, more educated, and warmer cities. Decomposing QOL using amenities chosen by LASSO, we find that a large portion of the cross‐sectional variation in QOL across cities is attributable to human‐made amenities. We also show that most temporal variation in QOL can be accounted for by amenities, particularly their increasing value.",1.0,https://doi.org/10.1111/jors.70027,True,JournalArticle,,2025-11-10,e4dbe798deeb7bb12347946d35d27dcf26003dd7,decomposingvariationsinqualityoflifethroughamenitiesevidencefromkoreancities,cho,10.1111/jors.70027,decomposingvariationsinqualityoflifethroughamenitiesevidencefromkoreancities|2025,1,2320,1,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,0,2,1,1,0,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0fdd579a8d6c5c2785fbe733b525ec9c93e61f75,10.1111/j.1435-5957.2006.00089.x,,"A cross-sectional analysis of residential property prices: the effects of income, commuting, schooling, the housing stock and spatial interaction in the english regions",2006.0,,B. Fingleton,"This article examines the distribution of residential property prices in 2001 across local areas in England using spatial econometric methods, showing that spatial variations in local income, income within commuting distance, the stock of residential properties and the quality of local schooling have significant effects. The residual spatial variation due to unknown factors is modelled by a proxy variable, but this does not rule out a significant spatial lag. The article argues that this represents endogenous interaction of property price levels between neighbouring areas, which is interpreted as the outcome of local market knowledge and preference, which produces greater price similarity between an area and its neighbours than one would anticipate from the levels of the exogenous price determinants. Copyright (c) 2006 the author(s). Journal compilation (c) 2006 RSAI.",29.0,https://doi.org/10.1111/j.1435-5957.2006.00089.x,True,,Economics,2006-11-17,0fdd579a8d6c5c2785fbe733b525ec9c93e61f75,acrosssectionalanalysisofresidentialpropertypricestheeffectsofincomecommutingschoolingthehousingstockandspatialinteractionintheenglishregions,fingleton,10.1111/j.1435-5957.2006.00089.x,acrosssectionalanalysisofresidentialpropertypricestheeffectsofincomecommutingschoolingthehousingstockandspatialinteractionintheenglishregions|2006,1,2380,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || residential property price determinants",0,1,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",098dd5995f350c19f68ca944f8e805f2cd7084a8,10.1093/restud/rdag079,,The Long Shadow of Housing Discrimination: Evidence from Racial Covenants,2026.0,The Review of Economic Studies,A. Sood; Kevin Ehrman-Solberg," Racial covenants in housing deeds were widely used in the United States during the early 20th century to segregate neighborhoods. In 1948, the Supreme Court decision in Shelley v. Kraemer made racial covenants unenforceable in court, stopping short of prohibiting them entirely. We surmise that even after losing their judicial enforceability, such covenants could have shaped segregation if they served as a focal point for coordination on an initial equilibrium in neighborhood formation, a context characterized by investment durability and path dependency. To test this hypothesis, we assemble novel parcel-level data on racial covenants and develop a quasi-experimental design that exploits delays between covenant execution and housing construction to isolate exposure to enforceable covenants at the time neighborhoods were built. Covenant enforceability indeed affected early-stage neighborhood formation in terms of housing characteristics, public infrastructure placement, and the composition of initial residents. These early differences proved durable: Enforceable covenants account for 6–24% of the observed neighborhood racial segregation from 1980 to 2020 and 3.1–4.4% of housing price differentials in the 21st century. We conclude that by coordinating beliefs and decisions during neighborhood formation, the law generated spatial inequality that has long outlived racial covenants themselves.",0.0,https://doi.org/10.1093/restud/rdag079,True,JournalArticle,,2026-07-18,098dd5995f350c19f68ca944f8e805f2cd7084a8,thelongshadowofhousingdiscriminationevidencefromracialcovenants,sood,10.1093/restud/rdag079,thelongshadowofhousingdiscriminationevidencefromracialcovenants|2026,1,2912,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7484bb278f29c86f0aa0b049a4979a956fa43736,10.1080/21681376.2024.2314396,,Flattening which curve? Property-price gradients in New South Wales during the COVID-19 pandemic,2024.0,"Regional Studies, Regional Science",Nicholas Sheard,"ABSTRACT This paper tests how the patterns of residential property values in the Australian state New South Wales changed during the COVID-19 pandemic. Between 2019 and 2021, property values became relatively high in medium-sized towns and cities, suggesting those locations became more attractive. In its capital and largest city Sydney, the premium on proximity to the central business district (CBD), where employment is heavily concentrated, decreased after 2019 and that decrease persisted beyond the end of pandemic measures. The premium in Sydney on proximity to the beach has increased, but on a trajectory consistent with an existing trend. The decreased premium on proximity to the CBD since 2019 appears to have initially been related to the value of access via public transport, but then also to the value of access by car. The findings suggest shifts in regional development and urban form that could have implications for how cities are planned and developed.",3.0,https://doi.org/10.1080/21681376.2024.2314396,True,JournalArticle,,2024-03-04,7484bb278f29c86f0aa0b049a4979a956fa43736,flatteningwhichcurvepropertypricegradientsinnewsouthwalesduringthecovid19pandemic,sheard,10.1080/21681376.2024.2314396,flatteningwhichcurvepropertypricegradientsinnewsouthwalesduringthecovid19pandemic|2024,1,2473,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3460ad0578b3b8ba1a442a838ebf0e7c34942eb9,10.1080/10691898.2008.11889569,,Modeling Home Prices Using Realtor Data,2008.0,,I. Pardoe,"It can be challenging when teaching regression concepts to find interesting real-life datasets that allow analyses that put all the concepts together in one large example. For example, concepts like interaction and predictor transformations are often illustrated through small-scale, unrealistic examples with just one or two predictor variables that make it difficult for students to appreciate how these concepts might be applied in more realistic multi-variable problems. This article addresses this challenge by describing a complete multiple linear regression analysis of home price data that covers many of the usual regression topics, including interaction and predictor transformations. The analysis also contains useful practical advice on model building—another topic that can be hard to illustrate realistically—and novel statistical graphics for interpreting regression model results. The analysis was motivated by the sale of a home by the author. The statistical ideas discussed range from those suitable for a second college statistics course to those typically found in more advanced linear regression courses.",19.0,https://doi.org/10.1080/10691898.2008.11889569,True,,Mathematics,2008-07-01,3460ad0578b3b8ba1a442a838ebf0e7c34942eb9,modelinghomepricesusingrealtordata,pardoe,10.1080/10691898.2008.11889569,modelinghomepricesusingrealtordata|2008,1,2626,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,spatial hedonic model housing,7b46a828ae3328415b07a84f540a43fcaec3d52c,10.1016/s1007-0214(05)70079-1,,Influence of spatial features on land and housing prices,2005.0,,Xiao-Lu Gao; Yasushi Asami,,12.0,https://doi.org/10.1016/s1007-0214(05)70079-1,True,,Mathematics,2005-06-01,7b46a828ae3328415b07a84f540a43fcaec3d52c,influenceofspatialfeaturesonlandandhousingprices,gao,10.1016/s1007-0214(05)70079-1,influenceofspatialfeaturesonlandandhousingprices|2005,1,1500,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",78bc9d720e5741bf564f697968291c6f8f90620f,10.1016/j.spasta.2024.100864,2409.08912,Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices,2024.0,Spatial Statistics,J. D. Toloza-Delgado; O. Melo; N. A. Cruz,"In the context of spatial econometrics, it is very useful to have methodologies that allow modeling the spatial dependence of the observed variables and obtaining more precise predictions of both the mean and the variability of the response variable, something very useful in territorial planning and public policies. This paper proposes a new methodology that jointly models the mean and the variance. Also, it allows to model the spatial dependence of the dependent variable as a function of covariates and to model the semiparametric effects in both models. The algorithms developed are based on generalized additive models that allow the inclusion of non-parametric terms in both the mean and the variance, maintaining the traditional theoretical framework of spatial regression. The theoretical developments of the estimation of this model are carried out, obtaining desirable statistical properties in the estimators. A simulation study is developed to verify that the proposed method has a remarkable predictive capacity in terms of the mean square error and shows a notable improvement in the estimation of the spatial autoregressive parameter, compared to other traditional methods and some recent developments. The model is also tested on data from the construction of a hedonic price model for the city of Bogota, highlighting as the main result the ability to model the variability of housing prices, and the wealth in the analysis obtained.",3.0,https://doi.org/10.1016/j.spasta.2024.100864,True,JournalArticle,Mathematics,2024-09-13,78bc9d720e5741bf564f697968291c6f8f90620f,jointspatialmodelingofmeanandnonhomogeneousvariancecombiningsemiparametricsarandgamlssmodelsforhedonicprices,toloza-delgado,10.1016/j.spasta.2024.100864,jointspatialmodelingofmeanandnonhomogeneousvariancecombiningsemiparametricsarandgamlssmodelsforhedonicprices|2024,1,2953,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || hedonic regression housing || spatial hedonic model housing",2,1,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,apartment prices hedonic,20734bef7533f01610af84f657dbdf329726bfce,10.1006/juec.1995.1030,,A Revealed Preference Approach to Ranking City Quality of Life,1995.0,,Matthew E. Kahn,"This paper presents a new method for ranking city quality of life. I use data from the 1980 and the 1990 Census of Population and Housing to rank cities. My approach relaxes the standard ""hedonic"" method's assumptions that all city local public goods are observed and that the implicit prices of all skills and apartment attributes are equal across cities. I find that Los Angeles and San Francisco have higher quality of life than Chicago and Houston in both 1980 and 1990. I find that quality of life in New York City fell during the 1980s.",93.0,https://doi.org/10.1006/juec.1995.1030,True,,Geography,1995-09-01,20734bef7533f01610af84f657dbdf329726bfce,arevealedpreferenceapproachtorankingcityqualityoflife,kahn,10.1006/juec.1995.1030,arevealedpreferenceapproachtorankingcityqualityoflife|1995,1,2042,4,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || apartment prices hedonic || hedonic housing prices || implicit prices housing attributes",2,0,2,2,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",91eece1ea1e5f617c5d641f6bdb29ca32073d203,10.1145/3718491.3718612,,Urban Street View Spatial Evaluation and Distribution of Residential Properties at Different Price Points,2024.0,Proceedings of the 4th Asia-Pacific Artificial Intelligence and Big Data Forum,Chen Geng,"In residents' pursuit of quality living environments, the quality of the residential surroundings is becoming an important consideration when purchasing a house. Previous studies have paid little attention to the impact of urban street views on residential prices. This study utilizes street view data and the SAM model based on deep learning technology to extract the visual features of street spaces around residential areas, qualitatively analyzing the association between urban street view visual features and the distribution of residential properties at different price points. The results of this study indicate that using the SAM model for extracting visual features of urban street views is feasible. Urban street view evaluation has an impact on urban real estate prices, and computer technology assists in quantifying and visualizing this impact.",0.0,https://dl.acm.org/doi/pdf/10.1145/3718491.3718612,True,Book,,2024-12-27,91eece1ea1e5f617c5d641f6bdb29ca32073d203,urbanstreetviewspatialevaluationanddistributionofresidentialpropertiesatdifferentpricepoints,geng,10.1145/3718491.3718612,urbanstreetviewspatialevaluationanddistributionofresidentialpropertiesatdifferentpricepoints|2024,1,2357,1,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",0,2,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",aca827a44946c0dc878d8de333a8c36f6a9b1b9a,10.1145/3650215.3650228,,Exploration of House Price Prediction Based on ICS-BP Neural Network,2023.0,ICMLCA,Dong Luo,"Since the real estate market is a complex, non-linear system with uncertainty and high complexity, house price forecasting is an important issue in the analysis and management of the real estate market. In house price forecasting, it is mainly faced with the complexity of the relationship between house price factors and the unsteadiness of house price data. In order to solve the above problems, the article adopts a BP (Back Propaganda) neural network model based on ICS (Improved Cuckoo Search). First, ICS is used to perform dimensionality reduction processing on house price factors, and then the dimensionality reduction house price factors are input into the BP neural network model, and the final prediction results are obtained by training and predicting the data. The results show that the accuracy of the model's prediction of house price data can reach an average of 92.54%, which is not only applicable to the prediction of actual data, but also provides an effective and feasible method for the analysis and management of the real estate market.",0.0,https://dl.acm.org/doi/pdf/10.1145/3650215.3650228,True,JournalArticle;Book,Computer Science,2023-10-27,aca827a44946c0dc878d8de333a8c36f6a9b1b9a,explorationofhousepricepredictionbasedonicsbpneuralnetwork,luo,10.1145/3650215.3650228,explorationofhousepricepredictionbasedonicsbpneuralnetwork|2023,1,2560,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,hedonic housing prices,33f88adea22caddc5b7fb7754748e096e7a6774f,10.1162/rest_a_00868,,Win or Lose: Residential Sorting After a School Choice Lottery,2020.0,Review of Economics and Statistics,Andrew J. Bibler; Stephen B. Billings,"Abstract We examine residential relocation and opting out of the public school system in response to school choice lottery outcomes. We show that rising kindergartners and sixth graders who lose a school choice lottery are 6 percentage points more likely to exit the district or change neighborhood schools (20% to 30% increase) and make up 0.14 to 0.35 standard deviations in average school test scores between lottery assignment and attendance the following year. Using hedonic-based estimates of land prices, we estimate that lottery losers pay a 9% to 11% housing price premium for access to a school with a 1 standard deviation higher mean test score.",16.0,https://direct.mit.edu/rest/article-pdf/102/3/457/1881525/rest_a_00868.pdf,True,,Sociology,2020-06-01,33f88adea22caddc5b7fb7754748e096e7a6774f,winorloseresidentialsortingafteraschoolchoicelottery,bibler,10.1162/rest_a_00868,winorloseresidentialsortingafteraschoolchoicelottery|2020,1,2156,1,hedonic housing prices,2,1,2,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic house price,4bc90bf144a67adcd110a7eb2f7529f23d1b686d,10.23860/diss-sudhakaran-pratheesh-2015,,Three Essays on Shellfish Management in Rhode Island,2015.0,,P. Sudhakaran,"Shellfish resources are the main fishery resources commercially harvested and cultured in Rhode Island. Wild shellfish management in Rhode Island is undertaken by the RI Department of Environmental Management (DEM) aiming to achieve, among other objectives, conserving naturally occurring shellfish populations in RI waters and managing public health outcomes due to, water quality issues. However, for any management scheme or regulation to be effective, policy must recognize the economic forces at work when evaluating proposed intervention or regulation of shellfishing. Regulations would influence the harvesters’ behavior through the change in the harvested quantity or market price of the product and this will affect the state of shellfish stock. As such, ignoring the market force could not only nullify the management effectiveness but could backfire and lead to unintended adverse impact on the primary target of management—the healthy stock of shellfish. Moreover, some of the regulations for shellfish resources might also affect the public. For example, people value houses near to the coast due to the aestheticism and serenity. However, construction of oyster farms near their backyard water might affect their view and calmness due to the frequent traffic in the water, which might decrease the value of those houses. Since the problem is directly linked to public, it is critically important to analyze whether regulations affect the houses and life of the coastal region. This dissertation addresses three research questions related to the management of shellfish resources in Rhode Island. In the first manuscript a market study was conducted to study the price and quantity relationships of commercially harvested shellfish in Rhode Island. The study analyzed the price sensitivity of shellfish products (three different market categories of quahog, scallops, and whelk) with respect to quantity landed of its own and other products using non-linear Almost Ideal Demand System (NL-AIDS) model. The study shows that the shellfish products considered in the analysis were price inflexible indicating that a significantly huge quantity of shellfish is required to change the price of the species. The study also showed that the shellfish species are substitutes to each other. The second manuscript measures the economic performance of shellfish transplant program conducted in some of the fishing areas to enrich the stock of quahog in Narragansett Bay area. The RI Department of Environment and Management (DEM) collect quahog from prohibited fishing areas with the help of fishermen and transplant to some of the selected open fishing areas. Using commercial harvest data of quahog from the bay area, the study investigated effect of transplant of quahog on quantity harvested and stock population in Narragansett Bay area. Moreover, profit from the transplant program was calculated to examine its net returns. The study showed that there is no statistical evidence to prove that transplantation is significantly influencing the harvest of quahog in Narragansett Bay area. The net returns estimates suggest that the transplant operation is not profitable. The third manuscript analyzes the effect of aquaculture on the public by looking at the impact of construction of oyster farms on the neighboring housing value in Rhode Island, using housing sale transaction data of Rhode Island from 2000 to 2012. The difference-in-difference method was combined within a Hedonic price model to evaluate the effect of farm construction occurring over time. The result showed that housing value is unaffected by construction of oyster farms in the neighborhood. This points to an important policy implication that people do not consider construction of oyster farms while purchasing property. The lack of consideration might be due to two reasons, first they consider only characteristics that are directly linked to their daily life, and secondly they might be actually supporting eco-friendly operations such as farms in their neighborhood.",1.0,https://digitalcommons.uri.edu/cgi/viewcontent.cgi?article=1396&context=oa_diss,True,,History,,4bc90bf144a67adcd110a7eb2f7529f23d1b686d,threeessaysonshellfishmanagementinrhodeisland,sudhakaran,10.23860/diss-sudhakaran-pratheesh-2015,threeessaysonshellfishmanagementinrhodeisland|2015,1,5553,3,hedonic house price || hedonic housing prices || housing characteristics prices hedonic,2,2,3,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,spatial hedonic model housing,d626b4a7696e6663796514b50cc34ddb6578fa9c,10.23860/diss-liu-tingting-2014,,THREE ESSAYS ON THE ECONOMICS OF ECOSYSTEM SERVICES AND LAND USE CHANGE,2014.0,,Ting-Ting Liu,"Over the past few decades, agricultural and forest lands in the northeast US have been lost to residential development. Combined with more intensive farming on remaining lands, these trends have led to losses in valuable ecosystem services from the agricultural and forest landscape. Narragansett Bay is also exhibiting an increasing array of eutrophic-associated symptoms, including low dissolved oxygen, fish kills, eelgrass loss, algae blooms, and loss of submerged aquatic vegetation (Narragansett Bay Estuary Program, 2008). This dissertation contains three essays to quantify and value the changes in ecosystem services and to evaluate the effectiveness of policy for land use management. Manuscript 1 seeks to illustrate a method for spatially quantifying hydrological ecosystem services (water quality and quantity) related to wildlife habitat and flood risks, as well as the production of ecosystem services (food and fiber) at the watershed scale. I also investigate the effects of stressors faced in the coming decades—land use change and climate change—as well as choices in land management practices on production of these ecosystem services. I demonstrate the approach in the Beaver River watershed in Rhode Island using a spatially-explicit, process-based hydrological model (SWAT). My key finding is that choices in land use and land management practices create tradeoffs across multiple ecosystem services and that the extent of these tradeoffs depends considerably on the scenarios and the ecosystem services being compared. Stressors such as urbanization, increased agriculture intensity and climate change make spatially explicit modeling necessary to understand the complex relationships between efficient land use and the complexity in the function of ecosystems. My second manuscript examines the direct and spillover effects of residential zoning policy on land development. Zoning has been widely used as a tool to manage residential development. Residential zoning policy regulation, particularly minimum lot size zoning restrictions in one area may affect the land development of the area itself as well as in the adjacent areas. Accounting for both the direct and the potential spillover effects of minimum lot size zoning restrictions is important for land use planning. However, limited research has been done to examine the spillover effect of minimum lot size zoning restrictions on nearby land development. In this study, I estimate the direct and spillover effect of minimum lot size zoning restrictions in Rhode Island. To address the nonrandom placement of residential zoning, I use propensity score matching and nearest neighborhood matching to preprocess the data. Additionally, to address simultaneity and the presence of spatially correlated unobserved characteristics, I use the soil construction constraint index as an instrumental variable for minimum lot size restriction. Results suggest that minimum lot size restrictions in the neighborhood significantly decrease the probability of urban development outside of the zoned area, up to a 2000 meters radius buffer. In my third manuscript, I examine the impact of water quality in Narragansett Bay on housing prices in coastal towns and municipalities using a hedonic housing price model. Compared with other water quality related hedonic studies, I combine an improved inversed distance weighted (IDW) interpolation method with water quality region, to best capture the water quality in Narragansett. Additionally, I compare different measures of Chlorophyll concentration as indicators of coastal water quality. Estimation results show that the coastal water quality indicator for Chlorophyll concentrations has a negative impact on the housing prices, and the negative impact of water quality attenuates with increasing distance from the shoreline. In the comparison of alternative measurements for water quality, I find a substantial difference among the estimations results. I further estimate potential increases in the value of the housing stock associated with different scenarios for water quality improvements in Narragansett Bay.",3.0,https://digitalcommons.uri.edu/cgi/viewcontent.cgi?article=1277&context=oa_diss,True,,Environmental Science,,d626b4a7696e6663796514b50cc34ddb6578fa9c,threeessaysontheeconomicsofecosystemservicesandlandusechange,liu,10.23860/diss-liu-tingting-2014,threeessaysontheeconomicsofecosystemservicesandlandusechange|2014,1,5634,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",2,2,2,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,residential property price determinants,4e338f7890627c293cd5480dec800ffcec7fef84,10.18778/0208-6018.342.06,,The Temporal Stability of Buyers’ Preferences for Property Localization on the Housing Market in Szczecin,2019.0,Acta Universitatis Lodziensis. Folia Oeconomica,Barbara Batóg; I. Foryś,"The housing market investors’ decisions are caused by their subjective expectations. These decisions are to do with financial resources, activity of local developers and buyers’ individual needs connected to the size of their household or their specific habits, as well as a sense of security and prestige of the neighborhood. The localization of a property is one of the most significant determinants of its price because of its spatial constancy. The attributes of a given residential area influence the value of properties and this phenomenon can be observed through intensity of transactions and transaction prices. On the other hand, the neighborhood’s influence changes over time. These changes are sometimes slow but could be very substantial. That is why buyers’ preferences can also change over time. The aim of this research is the analysis of the temporal stability of buyers’ localization preferences on the housing market in Szczecin. The fixed effects model, which allows for the comparison of changes in apartments’ prices in residential areas, has been applied. The data came from notarial deeds, from registers of real estate prices and values concerning transactions on the housing market in Szczecin.",1.0,https://czasopisma.uni.lodz.pl/foe/article/download/3357/5198,True,,Business,2019-08-22,4e338f7890627c293cd5480dec800ffcec7fef84,thetemporalstabilityofbuyerspreferencesforpropertylocalizationonthehousingmarketinszczecin,batóg,10.18778/0208-6018.342.06,thetemporalstabilityofbuyerspreferencesforpropertylocalizationonthehousingmarketinszczecin|2019,1,2719,1,residential property price determinants,0,2,4,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",f6355f6d796f324b0aa183bad31df03418c1ff38,10.2478/remav-2014-0032,,The Influence of Real Estate on Its Surroundings Based on the Example of the Manufaktura Complex in Lodz,2015.0,,Katarzyna Olbińska,"Abstract Real estate compared to other goods is distinguished by a high specificity. One of the signs of these specifics is the interdependence or correlation between property and its surroundings. On the one hand, the factor of closest neighborhood can largely shape the value of the property, while on the other hand, the function, use and development of real estate can strongly affect nearby property and the image of their entire neighborhood. The dynamics of the interdependence between the property and its environment reveal themselves most clearly in moments of transformation of real estate and its use. This study has a research character. The focus of this article is one of the aspects of interdependence - the impact of the property, its use and development on its closest surroundings. The aim of this article is to show the manifestations of different types of external effects generated by the property. Case study research was conducted on the housing market in Lodz for the purpose of the article. The study included the analysis of 3,444 transactions made during the period from July 2005 to December 2009 in the area of 27 precincts representing the proximal and distal environment of the Manufaktura complex. The study analyzed the impact of the transformation from a post-industrial area into a multifunctional one, with the dominant function of trade, on the prices and marketability of apartments located in the vicinity of the complex. The performed analyses have not clearly confirmed the impact of the carried out transformations on the price of housing in areas surrounding the Manufaktura complex. However, the analyses did reveal that the proximity of the multifunction complex may be one of the factors influencing the activity of the housing market in the area.",7.0,https://content.sciendo.com/downloadpdf/journals/remav/22/4/article-p5.pdf,True,,Business,2015-02-06,f6355f6d796f324b0aa183bad31df03418c1ff38,theinfluenceofrealestateonitssurroundingsbasedontheexampleofthemanufakturacomplexinlodz,olbińska,10.2478/remav-2014-0032,theinfluenceofrealestateonitssurroundingsbasedontheexampleofthemanufakturacomplexinlodz|2015,1,3294,1,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2e640676ba3b4f3213c798d84200cc2427936ec1,10.1108/jerer-11-2019-0045,,Decarbonising real estate,2020.0,Journal of European Real Estate Research,S. Wilkinson; S. Sayce," Purpose About 27 per cent of the total UK carbon emissions are attributed to residential buildings; therefore, improvements to the energy efficiency of the stock offers great potential. There are three main ways to achieve this. First is a mandatory approach, minimum energy efficiency standards are set and applied to new and existing buildings. Option 2 is voluntary, using energy ratings that classify performance to stimulate awareness and action. Third, financial measures, incentives and taxes, are applied to “nudge” behaviours. Most westernised countries have adopted a combination of Options 2 and 3, with the belief that the market will incentivize efficient properties. The belief is voluntary measures will stimulate demand, leading to value premiums. This paper aims to seek a deeper understanding of the relationship between energy efficiency and the value of residential property in Europe and, by so doing, to determine whether stronger policies are required to realise decarbonisation. Design/methodology/approach This paper reviews the current academic literature and large-scale quantitative studies conducted in Europe, mostly using hedonic pricing analysis to seek a relationship between energy performance certificates (EPCs) and either capital or rental values. It compares these to the reported findings of three case study projects that take a variety of different research approaches, all of which have the ambition to understand market behaviours and stimulate occupier or/and owner demand for energy efficient buildings. Findings The large-scale academic study results generally show a positive relationship between observed market prices and EPCs, which are commonly taken as surrogates for efficiency; however, outcomes are variable. One large study found energy upgrades may increase value, but not to the point where costs outweigh the value gain. Other studies found high returns on investment in energy efficiency technologies. The case study projects, however, revealed a more nuanced set of arguments in terms of the relationship between energy efficiency and market behaviours. Whilst there is some evidence that energy efficiency is beginning to impact on value, it is small compared to other value drivers; other drivers, including health, well-being and private sector finance deals, may prove more powerful market drivers. Further, the empirical findings reported point towards the emergence of a “brown” discount being more likely to be the long-term trend than a green premium. It is concluded that the current levels of action are unlikely to deliver the levels of decarbonisation urgently needed. Research limitations/implications This is a desktop study of other European studies that may have collected data on slightly different variables. Practical implications This study shows that more action is required to realise decarbonisation in new and existing residential property in the European states considered. The sector offers potential for substantial reductions, and other mandatory approaches need to be considered. Originality/value This is a timely review of the current outcomes of European programmes (EPCs) adopted in several countries to increase energy efficiency in the residential sector through a voluntary mechanism. The results show that more action is needed. ",0.0,https://centaur.reading.ac.uk/89550/1/Decarbonising%20Real%20Estate-%20paper%20submitted%20rev%202%20with%20Table%20and%20amended%20references%20%20clean%20in%20landscape_%20%28002%29.pdf,True,Review,Business,2020-04-06,2e640676ba3b4f3213c798d84200cc2427936ec1,decarbonisingrealestate,wilkinson,10.1108/jerer-11-2019-0045,decarbonisingrealestate|2020,1,4838,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,1,2,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,housing characteristics prices hedonic,2469c31ee76b38d5bd52bb7f18e02f433ca0ebb9,10.1007/s10901-019-09690-y,,"Administrative hierarchy, housing market inequality, and multilevel determinants: a cross-level analysis of housing prices in China",2019.0,Journal of Housing and the Built Environment,Rong-Hui Tan; Qingsong He; Ke-Hao Zhou; Yan Song; Hengzhou Xu,,0.0,https://cdr.lib.unc.edu/downloads/02871c17j,True,,,2019-06-29,2469c31ee76b38d5bd52bb7f18e02f433ca0ebb9,administrativehierarchyhousingmarketinequalityandmultileveldeterminantsacrosslevelanalysisofhousingpricesinchina,tan,10.1007/s10901-019-09690-y,administrativehierarchyhousingmarketinequalityandmultileveldeterminantsacrosslevelanalysisofhousingpricesinchina|2019,1,1500,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",640fffe1e1529dd6105ffc662a552d7cc71a8ae2,10.5539/jsd.v4n2p48,,"Impact of Sanitary Landfills on Urban Residential Property Value in Lagos State, Nigeria",2011.0,,O. A. Akinjare; C. Ayedun; A. Oluwatobi; O. Iroham,"Landfills have for long been generally thought to have diminutionary effect on residential property values. Various researches sparked off by this contention have proved contradictory in some instances. This study attempts to examine the impact of the four State established landfills on residential property values in Lagos State, each of varing sizes and having various degrees of contamination. A total of 229, 2 341 and 315 structured questionaires were administerd to Estate Surveyors and Valuers, residents within 1.2km distance from four landfills and Lagos State Waste Management Agency (LAWMA) workers respectively and on the avearge, 78% were retrieved. Evaluation using linear regression function of data drawn from administered questionaires showed a slight evident of statistical significance indicating that all residential property values increased with distances away from landfill sites at an average of 6% for the four landfills.",35.0,https://ccsenet.org/journal/index.php/jsd/article/download/10172/7272,True,Review,Geography,2011-04-06,640fffe1e1529dd6105ffc662a552d7cc71a8ae2,impactofsanitarylandfillsonurbanresidentialpropertyvalueinlagosstatenigeria,akinjare,10.5539/jsd.v4n2p48,impactofsanitarylandfillsonurbanresidentialpropertyvalueinlagosstatenigeria|2011,1,2449,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3c69ae567ab44b836ebe8f444043329e4120b250,10.5539/ijms.v3n2p64,,"Price Effects of Landfills on Residential Housing in Lagos, Nigeria",2011.0,,O. A. Akinjare; S. Oloyede; C. Ayedun; C. O. Oloke,"The question as to whether solid waste landfills affect residential property values has long been a subject of controversy and debate. Previous studies have resulted in mixed conclusions. This study examines four landfill sites in Lagos which differ in size, operating status and history. The relationship between each landfill and property values were measured based on distances of 1.2km radii away from the landfill locations and the measurements were based on the interval of 300meters up to 1 200 meters in concentric rings. The result of the study indicated that across the four landfill sites, increase in property values were evident as distance away from the landfills increased indicating that residential houses in close proximity to the landfills suffered value loss. Property appreciation relative to distance averaged 5.75% within the concentric rings for all four landfills.",8.0,https://ccsenet.org/journal/index.php/ijms/article/download/10376/7401,True,,Environmental Science,2011-04-28,3c69ae567ab44b836ebe8f444043329e4120b250,priceeffectsoflandfillsonresidentialhousinginlagosnigeria,akinjare,10.5539/ijms.v3n2p64,priceeffectsoflandfillsonresidentialhousinginlagosnigeria|2011,1,2389,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,4,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1e6daa6c15bd9d379c8d281f9900b569fb3aa7b2,10.70382/caijeres.v13i4.087,,"EFFECT OF DESIGNED AND BUILT INDUSTRIAL ESTATE ON PROPERTY VALUES IN ILUPEJU, LAGOS",2026.0,International journal of environmental research and earth science,Jumoke Mulikat Tomori; F. A. Olarewaju; A. Alege; I. Oladoja,"This study examines the effect of the planned design and built of the Ilupeju Industrial Estate on property values within the estate and its surrounding neighbourhoods in Lagos State, Nigeria. Ilupeju, one of the earliest planned industrial estates in Lagos, has experienced significant physical, commercial, and infrastructural transformation over the years. The research investigates how factors such as planned road networks, industrial infrastructure, accessibility, land-use planning, and proximity to manufacturing Resident and commercial activities influence the rental and capital values of residential and industrial properties. The study adopts a descriptive survey design, utilizing both primary data obtained through questionnaires administered to estate surveyors, property owners, and occupiers, and secondary data from valuation reports, property transactions, and relevant literature. Data are analyzed using descriptive and inferential statistical techniques to determine the relationship between industrial estate development and property values. Findings are expected to show that the planned nature of the Ilupeju Industrial Estate has positively influenced Residential and industrial property values through improved accessibility.",0.0,https://cambridgeresearchpub.com/ijeres/article/download/1385/1257,True,JournalArticle;Review,,2026-10-12,1e6daa6c15bd9d379c8d281f9900b569fb3aa7b2,effectofdesignedandbuiltindustrialestateonpropertyvaluesinilupejulagos,tomori,10.70382/caijeres.v13i4.087,effectofdesignedandbuiltindustrialestateonpropertyvaluesinilupejulagos|2026,1,2752,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",33e424089e98f8f99e302d450172d08a5e46e7c6,10.70382/bejasd.v7i2.017,,"MARKET VALUATION OF HERITAGE PROPERTIES: A CASE STUDY OF OLD RESIDENTIAL QUARTERS IN ILE-IFE, OSUN STATE",2025.0,Journal of African Sustainable Development,T. Taiwo; Saheed Omobolaji Rahman,"This study investigates the market valuation of heritage properties, focusing on old residential quarters in Ile-Ife, Osun State, Nigeria. Ile-Ife, recognized as the cradle of Yoruba civilization, is home to numerous cultural heritage sites that significantly influence the city's architectural landscape and urban development. Despite the rich cultural heritage, there is a paucity of empirical research examining how these heritage sites impact the market value of residential properties in the area. The research employs a mixed-methods approach, integrating quantitative spatial analysis with qualitative assessments. Quantitative data were collected through property transaction records, spatial measurements of proximity to heritage sites, and property valuation reports. Qualitative data were gathered via semi-structured interviews with property owners, real estate professionals, and urban planners. The study area encompasses residential properties within a 2-kilometer radius of selected heritage sites, including the Ooni's Palace, the National Museum, and the Ifa Temple. Findings indicate a positive correlation between proximity to heritage sites and increased property values. Properties situated closer to these cultural landmarks tend to command higher rental and sale prices. However, the study also identifies challenges such as urbanization pressures, inadequate infrastructure, and the high cost of maintaining heritage properties, which can negatively affect their market value. The study contributes to the discourse on heritage conservation by providing empirical evidence on the economic implications of preserving historical residential areas. It underscores the need for integrated urban planning strategies that balance development with the conservation of cultural heritage. The research also highlights the importance of policy interventions to mitigate the adverse effects of urbanization on heritage properties.",0.0,https://berkeleypublications.com/bjasd/article/download/538/488,True,JournalArticle,,2025-03-31,33e424089e98f8f99e302d450172d08a5e46e7c6,marketvaluationofheritagepropertiesacasestudyofoldresidentialquartersinileifeosunstate,taiwo,10.70382/bejasd.v7i2.017,marketvaluationofheritagepropertiesacasestudyofoldresidentialquartersinileifeosunstate|2025,1,3444,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,3,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",98d37d13904a710c80bd280e84d0c91e06a95ddb,10.54691/bcpbm.v38i.3766,,Russian housing price forecast based on BP neural Network,2023.0,BCP Business & Management,Si-Di Du; Xi-Qing Liu; S. Shi; Guan-Chen Zhou,"Contemporarily, Russia's real estate market has huge potential for investors. The purpose of this paper is to investigated the impact of various factors on the real estate market price based on various statistical models. We collected past housing price data from the Internet. Subsequently, PCA model was adopted to reduce the dimension of the data to simplify the calculation. Finally, the BP model is implemented to predict the price changes in the real estate market. After calculation, our forecast data and the reality of housing prices are slightly different, but the forecast of the real estate market is consistent with the reality. In order to further find the influence of various factors on housing price, we also calculated the characteristic importance of different factors and found the most important influencing factor, i.e., the region. These results can provide investors with an idea to analyze the real estate market prices, and help policy makers find the real estate market prices in line with the market rules to avoid the occurrence of bubbles.",0.0,https://bcpublication.org/index.php/BM/article/download/3766/3677,True,,,2023-03-02,98d37d13904a710c80bd280e84d0c91e06a95ddb,russianhousingpriceforecastbasedonbpneuralnetwork,du,10.54691/bcpbm.v38i.3766,russianhousingpriceforecastbasedonbpneuralnetwork|2023,1,2569,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,hedonic house price,8b9278471358ff729ac08010b9bafea03f36c01e,10.1023/b:real.0000035311.59920.74,,House Prices and the Structure of Local Government: An Application of Spatial Statistics,2004.0,,David M. Brasington,,54.0,https://autopapers.ssrn.com/sol3/papers.cfm?abstract_id=488787,True,,Economics,2004-01-21,8b9278471358ff729ac08010b9bafea03f36c01e,housepricesandthestructureoflocalgovernmentanapplicationofspatialstatistics,brasington,10.1023/b:real.0000035311.59920.74,housepricesandthestructureoflocalgovernmentanapplicationofspatialstatistics|2004,1,1500,2,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 arxiv,housing price text listing,,10.1007/978-3-030-84060-0_11,1907.12665,Airbnb Price Prediction Using Machine Learning and Sentiment Analysis,2019.0,arXiv,Pouya Rezazadeh Kalehbasti; Liubov Nikolenko; Hoormazd Rezaei,"Pricing a rental property on Airbnb is a challenging task for the owner as it determines the number of customers for the place. On the other hand, customers have to evaluate an offered price with minimal knowledge of an optimal value for the property. This paper aims to develop a reliable price prediction model using machine learning, deep learning, and natural language processing techniques to aid both the property owners and the customers with price evaluation given minimal available information about the property. Features of the rentals, owner characteristics, and the customer reviews will comprise the predictors, and a range of methods from linear regression to tree-based models, support-vector regression (SVR), K-means Clustering (KMC), and neural networks (NNs) will be used for creating the prediction model.",,https://arxiv.org/pdf/1907.12665v1,True,preprint,cs.LG;stat.ML,2019-07-29,doi_10_1007_978_3_030_84060_0_11,airbnbpricepredictionusingmachinelearningandsentimentanalysis,kalehbasti,10.1007/978-3-030-84060-0_11,airbnbpricepredictionusingmachinelearningandsentimentanalysis|2019,1,2326,3,hedonic house price || house price prediction machine learning || housing price text listing,0,1,1,6,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",549a2285265ba101d9558a0140313a72b9258ca6,10.1080/17421772.2017.1286373,1602.05757,A two-step approach to account for unobserved spatial heterogeneity,2016.0,,A. Billé; R. Benedetti; Paolo Postiglione,"ABSTRACT A two-step approach to account for unobserved spatial heterogeneity. Spatial Economic Analysis. Empirical analysis in economics often faces the difficulty that the data are correlated and heterogeneous in some unknown form. Spatial econometric models have been widely used to account for dependence structures, but the problem of directly dealing with unobserved spatial heterogeneity has been largely unexplored. The problem can be serious particularly if we have no prior information justified by economic theory. In this paper we propose a two-step procedure to identify endogenously spatial regimes in the first step and to account for spatial dependence in the second step. This procedure is applied to hedonic house price analysis.",46.0,https://arxiv.org/pdf/1602.05757,True,,Mathematics;Economics;Computer Science,2016-02-18,549a2285265ba101d9558a0140313a72b9258ca6,atwostepapproachtoaccountforunobservedspatialheterogeneity,billé,10.1080/17421772.2017.1286373,atwostepapproachtoaccountforunobservedspatialheterogeneity|2016,1,2246,2,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price",2,1,1,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,60dfdba30857ca7267b53c72edae402a2b06bc12,10.21834/ajbes.v3i12.125,,GIS and Hedonic Pricing for Green Space in Housing Area,2018.0,Asian Journal of Behavioural Studies,Norzailawati Mohd Noor; M. Asmawi; Alias Abdullah,"This paper explores a framework that combines Geographic information System (GIS) with Hedonic pricing method in improving the analysis and determining the value of green spaces with housing prices in Subang Jaya, Malaysia. The previous study found that proposed combination between GIS and Hedonic pricing method for the same purpose has not been fully explored particularly in Malaysia. Findings show the proposed combined method leads to improved understanding and representation of urban dynamics and regeneration process, furthermore will encourage a sustainable setting of the environmental amenities in improving the geospatial technologies respectively.Keywords: GIS, Hedonic Pricing Method, green Spaces and urban regenerationeISSN 2398-4295 © 2018. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.",0.0,https://ajbes.e-iph.co.uk/index.php/ajbes/article/download/125/pdf,True,Review,Business,2018-07-18,60dfdba30857ca7267b53c72edae402a2b06bc12,gisandhedonicpricingforgreenspaceinhousingarea,noor,10.21834/ajbes.v3i12.125,gisandhedonicpricingforgreenspaceinhousingarea|2018,1,2759,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices",2,1,2,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",511d291ed45bea7745e5e6b0311ed984ae280b38,10.1029/2017wr022133,,Policy Targeting to Reduce Economic Damages From Land Subsidence,2018.0,Water Resources Research,Christopher M. Wade; K. Cobourn; G. Amacher; E. Hester,"Groundwater pumping contributes significantly to land subsidence, which generates economic costs as changes in the frequency of flooding affect property values. We evaluate these costs by estimating the marginal damages from pumping, which define the corrective policy incentive to address land subsidence externalities. In an application to the southern Chesapeake Bay region of Virginia, we find that land subsidence due to groundwater pumping is greatest in inland rural areas, but that the damages from pumping are greatest in coastal urban areas. Our results suggest that cost‐minimizing groundwater policy is spatially heterogeneous and targets the damages from land subsidence due to pumping in different locations, not simply physical land subsidence itself. These results are driven by differences in the spatial patterns of aquifer characteristics, which determine subsidence, and housing density, which determines the damages from subsidence.",16.0,https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2017WR022133,True,,Environmental Science,2018-07-01,511d291ed45bea7745e5e6b0311ed984ae280b38,policytargetingtoreduceeconomicdamagesfromlandsubsidence,wade,10.1029/2017wr022133,policytargetingtoreduceeconomicdamagesfromlandsubsidence|2018,1,2453,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",54801e5cb3a98962ed69d862f282afc566e80d9a,10.1017/s0899367x00002002,,On Estimating Household Demand for Outdoor Recreation from Property Values: An Exploration,1989.0,,S. F. Edwards,"This paper explores how hedonic price analysis might be used to estimate the surplus benefits of local outdoor recreation when distance to the recreational site is captured in property values. The model is characterized by the endogenous choice of distance to a local recreational area by households in coastal property markets and by the capitalization of proximity in property values. Equilibrium occurs when the reduction in the cost of a property due to a marginal increase in distance to the recreational area equals the associated loss in recreational surplus resulting from increased travel costs. The theoretical model is applied in an exploratory analysis of the ""demand"" for distance to the nearest public beach from which total surplus benefits are estimated.",7.0,https://ageconsearch.umn.edu/record/28865/files/18020140.pdf,True,,Economics,1989-10-01,54801e5cb3a98962ed69d862f282afc566e80d9a,onestimatinghouseholddemandforoutdoorrecreationfrompropertyvaluesanexploration,edwards,10.1017/s0899367x00002002,onestimatinghouseholddemandforoutdoorrecreationfrompropertyvaluesanexploration|1989,1,2270,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,1,1,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,rent hedonic regression housing,fa17b05f4156dbabb8969bf9fc74a2d9422b39ea,10.70382/ajbegr.v10i4.039,,"Reimagining the Reverberation of Flooding on Rental Values of Residential Properties in Ibadan, Nigeria",2025.0,Journal of Built Environment and Geological Research,A. Abegunde; B. Oguntokun,"Flooding remains one of the most persistent environmental challenges confronting Nigerian cities, with Ibadan experiencing recurrent inundation that undermines housing quality and property market stability. This study examines the effects of flooding on the rental values of residential properties in Ibadan, focusing on the roles of environmental exposure, infrastructural conditions, and institutional efficiency. Drawing on a quantitative research design, data were collected through structured questionnaires administered to tenants and estate valuers across three flood-prone communities, Ogunpa, Kudeti, and Balogun. Descriptive and inferential analyses, including correlation and multiple regression, were employed to evaluate the relationships between flood exposure, governance indicators, and rental outcomes. Results revealed that flood-prone neighbourhoods experience average rent discounts of 20–28% compared to non-exposed areas, demonstrating that flood risk acts as a negative externality capitalised into rental pricing. Regression results showed that flood exposure (β = -0.416, p < 0.01) significantly depresses rent levels, while effective drainage maintenance (β = 0.312) and institutional efficiency (β = 0.328) mitigate this effect. These findings confirm the relevance of the Hedonic Pricing Theory, suggesting that tenants rationally adjust rents in response to perceived environmental and governance risks. The study concludes that integrating flood-risk data, enforcing planning regulations, and strengthening institutional coordination are essential to stabilising Ibadan’s housing market and promoting sustainable urban resilience.",0.0,https://africanscholarpub.com/ajbegr/article/download/953/934,True,JournalArticle,,2025-12-03,fa17b05f4156dbabb8969bf9fc74a2d9422b39ea,reimaginingthereverberationoffloodingonrentalvaluesofresidentialpropertiesinibadannigeria,abegunde,10.70382/ajbegr.v10i4.039,reimaginingthereverberationoffloodingonrentalvaluesofresidentialpropertiesinibadannigeria|2025,1,3160,3,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || rent hedonic regression housing,2,1,3,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,willingness to pay housing attributes hedonic,321020c85c887f7ad61bb8d1f09394712d8967af,10.70382/ajbegr.v8.i4.031,,"Effects of Crime Rate on Residential Property Values: A Case Study of Ibadan North Local Government Area, Oyo State",2025.0,Journal of Built Environment and Geological Research,Ibidapo Shonibare; Akinsanya Alade Mohammed,"The relationship between crime rate and property values has become a subject of growing concern in urban studies and real estate research. In many developing cities, insecurity increasingly dictates residential mobility, investment choices, and the overall sustainability of housing markets. This study investigates the effects of crime on residential property values in Ibadan North Local Government Area (LGA), Oyo State, Nigeria. Using a mixed-method approach, the research examines how different types of crime—including burglary, theft, and armed robbery—shape the residential property market and influence both capital and rental values. Crime constitutes a significant neighborhood externality. As Gibbons (2004) noted, “crime operates as a disamenity factor, reducing the attractiveness of communities and lowering housing demand” (p. 588). In the Nigerian context, studies have highlighted how rising insecurity in urban areas has altered housing preferences and property transactions (Ajayi, 2018; Oduwaye, 2019). Drawing from these insights, this study employed a structured survey of 200 respondents, complemented with police crime records and estate market data. By integrating hedonic pricing theory, which emphasizes that property values reflect the sum of locational and neighborhood characteristics (Rosen, 1974), and the Crime Prevention through Environmental Design (CPTED) framework (Cozens et al., 2005), the research situates crime as a measurable determinant of property market performance. The findings reveal a statistically significant negative correlation between crime rates and residential property values (R = -0.62, p < 0.05). High-density neighborhoods such as Agbowo and Yemetu, with recurrent incidents of burglary and theft, recorded rental values 20–30% lower than relatively secure areas like Bodija. This echoes Linden and Rockoff’s (2008) conclusion that “housing markets respond swiftly to changes in perceived safety, with buyers capitalizing crime risk into property prices” (p. 1165). Furthermore, respondents expressed willingness to pay a “safety premium” in exchange for living in secure environments, underscoring the importance of security as a core housing attribute. In conclusion, the study affirms that crime erodes residential property values in Ibadan North, discourages real estate investment, and diminishes the liveability of affected neighborhoods. It recommends strengthening community policing, integrating CPTED strategies into urban design, and promoting public–private partnerships for neighborhood security. Beyond its academic contribution, the study provides practical insights for estate managers, urban planners, and policymakers, who must address crime not merely as a social challenge but also as a factor shaping housing market dynamics and urban economic growth.",0.0,https://africanscholarpub.com/ajbegr/article/download/867/868,True,JournalArticle;Review,,2025-07-31,321020c85c887f7ad61bb8d1f09394712d8967af,effectsofcrimerateonresidentialpropertyvaluesacasestudyofibadannorthlocalgovernmentareaoyostate,shonibare,10.70382/ajbegr.v8.i4.031,effectsofcrimerateonresidentialpropertyvaluesacasestudyofibadannorthlocalgovernmentareaoyostate|2025,1,4334,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic || residential property price determinants || willingness to pay housing attributes hedonic",2,3,3,4,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0da98562261bd1b27b7edb9e5b8bf6e905797dd7,10.70382/ajbegr.v12i4.059,,"Impact and Enforcement Efficacy of Environmental Regulations on Property Values: A Study of Selected South East States, Nigeria",2026.0,Journal of Built Environment and Geological Research,R. O. Sado; E. Oladejo,"This study examines how environmental regulations and policies affect property values in South East Nigeria. It is premised on the growing recognition that environmental quality and regulatory frameworks significantly influence the demand and value of real estate. The aim of the study is to analyze the impact and enforcement efficacy of environmental regulations on property values: A case study of selected South East States, Nigeria, in order to identify key regulatory instruments that help to shape the property market in the study area. A quantitative research design was adopted, covering Anambra, Enugu, and Imo States. The population of the study comprised 113 estate surveying and valuation firms in the three selected states, with a sample frame of 95 firms based on retrieved questionnaires. Data were collected using structured questionnaires and analyzed using descriptive statistics of frequencies, percentages, and mean scores, and inferential statistics adopting the one sample t-test. Findings reveal that although environmental regulations and policies have significant impact on property values in the three selected South East States with p-values of 0.000 each respectively, their effectiveness is generally low due to weak enforcement, particularly in Anambra and Imo States. Key determinants of property value identified include land use planning, zoning regulations, housing policies, and taxation policies. It was concluded that ineffective implementation limits the positive impact of these policies on property values. The study recommends stricter enforcement of regulations, mandatory environmental impact assessments, improved urban planning, and the promotion of sustainable development practices to enhance property market outcomes.",0.0,https://africanscholarpub.com/ajbegr/article/download/1259/1241,True,JournalArticle;Review,,2026-06-03,0da98562261bd1b27b7edb9e5b8bf6e905797dd7,impactandenforcementefficacyofenvironmentalregulationsonpropertyvaluesastudyofselectedsoutheaststatesnigeria,sado,10.70382/ajbegr.v12i4.059,impactandenforcementefficacyofenvironmentalregulationsonpropertyvaluesastudyofselectedsoutheaststatesnigeria|2026,1,3266,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,spatial hedonic model housing,a24b60ad9460cb9fa41b96ff0b04ab52acb91b10,10.15396/afres2018_125,,Residential Rental Market in Ghana – Empirical Examination of Submarket Existence,2018.0,,E. Gavu,"Purpose – The purpose of this research is to empirically examine submarket existence based on an understanding of the residential rental housing market in Ghana.Approach – Based on literature review and market observations, we provide key concepts and an overview of the residential rental market in Ghana. Although there is a general consensus that submarket exists, empirical identification of same has employed varied methods. Based on real estate experts and stakeholder consultations, a priori delineations of submarket constructs are made based on spatial, structural and a nested approach. The existence of submarkets is tested using the Kruskal-Wallis H test and Hedonic modelling techniques.Findings – By using fieldwork data from Accra rental market, the analysis provides credence to the conceptualisation of submarkets and how to empirically test for same. It is argued that researchers should employ alternative methods to compare results in order to make far reaching conclusions.Research implications – examining the hypothesis that differential rental values exists for submarkets has implications forpolicy decisions to target submarket constructs differently to improve market maturity.Practical implications – the research provides stakeholder investors in the rental space an understanding of market dynamics for profit maximisation, and end-users to maximise utility in deciding where to live – and as such households could benefit from making informed investment decisions on housing.Originality / Value of work – This research is one of the first attempts to empirically delineate submarkets in the residential rental market in Ghana.",0.0,https://afres.architexturez.net/system/files/afres2018_125.pdf,True,Review,Business,,a24b60ad9460cb9fa41b96ff0b04ab52acb91b10,residentialrentalmarketinghanaempiricalexaminationofsubmarketexistence,gavu,10.15396/afres2018_125,residentialrentalmarketinghanaempiricalexaminationofsubmarketexistence|2018,1,3157,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || spatial hedonic model housing,2,1,4,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,129023b53e859d480f057e1aa633ff7548d6c727,10.54254/2754-1169/14/20230815,,Anchoring Effect in Chinas Real Estate Market,2023.0,"Advances in Economics, Management and Political Sciences",Siyu Chen; Wenhao Wang; Lu-Hong Zhong,"Since the reform and opening up, China's real estate industry has prospered rapidly and has become an important force driving rapid economic growth and promoting urbanization. In recent years, China's real estate industry has entered an inflection point of development, and real estate bubbles and cyclical sharp fluctuations in the real estate market have occurred from time to time. Behavioral finance has provided a new research direction for how to explain this fluctuation. Based on this, this essay uses the classic anchoring effect theory in behavioral finance theory to study the existence and characteristics of anchoring effect in China's real estate market from the micro level, and explains the entire mechanism through three applications. The first application empirically tested the existence of the anchoring effect in China's real estate market, and analyzed the difference between the anchoring effect in the eastern and western regions of China. It verified that the decision of housing prices in the central region was affected by the anchoring effect of the micro subject, while the anchoring effect in the eastern and western regions was not significant. The second application analyzed Hong Kong's secondary residential market, and a statistical model including anchoring effects was also presented in order to study the dynamics of house prices. The third application provides an improved Hedonic model, which combines various literature data to study the influence of age, growth area and other characteristics of home buyers on real estate prices under the anchoring effect.",1.0,https://aemps.ewapublishing.org/media/aa2b948c15574aa0a505b963feed819b.marked_3WynpOj.pdf,True,JournalArticle,,2023-09-13,129023b53e859d480f057e1aa633ff7548d6c727,anchoringeffectinchinasrealestatemarket,chen,10.54254/2754-1169/14/20230815,anchoringeffectinchinasrealestatemarket|2023,1,3099,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,3,4,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic house price,91ee2f7cf11b2d742696808a9e79600b39d9c89b,10.1108/ijhma-10-2015-0066,,A hedonic house price index in continuous time,2016.0,,S. Waltl,,19.0,http://www100.uni-graz.at/vwlwww/forschung/RePEc/wpaper/2015-04.pdf,True,,Economics,2016-10-03,91ee2f7cf11b2d742696808a9e79600b39d9c89b,ahedonichousepriceindexincontinuoustime,waltl,10.1108/ijhma-10-2015-0066,ahedonichousepriceindexincontinuoustime|2016,1,1500,2,hedonic house price || hedonic housing prices,2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cfde6915517f8fd980b0367c6f9dbca5f7baa860,10.4236/me.2022.133019,,Social Utilities versus House Prices as Scales of Residential Preferences for Homes’ Attributes 30 Years Apart,2022.0,Modern Economy,A. Phipps,"One of two scales of residential preferences in a resident’s mind is their social and environmental utilities for homes’ attributes’ levels. The other is their willingness to pay for these attributes’ levels as it conforms with prices in the local real estate market. Convergence between two scales of preferences has one mentally superseding the other as primary. Divergence will more likely produce a consumption disequilibrium as the gap in price between affordable socially and monetarily most preferred attributes’ levels of homes. Convergences and divergences between these two fluctuating scales of preferences are measured with three interrelated datasets for up to 74 respondents and 3,000 single-detached(-like) houses in each of Saskatoon SK in 1987 and Windsor ON in 2020. Results are that respondents have diverging monetized and social utilities for up to seven of 12 generic attributes of homes in 1987 and/or 2020. They therefore will frequently have large consumption disequilibria for these attributes. The maximum of these will average up to one-half more than the price of an attribute’s socially most preferred level if they want its monetarily most preferred level. Despite this, a typical respondent is not behaving as if asset accumulation potentials realized in prices for homes’ attributes levels have superseded social needs and desires for up to seven attributes’ levels, even at times such as after moving in or when planning on moving out.",1.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=116007,True,,,,cfde6915517f8fd980b0367c6f9dbca5f7baa860,socialutilitiesversushousepricesasscalesofresidentialpreferencesforhomesattributes30yearsapart,phipps,10.4236/me.2022.133019,socialutilitiesversushousepricesasscalesofresidentialpreferencesforhomesattributes30yearsapart|2022,1,2969,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,5,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",528fb65c8e80d2bdc0945ad2ef880219da734d81,10.4236/cus.2022.101007,,Losses of Residential Utility from Budget Constraints on Preferences for Homes,2022.0,Current Urban Studies,A. Phipps,"Budget-constrained residential preferences differ from unconstrained residential preferences if residents mentally devalue unaffordable attributes’ levels of available homes in comparison with affordable ones. Budget-constrained and unconstrained utilities of 70 recent-mover respondents in Saskatoon SK in 1987 and 74 inner-city respondents in Windsor ON in 2020 are quantified for 12 generic attributes of homes in conjoint choice experiments. Budget constraints on their utilities for homes’ attributes’ levels are operationalized by superimposing marginal implicit prices from a hedonic housing price model in each city. Residential utilities are then statistically compared both through time and for subsamples within full samples, and losses of utility are predicted. Respondents will experience an approximate one-quarter and onetenth loss of possible utility for a home in Saskatoon and Windsor, respectively, if they cannot afford their unconstrained most preferred attributes’ levels. Losses of utility are predicted even though budget-constrained utilities of subsamples of respondents are higher as hypothesized for affordable levels of four attributes, and lower for unaffordable levels of those attributes. In conclusion, theoretical and practical implications of these predictions of losses of residential utility are discussed for residents, housing providers and policymakers.",0.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=115978,True,,,,528fb65c8e80d2bdc0945ad2ef880219da734d81,lossesofresidentialutilityfrombudgetconstraintsonpreferencesforhomes,phipps,10.4236/cus.2022.101007,lossesofresidentialutilityfrombudgetconstraintsonpreferencesforhomes|2022,1,2893,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic housing prices || implicit prices housing attributes",2,1,4,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c7b242d28013321bceaeef0f994fd2d0dab032c0,10.4067/s0718-915x2013000200009,,Implicit Prices in Urban Real Estate Valuation,2013.0,,J. Nuñez Tabales; F. R. Rey Carmona; J. M. C. Y. Ocerin,"Econometric hedonic models encounter several theoretical and practical difficulties when applied to the real estate market, such as downward biases in the estimation of hedonic prices, subjective decisions in the measurement process of categorical attributes, frontier problems related to an imperfect information framework and uniequational specification. Many of these are linked to the parametric approach. Artificial Neural Networks (ANN) provide an attractive alternative: better dwelling prices estimates, avoidance of bias at different market segments, direct use of categorical data and full use of the information available. The price to be paid is the difficulties in the economic interpretation of network parameters. Nowadays, if the final objective to produce better estimates of the transaction prices, this methodology show lower errors, provided of a broad representative database of sales are recorded. A case study is presented for a medium size city in the South of Spain. Keywords: Urban Economics; Hedonic Models; Neural Networks.",12.0,http://www.scielo.cl/pdf/rconst/v12n2/art09.pdf,True,,Economics,2013-11-01,c7b242d28013321bceaeef0f994fd2d0dab032c0,implicitpricesinurbanrealestatevaluation,tabales,10.4067/s0718-915x2013000200009,implicitpricesinurbanrealestatevaluation|2013,1,2551,2,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate)",2,3,1,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,housing characteristics prices hedonic,f3338a7eb06d69a36952efd3811a6a59f21e908a,10.1590/1413-8050/ea36708,,Índice de preços Hedônicos para imóveis: Uma análise para o Município de Belo Horizonte.,2015.0,,Luiz Andrés Ribeiro Paixão,"This paper focuses on measuring the price appreciation of several real estate types between 1995 and 2003. We used as sample transaction values of apartments, houses, huts, shops, halls, warehouses and land in Belo Horizonte. The hedonic pricing method was used once its basic premise is that the price of a marketed good is related to its characteristics. As a result, we could realize that the properties valuation was small in the period, in many cases below the inflation rate, due to the poor economy, high interest rates, and housing credit small volume.",4.0,http://www.scielo.br/pdf/ecoa/v19n1/1980-5330-ecoa-19-01-00005.pdf,True,,Economics,2015-03-19,f3338a7eb06d69a36952efd3811a6a59f21e908a,indicedeprecoshedonicosparaimoveisumaanaliseparaomunicipiodebelohorizonte,paixão,10.1590/1413-8050/ea36708,indicedeprecoshedonicosparaimoveisumaanaliseparaomunicipiodebelohorizonte|2015,1,2060,6,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic || preços hedônicos imóveis,3,0,5,1,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,05133875334147dc5ac7d37a0479720642327db6,10.3386/w25701,,"Market Frictions, Arbitrage, and the Capitalization of Amenities",2019.0,,Amine Ouazad; Romain G. Ranciere,"The price-amenity arbitrage is a cornerstone of spatial economics, as the response of land and house prices to shifts in the quality of local amenities and public goods is typically used to reveal households' willingness to pay for amenities. With informational, time, and cash constraints, households' ability to arbitrage across locations with different amenities (demographics, crime, education, housing) depends on their ability to compare locations and to finance the swap of houses. Arbitrageurs with deep pockets and better search and matching technology can take advantage of price dispersions and unexploited trade opportunities. We develop a disaggregated search and matching model of the housing market with endogenously bargained prices, identified on transaction-level data from the universe of deeds for 6,400+ neighborhoods of the Chicago metropolitan area, matched with school-level test scores and geocoded criminal offenses. Price-amenity gradients reflect preferences and the capitalization of trading opportunities, which are arbitraged away in the frictionless limit. Thus the time-variation in hedonic pricing coefficients partly reflects the time variation in search and credit frictions. Our model is able to explain that, between the peak of the housing boom and its trough, the sign of the price-amenity gradient flipped, due to the decline in trading opportunities in lower-amenity neighborhoods and due to the lower capitalization of trading opportunities in house prices.",5.0,http://www.nber.org/papers/w25701.pdf,True,,Economics,2019-03-01,05133875334147dc5ac7d37a0479720642327db6,marketfrictionsarbitrageandthecapitalizationofamenities,ouazad,10.3386/w25701,marketfrictionsarbitrageandthecapitalizationofamenities|2019,1,3000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices || spatial hedonic model housing",2,2,3,4,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic house price,50bf3aceb15c317b23e85a508047a24651ec1462,10.24940/theijbm/2020/v8/i2/bm2001-063,,An Application of House Sales Methodology on Turkish Real Estate,2020.0,International journal of business management,Bouchra Labrahmi; U. Sener,"possible to work around this problem by using regular appraisals performed by real estate professionals, instead of transaction prices. These indices of expertise are particularly important for the commercial real estate market, given limited comparability of goods and reduced number of transactions in this market. However, even if the properties that make up the index are not all traded between two index calculation dates, it remains possible to determine the evolution of a transaction index. The real estate sector is very heterogeneous in nature, which makes it difficult to have a single price approach because the price formation of different goods is influenced by intrinsic characteristics (area, number of rooms, age, etc.) and geographical location of real estate. Similarly, changes in average prices may occur due to changes in the composition of dwellings sold from one period to another and thus result in a price evolution profile that does not reflect the actual conditions of supply and demand in the real estate market. By way of illustration, the transactions may concern mainly high-end apartments during a quarter and mainly involve economic apartments in the following quarter. To limit the effect of one or more of the above-mentioned constraints and depending on the nature and richness of the available databases, several approaches to developing real estate asset price Abstract : The application of house sales methodology in Turkey was investigated where House Price Index (HPI) was used as the dependent variable against New House Price Index (NHPI), Hedonic House Price Index (HHPI), and Existing House Price Index (EHPI). The data used were sourced from the online publications of Turkey statistical bulletin (TUIK) from 2010M1 to 2018M12. Descriptive analysis and regression method were employed, and the study found that new house price index has a positive and significant effect on house price index in Turkey and when NHPI increases with one unit, the house price index will positively increase with the value of 0.416993. The coefficient value of hedonic house price index is 0.115156 with p-value of 0.0640 implying that HHPI has a positive effect but it is not significant at 5% level of significance. The coefficient value of existing house price index is 0.543676 and its p-value is 0.0000 showing that EHPI has a positive and significance effect on house price index in Turkey. It was concluded new house price index and existing house price index could contribute significantly to the house pricing in Turkey. clarifications. policy maker on real estate since their contributions are significance on house in",0.0,http://www.internationaljournalcorner.com/index.php/theijbm/article/download/151426/105554,True,,Business,2020-02-29,50bf3aceb15c317b23e85a508047a24651ec1462,anapplicationofhousesalesmethodologyonturkishrealestate,labrahmi,10.24940/theijbm/2020/v8/i2/bm2001-063,anapplicationofhousesalesmethodologyonturkishrealestate|2020,1,4155,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || real estate hedonic regression",2,3,3,2,1,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ca00e2f7b4ae0ec78bef7df6906587c6b1f4ff0c,10.46254/ap01.20210120,,Compare Python Script and JMP JSL Script on the Housing Price Regression Models,2021.0,Proceedings of the International Conference on Industrial Engineering and Operations Management,Jason Chu; Mason Chen,,0.0,http://www.ieomsociety.org/china2021/papers/120.pdf,True,Conference,,2021-07-09,ca00e2f7b4ae0ec78bef7df6906587c6b1f4ff0c,comparepythonscriptandjmpjslscriptonthehousingpriceregressionmodels,chu,10.46254/ap01.20210120,comparepythonscriptandjmpjslscriptonthehousingpriceregressionmodels|2021,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic housing prices,e85d255e01b1fe86e41a3502eca2f79f337876f4,10.13189/cea.2020.080211,,Optimizing Property Income in Transit Oriented Development: A Case Study of Jakarta TOD,2020.0,,G. Gunawan; M. Berawi; M. Sari,"Transit-oriented development (TOD) can make a significant contribution to the development of Jabodebek Light Rail Transit (LRT) in Indonesia. The increase of property value affected by the existence of LRT station can be considered as one of the potential revenues for TOD projects. This study aims to estimate property income for housing, commercial, and office in the TOD-based areas. Interview is used to obtain the variable characteristics for the hedonic price modeling (HPM) suitability from the benchmark TOD areas with the case study TOD areas at the Jabodebek LRT line. The HPM coefficient of the value increase from the benchmark that has conformance characteristics with the case study will be used to calculate the magnitude of the increase of property value. It is found that due to the influence of the hedonic coefficient, the property income of Ciracas TOD is 414.5 billion rupiah with an internal rate of return (IRR) of 11.7%, the property income of Bekasi Timur TOD is 84 billion rupiah with IRR of 11.01%, the property income of Cibubur TOD is 152.97 billion rupiah with IRR of 19.51 %, and the property income of Jaticempaka TOD is 330.36 billion with IRR of 12.01%.",8.0,http://www.hrpub.org/download/20200430/CEA11-14815553.pdf,True,,Business,,e85d255e01b1fe86e41a3502eca2f79f337876f4,optimizingpropertyincomeintransitorienteddevelopmentacasestudyofjakartatod,gunawan,10.13189/cea.2020.080211,optimizingpropertyincomeintransitorienteddevelopmentacasestudyofjakartatod|2020,1,2687,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic",2,1,1,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9a69793c87e7c893665ba6b7390e4e37f897dcae,10.2298/pan1004405g,,“The Price You Pay”: The Impact of State-Funded Secondary School Performance on Residential Property Values in England,2010.0,,J. Glen; J. Nellis,"This paper examines the relationship between state-funded secondary school performance and local residential property values in seven major English cities. When choosing which secondary school they wish their children to attend, parents will be aware of the school’s performance in Key Stage 3, GCSE and A- level examinations. We suggest that GCSE examination results will be the measure of school performance that parental choice will be most closely correlated with. Therefore, secondary schools with good GCSE examination results will be “oversubscribed” in that more students will wish to attend these schools than there are places available. Schools will then have to develop mechanisms for rationing the available places - central to rationing strategies in English schools at the moment is geographical proximity of the family home to the school of choice. Parents will thus have a strong incentive to purchase houses in the “catchment” area of high performing schools. Our results suggest that this is the case, with high performing schools stimulating a price premium in local residential property markets of between 1% and 3% for each additional 10% point improvement in the pass rate in GCSE examinations.",9.0,http://www.doiserbia.nb.rs/ft.aspx?id=1452-595X1004405G,True,,Economics,2010-10-29,9a69793c87e7c893665ba6b7390e4e37f897dcae,thepriceyoupaytheimpactofstatefundedsecondaryschoolperformanceonresidentialpropertyvaluesinengland,glen,10.2298/pan1004405g,thepriceyoupaytheimpactofstatefundedsecondaryschoolperformanceonresidentialpropertyvaluesinengland|2010,1,2716,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,5,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2d3aac58348167204ad7a0e8c2b780d0c4717d42,10.1111/1540-6229.00557,,The measurement and determinants of single-family house prices,1991.0,,J. Peek; J. A. Wilcox,,120.0,http://www.bostonfed.org/economic/wp/wp1991/wp91_7.pdf,True,,Economics,1991-09-01,2d3aac58348167204ad7a0e8c2b780d0c4717d42,themeasurementanddeterminantsofsinglefamilyhouseprices,peek,10.1111/1540-6229.00557,themeasurementanddeterminantsofsinglefamilyhouseprices|1991,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,apartment prices hedonic,51134d2f7274ff4ef274c35586653f2235a55edc,10.1016/j.jhe.2022.101840,,What is the impact of macroprudential regulations on the Swedish housing market?,2022.0,Journal of Housing Economics,Mats Wilhelmsson,"Housing shortage and urbanization have led to higher housing prices and larger household debt. In order to control the growth of debts, Sweden's financial supervisory authority has introduced several borrower-based macroprudential tools in the last ten years. In 2010, a mortgage loan to value (LTV) ratio was introduced, and in 2016, amortization of 1 percent of the loan balance was mandated by the law if the mortgage was higher than 50 percent of the value of the property. Furthermore, in 2018, the amortization requirement was tightened as all households with a mortgage larger than 4.5 times the annual income was forced to amortize 1 percent of the loan balance. The question is whether the financial supervisory authority's use of macroprudential tools had the intended effect. We are combining a hedonic regression modeling approach with a regression discontinuity design in order to estimate the causal impact on house prices. The effect of the macroprudential tools is estimated on the single-family housing market as well as on the cooperative apartment market. We are using microdata of 1 million housing transactions over the period 2008 and 2019. Our estimates indicate that the amortization requirement in 2016 has a negative impact of around 7 percent on the Swedish housing market and a slightly lower impact of the 2018 amortization requirement. The 2010 LTV-ratio requirement did not have any impact on the prices of dwellings.",4.0,http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263789,True,,Economics,2022-04-01,51134d2f7274ff4ef274c35586653f2235a55edc,whatistheimpactofmacroprudentialregulationsontheswedishhousingmarket,wilhelmsson,10.1016/j.jhe.2022.101840,whatistheimpactofmacroprudentialregulationsontheswedishhousingmarket|2022,1,2948,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment prices hedonic || hedonic house price || hedonic housing prices",2,2,5,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bde987199c56619b2632c9ff10f3df8ca741fd56,10.32468/be.536,,Quality of Life in Urban Neighborhoods in Colombia:The Cases of Bogotá and Medellín,2008.0,,C. Medina; L. Morales; J. Nunez,"We use data from Bogota and Medellin to describe key quality of life indicators of each city and illustrate their spatial segregation at the census sector level and present evidence that the main two Colombian cities are highly spatially segregated according to their education levels and access to education, coverage of public services, households headed by women and key demographic variables like their levels of adolescent pregnancy. Not surprisingly, our estimated quality of life indexes resemble the mentioned segregation patterns in each city. We present evidence that the spatial agglomeration is statistically significant for each of the variables enumerated. We estimate hedonic models of house values and life satisfaction for Bogota and Medellin and find that the importance of the average level of education at the census sector level to determine house prices is striking. We also compare hedonic models for Bogota and Medellin. Bogota is better endowed than Medellin in the variables included in the analysis, in particular, it has higher education levels, and additionally, education is more equally distributed within census sectors. Bogota has also better access to gas, and has in general houses with better conditions. The models based on house values and life satisfaction approaches used in this article lead to similar conclusions in the aggregate when comparing their implied quality of life indexes. Although each approach allows us to rank the specific determinants of quality of life, and these determinant depend on the approach, their implied aggregated indexes suggest that they are just different faces of the same story. From a policy perspective, the evidence suggests that redesigning the current socioeconomic stratification system in a way that still allows reaching the poorest while preventing segregation to deepen, might be among the most important challenge to face in order to improve quality of life in main Colombian cities.",29.0,http://repositorio.banrep.gov.co/bitstream/20.500.12134/5553/1/be_536.pdf,True,,Geography,2008-11-06,bde987199c56619b2632c9ff10f3df8ca741fd56,qualityoflifeinurbanneighborhoodsincolombiathecasesofbogotaandmedellin,medina,10.32468/be.536,qualityoflifeinurbanneighborhoodsincolombiathecasesofbogotaandmedellin|2008,1,3470,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,3,2,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,e7545aa69fd7e3b0b4fddbe8806b08519280a674,10.3141/2245-16,,Analysis of House Prices to Assess Economic Impacts of New Public Transport Infrastructure,2011.0,,Lucía Mejía Dorantes; A. Páez; J. M. Vassallo,"Transportation infrastructure is known to affect the value of real estate property by virtue of changes in accessibility. The impact of transportation facilities is highly localized as well, and it is possible that spillover effects result from the capitalization of accessibility. The objective of this study was to review the theoretical background related to spatial hedonic models and the opportunities that they provided to evaluate the effect of new transportation infrastructure. An empirical case study is presented: the Madrid Metro Line 12, known as Metrosur, in the region of Madrid, Spain. The effect of proximity to metro stations on housing prices was evaluated. The analysis took into account a host of variables, including structure, location, and neighborhood and made use of three modeling approaches: linear regression estimation with ordinary least squares, spatial error, and spatial lag. The results indicated that better accessibility to Metrosur stations had a positive impact on real estate values and that the effect was marked in cases in which a house was for sale. The results also showed the presence of submarkets, which were well defined by geographic boundaries, and transport fares, which implied that the economic benefits differed across municipalities.",51.0,http://oa.upm.es/11288/,True,JournalArticle;Review,Business,,e7545aa69fd7e3b0b4fddbe8806b08519280a674,analysisofhousepricestoassesseconomicimpactsofnewpublictransportinfrastructure,dorantes,10.3141/2245-16,analysisofhousepricestoassesseconomicimpactsofnewpublictransportinfrastructure|2011,1,2789,9,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || house prices proximity hedonic || real estate hedonic regression || spatial hedonic model housing",2,2,2,4,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1c61b9c3d899e9189c440530688c2d26de672f95,10.31891/2307-5732-2022-313-5-265-273,,APPLICATION OF MACHINE LEARNING MODELS FOR PREDICTING PRICES ON THE REAL ESTATE MARKET,2022.0,Herald of Khmelnytskyi National University. Technical sciences,O. Pashkevych; S. Vahschyshchak; A. Boichuk; T. Styslo; Mykola Demchyna,"The work shows the results of using machine learning to forecast price changes in the real estate market. Economic models and factors affecting price formation are analyzed. This data was used as a basis for building a machine learning model. Special attention is focused on the selection of basic data for building such a model. Various types of regression models, which can be implemented in appropriate software environments, have been analyzed. As a result, a statistical model was created for predicting housing prices using linear regression. It is used to determine trends in price changes on the real estate market in the medium and long term. All libraries used in the development of statistical models are examined in detail, their advantages and disadvantages are analyzed. The following steps of model creation were considered and implemented: import of libraries and modules, developed data reading from the dataset, data analysis, cleaning and average statistical evaluation. As a result, linear regression was used to analyze an array of data obtained from an open resource – the real estate sales and rental site Zillow. The real estate objects described on this resource relate to the city of Seattle. Clustering was used according to the main parameters of real estate objects, in particular, area, location, age. In order to evaluate the ratio of various characteristics of the real estate object, 3D modeling was carried out using the Axes3D designer. Based on the analysis, data visualization was performed using various libraries. The analyzed data is displayed on a map using Folium. Data clustering and testing on real data were implemented, which showed quite good price forecast results. The obtained modeling result was checked on real estate objects and it was established that the accuracy of the model is 76%. Such a high result indicates the correctness of its construction and the rationality of using software solutions for its implementation. In the future, it can be used to analyze similar data sets in this field.",0.0,http://journals.khnu.km.ua/vestnik/wp-content/uploads/2022/12/vknu-ts-2022-n5313-265-273.pdf,True,JournalArticle,,2022-10-27,1c61b9c3d899e9189c440530688c2d26de672f95,applicationofmachinelearningmodelsforpredictingpricesontherealestatemarket,pashkevych,10.31891/2307-5732-2022-313-5-265-273,applicationofmachinelearningmodelsforpredictingpricesontherealestatemarket|2022,1,3550,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f2cc6a297bdb1aab7d471ec639e4bab628cd42ef,10.11591/ijeecs.v17.i3.pp1467-1473,,Proposed study on evaluating and forecasting the resident property value based on specific determinants by case base reasoning and artificial neural network approach,2020.0,,S. Diwan,"Real estate forecasting has become an integral part of the larger process of business planning and strategic management in real estate sector. This study covers residential estate markets and concentrates on property types, while previous studies that have considered country wide house price indices. There is a gap identified in the literature which need to study correlations between property types within a region or a city and whether they will provide diversification benefits for real estate investors such as risk reduction per unit of returns. This aim of the paper is to propose and develop a computer assisted real estate property price forecasting model. This proposed framework will examine the current uses of artificial intelligence, particularly combining case base reasoning and artificial neural network, in the business-forecasting field and considers suitable applications in real estate. The methodology consists of five phases: 1) Data gathering b) Data cleaning c) ANN Training d) CBR (Case Base Reasoning) similarity retrieval e) Result retrieval. This research will investigate the influence of residential real estate property characteristics on property values (prices) in global context, it revealed a high positive linear correlation between property characteristics and the property market values; an indication that these characteristics reasonably predict property market values. The results of the study will enable Real Estate Professionals to make fair estimates of the market values of residential real estate properties given the features/characteristics of such housing units.",8.0,http://ijeecs.iaescore.com/index.php/IJEECS/article/download/18752/13532,True,,Computer Science,2020-03-01,f2cc6a297bdb1aab7d471ec639e4bab628cd42ef,proposedstudyonevaluatingandforecastingtheresidentpropertyvaluebasedonspecificdeterminantsbycasebasereasoningandartificialneuralnetworkapproach,diwan,10.11591/ijeecs.v17.i3.pp1467-1473,proposedstudyonevaluatingandforecastingtheresidentpropertyvaluebasedonspecificdeterminantsbycasebasereasoningandartificialneuralnetworkapproach|2020,1,3114,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || residential property price determinants",0,4,3,3,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f2f2e8d4e0c68be7054271b21b3018d65c7e131c,10.20472/efc.2020.014.013,,THE CALCULATION OF REGIONAL INDEX OF DWELLING PRICES IN CROATIA,2020.0,"Proceedings of the 14th Economics & Finance Conference, Lisbon",Tanja Slišković; Tomislav Sekur,": In the existing literature in the field of housing economics, great attention has been paid to modeling housing prices. Residential property prices are generally very important indicator as they may indicate developments in the local economy. Moreover, many existing research have proven that there is a strong interdependence of the movement of housing prices with economic developments. Therefore, different market participants (home owners, potential buyers, lessors, tenants etc.), as well as subjects from broader economic environment (banks, local government, researchers etc.) are interested in trends on housing market, and in particular in housing prices. For that reason, constant improvement of housing prices statistics is very important for all mentioned entities. In Croatia, the issue of housing prices has become topical in the last 15 years due to the turbulent developments in the domestic housing market. Generally, the indicator which is most available to the general public is average price which is published by real estate agencies and advertisers. Such indicator fails to reflect real prices movements since it encompasses the difference in quality of the offered/sold housing units on the market. The more reliable indicator would be one that is “cleared” form the impact that qualitative characteristics of the housing unit have on its price. The most commonly used method for constructing such price indicators is hedonic approach. Hedonic indices provide information about real change in the price between two points of time. The methodology of calculation hedonic housing price index in Croatia has advanced significantly over the last 12 years. In this research we use hedonic model approach to calculate the index of dwelling prices for Croatia, as well as for four regions (The City of Zagreb, Middle and South Adriatic, Istria and North Adriatic and the region Other). The variables which are included in the model are price of the dwelling, location, floor area, number of rooms, the construction year and time of sale. Two different indices based on asked and realized prices are calculated within this research. The comparison between calculated indices shows that there are large price differences between geographically different Croatian dwelling markets. Acknowledgment: This work has been supported by the Croatian Science Foundation under the project no. 6785.",1.0,http://iises.net/proceedings/15th-economics-finance-conference-lisbon/table-of-content?cid=114&iid=013&rid=13128,True,Conference,Geography,,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,thecalculationofregionalindexofdwellingpricesincroatia,slišković,10.20472/efc.2020.014.013,thecalculationofregionalindexofdwellingpricesincroatia|2020,1,3904,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || cites:sirmans2006 || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,4,4,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ef5c7069d1e5054563b1e1ad22a5d9cd55c2bb54,10.1177/0042098012474699,,"Spatial Impacts, Local Labour Market Characteristics and Housing Prices",2013.0,,Liv Osland; I. Thorsen,,29.0,http://hdl.handle.net/11250/225721,True,JournalArticle,Economics,2013-02-11,ef5c7069d1e5054563b1e1ad22a5d9cd55c2bb54,spatialimpactslocallabourmarketcharacteristicsandhousingprices,osland,10.1177/0042098012474699,spatialimpactslocallabourmarketcharacteristicsandhousingprices|2013,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic housing prices,b57c3a75fc6f8929009a6e0fb90c9649b6c36f7f,10.1007/s00181-009-0267-9,,The welfare loss associated to characteristics of the goods: application to housing policy,2010.0,,C. Bilbao; A. Bilbao; J. Labeaga,"The subsidies in kind aimed at heterogeneous goods do not merely generate the traditional excess burden: there is an alternative welfare loss as the distortion also affects non-price characteristics of the good. The reason is that the beneficiary of this type of subsidy has to consume a good with some given characteristics which do not necessarily agree with his choice in the market. The welfare loss analysed here tries to overcome the lack of a correct measure of excess burden in the situations indicate above. Therefore, the paper constitutes an empirical novelty in the theory of excess burden. Moreover, we provide empirical evidence of the welfare loss after estimating a hedonic price model and using predicted prices in a quadratic almost ideal demand system for housing characteristics. The new excess burden is computed under standard assumptions from the theory of rationing. We identify the sources of the losses and we also provide a monetary measure suggesting that, under plausible parameter values, it can be quantitatively high.",7.0,http://hdl.handle.net/10651/8509,True,,Economics,2010-04-01,b57c3a75fc6f8929009a6e0fb90c9649b6c36f7f,thewelfarelossassociatedtocharacteristicsofthegoodsapplicationtohousingpolicy,bilbao,10.1007/s00181-009-0267-9,thewelfarelossassociatedtocharacteristicsofthegoodsapplicationtohousingpolicy|2010,1,2549,2,hedonic housing prices || housing characteristics prices hedonic,2,0,1,1,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",73274579fc23a002132e61e32aa7835203d7c03d,10.25236/ajee.2025.070508,,"Study on Spatial Distribution Characteristics and Influencing Factors of Housing Prices in Longgang District, Shenzhen",2025.0,Academic Journal of Environment & Earth Science,Xiaomin Lao; Xiaohui Zhuo; Bo Xiong; Hui Yin,": Housing prices have a direct impact on both the national economy and people's livelihoods. An in-depth exploration of the spatial distribution and influencing factors of housing prices is crucial for promoting the healthy development of the real estate industry. This study utilizes housing price data and POI (Point of Interest) big data, employing GIS spatial analysis methods to examine the distribution characteristics and spatial correlations of housing prices in Longgang District, Shenzhen. Furthermore, it explores the mechanisms by which various factors contribute to housing price heterogeneity. The findings are as follows: (a) Longgang District exhibits a polycentric spatial structure, with the housing prices peaks located in the western urban core and the eastern Longgang Central City; (b) Housing prices display significant spatial agglomeration, with high-value clusters located in Longcheng, Bantian, Buji, Jihua, and Nanwan Subdistricts, while low-value clusters are found at the junctions of Pingdi, Baolong, Longcheng, and Longgang Subdistricts; (c) The relative influence of factors on housing prices within the district ranks as follows: economic factors > transportation factors > neighborhood environment factors.",0.0,http://francis-press.com/uploads/papers/9aBVmz1ZgJHPApzc55hQCmF7sIBwi6tlGWbaFH6C.pdf,True,JournalArticle,,,73274579fc23a002132e61e32aa7835203d7c03d,studyonspatialdistributioncharacteristicsandinfluencingfactorsofhousingpricesinlonggangdistrictshenzhen,lao,10.25236/ajee.2025.070508,studyonspatialdistributioncharacteristicsandinfluencingfactorsofhousingpricesinlonggangdistrictshenzhen|2025,1,2741,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",094f75a7a322f1899f24f2132422bde05e3d8b2b,10.1177/2399808318784017,,Distance metric choice can both reduce and induce collinearity in geographically weighted regression,2018.0,Environment and Planning B Urban Analytics and City Science,A. Comber; Khánh Chi; M. Q. Huy; Quan Nguyen; Binbin Lu; P. Hoang; P. Harris,"This paper explores the impact of different distance metrics on collinearity in local regression models such as geographically weighted regression. Using a case study of house price data collected in Hà Nội, Vietnam, and by fully varying both power and rotation parameters to create different Minkowski distances, the analysis shows that local collinearity can be both negatively and positively affected by distance metric choice. The Minkowski distance that maximised collinearity in a geographically weighted regression was approximate to a Manhattan distance with (power = 0.70) with a rotation of 30°, and that which minimised collinearity was parameterised with power = 0.05 and a rotation of 70°. The results indicate that distance metric choice can provide a useful extra tuning component to address local collinearity issues in spatially varying coefficient modelling and that understanding the interaction of distance metric and collinearity can provide insight into the nature and structure of the data relationships. The discussion considers first, the exploration and selection of different distance metrics to minimise collinearity as an alternative to localised ridge regression, lasso and elastic net approaches. Second, it discusses the how distance metric choice could extend the methods that additionally optimise local model fit (lasso and elastic net) by selecting a distance metric that further helped minimise local collinearity. Third, it identifies the need to investigate the relationship between kernel bandwidth, distance metrics and collinearity as an area of further work.",29.0,http://eprints.whiterose.ac.uk/131530/1/EPB_Comber_accepted.pdf,True,JournalArticle,Mathematics,2018-07-11,094f75a7a322f1899f24f2132422bde05e3d8b2b,distancemetricchoicecanbothreduceandinducecollinearityingeographicallyweightedregression,comber,10.1177/2399808318784017,distancemetricchoicecanbothreduceandinducecollinearityingeographicallyweightedregression|2018,1,3101,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",5654b870d4405044056c4067b2b3fc923b7165be,10.1007/s11769-023-1341-5,,"Identifying Spatial Heterogeneity in the Effects of High-Tech Firm Density on Housing Prices: Evidence from Guangdong-Hong Kong-Macao Greater Bay Area, China",2023.0,Chinese Geographical Science,Yang Wang; Kang-Min Wu; Hong'ou Zhang; Yi Liu; Xiao-Li Yue,"Innovation capitalization is a new concept in innovation geography research. Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect. However, few studies investigate the spatial heterogeneity of innovation capitalization. Thus, case verification at the urban agglomeration scale is needed. Therefore, this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale. Examining the Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA), China as a case study, the study investigated the spatial heterogeneity of the influence of high-tech firms, representing innovation, on housing prices. This work verified the spatial heterogeneity of innovation capitalization. The study constructed a data set influencing housing prices, comprising 11 factors in 5 categories (high-tech firms, convenience of living facilities, built environment, the natural environment, and the fundamentals of the districts) for 419 subdistricts in the GHMGBA. On the global scale, the study finds that high-tech firms have a significant and positive influence on housing prices, with the housing price increasing by 0.0156% when high-tech firm density increases by 1%. Furthermore, a semi-geographically weighted regression (SGWR) analysis shows that the influence of high-tech firms on housing prices has spatial heterogeneity. The areas where high-tech firms have a significant and positive influence on housing prices are mainly in the Guangzhou-Foshan metropolitan area, western Shenzhen-Dongguan, north-central Zhongshan-Nansha district, and Guangzhou—all areas with densely distributed high-tech firms. These results confirm the spatial heterogeneity of innovation capitalization and the need for further discussion of its scale and spatial limitations. The study offers implications for relevant GHMGBA administrative authorities for spatially differentiated development strategies and housing policies that consider the role of innovation in successful urban development.",7.0,http://egeoscien.neigae.ac.cn/EN/10.1007/s11769-023-1341-5,True,,,2023-03-23,5654b870d4405044056c4067b2b3fc923b7165be,identifyingspatialheterogeneityintheeffectsofhightechfirmdensityonhousingpricesevidencefromguangdonghongkongmacaogreaterbayareachina,wang,10.1007/s11769-023-1341-5,identifyingspatialheterogeneityintheeffectsofhightechfirmdensityonhousingpricesevidencefromguangdonghongkongmacaogreaterbayareachina|2023,1,3631,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,1,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",362f7a72c5356bc2ceec9eca24ad9b45de1130c4,10.24136/oc.2019.031,,Compensation due to land use restrictions: the case of limited use area in the vicinity of Polish airports,2019.0,,I. Foryś; Michał Głuszak; Jan Konowalczuk,"Research background: The economic benefits that arise with the development of airport infrastructure are accompanied by negative externalities. Legal, technical and institutional instruments are used to mitigate or limit these effects. It involves state intervention in the use of real estate located in the vicinity of the airport, and the cost of such an intervention. On the other hand, as a result of state interventions, real estate market mechanisms are distorted. The balance on the market, prices and as a result the number of transactions is changing. Purpose of the article: The study evaluates adaptive efficiency, which is known as the ability of the real estate market system to adapt to the purpose of public intervention. The effectiveness of state intervention is measured as the difference between market transaction costs and costs after intervention. The former means the full coverage of all individual claims of property owners at market prices. However, after the intervention, these are costs of compensation and litigation (judicial, expert opinions, provisions for payment of damages), as well as the risk of the airport's insolvency. The state intervention system is also assessed through the prism of the lack of a methodology for assessing damages and subjective claims of property owners. The article focuses on the effects of the negative impact of airport noise resulting in limitations to residential buildings’ usability and depreciation of their market value. The study is based on the example of one regional airport. Methods: The study evaluates the current compensation model related to the introduction of Limited Land Use Areas around airports in Poland, based on Poznan-Lawica airport case study. In the empirical part of the paper, we use regression analysis to examine the value of compensations for loss of property value ruled by courts, and duration analysis to explore court procedure duration time. Findings & Value added: This research is one of the important basic research on socio-spatial connection near an airport in Poland. We argue that the current prac-tice related to compensation ruled by courts has substantial flaws (including the methodical error regarding the valuation of claims, where acoustic damage and value loss claims are treated as unrelated, thus both compensations are independently assessed). With the help of the Cox model, we demonstrate that the long distance from the airport and the location within the LUA increase the likelihood of court proceedings ending. The results are important due to the pending disputes and the costs threatening the functioning of airports in Poland.",3.0,http://economic-research.pl/Journals/index.php/oc/article/download/1736/1627,True,,Business,2019-12-29,362f7a72c5356bc2ceec9eca24ad9b45de1130c4,compensationduetolanduserestrictionsthecaseoflimiteduseareainthevicinityofpolishairports,foryś,10.24136/oc.2019.031,compensationduetolanduserestrictionsthecaseoflimiteduseareainthevicinityofpolishairports|2019,1,4159,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3265c9e0bcce594c113217915320b22ce5b86518,10.56557/jobari/2022/v28i27608,,ANALYSIS OF INFLUENCING FACTORS OF HOUSE PRICE IN CHANGSHA BASED ON THE MULTIPLE LINEAR REGRESSION MODEL,2022.0,Journal of basic and applied research international,Shimeng Gong; Li-Juan Hu,"House price has always been a hot issue concerned by the people. In recent years, the rapid growth of house prices across the country has become an important factor restricting economic development. Changsha has attracted much attention and reference because it has achieved good results in the regulation of the real estate market. By adopting the multiple linear regression model, we in this paper quantitatively analyze the factors that affect house prices in Changsha from four aspects: per capita disposable income, per capita consumption expenditure, total population at the end of the year, and investment in real estate development. It is helpful to grasp the development law of Changsha's real estate market and provide a certain reference basis for the regulation of the real estate market in other cities.",0.0,http://dx.doi.org/10.56557/jobari/2022/v28i27608,True,JournalArticle,,2022-04-06,3265c9e0bcce594c113217915320b22ce5b86518,analysisofinfluencingfactorsofhousepriceinchangshabasedonthemultiplelinearregressionmodel,gong,10.56557/jobari/2022/v28i27608,analysisofinfluencingfactorsofhousepriceinchangshabasedonthemultiplelinearregressionmodel|2022,1,2316,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5c5c506192e9ac6e15bbe6d910ab1dddb0d215cd,10.54097/fh050a12,,Investigating Factors Affecting House Prices - Chicago as an Example,2024.0,Highlights in Science Engineering and Technology,Chen Qian,"House is a very important necessity in human society and its price is also highly concerned. With the rapid development of the society, the house price is also rising rapidly, how to find some favourable ways to buy a house is very important for people. This paper will firstly analyse the relationship between house price and some factors by grouping them with box-and-line diagrams, then further calculate the correlation coefficients between the factors and house price, and further correlate the factors with house price. After that, this paper will use multiple linear regression to analyse the relationship using house price as y and other eight variables as x. After that, this paper will conclude that Space, Garage, Bathroom, Lot will have a substantial positive impact on the price. The price will have a significant negative effect due to the bedroom. But Room, Condition, Tax will not have any effect on Price. Finally, this paper will summaries the results of the analysis and provide some preferred methods for people to buy houses.",0.0,http://dx.doi.org/10.54097/fh050a12,True,JournalArticle,,2024-03-29,5c5c506192e9ac6e15bbe6d910ab1dddb0d215cd,investigatingfactorsaffectinghousepriceschicagoasanexample,qian,10.54097/fh050a12,investigatingfactorsaffectinghousepriceschicagoasanexample|2024,1,2546,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ba46f693643852d3929f18a12745e56eff4a2421,10.54097/92raxz19,,Forecast of Housing Prices in Beijing and Analysis of Influencing Factors,2024.0,Highlights in Science Engineering and Technology,Yuxi Liu,"Real estate is an important part of China's real economy, and housing prices are one of the basic elements in the entire real estate market. This paper analyzes the housing price data of Beijing, China, and firstly uses a multiple linear regression model to determine whether the factors that have an important impact on China's housing prices also play an effect on Beijing's housing prices. And then this paper analyzes how much each factor has an impact on Beijing's housing prices. Additionally, the ARIMA model is used to predict the trend of housing prices in Beijing in the next 10 months according to the monthly data of the past 10 years. Finally, the change point analysis method is used to study the effect of the Corona Virus Disease 2019 (COVID-19) on housing prices in Beijing. It aims to understand the operating mechanism behind housing prices and give policymakers, investors and home buyers more suggestions and instruction.",0.0,http://dx.doi.org/10.54097/92raxz19,True,JournalArticle,,2024-03-29,ba46f693643852d3929f18a12745e56eff4a2421,forecastofhousingpricesinbeijingandanalysisofinfluencingfactors,liu,10.54097/92raxz19,forecastofhousingpricesinbeijingandanalysisofinfluencingfactors|2024,1,2442,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d0376066fc82eec8da102d891dc028fab921c9cf,10.5220/0012991000004601,,Research on Housing Prices Forecasts Based on A Multiple Linear Regression Model,2024.0,"Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy",Yiwen Wang,,0.0,http://dx.doi.org/10.5220/0012991000004601,True,Conference,,,d0376066fc82eec8da102d891dc028fab921c9cf,researchonhousingpricesforecastsbasedonamultiplelinearregressionmodel,wang,10.5220/0012991000004601,researchonhousingpricesforecastsbasedonamultiplelinearregressionmodel|2024,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3db0010c979648962d30e2e5c0b1cbdac492f127,10.33543/j.1401.3440,,CALCULATING THE PRICE OF COMMERCIAL REAL ESTATE IN THE CZECH REPUBLIC,2024.0,AD ALTA: 14/01,Karel Brůha; Vojtěch Sloup; Iva Lorencová,"Property prices have surged recently, mainly due to limited real estate supply amid high demand supported by a large monetary base. This has driven up rental prices, especially in major cities compared to their peripheries. This manuscript examines rental prices in Prague, Brno, Ostrava, Pilsen, and České Budějovice from March to October 2023. It uses distribution functions, correlation coefficients, contour heat maps, and parameterized regression to define a probability price interval for office properties, identify a positive correlation between area and rental prices, and develop an algorithm for setting appropriate rental prices. The findings are useful for practitioners, real estate agents, and government institutions, though they are closely linked to macroeconomic variables. Future potential options are discussed.",0.0,http://dx.doi.org/10.33543/j.1401.3440,True,JournalArticle,,2024-06-30,3db0010c979648962d30e2e5c0b1cbdac492f127,calculatingthepriceofcommercialrealestateintheczechrepublic,brůha,10.33543/j.1401.3440,calculatingthepriceofcommercialrealestateintheczechrepublic|2024,1,2332,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,1,1,1,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,prix hédoniques logement,7479faf4f8f34c48a731da73a302614ced1386e5,10.4000/books.pufr.1110,,Capitalisation immobilière des gains d’accessibilité : étude de cas sur l’agglomération lyonnaise,2006.0,Quatre ans de recherche urbaine 2001-2004. Volume I,Ghislaine Deymier,"Les infrastructures de transport generent des gains d'accessibilite qui determinent l'evolution de la forme urbaine. L'accroissement des vitesses de circulation et, par consequent, la diminution des temps de deplacement contribuent, dans le contexte urbain, a l'augmentation des destinations accessibles et donc a l'allongement des distances de deplacement. Lors de l'evaluation des effets d'une infrastructure de transport urbaine, il convient alors d'analyser les gains d'accessibilite plutot que les gains de temps afin de tenir compte de la relative constance des budgets-temps de transport. L'articulation entre le systeme de transport et les modes d'occupation des sols se fonde sur la relation entre les differentiels d'accessibilite et de prix immobiliers. Le marche immobilier est l'un des elements structurants de l'espace urbain. Par consequent, il est important de comprendre comment se forme le prix du logement et comment les activites s'organisent au sein de la ville en fonction des variations de ce prix. Le peripherique Nord de Lyon, a travers son inscription a la fois spatiale au cœur du marche immobilier de l'agglomeration lyonnaise et temporelle, offre un cadre ideal d'apprehension de la dimension spatio-temporelle de la capitalisation immobiliere des effets engendres par une amelioration du reseau de transport. L'analyse des prix hedoniques, amelioree par la prise en compte du degre de dependance spatiale entre les observations constitue la base de la conceptualisation de notre modele de valorisation des benefices engendres par le peripherique Nord de Lyon dans les prix des logements sur les quatre segments du marche immobilier de l'agglomeration lyonnaise (individuel, collectif, neuf et ancien). L'integration de la dimension temporelle de la capitalisation permet de mettre en exergue, d'une part, le phenomene des anticipations en tenant compte de la lenteur du processus de mise en service de l'infrastructure et, d'autre part, fait apparaitre un phenomene d'apprentissage des gains d'accessibilite par un etalement de la zone de capitalisation des l'ouverture de l'ouvrage.",11.0,http://books.openedition.org/pufr/1110,True,,Political Science,,7479faf4f8f34c48a731da73a302614ced1386e5,capitalisationimmobilieredesgainsdaccessibiliteetudedecassurlagglomerationlyonnaise,deymier,10.4000/books.pufr.1110,capitalisationimmobilieredesgainsdaccessibiliteetudedecassurlagglomerationlyonnaise|2006,1,3613,1,prix hédoniques logement,0,3,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",aa4dd21ef89562a22c3e9bdb3786f1a024bb4334,10.48550/arxiv.2203.14751,2203.14751,Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach,2022.0,arXiv.org,Isaiah Hull; Anna Grodecka-Messi,"How do property prices respond to changes in local taxes and local public services? Attempts to measure this, starting with Oates (1969), have suffered from a lack of local public service controls. Recent work attempts to overcome such data limitations through the use of quasi-experimental methods. We revisit this fundamental problem, but adopt a different empirical strategy that pairs the double machine learning estimator of Chernozhukov et al. (2018) with a novel dataset of 947 time-varying local characteristic and public service controls for all municipalities in Sweden over the 2010-2016 period. We find that properly controlling for local public service and characteristic controls more than doubles the estimated impact of local income taxes on house prices. We also exploit the unique features of our dataset to demonstrate that tax capitalization is stronger in areas with greater municipal competition, providing support for a core implication of the Tiebout hypothesis. Finally, we measure the impact of public services, education, and crime on house prices and the effect of local taxes on migration.",2.0,http://arxiv.org/pdf/2203.14751,True,JournalArticle,Economics;Computer Science,2022-03-23,aa4dd21ef89562a22c3e9bdb3786f1a024bb4334,measuringtheimpactoftaxesandpublicservicesonpropertyvaluesadoublemachinelearningapproach,hull,10.48550/arxiv.2203.14751,measuringtheimpactoftaxesandpublicservicesonpropertyvaluesadoublemachinelearningapproach|2022,1,2618,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",985bdf3836772990583bc5befcca3813e168b892,10.19030/jber.v9i5.9026,1306.6137,Effects Of Zoning On Housing Option Value,2011.0,,Prathamesh Muzumdar,The research explores the subject of zoning effect on price value of a house in a certain designated zone. Zoning is defined as a land under planned use. This results in price value of the land. As the area of the land decreases an increase in price occurs. This research does not specify the magnitude of a certain unit by which a housing price will increase or decrease but it estimates the housing option value. This research helps the policy makers to improve long term policy decisions. This research will showcase zoning effects on housing option value for the specific zones in the city of Normal. The observations are taken for different parcels of total neighborhoods in the city of Normal. This research will use hedonic model for conducting a regression analysis on the subject. The hypothesis taken into consideration before starting the research is to prove that more 50 percent variation in the housing price is affected by the zoning characteristics. This research will try to prove the hypothesis by comparing the assessed values with the predicted values from the model.,4.0,http://arxiv.org/abs/1306.6137,True,,Economics;Mathematics,2011-05-01,985bdf3836772990583bc5befcca3813e168b892,effectsofzoningonhousingoptionvalue,muzumdar,10.19030/jber.v9i5.9026,effectsofzoningonhousingoptionvalue|2011,1,2587,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,2,2,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",9202c177e9b8160cfb4c6ffd15a80ded3dbc5be2,10.5399/osu/jtrf.43.1.741,,Effects of Light and Commuter Rail Transit on Land Prices: Experiences in San Diego County,2010.0,,R. Cervero,"Using hedonic price models, appreciable land-value premiums were found for multiple land uses in different rail corridors of San Diego County. The most appreciable benefits were for condominiums and single-family housing near commuter-rail stations in the north county, multifamily housing near light-rail stations, and commercial properties near downtown commuterrail stations and light-rail stops in the Mission Valley. Elsewhere, commercial properties accrued small or even negative capitalization benefits. Pro-development policies, worsening traffic congestion, and a generally healthy economy are thought to have generally boosted land values in San Diego County, though impacts are corridor- and land-use specific.",82.0,http://ageconsearch.umn.edu/record/206725,True,,Geography,2010-10-13,9202c177e9b8160cfb4c6ffd15a80ded3dbc5be2,effectsoflightandcommuterrailtransitonlandpricesexperiencesinsandiegocounty,cervero,10.5399/osu/jtrf.43.1.741,effectsoflightandcommuterrailtransitonlandpricesexperiencesinsandiegocounty|2010,1,2221,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",2,1,5,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bf84a226d199471074feb325dabe786163df315b,10.68479/001c.8515,,The Use of Alternative Measures of Accessibility in Estimating Spatial Relationships,1978.0,Journal of Regional Analysis & Policy,G. McDougall,"Examination of the literature discussing the effects of accessibility on the spatial distribution of economic phenomenon (in particular property values and rents, and population densities) reveals that there is no clear consensus among investigators as to what observed variable adequately or appropriately measures the concept of accessibility or spatial proximity. This paper summarizes the results from some simple comparisons when alternative measures of accessibility are used to statistically describe characteristics of house value, rent, and density gradients. It is argued that high correlations among alternative operational measures of accessibility are not sufficient to justify their interchange in commonly applied estimation",1.0,http://ageconsearch.umn.edu/record/128324,True,JournalArticle,Economics,1978-07-01,bf84a226d199471074feb325dabe786163df315b,theuseofalternativemeasuresofaccessibilityinestimatingspatialrelationships,mcdougall,10.68479/001c.8515,theuseofalternativemeasuresofaccessibilityinestimatingspatialrelationships|1978,1,2239,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,1,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic property valuation,b8bba3e2b7c7a12f68a848e56a384a13c7204dee,10.1111/iere.12389,,LEARNING IN A HEDONIC FRAMEWORK: VALUING BROWNFIELD REMEDIATION,2019.0,International Economic Review,La-La Ma,"Incomplete information in property value hedonic models can bias estimates of marginal willingness to pay (MWTP). Using brownfield remediation as an application, this article recovers hedonic values from a dynamic neighborhood choice framework that allows households to learn about brownfield contamination in a Bayesian fashion before choosing where to live. I find that ignoring learning yields nontrivial biases to the MWTP estimate. This has important implications for hedonic valuation if agents are imperfectly informed. Estimates are used to calculate information’s value had it been withheld from the public and to assess heterogeneity in information’s value along site and homebuyer demographics.",37.0,,False,,Economics,2019-02-20,b8bba3e2b7c7a12f68a848e56a384a13c7204dee,learninginahedonicframeworkvaluingbrownfieldremediation,ma,10.1111/iere.12389,learninginahedonicframeworkvaluingbrownfieldremediation|2019,1,1705,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic property valuation",2,1,2,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ed70211601abf752ae75e8476f975841dd967b22,10.1111/jiec.12957,,An integrated model of real estate market responses to coastal flooding,2019.0,Journal of Industrial Ecology,Handi Chandra‐Putra; C. Andrews,"Understanding and improving how humans adapt to climate change are priorities in our research community, and coastal settlements are good places to study adaptation. Severe storm events and sea‐level rise are threatening coastal communities with increasing levels of flood damage. Because ownership of coastal assets is distributed among many private and public actors, both individual property owners and public officials must take adaptive actions. This paper introduces an integrated agent‐based and hedonic pricing modeling system to simulate coastal real estate market performance under non‐equilibrium conditions that reflect the effects of storm events. The modeling system, which is used for policy analysis, is calibrated to conditions in two towns in Monmouth County, New Jersey, USA, which were badly damaged by Hurricane Sandy in 2012. The key findings are that (a) coastal real estate markets capitalize flood risk into property values but this discount diminishes rapidly as time passes between storm events, and (b) there is a distinct equity versus efficiency tradeoff in designing public policies to reduce the cost to society of coastal flooding. Stringent regulation of building practices reduces flood damage but drives away poorer home buyers and owners, whereas informational and incentive‐based policies are fairer but less effective. Hands‐off, market‐based retreat from risky areas is socially costly but allows less wealthy people to remain at the shore, albeit in vulnerable situations. Managed retreat should emphasize improved recreational access to coastal amenities while discouraging people from living there.",26.0,,False,,Business,2019-11-05,ed70211601abf752ae75e8476f975841dd967b22,anintegratedmodelofrealestatemarketresponsestocoastalflooding,chandra‐putra,10.1111/jiec.12957,anintegratedmodelofrealestatemarketresponsestocoastalflooding|2019,1,2641,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate)",2,1,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 crossref,implicit prices housing attributes,,10.1080/00420989550012997,,Determinants of Bargaining Outcome in Single-family Housing Transactions: An Empirical Examination,1995.0,Urban Studies,Shunfeng Song,"Using a hedonic regression model, this paper examines determinants of bargaining outcome in single-family housing transactions. It divides potential factors into three broad categories: dwelling attributes, site amenities, and seller's and buyer's characteristics. Results with use of 1990 data for the Reno-Sparks area in Nevada show that bargaining outcome is determined by seller's asking price, buyer's household income, and some of the dwelling attributes and site amenities. This paper concludes that factors affecting housing price do not necessarily affect bargaining outcome, and first-time home buyers do not statistically bargain less than repeat home buyers.",22.0,,,journal-article,,,doi_10_1080_00420989550012997,determinantsofbargainingoutcomeinsinglefamilyhousingtransactionsanempiricalexamination,song,10.1080/00420989550012997,determinantsofbargainingoutcomeinsinglefamilyhousingtransactionsanempiricalexamination|1995,1,1670,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic || implicit prices housing attributes",2,2,5,5,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",905a3854a270ed33499b6e478e8376f155a10423,10.1108/17538271311306020,,The importance of unobserved attributes in hedonic house price models,2013.0,,Liv Osland,,21.0,,False,,Economics,2013-11-10,905a3854a270ed33499b6e478e8376f155a10423,theimportanceofunobservedattributesinhedonichousepricemodels,osland,10.1108/17538271311306020,theimportanceofunobservedattributesinhedonichousepricemodels|2013,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || implicit prices housing attributes",2,1,1,1,0,7,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic housing prices,e09eb66a0e6faeeb45b54c4fe899a40e616a79b4,10.1016/j.jtrangeo.2018.05.022,,Externalities of auto traffic congestion growth: Evidence from the residential property values in the US Great Lakes megaregion,2018.0,Journal of Transport Geography,Jangik Jin; Peter C Rafferty,"Traffic congestion is getting worse and commuting cost is getting higher in every major metropolitan area. In this study, we attempt to explore the negative externalities of traffic congestion focusing on housing prices. We use a unique transportation dataset, the National Performance Management Research Data Set (NPMRDS), which contains information on every 5 min travel time across the extensive region to measure traffic congestion at the local level. In order to estimate the effect of traffic congestion on residential property values, we use a dynamic hedonic price model with a monthly panel dataset. Our findings show that while there is no significant relationship between congestion growth and housing price growth in rural and non-metro areas, the effects of congestion growth on housing price growth in urban and metro areas are obvious. We verify that traffic congestion functions as a disamenity in a local neighborhood. Transportation policies that aim to relieve traffic congestion should consider that severe traffic congestion may have an adverse effect on local property values.",18.0,,False,,Business,2018-06-01,e09eb66a0e6faeeb45b54c4fe899a40e616a79b4,externalitiesofautotrafficcongestiongrowthevidencefromtheresidentialpropertyvaluesintheusgreatlakesmegaregion,jin,10.1016/j.jtrangeo.2018.05.022,externalitiesofautotrafficcongestiongrowthevidencefromtheresidentialpropertyvaluesintheusgreatlakesmegaregion|2018,1,2099,1,hedonic housing prices,2,2,2,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,spatial hedonic model housing,5b5e03da5dba3d36048e9a53e6fd71851318f386,10.1016/j.seps.2020.100820,,Evaluating the value of an entrepreneurial city with a spatial hedonic approach: A case study of London,2020.0,,Hiroki Nakamura,"Abstract This study assesses the value of a city by using the housing price function with a geographically weighted regression model, including various social, economic, and environmental factors. To assess these values, various specific data scores—such as those related to ethnic groups, green areas, crime rates, education, unemployment rates, number of entrepreneurs, and environmental quality—were considered in a case study of London. The results indicate that some variables such as time to city center and entrepreneurship have a positive impact on the local areas’ value in London, as shown by housing prices, while those related to unemployment have a negative impact. Moreover, although the London city center has benefitted more in terms of value than its outskirts have, a few specific policies related to startups and entrepreneurship have succeeded in connecting these areas to existing companies and entrepreneurs. In general, large cities may be better equipped to promote such startup and entrepreneurship policies under local industry plans for future development.",15.0,,False,,Business,2020-09-01,5b5e03da5dba3d36048e9a53e6fd71851318f386,evaluatingthevalueofanentrepreneurialcitywithaspatialhedonicapproachacasestudyoflondon,nakamura,10.1016/j.seps.2020.100820,evaluatingthevalueofanentrepreneurialcitywithaspatialhedonicapproachacasestudyoflondon|2020,1,2083,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic housing prices || spatial hedonic model housing",2,1,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4e639bf1cfcb6b44043021eb5634e0e78694991e,10.1177/0275074020933968,,Bottom-Up or Top-Down Local Service Delivery? Assessing the Impacts of Special Districts as Community Governance Model,2021.0,,Brian Y. An,"This article assesses the impacts of bottom-up local governing institutions relative to top-down bureaucracies in local service delivery. Community services districts (CSDs) in California, a class of special districts that provides various neighborhood-level services, are examined to answer this question. An innovative characteristic of this institutional form is that it is residents who create them through a grass-root collective action to achieve a bottom-up governance structure, after opting out from a county service system. With changes in residential property values as performance metrics, the quantitative analysis utilizes district formation events and features a hedonic difference-in-difference regression. The results show that the creation of CSDs produces more significant impacts on property values than county authorities do. Yet, the effects are heterogeneous across the communities when the analysis is further drilled down to each district. The exploratory qualitative case study then uses interview data with district managers and document analysis to unveil what administrative factors explain the success and failure trajectory of bottom-up institution management. The case study identifies such factors as critical codeterminants, including managerial and board leadership, clarity of a problem statement, public support, and intergovernmental coordination with county and state agencies.",14.0,,False,JournalArticle,Business,2021-01-01,4e639bf1cfcb6b44043021eb5634e0e78694991e,bottomuportopdownlocalservicedeliveryassessingtheimpactsofspecialdistrictsascommunitygovernancemodel,an,10.1177/0275074020933968,bottomuportopdownlocalservicedeliveryassessingtheimpactsofspecialdistrictsascommunitygovernancemodel|2021,1,2416,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2,1,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5fa5ffbceda80cb4eadc65e47af1dbdf52eecff1,10.1007/978-3-030-58814-4_21,,Evaluating the Urban Quality Through a Hybrid Approach: Application in the Milan (Italy) City Area,2020.0,Communication Systems and Applications,A. Oppio; M. Bottero; Federico Dell’Anna; Marta Dell'Ovo; L. Gabrielli,"Urban planning can support the optimal exploitation of the resources available in an urban system with specific reference to the accessibility of goods and services by an increase of the quality of the public space. In this perspective, knowing the economic effects generated by urban planning choices can be fundamental to guide decision-making processes. For this purpose, a research work which proposes a method for defining a synthetic index through the Multi-Attribute Value Theory to describe urban quality is presented. The synthetic index proposed in this study reflects the public open spaces, accessibility and provision of services qualities of the urban context. Besides, this study estimates these advantages through the hedonic prices method, analyzing the residential market. The combined methodology has been applied in detail to three districts located in the Municipality of Milan (Italy). The simultaneous reading of the indices used to spatialise the urban quality levels allows identifying the extrinsic characteristics of the three neighbourhoods analyzed. Considering the changes induced on house prices, the results of the hedonic models show that the economic impacts generated by low levels of urban quality are significant. Accordingly, the policies that aim to transform the existing urban fabric become fundamental for the creation of economic and social value.",5.0,,False,JournalArticle,Computer Science;Business,2020-08-26,5fa5ffbceda80cb4eadc65e47af1dbdf52eecff1,evaluatingtheurbanqualitythroughahybridapproachapplicationinthemilanitalycityarea,oppio,10.1007/978-3-030-58814-4_21,evaluatingtheurbanqualitythroughahybridapproachapplicationinthemilanitalycityarea|2020,1,2390,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,1,2,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic house price,1e904244e524160b9e63bc29b60aefb773acae55,10.1108/jpif-11-2018-0088,,Income capitalisation method and hedonic model: an integrated approach,2019.0,Journal of Property Investment & Finance,G. Lisi," Purpose The purpose of this paper is to provide an integrated approach that combines the two methods usually used in the real estate appraisals, namely, the income capitalisation method and the hedonic model. Design/methodology/approach In order to pull out the link between the income capitalisation approach and the hedonic model, the standard hedonic price function is introduced into the basic model of income capitalisation instead of the house market value. It follows that, from the partial derivative, a direct relation between hedonic prices and discount rate can be obtained. Finally, by using the close relationship between income capitalisation and direct capitalisation, a mathematical relation between hedonic prices and capitalisation rate is also obtained. Findings The developed method allows to estimate the capitalisation rate using only hedonic prices. Indeed, selling and hedonic prices incorporate all of the information required to correctly estimate the capitalisation rate. Furthermore, given the close relation among going-in and going-out capitalisation rates and discount rate, the proposed method could also be useful for determining both the going-out capitalisation rate and the discount rate. Practical implications Obviously, it is always preferable to estimate the capitalisation rate by just using comparable transactional data. Nevertheless, the method developed in this paper is especially useful when: the rental income data are missing and/or not entirely reliable; the data on rental income and house price are related to different homes; the capitalisation rate, in fact, should compare the rent and value of identical homes. In these cases, therefore, the method can be a valuable alternative to direct estimation. Originality/value The large and important literature on real estate economics and real estate appraisal neglects the relationship between hedonic prices and capitalisation rate, thus considering the hedonic model and the income capitalisation approach as two separate and alternative methods. This paper, instead, shows that integration is possible and relatively simple. ",3.0,,False,,Economics,2019-02-21,1e904244e524160b9e63bc29b60aefb773acae55,incomecapitalisationmethodandhedonicmodelanintegratedapproach,lisi,10.1108/jpif-11-2018-0088,incomecapitalisationmethodandhedonicmodelanintegratedapproach|2019,1,3138,3,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price",2,2,4,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c48a29f280518ee9adc5e973f5410bbac3ecbc42,10.1108/jerer-07-2024-0054,,A clustering methodology for constructing local housing price indexes in France,2025.0,Journal of European Real Estate Research,Carmelo Micciche; Michel Baroni; Pierre Vidal," This paper introduces a novel methodology for computing local housing price indices at a national scale. The proposed approach addresses the limitations of traditional aggregated indices by capturing geographic heterogeneity in housing price dynamics. The methodology relies on the application of a heuristic for the max-p-regions problem, a regionalization process that stratifies a country’s housing market into geographically consistent clusters, followed by the application of hedonic regressions made robust to noisy and sparse data. Applied to French housing data from 2010 to 2019, this method produces 876 consistent submarkets and their corresponding indices. Our findings demonstrate the method's ability to reveal spatial patterns often obscured by national indices. While it captures trends at an aggregated level similar to official statistics, it also uncovers a much more contrasted trajectory of price changes across markets. The methodology developed in this paper enables the construction of the most precise index database in France. A highly accurate index is created for each part of the country. This database allows for an analysis of housing price evolutions in the French market that has never been done before. This analysis reveals much more nuanced price developments than those described by existing benchmark indices because of the lack of localized data. ",1.0,,False,JournalArticle,,2025-12-02,c48a29f280518ee9adc5e973f5410bbac3ecbc42,aclusteringmethodologyforconstructinglocalhousingpriceindexesinfrance,micciche,10.1108/jerer-07-2024-0054,aclusteringmethodologyforconstructinglocalhousingpriceindexesinfrance|2025,1,2411,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,1,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,machine learning hedonic pricing housing,5a90945c016152e66bebd16cd224f6064e08fba7,10.1109/compsac61105.2024.00382,,Explicable Machine Learning Models Using Rich Geospatial Data,2024.0,Annual International Computer Software and Applications Conference,A. Bramson; Masayoshi Mita,"Machine learning includes a variety of analysis tools with improved predictive performance compared to traditional statistical approaches. However, that performance comes at a cost of transparency and explainability because much of the models' details are not accessible or interpretable by humans. This problem is compounded by the ability of AI to make use of any correlated data to improve predictions, and practitioners' willingness to sacrifice insight for accuracy. However, there is growing push for “explainable AI”, especially in applications where people's lives, safety, or well-being are affected. Here we explore an intermediate solution suited to policy decision making: leverage the power of tree-based machine learning methods (such as LightGBM) but restrict oneself to variables with plausible causal influence on the target feature. We explore this “explicable AI” approach with an application to housing price prediction and show that using rich geospatial data can replace implicitly spatial variables (such as coordinates and neighborhood names) and achieve similar or better accuracy. And because the explanatory features are naturally interpretable, feature importance analyses provide genuine insights into contributing factors in a way that is similar to hedonic pricing models.",1.0,,False,JournalArticle;Conference,Computer Science,2024-07-02,5a90945c016152e66bebd16cd224f6064e08fba7,explicablemachinelearningmodelsusingrichgeospatialdata,bramson,10.1109/compsac61105.2024.00382,explicablemachinelearningmodelsusingrichgeospatialdata|2024,1,2303,4,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices || machine learning hedonic pricing housing || spatial hedonic model housing",2,1,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,1 crossref,apartment rent hedonic,,10.1108/02637470310508662,,Hedonic estimation of income multipliers,2003.0,Property Management,Christian T.L. Janssen,"Income multipliers are commonly used in the valuation of income streams. All income dollars, however, are not alike, and investors make distinctions based on the nature and source of the income. The income multiplier is adjusted for the characteristics of the investment. The present study deals with major apartment buildings and concerns the estimation of income multipliers from market observations. Regression analysis is used to estimate the gross and net income multipliers, as well as adjustments for differences in property features, zoning aspects, and economic conditions. The multipliers are then used in the valuation of two subject properties. An important issue is reconciling the estimates when the two multipliers lead to somewhat different values. The sample consists of high‐rise buildings in the West End area of downtown Vancouver, Canada, but the approach is general and widely applicable.",1.0,,,journal-article,,,doi_10_1108_02637470310508662,hedonicestimationofincomemultipliers,janssen,10.1108/02637470310508662,hedonicestimationofincomemultipliers|2003,1,1910,2,apartment rent hedonic || hedonic property valuation,2,1,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,200e23a5c3d3d901306f800431fa396fce6d7954,10.66884/kesj.2026.m1vdz55t,,"FLOOD VULNERABILITY AND HOUSING MARKET DYNAMICS IN PERI-URBAN IBADAN, NIGERIA",2026.0,Kaduna State University Environmental Sciences Journal,G. Morenikeji; Kehinde Olamiju,"Purpose: Peri-urban Ibadan in Nigeria Flood prone on the dynamics of housing rentals in peri-urban areas through using an integrated geospatial and housing market analysis. It was carried out in Akinyele, Ido and Egbeda Local Government Areas (LGAs) which have undergone a rapid peri-urban growth and frequent flooding.
Design/methodology/approach: Flood vulnerability was analysed based on a Flood Vulnerability Index (FVI) based on Digital Elevation Model (DEM)-based elevation and slope parameters, whereas housing market performance was analysed on the basis of longitudinal rental data over the period 2012–2024.
Findings: The findings showed that 20-25 percent of land area in the LGAs is very prone to floods with the Ido LGA having the largest low-lying landmass. The spatial variation in housing rental values is quite high, with Akinyele having the highest rents and the highest growth rate, followed by Egbeda with average performance, and Ido with the lowest values throughout. A semi-logarithmic OLS hedonic regression (R2 = 0.932, F (4,19) = 64.93, p < 0.001) established that the risk of floods is implicitly capitalised in housing rental values: an increase in the FVI by one unit is linked to an annual reduction in rental value of just around 16.3%.
Research limitations/Implications: The Flood Vulnerability Index (FVI) of the study is solely based on the DEM-derived parameters of elevations and slope. This simplistic methodology ignores important hydrological and anthropogenic controls like rainfall intensity, soil permeability, and existing drainage systems, land-use alterations, and built-environment controls (e.g., impervious surfaces or informal settlements). This means that the 20–25 percent highly vulnerable land estimate can underestimate or overestimate actual flood exposure especially in extreme events.
Practical implications: The paper recommends the incorporation of flood-sensitive land management in the peri-urban planning to increase the resilience of housing markets and sustainable urban development.
Originality/value: The study combined high-resolution geospatial mapping of flood vulnerability (FVI) based on the DEM with longitudinal data of the rental market (2012 to 2024) in peri-urban Ibadan. Although flood-risk mapping and hedonic pricing research has been conducted independently in Nigeria, the direct relationship between measured flood exposure at LGA scale and observed rental relationships in three fast-growing peri-urban centres is new. The study uses implicit risk capitalisation to a West African secondary city, which has been little studied and is potentially experiencing a high level of informal land market and peri-urban expansion due to climate change.
",0.0,,False,JournalArticle,,2026-05-01,200e23a5c3d3d901306f800431fa396fce6d7954,floodvulnerabilityandhousingmarketdynamicsinperiurbanibadannigeria,morenikeji,10.66884/kesj.2026.m1vdz55t,floodvulnerabilityandhousingmarketdynamicsinperiurbanibadannigeria|2026,1,3915,4,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || rent hedonic regression housing || spatial hedonic model housing",2,1,2,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic property valuation,c66d67cde43a7c08ecf6382350f39384039bab26,10.1108/jhrm-04-2025-0018,,"Green spaces, new faces: place marketing and neighborhood change. A case study of the Atlanta Beltline",2026.0,Journal of Historical Research in Marketing,Morgan M. Bryant," This study aims to examine how green space references in online real estate listings operate as signals of status and sustainability in gentrifying urban neighborhoods. It situates marketplace behavior within a longer historical trajectory of racialized urban policy, infrastructure development and economic and geographic isolation of Black communities. The study specifically positions contemporary green space marketing within the historical context of urban renewal and redevelopment policies that reshaped urban housing markets and racialized patterns of displacement. The study explores how branded versus generic green space signals influence housing prices, contributing to the displacement of long-standing residents under the guise of environmental improvement. The study uses mixed-methods of content analysis and hedonic pricing models. A sample of nearly 1,000 online real estate listings is analyzed to identify the presence of branded and generic green space language. These signals are integrated into regression models that quantify their relationship to list and sales prices. In addition, the Atlanta Beltline is incorporated as a qualitative case study of contemporary urban renewal, providing historical and policy context for interpreting pricing effects associated with green space signaling. Historical and policy context is provided through a review of relevant literature on redlining, urban renewal and racialized infrastructure development. Results show that branded green space signals (e.g. references to high-profile projects like the Beltline) are associated with significant price premiums, while generic green space references are negatively or insignificantly related to property value. These findings suggest that not all green space is valued equally in the marketplace; rather, the branding and redevelopment context surrounding green infrastructure plays a critical role in shaping housing market outcomes. This suggests that green signaling in real estate is not uniformly beneficial and may reflect broader status consumption dynamics. To the best of the author’s knowledge, this study is among the first to analyze green space references in real estate marketing as marketplace signals within the context of historical racial inequities. It connects theories of green consumption with urban policy history to highlight how contemporary marketing contributes to environmental gentrification. By framing green space marketing as part of a longer historical pattern linking urban renewal, infrastructure investment and racialized displacement, the study contributes to historical research in marketing by demonstrating how marketing communications participate in the valuation and revaluation of urban space over time. ",0.0,,False,JournalArticle;Review,,2026-09-14,c66d67cde43a7c08ecf6382350f39384039bab26,greenspacesnewfacesplacemarketingandneighborhoodchangeacasestudyoftheatlantabeltline,bryant,10.1108/jhrm-04-2025-0018,greenspacesnewfacesplacemarketingandneighborhoodchangeacasestudyoftheatlantabeltline|2026,1,3783,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic property valuation || real estate hedonic regression",2,3,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,housing characteristics prices hedonic,e9be629d998f1d16bb829b715ca637c3d9c618aa,10.32609/0042-8736-2025-6-94-113,,nalysis of hedonic price indices for Moscow’s secondary housing market and the impact of interest rates,2025.0,Voprosy Ekonomiki,N. Fokin,"In this paper we construct monthly hedonic price indices for the cost per square meter of secondary housing within the administrative boundaries of Moscow (including New Moscow Administrative District). The data is collected from the most popular platforms, such as Cian, Avito, Yandex, and others. The analysis covers a particularly interesting period from July 2023 to December 2024, during which there was a continuous tightening of monetary policy, which inevitably impacted the secondary housing market. Due to the absence of subsidized programs in this market, mortgage rates are closely aligned with the key interest rate. We analyze the impact of key rate dynamics on real (deflated by the Moscow CPI) hedonic indices, as well as average and median price indices, and find a statistically significant negative effect from the monetary policy tightening. The results of the analysis show that nominal indices (both hedonic and average/median price indices) exhibited growth up until the first quarter of 2024, after which they began to stagnate. By the end of 2024, nominal indices indicated an approximately 10% increase in the cost per square meter of housing in Moscow compared to July 2023. At the same time, real hedonic, as well as simple average and median prices per square meter of housing in Moscow, were below the levels at the start of the sample period. Over the course of about a year and a half, the hedonic price per square meter in secondary housing, according to our estimates, fell by approximately 4% to 5%. Hedonic and standard indices show quite different dynamics in the first half of the sample, as well as significantly different reactions to monetary policy tightening, highlighting the importance of accounting for property characteristics when analyzing the real estate market.",0.0,,False,JournalArticle,,2025-06-07,e9be629d998f1d16bb829b715ca637c3d9c618aa,nalysisofhedonicpriceindicesformoscowssecondaryhousingmarketandtheimpactofinterestrates,fokin,10.32609/0042-8736-2025-6-94-113,nalysisofhedonicpriceindicesformoscowssecondaryhousingmarketandtheimpactofinterestrates|2025,1,2812,3,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic,2,1,1,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),189c9774623c5d9391c5425e8173380358eeed11,10.1108/pm-06-2025-0080,,Off-grid energy systems and economic returns of luxury residential apartments in urban and peri-urban Ghana,2026.0,Property Management,E. Attakora-Amaniampong; M. W. Appau; P. D. Agorsor," Off-grid energy systems are emerging in luxury residential real estate as a response to demand for energy autonomy. Despite global momentum, there are few empirical studies on the economic viability of off-grid systems in Africa's real estate markets. This study assesses the economic returns of Off-Grid Energy Systems in Luxury homes in urban and peri-urban Ghana. A quantitative framework grounded in transaction cost economics (TCE) theory was adopted. The research examined 555 luxury units in urban and peri-urban Accra, analysing household energy costs, system characteristics, property values and developer financial disclosures. Statistical methods, including one-way analysis of variance, multiple regression and hedonic pricing models, were applied to assess cost efficiency, return on investment (ROI) and value premiums. Solar-hybrid systems involve a high capital investment (Ghana Cedis (GHS) 125,000–145,000) but generate significant annual savings (approximately GHS 9,000), an attractive ROI and price premiums of up to GHS 120,000. ROI is positively influenced by battery size, system age and location. Hedonic models confirm that off-grid features significantly affect property valuation. Off-grid housing represents both a sustainable and economically strategic solution. Developers gain a market advantage in a volatile energy environment, although high upfront costs and regulatory gaps remain significant barriers to scaling. This is the first empirical study in Africa to apply the theory of the circular economy to luxury off-grid housing, linking circular economy concepts with investment behaviour and offering new perspectives on energy independence in premium real estate markets. ",0.0,,False,JournalArticle,,2026-05-25,189c9774623c5d9391c5425e8173380358eeed11,offgridenergysystemsandeconomicreturnsofluxuryresidentialapartmentsinurbanandperiurbanghana,attakora-amaniampong,10.1108/pm-06-2025-0080,offgridenergysystemsandeconomicreturnsofluxuryresidentialapartmentsinurbanandperiurbanghana|2026,1,2743,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic || real estate hedonic regression",2,1,6,3,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,prix hédoniques logement,9314c9c166f136ed55e2bf8c04077a539e1949dc,10.70675/959651b2z90cdz473az9c8dzf3d9e83780e5,,L’influence de l’action publique sur la demande et l’offre de logement,2016.0,,J. Hubert,"Cette thèse porte sur les facteurs déterminant la demande et l’offre de logement, avec des applications dans l’ex-région Nord-Pas-de-Calais. Elle est constituée de cinq chapitres.Le premier propose une analyse de la littérature fondée sur les quelque 200 articles publiés dans la revue Journal of Housing Economics durant la période 2003-2012. Il met en lumière des résultats instructifs pour des chercheurs en économie du logement, novices et confirmés. Le second chapitre présente de manière détaillée et didactique la nouvelle base de données du service de « Demande de valeurs foncières » afin de rendre compte de la richesse de son contenu. Ce chapitre propose également quelques statistiques décrivant le marché du logement du Nord-Pas-de-Calais. Le chapitre 3 utilise une analyse hédonique des prix des transactions de logement pour expliciter les facteurs affectant leur prix. Hormis le service de « Demande de valeurs foncières », cette étude met en œuvre plusieurs bases spatialisées exploitées via un SIG. Le chapitre 4 utilise les résultats de ces estimations pour calculer la valeur du stock total de logements et de rente différentielle du Nord-Pas-de-Calais. Ces résultats apportent des clés de compréhensions quant à la création de valeur immobilière à l’échelle d’une des zones les plus densément peuplées en France. Ils montrent par exemple que près, de 50% de l’estimation d’un bien provient seulement de l’endroit où il se situe. Le chapitre 5 conclu la thèse sur l’approfondissement du modèle théorique DiPasquale-Wheaton avec pour objectif de présenter les liens existant entre les différents acteurs qui composent le marché du logement.",0.0,,False,,Political Science,2016-12-02,9314c9c166f136ed55e2bf8c04077a539e1949dc,linfluencedelactionpubliquesurlademandeetloffredelogement,hubert,10.70675/959651b2z90cdz473az9c8dzf3d9e83780e5,linfluencedelactionpubliquesurlademandeetloffredelogement|2016,1,2659,1,prix hédoniques logement,1,1,2,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,spatial hedonic model housing,1143b63af6f53e6cd1a151f05169eebabb42425b,10.5465/ambpp.2020.21096abstract,,The Social Value of a Ridesharing Platform: The Case of Uber,2020.0,Academy of Management Proceedings,Yongwook Paik; C. Makridis,"Ridesharing has become ubiquitous throughout the United States, leading to a proliferation of academic studies and policy debates about its welfare implications. However, the overall social value of a ridesharing platform is still unclear because prior literature focuses only on the supply side (i.e., drivers) or the demand side (i.e., passengers) of ridesharing when analyzing its welfare and does not consider non-users of the platform. In this study, we aim to quantify the social value of a ridesharing platform using a hedonic pricing model, exploiting plausibly exogenous variation in the staggered entry of UberX into different metropolitan areas between 2010 and 2016. We find that the entry of UberX into a metropolitan area leads to a 2.9% rise in median housing prices per square foot. A back-of-the-envelope calculation suggests that the entry of UberX is associated with a welfare gain of approximately $795 million. We also find substantial spatial heterogeneity, with benefits concentrated in areas with higher shares of college graduates, greater dependence on public transportation, and a lower value of vehicles per capita. By objectively computing the value of a ridesharing platform, our study can help regulators and firm managers generate effective policies. Moreover, this study contributes to the digital platform literature by introducing a relatively straightforward method—the hedonic pricing method—to quantitatively estimate the value of an innovative business platform that relies on online-to-offline services at the local level.",0.0,,False,,Business,2020-08-01,1143b63af6f53e6cd1a151f05169eebabb42425b,thesocialvalueofaridesharingplatformthecaseofuber,paik,10.5465/ambpp.2020.21096abstract,thesocialvalueofaridesharingplatformthecaseofuber|2020,1,2562,2,hedonic housing prices || spatial hedonic model housing,2,2,1,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,2bf7befeb78be2c9365a1d6179643a46fb465980,10.1080/00330124.2022.2148902,,The Efficacy of Mortgage Lending on Geographically Disparate and Differently Priced Residential Neighborhoods in Shanghai,2023.0,Professional Geographer,Cheng-He Guan; Kelly Yan; Bo Zhang; Ying Li; Xiao-Ling Zhang,"As both a vibrant industry and necessity for people’s lives, housing has attracted much attention in recent years. This article examines the efficacy of both loosening and tightening policies on mortgage lending in different priced housing markets in Shanghai, China, from 2014 to 2018 using daily average sale prices across 1,824 neighborhoods, with price-classified cluster analysis and policy-informed hedonic regressions. The results show that (1) the effects of mortgage lending policy interventions varied significantly and the overall spatial distribution gravitated toward subway networks and varied throughout the years; (2) the effects of interventions were time lagged but varied by different priced neighborhoods; and (3) although the accumulated number of both loosening and tightening policies were both significant, only the effects of tightening policies were significant overall. This article contributes to the existing literature by combining the spatial-temporal component in policy studies, providing detailed mortgage lending policy intervention variables, and expanding the scope of study from housing prices to housing transaction frequencies.",0.0,,False,,,2023-02-02,2bf7befeb78be2c9365a1d6179643a46fb465980,theefficacyofmortgagelendingongeographicallydisparateanddifferentlypricedresidentialneighborhoodsinshanghai,guan,10.1080/00330124.2022.2148902,theefficacyofmortgagelendingongeographicallydisparateanddifferentlypricedresidentialneighborhoodsinshanghai|2023,1,2167,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices",2,3,2,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,ab63cf056264801d0565c22b30ac506ba377c92b,10.1109/icaml51583.2020.00043,,Study on Spatial Distribution of Housing Price and its Influencing Factors of Cities and Towns Peripheral to China (Hubei) Pilot Free Trade Zone Wuhan Area,2020.0,2020 2nd International Conference on Applied Machine Learning (ICAML),Ying-Juan Du; Wei Huang,"As a new Free Trade Zone in Wuhan, China (Hubei) Pilot Free Trade Zone Wuhan Area (hereinafter referred as WA) provides certain innovation and practical guiding significance for being researched on. This paper studies the spatial distribution of housing price and its impact factors of cities and towns around Wuhan Free Trade Zone, which has important reference value for the future development rural areas under construction. Kriging interpolation method and spatial autocorrelation analysis were adopted in this paper to explore the spatial distribution pattern of housing prices of the target cities and towns around WA, and it is found that the spatial differentiation of housing prices was highlighted with the center of Optics Valley of China as the core, and a significant spatial agglomeration mode was discovered. In addition, the construction of hedonic price model revealed that the main impact factors of residential price include plot ratio, property management fee, road accessibility, nearest distance to the park and the nearest distance to commercial network station.",0.0,,False,Conference,,2020-10-01,ab63cf056264801d0565c22b30ac506ba377c92b,studyonspatialdistributionofhousingpriceanditsinfluencingfactorsofcitiesandtownsperipheraltochinahubeipilotfreetradezonewuhanarea,du,10.1109/icaml51583.2020.00043,studyonspatialdistributionofhousingpriceanditsinfluencingfactorsofcitiesandtownsperipheraltochinahubeipilotfreetradezonewuhanarea|2020,1,2085,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || spatial hedonic model housing",2,2,2,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,70ecf330359b80abac673dc82829dcb401ee2d40,10.1109/icccbda49378.2020.9095730,,"Study of Residential Price Index Based on Quantile Regression Model — A Case Study on Hangzhou, China",2020.0,2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA),Hai-Zhen Wen; Yao Liu,"With the rapid development of the real estate market, housing prices at different price levels are significantly different from each other. Therefore, this paper selects the second-hand housing transaction data from 2007 to 2015 in Hangzhou, uses the hedonic price model, and introduces the quantile regression to compile the first quantile house price index in Hangzhou. Then we compare it with Ordinary Least Squares estimation to verify its feasibility and necessity. The empirical results show that the variation trend of the price index at different quantiles is the same, but the fluctuation range is significantly different. The rate of increase in housing prices from the high to low prices decline. This study can help the agencies compiling price index to expand their thinking, and provide a more scientific basis for government policy making, real estate agencies and buyers' decision-making.",0.0,,False,Conference,Computer Science,2020-04-01,70ecf330359b80abac673dc82829dcb401ee2d40,studyofresidentialpriceindexbasedonquantileregressionmodelacasestudyonhangzhouchina,wen,10.1109/icccbda49378.2020.9095730,studyofresidentialpriceindexbasedonquantileregressionmodelacasestudyonhangzhouchina|2020,1,1904,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || real estate hedonic regression",2,3,3,1,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",41306b1b5729598796322e659f1e33d9d57b511b,10.35716/ijed/20002,,Assessing Impact of Freshwater Lake Ecosystem on Residential Property Prices using Hedonic Property Pricing approach: Evidence from Vellayanilake of Kerala,2020.0,,A. Vijayan; Elsamma Job,"The ecosystem services provided by wetlands can be direct or indirect. The direct services can be mostly valued through market prices, but the indirect service like aesthetic beauty and its impact on property prices surrounding the natural resource cannot be directly measured. To single out the economic effect of particular amenity which influenced the land property prices, the advanced valuation technique Hedonic property pricing was most popularly used. In this study, it was attempted to assess using the hedonic property pricing technique, the impact of the presence of the freshwater body, the Vellayani Lake on land property prices surrounding it. The results revealed that the marginal implicit price of getting one cent of land with lake view evaluated at mean property price of `2,44250 was `79171. The total aesthetic value of land with the scenic beauty of the lake was `275.92 crores.",0.0,,False,,Economics,,41306b1b5729598796322e659f1e33d9d57b511b,assessingimpactoffreshwaterlakeecosystemonresidentialpropertypricesusinghedonicpropertypricingapproachevidencefromvellayanilakeofkerala,vijayan,10.35716/ijed/20002,assessingimpactoffreshwaterlakeecosystemonresidentialpropertypricesusinghedonicpropertypricingapproachevidencefromvellayanilakeofkerala|2020,1,1900,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || hedonic property valuation",2,1,1,2,0,7,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic house price,7f11a8e96c9f42013f85c26db82df4ce25d9e6ec,10.2139/ssrn.3914797,,A New Approach to Measuring Intercity Differences in Housing Costs,2021.0,Social Science Research Network,Nathaniel Harris; Hyung Joon Chung,"Intercity housing price indexes that rely on median house price or pooled hedonic regressions adjust imperfectly for differences in housing characteristics. In addition, intercity house price indexes that rely on asset value are a biased measure of differences in the rental price of housing, because capitalization rates vary dramatically across cities. To mitigate these shortcomings, we create Fisher Ideal intercity housing price indexes for both rental and asset prices using a two-way Oaxaca-Blinder decomposition. Our method improves upon current house price indexes by using rental rather than asset prices, controlling for location and housing characteristics, and allowing implicit prices to vary across locations. ∗Department of Economics, George Washington University, gudwnsgo@gwu.edu †Department of Economics, George Washington University, nharris58@gwu.edu",0.0,,False,,,,7f11a8e96c9f42013f85c26db82df4ce25d9e6ec,anewapproachtomeasuringintercitydifferencesinhousingcosts,harris,10.2139/ssrn.3914797,anewapproachtomeasuringintercitydifferencesinhousingcosts|2021,1,1871,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,4,3,4,0,7,True,candidate,False,unclear,UNCLEAR_other,0 crossref,apartment rent hedonic,,10.1111/j.1467-9787.2010.00664.x,,ESTIMATION AND HYPOTHESIS TESTING FOR NONPARAMETRIC HEDONIC HOUSE PRICE FUNCTIONS,2010.0,Journal of Regional Science,Daniel P. McMillen; Christian L. Redfearn,,163.0,,,journal-article,,,doi_10_1111_j_1467_9787_2010_00664_x,estimationandhypothesistestingfornonparametrichedonichousepricefunctions,mcmillen,10.1111/j.1467-9787.2010.00664.x,estimationandhypothesistestingfornonparametrichedonichousepricefunctions|2010,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic house price",2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_no_abstract,0 crossref,apartment rent hedonic,,10.1111/j.1465-7295.2007.00110.x,,HEDONIC IMPUTATION AND THE PRICE INDEX PROBLEM: AN APPLICATION TO HOUSING,2008.0,Economic Inquiry,ROBERT J. HILL; DANIEL MELSER,"In this study, we show how use of the hedonic imputation method complicates the price index problem. In addition to the usual choice between formulas such as Fisher and Törnqvist, the fact that index compilers have some discretion over which prices are imputed implies that it is necessary to choose as well between different varieties of each formula. The functional form of the hedonic model must also be taken into account. We illustrate the importance of these issues in a housing context using house price data for three regions in Sydney over a 3‐yr period. ( JEL C43, E31, O47, R31)",74.0,,,journal-article,,,doi_10_1111_j_1465_7295_2007_00110_x,hedonicimputationandthepriceindexproblemanapplicationtohousing,hill,10.1111/j.1465-7295.2007.00110.x,hedonicimputationandthepriceindexproblemanapplicationtohousing|2008,1,1589,3,apartment rent hedonic || hedonic house price || hedonic housing prices,2,2,2,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 crossref,apartment rent hedonic,,10.1016/j.jhe.2010.04.001,,Hedonic versus repeat-sales housing price indexes for measuring the recent boom-bust cycle,2010.0,Journal of Housing Economics,Robert E. Dorsey; Haixin Hu; Walter J. Mayer; Hui-chen Wang,,69.0,,,journal-article,,,doi_10_1016_j_jhe_2010_04_001,hedonicversusrepeatsaleshousingpriceindexesformeasuringtherecentboombustcycle,dorsey,10.1016/j.jhe.2010.04.001,hedonicversusrepeatsaleshousingpriceindexesformeasuringtherecentboombustcycle|2010,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic housing prices || spatial hedonic model housing",2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_other,0 openalex,hedonic housing prices,https://openalex.org/W1526189538,10.1515/jbnst-2010-0612,,Housing Prices in Tokyo: A Comparison of Hedonic and Repeat Sales Measures,2010.0,Jahrbücher für Nationalökonomie und Statistik,Chihiro Shimizu; Kiyohiko Giichi Nishimura; Tsutomu Watanabe,"Summary Do indexes of house prices behave differently depending on the estimation method? If so, to what extent? To address these questions, we use a unique dataset that we compiled from individual listings in a widely circulated real estate advertisement magazine. The dataset contains more than 470,000 listings of housing prices between 1986 and 2008, including the period of the housing bubble and its burst.We find that there exists a substantial discrepancy in terms of turning points between hedonic and repeat sales indexes, even though the hedonic index is adjusted for structural changes and the repeat sales index is adjusted in the way Case and Shiller suggested. Specifically, the repeat sales measure signals turning points later than the hedonic measure: for example, the hedonic measure of condominium prices bottomed out at the beginning of 2002, while the corresponding repeat sales measure exhibits a reversal only in the spring of 2004. This discrepancy cannot be fully removed even if we adjust the repeat sales index for depreciation.",61.0,,True,article,,2010-12-01,https://openalex.org/W1526189538,housingpricesintokyoacomparisonofhedonicandrepeatsalesmeasures,shimizu,10.1515/jbnst-2010-0612,housingpricesintokyoacomparisonofhedonicandrepeatsalesmeasures|2010,1,2056,3,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices,2,4,4,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 crossref,apartment rent hedonic,,10.1016/j.jhe.2009.07.012,,Managing hedonic housing price indexes: The French experience,2009.0,Journal of Housing Economics,Christian Gouriéroux; Anne Laferrère,,55.0,,,journal-article,,,doi_10_1016_j_jhe_2009_07_012,managinghedonichousingpriceindexesthefrenchexperience,gouriéroux,10.1016/j.jhe.2009.07.012,managinghedonichousingpriceindexesthefrenchexperience|2009,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_other,0 crossref,apartment rent hedonic,,10.1006/juec.1996.0011,,New Hedonic Estimates of Regional Constant Quality House Prices,1996.0,Journal of Urban Economics,Edwin S. Mills; Ronald Simenauer,,47.0,,,journal-article,,,doi_10_1006_juec_1996_0011,newhedonicestimatesofregionalconstantqualityhouseprices,mills,10.1006/juec.1996.0011,newhedonicestimatesofregionalconstantqualityhouseprices|1996,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic house price || hedonic housing prices",2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,fdea450f2dc45c56b781a13d9e1a25b7cc317419,10.1016/j.chieco.2017.04.007,,"Unequal school enrollment rights, rent yields gap, and increased inequality: The case of Shanghai",2017.0,China economic review,Muyang Zhang; Jie Chen,"This paper first builds a simple theoretic model to explore how a special feature of enrollment policy of public primary schools in urban China, the unequal enrollment right between home owners and tenants, would produce rent-yields gap between different housings. The model also predicts that an enrollment policy featuring with tenant discrimination, accompanying with strict credit constraint, would reduce the chance of kids from middle-income families to attend better public schools while allow families with high initial wealth to access better high-quality public education at a lower cost. Using a hedonic pricing model, we find that, in Shanghai, rental yields of housings in neighborhoods associated with reputed public primary schools is on average 0.1–0.35 percentage-point lower than those associated with ordinary ones. We also explore how the rent-yields-gap varies across housing types, locations and changes over time. Nonetheless, our simulation computation suggests that the estimated opportunity cost of holding such schools in Shanghai is generally not a big amount and affordable for many families. Overall, the high entry costs of owing a housing is the major obstacle to access high-quality public primary education in urban China. These findings highlight how an education policy with features of inequality may contribute to education and residential segregation, and then reduce intergenerational mobility.",42.0,,False,,Economics,2017-04-27,fdea450f2dc45c56b781a13d9e1a25b7cc317419,unequalschoolenrollmentrightsrentyieldsgapandincreasedinequalitythecaseofshanghai,zhang,10.1016/j.chieco.2017.04.007,unequalschoolenrollmentrightsrentyieldsgapandincreasedinequalitythecaseofshanghai|2017,1,2434,1,hedonic housing prices,2,1,4,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 openalex,hedonic housing prices,https://openalex.org/W3125907624,10.1080/10835547.2007.12091199,,Improved Estimators of Hedonic Housing Price Models,2007.0,Journal of Real Estate Research,Helen X. H. Bao; Alan T. K. Wan,"In hedonic housing price modeling, real estate researchers and practitioners are often not completely ignorant about the parameters to be estimated. Experience and expertise usually provide them with tacit understanding of the likely values of the true parameters. Under this scenario the subjective knowledge about the parameter value can be incorporated as non-sample information in the hedonic price model. In this paper, we consider a class of Generalized Stein Variance Double k-class (GSVKK) estimators which allows real estate practitioners to introduce potentially useful information about the parameter values in the estimation of hedonic pricing models. The GSVKK estimator is a generalization of a family of shrinkage estimators introduced by Ohtani and Wan (2002, Econometric Reviews). Data from the Hong Kong real estate market are used to investigate the estimators' performance empirically. Compared with the traditional Ordinary Lease Squares approach, the GSVKK estimators have smaller predictive mean squared errors and lead to more precise parameter estimates. Some results on the theoretical properties of the GSVKK estimators are also presented.",41.0,,True,article,,2007-01-01,https://openalex.org/W3125907624,improvedestimatorsofhedonichousingpricemodels,bao,10.1080/10835547.2007.12091199,improvedestimatorsofhedonichousingpricemodels|2007,1,2166,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices,2,1,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 crossref,apartment rent hedonic,,10.1111/1540-6229.00382,,An Evaluation of Hedonic Price Indexes for Thirty‐four Large SMSAs,1986.0,Real Estate Economics,Dixie M. Blackley; James R. Follain; Haeduck Lee,"The purpose of this paper is twofold. First, a set of price indexes are developed for thirty‐four metropolitan areas for the years 1975 and 1978. Indexes are also broken down by central city and suburb for twenty two of the metropolitan areas. These indexes measure variation in the price per unit of owner‐occupied housing stock and the price per unit of housing services of rental housing. Confidence intervals are constructed for the price indexes. It is found that the indexes are reasonably precise measures in that the typical ratio of the confidence interval to the predicted median rent or value is about 13%. Also, analysis of the confidence intervals indicates substantial and statistically significant variation in the price of housing stock and services among the metropolitan areas studied. One of the major benefits of this set of indexes is that it can be used to address one of the most important questions in real estate — Why do housing prices and rents vary among metropolitan areas and over time? Research projects are currently underway that use the data to address these two questions.",34.0,,,journal-article,,,doi_10_1111_1540_6229_00382,anevaluationofhedonicpriceindexesforthirtyfourlargesmsas,blackley,10.1111/1540-6229.00382,anevaluationofhedonicpriceindexesforthirtyfourlargesmsas|1986,1,2107,3,apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices,2,2,2,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),34b3481704b37fd8ca54fb7c8ce5ec4318306509,10.1007/978-3-319-21470-2_15,,Spline Smoothing for Estimating Hedonic Housing Price Models,2015.0,Communication Systems and Applications,V. del Giudice; B. Manganelli; P. De Paola,,25.0,,False,JournalArticle,Computer Science,2015-06-22,34b3481704b37fd8ca54fb7c8ce5ec4318306509,splinesmoothingforestimatinghedonichousingpricemodels,giudice,10.1007/978-3-319-21470-2_15,splinesmoothingforestimatinghedonichousingpricemodels|2015,1,1000,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices,2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic housing prices,11150b500c450a47d8a9b6ac00ae7d6522f6fae7,10.1016/j.jeem.2019.06.006,,Environmental regulation as a double-edged sword for housing markets: Evidence from the NO Budget Trading Program,2019.0,Journal of Environmental Economics and Management,Sumit Agarwal; Yong-Heng Deng; Teng Li,"Abstract We investigate the effects of environmental regulations on housing markets using a quasi-experimental setting—the NOx Budget Trading Program (NBP). Hedonic theory predicts that house prices should rise as pollution levels decrease. However, environmental regulations may also affect labor markets, and thus housing demand. Employing a difference-in-differences framework, we find that house prices shifted up in the regulated areas with low manufacturing intensity, whereas in the areas with high manufacturing intensity, housing markets were weakened. We also find that in high-manufacturing-intensity areas, loan application volume declined, rejection rate augmented, and the probability of loan default increased.",20.0,,False,,Business,2019-07-01,11150b500c450a47d8a9b6ac00ae7d6522f6fae7,environmentalregulationasadoubleedgedswordforhousingmarketsevidencefromthenobudgettradingprogram,agarwal,10.1016/j.jeem.2019.06.006,environmentalregulationasadoubleedgedswordforhousingmarketsevidencefromthenobudgettradingprogram|2019,1,1725,2,hedonic house price || hedonic housing prices,2,1,2,0,1,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,fa202e59c6c5e1d237f19c1d1ac165980674e008,10.1016/j.chieco.2019.04.003,,Home value misestimation and household behavior: Evidence from China,2019.0,China economic review,Nan Gao; Ping-Han Liang,"Abstract Households often make errors when self-assessing their housing wealth. By using the Chinese Households Finance Survey, we show that Chinese urban households systematically overestimate their home value. This bias is significantly related to household consumption: a one standard deviation increase in the extent of estimation bias is associated with a 10% increase in household consumption. Our main results are robust against a variety of robustness checks, e.g., taking into account the expectations of future home price, using the interviewee's estimation as the instrumental variable for the household self-estimation, implementing the hedonic model on an external data source to assess real homevalue, etc.. Further, we show that overestimated households are more likely to take risk in financial market, and have a larger amount of immediate and conspicuous consumption. These findings have policy implications for the recent debate about property tax, indicating the unintended consequences of property tax through providing more precise information about home value.",11.0,,False,Review,Economics,2019-06-01,fa202e59c6c5e1d237f19c1d1ac165980674e008,homevaluemisestimationandhouseholdbehaviorevidencefromchina,gao,10.1016/j.chieco.2019.04.003,homevaluemisestimationandhouseholdbehaviorevidencefromchina|2019,1,2083,1,hedonic housing prices,2,2,3,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 crossref,apartment rent hedonic,,10.1111/1540-6229.00844,,An Application of Hedonic Indexing Methods to Monthly Changes in Housing Prices: 1965–1975*,1977.0,Real Estate Economics,Michael G. Ferri,,11.0,,,journal-article,,,doi_10_1111_1540_6229_00844,anapplicationofhedonicindexingmethodstomonthlychangesinhousingprices19651975,ferri,10.1111/1540-6229.00844,anapplicationofhedonicindexingmethodstomonthlychangesinhousingprices19651975|1977,1,1000,2,apartment rent hedonic || hedonic housing prices,2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_other,0 crossref,prix hédoniques logement,,10.3406/ecop.2007.7674,,La rénovation de la Goutte d'Or est-elle un succès ? Un diagnostic à l'aide d'indices de prix immobilier,2007.0,Économie & prévision,Alain Trannoy; Alessandra Michelangeli; Fabrice Barthélémy,"L’opération de rénovation urbaine concernant le quartier de la Goutte d’Or dans le 18ème arrondissement de Paris a débuté en 1985 et se poursuit jusqu’à nos jours. Nous nous intéressons à l’évaluation d ’ une telle opération par le marché du logement. À cette fin, nous utilisons une base de données sur les prix de transaction (380 000 observations sur la période 1981-2001) qui nous permet d’étudier la dynamique de ce marché à travers l’estimation d’indices de prix hédoniques pour Paris, pour les quartiers concernés par la rénovation, et pour un quartier dit de contrôle. À l’échelon d’un quartier, un effet de court terme peut être détecté, mais cet effet ne semble pas durable. Au niveau plus fin de l’infra-quartier, il apparaît également que la rénovation a eu pour effet d’atténuer le caractère d’externalité négative que présentait la zone par rapport aux logements situés dans un voisinage immédiat.",8.0,,,journal-article,,,doi_10_3406_ecop_2007_7674,larenovationdelagouttedorestelleunsuccesundiagnosticalaidedindicesdepriximmobilier,trannoy,10.3406/ecop.2007.7674,larenovationdelagouttedorestelleunsuccesundiagnosticalaidedindicesdepriximmobilier|2007,1,1911,1,prix hédoniques logement,1,1,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 crossref,apartment rent hedonic,,10.1080/02815738408730036,,"House price indexes: The hedonic technique, and some other methods applied to price movements of single family houses in Sweden",1984.0,Scandinavian Housing and Planning Research,Rune Wigren,,6.0,,,journal-article,,,doi_10_1080_02815738408730036,housepriceindexesthehedonictechniqueandsomeothermethodsappliedtopricemovementsofsinglefamilyhousesinsweden,wigren,10.1080/02815738408730036,housepriceindexesthehedonictechniqueandsomeothermethodsappliedtopricemovementsofsinglefamilyhousesinsweden|1984,1,1000,2,apartment rent hedonic || hedonic house price,2,1,2,0,0,6,True,candidate,True,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),5e37af8ef4d5af85377af315b096773f36257847,10.1007/s12076-019-00232-2,,Hedonic pricing models and residual house price volatility,2019.0,Letters in spatial and resource sciences,G. Lisi,,5.0,,False,,Economics,2019-06-27,5e37af8ef4d5af85377af315b096773f36257847,hedonicpricingmodelsandresidualhousepricevolatility,lisi,10.1007/s12076-019-00232-2,hedonicpricingmodelsandresidualhousepricevolatility|2019,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic house price,b8757da465da4bd490ab004e3c10d0ecb61e91bd,10.15396/eres2022_186,,Proposing a global model to overcome the bias-variance tradeoff in the context of hedonic house price models,2022.0,28th Annual European Real Estate Society Conference,S. Lang; Wolfgang A. Brunauer; Julian Granna,,4.0,,False,Conference,,,b8757da465da4bd490ab004e3c10d0ecb61e91bd,proposingaglobalmodeltoovercomethebiasvariancetradeoffinthecontextofhedonichousepricemodels,lang,10.15396/eres2022_186,proposingaglobalmodeltoovercomethebiasvariancetradeoffinthecontextofhedonichousepricemodels|2022,1,1000,1,hedonic house price,2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f00d413d1fc1aec9c71401512c0a45076cbdd8bf,10.1007/978-3-319-54873-9_12,,The Statistical Basis of Valuation: The Hedonic House Price Model,2017.0,,C. Price,,3.0,,False,,Economics,,f00d413d1fc1aec9c71401512c0a45076cbdd8bf,thestatisticalbasisofvaluationthehedonichousepricemodel,price,10.1007/978-3-319-54873-9_12,thestatisticalbasisofvaluationthehedonichousepricemodel|2017,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),3c8d020dead7349c9ea1d4cf7c5c16a018d717c2,10.1111/eufm.12200,,Getting it right or getting it cursed: Auction prices in a residential real estate bubble,2018.0,European Financial Management,Clare Branigan; Cal B. Muckley; P. Ryan,"This is the first study to test for a winner’s curse in a bubble market. Our hand-collected sample comprises the entire sequence of bids and the experience of the winning bidder at Irish residential real estate auctions, prior to the collapse of the bubble. Portfolios of practitioner-selected self-similar properties and a hedonic pricing model facilitate benchmark property price estimation. We show neither real estate investors nor owner occupiers shade auction bids to avoid the winner’s curse. Winning real estate investors, however, pay more for properties. Thus, real estate investors ride the wave of a property bubble and potentially exacerbate it.",2.0,,False,,Business,2018-12-19,3c8d020dead7349c9ea1d4cf7c5c16a018d717c2,gettingitrightorgettingitcursedauctionpricesinaresidentialrealestatebubble,branigan,10.1111/eufm.12200,gettingitrightorgettingitcursedauctionpricesinaresidentialrealestatebubble|2018,1,1658,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,1,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 openalex,hedonic housing prices,https://openalex.org/W3211343985,10.12068/j.issn.1005-3026.2015.08.031,,Shenyang Commercial Housing Price Index Based on Hedonic Price Model,2015.0,Journal of Northeastern University,Liang Zhao,,2.0,,False,article,,2015-08-15,https://openalex.org/W3211343985,shenyangcommercialhousingpriceindexbasedonhedonicpricemodel,zhao,10.12068/j.issn.1005-3026.2015.08.031,shenyangcommercialhousingpriceindexbasedonhedonicpricemodel|2015,1,1000,1,hedonic housing prices,2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),c77cafafe33e406e967c96ca9c80c10ed69a973f,10.2139/ssrn.3948243,,The quality-adjusted price evolution of houses and apartments in Flanders from 2005q1 until 2021q2,2021.0,Social Science Research Network,S. Damen,"I construct a quality-adjusted hedonic house price index for Flanders from 2005q1 until 2021q2. I use data from a large franchise network of real estate brokers with transactions from 37,850 houses and 12,200 apartments. The results indicate that the average yearly nominal house price growth was equal to 4.6% (2.1% after inflation). However, the yearly growth rate differs substantially in different sub periods with above average growth rates in 2005-2008 and 2017-2021 while nominal price growth was in line with inflation from 2009 until 2016. The price evolution of apartments is very similar to houses. In addition to a hedonic price index, I also calculate the evolution of the time-on-market and sale-to-list ratio as new indicators to assess the housing market. Looking at the impact of the COVID-19 crisis on the housing market, we see that house prices increased faster outside central cities since the second half of 2020. For apartments, there is a strong increase in the price of apartments at the coast and a strong decrease in the time-on-market of apartments with a terrace in comparison to apartments without a terrace. It is, however, too early to conclude whether or not these changes are temporary or permanent. The indices and indicators that I construct are available on my website www.svendamen.com/hedonic and updated every year in January.",1.0,,False,,Medicine,,c77cafafe33e406e967c96ca9c80c10ed69a973f,thequalityadjustedpriceevolutionofhousesandapartmentsinflandersfrom2005q1until2021q2,damen,10.2139/ssrn.3948243,thequalityadjustedpriceevolutionofhousesandapartmentsinflandersfrom2005q1until2021q2|2021,1,2366,3,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices,2,2,4,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 openalex,hedonic housing prices,https://openalex.org/W3137516421,10.1007/978-981-33-4114-2_12,,Evaluation of Hedonic Price Models that Explain Transit Induced Impact on Housing Prices,2021.0,Lecture notes in civil engineering,Karan Barpete; Arnab Jana,,1.0,,False,conference-paper,,2021-01-01,https://openalex.org/W3137516421,evaluationofhedonicpricemodelsthatexplaintransitinducedimpactonhousingprices,barpete,10.1007/978-981-33-4114-2_12,evaluationofhedonicpricemodelsthatexplaintransitinducedimpactonhousingprices|2021,1,1000,1,hedonic housing prices,2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic house price,abb7fb5e60edb4e86a12cac01b1fd4aee8679c01,10.4324/9781315235134-2,,The Hedonic House Price Function,2017.0,,S. Orford,,1.0,,False,,Economics,2017-09-08,abb7fb5e60edb4e86a12cac01b1fd4aee8679c01,thehedonichousepricefunction,orford,10.4324/9781315235134-2,thehedonichousepricefunction|2017,1,1000,1,hedonic house price,2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,apartment prices hedonic,b7a37c3fdc9ccb44fb8205496626f43ca065d0fa,10.37407/kres.2024.42.2.71,,Analyses of the Three Environmental Factors Affecting Apartment Prices: Focusing on Gyeongsangnamdo,2024.0,Korea Real Estate Society,Y. Jo; Jae-Wook Park; Dong-Keun Lee,"The purpose and significance of this study is to analyze the impact of complex environmental factors on apartment prices across the country based on actual transaction prices. For this, based on the actual transaction prices of apartments in four complexes within △△ district in ○○-si, Gyeongsangnam-do, which are all affected by three environmental factors: exposure to open spaces (neighborhood parks and natural mountainous areas), exposure to Nimby facility (livestock barn), and exposure to traffic noise. The influence of each factor was estimated through three-pair two-apartment correlation analysis and hedonic modeling. Three pairs of two apartments were selected for correlation analysis, and they have the same or similar physical factors such as exclusive use area, construction year, construction company, and orientation. However, the first pair of two apartments differs only in exposure to open space. The second pair of two apartments differs only in exposure to Nimby facilities. and the final pair of two apartments differs in simultaneous exposure to both open space and traffic noise. In addition, the actual transaction prices of 300 apartments in 4 complexes were investigated to estimate the impact of exposure to open space, to Nimby facility, and to traffic noise through a hedonic model. As a result of correlation analysis, the impact due to exposure to avoided facilities was -6.7%, the impact due to exposure to open space was +5.0%, and the impact due to noise exposure was -11.2%. From the results of the hedonic modelling, the impacts due to exposure to open space, Nimby facility and traffic noise were estimated to be 182,357 won/m 2 , -170,437 won/m 2 , and -195,141 won/m 2 , respectively. Although it was confirmed that traffic noise has the greatest impact on apartment prices, there is a limit in that the size of the analysis of the impact due to exposure to open space and exposure to nimby facilities was derived differently from correlation analysis and hedonic model analysis. Future research should be conducted to analyze the impact of open spaces by further subdividing them into natural landscapes such as mountains and rivers and artificial landscapes such as neighborhood parks.",0.0,,False,JournalArticle,,2024-06-30,b7a37c3fdc9ccb44fb8205496626f43ca065d0fa,analysesofthethreeenvironmentalfactorsaffectingapartmentpricesfocusingongyeongsangnamdo,jo,10.37407/kres.2024.42.2.71,analysesofthethreeenvironmentalfactorsaffectingapartmentpricesfocusingongyeongsangnamdo|2024,1,3230,1,apartment prices hedonic,2,3,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic house price,42a036736f1195f1e9a9b2a76fc8cfb8ce4b9fa6,10.15396/eres2019_51,,Assessing the investment performance of residential suburbs subject to aircraft noise,2019.0,26th Annual European Real Estate Society Conference,C. Eves,"An increasingly important issue facing developing cities is the requirement for more airports or more development of existing airports to cater for the increasing level of business, leisure and tourism air travel.In several major international cities, the airport is located in residential areas that have developed after the construction of the airport, with development further from the actual airport now under aircraft flight paths. In such residential affected suburbs, the issue of aircraft noise is raised, with an emphasis on the impact on property prices and values.There have been numerous research papers addressing the impact on price based on hedonic price models, with most of these studies showing a decline in prices for affected houses, and a smaller number of studies showing that location close to a major airport can result in higher prices due to the employment opportunities provided by major airports such as Heathrow, London.Although these studies have focussed on possible decreases or increases in house prices due to aircraft noise, there have been limited studies in relation to the long-term investment performance of houses under, adjoining or in view of aircraft flight paths and subject to aircraft noise.This paper analyses the residential property sales for 53 suburbs in Brisbane over the period 1988-2017 to assess the investment performance of these suburbs. The suburb selection includes those directly under the existing runway flight paths, several suburbs adjoining existing flight paths, and a range of suburbs that are currently not subject to aircraft noise but will be under the flight paths of the proposed second runway at Brisbane Airport that is under construction.The results show the long-term capital return based on median and average house prices, the long-term volatility and risk/return ratios for each suburb. These are compared based on the level of noise affectations.",0.0,,False,Conference,Business,,42a036736f1195f1e9a9b2a76fc8cfb8ce4b9fa6,assessingtheinvestmentperformanceofresidentialsuburbssubjecttoaircraftnoise,eves,10.15396/eres2019_51,assessingtheinvestmentperformanceofresidentialsuburbssubjecttoaircraftnoise|2019,1,2927,1,hedonic house price,2,3,3,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 crossref,preços hedônicos imóveis,,10.29327/2290393.5.1-1,,Diagnóstico de multicolinearidade em modelos hedônicos para avaliação em massa de imóveis para fins tributários,2026.0,Revista Valorem,Antônio Augusto Ferreira de Oliveira; Sandro Ricardo Vasconcelos Bandeira,"Este artigo tem como objetivo aprimorar o diagnóstico da multicolinearidade em modelos hedônicos utilizados na avaliação em massa de imóveis para fins tributários, superando abordagens restritas à matriz de correlações ou ao fator de inflação da variância (VIF) considerado isoladamente. Para tanto, propõe-se uma abordagem integrada que articula diagnósticos locais e globais, combinando matriz de correlações, VIF, número condicional e decomposição de variância segundo Belsley, Kuh e Welsch (BKW). A metodologia permite distinguir a multicolinearidade essencial, decorrente de quase-dependências entre variáveis explicativas, da multicolinearidade não essencial, associada a problemas de escala, parametrização ou alinhamento de regressoras com o intercepto. A aplicação empírica demonstrou que a reparametrização conceitualmente orientada reduziu a correlação entre variáveis físicas do terreno, diminuiu o maior VIF e melhorou o condicionamento global da matriz de regressoras, tornando o modelo mais estável e interpretável. Os resultados reforçam que a mitigação da multicolinearidade não deve ser confundida com a eliminação mecânica de variáveis, especialmente em mercados urbanos, nos quais atributos físicos, locacionais e socioeconômicos expressam dimensões interligadas da formação do valor. Conclui-se que o diagnóstico integrado, especialmente com o uso da decomposição BKW, amplia a capacidade de identificação das fontes de instabilidade inferencial e fornece base técnica mais robusta para justificar reparametrizações, comparar especificações alternativas e documentar escolhas metodológicas em avaliações em massa. Assim, contribui para elevar a governança, a explicabilidade e a segurança técnica dos modelos empregados na determinação de valores de mercado com finalidade tributária.",0.0,,,journal-article,,,doi_10_29327_2290393_5_1_1,diagnosticodemulticolinearidadeemmodeloshedonicosparaavaliacaoemmassadeimoveisparafinstributarios,oliveira,10.29327/2290393.5.1-1,diagnosticodemulticolinearidadeemmodeloshedonicosparaavaliacaoemmassadeimoveisparafinstributarios|2026,1,2805,1,preços hedônicos imóveis,1,1,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,prix hédoniques logement,0a320b8539445f20868d08159fe3b23f0fa9aa64,10.70675/b95b9923zc4c4z45a4za28fz9f233e41403b,,Social interactions and energy-efficiency investments in the building sector,2021.0,,Nathaly Cruz Garcia," Les stratégies de lutte contre le changement climatique et en faveur du processus de transition énergétique soulignent l'importance de la rénovation des bâtiments dans la réduction de la consommation d'énergie mondiale et des émissions de gaz à effet de serre. Néanmoins, le potentiel de ce secteur est limité par différents facteurs identifiés notamment dans la littérature sur le Gap d'Efficacité Energétique (EEG) [Jaffe and Stavins,1994 ] [Hirst and Brown, 1990] [Allcott and Greenstone, 2012] [Gerarden et al., 2017]. Ainsi, la transition vers un parc de bâtiments plus durables est confrontée à différentes défaillances de marché et autres qui, combinées, peuvent faire obstacle aux investissements en efficacité énergétique. Dans le cas du logement collectif, les enjeux de coordination, la complexité du processus, et le positionnement des individus vis-à-vis du marché jouent un rôle supplémentaire dans les décisions d'investissement. Dans cette thèse, nous postulons que les investissements en efficacité énergétique sont affectés, d'une part, par l'écosystème des acteurs du marché de la rénovation, et d'autre part, par le fait que les individus peuvent inclure la dynamique du marché et les choix collectifs dans leurs décisions d'investissement en efficacité énergétique. Dans le premier chapitre, nous étudions le processus complexe de prise de décision pour la rénovation de logements collectifs. Dans le deuxième chapitre, nous étudions la contingence de la valeur verte à la structure du marché. Dans le troisième chapitre, nous proposons une extension dynamique de la modélisation standard des prix hédoniques et analysons la façon dont les investissements précoces affectent l'équilibre des prix d'équilibre et donc le rendement attendu des investissements tardifs.",0.0,,False,,,,0a320b8539445f20868d08159fe3b23f0fa9aa64,socialinteractionsandenergyefficiencyinvestmentsinthebuildingsector,garcia,10.70675/b95b9923zc4c4z45a4za28fz9f233e41403b,socialinteractionsandenergyefficiencyinvestmentsinthebuildingsector|2021,1,2788,1,prix hédoniques logement,1,1,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),3d0c77693c59e2717490cee0c15aa84aa35dc787,10.15396/eres2019_167,,"Emotions, Uncertainty, Gender and Residential Real Estate Prices: Evidence from a Bubble Market",2019.0,26th Annual European Real Estate Society Conference,Clare Branigan; P. Ryan,"This paper investigates the impact of valuation uncertainty on residential property prices near the peak of a bubble. Our hand-collected sample comprises the sequence of bids and gender of the participating bidders at Irish residential real estate auctions, prior to the collapse of a bubble, which when it burst had disastrous implications for the banking system and the economy itself. Portfolios of practitioner- and hedonic pricing model-selected self-similar properties provide benchmark property price estimates and uncertainty is calculated by reference to various measures of dispersion related to prices achieved for comparable properties. We find, in aggregate, auction winners do not shade bids with increased valuation uncertainty. In addition, winning female bidders, in contrast to findings in the extant literature across a wide range of academic disciplines, including experimental bubble markets, are not less risk averse, or more likely to shy away from competitive situations than their male counterparts.",0.0,,False,Conference,Economics,,3d0c77693c59e2717490cee0c15aa84aa35dc787,emotionsuncertaintygenderandresidentialrealestatepricesevidencefromabubblemarket,branigan,10.15396/eres2019_167,emotionsuncertaintygenderandresidentialrealestatepricesevidencefromabubblemarket|2019,1,2024,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic property valuation,2,2,1,0,0,6,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),563d647e1235a91762d12ba8130ba12b970c0426,10.4324/9781315248004-18,,Estimation of a Hedonic Rent Index for the Residential Real Estate Market of Bari: A Contribution to Urban Planning,2016.0,,A. Khakee; A. Hull,,0.0,,False,,Business,2016-12-05,563d647e1235a91762d12ba8130ba12b970c0426,estimationofahedonicrentindexfortheresidentialrealestatemarketofbariacontributiontourbanplanning,khakee,10.4324/9781315248004-18,estimationofahedonicrentindexfortheresidentialrealestatemarketofbariacontributiontourbanplanning|2016,1,1000,1,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_other,0 openalex,hedonic housing prices,https://openalex.org/W3123572284,10.15396/eres2003_110,,Estimating hedonic housing price models with spline functions,2003.0,,Helen X. H. Bao; Wan Alan; Mats Håkan Wilhelmsson,,0.0,,False,conference-paper,,2003-06-10,https://openalex.org/W3123572284,estimatinghedonichousingpricemodelswithsplinefunctions,bao,10.15396/eres2003_110,estimatinghedonichousingpricemodelswithsplinefunctions|2003,1,1000,1,hedonic housing prices,2,1,1,0,0,6,True,candidate,True,unclear,UNCLEAR_no_abstract,0 crossref,implicit prices housing attributes,,10.1080/00420980120027465,,The External Effects of Local Attributes on Living Environment in Detached Residential Blocks in Tokyo,2001.0,Urban Studies,Xiaolu Gao; Yasushi Asami,"Measuring environment attributes in monetary terms is an effective approach for evaluating living environments and planning. This paper presents a study employing a hedonic pricing model on the externalities of local attributes in detached residential blocks in Tokyo. Results show that a group of local attributes, including both attributes of individual lots and of local districts, exert stable external effects. Moreover, many of these effects are changing with the size of land lot. These findings provide important information for urban planning and design in detached residential blocks. In the paper, the sub-division of land lots, an event arousing much concern in recent years, is analysed and concepts on facilitating local parks are suggested.",37.0,,,journal-article,,,doi_10_1080_00420980120027465,theexternaleffectsoflocalattributesonlivingenvironmentindetachedresidentialblocksintokyo,gao,10.1080/00420980120027465,theexternaleffectsoflocalattributesonlivingenvironmentindetachedresidentialblocksintokyo|2001,1,1755,2,hedonic property valuation residential || implicit prices housing attributes,2,0,1,1,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",45534596a3dd27c2a8412e2bf1c3524a7f490b2b,10.1016/j.scs.2018.05.022,,How residential compactness and attractiveness can be shaped by environmental amenities in an industrial city?,2018.0,Sustainable cities and society,B. Mondal; Dipendra Nath Das,"Abstract At a time when rapid urbanization and environmental degradation continue to be serious issues of concern, there is an escalating need to promote sustainable living across the world, with due emphasis on environmental amenities. The economic value of these amenities cannot be overlooked either, as they are inevitable in undertaking compact city plan. However, due to inadequate understanding of economic and aesthetic value of environmental amenities developers, decision-makers and policy makers ignored its importance, resulting in massive exploitation. This study conducted in the city of Ahmedabad in India, explores the significance of local environmental amenities and residential attractiveness to elicit an urban expansion pattern. The geographically weighted hedonic regression is employed in this research to estimate the value of local amenities and spatial clustering is used to examine the environment induced residential pattern. Outcome shows that in addition to structural attributes, local residents are willing to pay for LST, NDVI, air quality, and tree cover, in spite of these services being exceedingly location-specific. Besides, widespread residential spillover is observed in association with environmental amenities. These outcomes of the study could be substantially useful to understand the importance of environmental services and also be beneficial to commence a regional public policy.",26.0,,False,,Business,2018-08-01,45534596a3dd27c2a8412e2bf1c3524a7f490b2b,howresidentialcompactnessandattractivenesscanbeshapedbyenvironmentalamenitiesinanindustrialcity,mondal,10.1016/j.scs.2018.05.022,howresidentialcompactnessandattractivenesscanbeshapedbyenvironmentalamenitiesinanindustrialcity|2018,1,2426,1,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,0,1,4,0,5,True,candidate,False,unclear,UNCLEAR_other,0 crossref,apartment rent hedonic,,10.1006/jhec.2002.0308,,Household Expenditure Patterns for Housing Attributes: A Linear Expenditure System with Hedonic Prices,2002.0,Journal of Housing Economics,Mats Wilhelmsson,,24.0,,,journal-article,,,doi_10_1006_jhec_2002_0308,householdexpenditurepatternsforhousingattributesalinearexpendituresystemwithhedonicprices,wilhelmsson,10.1006/jhec.2002.0308,householdexpenditurepatternsforhousingattributesalinearexpendituresystemwithhedonicprices|2002,1,1000,2,apartment rent hedonic || hedonic housing prices,2,0,2,1,0,5,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",259691c3c6d22570f891346ba4a60239cd3de4ae,10.1017/s1355770x18000542,,Are land values related to ambient air pollution levels? Hedonic evidence from Mexico City,2019.0,Environment and Development Economics,L. Chakraborti; D. Heres; D. Hernandez,"Abstract This article investigates whether residents of Mexico City value air quality. Our results suggest that air quality improvement in PM10 is equivalent to a marginal willingness to pay (MWTP) of US$440.31 per property for the period 2006–2013. The corresponding MWTP for PM2.5 is US$880.63, for O3 is US$623.78, and for SO2 is as much as US$2091.50. These estimates are considerably larger in magnitude compared to the few other studies in similar settings. As a percentage of annual household income, these represent 2.44 per cent for PM10, 4.88 per cent for PM2.5, 3.46 per cent for O3 and 11.59 per cent for SO2. Our estimates of land value–pollution elasticities for PM10 (−0.26 and − 0.58) are within range of hedonic estimates for total suspended particulate matter in US cities around the 1970s. The corresponding elasticities range from − 0.55 to − 0.84 for PM2.5, from − 0.06 to − 0.49 for O3 and from − 0.11 to − 0.34 for SO2.",12.0,,False,,Geography,2019-02-06,259691c3c6d22570f891346ba4a60239cd3de4ae,arelandvaluesrelatedtoambientairpollutionlevelshedonicevidencefrommexicocity,chakraborti,10.1017/s1355770x18000542,arelandvaluesrelatedtoambientairpollutionlevelshedonicevidencefrommexicocity|2019,1,1942,1,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,0,1,1,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic housing prices,ec91d407567c6eda16be3e8788f2ee2b70efe006,10.1016/j.jhe.2021.101764,,The illusion of a hedonic price function: Nonparametric interpretable segmentation for hedonic inference,2021.0,,Hayato Nishi; Yasushi Asami; C. Shimizu,"Abstract In housing markets, one bundle of characteristics is regarded as corresponding to a single price via a hedonic function. However, under the conditions of market mixture, the correspondence can be multi-valued and the existence of a single function is an illusion. The market segmentation approach decomposes a complex correspondence into simple functions. In this paper, we propose an automatic segmentation method that satisfies the submarket’s equilibrium and substitutability conditions. Our empirical results show that our method provides a reasonable segmentation.",11.0,,False,,Computer Science,2021-03-08,ec91d407567c6eda16be3e8788f2ee2b70efe006,theillusionofahedonicpricefunctionnonparametricinterpretablesegmentationforhedonicinference,nishi,10.1016/j.jhe.2021.101764,theillusionofahedonicpricefunctionnonparametricinterpretablesegmentationforhedonicinference|2021,1,1578,2,hedonic housing prices || housing characteristics prices hedonic,2,0,1,1,0,5,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),e365e9c5ba827ff6138c40ece99fdf0d945b9b05,10.1109/icmla.2017.00-94,,A Spatio - Temporal Hedonic House Regression Model,2017.0,International Conference on Machine Learning and Applications,T. Oladunni; Sharad Sharma; Raymond Tiwang,,10.0,,False,JournalArticle;Conference,Computer Science,2017-12-01,e365e9c5ba827ff6138c40ece99fdf0d945b9b05,aspatiotemporalhedonichouseregressionmodel,oladunni,10.1109/icmla.2017.00-94,aspatiotemporalhedonichouseregressionmodel|2017,1,1000,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || real estate hedonic regression || spatial hedonic model housing",2,0,1,1,0,5,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,hedonic housing prices,1c730a20c7eb1ac7838746b8ac6b68fe6d7ced3f,10.1257/pandp.20231131,,Racial Gaps in Federal Flood Buyout Compensations,2023.0,AEA Papers and Proceedings,Kay Jowers; La-La Ma; Christopher Timmins,"In a managed retreat, or “buyout,” program, homeowners are offered their property's fair-market value to relocate from a flood-prone area. Using a nationwide sample of federal buyout acquisitions and the universe of US housing transactions over the last two decades, we estimate whether the discount in buyout price compensation, calculated using a hedonic prediction model, depends on a homeowner's race. We provide the first systematic evidence of inequitable compensation in a federal buyout program using nationwide administrative data. Disproportionate compensation, in an attempt to adapt to a warming climate, may further widen the wealth and social mobility gaps.",9.0,,False,JournalArticle,,2023-05-01,1c730a20c7eb1ac7838746b8ac6b68fe6d7ced3f,racialgapsinfederalfloodbuyoutcompensations,jowers,10.1257/pandp.20231131,racialgapsinfederalfloodbuyoutcompensations|2023,1,1671,1,hedonic housing prices,2,0,2,1,0,5,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),57c335bdc135cb1099b3803d3a608f03a056258c,10.1109/icmla.2016.0097,,Spatial Dependency and Hedonic Housing Regression Model,2016.0,International Conference on Machine Learning and Applications,T. Oladunni; Sharad Sharma,,6.0,,False,JournalArticle;Conference,Computer Science,2016-12-01,57c335bdc135cb1099b3803d3a608f03a056258c,spatialdependencyandhedonichousingregressionmodel,oladunni,10.1109/icmla.2016.0097,spatialdependencyandhedonichousingregressionmodel|2016,1,1000,3,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression || spatial hedonic model housing,2,0,1,2,0,5,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),4c495e1d8e58e3a43337bbf693b10e3c02146b87,10.18059/jmi.v2i2.27,,Real Estate Home Pricing: Hedonic model variables and Community Amenities Roles,2016.0,,T. Dorr,,2.0,,False,,Business,2016-10-14,4c495e1d8e58e3a43337bbf693b10e3c02146b87,realestatehomepricinghedonicmodelvariablesandcommunityamenitiesroles,dorr,10.18059/jmi.v2i2.27,realestatehomepricinghedonicmodelvariablesandcommunityamenitiesroles|2016,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression",2,0,1,1,0,5,True,candidate,True,unclear,UNCLEAR_no_abstract,0 crossref,apartment rent hedonic,,10.1016/0094-1190(78)90004-9,,"Hedonic prices, price indices and housing markets",1978.0,Journal of Urban Economics,Allen C. Goodman,,298.0,,,journal-article,,,doi_10_1016_0094_1190_78_90004_9,hedonicpricespriceindicesandhousingmarkets,goodman,10.1016/0094-1190(78)90004-9,hedonicpricespriceindicesandhousingmarkets|1978,1,1000,2,apartment rent hedonic || hedonic housing prices,2,0,1,0,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 crossref,hedonic housing prices,,10.1068/a34162,,The Definition and Identification of Housing Submarkets,2001.0,Environment and Planning A: Economy and Space,Craig A Watkins,"Although it is widely accepted that urban housing markets are too complex to be described by unitary market, equilibrium models, the role of submarkets has not been embraced in applied research. In this paper it is argued that this is unsurprising and can be traced to the failure to establish a theoretical or empirical basis for submarket modeling. I note that, throughout the housing economics literature, the term ‘submarket’ is subject to a range of definitions; the means of identifying submarkets has varied; empirical analyses have employed differing tests; and case studies have focused on a range of different cities and different time periods. This inconsistency has prevented the development of a coherent analytical approach. By using data from the Glasgow housing market a range of alternative definition and identification schemes are compared. The evidence suggests that submarkets are important and that, rather than being based exclusively on the similarity of property characteristics or geographical contiguity, the dimensions of housing submarkets are determined by both spatial and structural factors simultaneously.",251.0,,,journal-article,,,doi_10_1068_a34162,thedefinitionandidentificationofhousingsubmarkets,watkins,10.1068/a34162,thedefinitionandidentificationofhousingsubmarkets|2001,1,2138,2,hedonic housing prices || implicit prices housing attributes,0,1,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",725517c0f26df639a7e692031d664c717c34f19f,10.1111/1540-6229.00745,,"New Place-to-Place Housing Price Indexes for U.S. Metropolitan Areas, and Their Determinants",1996.0,,S. Malpezzi; Gregory H. Chun; Richard K. Green,,201.0,,False,,Business;Economics,1996-03-01,725517c0f26df639a7e692031d664c717c34f19f,newplacetoplacehousingpriceindexesforusmetropolitanareasandtheirdeterminants,malpezzi,10.1111/1540-6229.00745,newplacetoplacehousingpriceindexesforusmetropolitanareasandtheirdeterminants|1996,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",b7e7ef7a7371520eb3b29e1eff2990ea7d611ed8,10.1016/j.landusepol.2020.104919,,Understanding house price appreciation using multi-source big geo-data and machine learning,2020.0,Land Use Policy,Yu-Hao Kang; Fan Zhang; Wenzhe Peng; Song Gao; Jin-Meng Rao; Fábio Duarte; C. Ratti,"Abstract Understanding house price appreciation benefits place-based decision makings and real estate market analyses. Although large amounts of interests have been paid in the house price modeling, limited work has focused on evaluating the price appreciation rate. In this study, we propose a data-fusion framework to examine how well house price appreciation potentials can be predicted by combining multiple data sources. We used data sets including house structural attributes, house photos, locational amenities, street view images, transportation accessibility, visitor patterns, and socioeconomic attributes of neighborhoods to enrich our understanding of the real estate appreciation and its predictive modeling. As a case study, we investigate more than 20,000 houses in the Greater Boston Area, and discuss the spatial dependency of house price appreciations, influential variables and their relationships. In detail, we extract deep features from street view images and house photos using a deep learning model, merging features from multi-source data and modeling house price appreciation using machine learning models and the geographically weighted regression at two spatial scales: fine-scale point level and aggregated neighborhood level. Results show that the house price appreciation rate can be modeled with high accuracy using the proposed framework ( R 2 = 0.74 for gradient boosting machine at neighborhood-scale). We discovered that houses with low house prices and small house areas may have a higher house appreciation potential. Our results provide insights into how multi-source big geo-data can be employed in machine learning frameworks to characterize real estate price trends and help understand human settlements for policy-making.",190.0,,False,,Computer Science,2020-07-01,b7e7ef7a7371520eb3b29e1eff2990ea7d611ed8,understandinghousepriceappreciationusingmultisourcebiggeodataandmachinelearning,kang,10.1016/j.landusepol.2020.104919,understandinghousepriceappreciationusingmultisourcebiggeodataandmachinelearning|2020,1,2764,4,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,2,7,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f300cd3fcc1d1c9e1e554d644c2e3b3b26cfb3b1,10.1016/j.apgeog.2016.12.003,,Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique,2017.0,,Yang Wang; Shaojian Wang; Guang-Dong Li; Hong'ou Zhang; Lixia Jin; Yong-Xian Su; Kang-Min Wu,,155.0,,False,,Geography,2017-02-01,f300cd3fcc1d1c9e1e554d644c2e3b3b26cfb3b1,identifyingthedeterminantsofhousingpricesinchinausingspatialregressionandthegeographicaldetectortechnique,wang,10.1016/j.apgeog.2016.12.003,identifyingthedeterminantsofhousingpricesinchinausingspatialregressionandthegeographicaldetectortechnique|2017,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,3,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",783542bde70478f53dea2d9af920eb66c5e15483,10.1108/17538270910977572,,Housing price volatility and its determinants,2009.0,,C. Lee,,90.0,,False,,Economics,2009-08-07,783542bde70478f53dea2d9af920eb66c5e15483,housingpricevolatilityanditsdeterminants,lee,10.1108/17538270910977572,housingpricevolatilityanditsdeterminants|2009,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",aa1dfa2d3bc797fae69fed236764602c0e9c4f41,10.1080/00420987420080341,,Determinants of Urban House Prices,1974.0,,H. Richardson; J. Vipond; Robert Furbey,,76.0,,False,JournalArticle,Economics,1974-06-01,aa1dfa2d3bc797fae69fed236764602c0e9c4f41,determinantsofurbanhouseprices,richardson,10.1080/00420987420080341,determinantsofurbanhouseprices|1974,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || residential property price determinants",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",64e0e1c6b59d0309e540ea2150c2b7aab8cead09,10.1108/14635780310469102,,Determinants of housing prices in Spanish cities,2003.0,,Paloma Taltavull de La Paz,,62.0,,False,,Economics,2003-04-01,64e0e1c6b59d0309e540ea2150c2b7aab8cead09,determinantsofhousingpricesinspanishcities,paz,10.1108/14635780310469102,determinantsofhousingpricesinspanishcities|2003,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d29e0bf47fcebffe19e76cbf3d312c1fbdb0274b,10.1177/088541229801200301,,The Determinants of Residential Property Values: Implications for Metropolitan Planning,1998.0,,G. Knaap,,62.0,,False,JournalArticle;Review,Business,1998-02-01,d29e0bf47fcebffe19e76cbf3d312c1fbdb0274b,thedeterminantsofresidentialpropertyvaluesimplicationsformetropolitanplanning,knaap,10.1177/088541229801200301,thedeterminantsofresidentialpropertyvaluesimplicationsformetropolitanplanning|1998,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",9dee5cc3800299f7ddccac6e92a83eef1058fd76,10.1016/0094-1190(91)90047-b,,Housing price functions and ownership capitalization rates,1991.0,,P. Linneman; R. Voith,,54.0,,False,Review,Economics,1991-07-01,9dee5cc3800299f7ddccac6e92a83eef1058fd76,housingpricefunctionsandownershipcapitalizationrates,linneman,10.1016/0094-1190(91)90047-b,housingpricefunctionsandownershipcapitalizationrates|1991,1,1000,2,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3c4c92aaf0ec1b8ccdb97d7fa27a50bde3d403fc,10.1111/1468-2427.12873,,"The Limits of Homeownership: Racial Capitalism, Black Wealth, and the Appreciation Gap in Atlanta",2020.0,,Scott N. Markley; Taylor J. Hafley; Coleman A. Allums; Steven R. Holloway; Hee Cheol Chung,"Abstract In the wake of the 2007?08 housing crash, the Black?white wealth gap reached a staggering 20 to 1. Since then, a growing chorus of influential voices has proposed measures to increase the Black homeownership rate as a means to narrow the gap. Others, however, have argued that the uneven racial geography of home price appreciation necessarily restricts the degree to which Black households, in the aggregate, can build wealth via homeownership. We aim to contribute to these debates by theorizing a racial appreciation gap as a central feature of urban housing markets under racial capitalism, and analyzing how neighborhood racial and income characteristics have structured home price appreciation from before the height of the housing boom (2000?03) to its post-crisis recovery (2014?16). Focusing on the two counties that encompass Atlanta, Georgia, USA?an area famous for Black prosperity?we find that a neighborhood's racial composition has a more salient impact on home price change than its income. Results indicate that when a place is marked as Black, this may itself inhibit home price appreciation, suggesting that an enduring racial appreciation gap may limit the potential for Black homeownership to substantively narrow the racial wealth gap.",52.0,,False,,,2020-03-01,3c4c92aaf0ec1b8ccdb97d7fa27a50bde3d403fc,thelimitsofhomeownershipracialcapitalismblackwealthandtheappreciationgapinatlanta,markley,10.1111/1468-2427.12873,thelimitsofhomeownershipracialcapitalismblackwealthandtheappreciationgapinatlanta|2020,1,2266,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 crossref,apartment rent hedonic,,10.1023/a:1007729214108,,Price Indices Based on the Hedonic Repeat-Sales Method: Application to the Housing Market,1998.0,The Journal of Real Estate Finance and Economics,John M. Clapp; Carmelo Giaccotto,,49.0,,,journal-article,,,doi_10_1023_a_1007729214108,priceindicesbasedonthehedonicrepeatsalesmethodapplicationtothehousingmarket,clapp,10.1023/a:1007729214108,priceindicesbasedonthehedonicrepeatsalesmethodapplicationtothehousingmarket|1998,1,1000,4,apartment rent hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic,2,0,1,0,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",658598c8692029c7de140a118321ac538c74484d,10.1016/j.eswa.2023.120981,,A hybrid machine learning framework for forecasting house price,2023.0,Expert systems with applications,Choujun Zhan; Yonglin Liu; Zeqiong Wu; Mingde Zhao; Tommy W. S. Chow,,49.0,,False,JournalArticle,Computer Science,2023-07-01,658598c8692029c7de140a118321ac538c74484d,ahybridmachinelearningframeworkforforecastinghouseprice,zhan,10.1016/j.eswa.2023.120981,ahybridmachinelearningframeworkforforecastinghouseprice|2023,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",70a4acc430b60db9f5cfa5ef12abc71b0d80edf3,10.1198/073500104000000091,,Spatial Modeling of House Prices Using Normalized Distance-Weighted Sums of Stationary Processes,2004.0,,Sudipto Banerjee; A. Gelfand; John R. Knight; C. F. Sirmans,,43.0,,False,,Computer Science,2004-04-01,70a4acc430b60db9f5cfa5ef12abc71b0d80edf3,spatialmodelingofhousepricesusingnormalizeddistanceweightedsumsofstationaryprocesses,banerjee,10.1198/073500104000000091,spatialmodelingofhousepricesusingnormalizeddistanceweightedsumsofstationaryprocesses|2004,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0df59a7f1557fbb8bbf48a1ce104670d5313ed0a,10.1109/icpcsi.2017.8392275,,Predicting the housing price direction using machine learning techniques,2017.0,"2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI)",Debanjan Banerjee; Suchibrota Dutta,,39.0,,False,Conference,Computer Science,2017-09-01,0df59a7f1557fbb8bbf48a1ce104670d5313ed0a,predictingthehousingpricedirectionusingmachinelearningtechniques,banerjee,10.1109/icpcsi.2017.8392275,predictingthehousingpricedirectionusingmachinelearningtechniques|2017,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",83261931676ccb5dd148ae0780a13ea79e57abbe,10.1111/1540-6229.12170,,On the Relation between Local Amenities and House Price Dynamics,2018.0,,Eli Beracha; Benjamin Gilbert; Tyler Kjorstad; Kiplan S. Womack,,37.0,,False,,Economics,2018-09-01,83261931676ccb5dd148ae0780a13ea79e57abbe,ontherelationbetweenlocalamenitiesandhousepricedynamics,beracha,10.1111/1540-6229.12170,ontherelationbetweenlocalamenitiesandhousepricedynamics|2018,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6bf2617c5dce583da4d3e77c7f66b17ac6cf1a53,10.1016/j.cities.2018.01.018,,"The determinants of informal housing price in Beijing: Village power, informal institutions, and property security",2018.0,Cities,Meng-Zhu Zhang; Peng-Jun Zhao,"Abstract Although the how's of the informal housing market in China has been discussed widely; the answers remain conflicted. Using the theory of new institutional economics, this paper aims to answer two questions: what informal institutions secure the de facto tenure and facilitate the market? and How do informal institutions affect the pricing mechanism? We propose a conceptual framework to investigate the links between informal institutions, de facto tenure, and the price of informal housing. It is argued that villages have the ability to seek political patronage and regulate the market transactions to secure the tenure of informal housing. This ability is authorized by several formalities: the dual land system and the regime of village autonomy. The growing powers of market agencies in local development help strengthen the village's ability. The variances in ability between villages lead to a heterogeneity of tenure security, thus creating price differentials in the market. A higher tenure security increases the housing price. In conclusion, the informal housing market in China is “regulated” by a “semi-formal” system in which both informality and formality work and intertwine. A robust pricing mechanism has been emerging within the context of transition.",36.0,,False,,Business,2018-07-01,6bf2617c5dce583da4d3e77c7f66b17ac6cf1a53,thedeterminantsofinformalhousingpriceinbeijingvillagepowerinformalinstitutionsandpropertysecurity,zhang,10.1016/j.cities.2018.01.018,thedeterminantsofinformalhousingpriceinbeijingvillagepowerinformalinstitutionsandpropertysecurity|2018,1,2280,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,1,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f4e7ad57e8cecf6df00b9f645c303006086203bd,10.1016/j.jhe.2016.06.002,,The effect of mortgage interest deduction and mortgage characteristics on house prices,2016.0,,S. Damen; F. Vastmans; E. Buyst,,36.0,,False,,Economics,2016-12-01,f4e7ad57e8cecf6df00b9f645c303006086203bd,theeffectofmortgageinterestdeductionandmortgagecharacteristicsonhouseprices,damen,10.1016/j.jhe.2016.06.002,theeffectofmortgageinterestdeductionandmortgagecharacteristicsonhouseprices|2016,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 crossref,apartment rent hedonic,,10.1111/j.1465-7295.1991.tb00850.x,,ANALYZING RENT CONTROL: THE CASE OF LOS ANGELES,1991.0,Economic Inquiry,MICHAEL P. MURRAY; C. PETER RYDELL; C. LANCE BARNETT; CAROL E. HILLESTAD; KEVIN NEELS,"An accurate assessment of the economic effects of a rent control law requires that both the specific provisions of the ordinance and the characteristics of the local housing market be considered. Examining the case of Los Angeles we conclude: First, most of the transfers from landlords to tenants were realized early in the law's life, while most of the economic cost of rent control was incurred later. Second, ordinance provisions aimed at increasing landlords' incentives to maintain rent‐controlled dwellings also markedly reduce the size of the transfers to tenants. These results, we think, will apply to other jurisdictions.",27.0,,,journal-article,,,doi_10_1111_j_1465_7295_1991_tb00850_x,analyzingrentcontrolthecaseoflosangeles,murray,10.1111/j.1465-7295.1991.tb00850.x,analyzingrentcontrolthecaseoflosangeles|1991,1,1632,1,apartment rent hedonic,0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0fd064c6317c19cda97c542b59c1a210d7a079e5,10.1016/j.jhe.2011.08.002,,The house price determination process: Rational expectations with a spatial context,2011.0,,Hua Kiefer,,24.0,,False,,Economics,2011-12-01,0fd064c6317c19cda97c542b59c1a210d7a079e5,thehousepricedeterminationprocessrationalexpectationswithaspatialcontext,kiefer,10.1016/j.jhe.2011.08.002,thehousepricedeterminationprocessrationalexpectationswithaspatialcontext|2011,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price || hedonic housing prices || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",aa3e7bc90f5c74d83e16dca909464199c9e0d002,10.1016/j.jhe.2017.02.003,,The effect of local amenities on house price appreciation amid market shocks: The case of school quality,2017.0,,Mitchell R. Livy,,24.0,,False,,Economics,2017-06-01,aa3e7bc90f5c74d83e16dca909464199c9e0d002,theeffectoflocalamenitiesonhousepriceappreciationamidmarketshocksthecaseofschoolquality,livy,10.1016/j.jhe.2017.02.003,theeffectoflocalamenitiesonhousepriceappreciationamidmarketshocksthecaseofschoolquality|2017,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 crossref,implicit prices housing attributes,,10.1080/0042098993655,,The Inner-city Apartment versus the Suburb: Housing Sub-markets in a New Zealand City,1999.0,Urban Studies,Philip S. Morrison; Scott McMurray,"This paper addresses a new phenomenon in New Zealand—the growing demand for residence within the central business district. The construction of inner-city apartments has seen a ready response by local authorities keen to rejuvenate a demand for downtown services, by developers facing a slow-down in suburban growth and by absentee owners as well as owner-occupiers seeking to broaden their residential portfolios. The paper argues that, although we are witness to the emergence of a new and different housing sub-market, the inner-city apartment is in fact a natural extension of an existing demand for residence close to the city; its recent appearance as inner-city apartments is simply a reflection of the competition for inner-city land rather than the revealed preference of buyers for a wholly different type of housing. Far from turning their back on the single-dwelling unit, most apartment buyers seek dwelling attributes similar to those of their single-unit, suburban counterparts. What is unique to this new sub-market therefore is its location. The inner-city apartment is primarily a geographical reaction to the marked physical separation of residence, paid work and live entertainment which characterises the suburb rather than a switch of preference for a wholly new residential form. Although these are quite distinct housing forms, there remain strong market connections between the inner-city apartment and detached, single-unit dwellings in the suburbs. Fashionable though these new apartment units are, New Zealand housing continues to be dominated by low-density, suburban, single-unit dwellings.",20.0,,,journal-article,,,doi_10_1080_0042098993655,theinnercityapartmentversusthesuburbhousingsubmarketsinanewzealandcity,morrison,10.1080/0042098993655,theinnercityapartmentversusthesuburbhousingsubmarketsinanewzealandcity|1999,1,2620,3,apartment rent hedonic || hedonic housing prices || implicit prices housing attributes,0,1,4,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fab80a014e7c69903f33920d8b77abacdc5a7fbb,10.1111/1540-6229.t01-1-00201,,"FHA Mortgage Discount Points, House Prices and Consumer Behavior",1979.0,,K. L. Guntermann,,17.0,,False,,Economics,1979-06-01,fab80a014e7c69903f33920d8b77abacdc5a7fbb,fhamortgagediscountpointshousepricesandconsumerbehavior,guntermann,10.1111/1540-6229.t01-1-00201,fhamortgagediscountpointshousepricesandconsumerbehavior|1979,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 crossref,apartment rent hedonic,,10.1007/978-0-387-76815-1_9,,Using Hedonic Models to Measure Racial Discrimination and Prejudice in the U.S. Housing Market,2008.0,Hedonic Methods in Housing Markets,Jeffrey E. Zabel,,16.0,,,book-chapter,,,doi_10_1007_978_0_387_76815_1_9,usinghedonicmodelstomeasureracialdiscriminationandprejudiceintheushousingmarket,zabel,10.1007/978-0-387-76815-1_9,usinghedonicmodelstomeasureracialdiscriminationandprejudiceintheushousingmarket|2008,1,1000,1,apartment rent hedonic,2,0,1,0,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",11ebddb27aeaef3d08cf7a6dac9b7dd21eab0521,10.1142/s1793351x17500015,,Home Price Index: A Machine Learning Methodology,2017.0,International Journal of Semantic Computing,Joseph R. Barr; E. A. Ellis; A. Kassab; C. Redfearn; Nani Narayanan Srinivasan; Kurtis B. Voris,,16.0,,False,JournalArticle,Computer Science,2017-05-10,11ebddb27aeaef3d08cf7a6dac9b7dd21eab0521,homepriceindexamachinelearningmethodology,barr,10.1142/s1793351x17500015,homepriceindexamachinelearningmethodology|2017,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",59261e2e39b0c3b4a97c6493a726202144018f9e,10.1108/ijhma-06-2015-0028,,The effect of quality determinants on house prices of eight capital cities in Australia,2016.0,,Ti-Ching Peng; Chien-Fu Chen,,14.0,,False,,Economics,2016-09-06,59261e2e39b0c3b4a97c6493a726202144018f9e,theeffectofqualitydeterminantsonhousepricesofeightcapitalcitiesinaustralia,peng,10.1108/ijhma-06-2015-0028,theeffectofqualitydeterminantsonhousepricesofeightcapitalcitiesinaustralia|2016,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || residential property price determinants",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",db7b538b454552788bf9ab1b9b23610881d1c342,10.1080/09599916.2012.717100,,"Analysis of household location behaviour, local amenities and house prices in a sorting framework",2012.0,,M. van Duijn; J. Rouwendal,,14.0,,False,,Economics,2012-11-19,db7b538b454552788bf9ab1b9b23610881d1c342,analysisofhouseholdlocationbehaviourlocalamenitiesandhousepricesinasortingframework,duijn,10.1080/09599916.2012.717100,analysisofhouseholdlocationbehaviourlocalamenitiesandhousepricesinasortingframework|2012,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic house price",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",169f0a1bdf5551c551fd8dbb983472cd610e2923,10.1016/j.habitatint.2024.103151,,Population shrinkage and its influence on housing prices: A mediation perspective based on commercial amenities,2024.0,Habitat International,Mingke Xie; Zhang-Xian Feng; Hao-Ming Guan; Fei-Long Hao; Shi-Jun Wang; Xiaodong Chang,,14.0,,False,JournalArticle,,2024-09-01,169f0a1bdf5551c551fd8dbb983472cd610e2923,populationshrinkageanditsinfluenceonhousingpricesamediationperspectivebasedoncommercialamenities,xie,10.1016/j.habitatint.2024.103151,populationshrinkageanditsinfluenceonhousingpricesamediationperspectivebasedoncommercialamenities|2024,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",89150a647d9e1a08eb1878266bd50b940d68ac55,10.1111/cico.12436,,Black Homebuying after the Crisis: Appreciation Patterns in Fifteen Large Metropolitan Areas,2019.0,City & Community,Dan Immergluck; Stephanie Earl; Allison Powell,"Some have questioned the financial wisdom of homeownership and, especially, Black homeownership. This is understandable because the mortgage crisis dealt heavy blows to Black homeowners. One concern is that home values may not appreciate as much where Blacks purchase homes. We examine how Black homebuyers fared compared to White and Latino buyers in terms of home appreciation during the 2012 to 2017 recovery. We examine appreciation rates by race and ethnicity across 15 metros. We then estimate the relationship between appreciation and the race and ethnicity of the homebuyer, as well as characteristics of the neighborhood where the home is purchased. We find Blacks saw higher appreciation rates than Whites in high– and medium–appreciation metros, but not in low–appreciation metros. We also find that in medium– and high–appreciation metros, buyers in racially diverse neighborhoods tended to see higher levels of appreciation. Also in higher–appreciation metros, those buying in lower–income neighborhoods tended to see higher appreciation rates, while those in low–appreciation metros did not.",13.0,,False,JournalArticle,Political Science,2019-09-01,89150a647d9e1a08eb1878266bd50b940d68ac55,blackhomebuyingafterthecrisisappreciationpatternsinfifteenlargemetropolitanareas,immergluck,10.1111/cico.12436,blackhomebuyingafterthecrisisappreciationpatternsinfifteenlargemetropolitanareas|2019,1,2105,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e1ec1b999c9d283637d0daa85c523651f6de3bec,10.1007/s11356-023-30123-5,,The nonlinear relationship between air quality and housing prices by machine learning,2023.0,Environmental science and pollution research international,Wei-Wen Zhang; Sheng Pan; Zhi-Yuan Li; Zi-Qing Li; Zhaoyingzi Dong,,13.0,,False,JournalArticle,Medicine,2023-10-20,e1ec1b999c9d283637d0daa85c523651f6de3bec,thenonlinearrelationshipbetweenairqualityandhousingpricesbymachinelearning,zhang,10.1007/s11356-023-30123-5,thenonlinearrelationshipbetweenairqualityandhousingpricesbymachinelearning|2023,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 crossref,apartment rent hedonic,,10.2307/2330452,,Assessing Hedonic Indexes for Housing,1979.0,The Journal of Financial and Quantitative Analysis,Charles W. Noland,,12.0,,,journal-article,,,doi_10_2307_2330452,assessinghedonicindexesforhousing,noland,10.2307/2330452,assessinghedonicindexesforhousing|1979,1,1000,1,apartment rent hedonic,2,0,1,0,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,willingness to pay housing attributes hedonic,b6e75dff6727c5b0316020b1050e7a790688cd43,10.1016/j.jhe.2013.02.003,,Estimating household demand for housing attributes in rent-controlled markets,2013.0,,J. V. van Ommeren; M. Zijl,,12.0,,False,,Economics,2013-03-01,b6e75dff6727c5b0316020b1050e7a790688cd43,estimatinghouseholddemandforhousingattributesinrentcontrolledmarkets,ommeren,10.1016/j.jhe.2013.02.003,estimatinghouseholddemandforhousingattributesinrentcontrolledmarkets|2013,1,1000,4,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property) || hedonic housing prices || housing characteristics prices hedonic || willingness to pay housing attributes hedonic",0,1,2,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6f273edd7d4d076ad74ff6c37cbb97f200161903,10.1016/j.apgeog.2017.01.007,,"Housing-market bubble adjustment in coastal communities - A spatial and temporal analysis of housing prices in Midwest Pinellas County, Florida",2017.0,,Yin-Hsuen Chen; T. Fik,,12.0,,False,,Geography,2017-03-01,6f273edd7d4d076ad74ff6c37cbb97f200161903,housingmarketbubbleadjustmentincoastalcommunitiesaspatialandtemporalanalysisofhousingpricesinmidwestpinellascountyflorida,chen,10.1016/j.apgeog.2017.01.007,housingmarketbubbleadjustmentincoastalcommunitiesaspatialandtemporalanalysisofhousingpricesinmidwestpinellascountyflorida|2017,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic housing prices || housing characteristics prices hedonic || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d9aa8e7e33cedc2567dc8a94bbf2b485d5057b2d,10.1108/ijhma-10-2023-0139,,Determinants of housing prices: evidence from East Coast Malaysia,2024.0,International Journal of Housing Markets and Analysis,Siti Hafsah Zulkarnain; Abdol Samad Nawi; M. A. Esquivias; Anuar Husin," Purpose The purpose of this study is designed to achieve the learning process in producing studies involving economic issues and scenarios in business management in Malaysia. In addition, this study will provide exposure to the integration of managerial skills by using both microeconomics and macroeconomics concepts and theories to aid decision-making in a business environment. Design/methodology/approach The research method comprised qualitative methodology of literature review, case study and quantitative methodology of multiple linear regression (MLR). In this case, seven microeconomics and macroeconomics factors which are believed to significantly affect house price index (HPI) are taken into consideration which includes gross domestic product, consumer price index (CPI), government tax and subsidy on housing, overnight policy rate, unemployment rate (UNEMP), the median income (INC) and cost of production index. Findings This research has resulted in three significant factors affecting HPI from MLR, which include CPI, UNEMP and INC where the increase of these factors will cause a high increment of HPI. The other four factors are not significant. Originality/value Malaysia has been facing the stagnancy in house market these recent years due to issues such as massive oversupply, impacting Malaysia’s economy specifically focusing on domestic direct investment. To avoid oversupply issues, the vitality of future house demand and pricing forecast should be comprehended by involved bodies for more effective planning for the house development industry. To make a better and bigger impact, this research is intended to analyse the microeconomic and macroeconomic factors affecting the HPI to better understand the significance of each of these factors to the changes of HPI to resolve these economic issues. ",11.0,,False,JournalArticle;Review,,2024-01-11,d9aa8e7e33cedc2567dc8a94bbf2b485d5057b2d,determinantsofhousingpricesevidencefromeastcoastmalaysia,zulkarnain,10.1108/ijhma-10-2023-0139,determinantsofhousingpricesevidencefromeastcoastmalaysia|2024,1,2836,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b776d1c4bf5a8fe5b2ccd5a6ccf76e555ecdc49d,10.1080/10293523.2005.11082467,,The determinants of residential house prices in South Africa,2005.0,,B. Standish; B. Lowther; R. Morgan-Grenville; C. Quick,,11.0,,False,,Economics,2005-01-01,b776d1c4bf5a8fe5b2ccd5a6ccf76e555ecdc49d,thedeterminantsofresidentialhousepricesinsouthafrica,standish,10.1080/10293523.2005.11082467,thedeterminantsofresidentialhousepricesinsouthafrica|2005,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",87e810f2a3dd35b9b74b69d4277557b399a27db6,10.7763/joebm.2015.v3.208,,Determinants of Hong Kong's Housing Prices,2015.0,,A. Cheng; M. Fung,,10.0,,False,,Business,,87e810f2a3dd35b9b74b69d4277557b399a27db6,determinantsofhongkongshousingprices,cheng,10.7763/joebm.2015.v3.208,determinantsofhongkongshousingprices|2015,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 crossref,apartment rent hedonic,,10.1007/978-0-387-76815-1_3,,Hedonic Modeling of the Home Selling Process,2008.0,Hedonic Methods in Housing Markets,John R. Knight,,9.0,,,book-chapter,,,doi_10_1007_978_0_387_76815_1_3,hedonicmodelingofthehomesellingprocess,knight,10.1007/978-0-387-76815-1_3,hedonicmodelingofthehomesellingprocess|2008,1,1000,3,apartment rent hedonic || hedonic housing prices || hedonic property valuation,2,0,1,0,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f28b96fe92f004ac16c87bc0c2d3ada98f360756,10.1080/02673030701254152,,The Determinants of Home Price Appreciation Among Community Reinvestment Homeowners,2007.0,,Michael A. Stegman; Roberto G. Quercia; W. R. Davis,,9.0,,False,,Economics,2007-04-25,f28b96fe92f004ac16c87bc0c2d3ada98f360756,thedeterminantsofhomepriceappreciationamongcommunityreinvestmenthomeowners,stegman,10.1080/02673030701254152,thedeterminantsofhomepriceappreciationamongcommunityreinvestmenthomeowners|2007,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 crossref,apartment rent hedonic,,10.1111/1540-6229.00756,,A Test of a Buying Rule for “Underpriced” Apartment Buildings,1998.0,Real Estate Economics,Jane Londerville,"Apartment‐building repeat‐sales data are used to test a trading rule. Property values are estimated through regression; if the actual price is less than the estimated value, the property may be “undervalued.” Returns are calculated for these “undervalued” properties and adjusted for risk using the Sharpe ratio. A test is done of whether these ratios are significantly different for different portfolios. The “undervalued” properties have higher appreciation returns, but once these are adjusted for risk, the differences in returns are not significant. The paper contributes to the real estate efficiency literature and suggests a workable method for comparing the performance of real estate portfolios on a risk‐adjusted basis.",7.0,,,journal-article,,,doi_10_1111_1540_6229_00756,atestofabuyingruleforunderpricedapartmentbuildings,londerville,10.1111/1540-6229.00756,atestofabuyingruleforunderpricedapartmentbuildings|1998,1,1730,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic",0,2,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f3d8fb09231a923339d673bdef8251853cc33271,10.16538/j.cnki.jfe.2016.07.012,,"Real Estate Attributes, Income Gap and Change Trend of Housing Prices",2016.0,,Zhong-Fei Li; Shou-Dong Yu; Jun-Peng Zheng,,7.0,,False,,Economics,2016-07-01,f3d8fb09231a923339d673bdef8251853cc33271,realestateattributesincomegapandchangetrendofhousingprices,li,10.16538/j.cnki.jfe.2016.07.012,realestateattributesincomegapandchangetrendofhousingprices|2016,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9095003ffc06eca53d0fe6110393fdd6b400fe3e,10.1145/3369114.3369125,,Combining Domain Knowledge & Machine Learning: Making Predictions using Boosting Techniques,2019.0,International Conference on Advances in Artificial Intelligence,Utku Kubilay Cinar,"The latest hit on technology is the information and telecommunication novelties. Internet and big data are important source of information in order to understand this vast majority of the upcoming knowledge. Websites are progressively expanding and making it available to everyone who has access. Modern economic systems are built on data or knowledge. Thus, tech companies gather huge data and exercise their powers to digitalize the information to capture and utilize the knowledge within their reach. Information management enables firms to improve customer satisfaction, increase revenue, understand customer behavior, mitigate risk assessment, making a multidisciplinary approach. Another approach is to be able to identify business strategies. Information management researches (using machine learning algorithm) can help the field to discover what is significant on a customer behavior and reduce costs to its clients. Over the recent years, information management and machine learning algorithms are getting more and more close and on topic. Since, information management and machine learning intensely concerns with domain knowledge. Artificial Intelligence allows the machines to accumulate knowledge and adapts it. Information management and machine learning are affected by several factors such as business strategies, customer behavior, effectively using knowledge. Therefore, pulling only a single topic will not be enough to capture meaningful information from a huge chunk. Machine learning, and information systems also have the potential to help organizations in financial aspects. XG-Boost is one of the algorithms under the Decision-Trees equipped with boosting techniques similar to Microsoft's Light GBM algorithm. In recent years, XG-Boost algorithm has gained huge popularity due to its easy to use, speed, and performance. The aim of this paper is to show the use of the data in order to predict house prices XG-Boost algorithm and Neural Network model. The main goal here is to estimate the house prices using domain knowledge and machine learning algorithms. RMSE criteria was preferred. This paper suggests a generic way to gather knowledge on a very specific domain. Following the achieved result, the house price was estimated by processing the domain info through the machine learning algorithm here presented.",6.0,,False,Book;JournalArticle,Computer Science,2019-10-26,9095003ffc06eca53d0fe6110393fdd6b400fe3e,combiningdomainknowledgemachinelearningmakingpredictionsusingboostingtechniques,cinar,10.1145/3369114.3369125,combiningdomainknowledgemachinelearningmakingpredictionsusingboostingtechniques|2019,1,3341,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2d7f130b935d88f8e6331808b3ca723c2f192064,10.1080/09599916.2020.1740765,,Restrictions versus amenities: the differential impact of home owners associations on property marketability,2020.0,,K. Goodwin; C. R. La Roche; B. Waller,"ABSTRACT Common-interest developments (CIDs) or planned urban developments (PUDs) can include a multitude of property types such as condos, townhomes, coops and single-family residences. Many such developments are privately governed by a homeowners’ association (HOA) and managed by an HOA board of directors comprised of community homeowners. While such communities and their governing bodies have been widely criticised for their onerous rules, regulations and exclusionary practices, many argue that the amenities, benefits and utility afforded to its members provide a large degree of satisfaction for homeowners. In fact, one of the purposes of an HOA is to preserve and enhance home values by creating an environment with minimal negative externalities. Although it is generally assumed that an HOA does add value, these associations have also generated a number of controversial disputes. This paper examines the effects of an HOA on marketability to empirically shed light on the question of whether buyers find HOAs to be beneficial or burdensome. The results show that the impact of the HOA on price, marketing time and probability of sale are not even across price segments and exist even after controlling for the presence of gated communities.",6.0,,False,,Business,2020-04-09,2d7f130b935d88f8e6331808b3ca723c2f192064,restrictionsversusamenitiesthedifferentialimpactofhomeownersassociationsonpropertymarketability,goodwin,10.1080/09599916.2020.1740765,restrictionsversusamenitiesthedifferentialimpactofhomeownersassociationsonpropertymarketability|2020,1,2256,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,4,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fc5bc85c76c69374f7265703f89bb906bb6b0689,10.1080/13658816.2022.2100891,,Scalable spatiotemporal regression model based on Moran’s eigenvectors,2022.0,International Journal of Geographical Information Science,Hayato Nishi; Yasushi Asami; H. Baba; C. Shimizu,"Abstract We propose a scalable regression model with spatially and temporally varying coefficients based on Moran’s eigenvectors and efficient computation algorithms. Regression models that consider spatiotemporal non-stationarity are important because many real-world datasets, such as housing prices, are tied to geographical and temporal locations. Although geographically weighted regression (GWR) and its variants are widely used to model spatially varying coefficients, they cannot handle large datasets. We employ an alternative modelling method of spatially varying coefficients based on Moran’s eigenvectors and extend it to handle large spatiotemporal datasets. Additionally, we introduce a scalable learning algorithm that exploits the model structures based on the Kalman filter and the expectation–maximisation algorithm. Our scalable algorithm is efficient even for large datasets that cannot be handled by GWR. To evaluate the performance of the proposed model, we applied it to a housing market dataset collected in Tokyo, Japan. The results show that the predictive performance of the proposed model is comparable to that of GWR while increasing the computational speed. Moreover, larger datasets can accelerate the algorithm convergence.",6.0,,False,JournalArticle,Computer Science,2022-07-22,fc5bc85c76c69374f7265703f89bb906bb6b0689,scalablespatiotemporalregressionmodelbasedonmoranseigenvectors,nishi,10.1080/13658816.2022.2100891,scalablespatiotemporalregressionmodelbasedonmoranseigenvectors|2022,1,2255,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e2134ce08ed4558757de069f5eea19bd83d50760,10.1016/j.frl.2023.104400,,Community resilience and house prices: A machine learning approach,2023.0,Finance Research Letters,Yi Zheng,,6.0,,False,JournalArticle,,2023-09-01,e2134ce08ed4558757de069f5eea19bd83d50760,communityresilienceandhousepricesamachinelearningapproach,zheng,10.1016/j.frl.2023.104400,communityresilienceandhousepricesamachinelearningapproach|2023,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ebf80cfd13e864f1b2482c19826f76b660340cb1,10.11820/dlkxjz.2014.03.009,,Determinants and dynamics of spatial differentiation of housing price in Yangzhou,2014.0,,Yang Wang; Qiang Li; Jing Qin; Shao-Jian Wang,,6.0,,False,,Economics,,ebf80cfd13e864f1b2482c19826f76b660340cb1,determinantsanddynamicsofspatialdifferentiationofhousingpriceinyangzhou,wang,10.11820/dlkxjz.2014.03.009,determinantsanddynamicsofspatialdifferentiationofhousingpriceinyangzhou|2014,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 crossref,apartment rent hedonic,,10.2747/0272-3638.12.5.450,,"A SPATIAL ANALYSIS OF RENT SHIFTS IN THE CHICAGO RENTAL HOUSING MARKET, 1970-1980",1991.0,Urban Geography,Brigitte S. Waldorf,,5.0,,,journal-article,,,doi_10_2747_0272_3638_12_5_450,aspatialanalysisofrentshiftsinthechicagorentalhousingmarket19701980,waldorf,10.2747/0272-3638.12.5.450,aspatialanalysisofrentshiftsinthechicagorentalhousingmarket19701980|1991,1,1000,2,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || apartment rent hedonic",0,1,2,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),895d62850e3edf58474428e49c81ed24d27bf790,10.1007/s43071-021-00013-4,,A spatial model averaging approach to measuring house prices,2021.0,Journal of Spatial Econometrics,Ryan Greenaway-McGrevy; Kade Sorensen,,5.0,,False,,,2021-06-09,895d62850e3edf58474428e49c81ed24d27bf790,aspatialmodelaveragingapproachtomeasuringhouseprices,greenaway-mcgrevy,10.1007/s43071-021-00013-4,aspatialmodelaveragingapproachtomeasuringhouseprices|2021,1,1000,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price,0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),5a3936fb202b23751e85943007779ddc4ab3872b,10.1429/30314,,Hedonic-Based Price Indexes for the Housing Market in Italian Cities: Theory and Estimation,2009.0,,A. Michelangeli; Alberto Zanardi,,5.0,,False,,Economics,,5a3936fb202b23751e85943007779ddc4ab3872b,hedonicbasedpriceindexesforthehousingmarketinitaliancitiestheoryandestimation,michelangeli,10.1429/30314,hedonicbasedpriceindexesforthehousingmarketinitaliancitiestheoryandestimation|2009,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices",2,0,1,0,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7bd5e31c4ebb64037ef0ab02e74cbbf6b3d53414,10.1016/j.apgeog.2024.103322,,Neighborhood externalities modify the nexus between residential land and housing prices: A joint analysis using regression tree models,2024.0,Applied Geography,Weiye Xiao; Peiyu Tian; Feng Yuan,,5.0,,False,JournalArticle,,2024-07-01,7bd5e31c4ebb64037ef0ab02e74cbbf6b3d53414,neighborhoodexternalitiesmodifythenexusbetweenresidentiallandandhousingpricesajointanalysisusingregressiontreemodels,xiao,10.1016/j.apgeog.2024.103322,neighborhoodexternalitiesmodifythenexusbetweenresidentiallandandhousingpricesajointanalysisusingregressiontreemodels|2024,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0a8f8346927284b09dc1e42227b105534e47106a,10.1108/ijhma-10-2023-0135,,Factors affecting the housing prices in the metropolis of Tehran,2024.0,International Journal of Housing Markets and Analysis,S. Rajaei; A. Mottaghi; Hussein Elhaei Sahar; Behnaz Bahadori," Purpose This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable). Design/methodology/approach The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map. Findings Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities. Originality/value Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis. ",4.0,,False,JournalArticle,,2024-04-15,0a8f8346927284b09dc1e42227b105534e47106a,factorsaffectingthehousingpricesinthemetropolisoftehran,rajaei,10.1108/ijhma-10-2023-0135,factorsaffectingthehousingpricesinthemetropolisoftehran|2024,1,3554,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,2,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2c95d3c480099fcdd9ae90189622b4af16b7e673,10.1108/ijhma-02-2023-0020,,How frequent and visible criminal violence affects housing prices: evidence from Mexico City (2007–2011),2023.0,International Journal of Housing Markets and Analysis,Laura H. Atuesta; Monserrat Carrasco," Purpose Between 2006 and 2012, Mexico implemented a “frontal war against organized crime”. This strategy increased criminal violence and triggered negative consequences across the country’s economic, political and social spheres. This study aims to analyse how the magnitude and visibility of criminal violence impact the housing market of Mexico City. Design/methodology/approach The authors used different violent proxies to measure the effect of the magnitude and visibility of violence in housing prices. The structure of the data set is an unbalanced panel with no conditions of strict exogeneity. To address endogeneity, the authors calculate the first differences to estimate an Arellano–Bond estimator and use the lags of the dependent variable to instrumentalise the endogenous variable. Findings Results suggest that the magnitude of violence negatively impacts housing prices. Similarly, housing prices are negatively affected the closer the property is to visible violence, measured through narcomessages placed next to the bodies of executed victims. Lastly, housing prices are not always affected when a violent event occurs nearby, specifically, when neighbours or potential buyers consider this event as sporadic violence. Originality/value There are only a few studies of violence in housing prices using data from developing countries, and most of these studies are conducted with aggregated data at the municipality or state level. The authors are using geocoded information, both violence events and housing prices, to estimate more disaggregated effects. Moreover, the authors used different proxies to measure different characteristics of violence (magnitude and visibility) to estimate the heterogeneous effects of violence on housing prices. ",4.0,,False,JournalArticle,,2023-05-05,2c95d3c480099fcdd9ae90189622b4af16b7e673,howfrequentandvisiblecriminalviolenceaffectshousingpricesevidencefrommexicocity20072011,atuesta,10.1108/ijhma-02-2023-0020,howfrequentandvisiblecriminalviolenceaffectshousingpricesevidencefrommexicocity20072011|2023,1,2773,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0731671c97a0e27d246169336ff25e11c0f81c31,10.1109/eebda53927.2022.9744970,,Big Data Analyzing Techniques in Mathematical House Price Prediction Model,2022.0,"2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)",Jia-Hao Yang,"The Chinese house market has been flourishing in the past three decades as an increasingly bigger population moves into cities. To keep the rise of house price within a proper range and acceptable to the public, the government has adopted various approaches to bring the price under control. After intermittent fluctuations, house purchasing has become a hot topic for both the media and the public. It is of great significance to study the change of house price considering all the related factors. Some scientists prefer machine learning. Machine learning is a subject which involves many subjects such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. In this paper, we will discuss the details of the machine learning algorithms, three typical models (i.e., AdaBoost, Linear Regression and KNN) and their strengths and weaknesses. In the future, the growing complexity of all the factors that influence house price will cause more and more trouble for scientists pursuing more precise results. Also, based on its own specialty that computer is still not able to think like a man, machine learning cannot achieve 100% right, which often results in some silly outcomes.",4.0,,False,Conference,,2022-02-25,0731671c97a0e27d246169336ff25e11c0f81c31,bigdataanalyzingtechniquesinmathematicalhousepricepredictionmodel,yang,10.1109/eebda53927.2022.9744970,bigdataanalyzingtechniquesinmathematicalhousepricepredictionmodel|2022,1,2228,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,1 crossref,apartment rent hedonic,,10.1109/icmse.2009.5317673,,Analysis of residential product's value based on Structural Equation Model and Hedonic Price Theory,2009.0,2009 International Conference on Management Science and Engineering,Yan Liu; Yong-xiang Wu,,4.0,,,proceedings-article,,,doi_10_1109_icmse_2009_5317673,analysisofresidentialproductsvaluebasedonstructuralequationmodelandhedonicpricetheory,liu,10.1109/icmse.2009.5317673,analysisofresidentialproductsvaluebasedonstructuralequationmodelandhedonicpricetheory|2009,1,1000,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,0,1,0,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",280bd1c740314fbd052db13357879392ed94bb7f,10.1007/s12061-021-09403-5,,Do Urbanization and Energy-environment Policies Affect Housing Values? Evidence From Spatial Textual Analysis,2021.0,Applied spatial analysis and policy,Yi-Ming He; Meng Chen,,4.0,,False,,Economics,2021-09-07,280bd1c740314fbd052db13357879392ed94bb7f,dourbanizationandenergyenvironmentpoliciesaffecthousingvaluesevidencefromspatialtextualanalysis,he,10.1007/s12061-021-09403-5,dourbanizationandenergyenvironmentpoliciesaffecthousingvaluesevidencefromspatialtextualanalysis|2021,1,1000,1,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a1fb23eca0dbd0c5d233440bf64edfc4d698e665,10.4156/jcit.vol6.issue11.37,,Housing Price Forecasting Based on Least Squares Support Vector Regression with Particle Swarm Optimization,2011.0,,Rong-Peng Liu; Hong-Yang Ren,,4.0,,False,,Mathematics,2011-11-30,a1fb23eca0dbd0c5d233440bf64edfc4d698e665,housingpriceforecastingbasedonleastsquaressupportvectorregressionwithparticleswarmoptimization,liu,10.4156/jcit.vol6.issue11.37,housingpriceforecastingbasedonleastsquaressupportvectorregressionwithparticleswarmoptimization|2011,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",15b2bde6393741ff011895178e679ce0fd3fa258,10.1080/713702292,,The Use of Content Analysis in Identifying the Determinants of House Prices,1978.0,,J. Doling,,4.0,,False,JournalArticle;Review,Business,1978-02-01,15b2bde6393741ff011895178e679ce0fd3fa258,theuseofcontentanalysisinidentifyingthedeterminantsofhouseprices,doling,10.1080/713702292,theuseofcontentanalysisinidentifyingthedeterminantsofhouseprices|1978,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 crossref,preços hedônicos imóveis,,10.1590/1982-3533.2020v29n3art12,,"Índice de preços hedônicos para apartamentos: uma aplicação a dados fiscais de Belo Horizonte, 1995-2012",2020.0,Economia e Sociedade,Luiz Andrés Ribeiro Paixão; Viviane Luporini,"Resumo Esse trabalho tem como objetivo discutir as metodologias hedônicas mais difundidas para a construção de índices de preços e aplicá-las para uma base de dados fiscais do município de Belo Horizonte, entre 1995 e 2012. Os resultados para os índices de preços pelos diversos métodos hedônicos estimados registraram uma intensa valorização imobiliária em Belo Horizonte, a partir de 2005, impulsionada pelo aumento na oferta de crédito imobiliário, que se tornou possível após as melhorias no instituo de alienação fiduciária, em 2004. Além disso, a redução nas taxas de juros, o crescimento econômico observado no período e o aumento da renda real das famílias também contribuíram para o bom desempenho dos preços dos imóveis. Mesmo após a crise mundial de 2008/2009, a valorização imobiliária continuou intensa até meados de 2012, possivelmente devido às políticas anticíclicas implementadas pelo Governo Federal. Esses resultados demonstram que a utilização da metodologia hedônica em uma base de dados fiscais constitui uma estratégia promissora para a implementação de um futuro índice de preços oficial para imóveis no Brasil.",3.0,,,journal-article,,,doi_10_1590_1982_3533_2020v29n3art12,indicedeprecoshedonicosparaapartamentosumaaplicacaoadadosfiscaisdebelohorizonte19952012,paixão,10.1590/1982-3533.2020v29n3art12,indicedeprecoshedonicosparaapartamentosumaaplicacaoadadosfiscaisdebelohorizonte19952012|2020,1,2135,1,preços hedônicos imóveis,1,0,1,0,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1b6cb0abbe5638e619a4a5c36c577475a8ee7e64,10.1080/07352166.2020.1761257,,"Spatial dependence and spatial heterogeneity in the effects of immigration on home values and native flight in Louisville, Kentucky",2020.0,Journal of Urban Affairs,Anqing Xu; Charlie H. Zhang; M. Ruther,"ABSTRACT This article examines how changes in foreign-born populations are associated with home values and native flight in Louisville, the largest city in Kentucky. In particular, we use spatial autoregressive models (SAR) to explore the spillover effects of foreign-born populations beyond neighborhood boundaries and utilize geographically weighted regression (GWR) to tackle spatial heterogeneity that is complicating the immigrant/neighborhood relationship. Our findings show an insignificant role of immigrant growth in shaping median home values of Louisville, while increasing proportions of immigrants are positively associated with out-migration of non-Hispanic Whites. We also show how those relationships vary across space: the foreign-born population is a salient predictor in White flight in affluent northeastern suburban neighborhoods, compared to middle-class southern suburbs. These findings shed light on heterogeneous local responses within the metropolitan area when confronting immigrant suburbanization.",3.0,,False,,Geography,2020-06-24,1b6cb0abbe5638e619a4a5c36c577475a8ee7e64,spatialdependenceandspatialheterogeneityintheeffectsofimmigrationonhomevaluesandnativeflightinlouisvillekentucky,xu,10.1080/07352166.2020.1761257,spatialdependenceandspatialheterogeneityintheeffectsofimmigrationonhomevaluesandnativeflightinlouisvillekentucky|2020,1,2026,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f06c25ac3797097edbfba0499d0151eff5f6870d,10.1109/prmvia58252.2023.00028,,A Comparative Study Of CART Algorithm For Forecasting,2023.0,"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)",Juan Yan; Quan Zhou; Ya Xiao; Bin Pan,"CART algorithm is a tree structure used for classification rules in the form of decision tree from a group of unordered and irregular cases. Compared with other classification methods, it has the advantage that a busy large amount of data can is classified yen fully, and then valuable potential information can be found. The method is simple and intuitive, with fast classification speed and high accuracy, which is suitable for large-scale data processing. Moreover, the algorithm process is easy to understand and can though express the importance of attributes praying attributes. The significant sensitivity and unpredictability of house price make it difficult to construct its forecasting model. In this paper, through an example of house price, the influencing factors of house price are deeply analyzed and the existing research results are systematically sorted out, and the decision tree CART detailed is used to build a molybdenum metal price algorithm model and forecast the actual price. By comparing and analyzing the results by using Not principles, the average absolute error is 4.03%, and the accuracy rate of foreforetrend forecasting trend can reach 94.8%, which shows that the algorithm is not only not intuitive and intuitive, but also reasonable and reliable.",2.0,,False,Conference,,2023-03-01,f06c25ac3797097edbfba0499d0151eff5f6870d,acomparativestudyofcartalgorithmforforecasting,yan,10.1109/prmvia58252.2023.00028,acomparativestudyofcartalgorithmforforecasting|2023,1,2282,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9c435075f7982b32fc7121442af81db890063d6c,10.1142/s2345748116500263,,Which is Driver? Land Price or Housing Price: Examining the Urban Spatial Structure of Beijing,2016.0,,C. Ding; Yi Niu,,2.0,,False,,Economics,2016-09-01,9c435075f7982b32fc7121442af81db890063d6c,whichisdriverlandpriceorhousingpriceexaminingtheurbanspatialstructureofbeijing,ding,10.1142/s2345748116500263,whichisdriverlandpriceorhousingpriceexaminingtheurbanspatialstructureofbeijing|2016,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic house price,62b1054f7f05499495778ca937d1b679ab7d46f8,10.1007/s12076-009-0030-z,,Predicting housing prices at alternative locations and under alternative scenarios of the spatial job distribution,2009.0,,Liv Osland; I. Thorsen,,2.0,,False,,Economics,2009-09-30,62b1054f7f05499495778ca937d1b679ab7d46f8,predictinghousingpricesatalternativelocationsandunderalternativescenariosofthespatialjobdistribution,osland,10.1007/s12076-009-0030-z,predictinghousingpricesatalternativelocationsandunderalternativescenariosofthespatialjobdistribution|2009,1,1000,3,hedonic house price || hedonic housing prices || spatial hedonic model housing,0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",407faad9ae20aecb0f0bfdc7f26059d6e1c01425,10.4028/www.scientific.net/amm.415.722,,Influencing Factors Analysis of House Prices Based on Multiple Linear Regression,2013.0,,Cheng Wang,,2.0,,False,,Economics,2013-09-01,407faad9ae20aecb0f0bfdc7f26059d6e1c01425,influencingfactorsanalysisofhousepricesbasedonmultiplelinearregression,wang,10.4028/www.scientific.net/amm.415.722,influencingfactorsanalysisofhousepricesbasedonmultiplelinearregression|2013,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,computer vision property valuation,51fec5dc61f239c8af9d175c8844af2033fc82c2,10.63642/1357-1419.1021,,"Market calibration of ""cost"" models",2005.0,Journal of Property Tax Assessment & Administration,R. Gloudemans; W. Nelson,"Although the benefits of regression analysis and market modeling are well known, a practical limitation of high concern to potential users is how to interface models, once developed, into their Computer Assisted Mass Appraisal (CAMA) systems. How do jurisdictions that have used the cost approach in the past interface models developed with SPSS or other statistical packages into their cost-oriented CAMA systems? This paper will demonstrate how the District of Columbia successfully addressed this issue to implement a highly improved and accurate valuation system. Like many jurisdictions, the District has utilized a combination of the cost approach and market trending for the appraisal of residential properties. The District’s CAMA software, Appraisal Vision ® by Vision Appraisal Technology, contains the cost approach and an interface with SPSS for modeling. While the District desired to implement MRA, it also wanted to maintain its familiar cost structure, both for value explanation purposes and in order to avoid system modifications or reprogramming. However, the District’s cost models are “hybrid” models that contain a combination of rates, size and depreciation curves, and multipliers and therefore cannot be calibrated by traditional additive or multiplicative MRA. Undeterred, the District used SPSS’s “Nonlinear” MRA procedure to calibrate their cost structure using sales data in what can be called a fully “market calibrated cost model”. Although somewhat more complex, nonlinear MRA permits the user to calibrate virtually any model structure, thus supporting more sophisticated (complex) model structures and, importantly, fit models to a desired structure (rather than vice versa). Introduction of market calibrated cost models allowed the District to improve model performance and valuation accuracy without modifying its current CAMA system and without additional software or programming costs. Market Calibration of “Cost” Models Robert J. Gloudemans & Willam R. Nelson, Jr. Acknowledgments: the authors wish to acknowledge the support and encouragement of Thomas W. Branham, Chief Assessor within the Government of the District of Columbia, who has spearheaded the various technological and methodological improvements achieved in the District over the past several years. Without his leadership, vision, and on-going support, this paper would not have been possible.",1.0,,False,JournalArticle,Computer Science,2005-06-01,51fec5dc61f239c8af9d175c8844af2033fc82c2,marketcalibrationofcostmodels,gloudemans,10.63642/1357-1419.1021,marketcalibrationofcostmodels|2005,1,3399,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || computer vision property valuation",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",3c9caf715802e4b04d9f81994bbb50af4c642127,10.1108/jerer-02-2022-0004,,Drivers behind the accuracy of self-reported home valuations: evidence from an emerging economy,2022.0,Journal of European Real Estate Research,Mateusz Tomal,"PurposeThis paper aims to explore the drivers behind the accuracy of self-reported home valuations in the Warsaw (Poland) housing market.Design/methodology/approachIn order to achieve the research goal, firstly, unique data on subjective residential property values estimated by their owners were compared with market-justified ones. The latter was calculated using geographically weighted regression, which allowed for taking into account spatially heterogeneous buyers' housing preferences. An ordered logit model was then used to identify the factors influencing the probability of the occurrence of bias towards over or undervaluation.FindingsThe results of the study revealed that, on average, homeowners overvalued their properties by only 1.94%, and the fraction of interviewees estimating their properties accurately ranges from 20% to 68%, depending on the size of the margin of error adopted. The drivers of the valuation bias variation were the physical, locational and neighbourhood attributes of the property as well as the personal characteristics of the respondents, for which their age and employment situation played a key role.Originality/valueIn contrast to previous studies, this is the first to examine drivers behind the accuracy of self-reported home valuations in a Central and Eastern Europe country. In addition, this work is the first to consider heterogeneous housing preferences when calculating objective property values.",1.0,,False,,,2022-07-20,3c9caf715802e4b04d9f81994bbb50af4c642127,driversbehindtheaccuracyofselfreportedhomevaluationsevidencefromanemergingeconomy,tomal,10.1108/jerer-02-2022-0004,driversbehindtheaccuracyofselfreportedhomevaluationsevidencefromanemergingeconomy|2022,1,2451,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,3,4,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",14acb1ea3d8335492dc3ce1ebf386e9449efce19,10.1080/02673037.2026.2631435,,The impact of commercial land housing on residential land housing in China: a perspective of housing submarkets,2026.0,Housing Studies,Da-Wei Cai; Fangzhou Xia; Ling-Yu Li; Bo Huang,"Abstract While existing literature has explored the relationship between various housing submarkets from the supply and demand sides, few have examined the impact of commercial land (CL) housing on residential land (RL) housing. CL housing, a novel form of informal housing in China, violates land management regulations by converting commercial buildings for residential use, whereas RL housing constitutes the formal market. This study develops a theoretical framework and introduces the CL housing regulation to analyze the relationship between these two housing submarkets. Using a difference-in-differences approach and second-hand micro-housing transaction data from Beijing (2011–2020), we find that CL housing exerts a greater crowding-out effect on RL housing than a complementary effect. Following the implementation of the CL housing regulation, CL housing supply declined, prompting an increase in RL housing supply and a shift in some demand back to the RL housing submarket, resulting in lower RL housing prices. In addition, a regression discontinuity design reveals that the regulation curtailed investment demand while consumer demand persisted. To enhance the efficient use of CL housing, we propose policy recommendations for its repurposing. Our findings provide new evidence from China’s housing submarkets and offer a conceptual framework for other countries considering housing submarket dynamics in building use conversions.",1.0,,False,JournalArticle,,2026-02-27,14acb1ea3d8335492dc3ce1ebf386e9449efce19,theimpactofcommerciallandhousingonresidentiallandhousinginchinaaperspectiveofhousingsubmarkets,cai,10.1080/02673037.2026.2631435,theimpactofcommerciallandhousingonresidentiallandhousinginchinaaperspectiveofhousingsubmarkets|2026,1,2448,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",691a54d7a71befee9d921962fb33c6d730b6d18f,10.1145/3453187.3453321,,Factors Influencing Housing Prices: An Empirical Analysis From New York City,2020.0,EBIMCS,Dingrui Dong,"This paper studies the determinants of housing price dynam- ics in the New York City. This model is used to study the effect of house factors on the house prices appreciate rate. Using the records of real estate transactions between 2003 and 2017 in the New York City (short referred to as NYC), this paper found that the influencing factors of the property itself have different effects on the house prices appreciate rate. After removing the factor of real estate location, for the average house price appreciate rate, the number of real estate transactions has the greatest impact on house prices; in the case of different house price levels, the land area of the house has the greatest influence on house price growth dur- ing the boom and the depression of housing prices, while the structure and land area of the real estate does the greatest during the recovery period of housing prices",1.0,,False,JournalArticle;Book,Computer Science,2020-12-05,691a54d7a71befee9d921962fb33c6d730b6d18f,factorsinfluencinghousingpricesanempiricalanalysisfromnewyorkcity,dong,10.1145/3453187.3453321,factorsinfluencinghousingpricesanempiricalanalysisfromnewyorkcity|2020,1,1893,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 openalex,hedonic housing prices,https://openalex.org/W1608429913,10.2139/ssrn.1334089,,Hedonic Price Indexes for the Housing Market in Italian Cities,2008.0,SSRN Electronic Journal,Alessandra Michelangeli; Alberto Zanardi,,1.0,,True,preprint,,2008-01-01,https://openalex.org/W1608429913,hedonicpriceindexesforthehousingmarketinitaliancities,michelangeli,10.2139/ssrn.1334089,hedonicpriceindexesforthehousingmarketinitaliancities|2008,1,1000,1,hedonic housing prices,2,0,1,0,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",be36f220f0b46419f8ddc07e017a92f5689e995d,10.2139/ssrn.3601507,,House Price Forecasting Based on Hybrid Multi-regression Model,2020.0,Social Science Research Network,Shivdutt Vishwakarma; Swasti Singhal,,1.0,,False,,,,be36f220f0b46419f8ddc07e017a92f5689e995d,housepriceforecastingbasedonhybridmultiregressionmodel,vishwakarma,10.2139/ssrn.3601507,housepriceforecastingbasedonhybridmultiregressionmodel|2020,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",231363209ba68b9aa5c5a3003ecbe1db61296d71,10.24957/hsr.2018.26.4.53,,Estimating the Real Estate Price Index Based on Sample House Price: Focusing on the Use of Machine Learning Method,2018.0,,성완 배; 정석 유,,1.0,,False,,Economics,,231363209ba68b9aa5c5a3003ecbe1db61296d71,estimatingtherealestatepriceindexbasedonsamplehousepricefocusingontheuseofmachinelearningmethod,배,10.24957/hsr.2018.26.4.53,estimatingtherealestatepriceindexbasedonsamplehousepricefocusingontheuseofmachinelearningmethod|2018,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fe9e4c0d765cfdc05ea37a8e8bcd80943b013b5b,10.1117/12.813170,,Spatial statistics and GIS application study in spatial variability analysis of house prices: a case study of Dongguan,2009.0,GEOINFORMATICS,Zhi-Xiong Mei; Yang Ou; Wei-Ping Hu,,1.0,,False,,Geography;Engineering,2009-10-31,fe9e4c0d765cfdc05ea37a8e8bcd80943b013b5b,spatialstatisticsandgisapplicationstudyinspatialvariabilityanalysisofhousepricesacasestudyofdongguan,mei,10.1117/12.813170,spatialstatisticsandgisapplicationstudyinspatialvariabilityanalysisofhousepricesacasestudyofdongguan|2009,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",17757549437bae45b2067f130c12e89fcb629494,10.24957/hsr.2017.25.2.175,,The Comparison of Local Housing Price Determinants by Housing Type,2017.0,,Jae-Ik Kim,,1.0,,False,,Economics,2017-03-30,17757549437bae45b2067f130c12e89fcb629494,thecomparisonoflocalhousingpricedeterminantsbyhousingtype,kim,10.24957/hsr.2017.25.2.175,thecomparisonoflocalhousingpricedeterminantsbyhousingtype|2017,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4ef773d0acf70d26a0e78eaeaa9eef98639298e9,10.1108/ijhma-07-2026-0232,,"Accessibility and investment returns: evidence of yield compression in transit-oriented condominium markets in Bangkok, Thailand",2026.0,International Journal of Housing Markets and Analysis,Chunyarat Nititerapad; Terdpong Boonpan," This study aims to examine the relationship between accessibility and investment returns in transit-oriented condominium markets, focusing on rental yield as a key indicator of housing market performance. Previous studies have emphasized property price premiums associated with transit accessibility. Their impact on rental yield remains insufficiently understood. A quantitative cross-sectional research design was adopted using a structured dataset of 300 condominium units located within an 800-meter radius of transit stations along three rail mass transit lines in the Bangkok Metropolitan Region, which was collected from five major real estate platforms in Thailand between June 2025 and December 2025. The analysis focuses on the mid-market segment, meaning properties with prices not exceeding THB 150,000 per square meter or USD 5,000 per square meter. Multiple regression analysis based on ordinary least squares with stepwise regression used for variable selection technique to identify significant locational and structural determinants of rental yield. It also serves as an explanatory tool to identify and interpret the relationships between rental yield and key housing market factors across different submarkets. The results show that accessibility-related variables, both locational and structural, influence rental yields, including distance to transit stations, proximity to amenities, floor level and unit characteristics. However, the influence of key determinants varies across rail mass transit lines, highlighting spatial heterogeneity in housing market dynamics. Importantly, the findings provide evidence of yield compression, in which higher accessibility is associated with lower rental yields due to disproportionate increases in property values relative to rental income. The study is based on cross-sectional listing data and focuses on the mid-market segment within a specific urban context, which may limit the generalizability of the findings to higher-end properties. However, it provides useful insights into submarket-level housing dynamics and suggests that investment outcomes are shaped by local market conditions rather than uniform accessibility effects. The findings have important implications for real estate investors and developers by highlighting the trade-off between accessibility and investment returns. Decision-making should consider not only location advantages but also their impact on rental yield performance. The presence of yield compression also has implications for housing affordability in transit-oriented development contexts. This research offers a new perspective on rental yields as a critical topic for understanding housing market dynamics and significantly explains the phenomenon of yield compression arising from the relationship between accessibility and investment returns. In practice, investors should be aware that convenience, such as a highly accessible location, may not always offer the best returns. However, the balance between property value and rental income in each area should be carefully considered. This study contributes to the housing market literature by shifting the analytical focus from property prices to investment performance and by providing empirical evidence of yield compression and spatial variation in the determinants of rental yield within transit-oriented development contexts. ",0.0,,False,JournalArticle,,2026-09-28,4ef773d0acf70d26a0e78eaeaa9eef98639298e9,accessibilityandinvestmentreturnsevidenceofyieldcompressionintransitorientedcondominiummarketsinbangkokthailand,nititerapad,10.1108/ijhma-07-2026-0232,accessibilityandinvestmentreturnsevidenceofyieldcompressionintransitorientedcondominiummarketsinbangkokthailand|2026,1,4433,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,4,8,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5b7e70820e3d6ce581231f4e596e0f8583216d6a,10.66884/kesj.2025.2wxwb4br,,"EFFECTS OF HOUSING CONDITION ON RENTAL VALUES OF RESIDENTIAL PROPERTIES IN KADUNA METROPOLIS, NIGERIA",2025.0,Kaduna State University Environmental Sciences Journal,Matthew Mamman; M. S. Mohammad; Ishiyaku Bala; N. Iroaganachi,"Purpose: A neighbourhood’s housing environment encompasses the social services and utilities that make a community liveable. Because housing quality significantly affects both environments and their populations, it is vital to understand its drivers and measurement metrics. This study aims to assess the effects of housing quality on the rental values of residential properties in the Malali area of Kaduna Metropolis.
Design/methodology/approach: The study adopted quantitative research approaches and survey strategy because of the nature of the research questions with a sample size of 277. Structured questionnaires were distributed to the respondents. The data was analysed using mean ranking, frequency distribution tables and multiple regression analysis.
Findings: The result revealed that, the average (Mean) rent paid in different types of rented residential properties in Malali area of Kaduna metropolis, Nigeria, stands for flat ₦436,093, bungalow ₦547,043 and tenement ₦116,083.
Research limitations/Implication: This study was limited to two categories of independent variables Housing Physical Condition and Housing Non-Physical Condition within the Malali area of Kaduna. The regression model achieved an R2 of approximately 64.26%, indicating that these independent variables collectively account for a significant proportion of the total variation in rental values.
Practical implication: The study recommends that property owners/developers should continue to prioritise regular maintenance and timely repairs of fundamental property elements, as this ensures tenant satisfaction, increases turnover, and protects property value over time and to strategically invest in upgrading and maintaining flooring and ceilings to enhance interior aesthetics, boost perceived quality, and increase the desirability and potential rental value of their properties.
Originality/value: Property owners/developers should continue to prioritise regular maintenance and timely repairs of fundamental property elements, as this ensures tenant satisfaction, increases turnover, and protects property value over time.
",0.0,,False,JournalArticle;Review,,2025-12-01,5b7e70820e3d6ce581231f4e596e0f8583216d6a,effectsofhousingconditiononrentalvaluesofresidentialpropertiesinkadunametropolisnigeria,mamman,10.66884/kesj.2025.2wxwb4br,effectsofhousingconditiononrentalvaluesofresidentialpropertiesinkadunametropolisnigeria|2025,1,3307,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8b49de98072903d72495214521e44306ccf98ebd,10.1109/dsaa65442.2025.11247972,,An Event Study Framework for Analyzing Bidirectional Causality Between House Prices and Premium Café Entry,2025.0,International Conference on Data Science and Advanced Analytics,Sachin Date,"The relationship between cafes and house prices is an emerging field of study in urban data analytics. Recent research suggests that homes near cafes, especially premium cafes, start out dearer and appreciate faster than the broader U.S. housing market. However, significant endogeneity due to omitted variables and the possibility of bidirectional causality has made interpretation problematic. This uncertainty is compounded by limited methodical transparency, inadequate falsification criteria, and absence of rigorous analysis of bidirectional causation in the published work on this topic. Our study aims to fill these gaps. We present an analytical framework based on a Two-Way Fixed Effects (TWFE) event-study regression model (i.e., a generalized difference-in-differences design for staggered entry) combined with matching and covariate adjustment, to simultaneously study forward and reverse causality between house prices and premium café entry. Using Starbucks as a case study, we performed robust empirical analysis of the association between Starbucks' entry, and pre- and post-entry house price growth in over 500 U.S. neighborhoods across 45 states from 2012 to 2022. We controlled for nationwide price trends and shocks, neighborhood affluence, and short- and long-term price momentum. Our study included a placebo arm populated with “fake” Starbucks stores opened in random years to test whether pre- or post-entry price growth was driven solely by non-Starbucks related covariates. We found that while neighborhood characteristics and historical price momentum dominated, significant bidirectional effects consistent with causal influence between Starbucks' entry and house prices were also observed under stated assumptions. Placebo results falsified the hypothesis that the observed bidirectional effects were driven purely by non-Starbucks covariates. Our findings contribute real-world insights on urban amenity-price dynamics and offer a generalizable template for such empirical studies with strong policy and market relevance.",0.0,,False,JournalArticle;Conference,Computer Science,2025-10-09,8b49de98072903d72495214521e44306ccf98ebd,aneventstudyframeworkforanalyzingbidirectionalcausalitybetweenhousepricesandpremiumcafeentry,date,10.1109/dsaa65442.2025.11247972,aneventstudyframeworkforanalyzingbidirectionalcausalitybetweenhousepricesandpremiumcafeentry|2025,1,3052,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,4,4,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7c3a4bee2424ba8617a7df2d3a67edf0f6d5f73e,10.5040/9780755603565.0010,,Annotated Bibliography,2022.0,Technology in Modern German History,Silva Mathema,"This paper evaluates the impacts of new housing developments funded with the Low Income Housing Tax Credit (LIHTC), the largest federal project based housing program in the U.S., on the neighborhoods in which they are built. A discontinuity in the formula determining the magnitude of tax credits as a function of neighborhood characteristics generates pseudorandom assignment in the number of low income housing units built in similar sets of census tracts. Tracts where projects are awarded 30 percent higher tax credits receive approximately six more low income housing units on a base of seven units per tract. These additional new low income developments cause homeowner turnover to rise, raise property values in declining areas and reduce incomes in gentrifying areas in neighborhoods near the 30th percentile of the income distribution. LIHTC units significantly crowd out nearby new rental construction in gentrifying areas but do not displace new construction in stable or declining areas. Edmiston, Kelly D. “Nonprofit Housing Investment and Local Area Home Values.” Economic Review Q I (2012): 67–96. Available at: http://www.kansascityfed.org/publicat/econrev/pdf/12q1Edmiston.pdf Abstract: In the wake of the recent mortgage crisis, interest in neighborhood stabilization and redevelopment has shown renewed vigor. Decaying neighborhoods have been part of the urban landscape for decades, but their problems recently have been exacerbated by foreclosed and vacated properties, especially in lowand moderate-income areas. Edmiston analyzes the impact In the wake of the recent mortgage crisis, interest in neighborhood stabilization and redevelopment has shown renewed vigor. Decaying neighborhoods have been part of the urban landscape for decades, but their problems recently have been exacerbated by foreclosed and vacated properties, especially in lowand moderate-income areas. Edmiston analyzes the impact",0.0,,False,Review,,,7c3a4bee2424ba8617a7df2d3a67edf0f6d5f73e,annotatedbibliography,mathema,10.5040/9780755603565.0010,annotatedbibliography|2022,1,2923,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,3,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",22636334140a59a1c6a6004b40b4aed55a85d4fa,10.22423/kreus.2025.18.1.103,,A Study on the Impact of the International Exchange Complex District Development on Surrounding Real Estate: Focused on the Third General Residential Area of Gangnam-gu,2025.0,Konkuk Research Institute of Real Estate and Urban Studies,Minjung Joo; Sangyoub Lee,"This study empirically analyzes the impact of the International Exchange Complex Zone development in Seoul's Gangnam on land and building unit prices, focusing on the significance of spatial autocorrelation. Utilizing 1,339 actual transaction cases from 2006 to 2024, OLS, Spatial Lag Model (SAR), and Spatial Error Model (SEM) were comparatively analyzed. OLS regression results indicated that development phases, property area, macroeconomic indicators, and location variables significantly influenced both dependent variables. However, OLS residual diagnostics revealed statistically significant spatial autocorrelation, underscoring the necessity of applying spatial econometric models and leading to a 1000m optimal spatial threshold selection. SAR and SEM models, applied with this threshold, demonstrated comparable or improved explanatory power (R²/Pseudo R²) compared to OLS, with SEM showing the best fit by information criteria (AIC, BIC). Moran's I tests on their residuals confirmed successful resolution of spatial autocorrelation. Significant spatial coefficients (in SAR and SEM) proved strong spatial spillover effects and the influence of unobserved spatial characteristics, highlighting OLS estimation bias. This research demonstrates that spatial autocorrelation significantly impacts real estate price determination and that spatial econometric models are essential for reliable analysis and spatially informed policymaking. However, it acknowledges limitations in estimating pure causal effects, suggesting future research incorporate causal inference methodologies",0.0,,False,JournalArticle,,2025-08-31,22636334140a59a1c6a6004b40b4aed55a85d4fa,astudyontheimpactoftheinternationalexchangecomplexdistrictdevelopmentonsurroundingrealestatefocusedonthethirdgeneralresidentialareaofgangnamgu,joo,10.22423/kreus.2025.18.1.103,astudyontheimpactoftheinternationalexchangecomplexdistrictdevelopmentonsurroundingrealestatefocusedonthethirdgeneralresidentialareaofgangnamgu|2025,1,2588,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",903af78176b3e67aa98ed7a3f41f59770eb46e54,10.1108/pm-08-2024-0088,,"Dynamics of residential property values along rail line – a case of Kubwa station, Abuja, Nigeria",2025.0,Property Management,E. Komolafe; A. Oyetunji; Oyindamola 'Semilore Fawunmi,"PurposeUrbanization is transforming cities worldwide, with rapid growth and expansion being particularly evident in Nigerian cities like Abuja. The introduction of transportation infrastructure is a significant factor influencing rent dynamics. This paper is aimed at understanding the evolving patterns of rent at transportation hubs such as train stations, using the Abuja-Kaduna railway line in Nigeria as a representative case.Design/methodology/approachRental values of residential properties within a 3 km radius of the station before, during and after construction (between 2005 and 2023) were obtained from estate surveyors and valuers, who are property managers in Nigeria. Least squares regression and analysis of growth rate were used to analyze the data obtained.FindingsIt was found that the construction and operation of the Kubwa Train Station have led to substantial increases in the rental values of residential properties within its vicinity. Also, transportation infrastructure like rail stations leads to an increase in rental value.Practical implicationsThe study advises policymakers and urban planners to give precedence to investments in sustainable transportation infrastructure. Also, property investors are encouraged to invest along rail lines, as it enhances property value.Originality/valuePrevious studies on the dynamics of residential property values along rail lines are limited in developing countries. Hence, this study provides a broad perspective on the dynamics of rental values along rail lines.",0.0,,False,JournalArticle;Review,,2025-04-07,903af78176b3e67aa98ed7a3f41f59770eb46e54,dynamicsofresidentialpropertyvaluesalongraillineacaseofkubwastationabujanigeria,komolafe,10.1108/pm-08-2024-0088,dynamicsofresidentialpropertyvaluesalongraillineacaseofkubwastationabujanigeria|2025,1,2535,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2e2e6cdb4285e8172b031d001e9b47c38917af54,10.31303/krear.2023.92.21,,A Study on the External Effects of the Price Ceiling System on Private Housing Sites: Focusing on the regulated areas of Gyeonggi-do using the double-difference method,2023.0,Korea Real Estate Academy,Hye Yeon Kim; Tae Yang Kim; Yougha Park,"1. CONTENTS (1) RESEARCH OBJECTIVES This study attempted to analyze external effects on Hana Mimi, Gwangmyeong City, Gwangmyeong-si, Gwangmyeong-si, Gwangmyeong City and Gwacheon City, and Gwacheon City, Gyeonggi-do Province. (2) RESEARCH METHOD Panel analysis was used as the analysis method in consideration of the characteristics of the data used, and the double-difference method was applied to understand the policy effect of the sale price ceiling. The analysis tool utilized the open statistical package R, which is recognized as the most efficient analysis tool in panel analysis. (3) RESEARCH FINDINGS As a result of analysis, the total model, the price of nearby housing market, light, the price of the nearby housing market has been analyzed that there is an external effect of stabilization housing prices, and the upper limitations. 2. RESULTS According to the analysis, the implementation of the ceiling on the sale price applied to the three regions of Gyeonggi-do has the effect of lowering inventory housing prices in Hanam and Gwangmyeong, excluding Gwacheon. Policy proposals for institutional supplementation so that the sale price ceiling can operate as originally intended are as follows. First, it is necessary to reflect the sale price for essential expenditure costs. Second, it is necessary to flexibly apply basic construction costs, such as increasing raw material costs. Third, it is necessary to change the current apartment subscription system",0.0,,False,JournalArticle,,2023-12-31,2e2e6cdb4285e8172b031d001e9b47c38917af54,astudyontheexternaleffectsofthepriceceilingsystemonprivatehousingsitesfocusingontheregulatedareasofgyeonggidousingthedoubledifferencemethod,kim,10.31303/krear.2023.92.21,astudyontheexternaleffectsofthepriceceilingsystemonprivatehousingsitesfocusingontheregulatedareasofgyeonggidousingthedoubledifferencemethod|2023,1,2474,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a21a5a2bf56bc06f87bdd643ec59d32508619718,10.1080/23754931.2024.2402781,,The Fiscal Capitalization of Public Spending: Does Municipal Operating and Capital Spending Increase Property Values?,2024.0,Papers in applied geography,Valencia Prentice; Mohamad Bamanie; Umesh Jaiswal,"Abstract This study investigates the impact of local public spending on property values. The theory of fiscal capitalization, first proposed by Charles Tiebout and Wallace Oates, holds that a positive relationship exists between local public spending and property values. The theory posits that individuals and businesses make locational decisions based on their preferences for a community’s mix of taxes and spending on services. Although an extensive literature exists supporting fiscal capitalization, studies rarely investigate the separate effects of operating versus capital expenditures. Furthermore, few studies have investigated whether a distinct capitalization effect exists between the signal created by ongoing capital expenditures and a longer term amenity effect realized once projects are completed. This article extends the fiscal capitalization theory by assessing these distinctive effects on property values. The study uses a border discontinuity research design, limiting the sample to residential properties near municipal boundaries. The results show capital and operating expenditures have different effects on property values. In addition, the spending on capital improvements has short-term and long-term effects in the property market. The findings provide local policymakers with guidance on the relative benefits of operating versus capital spending when balancing the two choices.",0.0,,False,JournalArticle,,2024-09-23,a21a5a2bf56bc06f87bdd643ec59d32508619718,thefiscalcapitalizationofpublicspendingdoesmunicipaloperatingandcapitalspendingincreasepropertyvalues,prentice,10.1080/23754931.2024.2402781,thefiscalcapitalizationofpublicspendingdoesmunicipaloperatingandcapitalspendingincreasepropertyvalues|2024,1,2411,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",637b35b7958860a21e85feb713bca477838bb5ea,10.22423/kreus.2024.17.1.35,,Analysis of Policy Effects of Designation of Housing Speculation Area: Focusing on the Designations of Housing Speculation Areas in Years 2017 and 2018,2024.0,Konkuk Research Institute of Real Estate and Urban Studies,,"In a situation where regulations have become redundant and complex due to the revision of the policy of designating regulatory districts, this study examined the policy characteristics of the designation of housing speculation areas and empirically analyzed the individual policy effects of housing speculation areas. In order to estimate the time-varying policy effects and heterogeneous policy effects according to price levels, the difference-in-differences method that considers time-varying policy effects was used for submarkets classified by price levels of KRW 900,000,000. As a result of the analysis of Seoul, the designation of housing speculation areas in 2017 had the effect of increasing apartment prices, but the designation in 2018 played a role in stabilizing apartment prices, showing mixed policy effects. This suggests that it is difficult to predict the policy effect of designation of housing speculation areas. In addition, it was confirmed that the designation of speculation areas can have effects different from the policy intention depending on the subsequent real estate policy implemented after the designation, and can play a role in stabilizing housing prices depending on the housing market environment. This suggests that real estate policies must be elaborately prepared in order for the designation policy of housing speculation areas to be effective.",0.0,,False,JournalArticle,,2024-08-31,637b35b7958860a21e85feb713bca477838bb5ea,analysisofpolicyeffectsofdesignationofhousingspeculationareafocusingonthedesignationsofhousingspeculationareasinyears2017and2018,,10.22423/kreus.2024.17.1.35,analysisofpolicyeffectsofdesignationofhousingspeculationareafocusingonthedesignationsofhousingspeculationareasinyears2017and2018|2024,1,2386,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",33395030a1242a611d340b7d0353e570f818e758,10.1109/uemcon67449.2025.11267586,,Prediction of Housing Prices During Economic Variability Using Machine Learning and Deep Learning Techniques,2025.0,"Ubiquitous Computing, Electronics & Mobile Communication Conference",Maliha Momtaz; Faisal Quader; Rodney Leslie; G. Dimitoglou; Bill Hilbers,"Housing prices in the US market may be highly dependent on varying economic conditions such as inflation or other factors such as a pandemic, political instability, or engagement in armed conflicts. Monetary inflation, in particular, may cause sharp increases in the prices of various goods and services, including housing costs. In this work, we connect housing data and economic indicators using a housing and a consumer price index (CPI) dataset to predict home prices. The goal is to predict home values using machine learning (ML) and deep learning (DL) models (linear regression, recurrent neural networks, etc.). Various evaluation metrics and methods ($R^{2}$, adjusted $R^{2}$, and RMSE) are used to evaluate the performance of these algorithms. Our approach aims to help buyers, sellers, and policymakers better understand how economic changes affect housing prices. By accurately predicting price trends, stakeholders can make more informed decisions when buying or selling homes. This study also shows how combining different types of data can improve prediction accuracy. The results demonstrate that some machine learning models can effectively capture complex patterns in the housing market affected by economic factors.",0.0,,False,Conference,,2025-10-22,33395030a1242a611d340b7d0353e570f818e758,predictionofhousingpricesduringeconomicvariabilityusingmachinelearninganddeeplearningtechniques,momtaz,10.1109/uemcon67449.2025.11267586,predictionofhousingpricesduringeconomicvariabilityusingmachinelearninganddeeplearningtechniques|2025,1,2235,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,3,4,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ddb4167adfbea995b86dd4ed4f83bf6ff9971552,10.1080/01446193.2026.2724430,,Re-evaluating the drivers of urban housing affordability from a construction cost perspective: evidence from the emerging real estate market,2026.0,Construction Management and Economics,Peres Ofori; Albert Chan Ping Chuen; Rotimi Boluwatife Abidoye,"Abstract Rapid urbanisation in Sub-Saharan Africa has intensified housing affordability crises, yet drivers remain poorly understood. This study tests whether construction material costs determine property prices in Kumasi, Ghana, using a mixed-methods design (139 occupants, 12 experts). Regression and robustness checks reveal that for residential properties, the relationship is negligible (R2 = 0.001, p = 0.634); material cost fluctuations explain <0.1% of price variation. For commercial properties, a significant positive relationship exists (R2 = 0.374, p = 0.003). Land costs (GHȼ 60,000–200,000/plot), locational premiums, and demand-supply dynamics dominate residential pricing. These findings are transferable to emerging economies with weak land governance and speculative markets. Material cost mitigation policies are ineffective. Policy must shift to land market regulation, spatial planning, tenant protection, and building standards, advancing Sustainable Development Goals(SDGs) SDGs::3, 8, and 11. The paper provides the first regression-based falsification of the cost-push hypothesis in Ghana, demonstrates residential-commercial divergence, and quantifies the affordability-durability trade-off.",0.0,,False,JournalArticle,,2026-09-10,ddb4167adfbea995b86dd4ed4f83bf6ff9971552,reevaluatingthedriversofurbanhousingaffordabilityfromaconstructioncostperspectiveevidencefromtheemergingrealestatemarket,ofori,10.1080/01446193.2026.2724430,reevaluatingthedriversofurbanhousingaffordabilityfromaconstructioncostperspectiveevidencefromtheemergingrealestatemarket|2026,1,2218,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,3,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",74e5d01b6b08f5ea07bcd112fa9d7c41bae73554,10.1109/icesit53460.2021.9696701,,A Multi-modal Attention-based Seq2eq Model for Predicting Real-estate Prices,2021.0,2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT),P. Yao,"Some studies show that the closure and reopening orders brought by covid-19 have had a negative impact on the residential real estate market. Generally speaking, real estate sales decreased significantly during this period, such as office buildings, shopping centers and family houses. Although the overall situation is declining, there are also some new situations. For example, people's desire for spacious family space caused by home office leads to an increase in the demand for large houses in the suburbs. This paper mainly compares the sales differences between suburban family houses and urban family houses in San Francisco and New York in the real estate market during covid-19. The data come from multiple dimensions such as house listing price on the real estate sales website, Machine learning methods could be used for analysis. This paper proposed a multi-modal joint attention seq2seq method to analyze these differences and the reasons for the differences. The experimental results show that one of the possible reasons the house price change in San Francisco is that there are more high-tech job position and their family income is higher than the average level of other regions.",0.0,,False,Conference,Medicine,2021-11-22,74e5d01b6b08f5ea07bcd112fa9d7c41bae73554,amultimodalattentionbasedseq2eqmodelforpredictingrealestateprices,yao,10.1109/icesit53460.2021.9696701,amultimodalattentionbasedseq2eqmodelforpredictingrealestateprices|2021,1,2197,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,1,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cad3f7a66ed4b2f7d1868c568362b1863a593b87,10.35136/krer.33.1.3,,Relationship between False Price Reporting and Market Distortions in Real Estate,2023.0,Korea Real Estate Institute,Sungwon Lee; Chunil Kim; K. Ji,"In Korea, the housing market is undergoing a period of rapid price increases, and a policy to prevent false price reporting is implemented as a stabilization strategy. However, research on the relationship between false price reporting and market distortions in the real estate industry remains lacking. This study aims to empirically estimate whether false reporting of real estate price cause market distortions. Specifically, the prices of canceled trade suspected of false reporting and of normal trade are compared by using the data of Real Estate Trade Management System (RTMS) apartment sales in Seoul, Korea from Feb. 2020 to Dec. 2021. Double-adjustment in Propensity Score Matching is combined with Coarsened Exact Matching to obtain an accurate comparison. For the two groups of prices, pairs with similar attributes such as transaction time, location, apartment area, floor, and building age are matched. Results show no significant price difference between the two groups. False reporting of housing prices may have occurred in the specific period and region with high transaction cancelations, but its effects are negligible and cannot be concluded as a cause of market distortions.",0.0,,False,,,2023-03-30,cad3f7a66ed4b2f7d1868c568362b1863a593b87,relationshipbetweenfalsepricereportingandmarketdistortionsinrealestate,lee,10.35136/krer.33.1.3,relationshipbetweenfalsepricereportingandmarketdistortionsinrealestate|2023,1,2196,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",daf03a7c559a39abe6c5fd64122e366daf10994c,10.1109/icdsca53499.2021.9650136,,Computer mathematical statistics applied in the housing price investigation through machine learning and linear regression model,2021.0,2021 IEEE International Conference on Data Science and Computer Application (ICDSCA),Bo-Han Jia,"To Figure out the inner law of the housing price in Beijing and the key factors that influence the housing price, quantitative analysis is used through three statistical tools: autoregressive moving average model, linear regression model and random forest model. The first model shows that the coefficient of autoregressive part is greater than one, revealing an increasing trend of housing price. The second model shows that there are four factors, number of residents in Beijing, production of tertiary industry, per capita urban disposal income, number of elementary school students, have strong linear relationship with the housing price. From the third model, there are three factors, number of subways, number of buses and number of medical institutions, may have strong non-linear relationship with housing price. Then the conclusions that the increasing trend of housing price in Beijing may be kept in the future, there are 7 main factors having robust relationship with the housing price in Beijing, though some of them may not be linear and the statistical tools can be really helpful in analyzing the field of housing price or some other fields have been reached.",0.0,,False,Conference,,2021-10-29,daf03a7c559a39abe6c5fd64122e366daf10994c,computermathematicalstatisticsappliedinthehousingpriceinvestigationthroughmachinelearningandlinearregressionmodel,jia,10.1109/icdsca53499.2021.9650136,computermathematicalstatisticsappliedinthehousingpriceinvestigationthroughmachinelearningandlinearregressionmodel|2021,1,2175,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",30f2d71cfd0ad7302b6c3b52a245960f88f8f459,10.1177/10780874251412356,,Local Economic Development Policies and Market Effects: Policy Impacts in the land of OZ,2026.0,Urban Affairs Review,Brent C Smith,"The central objective of many place-based economic development policies is to incentivize private sector investment in areas that would otherwise be overlooked or underserved by market forces due to perceived risks, lack of infrastructure, or insufficient expected returns. One such policy in the United States, opportunity zones (OZs), provides tax incentives, in the form of deferred/eliminated capital gains taxes, to deliberately redirect real estate investments into targeted geographic areas. This analysis explores the effects of OZs on property values by using a nuanced approach that recognizes the operative word “place,” or proximity, along with time. This is essential to represent the diversity across OZ designated census tracts, and to compare property performance within designated OZs when compared to the entire market. Relying on a series of hierarchical linear equations, corrected for selection bias, the results indicate there are significant positive price effects in OZ tracts, for office, industrial and multifamily real estate, when compared against the larger market.",0.0,,False,JournalArticle,,2026-02-11,30f2d71cfd0ad7302b6c3b52a245960f88f8f459,localeconomicdevelopmentpoliciesandmarketeffectspolicyimpactsinthelandofoz,smith,10.1177/10780874251412356,localeconomicdevelopmentpoliciesandmarketeffectspolicyimpactsinthelandofoz|2026,1,2094,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0286d41a042ed46e4207bdd6b1b1842764a01afb,10.1109/ieem50564.2021.9672872,,Analysis of Housing Prices of Urban with Port and City Integration Taking Kaohsiung Example,2021.0,IEEE International Conference on Industrial Engineering and Engineering Management,Kuei-Chen Chiu,"Mortgage is an important part in the housing market, it cause a clear and strong relationship between monetary policies and housing market. The release of growing commodity causes the diversification of housing investment. Actually, the Financial Tsunami was cause by subprime-mortgage crisis in U.S. in 2007. A lot of models have been proposed to predict housing price over the course of decades of research, but some new models should be proposed to suitable the situation in the post era of financial tsunami and COVID-19. The subprime-mortgage crisis damaged the housing market of San Francisco, directly. The housing market in a port is different from in a city, shipping is an important factor to urban development, the paper build the system dynamic model of Kaohsiung housing market, which is a port the same as San Francisco, to predict Kaohsiung housing price. This study aims to extract the related factors to the housing prices of Kaohsiung by correlation analysis, find out the critical factors of the housing prices of Kaohsiung by regression analysis.",0.0,,False,JournalArticle;Conference,Computer Science,2021-12-13,0286d41a042ed46e4207bdd6b1b1842764a01afb,analysisofhousingpricesofurbanwithportandcityintegrationtakingkaohsiungexample,chiu,10.1109/ieem50564.2021.9672872,analysisofhousingpricesofurbanwithportandcityintegrationtakingkaohsiungexample|2021,1,2066,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",6165d69c156c016e6ad4f63089696afe6259f736,10.24957/hsr.2023.31.3.93,,A New Approach to Establishing an Apartment Price Index in Seoul based on Blinder-Oaxaca Decomposition,2023.0,Korean Association for Housing Policy Studies,Hyung Joon Chung; Jihee Ann,"We establish a house price index for Seoul based on a Fisher Ideal aggregation of a two-way Blinder-Oaxaca (BO) decomposition, the FIBO index. Applying the BO decomposition to the apartment market decomposes the intercity differences in the mean house prices into two parts: (i) differences that are due to the differences in housing characteristics across areas (endowment effects) and (ii) differences that arise from the interarea differences in implicit prices of those characteristics (coefficient effects). Using actual transaction data for apartments in Seoul from 2017 to 2022, the FIBO index is constructed based on the estimated coefficient effects. Robustness tests show that our method is far less influenced by changes in sample sizes and selection of the reference. We also show that the FIBO index more accurately reflects the actual movement and fluctuation of apartment prices compared to existing indices, which suggests that the FIBO index could serve as a good complementary house price index.",0.0,,False,JournalArticle,,2023-08-31,6165d69c156c016e6ad4f63089696afe6259f736,anewapproachtoestablishinganapartmentpriceindexinseoulbasedonblinderoaxacadecomposition,chung,10.24957/hsr.2023.31.3.93,anewapproachtoestablishinganapartmentpriceindexinseoulbasedonblinderoaxacadecomposition|2023,1,2013,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""implicit price"" | ""implicit prices"") + (housing | house | residential | property)",0,2,3,2,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",fc0bbdbf1e415dfb75ee691cb2550ca8e6e7cb42,10.1109/taai54685.2021.00018,,Housing Price Prediction by Using Generative Adversarial Networks,2021.0,International Conference on Technologies and Applications of Artificial Intelligence,Chia-Fen Hsieh; Tzu-Chieh Lin,"Housing price forecasting is a highly complex and vitally important field of research. Recent advancements in deep neural network technology allow researchers to develop highly accurate models to predict financial trends. Deep learning has recently achieved great success in many areas due to its powerful capabilities in the data processing. For instance, it has been widely used in financial areas such as stock market prediction, portfolio optimization, financial information processing, and trade execution strategies, etc. In this paper, we proposed a novel architecture of Generative Adversarial Network (GAN) with the Long Short-Term Memory (LSTM) for forecasting the price of houses. To train and evaluate our methodology. The dataset was House Sales in King County, USA, which includes several pings, floor or room age, etc. In the analysis of this input information, the network model will output the predicted house price.",0.0,,False,JournalArticle;Conference,Computer Science,2021-11-01,fc0bbdbf1e415dfb75ee691cb2550ca8e6e7cb42,housingpricepredictionbyusinggenerativeadversarialnetworks,hsieh,10.1109/taai54685.2021.00018,housingpricepredictionbyusinggenerativeadversarialnetworks|2021,1,1933,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,1,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",02b46ab8d787bc5d33edc5a9c8a769d0eb3b3249,10.1109/pci60110.2023.10325931,,Determination of the Location Valuation Index based on Public Transportation in Big Cities,2023.0,Panhellenic Conference on Informatics,Resmiye Nasiboglu; E. Nasibov,"In the countries’ economy, the housing acquisition and rental market has an important economic role. On the other hand, housing prices change quite rapidly due to effects such as inflation and earthquakes. Due to these rapid changes, it becomes difficult to stabilize and objectively evaluate housing prices. In evaluating houses, in addition to the characteristics of the house itself, the location where this house is located also has an important effect.In this study, the Location Valuation Index (LVI) was created to evaluate any location based on the ease of transportation from this location to other points of the city based on public transportation network. For this purpose, cost values between stops were used. The proposed model was run on data from izmir, Turkey’s third largest city, and a map of LVI values of the city was created.",0.0,,False,Conference,,2023-08-28,02b46ab8d787bc5d33edc5a9c8a769d0eb3b3249,determinationofthelocationvaluationindexbasedonpublictransportationinbigcities,nasiboglu,10.1109/pci60110.2023.10325931,determinationofthelocationvaluationindexbasedonpublictransportationinbigcities|2023,1,1846,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,4,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e86b71f295ab70d604cbc16f28d3b80674d5a6a4,10.55041/isjem01418,,PREDICTIVE MODELING FOR HOUSING PRICE TRENDS USING HISTORICAL DATA,2024.0,International Scientific Journal of Engineering and Management,Vidya sree P,"In ""Predictive Modeling for Housing Price Trends Using Historical Data,"" data analysis and predictive techniques are used to forecast future real estate pricing trends. By examining factors like location, size, economic indicators, and interest rates, this method creates predictive models. The aim is to provide valuable insights for real estate stakeholders, aiding decision-making in buying, selling, or investing in properties. These models assist in risk assessment and strategic planning, offering crucial insights for navigating the dynamic real estate market Key Words: Predictive Modeling, Housing Price Trends, Historical Data Analysis, Regression Analysis, Data Preprocessing, Feature Engineering, Model Deployment",0.0,,False,JournalArticle,,2024-03-23,e86b71f295ab70d604cbc16f28d3b80674d5a6a4,predictivemodelingforhousingpricetrendsusinghistoricaldata,p,10.55041/isjem01418,predictivemodelingforhousingpricetrendsusinghistoricaldata|2024,1,1725,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b01b56a3497e3173669e33f9a49300a4e5dd0c67,10.5817/cz.muni.p280-0068-2022-41,,Sdílené ubytování a jeho vliv na ceny nemovitostí: Případová studie z Prahy,2022.0,25th International Colloquium on Regional Sciences,Filip Emmer,"This work focuses on short-term rentals, which is essetnial part of the sharing economy. This particular segment and its impact on the property prices has been frequently discussed in both mass media and the academic research. The aim of this study is to identify whether or not short term rentals have statistically significant impact on the property prices. To fulfill the main research aim, multiple OLS regression model is applied. Results of this method indicate that Airbnb has local impact and contributes to price rise in areas with significant amount of short-term rental supply. On the other hand, model provides no evidence of impact on the whole city of Prague, which was selectí as a basis for this study.",0.0,,False,,,,b01b56a3497e3173669e33f9a49300a4e5dd0c67,sdileneubytovaniajehovlivnacenynemovitostipripadovastudiezprahy,emmer,10.5817/cz.muni.p280-0068-2022-41,sdileneubytovaniajehovlivnacenynemovitostipripadovastudiezprahy|2022,1,1718,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1a955d645bc31cae466d9144be438e323b3268ae,10.24191/myse.v7i2.10263,,"AN ASSESSMENT OF HERITAGE PROPERTY VALUES USING MULTIPLE REGRESSION ANALYSIS: GEORGE TOWN, PENANG ISLAND MALAYSIA",2020.0,,Nur Shahirah Ja’afar; Junainah Mohamad,"The aim of this paper was to estimate the value of heritage property using multiple regression analysis. The original dataset consists of 311 Pre-war Shophouses transacted from 2004 to 2018 in Northern-Eastern of Penang Island, Malaysia. After filtration process, only 248 units of Pre-war Shophouses heritage property were available and valid to be used. Three functional forms such as linear, semi-log and log-log have been constructed and their statistical performances have been compared. The results indicate that log-log functional form performed better than the other functional forms in predicting the price of heritage property.",0.0,,False,,Geography,2020-08-04,1a955d645bc31cae466d9144be438e323b3268ae,anassessmentofheritagepropertyvaluesusingmultipleregressionanalysisgeorgetownpenangislandmalaysia,ja’afar,10.24191/myse.v7i2.10263,anassessmentofheritagepropertyvaluesusingmultipleregressionanalysisgeorgetownpenangislandmalaysia|2020,1,1637,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,unclear,UNCLEAR_other,0 crossref,precios hedónicos vivienda,,10.2139/ssrn.2776232,,Determinantes Del Precio De La Vivienda Nueva No Vis En Medellln: Un Modelo Estructural (Price Determinants of No VIS New Housing In Medellin: A Structural Model),2016.0,SSRN Electronic Journal,John J. Garcia; Juan Carlos Upegui; Ermilson Velasquez,,0.0,,,journal-article,,,doi_10_2139_ssrn_2776232,determinantesdelpreciodelaviviendanuevanovisenmedelllnunmodeloestructuralpricedeterminantsofnovisnewhousinginmedellinastructuralmodel,garcia,10.2139/ssrn.2776232,determinantesdelpreciodelaviviendanuevanovisenmedelllnunmodeloestructuralpricedeterminantsofnovisnewhousinginmedellinastructuralmodel|2016,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || precios hedónicos vivienda",0,1,2,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6b4f541caa0b5974948e7e5e6ec37aedd224b479,10.12677/pm.2025.1510244,,Analysis of Factors Influencing Housing Prices in Hefei Based on LASSO Regression and Ridge Regression,2025.0,Pure Mathematics,Bang-Ji Wei,,0.0,,False,JournalArticle,,,6b4f541caa0b5974948e7e5e6ec37aedd224b479,analysisoffactorsinfluencinghousingpricesinhefeibasedonlassoregressionandridgeregression,wei,10.12677/pm.2025.1510244,analysisoffactorsinfluencinghousingpricesinhefeibasedonlassoregressionandridgeregression|2025,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",709f6f7e84bea6cad730a5d3e5f4458d3308b52f,10.25236/ajcis.020302,,Analysis of Commodity Housing Price Based on Partial Least Squares Regression,2019.0,,Qimeng Tao,,0.0,,False,,Economics,2019-09-25,709f6f7e84bea6cad730a5d3e5f4458d3308b52f,analysisofcommodityhousingpricebasedonpartialleastsquaresregression,tao,10.25236/ajcis.020302,analysisofcommodityhousingpricebasedonpartialleastsquaresregression|2019,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ed13100093d1e46e147a7080460517946ac465af,10.1007/978-3-030-36178-5_51,,Forecasting Housing Prices by Using Artificial Neural Networks,2019.0,,T. Yeşil; Fatma Akyüz; U. Kose,,0.0,,False,,Computer Science,2019-04-20,ed13100093d1e46e147a7080460517946ac465af,forecastinghousingpricesbyusingartificialneuralnetworks,yeşil,10.1007/978-3-030-36178-5_51,forecastinghousingpricesbyusingartificialneuralnetworks|2019,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",72d14d46adb2b6cca99a2e09a1bf07a1842c6d4f,10.22313/reik.2017.15.4.41,,Predicting Housing Price Using Spatial Panel Data Models in Korea,2017.0,,Hae Jung Chun,,0.0,,False,,Mathematics,2017-06-20,72d14d46adb2b6cca99a2e09a1bf07a1842c6d4f,predictinghousingpriceusingspatialpaneldatamodelsinkorea,chun,10.22313/reik.2017.15.4.41,predictinghousingpriceusingspatialpaneldatamodelsinkorea|2017,1,1000,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",b272bc0afd0d6ad1e6ba910be8ecf1b438d0f997,10.12677/sa.2023.124106,,Research on the Housing Prices in Beijing Based ON Spatial Econometric Model,2023.0,Statistics and Application,文彦 曹,,0.0,,False,JournalArticle,,,b272bc0afd0d6ad1e6ba910be8ecf1b438d0f997,researchonthehousingpricesinbeijingbasedonspatialeconometricmodel,曹,10.12677/sa.2023.124106,researchonthehousingpricesinbeijingbasedonspatialeconometricmodel|2023,1,1000,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",9beafa6308c63566243aa947e4908a83952cc320,10.1007/978-3-030-62743-0_61,,Spatial Econometric Analysis of House Prices in Guangdong Province Based on Big Data,2020.0,SPIoT,Yibing Lai; Qing-Hui Sun,,0.0,,False,JournalArticle,Computer Science;Economics,2020-11-04,9beafa6308c63566243aa947e4908a83952cc320,spatialeconometricanalysisofhousepricesinguangdongprovincebasedonbigdata,lai,10.1007/978-3-030-62743-0_61,spatialeconometricanalysisofhousepricesinguangdongprovincebasedonbigdata|2020,1,1000,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",947ac0410b42b38c95d5757dcc414dabbd804c81,10.12677/ass.2020.98156,,Application of Nonparametric Regression in House Price Forecast,2020.0,Advances in Social Sciences,Yun-Ying Lin,,0.0,,False,,Economics,,947ac0410b42b38c95d5757dcc414dabbd804c81,applicationofnonparametricregressioninhousepriceforecast,lin,10.12677/ass.2020.98156,applicationofnonparametricregressioninhousepriceforecast|2020,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d5398ebb21c08c2181e594fd3d849dfc54f73391,10.1117/12.837792,,Spatial and temporal variations in residential housing prices in Beijing,2009.0,Other Conferences,Yan Zhang; Meng Lv; Z-H Yin,,0.0,,False,Conference,Geography;Engineering,2009-10-13,d5398ebb21c08c2181e594fd3d849dfc54f73391,spatialandtemporalvariationsinresidentialhousingpricesinbeijing,zhang,10.1117/12.837792,spatialandtemporalvariationsinresidentialhousingpricesinbeijing|2009,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",99d9963d6c4294ddb5396a23121be92c7345ca00,10.1061/9780784413135.096,,The Comparative Analysis of the Commercial Housing Prices in Beijing and Harbin Based on the Stepwise Regression Model,2013.0,,Peng-Yang Li; Beibei Shen,,0.0,,False,,Economics,2013-11-12,99d9963d6c4294ddb5396a23121be92c7345ca00,thecomparativeanalysisofthecommercialhousingpricesinbeijingandharbinbasedonthestepwiseregressionmodel,li,10.1061/9780784413135.096,thecomparativeanalysisofthecommercialhousingpricesinbeijingandharbinbasedonthestepwiseregressionmodel|2013,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",6fb45a9ff50fa09de1a593122d0997eaa66cbc81,10.15781/t2125qs5c,,Overview of machine learning methods in predicting house prices and its application in R,2018.0,,Wei-Ta Chen,,0.0,,False,Review,Computer Science,2018-01-25,6fb45a9ff50fa09de1a593122d0997eaa66cbc81,overviewofmachinelearningmethodsinpredictinghousepricesanditsapplicationinr,chen,10.15781/t2125qs5c,overviewofmachinelearningmethodsinpredictinghousepricesanditsapplicationinr|2018,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",61ab71967d3cb90e765c1b69e4c0d4e71f258085,10.1007/978-981-15-1420-3_110,,"Prediction of Housing Prices Using Machine Learning, Time Series ARIMA Model and Artificial Neural Network",2020.0,,S. Mangaleswaran; S. Vigneshwari,,0.0,,False,,Computer Science,,61ab71967d3cb90e765c1b69e4c0d4e71f258085,predictionofhousingpricesusingmachinelearningtimeseriesarimamodelandartificialneuralnetwork,mangaleswaran,10.1007/978-981-15-1420-3_110,predictionofhousingpricesusingmachinelearningtimeseriesarimamodelandartificialneuralnetwork|2020,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,1 semanticscholar_bulk,hedonic property valuation,18ee6023e73e19baf97f8305f30f6f2e806f5503,10.15396/eres2013_344,,Spatial spillover effects of housing prices: An empirical study of Hong Kong,2013.0,,D. Lo,,0.0,,False,,Business,,18ee6023e73e19baf97f8305f30f6f2e806f5503,spatialspillovereffectsofhousingpricesanempiricalstudyofhongkong,lo,10.15396/eres2013_344,spatialspillovereffectsofhousingpricesanempiricalstudyofhongkong|2013,1,1000,3,hedonic housing prices || hedonic property valuation || spatial hedonic model housing,0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",20c6586dba15c14df63fd63115c3810a7d001d22,10.1016/j.cities.2026.107142,,Effects of housing price determinants in major international financial centers,2026.0,Cities,Lin Deng; Xueqing Zhang,,0.0,,False,JournalArticle,,2026-08-01,20c6586dba15c14df63fd63115c3810a7d001d22,effectsofhousingpricedeterminantsinmajorinternationalfinancialcenters,deng,10.1016/j.cities.2026.107142,effectsofhousingpricedeterminantsinmajorinternationalfinancialcenters|2026,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",05d14796055558f1cc168b355c4a4ee754c154e4,10.22423/kreus.2018.11.1.43,,A Study on the Determinants of Country Housing Prices,2018.0,Konkuk Research Institute of Real Estate and Urban Studies,S. Nam; Man-Hyeon Baek,,0.0,,False,,Economics,2018-08-30,05d14796055558f1cc168b355c4a4ee754c154e4,astudyonthedeterminantsofcountryhousingprices,nam,10.22423/kreus.2018.11.1.43,astudyonthedeterminantsofcountryhousingprices|2018,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,hedonic housing prices,5af5254946fa6fd330a2e28a31920cec8c38dc44,10.1109/icee.2012.1594,,The Improvement of Residential Hedonic Price Model in Macro Aspect,2012.0,International Conference on E-Business and E-Government,Hong Shen; Zhi Wang; Zhen-Zhen Jiang,,0.0,,False,,Economics,2012-05-11,5af5254946fa6fd330a2e28a31920cec8c38dc44,theimprovementofresidentialhedonicpricemodelinmacroaspect,shen,10.1109/icee.2012.1594,theimprovementofresidentialhedonicpricemodelinmacroaspect|2012,1,1000,1,hedonic housing prices,2,0,1,0,0,4,True,candidate,True,unclear,UNCLEAR_no_abstract,0 semanticscholar_bulk,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",e800891d7597db8e13065ac16fe182a4a38d6c38,10.1002/ajae.70060,,Public lands and urban quality of life,2026.0,American Journal of Agricultural Economics,S. B. Akhundjanov; P. Jakus," Federal and state‐owned public lands occupy a significant proportion of many U.S. urban regions, contributing to two highly‐valued, nontradable goods: viewscapes and outdoor recreational opportunities. Yet, public lands have not been adequately addressed in the urban quality‐of‐life (QOL) literature. We argue that much of the previous literature has (1) used a geographic level of aggregation that has not been extensive enough to capture all the public lands that influence wage and housing equilibria, (2) often defined public lands measures too narrowly and has excluded over 50% of public land influencing hedonic equilibria, and (3) has rarely attempted to control for public lands endogeneity when quasi‐experimental approaches are infeasible. Using GIS and Census data for U.S. urban areas, along with instruments rooted in the 19th century land disposal policies, we estimate how publicly owned federal and state lands contribute to urban QOL. Our preferred specifications are based on Bureau of Census defined Combined Statistical Areas that are linear in public land variables, and the results are robust regardless of geographic aggregation and model specification. Welfare calculations indicate that the annual urban household willingness‐to‐pay estimate for a one percent increase in public land ranges between $80.58 and $130.29. Our study contributes to the literature by establishing land grant railroads as an exogenous instrument for public lands and by providing a framework for evaluating the welfare consequences of proposed land exchanges and other changes in land ownership or management.",0.0,,False,JournalArticle,,2026-03-02,e800891d7597db8e13065ac16fe182a4a38d6c38,publiclandsandurbanqualityoflife,akhundjanov,10.1002/ajae.70060,publiclandsandurbanqualityoflife|2026,1,2615,1,"(""willingness to pay"" | capitalization | capitalisation) + hedonic + (housing | house | residential | property)",2,0,2,0,1,-1,True,candidate,False,unclear,UNCLEAR_other,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",87ecd21a30ea9d1def62f8bb1254d9d3e4f11df2,10.18421/tem121-17,,House Price Prediction Model Using Random Forest in Surabaya City,2023.0,TEM Journal,Rinabi Tanamal; Nathalia Minoque; Trianggoro Wiradinata; Y. Soekamto; T. Ratih,"A home is one of many fundamental human needs. Therefore, it is essential to arrange so that each family has a separate dwelling. Several prediction algorithms are presented in this study to forecast future property values. By interviewing real estate agents, combining many interviews with multiple agents engaged in the purchasing and selling of homes. Consequently, this study investigates Surabaya Real estate price forecasting models employing Random Forest machine learning algorithms and adopting seventeen regularly used characteristics from real estate agents, which are the most influential factor in determining house prices. The final model may assist in determining the appropriate price for the house. Several research trials have been conducted to achieve a high predictive value; however, the highest predictive value was achieved by using 80% of the data set for training and 20% of the data set for testing to provide output values with an 88% accuracy rate.",21.0,https://www.temjournal.com/content/121/TEMJournalFebruary2023_126_132.pdf,True,,,2023-02-27,87ecd21a30ea9d1def62f8bb1254d9d3e4f11df2,housepricepredictionmodelusingrandomforestinsurabayacity,tanamal,10.18421/tem121-17,housepricepredictionmodelusingrandomforestinsurabayacity|2023,1,2476,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",bb63c63917e51e1931ae23ee6ef45b634acc07e2,10.1080/10095020.2018.1503775,,Road distance and travel time for an improved house price Kriging predictor,2018.0,Geo-Spatial Information Science,H. Crosby; T. Damoulas; Alex Caton; Paul Davis; João Porto de Albuquerque; S. Jarvis,"ABSTRACT The paper designs an automated valuation model to predict the price of residential property in Coventry, United Kingdom, and achieves this by means of geostatistical Kriging, a popularly employed distance-based learning method. Unlike traditional applications of distance-based learning, this papers implements non-Euclidean distance metrics by approximating road distance, travel time and a linear combination of both, which this paper hypothesizes to be more related to house prices than straight-line (Euclidean) distance. Given that – to undertake Kriging – a valid variogram must be produced, this paper exploits the conforming properties of the Minkowski distance function to approximate a road distance and travel time metric. A least squares approach is put forth for variogram parameter selection and an ordinary Kriging predictor is implemented for interpolation. The predictor is then validated with 10-fold cross-validation and a spatially aware checkerboard hold out method against the almost exclusively employed, Euclidean metric. Given a comparison of results for each distance metric, this paper witnesses a goodness of fit () result of 0.6901 ± 0.18 SD for real estate price prediction compared to the traditional (Euclidean) approach obtaining a suboptimal value of 0.66 ± 0.21 SD.",18.0,https://www.tandfonline.com/doi/pdf/10.1080/10095020.2018.1503775?needAccess=true,True,JournalArticle,Mathematics;Computer Science,2018-07-03,bb63c63917e51e1931ae23ee6ef45b634acc07e2,roaddistanceandtraveltimeforanimprovedhousepricekrigingpredictor,crosby,10.1080/10095020.2018.1503775,roaddistanceandtraveltimeforanimprovedhousepricekrigingpredictor|2018,1,2809,3,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || automated valuation model residential",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,machine learning hedonic pricing housing,0a14f4d05e3188bbfdf21ba4b01775bf34ad8efd,10.1080/09599916.2022.2070525,,House price prediction with gradient boosted trees under different loss functions,2022.0,Journal of Property Research,A. Hjort; J. Pensar; I. Scheel; D. E. Sommervoll,"ABSTRACT Many banks and credit institutions are required to assess the value of dwellings in their mortgage portfolio. This valuation often relies on an Automated Valuation Model (AVM). Moreover, these institutions often report the models accuracy by two numbers: The fraction of predictions within and range from the true values. Until recently, AVMs tended to be hedonic regression models, but lately machine learning approaches like random forest and gradient boosted trees have been increasingly applied. Both the traditional approaches and the machine learning approaches rely on minimising mean squared prediction error, and not the number of predictions in the and range. We investigate whether introducing a loss function closer to the AVMs actual loss measure improves performance in machine learning approaches, specifically for a gradient boosted tree approach. This loss function yields an improvement from to of predictions within of the true value on a data set of transactions from the Norwegian housing market between 2013 and 2015, with the biggest improvements in performance coming from the lower price segments. We also find that a weighted average of models with different loss functions improves performance further, yielding of the observations within of the true value.",59.0,https://www.tandfonline.com/doi/pdf/10.1080/09599916.2022.2070525?needAccess=true,True,,,2022-05-24,0a14f4d05e3188bbfdf21ba4b01775bf34ad8efd,housepricepredictionwithgradientboostedtreesunderdifferentlossfunctions,hjort,10.1080/09599916.2022.2070525,housepricepredictionwithgradientboostedtreesunderdifferentlossfunctions|2022,1,2793,6,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic house price || hedonic housing prices || machine learning hedonic pricing housing",2,2,3,4,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7b030e3e2ab48150e1fc58d4ba2af5c1b31c6118,10.2478/amns.2022.1.00052,,Analysis and prediction of second-hand house price based on random forest,2022.0,Applied Mathematics and Nonlinear Sciences,Yan Zhang; Jin-Xin Huang; Jiahui Zhang; Shu-Yi Liu; Samer Shorman,"Abstract Using Python language and combined with data analysis and mining technology, the authors capture and clean the housing source data of second-hand houses in Chengdu from Beike Network, and visually analyse the cleaned data. Then, a Random Forest (RF) model is established for 38,363 data elements. According to the visual analysis results, the model variables are revalued, the key factors affecting house prices are studied and the optimised model is used to predict house prices. The experiment shows that the deviation between the house price predicted by the RF model and that predicted by the real house price is small; it also indicates the accuracy of the RF model and demonstrates its good application value.",15.0,https://www.sciendo.com/pdf/10.2478/amns.2022.1.00052,True,,,2022-01-01,7b030e3e2ab48150e1fc58d4ba2af5c1b31c6118,analysisandpredictionofsecondhandhousepricebasedonrandomforest,zhang,10.2478/amns.2022.1.00052,analysisandpredictionofsecondhandhousepricebasedonrandomforest|2022,1,2224,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a0bb06cac1a04d3884edd314c1d07d8f1e86b05b,10.1515/remav-2020-0028,,Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach,2020.0,Real Estate Management and Valuation,Changro Lee; K. K. Park,"Abstract Although deep learning-based valuation models are spreading throughout the real estate industry following the artificial intelligence boom, property owners and investors continue to doubt the accuracy of the results. In this study, we specify a neural network for predicting house prices. We suggest a standard feed-forward network with two hidden layers, and show that it is sufficiently reasonable to apply its prediction to real-world projects such as property valuation. In addition, we propose a Bayesian neural network for describing uncertainty in house price predictions while providing a means to quantify uncertainty for each prediction. We choose Gangnam-gu, Seoul for the analysis, and predict house prices in the area using both networks. Although the Bayesian neural network did not perform better than the conventional network, it could provide a tool to measure the uncertainty inherent in predicted prices. The findings of this study show that a Bayesian approach can model uncertainty in property valuation, thereby promoting the adoption of deep learning tools in the real estate industry.",16.0,https://www.sciendo.com/pdf/10.1515/remav-2020-0028,True,,,2020-12-01,a0bb06cac1a04d3884edd314c1d07d8f1e86b05b,representinguncertaintyinpropertyvaluationthroughabayesiandeeplearningapproach,lee,10.1515/remav-2020-0028,representinguncertaintyinpropertyvaluationthroughabayesiandeeplearningapproach|2020,1,2617,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",531d6d0273e00a3bc037f290a1eed143097708f2,10.14419/ijet.v7i4.30.22378,,Modelling House Price Using Ridge Regression and Lasso Regression,2018.0,International journal of engineering and technology,Sen Xin; K. Khalid,"House price prediction is important for the government, finance company, real estate sector and also the house owner. The data of the house price at Ames, Iowa in United State which from the year 2006 to 2010 is used for multivariate analysis. However, multicollinearity is commonly occurred in the multivariate analysis and gives a serious effect to the model. Therefore, in this study investigates the performance of the Ridge regression model and Lasso regression model as both regressions can deal with multicollinearity. Ridge regression model and Lasso regression model are constructed and compared. The root mean square error (RMSE) and adjusted R-squared are used to evaluate the performance of the models. This comparative study found that the Lasso regression model is performing better compared to the Ridge regression model. Based on this analysis, the selected variables includes the aspect of house size, age of house, condition of house and also the location of the house.",21.0,https://www.sciencepubco.com/index.php/ijet/article/download/22378/10978,True,,Computer Science,2018-11-30,531d6d0273e00a3bc037f290a1eed143097708f2,modellinghousepriceusingridgeregressionandlassoregression,xin,10.14419/ijet.v7i4.30.22378,modellinghousepriceusingridgeregressionandlassoregression|2018,1,2489,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",490d4d485dd5637fff425cc76757634968be50d7,10.1016/j.procs.2022.09.078,,Machine learning in house price analysis: regression models versus neural networks,2022.0,International Conference on Knowledge-Based Intelligent Information & Engineering Systems,I. Foryś,,28.0,https://www.sciencedirect.com/science/article/pii/S1877050922009516/pdf,True,JournalArticle,Computer Science,,490d4d485dd5637fff425cc76757634968be50d7,machinelearninginhousepriceanalysisregressionmodelsversusneuralnetworks,foryś,10.1016/j.procs.2022.09.078,machinelearninginhousepriceanalysisregressionmodelsversusneuralnetworks|2022,1,1500,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a03a47a7745d227abfc80b28bb977544f2c1afd1,10.1016/j.procs.2022.01.100,,House Price Prediction using Random Forest Machine Learning Technique,2021.0,International Conference on Information Technology and Quantitative Management,A. B. Adetunji; N. Akande; F. Ajala; O. Oyewo; Y. Akande; Gbenle Oluwadara,,166.0,https://www.sciencedirect.com/science/article/pii/S1877050922001016/pdf,True,JournalArticle,Computer Science,,a03a47a7745d227abfc80b28bb977544f2c1afd1,housepricepredictionusingrandomforestmachinelearningtechnique,adetunji,10.1016/j.procs.2022.01.100,housepricepredictionusingrandomforestmachinelearningtechnique|2021,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",10bbdef60dc6884e43b1a559b573e9e2f3d29a4a,10.1016/j.procs.2020.06.111,,Housing Price Prediction via Improved Machine Learning Techniques,2020.0,Procedia Computer Science,Quang Truong; Minh Nguyen; Hy Dang; Bo Mei,"Abstract House Price Index (HPI) is commonly used to estimate the changes in housing price. Since housing price is strongly correlated to other factors such as location, area, population, it requires other information apart from HPI to predict individual housing price. There has been a considerably large number of papers adopting traditional machine learning approaches to predict housing prices accurately, but they rarely concern about the performance of individual models and neglect the less popular yet complex models. As a result, to explore various impacts of features on prediction methods, this paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models. This paper will also comprehensively validate multiple techniques in model implementation on regression and provide an optimistic result for housing price prediction.",174.0,https://www.sciencedirect.com/science/article/pii/S1877050920316318/pdf,True,,Computer Science,,10bbdef60dc6884e43b1a559b573e9e2f3d29a4a,housingpricepredictionviaimprovedmachinelearningtechniques,truong,10.1016/j.procs.2020.06.111,housingpricepredictionviaimprovedmachinelearningtechniques|2020,1,2408,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",da88ef8e3cfc6b2448b50502622c009fefeb1578,10.63887/jtie.2026.2.4.13,,A Study on House Price Prediction Based on Multi-Model Fusion: Centered on PCA and Random Forest,2026.0,Journal of Technology Innovation and Engineering,Rui-Qi Zhou; Yi-Ming Xu,"Based on the Ames housing price dataset, this paper constructs and compares the performance of various machine learning models for real estate price forecasting. To address the challenges of high-dimensional features and missing values, we first improve data quality through data cleaning and feature selection. Building on this foundation, we propose a modeling framework that combines principal component analysis with regression trees, effectively achieving feature dimensionality reduction and model complexity control. Additionally, a random forest regression model was introduced to enhance the model’s generalization ability and stability through multi-tree ensemble and random feature selection mechanisms, with performance evaluated using out-of-bag error. Furthermore, log-transformed linear regression and ridge regression models were constructed as controls to analyze the impact of linear relationships and regularization on prediction performance. Experimental results indicate that ensemble learning methods possess distinct advantages in capturing nonlinear relationships and feature interactions, with the Random Forest model demonstrating the best performance in terms of prediction accuracy and stability. The research methodology exhibits good generalizability and can provide effective modeling approaches for complex multivariate regression problems.",0.0,https://www.sci-open.net/index.php/JTIE/article/download/17341/18247,True,JournalArticle,,2026-07-14,da88ef8e3cfc6b2448b50502622c009fefeb1578,astudyonhousepricepredictionbasedonmultimodelfusioncenteredonpcaandrandomforest,zhou,10.63887/jtie.2026.2.4.13,astudyonhousepricepredictionbasedonmultimodelfusioncenteredonpcaandrandomforest|2026,1,2872,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",36381c0d9eba33d8b04209483e5f53e367c8e388,10.13052/rp-9788743808268a104,,"Advanced Machine Learning Techniques for Predicting House Prices: A Comparative Analysis",2025.0,"First International Conference on Computer, Computation and Communication (IC3C-2025)",Gautam Kumar,,0.0,https://www.riverpublishers.com/pdf/ebook/chapter/RP_9788743808268C104.pdf,True,Conference,,,36381c0d9eba33d8b04209483e5f53e367c8e388,advancedmachinelearningtechniquesforpredictinghousepricesacomparativeanalysis,kumar,10.13052/rp-9788743808268a104,advancedmachinelearningtechniquesforpredictinghousepricesacomparativeanalysis|2025,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2496df96e711af71e0ee891ef853f03e7e12bc6f,10.53560/ppasa(63-2)708,,A Robust Stacked Ensemble Approach for House Price Prediction,2026.0,Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences,Muhammad Khateeb Khan; Asad Ullah Khan; Salman Ahmed; Wajid Ali,"Real estate price prediction plays a fundamental role in decision-making and ensuring market stability for buyers, sellers, and property owners. Since the real estate sector has a significant impact on the global economy, accurate prediction models are crucial. In this study, we used the Ames Housing dataset and performed thorough preprocessing to handle missing values, reduce multicollinearity, and enhance feature quality. We introduced three new features, total square footage, total porch area and the age of the house to capture the better property characteristics. After these steps, we evaluated a range of machine learning (ML) models, including Linear Regression, Ridge, Lasso, Decision Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost and explored hybrid combinations with weighed ensemble and stacked ensemble strategies. We proposed CXL Stacked Ensemble (CatBoost + XGBoost + LightGBM) model with ridge regression as meta-learner and the model outperformed with R² of 0.9333 and a normalized RMSLE of 0.1132, demonstrating the superior model predictive capability. To validate its robustness fold-wise RMSE comparisons and significance tests (paired t-test and Wilcoxon signed-rank test) were conducted. Additionally, cross-validation and residual error analysis validated model reliability. This work depicts the effectiveness of stacked ensemble methods for real state price predication and provides practical insights for stakeholders to make informed decision.",0.0,https://www.ppaspk.org/index.php/PPAS-A/article/download/1660/1117,True,JournalArticle,,2026-06-09,2496df96e711af71e0ee891ef853f03e7e12bc6f,arobuststackedensembleapproachforhousepriceprediction,khan,10.53560/ppasa(63-2)708,arobuststackedensembleapproachforhousepriceprediction|2026,1,3003,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",321664826269788c8435ead0315a2539fb9837f3,10.21837/pm.v19i17.1003,,ENHANCING THE ACCURACY OF MALAYSIAN HOUSE PRICE FORECASTING: A COMPARATIVE ANALYSIS ON THE FORECASTING PERFORMANCE BETWEEN THE HEDONIC PRICE MODEL AND ARTIFICIAL NEURAL NETWORK MODEL,2021.0,PLANNING MALAYSIA,Nurul Fazira Sa’at; N. H. Adi Maimun; N. H. Idris,"The Hedonic Price Model (HPM), a prominent model used in real estate appraisal and economics, has been argued to be marred with nonlinearity, multicollinearity and heteroscedasticity problems that affect the accuracy of price predictions. An alternative method called Artificial Neural Network Model (ANN) was identified as capable of addressing the shortcomings of HPM and produces superior predictive performance. Hence, this study aims to evaluate the forecasting performance between HPM and ANN using Malaysian housing transaction data from the period between 2009 to 2018, sourced from the Valuation and Property Service Department, Johor Bahru. The models’ performance was evaluated and compared based on their statistical and predictive performance. Results showed that ANN outperformed HPM in both statistical and predictive performance. This study benefits the expansion of academic and practical knowledge in enhancing the accuracy of house price forecasting.",9.0,https://www.planningmalaysia.org/index.php/pmj/article/download/1003/699,True,,,2021-10-17,321664826269788c8435ead0315a2539fb9837f3,enhancingtheaccuracyofmalaysianhousepriceforecastingacomparativeanalysisontheforecastingperformancebetweenthehedonicpricemodelandartificialneuralnetworkmodel,sa’at,10.21837/pm.v19i17.1003,enhancingtheaccuracyofmalaysianhousepriceforecastingacomparativeanalysisontheforecastingperformancebetweenthehedonicpricemodelandartificialneuralnetworkmodel|2021,1,2469,5,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || hedonic property valuation",2,1,2,1,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",03d00ed18477478e82bd3d2122c9ef5a4ba342e4,10.56397/jwe.2023.09.03,,Housing Price Prediction Using Machine Learning Algorithm,2023.0,Journal of World Economy,Ling Zhang,"This study examines the effectiveness of various machine learning models, including K-Nearest Neighbors (KNN), Ridge Regression, Random Forest, and Extreme Gradient Boosting (XGBoost), in predicting housing prices, using Multiple Linear Regression as a traditional baseline for comparison. Utilizing a dataset of residential property sales in Ames, Iowa, the XGBoost model was found to significantly outperform the baseline, highlighting the efficacy of machine learning techniques in this domain. Furthermore, the study applied the TreeSHAP package to enhance the interpretability of the XGBoost model, effectively bridging the gap between prediction accuracy and interpretability. The results underscore the potential of machine learning methods, particularly XGBoost, in housing price prediction, suggesting a need for further research to validate these findings using different datasets and exploring other machine learning algorithms.",5.0,https://www.pioneerpublisher.com/jwe/article/download/392/347,True,JournalArticle,,2023-09-01,03d00ed18477478e82bd3d2122c9ef5a4ba342e4,housingpricepredictionusingmachinelearningalgorithm,zhang,10.56397/jwe.2023.09.03,housingpricepredictionusingmachinelearningalgorithm|2023,1,2439,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic housing prices,e4c2026ce7cfcd85400e73fbb18cd675b84074b2,10.1057/s41599-025-06153-4,,Asymmetric impacts of artificial intelligence on housing price valuation across education levels,2025.0,Humanities and Social Sciences Communications,S. An; Yena Song; H. Jang; Kwangwon Ahn,"This study examines the scenarios under which asymmetric estimations can arise from employing artificial intelligence (AI) models to assess housing prices. Advances in the pricing instrument effectively tackle the inherent nonlinearity issue in relevant datasets, and AI technologies have demonstrated superior predictive power compared with traditional approaches. However, AI models may penalize minority groups against the majority in a social manner. We empirically explore the potential negative externalities, specifically the asymmetric estimations that can arise across social groups when using machine learning technology to assess housing prices. Our findings highlight three notable observations. First, education levels are significantly and positively associated with housing prices. Second, AI models can appraise housing prices more precisely compared with conventional hedonic models. Finally, AI models tend to overestimate the housing prices of well-educated groups and underestimate those of less-educated groups. These results indicate that AI models improve the predictive power of price assessments; however, indiscriminate adoption and application of AI-based predictions may aggravate social inequality. Our findings provide insights into ways to alleviate inequality in urban areas; thus, policymakers can refer to our empirical evidence when designing initiatives to enhance social inclusion and coherence, and when considering strategies to realize balanced urban development.",1.0,https://www.nature.com/articles/s41599-025-06153-4.pdf,True,JournalArticle,,2025-12-01,e4c2026ce7cfcd85400e73fbb18cd675b84074b2,asymmetricimpactsofartificialintelligenceonhousingpricevaluationacrosseducationlevels,an,10.1057/s41599-025-06153-4,asymmetricimpactsofartificialintelligenceonhousingpricevaluationacrosseducationlevels|2025,1,3003,3,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices || machine learning hedonic pricing housing",2,2,1,1,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ca70739fecc59d91bc7eb046c19576980e64468f,10.1038/s41598-026-62854-z,,House price prediction using a hybrid GRU–MLP based on binary whale optimization algorithm and ant colony optimization for hyperparameter tuning,2026.0,Scientific Reports,Sarah M. Alhammad; Yasser Fouad; Amira A. Mahmoud; Ahmed M. Osman; Hazem M. El-Bakry; Ahmed M. Elshewey,"Accurate house price prediction is essential for real estate valuation, investment planning, and intelligent property decision-support systems. This study proposes an optimized hybrid deep learning framework that integrates a Gated Recurrent Unit and Multilayer Perceptron model with the Binary Whale Optimization Algorithm for feature selection and Ant Colony Optimization for hyperparameter tuning. The proposed framework was evaluated using a publicly available Kaggle house price regression dataset containing 500 housing records with structural, locational, and amenity-related attributes. The dataset was divided into training, validation, and testing subsets using a 70:20:10 ratio, and leakage-free normalization was applied using only the training data. Experimental results show that the proposed BWOA–ACO–GRU–MLP model outperformed standalone GRU, MLP, CNN, LSTM, and BiLSTM models. It achieved an MSE of 0.0146, MAE of 0.1051, RMSE of 0.1208, MAPE of 0.0112, MedAE of 0.0969, and an R2 of 99.04%. These results demonstrate that combining feature selection, hyperparameter optimization, and hybrid neural regression improves prediction accuracy and model stability for house price estimation. The proposed framework provides a reliable data-driven approach for smart real estate valuation applications.",0.0,https://www.nature.com/articles/s41598-026-62854-z.pdf,True,JournalArticle,Medicine,2026-07-25,ca70739fecc59d91bc7eb046c19576980e64468f,housepricepredictionusingahybridgrumlpbasedonbinarywhaleoptimizationalgorithmandantcolonyoptimizationforhyperparametertuning,alhammad,10.1038/s41598-026-62854-z,housepricepredictionusingahybridgrumlpbasedonbinarywhaleoptimizationalgorithmandantcolonyoptimizationforhyperparametertuning|2026,1,2813,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",f22377622bf5738294e171035bed6a58b5eb8373,10.1038/s41598-026-52995-6,,IALA-BPNN: superior house price prediction through multi-strategy optimized Artificial Lemming Algorithm and BP neural network,2026.0,Scientific Reports,Qi-Feng Guo; Sheng-Peng Li; Tianqi Xia; Da-Ren Chen,"Against this backdrop of a global economic downturn, the real estate market exhibits characteristics of both a consumer good and an investment asset, with housing prices displaying fluctuating nonlinear trends. Predicting these trends holds significant importance for governments, developers, and individuals. Addressing the vulnerability of BP neural networks (BPNN) to initial weights and biases in housing price forecasting, this study enhances the Artificial Lemming Algorithm (ALA) by incorporating pinhole imaging learning, evolutionary mutation perturbation, and golden sine development strategies. This improved ALA is applied to update the BPNN network structure, establishing the novel IALA-BPNN housing price prediction model. Experiments using four public datasets compare IALA-BPNN against eight comparable hybrid models, validating the model’s success and the effectiveness of the three improvement strategies. Further comparisons with four machine learning models (RF, SVM, LSTM, CNN) demonstrate the model’s strong competitiveness in housing price forecasting. Finally, a Shanghai housing price dataset is constructed. Comparing IALA-BPNN with the top-performing models from the previous experiments reveals that IALA-BPNN achieves significant improvements across evaluation metrics: MAE (13.63%), MAPE (14.18%), RMSE (14.99%), R2 (9.38%), SMAPE (19.15%), SD (8.46%), and Time (34.30%), demonstrating its practical application potential.",0.0,https://www.nature.com/articles/s41598-026-52995-6_reference.pdf,True,JournalArticle,Medicine,2026-05-20,f22377622bf5738294e171035bed6a58b5eb8373,ialabpnnsuperiorhousepricepredictionthroughmultistrategyoptimizedartificiallemmingalgorithmandbpneuralnetwork,guo,10.1038/s41598-026-52995-6,ialabpnnsuperiorhousepricepredictionthroughmultistrategyoptimizedartificiallemmingalgorithmandbpneuralnetwork|2026,1,2953,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",00176b68f2247097a05ac361dde76933bf71f104,10.1038/s41598-025-34099-9,,"Evaluating human–machine collaboration through a comparative analysis of experts, machine learning, and hybrid approaches in real estate valuation",2026.0,Scientific Reports,Christopher Kmen; Gerhard Navratil; Markus Kattenbeck; Ioannis Giannopoulos,"Accurate prediction of real estate prices remains a major challenge due to dynamic market conditions and the limitations of traditional valuation methods. Empirical studies that directly compare human experts, machine learning (ML) models, and hybrid approaches are rare. This study examines the predictive accuracy and efficiency of an XGBoost-based ML model, real estate experts, and a hybrid human–machine approach. A model was trained using 21,736 real estate transactions from Vienna (2018–2022). We then conducted an experimental procedure with 13 experts who evaluated newly built apartments sold in 2023 under three conditions: limited information, state-of-the-art expert methods, and collaboration between experts and ML model. The results show that the ML model achieves accuracy comparable to that of experts while significantly reducing the time required for the task. Within the hybrid approach, experts were able to achieve the highest accuracy in comparison to other methods. These results underscore the potential of human-ML collaboration.",5.0,https://www.nature.com/articles/s41598-025-34099-9_reference.pdf,True,JournalArticle,Medicine,2026-01-17,00176b68f2247097a05ac361dde76933bf71f104,evaluatinghumanmachinecollaborationthroughacomparativeanalysisofexpertsmachinelearningandhybridapproachesinrealestatevaluation,kmen,10.1038/s41598-025-34099-9,evaluatinghumanmachinecollaborationthroughacomparativeanalysisofexpertsmachinelearningandhybridapproachesinrealestatevaluation|2026,1,2557,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",06cc3204e06bda2b77cc3ae4f4530e7535cefe99,10.3390/realestate2030012,,Machine Learning Algorithms and Explainable Artificial Intelligence for Property Valuation,2025.0,Real Estate,G. Maselli; Antonio Nesticò,"The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation.",7.0,https://www.mdpi.com/2813-8090/2/3/12/pdf?version=1754032016,True,JournalArticle,,2025-08-01,06cc3204e06bda2b77cc3ae4f4530e7535cefe99,machinelearningalgorithmsandexplainableartificialintelligenceforpropertyvaluation,maselli,10.3390/realestate2030012,machinelearningalgorithmsandexplainableartificialintelligenceforpropertyvaluation|2025,1,3141,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_citations,cites:sirmans2006,0e8ea76cf6036d5cb5aee397f4bffefcfdbbcbd6,10.3390/realestate1010005,,Impact of Green Features on Rental Value of Residential Properties: Evidence from South Africa,2024.0,Real Estate,T. B. Odubiyi; R. Abidoye; C. Aigbavboa; W. Thwala; A. S. Ademiloye; O. Oshodi,"In recent years, scholars have called for an increase in the usage of green features in the built environment to address climate change issues. Governments across the developed world are implementing legislation to support this increased uptake. However, little is known about how the inclusion of green features influences the rental value of residential properties located in developing countries. Data on 389 residential properties were extracted and collected from a webpage. Text mining and machine learning models were used to evaluate the impact of green features on the rental value of residential properties. The results indicated that floor area, number of bathrooms, and availability of furniture are the top three attributes affecting the rental value of residential properties. The random forest model generated better predictions when compared with other modelling techniques. It was also observed that green features are not the most common words mentioned in rental adverts for residential properties. The results suggest that green features add limited value to residential properties in South Africa. This finding suggests that there is a need for stakeholders to create and implement policies targeted at incentivising the inclusion of green features in existing and new residential properties in South Africa.",7.0,https://www.mdpi.com/2813-8090/1/1/5/pdf?version=1710906638,True,JournalArticle,,2024-03-20,0e8ea76cf6036d5cb5aee397f4bffefcfdbbcbd6,impactofgreenfeaturesonrentalvalueofresidentialpropertiesevidencefromsouthafrica,odubiyi,10.3390/realestate1010005,impactofgreenfeaturesonrentalvalueofresidentialpropertiesevidencefromsouthafrica|2024,1,2829,1,cites:sirmans2006,0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",19d8e53e8f4132ccb14bd30719600eb7c8f3a8d5,10.3390/analytics3010003,2402.04082,An Optimal House Price Prediction Algorithm: XGBoost,2024.0,Analytics,Hemlata Sharma; Hitesh Harsora; Bayode Ogunleye,"An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning (ML) techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare XGBoost, support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction. Our findings present valuable insights and tools for stakeholders, facilitating more accurate property price estimates and, in turn, enabling more informed decision making to meet the housing needs of diverse populations while considering budget constraints.",60.0,https://www.mdpi.com/2813-2203/3/1/3/pdf?version=1704172113,True,JournalArticle,Computer Science;Mathematics,2024-01-02,19d8e53e8f4132ccb14bd30719600eb7c8f3a8d5,anoptimalhousepricepredictionalgorithmxgboost,sharma,10.3390/analytics3010003,anoptimalhousepricepredictionalgorithmxgboost|2024,1,2779,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic house price || hedonic regression housing || house price prediction machine learning || housing price text listing",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",f3a4adb60cf58a077f8978abbbf5d344a356a194,10.3390/bdcc6030096,,Improving Real Estate Rental Estimations with Visual Data,2022.0,Big Data and Cognitive Computing,Ilia Azizi; Iegor Rudnytskyi,"Multi-modal data are widely available for online real estate listings. Announcements can contain various forms of data, including visual data and unstructured textual descriptions. Nonetheless, many traditional real estate pricing models rely solely on well-structured tabular features. This work investigates whether it is possible to improve the performance of the pricing model using additional unstructured data, namely images of the property and satellite images. We compare four models based on the type of input data they use: (1) tabular data only, (2) tabular data and property images, (3) tabular data and satellite images, and (4) tabular data and a combination of property and satellite images. In a supervised context, the branches of dedicated neural networks for each data type are fused (concatenated) to predict log rental prices. The novel dataset devised for the study (SRED) consists of 11,105 flat rentals advertised over the internet in Switzerland. The results reveal that using all three sources of data generally outperforms machine learning models built on only tabular information. The findings pave the way for further research on integrating other non-structured inputs, for instance, the textual descriptions of properties.",9.0,https://www.mdpi.com/2504-2289/6/3/96/pdf?version=1663293542,True,JournalArticle,Computer Science,2022-09-09,f3a4adb60cf58a077f8978abbbf5d344a356a194,improvingrealestaterentalestimationswithvisualdata,azizi,10.3390/bdcc6030096,improvingrealestaterentalestimationswithvisualdata|2022,1,2753,2,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",aa826b48c46d7896c2552b27d90272e477450e82,10.3390/math11143160,,GERPM: A Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model,2023.0,Mathematics,Guang Hu; Yue Tang,"Accurate prediction of urban residential rents is of great importance for landlords, tenants, and investors. However, existing rents prediction models face challenges in meeting practical demands due to their limited perspectives and inadequate prediction performance. The existing individual prediction models often lack satisfactory accuracy, while ensemble learning models that combine multiple individual models to improve prediction results often overlook the impact of spatial heterogeneity on residential rents. To address these issues, this paper proposes a novel prediction model called GERPM, which stands for Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model. GERPM comprehensively analyzes the influencing factors of residential rents from multiple perspectives and leverages a geographically weighted stacking ensemble learning approach. The model combines multiple machine learning and deep learning models, optimizes parameters to achieve optimal predictions, and incorporates the geographically weighted regression (GWR) model to consider spatial heterogeneity. By combining the strengths of deep learning and machine learning models and taking into account geographical factors, GERPM aims to improve prediction accuracy and provide robust predictions for urban residential rents. The model is evaluated using housing data from Nanjing, a major city in China, and compared with representative individual prediction models, the equal weight combination model, and the ensemble learning model. The experimental results demonstrate that GERPM outperforms other models in terms of prediction performance. Furthermore, the model’s effectiveness and robustness are validated by applying it to other major cities in China, such as Shanghai and Hangzhou. Overall, GERPM shows promising potential in accurately predicting urban residential rents and contributing to the advancement of the rental market.",4.0,https://www.mdpi.com/2227-7390/11/14/3160/pdf?version=1689689674,True,JournalArticle,,2023-07-18,aa826b48c46d7896c2552b27d90272e477450e82,gerpmageographicallyweightedstackingensemblelearningbasedurbanresidentialrentspredictionmodel,hu,10.3390/math11143160,gerpmageographicallyweightedstackingensemblelearningbasedurbanresidentialrentspredictionmodel|2023,1,3459,1,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",c378256fcd0cd78c5853a50edb81b324a4bac7e7,10.3390/ijgi9050288,,Augmenting Geostatistics with Matrix Factorization: A Case Study for House Price Estimation,2020.0,ISPRS Int. J. Geo Inf.,Aisha Sikder; Andreas Züfle,"Singular value decomposition (SVD) is ubiquitously used in recommendation systems to estimate and predict values based on latent features obtained through matrix factorization. But, oblivious of location information, SVD has limitations in predicting variables that have strong spatial autocorrelation, such as housing prices which strongly depend on spatial properties such as the neighborhood and school districts. In this work, we build an algorithm that integrates the latent feature learning capabilities of truncated SVD with kriging, which is called SVD-Regression Kriging (SVD-RK). In doing so, we address the problem of modeling and predicting spatially autocorrelated data for recommender engines using real estate housing prices by integrating spatial statistics. We also show that SVD-RK outperforms purely latent features based solutions as well as purely spatial approaches like Geographically Weighted Regression (GWR). Our proposed algorithm, SVD-RK, integrates the results of truncated SVD as an independent variable into a regression kriging approach. We show experimentally, that latent house price patterns learned using SVD are able to improve house price predictions of ordinary kriging in areas where house prices fluctuate locally. For areas where house prices are strongly spatially autocorrelated, evident by a house pricing variogram showing that the data can be mostly explained by spatial information only, we propose to feed the results of SVD into a geographically weighted regression model to outperform the orginary kriging approach.",4.0,https://www.mdpi.com/2220-9964/9/5/288/pdf?version=1588073235,True,JournalArticle,Computer Science,2020-04-28,c378256fcd0cd78c5853a50edb81b324a4bac7e7,augmentinggeostatisticswithmatrixfactorizationacasestudyforhousepriceestimation,sikder,10.3390/ijgi9050288,augmentinggeostatisticswithmatrixfactorizationacasestudyforhousepriceestimation|2020,1,3066,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3aee2f900a81d885cb36f5b08a2f98701871aec4,10.3390/ijgi9040227,,Missing Data Imputation for Geolocation-based Price Prediction Using KNN-MCF Method,2020.0,ISPRS Int. J. Geo Inf.,Karshiev Sanjar; Olimov Bekhzod; Jaesoo Kim; Anand Paul; Jeonghong Kim,"Accurate house price forecasts are very important for formulating national economic policies. In this paper, we offer an effective method to predict houses’ sale prices. Our algorithm includes one-hot encoding to convert text data into numeric data, feature correlation to select only the most correlated variables, and a technique to overcome the missing data. Our approach is an effective way to handle missing data in large datasets with the K-nearest neighbor algorithm based on the most correlated features (KNN–MCF). As far as we are concerned, there has been no previous research that has focused on important features dealing with missing observations. Compared to the typical machine learning prediction algorithms, the prediction accuracy of the proposed method is 92.01% with the random forest algorithm, which is more efficient than the other methods.",45.0,https://www.mdpi.com/2220-9964/9/4/227/pdf?version=1586492572,True,JournalArticle,Computer Science,2020-04-08,3aee2f900a81d885cb36f5b08a2f98701871aec4,missingdataimputationforgeolocationbasedpricepredictionusingknnmcfmethod,sanjar,10.3390/ijgi9040227,missingdataimputationforgeolocationbasedpricepredictionusingknnmcfmethod|2020,1,2363,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",071e12e47cb753c6fbc77c85f01df0ae2c5ee431,10.3390/ijgi8080349,,Modeling Housing Rent in the Atlanta Metropolitan Area Using Textual Information and Deep Learning,2019.0,ISPRS Int. J. Geo Inf.,Xiao-Lu Zhou; Wei-Tian Tong; Dongying Li,"The rental housing market plays a critical role in the United States real estate market. In addition, rent changes are also indicators of urban transformation and social phenomena. However, traditional data sources for market rent prediction are often inaccurate or inadequate at covering large geographies. With the development of housing information exchange platforms such as Craigslist, user-generated rental listings now provide big data that cover wide geographies and are rich in textual information. Given the importance of rent prediction in urban studies, this study aims to develop and evaluate models of rental market dynamics using deep learning approaches on spatial and textual data from Craigslist rental listings. We tested a number of machine learning and deep learning models (e.g., convolutional neural network, recurrent neural network) for the prediction of rental prices based on data collected from Atlanta, GA, USA. With textual information alone, deep learning models achieved an average root mean square error (RMSE) of 288.4 and mean absolute error (MAE) of 196.8. When combining textual information with location and housing attributes, the integrated model achieved an average RMSE of 227.9 and MAE of 145.4. These approaches can be applied to assess the market value of rental properties, and the prediction results can be used as indicators of a variety of urban phenomena and provide practical references for home owners and renters.",33.0,https://www.mdpi.com/2220-9964/8/8/349/pdf?version=1564724590,True,JournalArticle,Computer Science,2019-08-02,071e12e47cb753c6fbc77c85f01df0ae2c5ee431,modelinghousingrentintheatlantametropolitanareausingtextualinformationanddeeplearning,zhou,10.3390/ijgi8080349,modelinghousingrentintheatlantametropolitanareausingtextualinformationanddeeplearning|2019,1,2966,1,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,2,3,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,real estate hedonic regression,234ac161f4d3a76cf9cb2ba5cd02e2402ab966c9,10.3390/ijgi7050168,,Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments,2018.0,ISPRS Int. J. Geo Inf.,M. Čeh; M. Kilibarda; A. Lisec; B. Bajat,"The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008–2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1) the non-linear nature of the prediction assignment task; (2) input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3) the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS) ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R2 values, sales ratios, mean average percentage error (MAPE), coefficient of dispersion (COD)) revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction.",188.0,https://www.mdpi.com/2220-9964/7/5/168/pdf?version=1548036602,True,JournalArticle,Mathematics;Computer Science,2018-05-02,234ac161f4d3a76cf9cb2ba5cd02e2402ab966c9,estimatingtheperformanceofrandomforestversusmultipleregressionforpredictingpricesoftheapartments,čeh,10.3390/ijgi7050168,estimatingtheperformanceofrandomforestversusmultipleregressionforpredictingpricesoftheapartments|2018,1,2883,3,apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression,2,2,1,4,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",80f35b33854990fddc9a0f407b519428d8535d17,10.3390/ijgi5010004,,An Extended Semi-Supervised Regression Approach with Co-Training and Geographical Weighted Regression: A Case Study of Housing Prices in Beijing,2016.0,ISPRS Int. J. Geo Inf.,Yi Yang; Jiping Liu; Shenghua Xu; Yangyang Zhao,"This paper proposes an extended semi-supervised regression approach to enhance the prediction accuracy of housing prices within the geographical information science field. The method, referred to as co-training geographical weighted regression (COGWR), aims to fully utilize the positive aspects of both the geographical weighted regression (GWR) method and the semi-supervised learning paradigm. Housing prices in Beijing are assessed to validate the feasibility of the proposed model. The COGWR model demonstrated a better goodness-of-fit than the GWR when housing price data were limited because a COGWR is able to effectively absorb no-price data with explanatory variables into its learning by considering spatial variations and nonstationarity that may introduce significant biases into housing prices. This result demonstrates that a semisupervised geographic weighted regression may be effectively used to predict housing prices.",18.0,https://www.mdpi.com/2220-9964/5/1/4/pdf?version=1452602180,True,JournalArticle,Computer Science;Geography,2016-01-12,80f35b33854990fddc9a0f407b519428d8535d17,anextendedsemisupervisedregressionapproachwithcotrainingandgeographicalweightedregressionacasestudyofhousingpricesinbeijing,yang,10.3390/ijgi5010004,anextendedsemisupervisedregressionapproachwithcotrainingandgeographicalweightedregressionacasestudyofhousingpricesinbeijing|2016,1,2437,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",53bb7bb713ed608d57fea08775644003beb501b0,10.3390/ijgi15010046,,A Machine Learning Approach Using Spatially Explicit K-Nearest Neighbors for House Price Predictions,2026.0,ISPRS Int. J. Geo Inf.,Meifang Chen; Changho Lee; Yong-Wan Chun,"Spatial data has distinctive properties that differentiate it from non-spatial data. One prominent characteristic is spatial autocorrelation (SA). When machine learning techniques are applied for spatial data modeling, they require spatially explicit consideration. If these inherent spatial structures are ignored, models may produce biased predictions. However, integrating this property into the model yields additional spatial insight, thereby enhancing learning and improving predictive accuracy. This study examines spatially explicit K-nearest neighbors (SE-KNN) by integrating SA as a spatially explicit property, λ, into the learning algorithm. The innovation of SE-KNN lies in its alignment with the principle of spatial autocorrelation, as KNN’s core learning assumption—that similar observations tend to have similar outcomes—naturally parallels spatial dependence. The proposed SE-KNN is applied to a house price prediction model using house sales data from Franklin County, Ohio to demonstrate a spatially dependent, data-rich, and real-world problem. The results show that SE-KNN achieved the best prediction accuracy compared to mean of absolute error (MAE) of three other machine learning approaches (i.e., standard KNN, linear regression, and artificial neural networks). The proposed method effectively captures the spatial structures in the housing market and leaves only a trace amount of SA in the residuals.",2.0,https://www.mdpi.com/2220-9964/15/1/46/pdf?version=1768991780,True,JournalArticle,Computer Science,2026-01-21,53bb7bb713ed608d57fea08775644003beb501b0,amachinelearningapproachusingspatiallyexplicitknearestneighborsforhousepricepredictions,chen,10.3390/ijgi15010046,amachinelearningapproachusingspatiallyexplicitknearestneighborsforhousepricepredictions|2026,1,2930,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",414fccdeb81b4c74fecec8265eb146b06dc6c1c5,10.3390/ijgi11080450,2202.04358,"House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China",2022.0,ISPRS Int. J. Geo Inf.,Zimo Wang; Yicheng Wang; Sen-Sen Wu; Zhen-Hong Du,"Confronted with the spatial heterogeneity of the real estate market, some traditional research has utilized geographically weighted regression (GWR) to estimate house prices. However, its predictive power still has some room to improve, and its kernel function is limited in some simple forms. Therefore, we propose a novel house price valuation model, which is combined with geographical neural network-weighted regression (GNNWR) to improve the accuracy of real estate appraisal with the help of neural networks. Based on the Shenzhen house price dataset, this work conspicuously captures the variable spatial regression relationships at different regions of different variables, which GWR has difficulty realizing. Moreover, we focus on the performance of GNNWR, verify its robustness and superiority, and refine the experiment process with 10-fold cross-validation. In contrast with the ordinary least squares (OLS) model, our model achieves an improvement of about 50% on most of the metrics. Compared with the best GWR model, our thorough experiments reveal that our model improves the mean absolute error (MAE) by 13.5% and attains a decrease of the mean absolute percentage error (MAPE) by 13.0% in the evaluation on the validation dataset. It is a practical and powerful way to assess house prices, and we believe our model could be applied to other valuation problems concerning geographical data to promote the prediction accuracy of socioeconomic phenomena.",30.0,https://www.mdpi.com/2220-9964/11/8/450/pdf?version=1661353256,True,JournalArticle,Medicine,2022-02-09,414fccdeb81b4c74fecec8265eb146b06dc6c1c5,housepricevaluationmodelbasedongeographicallyneuralnetworkweightedregressionthecasestudyofshenzhenchina,wang,10.3390/ijgi11080450,housepricevaluationmodelbasedongeographicallyneuralnetworkweightedregressionthecasestudyofshenzhenchina|2022,1,2969,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",14d49fbf547fa4f5eb292cfa2b50afd9c6d0fc45,10.3390/ijgi11030178,,A Map-Based Recommendation System and House Price Prediction Model for Real Estate,2022.0,ISPRS Int. J. Geo Inf.,M. Mubarak; A. Tahir; Fizza Waqar; Ibraheem Haneef; G. Mcardle; M. Bertolotto; Muhammad Saeed,"In 2015, global real estate was worth $217 trillion, which is approximately 2.7 times the global GDP; it also accounts for roughly 60% of all conventional global resources, making it one of the key factors behind any country’s economic growth and stability. The accessibility of spatial big data will help real estate investors make better judgement calls and earn additional profit. Since location is deemed necessary for real estate and consequent decision-making, digital maps have become a prime resource for real estate purchases, planning and development. Personalisation can assist in making judgments by identifying user desires and inclinations, which can then be recorded or captured as a user performs some interactions with a digital map. A personalised real estate portal can use this information to suggest properties, assist homeowners and provide valuable real estate analytics. This article presents a novel framework for recommending real estate to users. By monitoring user interactions through an online real estate portal, the framework can make personalised recommendations of real estate based on content, collaboration and location. The effectiveness of the recommendations was tested by the user feedback mechanism through a method of mean absolute precision, and the results show that 79% precise suggestions were generated, i.e., out of 5 recommendations produced, users were interested in at least 3. Along with that, a separate house price prediction model based on neural networks and classical regression techniques was also implemented to assist users in making an informed decision regarding prospects of real estate purchase.",13.0,https://www.mdpi.com/2220-9964/11/3/178/pdf?version=1646718704,True,JournalArticle,Computer Science,2022-03-07,14d49fbf547fa4f5eb292cfa2b50afd9c6d0fc45,amapbasedrecommendationsystemandhousepricepredictionmodelforrealestate,mubarak,10.3390/ijgi11030178,amapbasedrecommendationsystemandhousepricepredictionmodelforrealestate|2022,1,3159,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,real estate hedonic regression,0ed3e215f7a2e67852574cd78c3b11af4fa25db9,10.3390/ijgi11020125,,Spatial Determinants of Real Estate Appraisals in The Netherlands: A Machine Learning Approach,2022.0,ISPRS Int. J. Geo Inf.,Evert Guliker; E. Folmer; M. V. van Sinderen,"With the rapidly increasing house prices in the Netherlands, there is a growing need for more localised value predictions for mortgage collaterals within the financial sector. Many existing studies focus on modelling house prices for an individual city; however, these models are often not interesting for mortgage lenders with assets spread out all over the country. That is why, with the current abundance of national geospatial datasets, this paper implements and compares three hedonic pricing models (linear regression, geographically weighted regression, and extreme gradient boosting—XGBoost) to model real estate appraisals values for five large municipalities in different parts of the Netherlands. The appraisal values used to train the model are provided by Stater N.V., which is the largest mortgage service provider in the Netherlands. Out of the three implemented models, the XGBoost model has the highest accuracy. XGBoost can explain 83% of the variance with an RMSE of €65,312, an MAE of €43,625, and an MAPE of 6.35% across the five municipalities. The two most important variables in the model are the total living area and taxation value, which were taken from publicly available datasets. Furthermore, a comparison is made between indexation and XGBoost, which shows that the XGBoost model is able to more accurately predict the appraisal values of different types of houses. The remaining unexplained variance is most probably caused by the lack of good indicators for the condition of the house. Overall, this paper highlights the benefits of open geospatial datasets to build a national real estate appraisal model.",27.0,https://www.mdpi.com/2220-9964/11/2/125/pdf?version=1645666843,True,JournalArticle,Computer Science,2022-02-09,0ed3e215f7a2e67852574cd78c3b11af4fa25db9,spatialdeterminantsofrealestateappraisalsinthenetherlandsamachinelearningapproach,guliker,10.3390/ijgi11020125,spatialdeterminantsofrealestateappraisalsinthenetherlandsamachinelearningapproach|2022,1,3139,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || real estate hedonic regression",2,2,2,5,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",98d2a3ad95444036c7a2fe45df5db141650243cb,10.3390/ijgi11010053,,"A Pricing Model for Urban Rental Housing Based on Convolutional Neural Networks and Spatial Density: A Case Study of Wuhan, China",2022.0,ISPRS Int. J. Geo Inf.,Hang Shen; Lin Li; Haihong Zhu; Feng Li,"With the development of urbanization and the expansion of floating populations, rental housing has become an increasingly common living choice for many people, and housing rental prices have attracted great attention from individuals, enterprises and the government. The housing rental prices are principally estimated based on structural, locational and neighborhood variables, among which the relationships are complicated and can hardly be captured entirely by simple one-dimensional models; in addition, the influence of the geographic objects on the price may vary with the increase in their quantities. However, existing pricing models usually take those structural, locational and neighborhood variables as one-dimensional inputs into neural networks, and often neglect the aggregated effects of geographical objects, which may lead to fluctuating rental price estimations. Therefore, this paper proposes a rental housing price model based on the convolutional neural network (CNN) and the synthetic spatial density of points of interest (POIs). The CNN can efficiently extract the complex characteristics among the relevant variables of housing, and the two-dimensional locational and neighborhood variables, based on the synthetic spatial density, effectively reflect the aggregated effects of the urban facilities on rental housing prices, thereby improving the accuracy of the model. Taking Wuhan, China, as the study area, the proposed method achieves satisfactory and accurate rental price estimations (coefficient of determination (R2) = 0.9097, root mean square error (RMSE) = 3.5126) in comparison with other commonly used pricing models.",11.0,https://www.mdpi.com/2220-9964/11/1/53/pdf?version=1641971246,True,JournalArticle,Computer Science,2022-01-11,98d2a3ad95444036c7a2fe45df5db141650243cb,apricingmodelforurbanrentalhousingbasedonconvolutionalneuralnetworksandspatialdensityacasestudyofwuhanchina,shen,10.3390/ijgi11010053,apricingmodelforurbanrentalhousingbasedonconvolutionalneuralnetworksandspatialdensityacasestudyofwuhanchina|2022,1,3154,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",52c7622ab3374ca44aeb5523d520cbd8cf040cbf,10.3390/electronics11213448,,Application of Artificial Intelligence for Predicting Real Estate Prices: The Case of Saudi Arabia,2022.0,Electronics,Elham Alzain; A. Alshebami; Theyazn H. H. Aldhyani; S. N. Alsubari,"The housing market is a crucial economic indicator to which the government must pay special attention because of its impact on the lives of freshly minted city inhabitants. As a guide for government regulation, individual property purchases, third-party evaluation, and understanding how housing prices are distributed geographically may be of great practical use. Therefore, much research has been conducted on how to arrive at a more accurate and efficient way of calculating housing prices in the current market. The goal of this study was to use the artificial neural network (ANN) technique to correctly identify real estate prices. The novelty of the proposed research is to build a prediction model based on ANN for predicting future house prices in Saudi Arabia. The dataset was collected from Aqar in four main Saudi Arabian cities: Riyadh, Jeddah, Dammam, and Al-Khobar. The results showed that the experimental and predicted values were very close. The results of the proposed system were compared with different existing prediction systems, and the developed model achieved high performance. This forecasting system can also help increase investment in the real estate sector. The ANN model could appropriately estimate the housing prices currently available on the market, according to the findings of the assessments of the model. Thus, this study provides a suitable decision support or adaptive suggestion approach for estimating the ideal sales prices of residential properties. This solution is urgently required by both investors and the general population as a whole.",33.0,https://www.mdpi.com/2079-9292/11/21/3448/pdf?version=1666700097,True,,,2022-10-25,52c7622ab3374ca44aeb5523d520cbd8cf040cbf,applicationofartificialintelligenceforpredictingrealestatepricesthecaseofsaudiarabia,alzain,10.3390/electronics11213448,applicationofartificialintelligenceforpredictingrealestatepricesthecaseofsaudiarabia|2022,1,3087,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",e16f8b3f5504168f65400bdccdb0fe5471728fdd,10.3390/info16121049,,Artificial Intelligence and Real Estate Valuation: The Design and Implementation of a Multimodal Model,2025.0,Inf.,Nikolaos Karanikolas; Eleni Kyriakidou; Eleni Athanasouli,"The valuation of real estate is a fundamental process for the proper functioning of the market and the formulation of public policies. The mother of the traditional methodologies is the comparative method, which is based on data that is often incomplete or unreliable, especially in real estate markets with limited transparency. In contrast, online advertisements offer a wealth of unstructured information, which requires advanced analysis techniques. Even if they reflect price demands rather than real estate’s final market values, they provide a wealth of data that is particularly useful for market transparency. Real estate advertisements are also enriched with photographs and videos of the property. This paper proposes a methodological framework that integrates artificial intelligence techniques (natural language processing and computer vision) to extract structured features from text and photographs of advertisements. The resulting dataset feeds the comparative method of property valuation, applied to thousands of properties. Geographic analysis and neighborhood characteristics of properties enrich the proposed algorithm, covering all the available geodata that affect real estate values. The empirical analysis of apartments in the city of Thessaloniki demonstrates significant improvements in valuation accuracy and the completeness of the characteristics. At the same time, the prediction of the energy class of properties, among other traits that the proposed methodology can accurately calculate, further enriches the valuation process. The work highlights the potential integration of artificial intelligence into modern valuation practice, offering a transparent, scalable, and auditable tool for professionals and policymakers.",6.0,https://www.mdpi.com/2078-2489/16/12/1049/pdf?version=1764667660,True,JournalArticle,Computer Science,2025-12-01,e16f8b3f5504168f65400bdccdb0fe5471728fdd,artificialintelligenceandrealestatevaluationthedesignandimplementationofamultimodalmodel,karanikolas,10.3390/info16121049,artificialintelligenceandrealestatevaluationthedesignandimplementationofamultimodalmodel|2025,1,3254,4,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"") || cites:sirmans2006 || computer vision property valuation",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",cdc81ce8be295ca18d0559f0457938f3e6153618,10.3390/app8112321,1809.04933,Identifying Real Estate Opportunities using Machine Learning,2018.0,Applied Sciences,A. Baldominos; I. Blanco; Antonio José Moreno; Rubén Iturrarte; Óscar Bernárdez; C. Afonso,"The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.",114.0,https://www.mdpi.com/2076-3417/8/11/2321/pdf?version=1542794649,True,JournalArticle,Mathematics;Computer Science;Engineering,2018-09-13,cdc81ce8be295ca18d0559f0457938f3e6153618,identifyingrealestateopportunitiesusingmachinelearning,baldominos,10.3390/app8112321,identifyingrealestateopportunitiesusingmachinelearning|2018,1,3214,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || house price prediction machine learning || housing price text listing || real estate valuation deep learning",0,2,6,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7d720d03f070a188f835742813c63047cf0c3f9c,10.3390/app131910732,,Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning,2023.0,Applied Sciences,Juryon Paik; Seungyun Baek; Jun-Wan Kim; Kwangho Ko,"The South Korean residential real estate market is influenced by both the traditional dynamics of demand and supply and external factors such as housing policies and macroeconomic conditions. Considering the proportion of housing assets in individual wealth, market fluctuations can have significant implications. While previous studies have utilized variables such as GDP growth rate, patent issuance, and birth rate, and employed models such as LSTM and ARIMA for housing price predictions, many have overlooked the influence of local factors. In particular, there has been insufficient investigation into the impact of subway stations and living social overhead capital facilities on housing prices, especially in metropolitan areas. This study seeks to bridge this gap by analyzing the usage trends of subway stations, evaluating the impact of living social overhead capital facilities on housing values, and deriving the optimal machine learning model for price predictions near subway stations. We compared and analyzed a total of eight machine learning regression models, including Linear Regression, Decision Tree, Random Forest, LightGBM, Ridge, Lasso, Elastic Net, and XGBoost, all of which are popular regression models, especially in the context of machine learning and data science. Through comparative analysis of these machine learning techniques, we aim to provide insights for more rational housing price determinations, thereby promoting stability in the real estate market.",7.0,https://www.mdpi.com/2076-3417/13/19/10732/pdf?version=1695799534,True,JournalArticle,,2023-09-27,7d720d03f070a188f835742813c63047cf0c3f9c,influenceofsocialoverheadcapitalfacilitiesonhousingpricesusingmachinelearning,paik,10.3390/app131910732,influenceofsocialoverheadcapitalfacilitiesonhousingpricesusingmachinelearning|2023,1,2992,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",385193670b0a77556d2633a857a7a83c459a955f,10.3390/app122010660,,Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal,2022.0,Applied Sciences,M. McCord; D. Lo; P. Davis; John McCord; L. Hermans; P. Bidanset,"Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approaches and machine learning (ML) algorithms have been shown to help improve house price prediction and mass appraisal assessment. Nonetheless, the adoption a of ML within mass appraisal has been protracted and subject to scrutiny by assessment jurisdictions due to their failure to account for spatial autocorrelation and limited practicality in terms of value significant estimates needed for tribunal defense and explainability. Existing research comparing traditional regression approaches has tended to examine unsupervised ML methods such as Random Forest (RF) models which remain more esoteric and less transparent in producing value significant estimates necessary for mass appraisal explainability and defense. Therefore, the purpose of this study is to apply the supervised Regularized regression technique which offers a more transparent alternative, and integrate this with a more nuanced geo-statistical technique, the Eigenvector Spatial Filter (ESF) approach, to more accurately account for spatial autocorrelation and enhance prediction accuracy whilst improving explainability needed for mass appraisal exercises. By undertaking such an approach, the research demonstrates the application of this method can be easily adopted for property tax jurisdictions in a framework which is more interpretable, transparent and useable within mass appraisal given its simple and appealing approach. The findings reveal that the integration of the ESFs improves model explainability, prediction accuracy and spatial residual error compared to baseline classical regression and Elastic-net regularized regression architectures, whilst offering the necessary ‘front-facing’ and flexible structure for in-sample and out-of-sample assessment needed by the assessment community for valuing the unsold housing stock. In terms of policy and practice, the study demonstrates some important considerations for mass appraisal tax assessment and for the improvement of taxation assessment and the alleviation of horizontal and vertical inequity.",14.0,https://www.mdpi.com/2076-3417/12/20/10660/pdf?version=1666350660,True,,,2022-10-21,385193670b0a77556d2633a857a7a83c459a955f,applyingthegeostatisticaleigenvectorspatialfilterapproachintoregularizedregressionforimprovingpredictionaccuracyformassappraisal,mccord,10.3390/app122010660,applyingthegeostatisticaleigenvectorspatialfilterapproachintoregularizedregressionforimprovingpredictionaccuracyformassappraisal|2022,1,3785,3,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",245c544deb37ae431fc42a9673596d8aff1015e1,10.3390/geosciences9030143,,Estimating Residential Property Values on the Basis of Clustering and Geostatistics,2019.0,Géosciences,B. Całka,"The article presents a two-stage model for estimating the value of residential property. The research is based on the application of a sequence of known methods in the process of developing property value maps. The market is divided into local submarkets using data mining, and, in particular, data clustering. This process takes into account only a property’s non-spatial (structural) attributes. This is the first stage of the model, which isolates local property markets where properties have similar structural attributes. To estimate the impact of the spatial factor (location) on property value, the second stage involves performing an interpolation for each cluster separately using ordinary kriging. In this stage, the model is based on Tobler’s first law of geography. The model results in property value maps, drawn up separately for each of the clusters. Experimental research carried out using the example of Siedlce, a city in eastern Poland, proves that the estimation error for a property’s value using the proposed method, evaluated using the mean absolute percentage error, does not exceed 10%. The model that has been developed is universal and can be used to estimate the value of land, property, and buildings.",28.0,https://www.mdpi.com/2076-3263/9/3/143/pdf?version=1553416075,True,,Mathematics,2019-03-01,245c544deb37ae431fc42a9673596d8aff1015e1,estimatingresidentialpropertyvaluesonthebasisofclusteringandgeostatistics,całka,10.3390/geosciences9030143,estimatingresidentialpropertyvaluesonthebasisofclusteringandgeostatistics|2019,1,2730,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",41ef3ce62cea55327ba05e5ae1fd5f66c1569b42,10.3390/buildings15091405,,"Housing Price Modeling Using a New Geographically, Temporally, and Characteristically Weighted Generalized Regression Neural Network (GTCW-GRNN) Algorithm",2025.0,Buildings,Saeed Zali; P. Pahlavani; O. Ghorbanzadeh; Ali Khazravi; Mohammad Ahmadlou; Sara Givekesh,"The location of housing has a significant influence on its pricing. Generally, spatial self-correlation and spatial heterogeneity phenomena affect housing price data. Additionally, time is a crucial factor in housing price modeling, as it helps understand market trends and fluctuations. Currency market fluctuations also directly affect housing prices. Therefore, in addition to the physical features of the property, such as the area of the residential unit and building age, the rate of exchange (dollar price) is added to the independent variable set. This study used the real estate transaction records from Iran’s registration system, covering February, May, August, and November in 2017–2019. Initially, 7464 transactions were collected, but after preprocessing, the dataset was refined to 7161 records. Unlike feedforward neural networks, the generalized regression neural network does not converge to local minimums, so in this research, the Geographically, Temporally, and Characteristically Weighted Generalized Regression Neural Network (GTCW-GRNN) for housing price modeling was developed. In addition to being able to model the spatial–time heterogeneity available in observations, this algorithm is accurate and faster than MLR, GWR, GRNN, and GCW-GRNN. The average index of the adjusted coefficient of determination in other methods, including the MLR, GWR, GTWR, GRNN, GCW-GRNN, and the proposed GTCW-GRNN in different modes of using Euclidean or travel distance and fixed or adaptive kernel was equal to 0.760, 0.797, 0.854, 0.777, 0.774, and 0.813, respectively, which showed the success of the proposed GTCW-GRNN algorithm. The results showed the importance of the variable of the dollar and the area of housing significantly.",2.0,https://www.mdpi.com/2075-5309/15/9/1405/pdf?version=1745323671,True,JournalArticle,,2025-04-22,41ef3ce62cea55327ba05e5ae1fd5f66c1569b42,housingpricemodelingusinganewgeographicallytemporallyandcharacteristicallyweightedgeneralizedregressionneuralnetworkgtcwgrnnalgorithm,zali,10.3390/buildings15091405,housingpricemodelingusinganewgeographicallytemporallyandcharacteristicallyweightedgeneralizedregressionneuralnetworkgtcwgrnnalgorithm|2025,1,3246,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a1d448944155043a92099df6473158694642135e,10.3390/buildings14103172,,Machine Learning Valuation in Dual Market Dynamics: A Case Study of the Formal and Informal Real Estate Market in Dar es Salaam,2024.0,Buildings,Frank Nyanda; Henry Muyingo; Mats Wilhelmsson,"The housing market in Dar es Salaam, Tanzania, is expanding and with it a need for increased market transparency to guide investors and other stakeholders. The objective of this paper is to evaluate machine learning (ML) methods to appraise real estate in formal and informal housing markets in this nascent market sector. Various advanced ML models are applied with the aim of improving property value estimates in a market with limited access to information. The dataset used included detailed property characteristics and transaction data from both market types. Regression, decision trees, neural networks, and ensemble methods were employed to refine property appraisals across these settings. The findings indicate significant differences between formal and informal market valuations, demonstrating ML’s effectiveness in handling limited data and complex market dynamics. These results emphasise the potential of ML techniques in emerging markets where traditional valuation methods often fail due to the scarcity of transaction data.",2.0,https://www.mdpi.com/2075-5309/14/10/3172/pdf?version=1728119444,True,JournalArticle,,2024-10-05,a1d448944155043a92099df6473158694642135e,machinelearningvaluationindualmarketdynamicsacasestudyoftheformalandinformalrealestatemarketindaressalaam,nyanda,10.3390/buildings14103172,machinelearningvaluationindualmarketdynamicsacasestudyoftheformalandinformalrealestatemarketindaressalaam|2024,1,2541,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",082220f79fc841b35c4f641fd8db60c66a6d0084,10.3390/buildings13102464,,Investigation of Real Estate Tax Leakage Loss Rates with ANNs,2023.0,Buildings,Mehmet Yılmaz; Bülent Bostancı,"In Türkiye, many changes have been made in the law within the past fifty years to determine the real estate tax value close to the real market value. However, the changes did not establish a fair valuation system for determining real estate tax. Despite the regulations and records of immovable properties with a geographic information system (GIS)-based inventory in recent years, the problem of leakage loss in real estate tax was still not resolved. Within the scope of this study, a mass appraisal model was created with a dataset of 499 independent sections including trading values from the last year in the district of Kayseri to determine the real estate tax leakage loss rates. Multiple regression analysis (MRA) and artificial neural network (ANN) methods, widely used in mass appraisal, were used in the analysis. Considering the analysis of the test data and the model performances, the ANN model was found to give better results than the MRA model. To conclude this study, the housing values obtained with the mass appraisal methods and the real estate tax values obtained with the existing system were compared, and a 3.7-fold difference was found between them.",5.0,https://www.mdpi.com/2075-5309/13/10/2464/pdf?version=1695875311,True,JournalArticle,,2023-09-28,082220f79fc841b35c4f641fd8db60c66a6d0084,investigationofrealestatetaxleakagelossrateswithanns,yılmaz,10.3390/buildings13102464,investigationofrealestatetaxleakagelossrateswithanns|2023,1,2675,1,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c4b70475caa7925d9b58d7180ef1d87065a8d259,10.3390/sym12081329,,"Research on Accurate House Price Analysis by Using GIS Technology and Transport Accessibility: A Case Study of Xi'an, China",2020.0,Symmetry,Chaochen Xue; Yongfeng Ju; Shuguang Li; Qilong Zhou; Qingqing Liu,"Based on the symmetrical public transportation network data of Xi’an, China obtained by geographic information system (GIS) technology in 2019, three urban public transportation indexes of walking accessibility, bus accessibility, and metro accessibility were established, and a real estate price prediction model was built by using several machine learning algorithms to predict and analysis the housing price in Xi’an, China. Firstly, the symmetrical road network data and real estate property data of Xi’an were collected and preprocessed, secondly, the spatial syntax theory and distance calculation method were applied to establish three indexes of traffic accessibility; finally, taking the house property data and the calculated traffic accessibility indexes as the characteristic index, the real estate price prediction model of Xi’an was constructed by using the random forest algorithm (RF), lightweight gradient lift algorithm (LGBM), and gradient lifting regression tree algorithm (GBDT). The prediction accuracy of the final model is 89.2% and the root-mean-square error is 1761.84. The results show that the accessibility of bus and metro to some extent represent the convenience of public transportation in different areas of urban space. The higher the accessibility index is, the more convenient the traffic is. The real estate price model has high prediction accuracy and can reflect the real situation of urban real estate price. The importance of the three accessibility features to the real estate price prediction model are nearly more than 20%, which indicates that the accessibility of urban public transportation has an important impact on the change of urban real estate price, and the development of urban public transportation plays an important role in the real estate economy.",16.0,https://www.mdpi.com/2073-8994/12/8/1329/pdf?version=1597035960,True,JournalArticle,Computer Science,2020-08-10,c4b70475caa7925d9b58d7180ef1d87065a8d259,researchonaccuratehousepriceanalysisbyusinggistechnologyandtransportaccessibilityacasestudyofxianchina,xue,10.3390/sym12081329,researchonaccuratehousepriceanalysisbyusinggistechnologyandtransportaccessibilityacasestudyofxianchina|2020,1,3306,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic housing prices,8ed28a93a8c3390974e66a132ecf943129cc00ae,10.3390/land12040740,,The Use of Machine Learning in Real Estate Research,2023.0,Land,L. Choy; Winky K.O. Ho,"This research seeks to demonstrate how machine learning, a branch of artificial intelligence, is able to deliver more accurate pricing predictions, using the real estate market as an example. Utilizing 24,936 housing transaction records, this paper employs Extra Trees (ET), k–Nearest Neighbors (KNN), and Random Forest (RF) to predict property prices and then compares their results with those of a hedonic price model. In particular, this paper uses a feature (property age x square footage) instead of property age in order to isolate the effect of land depreciation on property prices. Our results suggest that these three algorithms markedly outperform the traditional statistical techniques in terms of explanatory power and error minimization. Machine learning is expected to play an increasing role in shaping our future. However, it may raise questions about the privacy, fairness, and job displacement issues. It is therefore important to pay close attention to the ethical implications of machine learning and ensure that the technology is used responsibly and ethically. Researchers, legislators, and industry players must work together to create appropriate standards and legislation to govern the use of machine learning.",59.0,https://www.mdpi.com/2073-445X/12/4/740/pdf?version=1679729459,True,,,2023-03-25,8ed28a93a8c3390974e66a132ecf943129cc00ae,theuseofmachinelearninginrealestateresearch,choy,10.3390/land12040740,theuseofmachinelearninginrealestateresearch|2023,1,2735,4,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || machine learning hedonic pricing housing",2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic housing prices,4410a1dad99aee4702c611a6173645faaad1ebdf,10.3390/land11081138,,An Intelligent Homogeneous Model Based on an Enhanced Weighted Kernel Self-Organizing Map for Forecasting House Prices,2022.0,Land,Ching-Hsue Cheng; Ming-Chi Tsai,"Accurately forecasting housing prices will enable investors to attain profits, and it can provide information to stakeholders that housing prices in the community are falling, stabilizing, or rising. Previous studies on housing price forecasting mostly used hedonic pricing and weighted regression methods, which led to the lack of consideration of the nonlinear relationship model and its explanatory power. Furthermore, the attribute data of housing price forecasts are a heterogeneous study, and they are difficult to forecast accurately. Therefore, this study proposes an intelligent homogeneous model based on an enhanced weighted kernel self-organizing map (EW-KSOM) for forecasting house prices; that is, this study proposes an EW-KSOM algorithm to cluster the collected data and then applies random forest, extra tree, multilayer perception, and support vector regression to forecast the house prices of full, district, and apartment complex data. In the experimental comparison, we compare the performance of the proposed enhanced weighted kernel self-organizing map with the listing clustering methods. The results show that the best forecast algorithm is the combined EW-KSOM and random forest under the root mean square error and root-relative square error, and the proposed method can effectively improve the forecast capability of housing prices and understand the influencing factors of housing prices in full and important districts. Furthermore, we obtain that the top five key factors influencing house prices are transferred land area, house age, building transfer total area, population percentage, and the total number of floors. Lastly, the research results can provide references for investors and related organizations.",2.0,https://www.mdpi.com/2073-445X/11/8/1138/pdf?version=1658659023,True,,,2022-07-24,4410a1dad99aee4702c611a6173645faaad1ebdf,anintelligenthomogeneousmodelbasedonanenhancedweightedkernelselforganizingmapforforecastinghouseprices,cheng,10.3390/land11081138,anintelligenthomogeneousmodelbasedonanenhancedweightedkernelselforganizingmapforforecastinghouseprices|2022,1,3243,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || apartment prices hedonic || hedonic house price || hedonic housing prices",2,2,3,2,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",45efb25c539abcec425466dd3f205a8d35bce89c,10.3390/land11112100,,Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times,2022.0,Land,R. Mora-García; M. Cespedes-Lopez; V. Pérez-Sánchez,"Machine learning algorithms are being used for multiple real-life applications and in research. As a consequence of digital technology, large structured and georeferenced datasets are now more widely available, facilitating the use of these algorithms to analyze and identify patterns, as well as to make predictions that help users in decision making. This research aims to identify the best machine learning algorithms to predict house prices, and to quantify the impact of the COVID-19 pandemic on house prices in a Spanish city. The methodology addresses the phases of data preparation, feature engineering, hyperparameter training and optimization, model evaluation and selection, and finally model interpretation. Ensemble learning algorithms based on boosting (Gradient Boosting Regressor, Extreme Gradient Boosting, and Light Gradient Boosting Machine) and bagging (random forest and extra-trees regressor) are used and compared with a linear regression model. A case study is developed with georeferenced microdata of the real estate market in Alicante (Spain), before and after the pandemic declaration derived from COVID-19, together with information from other complementary sources such as the cadastre, socio-demographic and economic indicators, and satellite images. The results show that machine learning algorithms perform better than traditional linear models because they are better adapted to the nonlinearities of complex data such as real estate market data. Algorithms based on bagging show overfitting problems (random forest and extra-trees regressor) and those based on boosting have better performance and lower overfitting. This research contributes to the literature on the Spanish real estate market by being one of the first studies to use machine learning and microdata to explore the incidence of the COVID-19 pandemic on house prices.",73.0,https://www.mdpi.com/2073-445X/11/11/2100/pdf?version=1669037631,True,,Medicine,2022-11-21,45efb25c539abcec425466dd3f205a8d35bce89c,housingpricepredictionusingmachinelearningalgorithmsincovid19times,mora-garcía,10.3390/land11112100,housingpricepredictionusingmachinelearningalgorithmsincovid19times|2022,1,3368,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8550fbc72c6b46622fd732c58d24972f4ea40c81,10.3390/land11010004,,Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective,2021.0,Land,Hang Shen; Lin Li; Haihong Zhu; Yu Liu; Zhenwei Luo,"Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods can hardly predict very satisfactory prices, since the rental house prices involve both complicated nonlinear characteristics and spatial heterogeneity. The linear-based GWR model cannot characterize the nonlinear complexity of rental house prices, while existing deep-learning methods cannot explicitly model the spatial heterogeneity. This paper proposes a fully connected neural network–geographically weighted regression (FCNN–GWR) model that combines deep learning with GWR and can handle both of the problems above. In addition, when calculating the geographical location of a house, we propose a set of locational and neighborhood variables based on the quantities of nearby points of interests (POIs). Compared with traditional locational and neighborhood variables, the proposed “quantity-based” locational and neighborhood variables can cover more geographic objects and reflect the locational characteristics of a house from a comprehensive geographical perspective. Taking four major Chinese cities (Wuhan, Nanjing, Beijing, and Xi’an) as study areas, we compare the proposed method with other commonly used methods, and this paper presents a more precise estimation model for rental house prices. The method proposed in this paper may serve as a useful reference for individuals and enterprises in their transactions relevant to rental houses, and for the government in terms of the policies and positions of public rental housing.",18.0,https://www.mdpi.com/2073-445X/11/1/4/pdf?version=1640083411,True,,,2021-12-21,8550fbc72c6b46622fd732c58d24972f4ea40c81,exploringapricingmodelforurbanrentalhousesfromageographicalperspective,shen,10.3390/land11010004,exploringapricingmodelforurbanrentalhousesfromageographicalperspective|2021,1,3249,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,4,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,automated valuation model residential,48fe379d80519fbf670baf2989a14bc4c4f36883,10.3390/rs11243047,,"Sensitivity Analysis of Machine Learning Models for the Mass Appraisal of Real Estate. Case Study of Residential Units in Nicosia, Cyprus",2019.0,Remote Sensing,T. Dimopoulos; N. Bakas,"A recent study of property valuation literature indicated that the vast majority of researchers and academics in the field of real estate are focusing on Mass Appraisals rather than on the further development of the existing valuation methods. Researchers are using a variety of mathematical models used within the field of Machine Learning, which are applied to real estate valuations with high accuracy. On the other hand, it appears that professional valuers do not use these sophisticated models during daily practice, rather they operate using the traditional five methods. The Department of Lands and Surveys in Cyprus recently published the property values (General Valuation) for taxation purposes which were calculated by applying a hybrid model based on the Cost approach with the use of regression analysis in order to quantify the specific parameters of each property. In this paper, the authors propose a number of algorithms based on Artificial Intelligence and Machine Learning approaches that improve the accuracy of these results significantly. The aim of this work is to investigate the capabilities of such models and how they can be used for the mass appraisal of properties, to highlight the importance of sensitivity analysis in such models and also to increase the transparency so that automated valuation models (AVM) can be used for the day-to-day work of the valuer.",56.0,https://www.mdpi.com/2072-4292/11/24/3047/pdf?version=1576574207,True,JournalArticle;Review,Computer Science,2019-12-17,48fe379d80519fbf670baf2989a14bc4c4f36883,sensitivityanalysisofmachinelearningmodelsforthemassappraisalofrealestatecasestudyofresidentialunitsinnicosiacyprus,dimopoulos,10.3390/rs11243047,sensitivityanalysisofmachinelearningmodelsforthemassappraisalofrealestatecasestudyofresidentialunitsinnicosiacyprus|2019,1,2892,4,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || automated valuation model residential",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),99ca7e0e5fa67a40a7155332e806f35ec242ba91,10.3390/su18136497,,"Data-Driven Green Value Assessment of Urban Real Estate: A Multimodal Intelligent Valuation Framework Integrating Image, Text, and Spatial Information",2026.0,Sustainability,Wen Fu; Lei Zhang,"Traditional approaches to urban real estate green value assessment rely heavily on single structured data sources. Such methods often provide limited interpretability and fail to capture multidimensional green attributes accurately. To address these limitations, this study constructs a multimodal assessment framework that integrates image, text, and spatial information. A housing price prediction model is developed based on a Multi-Layer Perceptron architecture. Results show that the proposed method is superior to traditional models (such as the Hedonic pricing model, Ridge regression, and eXtreme Gradient Boosting, as well as single-modality control models). The core evaluation metric, mean squared error, reaches 0.0505 ± 0.0021. SHapley Additive exPlanations analysis shows that the text modality provides the largest contribution to model prediction, accounting for 51.45% of the global contribution. However, this dominance reflects the model’s dependence on textual green signals rather than the establishment of causal relationships. The result may also be influenced by marketing language bias and symbolic sustainability signals. The image modality contributes 38.48%, while the spatial modality contributes 10.07%, indicating a complementary relationship among the three modalities. Green premium analysis confirms that the model achieves higher prediction accuracy for high-priced residences and effectively captures differences in green premium across housing price tiers. This study provides a new technical pathway for real estate green value assessment.",0.0,https://www.mdpi.com/2071-1050/18/13/6497/pdf?version=1783307101,True,JournalArticle,,2026-06-25,99ca7e0e5fa67a40a7155332e806f35ec242ba91,datadrivengreenvalueassessmentofurbanrealestateamultimodalintelligentvaluationframeworkintegratingimagetextandspatialinformation,fu,10.3390/su18136497,datadrivengreenvalueassessmentofurbanrealestateamultimodalintelligentvaluationframeworkintegratingimagetextandspatialinformation|2026,1,3077,8,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || real estate hedonic regression || spatial hedonic model housing",2,1,1,5,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic housing prices,de00ce33714ab93ed7f1db6c02278a9ee1e1890c,10.3390/su14095307,,Understanding Housing Prices Using Geographic Big Data: A Case Study in Shenzhen,2022.0,Sustainability,Xufeng Jiang; Ze-Lu Jia; Le-Fei Li; Tianhong Zhao,"Understanding the spatial pattern of urban house prices and its association with the built environment is of great significance to housing policymaking and urban planning. However, many studies on the influencing factors of urban housing prices conduct qualitative analyses using statistical data and manual survey data. In addition, traditional housing price models are mostly linear models that cannot explain the distribution of housing prices in urban areas. In this paper, we propose using geographic big data and zonal nonlinear feature machine learning models to understand housing prices. First, the housing price influencing factor system is built based on the hedonic pricing model and geographic big data, and it includes commercial development, transportation, infrastructure, location, education, environment, and residents’ consumption level. Second, a spatial exploratory analysis framework for house price data was constructed using Moran’s I tools and geographic detectors. Finally, the XGBoost model is developed to assess the importance of the variables influencing housing prices, and the zonal nonlinear feature model is built to predict housing prices based on spatial exploration results. Taking Shenzhen as an example, this paper explored the distribution law of housing prices, analyzed the influencing factors of housing prices, and compared the different housing price models. The results show that the zonal nonlinear feature model has higher accuracy than the linear model and the global model.",11.0,https://www.mdpi.com/2071-1050/14/9/5307/pdf?version=1651130231,True,Review,,2022-04-28,de00ce33714ab93ed7f1db6c02278a9ee1e1890c,understandinghousingpricesusinggeographicbigdataacasestudyinshenzhen,jiang,10.3390/su14095307,understandinghousingpricesusinggeographicbigdataacasestudyinshenzhen|2022,1,3023,5,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic house price || hedonic housing prices || machine learning hedonic pricing housing || spatial hedonic model housing",2,3,2,2,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",137b93fc5e22064ef7575f158dfe102b8f1fc46d,10.3390/su14159056,,A Comparative Study of Machine Learning and Spatial Interpolation Methods for Predicting House Prices,2022.0,Sustainability,Jeonghyeon Kim; Youngho Lee; Myeongdong Lee; Seong-Yun Hong,"As the volume of spatial data has rapidly increased over the last several decades, there is a growing concern about missing and incomplete observations that may result in biased conclusions. Several recent studies have reported that machine learning techniques can more efficiently address this limitation in emerging data sets than conventional interpolation approaches, such as inverse distance weighting and kriging. However, most existing studies focus on data from environmental sciences; so, further evaluations are required to assess their strengths and limitations for socioeconomic data, such as house price data. In this study, we conducted a comparative analysis of four commonly used methods: neural networks, random forests, inverse distance weighting, and kriging. We applied these methods to the real estate transaction data of Seoul, South Korea, and demonstrated how the values of the houses at which no transactions are recorded could be predicted. Our empirical analysis suggested that the neural networks and random forests can provide a more accurate estimation than the interpolation counterparts. Of the two machine learning techniques, the results from a random forest model were slightly better than those from a neural network model. However, the neural network appeared to be more sensitive to the amount of training data, implying that it has the potential to outperform the other methods when there are sufficient data available for training.",33.0,https://www.mdpi.com/2071-1050/14/15/9056/pdf?version=1658645047,True,,,2022-07-24,137b93fc5e22064ef7575f158dfe102b8f1fc46d,acomparativestudyofmachinelearningandspatialinterpolationmethodsforpredictinghouseprices,kim,10.3390/su14159056,acomparativestudyofmachinelearningandspatialinterpolationmethodsforpredictinghouseprices|2022,1,2971,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,housing characteristics prices hedonic,0122a631e41bf9758c29784cc01333d32c8da9c7,10.3390/su132011489,,Computational Valuation Model of Housing Price Using Pseudo Self Comparison Method,2021.0,Sustainability,S. Choi; Mun Yong Yi,"Hedonic pricing method (HPM), which is commonly used for estimating real estate property values, considers the property’s internal and external characteristics for its valuation. Despite its popularity, however, the method lacks the mechanism that directly reflects the target property’s price fluctuation and the real estate market’s volatility over time. To overcome these limitations, we propose Pseudo Self Comparison Method (PSCM), which reduces the real estate valuation problem to finding a pseudo self, which is defined as a housing property that can most closely approximate the characteristics of the target housing property, and adjusting its previous transaction price to be in sync with the real estate market change. The proposed PSCM is tested for two scenarios in which the volatility of the real estate market varies greatly, using the transaction data compiled from Seoul, the capital of South Korea, and its surrounding region, Gyeonggi. The study results show almost five times lower estimation errors when predicting housing transaction prices using the PSCM compared to the HPM in both scenarios and in both areas. The proposed method is particularly useful for mass valuation of apartments or densely located housing units.",5.0,https://www.mdpi.com/2071-1050/13/20/11489/pdf?version=1634881151,True,,,2021-10-18,0122a631e41bf9758c29784cc01333d32c8da9c7,computationalvaluationmodelofhousingpriceusingpseudoselfcomparisonmethod,choi,10.3390/su132011489,computationalvaluationmodelofhousingpriceusingpseudoselfcomparisonmethod|2021,1,2746,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic",2,3,2,2,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",04f422e09f33240e186db483f7eceae8e3bd6b03,10.3390/su12187520,,Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM),2020.0,Sustainability,Hyunsoo Kim; Youngwoo Kwon; Yeol Choi,"Providing adequate public rental housing (PRH) of a decent quality at a desirable location is a major challenge in many cities. Often, a prominent opponent of PRH development is its host community, driven by a belief that PRH depreciates nearby property values. While this is a persistent issue in many cities around the world, this study proposed a new approach to assessing the impact of PRH on nearby property value. This study utilized a machine learning technique called long short-term memory (LSTM) to construct a set of housing price prediction models based on 547,740 apartment transaction records from the city of Busan, South Korea. A set of apartment characteristics and proximity measures to PRH were included in the modeling process. Four geographic boundaries were analyzed: The entire region of Busan, all neighborhoods of PRH, the neighborhoods of PRH in the “favorable,” and the “less favorable” local housing market. The study produced accurate and reliable price predictions, which indicated that the proximity to PRH has a meaningful impact on nearby housing prices both at the city and the neighborhood level. The approach taken by the study can facilitate improved decision making for future PRH policies and programs.",20.0,https://www.mdpi.com/2071-1050/12/18/7520/pdf,True,,Business,2020-09-11,04f422e09f33240e186db483f7eceae8e3bd6b03,assessingtheimpactofpublicrentalhousingonthehousingpricesinproximitybasedontheregionalandlocallevelofpricepredictionmodelsusinglongshorttermmemorylstm,kim,10.3390/su12187520,assessingtheimpactofpublicrentalhousingonthehousingpricesinproximitybasedontheregionalandlocallevelofpricepredictionmodelsusinglongshorttermmemorylstm|2020,1,2741,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7565f7ec9aefc380cf897b8d5ea10d2d098d1500,10.3390/su12145679,,Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices,2020.0,Sustainability,Yunjong Kim; Seung-Ho Choi; Mun Yong Yi,"In this paper, we propose a novel procedure designed to apply comparable sales method to the automated price estimation of real estates, in particular, that of apartments. Apartments are the most popular residential housing type in Korea. The price of a single apartment is influenced by many factors, making it hard to estimate accurately. Moreover, as an apartment is purchased for living, with a sizable amount of money, it is mostly traded infrequently. Thus, its past transaction price may not be particularly helpful to the estimation after a certain period of time. For these reasons, the up-to-date price of an apartment is commonly estimated by certified appraisers, who typically rely on comparable sales method (CSM). CSM requires comparable properties to be identified and used as references in estimating the current price of the property in question. In this research, we develop a procedure to systematically apply this procedure to the automated estimation of apartment prices and assess its applicability using nine years’ real transaction data from the capital city and the most-populated province in South Korea and multiple scenarios designed to reflect the conditions of low and high fluctuations of housing prices. The results from extensive evaluations show that the proposed approach is superior to the traditional approach of relying on real estate professionals and also to the baseline machine learning approach.",24.0,https://www.mdpi.com/2071-1050/12/14/5679/pdf?version=1595295841,True,,Business,2020-07-15,7565f7ec9aefc380cf897b8d5ea10d2d098d1500,applyingcomparablesalesmethodtotheautomatedestimationofrealestateprices,kim,10.3390/su12145679,applyingcomparablesalesmethodtotheautomatedestimationofrealestateprices|2020,1,2939,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",199747ec923aea52036ef30630a3263acbfb5ff2,10.3390/su10041298,,"Walled Buildings, Sustainability, and Housing Prices: An Artificial Neural Network Approach",2018.0,,R. Li; Kai-Yin Cheng; M. Shoaib,"Various researchers have explored the adverse effects of walled buildings on human health. However, few of them have examined the relationship between walled buildings and private housing estates in Hong Kong. This study endeavors to fill the research gap by exploring the connections among walled-building effects, housing features, macroeconomic factors, and housing prices in private housing estates. Specifically, it reveals the relationship between walled buildings and housing prices. Eight privately owned housing estates are selected with a total of 11,365 observations. Results are analyzed to study the factors that affect the housing price. Firstly, unit root tests are carried out to evaluate if the time series variables follow the unit root process. Secondly, the relationship between walled buildings and housing price is examined by conducting an artificial neural network. We assumed that the housing price reduces due to walled-building effects, given that previous literature showed that heat island effect, and blockage of natural light and views, are common in walled-building districts. Moreover, we assume that housing price can also be affected by macroeconomic factors and housing features, and these effects vary among private housing estates. We also study these impacts by using the two models. Recommendations and possible solutions are suggested at the end of the research paper.",41.0,https://www.mdpi.com/2071-1050/10/4/1298/pdf?version=1525349533,True,,Business,2018-04-23,199747ec923aea52036ef30630a3263acbfb5ff2,walledbuildingssustainabilityandhousingpricesanartificialneuralnetworkapproach,li,10.3390/su10041298,walledbuildingssustainabilityandhousingpricesanartificialneuralnetworkapproach|2018,1,2909,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic housing prices,aacf1da2f2c282d86f6907614f853ed1c1c7ce56,10.3390/a19020098,,Machine Learning Applications for Sustainable Housing Policy: Understanding Price Determinants to Inform Affordable Housing Strategies,2026.0,Algorithms,Fan Zhang; Yi-Fang Luo; Yuqing Dong; Qi-Kai Zhang; Aihua Han,"Understanding how housing attributes are capitalized into prices is central to addressing urban affordability challenges. Using 2799 second-hand housing transactions from Wenzhou, China, this study examines residential price formation under pronounced spatial and structural heterogeneity. Multiple predictive models are evaluated within a unified 10-fold cross-validation framework. Results indicate that Random Forest delivers the strongest predictive performance, achieving a normalized mean squared error below 0.10 and explaining over 90% of out-of-sample price variation, substantially outperforming hedonic regression, regression trees, bagging, boosting, and support vector models. Permutation-based importance analysis identifies district location, building scale, and floor area as the dominant price determinants, while the influence of renovation quality, transportation access, and educational amenities varies across districts and dwelling types. These findings reveal strong nonlinearities and heterogeneous valuation mechanisms in rapidly urbanizing housing markets. Methodologically, the study demonstrates how interpretable machine learning complements traditional hedonic analysis, while providing policy-relevant insights into housing affordability dynamics in medium-sized Chinese cities.",0.0,https://www.mdpi.com/1999-4893/19/2/98/pdf,True,JournalArticle,Computer Science,2026-01-26,aacf1da2f2c282d86f6907614f853ed1c1c7ce56,machinelearningapplicationsforsustainablehousingpolicyunderstandingpricedeterminantstoinformaffordablehousingstrategies,zhang,10.3390/a19020098,machinelearningapplicationsforsustainablehousingpolicyunderstandingpricedeterminantstoinformaffordablehousingstrategies|2026,1,2809,4,hedonic housing prices || hedonic price model amenities residential || machine learning hedonic pricing housing || spatial hedonic model housing,2,0,3,7,0,5,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1394e6041b939e855033f7dfa0b687125fb7a489,10.3390/a18020093,,Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks,2025.0,Algorithms,Iosif Viktoratos; A. Tsadiras,"Accurately estimating house values is a critical challenge for real-estate stakeholders, including homeowners, buyers, sellers, agents, and policymakers. This study introduces a novel approach to this problem using Kolmogorov–Arnold networks (KANs), a type of neural network based on the Kolmogorov–Arnold theorem. The proposed KAN model was tested on two datasets and demonstrated superior performance compared to existing state-of-the-art methods for predicting house prices. By delivering more precise price forecasts, the model supports improved decision-making for real-estate stakeholders. Additionally, the results highlight the broader potential of KANs for addressing complex prediction tasks in data science. This study aims to provide an innovative and effective solution for accurate house price estimation, offering significant benefits for the real-estate industry and beyond.",3.0,https://www.mdpi.com/1999-4893/18/2/93/pdf,True,JournalArticle,Computer Science,2025-02-07,1394e6041b939e855033f7dfa0b687125fb7a489,advancingrealestateforecastinganovelapproachusingkolmogorovarnoldnetworks,viktoratos,10.3390/a18020093,advancingrealestateforecastinganovelapproachusingkolmogorovarnoldnetworks|2025,1,2390,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",60baaaab9e349694711826de7788e8ea4b27978d,10.47611/jsrhs.v13i3.6683,,Making Housing Predictions Using ML Without Live Market Data,2024.0,Journal of student-scientists' research,Justin Chen; Soyoun Choi,"This paper explores the importance of machine learning and the benefits machine learning can bring to the housing industry. The housing industry makes up an average of more than 15% of the US’s yearly GDP and plays a significant role in the US economy. Additionally, the housing industry, specifically house prices, determines the environment each person grows up in by filtering out those who are unable to afford the costs of living in any given area. Having accurate house price information on hand can save many hours spent looking for affordable housing. After a brief introduction of Zillow, a house listing website that also contains a feature that provides highly accurate predictions of its users’ house prices, the central question, “How high a degree of accuracy can house prices be predicted without information regarding the state of the current economy or house market?” is explored. While Zillow uses data such as market trends and past selling prices, only information regarding the physical traits of the house, such as its geographical location and its room count, are considered within this paper (Note that the scaling factor for many variables is messed up, meaning that despite numbers that don’t intuitively seem correct, the relationship between each pair of data points remains unaffected). After visually identifying correlations between house value and the other variables with simple scatter plots, the relationships between different variables were further explored with different regression models, and a neural network was made based off of the previous exploration. ",0.0,https://www.jsr.org/hs/index.php/path/article/download/6683/3211,True,JournalArticle,,2024-08-31,60baaaab9e349694711826de7788e8ea4b27978d,makinghousingpredictionsusingmlwithoutlivemarketdata,chen,10.47611/jsrhs.v13i3.6683,makinghousingpredictionsusingmlwithoutlivemarketdata|2024,1,3098,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d6ce91755158f5e7f936d64c3b1263a530cacf9a,10.47611/jsrhs.v11i4.3151,,Using Supervised Machine Learning to Predict House Prices,2022.0,Journal of student-scientists' research,Alexander Tsai; Hieu Nguyen,"Given the recent influx of prices in the housing market, determining a fair housing price has been of high interest for many homebuyers and sellers alike. In this project, various machine learning models are used to predict the price of a house based on physical features and characteristics such as lot size and neighborhood. Extensive data preprocessing and feature engineering were employed to aid the models’ performance compared to other models in the market. The best models have been able to predict U.S houses’ prices within a RMSE value of $23,000 when the mean price of a house in the dataset is $180,000. In future research, this model can be implemented in various other places within the U.S and additional features can improve performance further.",0.0,https://www.jsr.org/hs/index.php/path/article/download/3151/1640,True,JournalArticle,,2022-11-30,d6ce91755158f5e7f936d64c3b1263a530cacf9a,usingsupervisedmachinelearningtopredicthouseprices,tsai,10.47611/jsrhs.v11i4.3151,usingsupervisedmachinelearningtopredicthouseprices|2022,1,2261,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",16ff9ab5caa1f21f40d959d0083736cbf7d703ab,10.1051/itmconf/20258001031,,"Comparing Machine Learning Models for House Price Prediction: Linear Regression, Decision Tree, and Random Forest",2025.0,ITM Web of Conferences,Y. Jing,"Predicting housing prices accurately is crucial for informed real estate decisions. This paper compares the performance of three machine learning models—Linear Regression, Decision Tree, and Random Forest— on the California Housing Prices dataset from scikit-learn. Through comprehensive data preprocessing and hyperparameter tuning using grid search with cross-validation, the paper evaluates model performance using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and R-squared (R²) metrics. Experimental results show that Random Forest achieves the highest prediction accuracy with the smallest MAE (0.33), MSE (0.26), and RMSE (0.51) values, and the R² value (0.78) closest to 1. The Decision Tree model demonstrates moderate accuracy but shows signs of overfitting, while Linear Regression exhibits the lowest accuracy but best robustness with minimal difference between training and test performance. These findings suggest that ensemble methods like Random Forest are particularly effective for housing price prediction, though model choice should consider the specific trade-offs between accuracy and robustness. The study provides valuable insights for selecting appropriate models in real estate price prediction applications.",3.0,https://www.itm-conferences.org/articles/itmconf/pdf/2025/11/itmconf_acaai2025_01031.pdf,True,JournalArticle;Conference,,,16ff9ab5caa1f21f40d959d0083736cbf7d703ab,comparingmachinelearningmodelsforhousepricepredictionlinearregressiondecisiontreeandrandomforest,jing,10.1051/itmconf/20258001031,comparingmachinelearningmodelsforhousepricepredictionlinearregressiondecisiontreeandrandomforest|2025,1,2772,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",bbc333a961f64c0245c510956184745b487f1d31,10.1051/itmconf/20257002018,,Research on House Price Prediction based on Machine Learning,2025.0,ITM Web of Conferences,Xiangjun Yang,"Accurately predicting house prices is of vital importance to individual home buyers and investment groups, which not only profoundly affects the formulation of home-buying strategies, but also is closely related to the smooth operation of the economy and the overall development of the society. In recent years, machine learning techniques have shown remarkable potential in house price prediction, as these models can mine the complex nonlinear correlations in large amounts of historical data to produce more detailed and accurate predictions. This study aims to evaluate and compare the performance of various machine learning models on the task of house price prediction. For the house price prediction task, Random Forests generally perform better than Linear Regression and Single Decision Tree because they can better capture complex patterns in the data and reduce the risk of overfitting. Linear regression models are simple and easy to interpret, but may not be accurate enough when dealing with nonlinear relationships and outliers. The advantages of random forests are reflected in higher predictive accuracy, robustness to outliers, and the ability to handle interactions between variables automatically.",4.0,https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02018.pdf,True,JournalArticle;Conference,,,bbc333a961f64c0245c510956184745b487f1d31,researchonhousepricepredictionbasedonmachinelearning,yang,10.1051/itmconf/20257002018,researchonhousepricepredictionbasedonmachinelearning|2025,1,2717,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",719ddda153b52e86649ce1056722027072dd1a5a,10.5194/isprs-annals-v-4-2022-49-2022,,STACKING-BASED UNCERTAINTY MODELLING OF STATISTICAL AND MACHINE LEARNING METHODS FOR RESIDENTIAL PROPERTY VALUATION,2022.0,"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences",A. Jafari; M. R. Delavar; A. Stein,"Abstract. Estimating real estate prices helps to adapt informed policies to regulate the real estate market and assist sellers and buyers to have a fair business. This study aims to estimate the price of residential properties in District 5 of Tehran, Capital of Iran, and model its associated uncertainty. The study implements the Stacking technique to model uncertainties by integrating the outputs of basic models. Basic models must have a good performance for their combinations to have acceptable results. This study employs four statistical and machine learning models as basic models: Random Forest (RF), Ordinary Least Squares (OLS), Weighted K-Nearest Neighbour (WKNN), and Support Vector Regression (SVR) to estimate the price of residential properties. The results show that the integrated output is more accurate for the quadruple combination mode than for any of the binary and triple combinations of the basic models. Comparing the Stacking technique with the Voting technique, it is shown that the Mean Absolute Percentage Error (MAPE) reduces from 10.18% to 9.81%. Hence we conclude that our method performs better than the Voting technique. ",2.0,https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-4-2022/49/2022/isprs-annals-V-4-2022-49-2022.pdf,True,,,2022-05-18,719ddda153b52e86649ce1056722027072dd1a5a,stackingbaseduncertaintymodellingofstatisticalandmachinelearningmethodsforresidentialpropertyvaluation,jafari,10.5194/isprs-annals-v-4-2022-49-2022,stackingbaseduncertaintymodellingofstatisticalandmachinelearningmethodsforresidentialpropertyvaluation|2022,1,2658,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",466453a0acb823acd777ace6ef870a5a22aee178,10.71097/ijsat.v16.i2.6130,,Approaches and comparison of valuation of Residential properties by using Deep Learning Technique.,2025.0,International Journal for Sciences and Technology,Shrinath Zine; Sharan Kori,"This study presents an integrated approach to real estate price prediction and property valuation by combining advanced deep learning models with traditional regression techniques. The research leverages machine learning algorithms, including Artificial Neural Networks (ANNs) and Long Short-Term Memory (LSTM) networks, to improve the accuracy and efficiency of predicting residential property prices. A comparative analysis of valuation techniques such as the income approach, sales comparison method, and cost method is also conducted using a case study focused on residential properties in Pune City. The study explores spatial, temporal, and economic variables influencing market trends and integrates them into a hybrid model framework. By combining modern AI-driven predictive tools with traditional real estate valuation practices, this work aims to enhance decision-making for stakeholders such as investors, developers, and urban planners.",0.0,https://www.ijsat.org/papers/2025/2/6130.pdf,True,JournalArticle,,2025-06-11,466453a0acb823acd777ace6ef870a5a22aee178,approachesandcomparisonofvaluationofresidentialpropertiesbyusingdeeplearningtechnique,zine,10.71097/ijsat.v16.i2.6130,approachesandcomparisonofvaluationofresidentialpropertiesbyusingdeeplearningtechnique|2025,1,2449,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1b966ba824b734e331501c2e9a87fb8606deb07c,10.38124/ijisrt/25may475,,Residential Property Price Forecasting with a Machine Learning Approach,2025.0,International Journal of Innovative Science and Research Technology,N. Bhavana; A. Bhargavi,"The location, economic trends, infrastructure, and regulatory changes are just a few of the many variables that impact the dynamic and complicated housing market. For investors, buyers, sellers, and legislators to make wise choices, accurate home price forecasting is essential. Conventional assessment techniques mostly rely on manual value, which is frequently biased and inconsistent. By revealing hidden patterns in vast and intricate datasets, machine learning has become a potent tool for modeling and predicting real estate prices in recent years. Using a variety of property-related characteristics and historical sales data, this study suggests a strong machine learning framework for predicting home values. Numerous factors are included in the model, including location, square footage, number of bedrooms and baths, property age, ease of access to amenities, and neighborhood data. To find the best model, a number of techniques are investigated and contrasted, such as Linear Regression, Decision Trees, Random Forests, and Gradient Boosting. Performance optimization involves several crucial phases, including feature selection, data preprocessing, and hyperparameter adjustment. Model correctness is evaluated using the evaluation metrics Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. The findings show that ensemble-based models perform better in terms of prediction, especially Gradient Boosting. This study offers a flexible and scalable method for real-time price estimation that can be incorporated into real estate platforms, improving the efficiency and transparency of real estate transactions.",0.0,https://www.ijisrt.com/assets/upload/files/IJISRT25MAY475.pdf,True,JournalArticle,,2025-05-22,1b966ba824b734e331501c2e9a87fb8606deb07c,residentialpropertypriceforecastingwithamachinelearningapproach,bhavana,10.38124/ijisrt/25may475,residentialpropertypriceforecastingwithamachinelearningapproach|2025,1,3145,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",78275fd0eaf66fb56137b4c71d1c9dd2719494f5,10.38124/ijisrt/ijisrt24oct1873,,House Price Pridiction Using Machine Learning,2024.0,International Journal of Innovative Science and Research Technology,Dev Mulchandani; Swati Shamkuwar,"This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This proposed model considers as the most accurate model used for calculating the house price and provides a most accurate prediction. This provides a brief introduction which will be needed to predict the house price. This project consists of what and how the house price model works with the assistance of machine learning technique using scikit-learn and which datasets we will be using in our proposed model. Predicting the price of a house helps for determine the selling price of the house in a particular region and it help people to find the correct time to buy a home. In this task on House Price Prediction using machine learning, our task is to use data to create a machine learning model to predict house prices in the given region. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to improve the efficiency of the model. for this project we are using supervised learning, which is a part of machine learning. We have to go through different attributes of the dataset",0.0,https://www.ijisrt.com/assets/upload/files/IJISRT24OCT1873.pdf,True,JournalArticle;Review,,2024-11-12,78275fd0eaf66fb56137b4c71d1c9dd2719494f5,housepricepridictionusingmachinelearning,mulchandani,10.38124/ijisrt/ijisrt24oct1873,housepricepridictionusingmachinelearning|2024,1,2797,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3fe9779bc5ed68ec00d0a7c7dcf3b20fe62799b5,10.36948/ijfmr.2026.v08i02.73152,,Exploring House Prices and Features with Tableau,2026.0,International Journal For Multidisciplinary Research,Koppula Purna Venkateswara Rao; P. Sravani; Porumalla Gowthami; Chandragiri Veeraswamy; Mutyala Satish,"The real estate sector requires accurate housing price estimation for effective decision making. Traditional property valuation methods are often manual and inefficient. With the availability of large real estate datasets, data analytics and machine learning techniques can improve housing market analysis and price prediction. This study proposes a housing price prediction and visualization system that integrates Tableau dashboards with machine learning models. Tableau is used to visualize housing market trends, while algorithms such as Linear Regression, KNN, and Random Forest are applied to predict housing prices. The dataset is preprocessed, and models are evaluated using MAE, RMSE, and R² Score. The trained model is deployed using a Flask web application, where users can input property details and obtain predicted prices. The system also provides future price estimation and profit analysis based on growth rate. The proposed system enables effective housing market analysis and supports data-driven decision making.",0.0,https://www.ijfmr.com/papers/2026/2/73152.pdf,True,JournalArticle,,2026-03-31,3fe9779bc5ed68ec00d0a7c7dcf3b20fe62799b5,exploringhousepricesandfeatureswithtableau,rao,10.36948/ijfmr.2026.v08i02.73152,exploringhousepricesandfeatureswithtableau|2026,1,2533,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f69a5bd1259a127997971ab7665e226f6509dc99,10.36948/ijfmr.2023.v05i06.11519,,House Price Prediction Using Linear Regression Model,2023.0,International Journal For Multidisciplinary Research,J. Parekh,"Machine learning is a subset of Artificial Intelligence. Artificial intelligence (AI) and machine learning (ML) are key technologies in solving problems and addressing a wide range of issues in many different fields. Due to its capacity to automate processes, analyze massive volumes of data, and make precise judgments. Generally, it is used in voice assistants, recommendation systems, autonomous vehicles as well as in fraud detection. It also plays a vital role in the real estate sector, accurate house price prediction helps buyers, sellers, and investors make accurate decisions. There is a need for technology to predict housing values because they rise annually. Predicting house prices can assist developers in setting a property's selling price as well as buyers in scheduling the ideal time to buy a home. Four factors influence the price of a house which are area, bedrooms, bathrooms, and location. This study uses a methodology to forecast the price of houses based on relevant features, specifically by applying a linear regression model. Through the use of machine learning methods such as Random Forest, K-Means, Decision Tree, and Linear regression. This strategy will make it easier for people to invest money in a legacy without going via a broker. The study's findings indicate that the Linear regression yields the best accuracy.",2.0,https://www.ijfmr.com/papers/2023/6/11519.pdf,True,JournalArticle,,2023-12-31,f69a5bd1259a127997971ab7665e226f6509dc99,housepricepredictionusinglinearregressionmodel,parekh,10.36948/ijfmr.2023.v05i06.11519,housepricepredictionusinglinearregressionmodel|2023,1,2852,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8b3f3a2bc764ad2eb5fc96d5acd25ed14bf4ee97,10.36948/ijfmr.2023.v05i04.5255,,Prediction and Analysis of House Price Through Machine Learning Approach,2023.0,International Journal For Multidisciplinary Research,Manivel Kandasamy; Dr. Raju Shanmugam; Ayush Dave; Charvin Chawda; Krish Shah; Umang Seladiya,"In recent years, picture detection, spam restructuring, natural speech command, product recommendation, and medical diagnosis have all benefited greatly from machine learning. The current machine learning algorithm aids in improving medical advancements, public safety, and security alerts. In the current work, we talk about the machine learning algorithm-generated forecast of future home prices. We compare and investigate numerous prediction methods for the selection of prediction methods. We employ lasso regression as our model because to its flexible and probabilistic model selection process We construct a housing cost prediction model in the considering such machine learning algorithm models as XG Boost, lasso regression, and neural systems on look at their order precision execution. At that point, we urge a real estate agent or home seller to employ a housing cost estimates model to assist them with better information based on property valuation. In regards to precision those tests show that the lasso regression algorithm constantly beats alternative models when utilized to predict costs associated with housing.",1.0,https://www.ijfmr.com/papers/2023/4/5255.pdf,True,JournalArticle,,2023-08-24,8b3f3a2bc764ad2eb5fc96d5acd25ed14bf4ee97,predictionandanalysisofhousepricethroughmachinelearningapproach,kandasamy,10.36948/ijfmr.2023.v05i04.5255,predictionandanalysisofhousepricethroughmachinelearningapproach|2023,1,2633,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ab5dc83d2fa2ebbd72d7238968a1f2f003282eb8,10.36948/ijfmr.2023.v05i02.1849,,Predicting House Price with Deep Learning: A Comparative Study of Machine Learning Models,2023.0,International Journal For Multidisciplinary Research,S. Kumar; S. Bhatt; Hasan Phudinawala,"In the real estate market, one of the most important tasks is to accurately predict house prices, as this information can assist buyers and sellers in making more educated decisions about the value of their respective properties. In this investigation, we investigate the application of deep learning (DL) strategies for forecasting housing prices and evaluate how well these strategies perform in comparison to more conventional machine learning approaches.",6.0,https://www.ijfmr.com/papers/2023/2/1849.pdf,True,,,2023-03-21,ab5dc83d2fa2ebbd72d7238968a1f2f003282eb8,predictinghousepricewithdeeplearningacomparativestudyofmachinelearningmodels,kumar,10.36948/ijfmr.2023.v05i02.1849,predictinghousepricewithdeeplearningacomparativestudyofmachinelearningmodels|2023,1,1958,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a6b7be12c5485726891ca3625471e23814fee1f8,10.71222/1s8mmk10,,Comparative Study of Machine Learning Models for U.S. Housing Price Prediction,2026.0,"Journal of Computer, Signal, and System Research",Wen-Guang Zhou; Wen-Jiao Zhou,"This study compares machine learning models used to predict US house prices using a large-scale real estate dataset covering all US states, cities, and zip code regions. The goal is to evaluate prediction accuracy and identify effective preprocessing strategies for high-cardinality location features. After cleaning the data through missing value handling, deduplication, and outlier removal based on interquartile range (IQR), leakage-aware feature engineering was applied, including aggregating zip codes into ZIP3, grouping by the top K cities, and date decomposition, while excluding target derived variables. This study trains and evaluates three models-linear regression, random forest, and XGBoost-on a reserved test set using mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²). The results show that XGBoost achieves the best performance, outperforming linear regression and random forest, and feature importance indicates that location indicators play a dominant role in prediction gain. The findings demonstrate that the XGBoost model outperforms linear regression and random forest models in predicting US house prices.",0.0,https://www.gbspress.com/index.php/JCSSR/article/download/613/622,True,JournalArticle,,2026-03-14,a6b7be12c5485726891ca3625471e23814fee1f8,comparativestudyofmachinelearningmodelsforushousingpriceprediction,zhou,10.71222/1s8mmk10,comparativestudyofmachinelearningmodelsforushousingpriceprediction|2026,1,2671,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7f339c1ab989769c86c527166573bc6b3ea01d2f,10.54254/2753-8818/2025.dl27031,,Applying Hypothesis Testing and XGBoost to Analyze and Predict Housing Price Trends in Guangzhou,2025.0,Theoretical and Natural Science,Junyang Liu,"With the increasing application of machine learning technologies in the real estate sector, traditional models face challenges in terms of prediction accuracy and capturing nonlinear relationships. This study aims to integrate classical statistical methods with advanced machine learning algorithms to thoroughly explore the core factors influencing housing prices in Guangzhou and achieve high-precision regional price predictions. By employing statistical hypothesis testing to preliminarily screen factors, the significance of their relationships with housing prices is verified. On this basis, an XGBoost learning model is constructed to perform modeling and prediction, leveraging its strong capabilities in handling nonlinear relationships, interaction effects, and missing data. The study identifies the number of new houses, GDP per capita, the number of subway stations, and the number of large supermarkets as key factors verified to have a significant positive impact on housing prices in Guangzhou. Housing prices in Guangzhou's core districts have taken the lead in recovery, while development zones have shown steady growth with the greatest potential. In contrast, the peripheral suburban areas continue to face downward pressure.",0.0,https://www.ewadirect.com/proceedings/tns/article/view/27031/pdf,True,JournalArticle,,2025-09-24,7f339c1ab989769c86c527166573bc6b3ea01d2f,applyinghypothesistestingandxgboosttoanalyzeandpredicthousingpricetrendsinguangzhou,liu,10.54254/2753-8818/2025.dl27031,applyinghypothesistestingandxgboosttoanalyzeandpredicthousingpricetrendsinguangzhou|2025,1,2745,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ea2569a40c28d3872ad58bfa06e5f2001dedf617,10.54254/2753-8818/2025.22572,,Hybrid House Price Prediction Model by Integration of Simple Linear Regression and Cubic Spline Interpolation,2025.0,Theoretical and Natural Science,Yiwen Tang,"In this day and age, house-purchase has become a crucial consideration for almost everyone, whether seeking a residence or making an investment. Therefore, analyzing the relationship between these factors and house prices is vitally important for both buyers and sellers to make informed decisions. Some researchers have used a linear regression model that can predict the house price for a company or individual. This paper focuses on a target sample data set of Houses in London from Kaggle. The author firstly provides an analysis of two methods to model the relationship between the dependent variable, House Price, and an independent variable, Square Meters. These methods are the Simple Linear Regression Model (SLR) and Cubic Spline Interpolation polynomial (CSI), respectively. Then, a Hybrid House Price Prediction Model is established to predict the house price with specific Square Meters by integrating SLR and CSI. Finally, the author uses Multiple Linear Regression to model the effects of various independent variables from the target sample to the House Price. The research significance of this paper mainly includes increasing the accuracy and comprehensiveness of the House Prediction Model by constructing a Hybrid Model and using the MLR model, respectively.",0.0,https://www.ewadirect.com/proceedings/tns/article/view/22572/pdf,True,JournalArticle,,2025-05-06,ea2569a40c28d3872ad58bfa06e5f2001dedf617,hybridhousepricepredictionmodelbyintegrationofsimplelinearregressionandcubicsplineinterpolation,tang,10.54254/2753-8818/2025.22572,hybridhousepricepredictionmodelbyintegrationofsimplelinearregressionandcubicsplineinterpolation|2025,1,2778,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",85d7c2812036f9f54351dee08df7c5f83d99e95c,10.54254/2753-8818/2025.22048,,Dimensionality-Prediction Dilemma: The Dual Effects of PCA in Housing Price Regression Modeling,2025.0,Theoretical and Natural Science,Tangrui Xiong,"This study evaluates the practical dimensionality reduction efficacy of Principal Component Analysis (PCA) and its consequential impacts on regression-based housing price prediction by comparing three modelsmultiple linear regression, ridge regression, and LASSO regressionapplied to California housing data, while preliminarily exploring the underlying mechanisms. The research first establishes a spatial-economic-social framework influencing housing prices through data dimensionality reduction. This process initiates with KMO-Bartlett validation of the dataset, followed by variable screening conducted under comprehensive consideration of variables practical significance, and subsequently implements Kaiser criterion-guided PCA to derive three primary factors governing housing prices within this spatiotemporal context: Spatial Distribution & Density, Geographic Location, and Economic Status, thereby yielding interpretable components reflecting spatial, geographic, and economic dimensions. Further findings reveal that all models exhibit quantitatively comparable declines in predictive performance post-PCA implementation, with ridge and LASSO regression demonstrating nearly equivalent performance to ordinary linear regression, suggesting limited benefits of regularization in this scenario. These results challenge the presumed utility of PCA in regression workflows, highlighting that simplistic PCA dimensionality reduction may discard inherent latent predictive signals within housing datasets. These insights advocate for circumspect adoption of PCA in real estate analytics and emphasize the necessity for domain-specific regularization strategies to balance interpretability with predictive fidelity.",0.0,https://www.ewadirect.com/proceedings/tns/article/view/22048/pdf,True,JournalArticle,,2025-04-17,85d7c2812036f9f54351dee08df7c5f83d99e95c,dimensionalitypredictiondilemmathedualeffectsofpcainhousingpriceregressionmodeling,xiong,10.54254/2753-8818/2025.22048,dimensionalitypredictiondilemmathedualeffectsofpcainhousingpriceregressionmodeling|2025,1,3221,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d1c948d264dac23935c09f10d88dd082602f66c9,10.54254/2753-8818/42/2024ch0213,,Factors affected housing prices: taking Boston as an example,2024.0,Theoretical and Natural Science,Ao Wang,"Abstract. Real estate price prediction plays a vital role in urban planning, investment decision-making, and risk management. However, existing prediction models often show problems such as insufficient generalization ability and susceptibility to outliers when faced with complex nonlinear relationships, multidimensional features, and noisy data. Therefore, choosing a model that can accurately capture complex patterns and has strong robustness has become the focus of research. This paper introduces the random forest model and compares it with multivariate linear regression, XGBoost, and support vector machine (SVM). Compared with the traditional regression model, the random forest model combines the flexibility of decision trees and the multi-level feature extraction ability of deep learning, and can better handle the complex nonlinear relationships in the Boston housing price dataset. The experimental results show that the random forest model has achieved excellent performance in all evaluation indicators, and the model accuracy indicators are distributed as MSE=8.2502, RMSE=2.8723, MAE=2.0668, and R^2=0.8875. These results show that the random forest model not only outperforms other models in prediction accuracy but also shows significant advantages in dealing with data complexity and improving generalization ability. Therefore, the random forest model provides an efficient and reliable tool for future real estate price prediction research and applications.",1.0,https://www.ewadirect.com/proceedings/tns/article/view/16420/pdf,True,JournalArticle,,2024-11-01,d1c948d264dac23935c09f10d88dd082602f66c9,factorsaffectedhousingpricestakingbostonasanexample,wang,10.54254/2753-8818/42/2024ch0213,factorsaffectedhousingpricestakingbostonasanexample|2024,1,2983,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",31d195fbe803bb066a9fb0cae093a628f2a6bafd,10.54254/2753-8818/39/20240571,,The research on factors influencing house value-take California as an example,2024.0,Theoretical and Natural Science,Tian-Zheng Li; Xuezhou Yang,"Housing price is a popular and important topic in todays society. This article aims to find the factors that have impacts on the housing price. To find the relationships between factors, this article uses Multiple Linear Regression as the method to perform a significant analysis of factors. 1000 samples of Californias block groups in 1990 are selected for this research. Based on the assumption, this research chooses 8 explanatory variables for the analysis. Because of the relationships between explanatory variables, the article also adds interaction terms between latitude and longitude, and population and total bedrooms to solve the multicollinearity problem among explanatory variables. To optimize model analysis effectiveness, this research compares the significance, VIF value, and GVIF value of explanatory variables. The analysis result shows that the geographical location (Latitude and longitude), the housing median age, the total bedrooms, the population, and the median income make significant impacts on the housing value. Among these factors, the median income is the main factor.",1.0,https://www.ewadirect.com/proceedings/tns/article/view/13306/pdf,True,JournalArticle,,2024-06-21,31d195fbe803bb066a9fb0cae093a628f2a6bafd,theresearchonfactorsinfluencinghousevaluetakecaliforniaasanexample,li,10.54254/2753-8818/39/20240571,theresearchonfactorsinfluencinghousevaluetakecaliforniaasanexample|2024,1,2601,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",47b15d1c041d16e71023960b6fa659d9c6a0436a,10.54254/2754-1169/66/20241235,,House Price Prediction Based on Joint Clustering and Linear Regression,2024.0,"Advances in Economics, Management and Political Sciences",Guanyu Chen,"The corona virus disease (COVID-19) epidemic has a great impact on the real estate industry. The epidemic has caused a downward trend in housing values. It is essential for those who want to buy property during this period of economic recovery to understand the key factors influencing house pricing. In order to identify the factors that influence real estate prices and make house price prediction, this study uses a mathematical model called linear regression to analyze the data. The model is further improved and streamlined by focusing on essential elements. The study successfully pinpoints the most critical factors affecting home prices and makes relatively accurate house price prediction. Notably, the house's number of levels and bedrooms are essential determinants for house price prediction. Understanding these variables enables real estate agencies to concentrate on particular areas for improvement, thereby optimizing the total housing market, which gives buyers a clearer idea of the types of homes they can pick. Real estate brokers can work more productively, increase their competitive edge, and guarantee long-term success in the post-pandemic recovery phase. Furthermore, by building homes with more logical conditions for buyers, brokers may protect their brand identity and nurture client loyalty.",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/9364/pdf,True,JournalArticle,,2024-01-05,47b15d1c041d16e71023960b6fa659d9c6a0436a,housepricepredictionbasedonjointclusteringandlinearregression,chen,10.54254/2754-1169/66/20241235,housepricepredictionbasedonjointclusteringandlinearregression|2024,1,2823,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",47e4ca2c9ee69fa225e47a66f1bd5b2fd5074eb1,10.54254/2754-1169/66/20241234,,Shanghai House Price Prediction Using Random Forest,2024.0,"Advances in Economics, Management and Political Sciences",Shenghan Wu,"With the increasing complexity of the urban real estate market, accurate prediction of housing prices has become an important task. One of the key applications of machine learning is how to raise and accurately estimate costs. Various factors will affect the price of houses. Most of the current frameworks are all using as detailed features as possible to increase their predicting accuracy. But in real-life conditions, many non-local clients also want to have a clear prediction of the house price. These consumers are not from the area; thus they are unaware of the house's surroundings, including nearby amenities. What they know about the house is only its housing characteristics. The objective of the paper is to help these clients put their resources into a bequest properly. The paper collected a wide range of Shanghai real estate data about housing characteristics as characteristics. By harnessing the ensemble learning capabilities of random forests, the paper aims to capture complex relationships and non-linear effects between features, thereby improving prediction accuracy.",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/9363/pdf,True,JournalArticle,,2024-01-05,47e4ca2c9ee69fa225e47a66f1bd5b2fd5074eb1,shanghaihousepricepredictionusingrandomforest,wu,10.54254/2754-1169/66/20241234,shanghaihousepricepredictionusingrandomforest|2024,1,2592,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",30ee417f35fae286e354b8696a870dca54aee886,10.54254/2754-1169/50/20230580,,House Prices Prediction Advanced Regression Techniques,2023.0,"Advances in Economics, Management and Political Sciences",Yue Zong,"In recent years, with the development of the real estate industry, housing prices have continued to rise. The nation, society, and individuals are all concerned about these prices. For commodity housing prices, there are many factors that influence the housing prices. Apart from national regulations, factors such as lighting, layout, and environment of the houses themselves also have a certain impact on the prices, leading to significant fluctuations in the real estate market. Therefore, researching an accurate model for predicting housing prices has practical significance. It can guide residents in housing consumption and provide policy recommendations for government price regulation. Machine learning methods have become a new type of prediction method in this regard. Based on the theories of data analysis and machine learning, a dataset consisting of 2920 data points with 81 attributes was selected from the publicly available Kaggle housing dataset. The data was normalized and analyzed for feature selection. The ranking of attributes most correlated with housing prices was obtained. Subsequently, a neural network model was built, parameters were adjusted, and the trained network was used to predict housing prices. On the Kaggle leaderboard, the RMSE test result stands at 0.1198, positioning our model among the top performers among all machine learning methods.",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/7085/pdf,True,JournalArticle,,2023-12-01,30ee417f35fae286e354b8696a870dca54aee886,housepricespredictionadvancedregressiontechniques,zong,10.54254/2754-1169/50/20230580,housepricespredictionadvancedregressiontechniques|2023,1,2884,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",4b2486febbcf1067bb316559cd8185fb5a45e9a0,10.54254/2754-1169/43/20232154,,Application of Machine Learning in Boston House Price Prediction,2023.0,"Advances in Economics, Management and Political Sciences",Yuanheng Zhang,"House prices are an important economic indicator for a country or region, rising house prices are often associated with economic growth and increased employment opportunities, while a decline in house prices may indicate an economic slowdown or other unfavorable factors. Governments and relevant agencies need to understand the dynamics of housing prices to formulate appropriate housing policies and plan urban development. As one of the major economic centers in the United States, the fluctuations in Boston's housing prices can reflect the local economic conditions and development trends. This paper selects data from Boston in 1970. Each record in this database describes a Boston suburb or town. The proposed method was evaluated using 5 metrics. By comparison, the paper ends up with the result that XGBoost works best out of the four regression models.",1.0,https://www.ewadirect.com/proceedings/aemps/article/view/6223/pdf,True,JournalArticle,,2023-11-10,4b2486febbcf1067bb316559cd8185fb5a45e9a0,applicationofmachinelearninginbostonhousepriceprediction,zhang,10.54254/2754-1169/43/20232154,applicationofmachinelearninginbostonhousepriceprediction|2023,1,2362,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",64e6e0d0e56399a90ea269a5db31660877659764,10.54254/2754-1169/2025.cau26835,,Analysis of Real Estate Market Pricing Rules and Construction of Prediction Model from the Perspective of Big Data,2025.0,"Advances in Economics, Management and Political Sciences",Xinyi Cao,"Amidst the digital revolution, data resources are surging exponentially. Cutting-edge technologies like big data and AI are rapidly evolving, creating new opportunities for industries. Yet, the real estate market is becoming more complex and unstable. Frequent policy changes, supply-demand imbalances, and economic volatility are heightening industry uncertainty. Meanwhile, consumers are demanding better living experiences, smarter amenities, and greener homes. These diverse and refined needs are driving a major shift in the real estate landscape. This paper employs a method of literature review and case study. It investigates the influence of big data in the prediction of price and the construction of the real estate market. This big data analysis on housing prices holds significant value by empowering homebuyers to understand explicit/implicit factors for informed decisions, guiding real estate firms on land/pricing strategies, supporting government policy formulation with data, aiding macroeconomic stability through risk identification in price patterns, and enriching academic theory in real estate economics through systematic analysis and novel methods.",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/26835/pdf,True,JournalArticle;Review,,2025-09-16,64e6e0d0e56399a90ea269a5db31660877659764,analysisofrealestatemarketpricingrulesandconstructionofpredictionmodelfromtheperspectiveofbigdata,cao,10.54254/2754-1169/2025.cau26835,analysisofrealestatemarketpricingrulesandconstructionofpredictionmodelfromtheperspectiveofbigdata|2025,1,2674,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4b59e1e5c66091609bd65d9f3681ccfd5fbc7390,10.54254/2754-1169/2024.ld19612,,The Research on Influencing Factors of Housing Price,2025.0,"Advances in Economics, Management and Political Sciences",Kunlong Du; Yanyi Li; Ruike Liu,"This paper analyzed the various factors affecting housing prices in the Boston area. Given that housing prices are one of the most highly influential elements in the social economy and individual decisions, this study aimed to trace the main key determinants driving fluctuations in this market. The results from an MLR model applied on 509 observations of characteristics describing housing in the Boston area, using a dataset from Kaggle against housing prices, have been reported. The empirical results of the study reveal that crime rate, number of rooms, air quality (nitrogen oxide concentration), pupil-teacher ratio, property tax rate, distance to employment centers, and highway accessibility affect housing prices. A higher crime rate and nitrogen oxide concentration lead to a decrease in housing prices, whereas an increase in the number of rooms and better highway accessibility leads to an increase in housing prices. Other variables that the study identified as likely to have multicollinearity problems are property tax and highway accessibility. Such conclusions assist in gaining a better understanding of the dynamics associated with determinants in housing price and offer invaluable advice to policymakers, investors, and potential homebuyers in making decisions regarding real estate policy and investments within Boston's real estate market.",1.0,https://www.ewadirect.com/proceedings/aemps/article/view/19612/pdf,True,JournalArticle,,2025-01-07,4b59e1e5c66091609bd65d9f3681ccfd5fbc7390,theresearchoninfluencingfactorsofhousingprice,du,10.54254/2754-1169/2024.ld19612,theresearchoninfluencingfactorsofhousingprice|2025,1,2864,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",701f7563ea70de153f3bb30c96bc37a70ccaaec5,10.54254/2754-1169/2024.18705,,Comparative Analysis of the Effectiveness of Different Algorithms for House Price Prediction in Machine Learning,2024.0,"Advances in Economics, Management and Political Sciences",Zhicong Deng,"In today's economic climate, where housing is a major focus, predicting house prices has become essential for both buyers and investors. Accurate price prediction allows for more informed decision-making, helping to minimize losses and maximize potential profits. The paper explores the different machine learning algorithms to predict house prices, utilizing the classic Boston Housing dataset from Kaggle. The study compares the performance of four algorithms: Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and Extreme GB (XGBoost). Experimental results demonstrate that GB and XGBoost outperform the other models, delivering the highest prediction accuracy. These algorithms excel in capturing complex, nonlinear relationships in the data, making them particularly effective for house price prediction. The findings suggest the models like GB and XGBoost can enhance the accuracy of prediction, offering valuable insights into the real estate market. The study also highlights the importance of continuously refining machine learning models to adapt to changing market conditions. By improving predictive models, the study contributes to an understanding of home price dynamics, which is critical for both home buyers and real estate investors, which is crucial for both homebuyers and real estate investors.",1.0,https://www.ewadirect.com/proceedings/aemps/article/view/18705/pdf,True,JournalArticle,,2024-12-26,701f7563ea70de153f3bb30c96bc37a70ccaaec5,comparativeanalysisoftheeffectivenessofdifferentalgorithmsforhousepricepredictioninmachinelearning,deng,10.54254/2754-1169/2024.18705,comparativeanalysisoftheeffectivenessofdifferentalgorithmsforhousepricepredictioninmachinelearning|2024,1,2829,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b0c1fb4826e41db24d5926154604901bd02c7423,10.54254/2754-1169/77/20241802,,Influential Factors on California Regional Housing Price Analysed by Multiple Linear Regression,2024.0,"Advances in Economics, Management and Political Sciences",Yanqiang Cao,"While metropolises keep expanding with the increasing population in recent decades, the need for housing rises inevitably. This paper aims to find the most explanatory factors for the housing price in California; with 20433 entries of data found in Kaggle, the method of multiple linear regression (MLR) is applied to find the most influential factors. 500 entries in the dataset are chosen randomly and are divided into 2 datasets for training and testing purposes. Models have been developed in R by using the training dataset. After comparing the adjusted R square and variability of the models, the most convincible model will be selected to find out the result of this investigation on the test dataset. After model diagnostics, the result of this analysis is that the regional median income level has a strong positive correlation with the housing price, and it is the most influential factor. Other influential factors will be introduced in the conclusion.",1.0,https://www.ewadirect.com/proceedings/aemps/article/view/11435/pdf,True,JournalArticle,,2024-04-18,b0c1fb4826e41db24d5926154604901bd02c7423,influentialfactorsoncaliforniaregionalhousingpriceanalysedbymultiplelinearregression,cao,10.54254/2754-1169/77/20241802,influentialfactorsoncaliforniaregionalhousingpriceanalysedbymultiplelinearregression|2024,1,2463,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",6b33b5003b996cd04e647ce3a57c9b0aa30781c0,10.54254/2755-2721/32/20230222,,"House price prediction using machine learning: A case study in Seattle, U.S.",2024.0,Applied and Computational Engineering,Jiapei Liao,"In recent years, predicting housing prices has become a prominent research topic. The motivation behind this research is the lack of precise analysis and comprehensive comparison of different machine learning models for Seattle housing prices. To address this issue, this paper utilizes a dataset obtained from Kaggle, consisting of Seattle housing prices. This study focuses on analyzing and comparing the performance of different machine learning models in predicting housing prices in Seattle. After preprocessing the data, eight different machine learning models are applied to predict housing prices in Seattle. A comprehensive comparison of these models is conducted to analyze their differences. The experimental results show that the differences in performance among the models are not substantial. However, StackingAveragedModels emerges as the top-performing model, with an RMSLE of 0.2328 and an R2 of 0.7771. These findings contribute to a better understanding of the predictive capabilities of different machine learning models for Seattle housing prices.",1.0,https://www.ewadirect.com/proceedings/ace/article/view/9948/pdf,True,JournalArticle,,2024-01-22,6b33b5003b996cd04e647ce3a57c9b0aa30781c0,housepricepredictionusingmachinelearningacasestudyinseattleus,liao,10.54254/2755-2721/32/20230222,housepricepredictionusingmachinelearningacasestudyinseattleus|2024,1,2568,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ec7b52328bae99bbb22d4e8a7dd5b9a8c52e95ad,10.54254/2755-2721/29/20231181,,Machine learning techniques for predicting home rental prices in India,2023.0,Applied and Computational Engineering,P. Jayadharshini; S. Santhiya; S. Keerthika; N. Abinaya; S. Priyanka,"Predicting the selling price of houses has become increasingly crucial as land and housing prices rise annually. This task is particularly challenging for metropolitan areas like Chennai and Bangalore. Therefore, there is a growing demand for an easier and more effective approach to forecast house rental prices, ensuring future generations have access to reliable predictions. Several key factors, such as the house's location and area, significantly influence rental prices. In this paper, a dataset comprising ten similar crucial features is utilized. The model is developed using a Python library, where the data is preprocessed and prepared to ensure cleanliness for constructing the model. Various machine learning algorithms, including Random Forest, Linear Regression, Decision Tree Regression, and Gradient Boosting, are employed. Through feature extraction, it is determined that area and property type are the most important features that significantly impact rental prices. Among the techniques used, gradient boosting yields the most satisfactory predictive results for rent based on evaluation metrics like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-Squared Metric (R2).",1.0,https://www.ewadirect.com/proceedings/ace/article/view/9326/pdf,True,JournalArticle,,2023-12-26,ec7b52328bae99bbb22d4e8a7dd5b9a8c52e95ad,machinelearningtechniquesforpredictinghomerentalpricesinindia,jayadharshini,10.54254/2755-2721/29/20231181,machinelearningtechniquesforpredictinghomerentalpricesinindia|2023,1,2707,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,5,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f56d7d4780d15d2ba7a052ebeff827ad26ae1063,10.54254/2755-2721/13/20230724,,Predicting houses price by deep learning neural network,2023.0,Applied and Computational Engineering,Zi-Dong Xu,"Deep learning is widely used in various fields, the article proposed a deep learning method to predict house prices through different characteristics of real estate, establish a prediction model, and carry out simulation experiments. First, extracting data from property transactions records, it is difficult to directly input the raw-data into the deep learning model, and there may be overfitting in the model training, so data will be pretreated. Second, Fully-Connected Neural Network is used to model different features influences to price. The sample data will be randomly divided into a training set and a validation set, of which 70% of the samples are used for building and training the model, and the remaining 30% are used for model accuracy verification. Experimental results show that the model can achieve a high accuracy in predicting houses price. The model can be used as a reference for the evaluation of housing prices.",0.0,https://www.ewadirect.com/proceedings/ace/article/view/4500/pdf,True,JournalArticle,,2023-10-23,f56d7d4780d15d2ba7a052ebeff827ad26ae1063,predictinghousespricebydeeplearningneuralnetwork,xu,10.54254/2755-2721/13/20230724,predictinghousespricebydeeplearningneuralnetwork|2023,1,2438,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",aac7869c5e96506fc354b572a6188e5baf5e174a,10.54254/2755-2721/2025.bj27450,,House Price Prediction Leveraging Feature Engineering and Model Comparison: A Case Study of Ames and Iowa Datasets,2025.0,Applied and Computational Engineering,Zixuan Wang,"Developing a precise prediction of house prices has been an unsettled task for many years. It is the social as well as economic need for the welfare and comfort of the citizens. The objective and aim of the research is to provide digital-based solutions to real estate prices, which focuses on economic, geographic, and demographic variables. The paper uses Advanced Regression Techniques on datasets from Ames, Iowa. The study identifies significant correlations between housing prices and 79 explanatory variables describing every aspect of residential homes. For example, the Lot configuration. Physical locations within Ames city limits, original construction date, and so on. The paper employs Lasso, Ridge, XGBoost and LightGBM to verify the most suitable model in practice in this field, offering insights for policymakers, investors, and homebuyers. This research contributes to the existing literature by providing a comparative analysis across different housing markets and suggesting strategies for price stabilisation.",0.0,https://www.ewadirect.com/proceedings/ace/article/view/27450/pdf,True,JournalArticle,,2025-10-02,aac7869c5e96506fc354b572a6188e5baf5e174a,housepricepredictionleveragingfeatureengineeringandmodelcomparisonacasestudyofamesandiowadatasets,wang,10.54254/2755-2721/2025.bj27450,housepricepredictionleveragingfeatureengineeringandmodelcomparisonacasestudyofamesandiowadatasets|2025,1,2530,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,5,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1ea3eb6493d53d6265e6d3023a34345028e10a53,10.54254/2755-2721/4/20230505,,Machine learning models for house price prediction,2023.0,Applied and Computational Engineering,Ling Fang,"Housing prices have changed over the years, and there has been an increasing need to predict prices of future homes. This paper gives an overview of various machine learning models that can predict housing prices. There are numerous possible methods of pre-processing the data, so this paper explores ways to handle missing values and categorical data. In this study, the models of regression tree, random forest, XGBoost, gradient boosting, and LightGBM are described and used to predict housing prices. Machine learning models also have hyperparameters that can be adjusted, which can affect predictive accuracy. The methods are evaluated on several benchmark datasets. Based on the results, our approach is effective for the task of house price prediction.",2.0,https://www.ewadirect.com/proceedings/ace/article/view/2318/pdf,True,JournalArticle;Review,,2023-06-14,1ea3eb6493d53d6265e6d3023a34345028e10a53,machinelearningmodelsforhousepriceprediction,fang,10.54254/2755-2721/4/20230505,machinelearningmodelsforhousepriceprediction|2023,1,2259,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",768894e40c1ad4c0be4821db372ae199252fed64,10.54254/2755-2721/6/20230928,,Optimization of linear regression in house price prediction,2023.0,Applied and Computational Engineering,Liang Zhu,"House price prediction plays a very important role in housing transactions. Linear regression based algorithms show good effects in predicting house prices. They have strong interpretability and fast operation speed. However, people ignore the estimation of deviations in linear regression (LR) algorithms. In this paper, k-nearest neighbor (KNN) algorithm is supposed to estimate deviations that are added to the result of linear regression to predict house prices accurately. Furthermore, deviation regression (DR) algorithm is supposed to make the prediction result more accurate. By utilizing Boston House Price data from Kaggle, extensive experiments are conducted and demonstrate the superior performance and compatibility of DR.",1.0,https://www.ewadirect.com/proceedings/ace/article/view/2126/pdf,True,JournalArticle,,2023-06-14,768894e40c1ad4c0be4821db372ae199252fed64,optimizationoflinearregressioninhousepriceprediction,zhu,10.54254/2755-2721/6/20230928,optimizationoflinearregressioninhousepriceprediction|2023,1,2235,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2803b0b59ff3896da756d3ac3247eb17b1d40ad9,10.54254/2755-2721/4/2023362,,House price prediction based on machine learning,2023.0,Applied and Computational Engineering,Hanwen Li,"Machine learning is commonly used in the real estate market. It is vital to apply the idea of machine learning in this field to predict house prices based on various features. The paper will focus on how to use the most appropriate machine learning models for house price prediction. It will use LightGBM(Light Gradient Boosting Machine), Gradient Boosting, and XGBoost(Extreme Gradient Boosting) to train models to predict house prices using the existing data from the Kaggle website. After three models make predictions, they will get an RMSE (root mean square error), whichis0.02975, 0.02537, and 0.01364. Based on the result, the XGBoost model is the best one among these three models used for house price prediction.",8.0,https://www.ewadirect.com/proceedings/ace/article/view/2092/pdf,True,JournalArticle,,2023-06-14,2803b0b59ff3896da756d3ac3247eb17b1d40ad9,housepricepredictionbasedonmachinelearning,li,10.54254/2755-2721/4/2023362,housepricepredictionbasedonmachinelearning|2023,1,2221,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",191e707f1da108b5b31f8a013f58cac6d9c3e616,10.54254/2755-2721/2024.18220,,Predicting House Prices with a Linear Regression Model,2024.0,Applied and Computational Engineering,Liming Yan,"House price forecasting is an important area of economic and social research. Among the many house price forecasting methods, the linear regression model is widely used because of its simplicity, easy interpretation, and high computational efficiency. This paper aims to investigate the effectiveness of linear regression models in house price forecasting. This paper will first introduce the basic theory of linear regression model, and discuss the factors that affect the housing price, then build and evaluate the housing price prediction model, and then verify the constructed data model through real data. Finally, we discuss the accuracy of the prediction, analyze the results of the passing model, and find that housing prices can be predicted more accurately for cases where the variables are relatively simple and differentiated, such as the ownership of specific facilities. However, linear regression prediction still has some defects, and there are more effective and general methods for housing price prediction to solve the problems that linear regression method cannot pay attention to, which will be the subject of future research.",1.0,https://www.ewadirect.com/proceedings/ace/article/view/18220/pdf,True,JournalArticle,,2024-12-12,191e707f1da108b5b31f8a013f58cac6d9c3e616,predictinghousepriceswithalinearregressionmodel,yan,10.54254/2755-2721/2024.18220,predictinghousepriceswithalinearregressionmodel|2024,1,2647,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9744b3fa9eeeb19d80246fb6abab550b0276ccb0,10.54254/2755-2721/49/20241058,,House price prediction based on different models of machine learning,2024.0,Applied and Computational Engineering,Chuhan Ni,"Housing price prediction is a typical regression problem in machine learning. Common algorithms include linear regression, support vector regression, random forest, and extreme gradient boosting models based on integrated learning methods. Among the specific problems, different models in the specific problem will get different results. This research will compare these three models to show which model is more accurate and robust. Given the practical problem of housing price prediction, various characteristics of houses are carried out. The research will analyze and study, apply a variety of regression models, and compare the performance of the above three models on this problem, make the horizontal comparison of the advantages and disadvantages of different models, and analyze the difference in effect Line analysis and summary.",2.0,https://www.ewadirect.com/proceedings/ace/article/view/10770/pdf,True,JournalArticle,,2024-03-22,9744b3fa9eeeb19d80246fb6abab550b0276ccb0,housepricepredictionbasedondifferentmodelsofmachinelearning,ni,10.54254/2755-2721/49/20241058,housepricepredictionbasedondifferentmodelsofmachinelearning|2024,1,2338,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1fe3d995ec0f3d2e0682124e48792005be125338,10.54254/3029-0880/2025.30128,,The application of machine learning in house price prediction,2025.0,Advances in Operation Research and Production Management,Zihao Chen,"In recent years, with the significant impact of housing price changes on economic and social stability, scientifically predicting housing price trends can prompt local governments to formulate policies related to housing prices, and investors can make investments based on the corresponding housing prices. At the same time, homebuyers can also make housing purchase plans according to housing prices. Traditional methods for predicting housing prices are primarily based on experience or simple statistical models, which cannot reasonably consider complex factors. This paper investigates the efficacy of machine learning methodologies in the prediction of housing market valuations. Firstly, it discusses the characteristics and value of supervised, unsupervised, and decision tree algorithms in the machine learning field when applied to housing price prediction. Secondly, it explores the reasons affecting housing prices, such as economic characteristics like national income, regional features, and the influence of supply and demand. Then, it applies a public housing price dataset to establish a decision tree for predicting housing prices. Based on basic features, it expands the feature library. It seeks suitable feature combinations to analyze housing prices better and draw conclusions on the value of machine learning methods in housing price prediction. This article summarizes successful experiences from existing application cases and studies the problems and deficiencies in the application process.",0.0,https://www.ewadirect.com/journal/aorpm/article/view/30128/pdf,True,JournalArticle,,2025-11-27,1fe3d995ec0f3d2e0682124e48792005be125338,theapplicationofmachinelearninginhousepriceprediction,chen,10.54254/3029-0880/2025.30128,theapplicationofmachinelearninginhousepriceprediction|2025,1,3017,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ae50a3ce2963341c6d8be4416d87393789ad47c1,10.54254/3029-0880/2025.27554,,Prediction of the median value of owner-occupied homes (MEDV) in the Boston housing dataset using regression models,2025.0,Advances in Operation Research and Production Management,Xiaojin Zhou,"The Boston housing dataset is one of the significant tools used to examine the influencing factors of housing price. Meanwhile, housing price prediction is crucial for government regulation, business decision-making, and individual homebuyers. Existing studies fall short in balancing model interpretability, computational efficiency, and generalization ability. Hence, this study, based on the Boston housing dataset, focuses on the prediction of the median value of owner-occupied homes (MEDV). It constructs three models, including linear regression, decision tree regression, and Bayesian regression, and evaluates their performance using mean squared error (MSE), mean absolute error (MAE), and the coefficient of determination (R2). It examines the effects 13 features, including per capita crime rate by town (CRIM) and average number of rooms per dwelling (RM) on MEDV. Furthermore, it trains and visualizes the results of each model. The results show that decision tree regression achieves the highest R2, effectively capturing nonlinear relationships but being prone to overfitting. Linear regression and Bayesian regression perform better in terms of MSE and MAE; the former is simple in structure and fast to train, while the latter can output probability distributions to assess uncertainty. Each model has its strengths, and the choice should depend on the application scenario. The limitations related to dataset timeliness and the lack of extensive hyperparameter tuning are acknowledged, providing useful insights for housing price prediction research and practice.",0.0,https://www.ewadirect.com/journal/aorpm/article/view/27554/pdf,True,JournalArticle,,2025-10-14,ae50a3ce2963341c6d8be4416d87393789ad47c1,predictionofthemedianvalueofowneroccupiedhomesmedvinthebostonhousingdatasetusingregressionmodels,zhou,10.54254/3029-0880/2025.27554,predictionofthemedianvalueofowneroccupiedhomesmedvinthebostonhousingdatasetusingregressionmodels|2025,1,3082,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,4,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7c1042d804dc05284709a441fe214921128b7cb9,10.24294/ijmss.v6i4.3213,,Comparison of Ridge Regression and GA-RF Models for Boston House Price Prediction,2023.0,International Journal of Mathematics and Systems Science,Liang Ye,"The purpose of this paper is to explore the performance of ridge regression and the random forest model improved by genetic algorithm in predicting the Boston house price data set and conduct a comparative analysis. To achieve it, the data is divided into training set and test set according to the ratio of 70-30. The RidgeCV library is used to select the best regularization parameter for the Ridge regression model, and for the random forest model, the genetic algorithm is used to optimize the model's hyperparameters. The result shows that compared with ridge regression, the random forest model improved by genetic algorithm can perform better in the regression problem of Boston house prices.",2.0,https://www.enpress-publisher.com/files/journals/31/articles/3213/public/3213-10517-1-PB.pdf,True,JournalArticle,,2023-11-10,7c1042d804dc05284709a441fe214921128b7cb9,comparisonofridgeregressionandgarfmodelsforbostonhousepriceprediction,ye,10.24294/ijmss.v6i4.3213,comparisonofridgeregressionandgarfmodelsforbostonhousepriceprediction|2023,1,2199,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",60165a1034e15dbe03f24a89b680854c23978e94,10.1051/e3sconf/202341803001,,A Comparative Study of Urban House Price Prediction using Machine Learning Algorithms,2023.0,E3S Web of Conferences,Lale El Mouna; H. Silkan; Youssef Haynf; Mohamedade Farouk Nann; Stéphane C. K. Tekouabou,"Accurate housing price forecasts are essential for several reasons. First, it allows individuals to make informed decisions about buying or selling real estate and to determine appropriate prices. Secondly, it helps real estate agents and investors make better investment decisions and negotiate contracts more effectively. In addition, housing prices are often an indication of the general state of the economy. A price decrease may indicate an economic recession, while an increase in prices may signal economic growth. In this study, we proposed to address this subject by predicting house prices using machine learning by choosing three types of machine learning: Linear Regression (LN), Random Forest (RF) and GradientBoosting (GB). We tested our models on the Melbourne real estate dataset, which includes 34,857 property sales and 21 features.",13.0,https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/55/e3sconf_acc2023_03001.pdf,True,JournalArticle;Conference,,,60165a1034e15dbe03f24a89b680854c23978e94,acomparativestudyofurbanhousepricepredictionusingmachinelearningalgorithms,mouna,10.1051/e3sconf/202341803001,acomparativestudyofurbanhousepricepredictionusingmachinelearningalgorithms|2023,1,2350,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",73f6ad0cfe50fd4f9938d57900a75a91bb9a4b9e,10.28991/cej-sp2023-09-04,,Forecasting the Real Estate Housing Prices Using a Novel Deep Learning Machine Model,2023.0,Civil Engineering Journal,H. H. Mohamed; A. Ibrahim; Omar A. Hagras,"There is an urgent need to forecast real estate unit prices because the average price of residential real estate is always fluctuating. This paper provides a real estate price prediction model based on supervised regression deep learning with 3 hidden layers, a Relu activation function, 100 neurons, and a Root Mean Square Propagation optimizer (RMS Prop). The model was developed using actual data collected from 28 Egyptian cities between 2014 and 2022. The model can forecast the price of a real estate unit based on 27 different variables. The model is created in two stages: adjusting the parameters to obtain the best ones using a sensitivity k-fold technique, then optimizing the result. 85 percent of the real estate unit data gathered was used in training and developing the model, while the other 15 percent was used in validating and testing. By using a dropout regularization technique of 0.60 on the model layers, the final developed model had a maximum error of 10.58%. After validation, the model had a maximum error of about 9.50%. A graphical user interface (GUI) tool is developed to make use of the final predictive model, which is very simple for real estate developers and decision-makers to use. Doi: 10.28991/CEJ-SP2023-09-04 Full Text: PDF",7.0,https://www.civilejournal.org/index.php/cej/article/download/3947/pdf,True,,,2023-03-04,73f6ad0cfe50fd4f9938d57900a75a91bb9a4b9e,forecastingtherealestatehousingpricesusinganoveldeeplearningmachinemodel,mohamed,10.28991/cej-sp2023-09-04,forecastingtherealestatehousingpricesusinganoveldeeplearningmachinemodel|2023,1,2764,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ca4fb2ebb95cdf0f90c126bff164537a2b769e09,10.1017/s1351324923000360,,Describe the house and I will tell you the price: House price prediction with textual description data,2023.0,Natural Language Engineering,Han Zhang; Yansong Li; Paula Branco,"Abstract House price prediction is an important problem that could benefit home buyers and sellers. Traditional models for house price prediction use numerical attributes such as the number of rooms but disregard the house description text. The recent developments in text processing suggest these can be valuable attributes, which motivated us to use house descriptions. This paper focuses on the house asking/advertising price and studies the impact of using house description texts to predict the final house price. To achieve this, we collected a large and diverse set of attributes on house postings, including the house advertising price. Then, we compare the performance of three scenarios: using only the house description, only numeric attributes, or both. We processed the description text through three word embedding techniques: TF-IDF, Word2Vec, and BERT. Four regression algorithms are trained using only textual data, non-textual data, or both. Our results show that by using exclusively the description data with Word2Vec and a Deep Learning model, we can achieve good performance. However, the best overall performance is obtained when using both textual and non-textual features. An $R^2$ of 0.7904 is achieved by the deep learning model using only description data on the testing data. This clearly indicates that using the house description text alone is a strong predictor for the house price. However, when observing the RMSE on the test data, the best model was gradient boosting using both numeric and description data. Overall, we observe that combining the textual and non-textual features improves the learned model and provides performance benefits when compared against using only one of the feature types. We also provide a freely available application for house price prediction, which is solely based on a house text description and uses our final developed model with Word2Vec and Deep Learning to predict the house price.",22.0,https://www.cambridge.org/core/services/aop-cambridge-core/content/view/807584C6B9D76F555DA61E220BF39F9B/S1351324923000360a.pdf/div-class-title-describe-the-house-and-i-will-tell-you-the-price-house-price-prediction-with-textual-description-data-div.pdf,True,JournalArticle,Computer Science,2023-07-18,ca4fb2ebb95cdf0f90c126bff164537a2b769e09,describethehouseandiwilltellyouthepricehousepricepredictionwithtextualdescriptiondata,zhang,10.1017/s1351324923000360,describethehouseandiwilltellyouthepricehousepricepredictionwithtextualdescriptiondata|2023,1,3457,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c594a42b1268a4e85ddccbe2b0dfc68f5c36f1b8,10.1007/s10614-022-10298-8,,A Novel Hybrid House Price Prediction Model,2020.0,Computational Economics,Süreyya Özöğür Akyüz; Birsen Eygi Erdogan; Ö. Yıldız; Pınar Karadayı Ataş,"The real estate sector is evolving and changing rapidly with the increase in housing demand, and new luxury housing projects appear every day. The reliability of housing market investments is largely dependent on accurate pricing.The aim of this study is to introduce a dynamic pricing procedure that estimates house prices using the most important characteristics of a house. For this purpose, a hybrid algorithm using linear regression, clustering analysis, nearest neighbor classification and Support Vector Regression (SVR) method is proposed. Our hybrid algorithm involves using the output of one method as the input of another method for home price prediction to deal with the heteroscedastic nature of the housing data. In other words, the aim of this study is to present a hybrid algorithm that will create different housing clusters from the available data set, classify the houses to which the cluster is unknown, and make price predictions by creating separate prediction models for each class. Housing data collected through manual web scraping of Kadıköy district in Istanbul were used for training and validation of the proposed algorithm. In addition to these data, we validated our algorithm on the KAGGLE house dataset, which covers a wide range of features. The results of the hybrid algorithm were compared using multiple linear regression, Lasso, ridge regression, Support Vector Regression (SVR), AdaBoost, decision tree, random forest and XGBoost regression. Experimental results show that the proposed hybrid model is superior in terms of both Residual Mean Square Error (RMSE), Mean Absolute Value Percent Error (MAPE) and adjusted Rsquare measures for both Kadıköy and KAGGLE housing dataset.",26.0,https://www.authorea.com/doi/pdf/10.22541/au.160284063.37909495,True,,Mathematics,2020-10-16,c594a42b1268a4e85ddccbe2b0dfc68f5c36f1b8,anovelhybridhousepricepredictionmodel,akyüz,10.1007/s10614-022-10298-8,anovelhybridhousepricepredictionmodel|2020,1,3217,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",baaec29e29c5f374e6501b179a01b6732b900c04,10.2991/aebmr.k.220307.253,,House Price Prediction Based on Machine Learning: A Case of King County,2022.0,Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022),Yijia Wang; Q. Zhao,"This paper focuses on formulating a feasible method for house price prediction. A dataset containing features and house price of King County in the US is used. During the data preprocessing, extreme values are winsorized and highly correlated features are removed. Eight models including Catboost, lightGBM and XGBoost serve as candidate models. They are evaluated by several evaluation indicators, including rooted mean square error, R-squared score, adjusted R-squared score and K-fold cross validation score. The model that has low RMSE, achieves a high R-squared score and adjusted R-squared score, especially in the test set, and acquires a high score in cross validation is considered a better model. This paper finds out that Catboost performs the best among all models and can be used for house price prediction. Location, living space and condition of the house are the most important features influencing house price. After comparison and contrast with other papers, it is attested that findings in this paper conform to real life. This paper formulates a model that fits better than preceding studies for house price prediction and makes necessary supplement to the exploration of features that influence house price from a microscope.",6.0,https://www.atlantis-press.com/article/125971834.pdf,True,Conference,,,baaec29e29c5f374e6501b179a01b6732b900c04,housepricepredictionbasedonmachinelearningacaseofkingcounty,wang,10.2991/aebmr.k.220307.253,housepricepredictionbasedonmachinelearningacaseofkingcounty|2022,1,2746,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b9e1d09ff13f801ef90f3cd15f899f008911b19e,10.4314/just.v43i3.7,,Exploration of Machine Learning Algorithms for Housing Price Prediction in Megacities,2025.0,Journal of Science and Technology (Ghana),R. Ogundeji; N. Badmus; Malik Olasubomi Akintola,"Effective predictive models are good for tackling pressing issues faced by residents and stakeholders regarding the cost of houses in cities. Thus, buyers and sellers of houses have challenges in making decisions due to a lack of adequate data-driven decision-making. This study explores the feasibility of using machine learning techniques to predict house prices in Lagos and Abuja, Nigeria. The data used for this research were obtained from a property listing website and encompass key features such as location, number of rooms, property type and other basic characteristics. Several models including Linear Regression, Bayesian Linear Regression, Support Vector Regressor and Ridge Regression, were trained using these data. The performances of models considered for this study were evaluated using the Mean Absolute Error and the R-Squared scores, and the Support Vector Regressor performed best. Also, the model predicted Lagos house prices better than Abuja house prices with the highest R-squared and lowest Mean Absolute Error (MAE) values. An independent t-test was also conducted to test for significant differences in the price of houses in Lagos and Abuja. The results indicate there is no significant difference in the prices. The study concludes that machine learning is a useful tool for house price prediction to its accuracy in predictions, highly dependent on the availability and quality of comprehensive datasets.",0.0,https://www.ajol.info/index.php/just/article/download/302800/284678,True,JournalArticle,,2025-09-04,b9e1d09ff13f801ef90f3cd15f899f008911b19e,explorationofmachinelearningalgorithmsforhousingpricepredictioninmegacities,ogundeji,10.4314/just.v43i3.7,explorationofmachinelearningalgorithmsforhousingpricepredictioninmegacities|2025,1,2936,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",496341a43811427014a9dcda0319cebcd0a98aa3,10.4314/jasem.v29i1.6,,Evaluation of Price Prediction of Houses in a Real Estate via Machine Learning,2025.0,Journal of Applied Sciences and Environmental Management,A. Ibrahim; O. Ayilara-Adewale; A. Alabi; D. A. Olusesi,"Traditional (manual) methods of determining real estate house prices are in some cases prone to mistakes which may be due to distractions, lack of attentiveness or vulnerability to real estate agent fraud. This work focuses on evaluation house price prediction in real estate using more recent methods. House pricing using such methods as House Pricing Index and Random Forest Machine Learning Technique has been discussed, a new approach is proposed as a model utilizing the Extra Tree regression because it introduces an additional level of randomness in the tree-building process. Kaggle Boston housing dataset with 506 entries and 14 features was employed to train and test the developed model whose efficiency was then determined via mean absolute error and mean squared error. Additionally, a comparison was made between a random forest regression model and the proposed prediction model which revealed that the new prediction model yielded better performance than the random forest regression.",1.0,https://www.ajol.info/index.php/jasem/article/download/288025/271318,True,JournalArticle,,2025-02-02,496341a43811427014a9dcda0319cebcd0a98aa3,evaluationofpricepredictionofhousesinarealestateviamachinelearning,ibrahim,10.4314/jasem.v29i1.6,evaluationofpricepredictionofhousesinarealestateviamachinelearning|2025,1,2500,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c06d5f638d16e50d415bef8ac040c45cf86c66f3,10.4314/afrrev.v5i5.13,,Determinants of Residential Property Value in Nigeria – A Neural Network Approach,2011.0,,So Igbinosa,"This study investigated, by means of artificial intelligent system, the influence of residential real estate property characteristics on property values (prices) in Nigeria, using two major cities (Benin and Lagos) as examples. It revealed a high positive linear correlation between property characteristics and the property market values; an indication that these characteristics reasonably predict property market values. The study demonstrated that although several property characteristics can be identified with residential real estate properties, only are few important ones have significant impact on the market values of such properties. It identified nine (9) property characteristics that have relatively strong impact on market values (prices) and to that extent influence the sales and purchase decisions of sellers and buyers in Nigeria. The results of the study should enable Real Estate Professionals to make fair estimates of the market values of residential real estate properties given the features/characteristics of such housing units. This would aid rapid valuation, help to improve housing quality and make possible mass evaluation of properties. The study recommends to real estate practitioners and other professionals, amongst others, to use the knowledge of significant property features/characteristics for more efficient valuation, improved quality of their sales/purchase decisions and proper management of residential housing units.",30.0,https://www.ajol.info/index.php/afrrev/article/download/72305/61234,True,,Economics,2011-11-18,c06d5f638d16e50d415bef8ac040c45cf86c66f3,determinantsofresidentialpropertyvalueinnigeriaaneuralnetworkapproach,igbinosa,10.4314/afrrev.v5i5.13,determinantsofresidentialpropertyvalueinnigeriaaneuralnetworkapproach|2011,1,2962,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || residential property price determinants",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0b3579302b76bd1fc77be4d0b78d92273cd16a72,10.30574/wjaets.2023.8.1.0048,,House price prediction with Convolutional Neural Network (CNN),2023.0,World Journal of Advanced Engineering Technology and Sciences,Mohit Jain; Arjun Srihari,"In this paper, we examine the applicability of Convolutional Neural Networks (CNNs) for predicting the cost of houses with an inclusion of visual and non-visual elements. Traditional end-to-end patterns of supervised machine learning usually incorporate a finite set of numerical or categorical features, ignoring spatial and aesthetic information. CNNs, which can parse complex patterns in unstructured data, represent a way to improve predictive accuracy at the price of including property images with numerical and geographical coordinates. This variety of inputs is integrated within the proposed model to reflect the visual and contextual aspects of real estate valuation. The training and testing set used property images, georeferenced information, and typical numerical features. Incorporation of architectural features, including design, quality and neighbourhood aesthetics, showed a higher variance than the baseline machine learning model. Analysis of the outcomes obtained in experiments showed that CNN-based models performed better than the benchmark models, such as regression, gradient boosting, and random forest regression, in identifying the subtle interconnection between attractiveness and property values. The results further support the ability of CNNs to use multimodal information to solve diverse prediction problems within real estate. This research emphasizes the potential value of disseminating multiple data sources and merging relatively sophisticated neural structures into real estate applications, indicating the future viability of such paradigms in broader property market technologies. Subsequent studies can look at topics such as increasing the capacity of model structures and expanding the dataset to incorporate time dependency and other regions.",11.0,https://wjaets.com/sites/default/files/WJAETS-2023-0048.pdf,True,JournalArticle,,2023-02-28,0b3579302b76bd1fc77be4d0b78d92273cd16a72,housepricepredictionwithconvolutionalneuralnetworkcnn,jain,10.30574/wjaets.2023.8.1.0048,housepricepredictionwithconvolutionalneuralnetworkcnn|2023,1,3290,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2e52b8120d504cddfa568dcf1f968646f2fe6dea,10.62051/e9x5cc73,,Predicting House Prices Using Machine Learning Models,2025.0,Transactions on Computer Science and Intelligent Systems Research,Tianyi Zhao,"Predicting house prices is crucial for stakeholders such as buyers, developers, financial institutions, and policymakers. This paper explores how machine learning models can help with house price prediction. Using the Ames Housing Dataset, we compare three models: linear regression, decision tree, and random forest. The goal is to understand which model gives better results in terms of accuracy and how well it works with new data. Data preprocessing, including imputation for missing values, standardization, and feature selection, was performed to ensure compatibility with the models. The models were tested using three common evaluation tools: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the R² score. The results show that the random forest model performs the best. It has the lowest error and the highest R² score among the three. This is because random forest uses many decision trees and combines their results, which helps reduce overfitting and improves prediction. The study highlights the potential of machine learning in improving house price prediction accuracy and discusses future avenues for model optimization. These include incorporating real-time data, enhancing model adaptability, and tailoring predictions for specific stakeholder needs. The findings suggest that machine learning models, particularly random forest, can provide valuable insights into housing market trends and support more informed decision-making.",0.0,https://wepub.org/index.php/TCSISR/article/download/5534/6065,True,JournalArticle,,2025-07-10,2e52b8120d504cddfa568dcf1f968646f2fe6dea,predictinghousepricesusingmachinelearningmodels,zhao,10.62051/e9x5cc73,predictinghousepricesusingmachinelearningmodels|2025,1,2960,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4607a6f6bacc8e2fda9ce2607ef5b9fe00368561,10.62051/ev013e77,,Evaluating Machine Learning Models in Housing Price Forecasting,2025.0,Transactions on Computer Science and Intelligent Systems Research,Kai-Lin Wang,"This study aims to compare the performance of different machine learning algorithms in predicting housing prices and to construct an experimental process based on data preprocessing, feature selection, and comparative analysis of multiple regression models. Initially, a Random Forest will filter out the ten most significant variables. This variable will be utilized to build Multivariable Linear Regression (MLR). To find the best subset, lasso, best subset, and 5-fold Cross-Validation methods will be applied to find the final model. At the same time, this study incorporates training and testing using models such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Bagging, Random Forest (RF), Boosting, and Bayesian Additive Regression Trees (BART). By comparing the error performance of the model on the training set and the test set, the mean squared error (MSE) will be calculated to evaluate the strengths and weaknesses of each model in housing price prediction. The goal is to find the modeling method and variable combination that best suits the data set.",0.0,https://wepub.org/index.php/TCSISR/article/download/5497/6030,True,JournalArticle,,2025-07-10,4607a6f6bacc8e2fda9ce2607ef5b9fe00368561,evaluatingmachinelearningmodelsinhousingpriceforecasting,wang,10.62051/ev013e77,evaluatingmachinelearningmodelsinhousingpriceforecasting|2025,1,2577,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2fcf45aa2743e8132fb5777a73fd2173df413369,10.62051/d5x1ss82,,Comparing Linear Regression and Random Forest for Housing Price Prediction: Insights from the Boston Housing Dataset,2025.0,Transactions on Computer Science and Intelligent Systems Research,Guo-Guo Chen,"Housing price prediction is a critical task in real estate and economic analysis, providing valuable insights for stakeholders such as homebuyers, sellers, and policymakers. This study focuses on the Boston Housing dataset, a benchmark with 505 samples and 14 features, to predict the median value of owner-occupied homes (MEDV) using Linear Regression and Random Forest Regression. Exploratory data analysis reveals non-linear patterns, such as the right-skewed distribution of MEDV (skewness = 1.11) and strong correlations with features like LSTAT (-0.74) and RM (0.70). The dataset was standardized and split into 80-20 training and testing sets for model evaluation. Results show that Random Forest outperforms Linear Regression, achieving an MSE of 7.58 and R² of 0.864 compared to 19.38 and 0.652, respectively. Feature importance analysis highlights LSTAT and RM as key predictors, emphasizing socio-economic and structural influences. While Random Forest excels in capturing non-linear relationships, Linear Regression offers interpretability for policy insights. However, the dataset’s historical context and small size limit its applicability to modern markets, suggesting future research with larger, contemporary datasets and advanced models.",2.0,https://wepub.org/index.php/TCSISR/article/download/5464/5998,True,JournalArticle,,2025-07-10,2fcf45aa2743e8132fb5777a73fd2173df413369,comparinglinearregressionandrandomforestforhousingpricepredictioninsightsfromthebostonhousingdataset,chen,10.62051/d5x1ss82,comparinglinearregressionandrandomforestforhousingpricepredictioninsightsfromthebostonhousingdataset|2025,1,2755,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",6ddd2a78ed71117753ca02750caa06192f794918,10.62051/dpz1db11,,Predicting the Rise in California Home Prices and Factors Affecting,2024.0,Transactions on Computer Science and Intelligent Systems Research,Yufan Bai,"California’s real estate market has been in the spotlight for its unique geography and economic advantages, and home prices have been volatile. Therefore, accurate prediction of house price increases is of great importance to home buyers, investors and policy makers. This paper utilizes the California house price dataset, combines macroeconomic indicators and micro property attributes, and analyzes house price prediction through random forest, decision tree and neural network models. The study results show that the Random Forest model performs the best in predicting house prices with an R²value of 0.817210, which is significantly higher than the other models at 0.817210. the influence of R²leads to a slightly poorer neural network performance (CNN). Further through visualization studies and feature importance analysis median income, longitude, latitude, and distance from the sea are the key factors affecting the house price in California. House prices are strongly positively correlated with median income, while distance from the sea is negatively correlated with house prices. These key factors can also directly affect future home buying trends and house price direction. The research in this paper provides a strong reference for homebuyers, investors, and policymakers to help them make more informed decisions in the real estate market and promote a smooth and healthy market development.",0.0,https://wepub.org/index.php/TCSISR/article/download/4160/4678,True,JournalArticle,,2024-11-25,6ddd2a78ed71117753ca02750caa06192f794918,predictingtheriseincaliforniahomepricesandfactorsaffecting,bai,10.62051/dpz1db11,predictingtheriseincaliforniahomepricesandfactorsaffecting|2024,1,2908,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ae40f8865a232e508594d1da2838294d9a13cdd0,10.62051/cman5j03,,Feature Selection and Regression for House Value Prediction,2024.0,Transactions on Computer Science and Intelligent Systems Research,Zhaowen Gu,"This report investigates the application of feature selection and regression techniques, including LASSO, Ridge Regression, and Elastic Net, in predicting house values. By analyzing these methods, we aim to identify the most influential factors driving house prices and assess their relative importance. Our study involves a comprehensive evaluation of various linear regression models to determine their effectiveness and consistency in predicting house prices. The results reveal that while all examined techniques demonstrate robust performance, certain methods, particularly Elastic Net, offer superior predictive accuracy and better handle multicollinearity among features. This report provides valuable insights for real estate professionals and data scientists seeking to refine their predictive models and make informed decisions based on comprehensive data analysis.",0.0,https://wepub.org/index.php/TCSISR/article/download/4069/4444,True,JournalArticle,,2024-10-24,ae40f8865a232e508594d1da2838294d9a13cdd0,featureselectionandregressionforhousevalueprediction,gu,10.62051/cman5j03,featureselectionandregressionforhousevalueprediction|2024,1,2375,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",8df71f818af16b5ce055a4247e58d7652980bffb,10.1109/icrito66076.2025.11241262,,"CRISP-DM-Based Residential Property Valuation in Bolton, UK: Comparative Analysis of Supervised Machine Learning Models",2025.0,"2025 12th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)",E. G. Nwibo; Anchal Garg; Pradeep Hewage; Sarika Jain,"The boom in the real estate market necessitate easier and modern prediction models to accurately estimate properties' values. In this research, seven machine learning algorithms for house price prediction on residential houses in Bolton, Greater Manchester, were evaluated to cover the research gap in local UK market research. Following the CRISP-DM guideline, analysis of 13,933 transactions of 2,930 unique properties over a period of 12 months based on 17 property attributes with GDPR compliance were made. The pipeline for preprocessing end-toend comprised correlation analysis, detection of outliers using IQR, log transformation for skewness correction, and Min-Max normalization. Seven different models were experimented with: Linear SVR, Polynomial SVR, RBF SVR, K-Nearest Neighbors, Decision Tree, Random Forest implementations (RF-100, RF-200, RF-500), and XGBoost. Performance assessment with 70/30 traintest split showed Random Forest with 500 estimators yielded superior results ($R^{2}=0.93$, MAE $=\mathbf{0. 1 0 8 6}$, MSE $=\mathbf{0. 0 3 3 2}$, RMSE $=0.1822$), then XGBoost ($R^{2}=0.92$). Total Living Area was the superior predictor ($r=0.67$), confirming rudimentary real estate appraisal fundamentals. These findings offer proof of the capacity of machine learning to enhance property valuation accuracy, with implications for applied utilization by real estate professionals, financial institutions, investors, and policymakers in regional market analysis.",0.0,https://ub-ir.bolton.ac.uk/view/delivery/44UOBO_INST/1210424020008841/1310948490008841,True,Conference,,2025-09-18,8df71f818af16b5ce055a4247e58d7652980bffb,crispdmbasedresidentialpropertyvaluationinboltonukcomparativeanalysisofsupervisedmachinelearningmodels,nwibo,10.1109/icrito66076.2025.11241262,crispdmbasedresidentialpropertyvaluationinboltonukcomparativeanalysisofsupervisedmachinelearningmodels|2025,1,2982,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b016358b1d6d1b0e28b14a1bab3abd3080ab8933,10.17762/turcomat.v12i11.6053,,Prediction Of The Accuracy Of Student Boarding House Prices Around Widyatama University Using Neural Network Backpropagation Algorithm,2021.0,Turkish Journal of Computer and Mathematics Education,Ari Purno Wahyu; Raka Maheka Auramuwahid,"Boarding room rental services are a business that has been around for a long time. One of the target consumers of this business is from students who plan to stay temporarily during the lecture period so that they are close to the campus location where the student is studying. A strategic location such as in the area of one of the campuses in Bandung, namely the Widyatama University Campus, provides added value for boarding owners and also consumers who will rent, but there are several problems in this boarding rental business, namely the lack of knowledge of information on boarding rental prices around the area. campus due to the lack of effort made by boarding house owners in surveying boarding rental price data. In addition, boarding house owners find it difficult to determine the range of rental prices in accordance with boarding facilities. This problem is also experienced by prospective boarding house tenants in estimating whether the rental price offered is ideal and comparable to the facilities that will be obtained. This research conducts trials and predicts the range of rental prices in accordance with the parameters of the facilities that have been defined based on a survey of boarding price data via the internet and predicts how accurate the model used is. The method used is an artificial neural network with a backpropagation algorithm. The results of this study indicate that the 14-12-10-2-1 model with an epoch of 100 produces an accuracy rate of 87.18% and an MSE of 0.0033.",0.0,https://turcomat.org/index.php/turkbilmat/article/download/6053/5050,True,JournalArticle;Review,Business,2021-05-10,b016358b1d6d1b0e28b14a1bab3abd3080ab8933,predictionoftheaccuracyofstudentboardinghousepricesaroundwidyatamauniversityusingneuralnetworkbackpropagationalgorithm,wahyu,10.17762/turcomat.v12i11.6053,predictionoftheaccuracyofstudentboardinghousepricesaroundwidyatamauniversityusingneuralnetworkbackpropagationalgorithm|2021,1,3013,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ec6fdc763ceba51256f30c7d6a71f88e8490179d,10.17762/turcomat.v12i2.1870,,Prediction of House Price Using XGBoost Regression Algorithm,2021.0,,Et. al. J. Avanijaa,"House price fluctuates each and every year due to changes in land value and change in infrastructure in and around the area. Centralised system should be available for prediction of house price in correlation with neighbourhood and infrastructure, will help customer to estimate the price of the house. Also, it assists the customer to come to a conclusion where to buy a house and when to purchase the house. Different factors are taken into consideration while predicting the worth of the house like location, neighbourhood and various amenities like garage space etc. Developing a model starts with Pre-processing data to remove all sort of discrepancies and fill null values or remove data outliers and make data ready to be processed. The categorical attribute can be converted into required attributes using one hot encoding methodology. Later the house price is predicted using XGBoost regression technique.",60.0,https://turcomat.org/index.php/turkbilmat/article/download/1870/1615,True,,Mathematics,2021-04-11,ec6fdc763ceba51256f30c7d6a71f88e8490179d,predictionofhousepriceusingxgboostregressionalgorithm,avanijaa,10.17762/turcomat.v12i2.1870,predictionofhousepriceusingxgboostregressionalgorithm|2021,1,2414,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c52e611665e6d95e655abca687626b44f8eaf37d,10.54254/2753-8818/52/2024ch0140,,Research on housing prices prediction-take Boston as an example,2024.0,Theoretical and Natural Science,Bo-Xuan Liu,"The problem of predicting Boston housing prices belongs to the field of artificial intelligence, specifically in the domain of regression problems, which is a crucial area of study in machine learning. This article aims to get an accurate prediction model of Boston housing prices by comparing four regression models based on the datasets derived from StabLib library. Multiple Linear Regression model, Random Forest Regression model, Extreme Gradient Boosting Regression model and Support Vector Machine Regression model are taken into consideration. Five evaluation index R-squared, adjusted R-squared, mean absolute error, mean squared error and root mean squared error are compared in terms of generalization ability of model. Eventually, Extreme Gradient Boosting Regression model was found to be the most effective model when predicting housing prices in Boston. The model has certain positive applications in real life, which can help government formulate real estate policies and people make wiser house purchasing strategies.",0.0,https://tns.ewapub.com/article/view/15617.pdf,True,JournalArticle,,2024-09-27,c52e611665e6d95e655abca687626b44f8eaf37d,researchonhousingpricespredictiontakebostonasanexample,liu,10.54254/2753-8818/52/2024ch0140,researchonhousingpricespredictiontakebostonasanexample|2024,1,2534,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9997b189d1b1db09bcd6685170f001fc37c02a66,10.54254/2753-8818/52/2024ch0110,,Research on the influencing factors of housing price based on multiple linear regression and random forest,2024.0,Theoretical and Natural Science,Xin-Ran Jiang; Chang-Rong Li,"This paper aims to use multiple linear regression model and random forest models to analyze and study the factors affecting the housing price in Boston. The multiple linear regression model describes the relationship between multiple independent variables and one dependent variable through linear equations, and the random forest improves the accuracy and robustness by constructing multiple decision trees and combining their prediction results. To deal with complex nonlinear relationships and high dimensional data. Housing price is an important index to reflect the level and condition of economic and social development of a region, so it is of theoretical value and practical significance to explore its influencing factors and ways and degrees. Multiple factors are selected to analyze the weight and importance of each influencing factor, so as to help the government and decision makers to formulate more accurate policies, promote the stable development of the market, and provide scientific decision-making support for real estate developers, investors and ordinary buyers. In this study, the random forest model based on decision tree was used to clean, select and reduce the acquired housing price data, and to find out the main factors affecting housing price from the perspective of information gain, so as to obtain a relatively complete mathematical model and provide a reference scheme for future research by scholars.",1.0,https://tns.ewapub.com/article/view/15392.pdf,True,JournalArticle,,2024-09-10,9997b189d1b1db09bcd6685170f001fc37c02a66,researchontheinfluencingfactorsofhousingpricebasedonmultiplelinearregressionandrandomforest,jiang,10.54254/2753-8818/52/2024ch0110,researchontheinfluencingfactorsofhousingpricebasedonmultiplelinearregressionandrandomforest|2024,1,2937,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",85ba1a9fd85fc626cbb78eb16244ae06366cce7a,10.4038/bhumi.v10i2.103,,"An Investigation of the Impact of Amenities on Condominium Apartment Prices in Colombo, Sri Lanka with Machine Learning",2023.0,Bhumi The Planning Research Journal,R. Mahamood; Gayani Ranasinghe,"Condominium prices of a location are heavily impacted by the availability of neighbourhood amenities, which attract people to that location. Studies that employ machine learning to evaluate the impacts of amenities on condominium prices are a few, despite indications of increased accuracy. Hedonic models are used in the majority of current studies on housing pricing. Yet, the application of machine learning will improve accuracy and be more effective for the identification of multi-collinearity and non-linear relationships between property prices and the availability of amenities for a wide range of parameters. The aim of this study is to investigate the possible relationship between urban amenities surrounding condominium apartments and their market prices. The study uses data from the Google Maps Platform to examine the link between neighbourhood amenities and the prices of 236 condominiums in Colombo, Sri Lanka. 56 significant amenity characteristics were found using the eXtreme gradient boosting (XGB) algorithm, and the variety of correlations between amenities and condominium prices as restricted positive, accelerated positive, crooked, humped, and negative was explained. Results demonstrated that while a beautiful urban environment requires a variety of facilities, the popularity and other attributes of amenities affect condominium prices in numerous non-linear ways. Therefore, public and private organizations must work together to create integrative strategies that enhance and maintain the variety and accessibility of urban amenities.",2.0,https://storage.googleapis.com/jnl-sljo-j-btprj-files/journals/1/articles/103/659cd529a2ab0.pdf,True,JournalArticle,,2023-12-31,85ba1a9fd85fc626cbb78eb16244ae06366cce7a,aninvestigationoftheimpactofamenitiesoncondominiumapartmentpricesincolombosrilankawithmachinelearning,mahamood,10.4038/bhumi.v10i2.103,aninvestigationoftheimpactofamenitiesoncondominiumapartmentpricesincolombosrilankawithmachinelearning|2023,1,3067,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || apartment prices hedonic || hedonic housing prices || housing characteristics prices hedonic || machine learning hedonic pricing housing",2,4,4,5,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",82b1a7bbbca62c379a144ada2c95b782c6d6aa48,10.5753/eniac.2019.9300,,Housing Prices Prediction with a Deep Learning and Random Forest Ensemble,2019.0,Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2019),Bruno Klaus de Aquino Afonso; L. Melo; Willian Oliveira; S. Sousa; L. Berton,"The development of a housing prices prediction model can assist a house seller or a real estate agent to make better-informed decisions based on house price valuation. Only a few works report the use of machine learning (ML) algorithms to predict the values of properties in Brazil. This study analyzes a dataset composed of 12,223,582 housing advertisements, collected from Brazilian websites from 2015 to 2018. Each instance comprises twenty-four features of five different data types: integer, date, string, float, and image. To predict the property prices, we ensemble two different ML architectures, based on Random Forest (RF) and Recurrent Neural Networks (RNN). This study demonstrates that enriching the dataset and combining different ML approaches can be a better alternative for prediction of housing prices in Brazil.",52.0,https://sol.sbc.org.br/index.php/eniac/article/download/9300/9202,True,,Computer Science,2019-10-15,82b1a7bbbca62c379a144ada2c95b782c6d6aa48,housingpricespredictionwithadeeplearningandrandomforestensemble,afonso,10.5753/eniac.2019.9300,housingpricespredictionwithadeeplearningandrandomforestensemble|2019,1,2330,2,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2585bec8a6a8e1b093adbd26c9ecf6cb4d53e2e5,10.32520/stmsi.v15i2.5726,,House Price Prediction using the Random Forest Regression Algorithm,2026.0,SISTEMASI,Fika Halimah Balqis; Qurrotul Aini,,0.0,https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/download/5726/1150,True,JournalArticle,,2026-02-27,2585bec8a6a8e1b093adbd26c9ecf6cb4d53e2e5,housepricepredictionusingtherandomforestregressionalgorithm,balqis,10.32520/stmsi.v15i2.5726,housepricepredictionusingtherandomforestregressionalgorithm|2026,1,1500,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",922f998abd65e29da008f8bef799d74593b84a64,10.33317/ssurj.504,,House Price Prediction of Real Time Data (DHA Defence) Karachi Using Machine Learning,2022.0,Sir Syed University Research Journal of Engineering & Technology,Lata Bai Gokalani; B. Das; Dilip Kumar Ramnani; Mahender Kumar; M. Shah,"Pakistan’s real estate market has a large impact in GDP growth. Investment in real estate sector in Pakistan is encumbered with lucrative opportunities. The market demand for housing is ever increasing year by year. House sales prices keep on changing and increasing frequently, so there is a need for a system to forecast house sales prices in the future. Several factors that influence house sales price includes; location, physical attributes, number of bedrooms as well as several other economic factors. One of the main motivation of choosing Karachi for the house prediction is that Karachi is capital of Sindh and it has significant importance in country's economic as it is the major commercial and industrial center of Sindh. It is one of the main contribution of the work is that through this the house prediction model based on DHA Karachi data is developed and as per best of our knowledge till today there is no prediction of housing for the country’s important has been developed. has This research paper mainly focuses on real time Defense Housing Authority (DHA) Karachi data, applying different regression algorithms like Decision tree, Random forest and linear regression to find the sales price prediction of the house and compare the performance of these models. Random Forest algorithm gives 98% of accuracy. The proposed work will be very much helpful for the common people, real-estate people, investors and builders to inform them about making decision of selling or buying at Defense Housing Authority (DHA) Karachi.",1.0,https://sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/download/504/176,True,,,2022-12-25,922f998abd65e29da008f8bef799d74593b84a64,housepricepredictionofrealtimedatadhadefencekarachiusingmachinelearning,gokalani,10.33317/ssurj.504,housepricepredictionofrealtimedatadhadefencekarachiusingmachinelearning|2022,1,3041,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),05c8066a4f1ebf0565508520c4002c90671161c4,10.2478/remav-2025-0003,,Algorithm-Driven Hedonic Real Estate Pricing – An Explainable AI Approach,2024.0,Real Estate Management and Valuation,S. Tarasov; Bartłomiej Dessoulavy-Śliwiński,"Abstract Data-driven machine learning algorithms triggered a fundamental change in hedonic real estate pricing. However, their adaptive nonparametric structure makes inference and out-ofsample prediction challenging. This study introduces an explainable approach to interpreting machine learning predictions, which has not been done before in the local market context. Specifically, Random Forest and Extreme Gradient Boosting models are developed for residential real estate price prediction in Warsaw in 2021 on 10,827 property transactions. Model-agnostic Explainable Artificial Intelligence (XAI) methods are then used to investigate the black box decision making. The results show the practicability of applying XAI frameworks in the real estate market context to decode the rationale behind data-driven algorithms. Information about the relationships between input variables is extracted in greater detail. Accurate, reliable and transparent real estate valuation support tools can offer substantial advantages to participants in the real estate market, including banks, insurers, pension and sovereign wealth funds, as well public authorities and private individuals.",6.0,https://sciendo.com/pdf/10.2478/remav-2025-0003,True,JournalArticle,,2024-11-27,05c8066a4f1ebf0565508520c4002c90671161c4,algorithmdrivenhedonicrealestatepricinganexplainableaiapproach,tarasov,10.2478/remav-2025-0003,algorithmdrivenhedonicrealestatepricinganexplainableaiapproach|2024,1,2674,3,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic property valuation",2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fc2f6eafea321324e89cf37e787a95cea0703b75,10.53730/ijhs.v6ns3.8255,,Machine learning based prediction of house price,2022.0,International Journal of Health Sciences,T. Suganya; M. Gowtham; H. Raja,"Getting a house of our wishes within our budget in a residential area of our customization is quite a tedious process. In order to overcome this, we have developed a model to get a houses of our interest with religious belief and budget this Implemented model is of linear regression and k nearest neighbor’s algorithm with gradient descent optimization to make an optimal model for predicting house prices using the dataset. Performed feature engineering and selection using lasso and ridge penalties to eliminate features which had little or no impact on the residual sum of squares error. Then exposes Jupyter notebook cells as REST Endpoints to make prediction with new information. Finally, we are trying to send email alert to the concern user to give alert of the house price",2.0,https://sciencescholar.us/journal/index.php/ijhs/article/download/8255/4853,True,,,2022-06-01,fc2f6eafea321324e89cf37e787a95cea0703b75,machinelearningbasedpredictionofhouseprice,suganya,10.53730/ijhs.v6ns3.8255,machinelearningbasedpredictionofhouseprice|2022,1,2282,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bbc5cfca4ebec1ddeec39f4d061990c47c01851a,10.36825/riti.13.32.003,,Desarrollo de aplicación web prototipo para estimación de valores de viviendas en México aplicando modelos de aprendizaje automático,2025.0,Revista de Investigación en Tecnologías de la Información,Fabián Espinoza Garza; Yobani Martínez Ramírez; Alan David Ramírez Noriega; Iván Noel Álvarez Sánchez,"The development of a prototype web application for estimating home values in Mexico using machine learning models (ML) is presented. The evolution of valuation in Mexico is described, along with the opportunity that machine learning represents to improve accuracy and efficiency. The article is based on open data from the National Housing Information and Indicators System (SNIIV) and employs Random Forest (RF) and Linear Regression (LR) models, highlighting the superiority of the former in performance. The methodology, architecture, and technological tools used to build the application are detailed, and the accuracy results are presented by state, demonstrating the technical feasibility of the prototype despite limitations such as performance variability and the lack of regulations for its application in real-life cases.",0.0,https://riti.es/index.php/riti/article/download/406/728,True,JournalArticle,,2025-11-01,bbc5cfca4ebec1ddeec39f4d061990c47c01851a,desarrollodeaplicacionwebprototipoparaestimaciondevaloresdeviviendasenmexicoaplicandomodelosdeaprendizajeautomatico,garza,10.36825/riti.13.32.003,desarrollodeaplicacionwebprototipoparaestimaciondevaloresdeviviendasenmexicoaplicandomodelosdeaprendizajeautomatico|2025,1,2331,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",60e607d4a12a46fe9b46a79601a17a5e6b6a94a8,10.7764/rdlc.22.1.163,,Machine learning methods for prediction real estate sales prices in Turkey,2023.0,Revista de la Construcción,Cihan Çılgın; Hadi Gökçen,"Owning a house is one of the most important decisions that low and middle income people make in their lives. The real estate market is a significant factor of the national economy as much as it is important for individuals. Therefore, predicting real estate values or real estate valuation is beneficial and necessary not only for buyers, but also for real estate agents, economists and policy makers. This issue represents an active area of research, as individuals, companies and governments hold considerable assets in real estate. In this context, the aim of the study is to predict real estate prices with Machine Learning methods using the real estate sales data set in June and July 2021 belonging to the province of Ankara. In particular, it is to perform a comprehensive comparison on Machine Learning regression types methods that give successful prediction results in various but similar tasks, which are not included in the real estate literature. Real estate data obtained over the Internet was first included in a detailed data preprocessing process, and then Linear, Lasso and Ridge Regression, XGBoost and Artificial Neural Networks (ANN) methods were used on this dataset. According to empirical findings, XGBoost and ANNs appear as very important alternatives in predicting real estate sales prices.",8.0,https://revistadelaconstruccion.uc.cl/index.php/RDLC/article/download/51473/49333,True,JournalArticle,,,60e607d4a12a46fe9b46a79601a17a5e6b6a94a8,machinelearningmethodsforpredictionrealestatesalespricesinturkey,çılgın,10.7764/rdlc.22.1.163,machinelearningmethodsforpredictionrealestatesalespricesinturkey|2023,1,2817,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",194c32966936219ef7d227a03d9506db0f78f073,10.18775/ijied.1849-7551-7020.2015.95.2003,,"Sentiment Analysis, Social Media and Urban Economics: The Case of Singaporean HDB and Covid-19",2023.0,International Journal of Innovation and Economic Development,Srinaath Anbu Durai; Zhaoxia Wang,"Twitter sentiment analysis has been employed as a prognostic tool for predicting prices and trends in both stock and housing markets. Early studies in this domain drew inspiration from behavioural economics, establishing a link between sentiments or emotions and economic decision-making. However, recent investigations in this field have shifted their focus from the data utilized to the algorithms employed. A comprehensive literature review, with an emphasis on the data aspect, reveals a scarcity of research considering the influence of sentiments arising from external factors on stock or housing markets, despite abundant evidence in behavioural economics suggesting that sentiments induced by external factors impact economic decisions. To bridge this gap, this study explores the impact of Twitter sentiment related to the Covid-19 pandemic on housing prices in Singapore. Employing SNSCRAPE for tweet collection, sentiment analysis is conducted using VADER. Granger Causality is applied to investigate the relationship between Covid-19 cases and sentiment, while neural networks serve as prediction models. The research compares the predictive capacity of Twitter sentiment regarding Covid-19 with traditional housing price predictors, such as structural and neighbourhood characteristics. Findings indicate that utilizing Twitter sentiment related to Covid-19 yields superior predictions compared to relying solely on traditional predictors, outperforming two specific traditional predictors. Consequently, this study underscores the significance of considering Twitter sentiment related to external factors as crucial in economic predictions, demonstrating practical applications of sentiment analysis on Twitter data in real-world economic scenarios.",0.0,https://researchleap.com/wp-content/uploads/2024/02/03_Sentiment-Analysis-Social-Media-and-Urban-Economics.pdf,True,JournalArticle;Review,,2023-12-01,194c32966936219ef7d227a03d9506db0f78f073,sentimentanalysissocialmediaandurbaneconomicsthecaseofsingaporeanhdbandcovid19,durai,10.18775/ijied.1849-7551-7020.2015.95.2003,sentimentanalysissocialmediaandurbaneconomicsthecaseofsingaporeanhdbandcovid19|2023,1,3263,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic house price,adf56929a00d06585be1de8b25cb19aa042a377a,10.1109/icsdm.2015.7298035,,Comparing multilevel modelling and artificial neural networks in house price prediction,2015.0,International Conference on Spatial Data Mining and Geographical Knowledge Services,Yingyu Feng; Kelvyn Jones,,28.0,https://research-information.bris.ac.uk/en/publications/4cb022a6-f18c-4859-819a-f562397648bc,True,JournalArticle;Conference,Computer Science,2015-07-08,adf56929a00d06585be1de8b25cb19aa042a377a,comparingmultilevelmodellingandartificialneuralnetworksinhousepriceprediction,feng,10.1109/icsdm.2015.7298035,comparingmultilevelmodellingandartificialneuralnetworksinhousepriceprediction|2015,1,1500,2,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic house price",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f8713e591c7667560c2cd38cdec3be9c97dbf77b,10.1108/pm-09-2019-0053,,Neural networks support vector machine for mass appraisal of properties,2020.0,,Joseph Awoamim Yacim; D. Boshoff,"The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.,The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.,The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.,The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.",13.0,https://repository.up.ac.za/bitstream/2263/81959/1/Yacim_Neural_2020.pdf,True,,Computer Science,2020-03-29,f8713e591c7667560c2cd38cdec3be9c97dbf77b,neuralnetworkssupportvectormachineformassappraisalofproperties,yacim,10.1108/pm-09-2019-0053,neuralnetworkssupportvectormachineformassappraisalofproperties|2020,1,2924,4,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f6dc3049603a372f1008008a5729d92968782f6a,10.1080/09599916.2024.2403998,,On the uncertainty of real estate price predictions,2024.0,Journal of Property Research,João A. Bastos; J. Paquette,"ABSTRACT Uncertainty quantification associated with real estate appraisal has largely been overlooked in the literature. In this paper, we address this gap by analysing the uncertainty in automated property valuations using conformal prediction, a distribution-free procedure for constructing prediction intervals with valid coverage in finite samples. Through an empirical study of property prices in the San Francisco Bay Area, we find that prediction intervals obtained using conformal quantile regression have exact coverage. In contrast, prediction intervals obtained from non-conformal quantile regressions severely undercover the data. Furthermore, we show that the intervals adapt to various characteristics of the dwellings, which is crucial given the heterogeneous nature of real estate data. Indeed, we observe that larger and older properties, those in both low and high-income neighbourhoods, as well as those on the market for less than one year are more challenging to evaluate.",3.0,https://repositorio.ulisboa.pt/bitstreams/649995ec-b0df-4d4d-ae9b-2c65b7e2f9c5/download,True,JournalArticle,,2024-09-22,f6dc3049603a372f1008008a5729d92968782f6a,ontheuncertaintyofrealestatepricepredictions,bastos,10.1080/09599916.2024.2403998,ontheuncertaintyofrealestatepricepredictions|2024,1,2493,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",71ac66a3e6a6e2422b2c881d3156a364df02c7d3,10.2478/remav-2025-0001,,Housing Price Prediction - Machine Learning and Geostatistical Methods,2024.0,Real Estate Management and Valuation,R. Cellmer; Katarzyna Kobylińska,"Abstract Machine learning algorithms are increasingly often used to predict real estate prices because they generate more accurate results than conventional statistical or geostatistical methods. This study proposes a methodology for incorporating information about the spatial distribution of residuals, estimated by kriging, into selected machine learning algorithms. The analysis was based on apartment prices quoted in the Polish capital of Warsaw. The study demonstrated that machine learning combined with geostatistical methods significantly improves the accuracy of housing price predictions. Local factors that influence housing prices can be directly incorporated into the model with the use of dedicated maps.",6.0,https://reference-global.com/2/v2/download/article/10.2478/remav-2025-0001.pdf,True,JournalArticle,,2024-10-01,71ac66a3e6a6e2422b2c881d3156a364df02c7d3,housingpricepredictionmachinelearningandgeostatisticalmethods,cellmer,10.2478/remav-2025-0001,housingpricepredictionmachinelearningandgeostatisticalmethods|2024,1,2220,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0563f027cf714e008ab3994408f3e369f4c1ed3b,10.56801/rebicte.v9i.156,,Estimate House Price Using Machine Learning,2023.0,Research Briefs on Information & Communication Technology Evolution,Rishita Gadde; Md Amiruzzaman; Richard Burns; Md. Rajibul Islam; Rizal Mohd Nor,"The House Price Index (HPI) is commonly used to estimate the changes in housing prices. The sale price of the house is correlated with many other factors like geographical location, size of the house, age of the house, the area and population of the neighborhood etc. There has been a considerably large number of datasets released in the literature of various locations to explore the correlation of the sale price of houses with their corresponding features. However, all the features don’t affect the sale price in equal proportion. Some features strongly correlate with each other and, while some features don’t carry any importance or are probably redundant. As a result, to explore various impacts of features on sales prices, we performed a detailed data analysis on the original house dataset. This report also comprehensively validates multiple steps of data analysis with supporting statistics and visualizations to provide an optimistic result of various features and their impact on the sales price of houses.",1.0,https://rebicte.org/index.php/rebicte/article/download/156/195,True,JournalArticle,,2023-05-02,0563f027cf714e008ab3994408f3e369f4c1ed3b,estimatehousepriceusingmachinelearning,gadde,10.56801/rebicte.v9i.156,estimatehousepriceusingmachinelearning|2023,1,2521,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",39cde0780a2ac86beb9466ecf9d772daed842f08,10.1108/jpif-08-2021-0073,,Artificial intelligence algorithms to predict Italian real estate market prices,2021.0,Journal of Property Investment & Finance,Luca Rampini; Fulvio Re Cecconi,"PurposeThe assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.Design/methodology/approachAn extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.FindingsThe models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).Research limitations/implicationsAll the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.Practical implicationsThe accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.Originality/valueTo date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.",31.0,https://re.public.polimi.it/bitstream/11311/1217365/5/Published_version.pdf,True,,,2021-12-07,39cde0780a2ac86beb9466ecf9d772daed842f08,artificialintelligencealgorithmstopredictitalianrealestatemarketprices,rampini,10.1108/jpif-08-2021-0073,artificialintelligencealgorithmstopredictitalianrealestatemarketprices|2021,1,3254,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",dc9d287b9443746d85f13a05a9f9a46bf9934de3,10.29313/bcss.v3i2.9084,,Penerapan Metode Regresi Komponen Utama Kernel untuk Prediksi Harga Rumah,2023.0,Bandung Conference Series: Statistics,M. Verdiansyah; Suwanda,"Abstract. Linear regression analysis is a statistical method used to model the relationship between the dependent variable and one or more independent variables expressed in the form of a regression equation. The Ordinary Least Square (OLS) can be used to estimate regression parameters. The regression parameter estimator obtained will be good if the assumptions that apply to MKT are met. One of the assumptions that must be met is that there is a linear relationship between the dependent variable and each independent variable. If linearity is violated, kernels can be used. In addition, another assumption that must be met is the absence of multicollinearity between independent variables. An alternative method that can be used to overcome multicollinearity is using Principal Component Analysis. If the linearity and multicollinearity assumptions are violated, the Kernel Principal Component Regression method can be applied. This study will apply the Kernel Principal Component Regression (KPCR) method, where the principal component is a nonlinear combination of independent variables through a kernel function. This KPCR method is implemented to predict house prices in South Jakarta based on building area, land area, number of bedrooms, number of bathrooms, and number of car capacity in the garage. Based on the results of the study, 3 main components of the kernel were formed with a prediction model Y ̂ = 76,3612 + 409,5373 KUK1 + 328,4967 KUK2 – 291,7757 KUK3 and a coefficient of determination (R2) of 70.4640%. Abstrak. Analisis regresi linear merupakan suatu metode statistika yang digunakan untuk memodelkan hubungan antara variabel dependen dengan satu atau lebih variabel independen yang dinyatakan dalam bentuk persamaan regresi. Dalam menaksir parameter regresi dapat menggunakan Metode Kuadrat Terkecil (MKT). Penaksir parameter regresi yang diperoleh akan baik jika asumsi-asumsi yang berlaku untuk MKT dipenuhi. Salah satu asumsi yang harus dipenuhi adalah terdapat hubungan linear antara variabel dependen dan masing-masing variabel independen. Apabila linearitas terlanggar, dapat menggunakan kernel. Selain itu, asumsi lainnya yang harus terpenuhi adalah tidak adanya multikolinearitas antar variabel independen. Metode alternatif yang dapat digunakaan untuk mengatasi multikolinearitas adalah menggunakan Analisis Komponen Utama. Apabila asumsi linearitas dan multikolinearitas terlanggar, dapat diterapkan metode Regresi Komponen Utama Kernel. Dalam penelitian ini, akan diterapkan metode Regresi Komponen Utama Kernel (RKUK), dimana komponen utama merupakan kombinasi nonlinier dari variabel independen melalui fungsi kernel. Metode RKUK ini diimplementasikan untuk memprediksi harga rumah di Jakarta Selatan berdasarkan luas bangunan, luas tanah, jumlah kamar tidur, jumlah kamar mandi, dan jumlah kapasitas mobil dalam garasi. Berdasarkan hasil penelitian, terbentuk 3 buah komponen utama kernel dengan model prediksi Y ̂ = 76,3612 + 409,5373 KUK1 + 328,4967 KUK2 – 291,7757 KUK3 serta nilai koefisien determinasi (R2) sebesar 70,4640%.",1.0,https://proceedings.unisba.ac.id/index.php/BCSS/article/download/9084/3528,True,JournalArticle;Conference,,2023-08-02,dc9d287b9443746d85f13a05a9f9a46bf9934de3,penerapanmetoderegresikomponenutamakerneluntukprediksihargarumah,verdiansyah,10.29313/bcss.v3i2.9084,penerapanmetoderegresikomponenutamakerneluntukprediksihargarumah|2023,1,4573,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",38bdcfa5cbca15dd16078daa707d480e6cf8cf31,10.18690/um.epf.5.2024.8,,Prediction of Residential Real Estate Selling Prices in Serbia Using Artifical Neural Networks,2024.0,Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development,Ljiljana Matic; Vesna Ranković; Tijana Geroski; Zoran Kalinić,"The increasing housing prices over the past decades have added complexity to the real estate appraisal process. Therefore, it is important to create a proper prediction model which can encapsulate complex dependence of the property price from variable inputs. But, the problem of predicting real estate prices is highly non-linear and depends on many parameters. This research explores the potential of utilizing artificial neural networks (ANNs) to forecast the selling prices of apartments in Belgrade, Serbia, based on various apartment parameters. The findings demonstrate high efficiency of the ANN models in property valuation and, if all the preconditions of property value modelling are met, the ANN technique stands as a reliable valuation approach that could be used by both real estate researchers and professionals.",0.0,https://press.um.si/index.php/ump/catalog/download/870/1282/4109,True,,,2024-05-20,38bdcfa5cbca15dd16078daa707d480e6cf8cf31,predictionofresidentialrealestatesellingpricesinserbiausingartificalneuralnetworks,matic,10.18690/um.epf.5.2024.8,predictionofresidentialrealestatesellingpricesinserbiausingartificalneuralnetworks|2024,1,2327,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",440205e73bb814f65e1e006a9582dc5c823e9e20,10.12677/hjdm.2017.72004,,Analysis of Beijing Second-Hand House Price Based on Random Forest,2017.0,,Xiao-Tong Li,,2.0,https://pdf.hanspub.org/HJDM20170200000_61628239.pdf,True,,Geography;Economics,2017-05-04,440205e73bb814f65e1e006a9582dc5c823e9e20,analysisofbeijingsecondhandhousepricebasedonrandomforest,li,10.12677/hjdm.2017.72004,analysisofbeijingsecondhandhousepricebasedonrandomforest|2017,1,1500,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0e3ac5b13b1c6cb3be3a17167e2f08d4be41fd86,10.34190/ecsm.10.1.1020,,Resale HDB Price Prediction Considering Covid-19 through Sentiment Analysis,2023.0,European Conference on Social Media,Srinaath Anbu Durai,"Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon-based tool, VADER, is used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid-19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. In a micro sense, this paper demonstrates the use of sentiment analysis of Twitter data in urban economics. In a macro sense, the paper demonstrates the extent to which social media is able to capture the behavioral economic cues of a population.",3.0,https://papers.academic-conferences.org/index.php/ecsm/article/download/1020/1065,True,JournalArticle;Conference;Review,,2023-05-05,0e3ac5b13b1c6cb3be3a17167e2f08d4be41fd86,resalehdbpricepredictionconsideringcovid19throughsentimentanalysis,durai,10.34190/ecsm.10.1.1020,resalehdbpricepredictionconsideringcovid19throughsentimentanalysis|2023,1,3541,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,1,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",6e9c26a825457de53154364c62dc8ae8b8baf5a1,10.1111/tgis.13303,,Spatial Performance Approach to Machine Learning Algorithms: A GIS‐Based Comparison Analysis for Real Estate Valuation,2025.0,Transactions on GIS,Nihal Genç; H. E. Colak; Ferdi Ozbilgin,"Machine learning algorithms are frequently used in mass valuation activities today. These algorithms are believed to perform differently depending on various factors such as different functions, model parameters, data quantity, and quality. Another factor that is often overlooked is location. In this study, the performance of a set of machine learning algorithms in predicting real estate values was evaluated from a spatial perspective. To highlight the spatial performance of the algorithms, a study area was chosen where the factors affecting value are spatially heterogeneous. Value predictions were made using Linear Regression, Random Forest, Support Vector Machine, Regression Trees, Gaussian Process Regression, Artificial Neural Network, and Least‐Squares Boosting algorithms. Bayesian Optimization was used to improve the performance of the algorithms. The performances of the algorithms were compared using R2, RMSE, MSE, MAPE, and MAE metrics. The Random Forest model showed the best performance according to these metrics. Spatial predictions were made in the GIS environment using the IDW spatial interpolation method based on test data. The spatial distribution of the predictions and actual housing values were shown by producing maps. The spatial distribution of the algorithms was compared both with the actual values and with each other. It was found that, aside from the Random Forest model, other models also performed quite well in some locations and even produced better results than the Random Forest model. This demonstrates that location affects the performance of ML algorithms and that performance may vary depending on location. This study is expected to contribute to many fields of science where ML algorithms are used, especially in real estate valuation, and to bring a new perspective to the literature.",12.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/tgis.13303,True,JournalArticle,,2025-01-27,6e9c26a825457de53154364c62dc8ae8b8baf5a1,spatialperformanceapproachtomachinelearningalgorithmsagisbasedcomparisonanalysisforrealestatevaluation,genç,10.1111/tgis.13303,spatialperformanceapproachtomachinelearningalgorithmsagisbasedcomparisonanalysisforrealestatevaluation|2025,1,3339,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a8361723d27a163108c36779a49323e1a1ffb9ff,10.1111/tgis.13273,,Developing GeoAI Integrated Mass Valuation Model Based on LADM Valuation Information Great Britain Country Profile,2024.0,Trans. GIS,M. Mete,"Access to information pertaining to land facilitates strategic planning for individuals, businesses, and governmental entities and allows them to anticipate and address contemporary challenges such as climate change, housing requirements, and economic prosperity. Improvements in information technologies contribute to data creation, analysis, and dissemination processes that enable gaining knowledge about land‐related events. Greater use of location data within property technologies gives rise to an efficient and sustainable land administration system. GeoAI can contribute greatly to the solution of spatial problems by extracting information from complex data through geospatial intelligence. Property valuation requires a spatially explicit model to evaluate locational factors with spatial analyses. In this study, a Land Administration Domain Model (LADM)‐based mass valuation model is developed for Great Britain by integrating GeoAI techniques. First, the LADM Valuation Information package is matched with the country's current organizational structure and it is extended according to the needs to create a country profile. After developing the conceptual model, a database schema is created and automatic conversion to the physical model is conducted. Loading different data sources into the database, a mass valuation application is carried out through spatial analysis and Random Forest regression analysis. As a result, a highly accurate and holistic valuation model is implemented based on the international land administration standards.",7.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/tgis.13273,True,JournalArticle,Computer Science,2024-11-19,a8361723d27a163108c36779a49323e1a1ffb9ff,developinggeoaiintegratedmassvaluationmodelbasedonladmvaluationinformationgreatbritaincountryprofile,mete,10.1111/tgis.13273,developinggeoaiintegratedmassvaluationmodelbasedonladmvaluationinformationgreatbritaincountryprofile|2024,1,3056,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e74dce110afd377849f60841d3e42a1329bf5be7,10.1002/eng2.12599,,A boosting ensemble learning based hybrid light gradient boosting machine and extreme gradient boosting model for predicting house prices,2022.0,Engineering Reports,Racheal Sibindi; R. Mwangi; A. Waititu,"The implementation of tree‐ensemble models has become increasingly essential in solving classification and prediction problems. Boosting ensemble techniques have been widely used as individual machine learning algorithms in predicting house prices. One of the techniques is LGBM algorithm that employs leaf wise growth strategy, reduces loss and improves accuracy during training which results in overfitting. However, XGBoost algorithm uses level wise growth strategy which takes time to compute resulting in higher computation time. Nevertheless, XGBoost has a regularization parameter, implements column sampling and weight reduction on new trees which combats overfitting. This study focuses on developing a hybrid LGBM and XGBoost model in order to prevent overfitting through minimizing variance whilst improving accuracy. Bayesian hyperparameter optimization technique is implemented on the base learners in order to find the best combination of hyperparameters. This resulted in reduced variance (overfitting) in the hybrid model since the regularization parameter values were optimized. The hybrid model is compared to LGBM, XGBoost, Adaboost and GBM algorithms to evaluate its performance in giving accurate house price predictions using MSE, MAE and MAPE evaluation metrics. The hybrid LGBM and XGBoost model outperformed the other models with MSE, MAE and MAPE of 0.193, 0.285, and 0.156 respectively.",152.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/eng2.12599,True,,,2022-11-22,e74dce110afd377849f60841d3e42a1329bf5be7,aboostingensemblelearningbasedhybridlightgradientboostingmachineandextremegradientboostingmodelforpredictinghouseprices,sibindi,10.1002/eng2.12599,aboostingensemblelearningbasedhybridlightgradientboostingmachineandextremegradientboostingmodelforpredictinghouseprices|2022,1,2913,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",118dcf81f68dc75cdff57e6316741c2a375934f4,10.3991/ijim.v17i23.38073,,Web Application with Machine Learning for House Price Prediction,2023.0,International Journal of Interactive Mobile Technologies,Raúl Jáuregui-Velarde; L. Andrade-Arenas; Domingo Hernández Celis; Roberto Carlos Dávila Morán; Michael Cabanillas-Carbonell,"Every year, the price of a house changes due to different aspects, so accurately estimating the buying and selling price is a problem for real estate agencies. Therefore, the research work aims to build a Machine Learning (ML) model in Azure ML Studio and a web application to predict the buying and selling price of two types of houses: urban and rural houses, according to their characteristics, to minimize the forecast error in prediction. Following the basic stages of machine learning construction, we build the prediction model and the Rational Unified Process (RUP) methodology to build the web application. As a result, we obtained a model trained with a linear regression algorithm and a predictive ML model with a coefficient of determination of 95% and a web application that consumes the prediction model through an Application Programming Interface (API) that facilitates price prediction to customers. The quality of the prediction system was evaluated by expert judgment; they evaluated efficiency, usability, and functionality. After the calculation, they obtained an average quality of 4.88, which indicates that the quality is very high. In conclusion, the developed prediction system facilitates real estate agencies and their customers the accurate prediction of the price of urban and rural housing, minimizing accuracy errors in price prediction. Benefiting all people interested in the real estate world.",7.0,https://online-journals.org/index.php/i-jim/article/download/38073/14301,True,JournalArticle,Computer Science,2023-12-12,118dcf81f68dc75cdff57e6316741c2a375934f4,webapplicationwithmachinelearningforhousepriceprediction,jáuregui-velarde,10.3991/ijim.v17i23.38073,webapplicationwithmachinelearningforhousepriceprediction|2023,1,2928,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7b5fd87c28c6295cbb1446d83f58278d8ae025d2,10.30598/barekengvol19iss1pp107-118,,MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS,2025.0,BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN,Sugha Faiz Al Maula; Nicoletta Almira Dyah Setiawan; Elly Pusporani; S. Z. Jannah,"The increasing demand for housing in urban agglomerations, particularly in areas like Jakarta, has made homeownership a significant challenge for many, especially first-time buyers and the lower-middle class. Post-pandemic shifts have further influenced housing preferences, driving interest towards suburban areas with green spaces. Despite government efforts through mortgage subsidy programs, affordability remains a concern, particularly in peripheral regions. This study aims to analyze housing prices in various Jakarta regions using machine learning models, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), Light Gradient Boosting Machine (LGBM), and Random Forest. A dataset of 554 house prices from West Jakarta, South Jakarta, Central Jakarta, and South Tangerang was used. The analysis focused on key predictors like land area, building area, bedrooms, and carports, with R² and Mean Squared Error (MSE) metrics evaluating model performance. Results showed that LGBM and Random Forest outperformed others with 0.8 R2 and low MSE, with building and land area as the most significant factors influencing prices. The study concludes that property size is a primary determinant of house prices, and there is a need for policy interventions to make housing more affordable. Additionally, apartment rentals offer a viable alternative, especially in central urban areas, where proximity to economic activities and facilities is crucial. The findings suggest that enhancing marketplace features with predictive tools could further assist buyers in making informed decisions.",1.0,https://ojs3.unpatti.ac.id/index.php/barekeng/article/download/12906/9511,True,JournalArticle,,2025-01-13,7b5fd87c28c6295cbb1446d83f58278d8ae025d2,modelinghousesellingpricesinjakartaandsouthtangerangusingmachinelearningpredictionanalysis,maula,10.30598/barekengvol19iss1pp107-118,modelinghousesellingpricesinjakartaandsouthtangerangusingmachinelearningpredictionanalysis|2025,1,3105,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,5,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",4de8a99bf100e28715f5d6f82774815bd2d6650d,10.37256/rrcs.2320232639,,An Analysis of House Price Prediction Using Ensemble Learning Algorithms,2023.0,Research Reports on Computer Science,Sai Venkat Boyapati; M. Karthik; K. Subrahmanyam; B. Reddy,"It is very important to understand the market drifts in the wake of booming civilization and ever-changing market requirements. The principal purpose of the study is the prediction of house prices based on current conditions. From historical data on property markets, literature attempts to draw useful insights. Business trends must be understood so that individuals may prepare their budgetary needs accordingly. A society that is ever-expanding is driven by the growing real estate industry. A lot of clients have been duped by agents setting up a fake market rate. As a result, the real estate industry has become less transparent in recent years. Due to decreased accuracy and overfitting of data, the previous model reduced efficiency, whereas the newly developed model resolves such issues and provides a rich user interface with a better model. An important part of this study is to develop an extensive model that is beneficial to both business societies and individuals. This is the main objective of this study. In order to simplify the client’s fieldwork and free up his time and money, this software is intended to assist him. Machine learning algorithms enable models to be enlightened such as root mean square error, random forest, support vector machine, k-nearest neighbors, mean squared error, extreme gradient boost, mean absolute error, R-squared score, linear regression, AdaBoost, CatBoost.",6.0,https://ojs.wiserpub.com/index.php/RRCS/article/download/2639/1395,True,JournalArticle,,2023-05-29,4de8a99bf100e28715f5d6f82774815bd2d6650d,ananalysisofhousepricepredictionusingensemblelearningalgorithms,boyapati,10.37256/rrcs.2320232639,ananalysisofhousepricepredictionusingensemblelearningalgorithms|2023,1,2912,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6df251147348482e626686a7af688e852f8411f7,10.24843/jim.2024.v12.i01.p06,,"Comparison Of Decision Tree, Linear Regression, and Random Forest Regressor Models for Predicting House Prices",2024.0,Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi),Ni Putu Sutramiani,"A home is a basic requirement that offers comfort and security to its occupants. Because they are subject to price fluctuations, houses are also a potential option in an investing setting. As a result, buyers and investors require a system that can forecast house values. This study compares the effectiveness of decision trees, linear regression, and random forest regressors as models for predicting home prices. The dataset for predicting home prices was used in this study to conduct data exploration, pre-processing, modeling, and model comparison stages. The study's findings demonstrate that the random forest regressor offers the best prediction performance with lower assessment metrics, including MAE, MSE, RMSE, and R2 Score, making it the best option for predicting house prices and other financial outcomes.",2.0,https://ojs.unud.ac.id/index.php/merpati/article/download/112815/55168,True,JournalArticle,,2024-05-23,6df251147348482e626686a7af688e852f8411f7,comparisonofdecisiontreelinearregressionandrandomforestregressormodelsforpredictinghouseprices,sutramiani,10.24843/jim.2024.v12.i01.p06,comparisonofdecisiontreelinearregressionandrandomforestregressormodelsforpredictinghouseprices|2024,1,2320,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",bcb8fc58a0bfe6444a3a192745687553d510518b,10.24843/jnatia.2023.v01.i03.p18,,Uji Performansi Algoritma Linear Regression dan Random Forest Regression pada Implementasi Sistem Prediksi Harga Rumah,2023.0,Jurnal Nasional Teknologi Informasi dan Aplikasnya,I. Wijaya; Ida Bagus Gede Dwidasmara,"Currently the house has become one of the needs that must be met. The price of a house is the main parameter that determines whether a person or organization buys or invests. In general, house prices are influenced by several factors, including building area, land area, number of bedrooms, number of bathrooms and number of garages. Currently, there are many websites devoted to providing information about buying and selling houses. This of course makes it easier for someone when looking for a house with the desired specifications without the need to come directly to the location. However, the house buying and selling platform does not provide a house price prediction feature that is in accordance with user specifications. This means someone who is planning to buy a house does not get an initial idea of the costs that must be spent to own the desired home. Therefore, in this study, researchers will design a web app-based house price prediction system that can make it easier for users to get predictions of the desired house price. In this study the prediction algorithms to be used are linear regression and random forest. Both algorithms will be analyzed for their performance and then the algorithm with the best level of accuracy will be applied as a predictive model which will be integrated with the user interface display.",0.0,https://ojs.unud.ac.id/index.php/jnatia/article/download/102444/50654,True,JournalArticle,,2023-07-17,bcb8fc58a0bfe6444a3a192745687553d510518b,ujiperformansialgoritmalinearregressiondanrandomforestregressionpadaimplementasisistemprediksihargarumah,wijaya,10.24843/jnatia.2023.v01.i03.p18,ujiperformansialgoritmalinearregressiondanrandomforestregressionpadaimplementasisistemprediksihargarumah|2023,1,2841,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ed42fa40fbc0a004411002765140cc1b55117d56,10.36253/aestim-13703,,House price prediction modeling using machine learning techniques: a comparative study,2023.0,Aestimum,Ayten Yağmur; M. Kayakuş; Mustafa Terzioğlu,"In the literature, there are two basic approaches regarding the determination of house prices. One of them is the prediction of house price using macroeconomic variables in the country where the house is produced, and another one is the price prediction models, which we can express as micro-variables, by considering the features of the house. In this study, the price of the house was attempted to be predicted using machine learning methods by establishing a model with micro variables that reveal the features of the house. The study was conducted in Turkey’ Antalya province, where household housing demand of foreigners is also high. The house advertisements in locations belonging to the lower, middle- and upper-income groups were selected as the sample. In the results, it was observed that the artificial neural network (ANN) method made predictions with more meaningful results compared to support vector regression (SVR) and multiple linear regression (MLR). These results appear to be a viable model for institutions that supply housing, mediate housing sales, and provide housing financing and valuation. It is considered that this model, which can be used to predict fluctuating house prices, especially in developing countries, will regulate the housing market.",18.0,https://oaj.fupress.net/index.php/ceset/article/download/13703/11696,True,,,2023-02-13,ed42fa40fbc0a004411002765140cc1b55117d56,housepricepredictionmodelingusingmachinelearningtechniquesacomparativestudy,yağmur,10.36253/aestim-13703,housepricepredictionmodelingusingmachinelearningtechniquesacomparativestudy|2023,1,2777,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",303e04942037b7fc2fc530828eb5ab99016dd3ec,10.31893/multiscience.2023ss0206,,Predicting future housing prices: a machine learning approach,2023.0,Multidisciplinary Science Journal,P. Verma; Satyendra Arya; Chetana Asbe,"In our ecosystem, the real estate sector is the least transparent. Daily changes in housing prices as well as sometimes exaggerated prices rather than valuations are a part of life in the housing market. Our research project's major focus is on predicting future housing prices using actual machine learning. Here, we want to concentrate our judgments on each fundamental factor that goes into calculating the price. Machine learning has played a significant role in picture identification, spam restructuring, normal speech command, product suggestion, and medical diagnosis in recent years. The current machine learning method aids us in improving security warnings, maintaining public safety, and improving medicinal advancements. Machine learning technology also improves customer service and makes automobiles safer. The current research discusses the prediction of future housing prices provided by a machine learning system. The data was preprocessed after it was collected. In this procedure, we employ the Forest Neural Gradient Boosting Algorithm (FNGBA). We evaluate and compare several prediction techniques for the selection of prediction methods. Our findings demonstrate the necessity for a successful approach to the problem and the capability of our method to provide predictions that can be compared to existing models of housing price prediction. When compared to employing independent methods, the findings showed that this technique delivers the least mistake and the maximum accuracy. ",6.0,https://malque.pub/ojs/index.php/msj/article/download/1249/627,True,JournalArticle,,2023-08-18,303e04942037b7fc2fc530828eb5ab99016dd3ec,predictingfuturehousingpricesamachinelearningapproach,verma,10.31893/multiscience.2023ss0206,predictingfuturehousingpricesamachinelearningapproach|2023,1,3008,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,1,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",28d7d0e2158a32ae7b12b56229e2ede6fd95c7fe,10.56028/aetr.6.1.549.2023,,Determining the Best Model among Candidate Machine Learning Models for Chicago Suburb House Price Data,2023.0,Advances in Engineering Technology Research,Rui Zhang,"With the development of the society, there is an increasing need of predicting the house price in order to purchase properties economically. Myriads of machine learning methods have been developed to achieve this goal. In this paper, we narrow our focus on three types of machine learning methods-Linear Regression, Polynomial Regression, and Neural Network (Multilayer Perceptron Regressor) and train all of them on Chicago suburb house price dataset and employ all of them to make predictions. Performance is evaluated using mean squared error (MSE) and R square (R2) metrics. Preliminary experimental results demostrate that Neural Network outperforms the other candidate methods in all cases. Polynomial Regression of degree 2 outperforms Linear Regression in most cases but performs arbitrarily bad in worst cases. Therefore, Neural Network (MLP Regressor) is the best method for predicting Chicago suburb house price.",0.0,https://madison-proceedings.com/index.php/aetr/article/download/1356/1353,True,JournalArticle,,2023-07-18,28d7d0e2158a32ae7b12b56229e2ede6fd95c7fe,determiningthebestmodelamongcandidatemachinelearningmodelsforchicagosuburbhousepricedata,zhang,10.56028/aetr.6.1.549.2023,determiningthebestmodelamongcandidatemachinelearningmodelsforchicagosuburbhousepricedata|2023,1,2424,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",63c98402bec1d98255a7046e0ebd29193c04cdd0,10.56028/aemr.1.2.71,,Research on Housing Price in Boston,2022.0,Advances in Economics and Management Research,Zhi-Heng Ma; Tan Wang; Yubo Zhang,"In the United States, due to the characteristics of sparsely populated areas, its housing prices are not quite the same as those in China. At the same time, due to factors such as the freedom of firearms in the United States, many people consider more factors in purchasing houses, resulting in great differences in housing prices in the United States. The housing price problem has also become a major concern. This paper first analyzes fourteen variables, respectively, thirteen independent variables were analyzed to find the relationship between independent variables and housing prices, establish regression model analysis, and then through the frequency histogram to find the relationship between independent variables and frequency, through the relationship between frequency and housing prices to illustrate the relationship between independent variables and housing prices. By analyzing the correlation to find out the relationship between the variables and housing prices. Through the statistical description and analysis model of data, we can establish the normal distribution relationship between independent variables and dependent variables, and approximate the normal distribution parameters through the average and variance of independent variables. The relationship between each independent variable and dependent variable is fitted, and the influence of each variable on housing prices is finally obtained.",0.0,https://madison-proceedings.com/index.php/aemr/article/download/323/267,True,,,2022-09-21,63c98402bec1d98255a7046e0ebd29193c04cdd0,researchonhousingpriceinboston,ma,10.56028/aemr.1.2.71,researchonhousingpriceinboston|2022,1,2926,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f993176ad78e5de60dbd59429d1ddcc5432c9e95,10.1007/s41060-024-00682-y,,Graph neural networks for house price prediction: do or don’t?,2024.0,International Journal of Data Science and Analysis,Margot Geerts; S. Vanden Broucke; Jochen De Weerdt,,10.0,https://lirias.kuleuven.be/retrieve/cacb25c7-ab31-446b-b976-ff006530dbf6,True,JournalArticle,Computer Science,2024-11-22,f993176ad78e5de60dbd59429d1ddcc5432c9e95,graphneuralnetworksforhousepricepredictiondoordont,geerts,10.1007/s41060-024-00682-y,graphneuralnetworksforhousepricepredictiondoordont|2024,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",f3bf12c7c903d2eae7ba97f32b506850ec57d136,10.1365/s41056-022-00065-z,,Adverse selection in iBuyer business models—don’t buy lemons!,2022.0,Zeitschrift für Immobilienökonomie,Eirik Helgaker; A. Oust; A. J. Pollestad,"The rise of instant buyer (iBuyer) businesses in the past years has made automated valuation models (AVMs) an important part of the property market. Although iBuyer services are in demand, large actors within the segment have reported dissatisfying profits over time. The business model is subject to adverse selection as homeowners based on their superior knowledge of their home are more likely to accept overpriced bids than underpriced bids, making the iBuyer purchase more overpriced dwellings. In this paper, we use a dataset consisting of 84,905 apartment transactions from Oslo, the Norwegian capital. We use 80% of the dataset to train three different AVMs similar to those used by iBuyers. Next, we construct some simple purchasing rules from the predictive accuracies found in the training dataset. Finally, taking the remaining 20% of the data in a test dataset, we introduce an adverse selection indicator based on accepted probability distributions and calculate the average expected resale profits per apartment for a hypothetical iBuyer. We find that adverse selection has a large negative impact on average profits for the hypothetical iBuyer. Furthermore, the simple purchasing rules are able to improve the profit by 1 percentage point per apartment when adverse selection is present.",7.0,https://link.springer.com/content/pdf/10.1365/s41056-022-00065-z.pdf,True,,,2022-12-05,f3bf12c7c903d2eae7ba97f32b506850ec57d136,adverseselectioninibuyerbusinessmodelsdontbuylemons,helgaker,10.1365/s41056-022-00065-z,adverseselectioninibuyerbusinessmodelsdontbuylemons|2022,1,2803,1,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f997df888ed6b26ae76370363c48e7bbc4000b13,10.1007/s42979-024-03578-7,,Optimizing Polynomial and Regularization Techniques for Enhanced Housing Price Prediction Accuracy,2025.0,SN Computer Science,Preethi; D. Murthy; Vani A. Hiremani; Raghavendra M. Devadas; R. Sapna,"This study investigates the effectiveness of various regression models for predicting housing prices using the California Housing dataset. The models evaluated include Linear Regression, Ridge Regression, Best Polynomial Ridge Regression, Lasso Regression, Elastic Net Regression, and Support Vector Regression (SVR) with an RBF kernel. The analysis reveals that SVR with an RBF kernel exhibits the poorest performance, characterized by the highest Mean Squared Error (MSE) and the lowest R² score, indicating limited effectiveness for this dataset. Conversely, Linear Regression, Ridge Regression, and Best Polynomial Ridge Regression demonstrate significantly lower MSE values and nearly identical R² scores, each explaining approximately 60% of the variance in housing prices. Among these, Best Polynomial Ridge Regression marginally outperforms Linear and Ridge Regression, suggesting that including polynomial features enhances model performance. Although Lasso Regression shows slightly higher MSE than the leading models, it still performs better than Elastic Net and SVR. Overall, the study identifies linear regression, ridge regression, and best polynomial ridge regression as the most effective models for this dataset, with SVR and an RBF kernel being the least effective.",12.0,https://link.springer.com/content/pdf/10.1007/s42979-024-03578-7.pdf,True,JournalArticle,Computer Science,2025-01-23,f997df888ed6b26ae76370363c48e7bbc4000b13,optimizingpolynomialandregularizationtechniquesforenhancedhousingpricepredictionaccuracy,preethi,10.1007/s42979-024-03578-7,optimizingpolynomialandregularizationtechniquesforenhancedhousingpricepredictionaccuracy|2025,1,2784,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",880bd6cddb821c525f1a2faee4bef1fa57f6fbab,10.1007/s42452-025-06922-5,,Optimizing multivariate adaptive regression splines (MARS) with coordinate descent to accurately select the best model for house price prediction,2025.0,Discover Applied Sciences,Ndagijimana Adonia; Waweru Mwangi; Kimwele Michael,"Multivariate Adaptive Regression Splines (MARS) have proved to be a powerful tool for modeling non-parametric regression analysis problems and for selecting the model in multi-dimensional data. A major challenge lies in model over-fitting and under-fitting on multi-dimensional data. Applying optimization methods to design and select the model can reduce the over-fitting and under-fitting in MARS modeling. Particularly, information-based model selection criteria have shown to be effective for modeling in MARS. This study proposes a model selection method in MARS using coordinate descent (CD-MARS) that can accurately select the best model. We have integrated coordinate descent (CD) features in the information-based model selection and evaluation criteria formulation. In this formulation, the CD was used as a penalty term to the negative log-likelihood in information-based model selection and evaluation criteria. To test the model, the study generated a dataset for testing the model and then the study applied house prices dataset that is publicly available online to evaluate the model’s success. The dataset have the variables that do and do not add to the dependent variable. We measure the performance of the proposed model using mean squared error (MSE), the Coefficient of Determination (Rsquared) and Mean absolute error. The results show that, the model’s mean squared error (MSE) was small compared to that of MARS. Additionally, the CD-MARS model scored the Rsquared of 0.931 (93.1%) whereas the traditional MARS score was 0.8952 (89.52%). Thus, the proposed model improves the traditional MARS by 3.58%. This indicates that the proposed model fits well the data compared to the traditional MARS and can produce good generalizability in MARS modeling. The optimized MARS model with coordinate descent can greatly improve the MARS performance and minimize the prediction error The material and finish quality, living area, Remodel date, the square feet and Bedrooms are important considerations while predicting the house sale price. The model minimizes the error and provides good generalizability compared to the existing approaches. The optimized MARS model with coordinate descent can greatly improve the MARS performance and minimize the prediction error The material and finish quality, living area, Remodel date, the square feet and Bedrooms are important considerations while predicting the house sale price. The model minimizes the error and provides good generalizability compared to the existing approaches.",1.0,https://link.springer.com/content/pdf/10.1007/s42452-025-06922-5.pdf,True,JournalArticle,,2025-08-25,880bd6cddb821c525f1a2faee4bef1fa57f6fbab,optimizingmultivariateadaptiveregressionsplinesmarswithcoordinatedescenttoaccuratelyselectthebestmodelforhousepriceprediction,adonia,10.1007/s42452-025-06922-5,optimizingmultivariateadaptiveregressionsplinesmarswithcoordinatedescenttoaccuratelyselectthebestmodelforhousepriceprediction|2025,1,4039,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1cbdf7f0f997ee3da2127dbb3c2e026c16ec662e,10.1007/s41060-025-00862-4,2312.06531,Uncertainty quantification in automated valuation models with spatially weighted conformal prediction,2023.0,International Journal of Data Science and Analysis,A. Hjort; G. Hermansen; Johan Pensar; Jonathan P. Williams,"Nonparametric machine learning models, such as random forests and gradient boosted trees, are frequently used to estimate house prices due to their predictive accuracy, but a main drawback of such methods is their limited ability to quantify prediction uncertainty. Conformal prediction (CP) is a model-agnostic framework for constructing confidence sets around predictions of machine learning models with minimal assumptions. However, due to the spatial dependencies observed in house prices, direct application of CP leads to confidence sets that are not calibrated everywhere, i.e., the confidence sets will be too large in certain geographical regions and too small in others. We survey various approaches to adjust the CP confidence set to account for this and demonstrate their performance on a data set from the housing market in Oslo, Norway. Our findings indicate that calibrating the confidence sets on a spatially weighted version of the non-conformity scores makes the coverage more consistently calibrated across geographical regions. We also perform a simulation study on synthetically generated sale prices to empirically explore the performance of CP on housing market data under idealized conditions with known data-generating mechanisms.",6.0,https://link.springer.com/content/pdf/10.1007/s41060-025-00862-4.pdf,True,JournalArticle;Review,Computer Science;Mathematics,2023-12-11,1cbdf7f0f997ee3da2127dbb3c2e026c16ec662e,uncertaintyquantificationinautomatedvaluationmodelswithspatiallyweightedconformalprediction,hjort,10.1007/s41060-025-00862-4,uncertaintyquantificationinautomatedvaluationmodelswithspatiallyweightedconformalprediction|2023,1,2755,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",e2ec7703f651dc597c7a3d060bea539c780c6126,10.1007/s11146-025-10030-x,,Comparing Housing Valuation Techniques and Stacked Generalization: Exploiting Explainable AI,2025.0,Journal of real estate finance and economics,Elias Bjørgve; A. Oust; A. J. Pollestad; Cato Sandnes; O. Sønstebø,"This study investigates the use of diverse model selections in stacked generalization to improve the predictive accuracy of Automated Valuation Models (AVMs). The research focuses on three commonly used valuation techniques: the Comparable Sales Method (CSM), the linear Least Absolute Deviation (LAD), and the nonlinear XGBoost (XGB). A dataset consisting of 164,619 apartment transactions from Oslo between 2008 and 2022 is utilized for testing, with 25% of the data used for out-of-sample predictions. While the stacked model combining XGB, CSM, and LAD achieves the best performance with a Median Absolute Percentage Error (MdAPE) of 5.17%, the individual XGB model performs nearly as well, achieving an MdAPE of 5.24%. Analysis reveals that stacking provides marginal improvements and primarily relies on XGB predictions. However, the computational cost of stacking raises questions about its practicality. The research highlights the limitations and benefits of different housing valuation techniques and varying data sizes, offering practical insights to enhance the performance of future AI-AVMs. By utilizing explainable AI, this study contributes to a better understanding of how different models collaborate and diverge, revealing their competitive advantages in house price valuation.",5.0,https://link.springer.com/content/pdf/10.1007/s11146-025-10030-x.pdf,True,JournalArticle,,2025-09-29,e2ec7703f651dc597c7a3d060bea539c780c6126,comparinghousingvaluationtechniquesandstackedgeneralizationexploitingexplainableai,bjørgve,10.1007/s11146-025-10030-x,comparinghousingvaluationtechniquesandstackedgeneralizationexploitingexplainableai|2025,1,2796,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e2bbd1830ab2ddb1b60e132bc7389b7b675579ce,10.1007/s11146-024-10002-7,,Towards a Better Uncertainty Quantification in Automated Valuation Models,2024.0,Journal of real estate finance and economics,A. J. Pollestad; Arild Brandrud Næss; A. Oust,"This study introduces a novel framework for quantifying prediction uncertainty in automated valuation models (AVMs), crucial tools in modern real estate finance. While non-linear AVMs excel in predictive performance, their limited methods for assessing prediction uncertainty reduces reliability and practical utility. We address this gap by proposing an approach for quantifying the uncertainty associated with predicted house prices and by introducing a model-specific AVM uncertainty estimate (AVMU) for AVM comparisons. Using a dataset of 51,747 historical apartment transactions in Oslo, Norway, we train three AVMs (XGBoost, random forest, support vector machine) to predict sales prices. Thereafter, we develop three base uncertainty estimators (direct loss estimation, bootstrap ensemble, quantile regression) and three meta estimators (average regressor, voting regressor, stacked generalization) for uncertainty quantification. Conformal calibration aligns the outputted uncertainty estimates from the six estimators with standard deviations of corresponding prediction errors. Having strong positive correlations with observed absolute prediction errors, the calibrated uncertainty estimators are shown to effectively capture prediction uncertainty. While the direct loss estimation excels among base estimators, the voting regressor and stacked generalization meta estimators consistently outperform it. Furthermore, by using the AVMU estimate from the stacked generalization meta estimator we can successfully identify the best-performing AVM for three separate apartment portfolios without knowing true sales prices. This alignment of the mean estimated prediction uncertainty with observed deviations underlines the utility of pre-factual AVMU estimates for model comparisons. In conclusion, our framework helps bridge prediction accuracy and uncertainty for AVMs, enhancing their reliability and supporting informed decision making for stakeholders.",13.0,https://link.springer.com/content/pdf/10.1007/s11146-024-10002-7.pdf,True,JournalArticle,,2024-10-25,e2bbd1830ab2ddb1b60e132bc7389b7b675579ce,towardsabetteruncertaintyquantificationinautomatedvaluationmodels,pollestad,10.1007/s11146-024-10002-7,towardsabetteruncertaintyquantificationinautomatedvaluationmodels|2024,1,3465,3,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic house price,41c05e4c190bfd812f576bb4bc53a0c147331a7d,10.1007/s11146-022-09915-y,,Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach,2022.0,Journal of real estate finance and economics,Juergen Deppner; Marcelo Cajias,"Data-driven machine learning algorithms have initiated a paradigm shift in hedonic house price and rent modeling through their ability to capture highly complex and non-monotonic relationships. Their superior accuracy compared to parametric model alternatives has been demonstrated repeatedly in the literature. However, the statistical independence of the data implicitly assumed by resampling-based error estimates is unlikely to hold in a real estate context as price-formation processes in property markets are inherently spatial, which leads to spatial dependence structures in the data. When performing conventional cross-validation techniques for model selection and model assessment, spatial dependence between training and test data may lead to undetected overfitting and overoptimistic perception of predictive power. This study sheds light on the bias in cross-validation errors of tree-based algorithms induced by spatial autocorrelation and proposes a bias-reduced spatial cross-validation strategy. The findings confirm that error estimates from non-spatial resampling methods are overly optimistic, whereas spatially conscious techniques are more dependable and can increase generalizability. As accurate and unbiased error estimates are crucial to automated valuation methods, our results prove helpful for applications including, but not limited to, mass appraisal, credit risk management, portfolio allocation and investment decision making.",38.0,https://link.springer.com/content/pdf/10.1007/s11146-022-09915-y.pdf,True,,,2022-07-13,41c05e4c190bfd812f576bb4bc53a0c147331a7d,accountingforspatialautocorrelationinalgorithmdrivenhedonicmodelsaspatialcrossvalidationapproach,deppner,10.1007/s11146-022-09915-y,accountingforspatialautocorrelationinalgorithmdrivenhedonicmodelsaspatialcrossvalidationapproach|2022,1,2959,5,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic property valuation",2,2,1,2,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",124378e49dbbe86259a0143b4e7ead1831faca2f,10.1007/s11146-021-09861-1,,Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM),2021.0,Journal of real estate finance and economics,T. Sing; J. Yang; Shi-Ming Yu,"This paper develops an artificial intelligence based automated valuation model (AI-AVM) using the boosting tree ensemble technique to predict housing prices in Singapore. We use more than 300,000 private and public housing transactions in Singapore for the period from 1995 to 2017 in the training of the AI-AVM models. The boosting model is the best predictive model that produce the most robust and accurate predictions for housing prices compared to the decision tree and multiple regression analysis (MRA) models. The boosting AI-AVM models explain 91.33% and 94.28% of the price variances, and keep the mean absolute percentage errors at 8.55% and 5.34% for the public housing market and the private housing market, respectively. When subject the AI-AVM to the out-of-sample forecasting using the 2018 housing sale samples, the prediction errors remain within a narrow range of between 5% and 9%.",41.0,https://link.springer.com/content/pdf/10.1007/s11146-021-09861-1.pdf,True,JournalArticle,Medicine,2021-11-05,124378e49dbbe86259a0143b4e7ead1831faca2f,boostedtreeensemblesforartificialintelligencebasedautomatedvaluationmodelsaiavm,sing,10.1007/s11146-021-09861-1,boostedtreeensemblesforartificialintelligencebasedautomatedvaluationmodelsaiavm|2021,1,2401,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c88fa0cc44592135ad5aed6d68e1d50d4fefe425,10.1007/s11146-019-09712-0,,Appraisal Accuracy and Automated Valuation Models in Rural Areas,2020.0,Journal of real estate finance and economics,A. Bogin; Jessica Shui,"Accurate and unbiased property value estimates are essential to credit risk management. Along with loan amount, they determine a mortgage’s loan-to-value ratio, which captures the degree of homeowner equity and is a key determinant of borrower credit risk. For home purchases, lenders generally require an independent appraisal, which, in addition to a home’s sales price, is used to calculate a value for the underlying collateral. A number of empirical studies have shown that property appraisals tend to be biased upwards, and over 90 percent of the time, either confirm or exceed the associated contract price. Our data suggest that appraisal bias is particularly pervasive in rural areas where over 25 percent of rural properties are appraised at more than five percent above contract price. Given this significant upward bias, we examine a host of alternate valuation techniques to more accurately estimate rural property values.",42.0,https://link.springer.com/content/pdf/10.1007/s11146-019-09712-0.pdf,True,,Business,2020-02-01,c88fa0cc44592135ad5aed6d68e1d50d4fefe425,appraisalaccuracyandautomatedvaluationmodelsinruralareas,bogin,10.1007/s11146-019-09712-0,appraisalaccuracyandautomatedvaluationmodelsinruralareas|2020,1,2435,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",b5db3b0406a10459ffc13d01ce60ae766927928b,10.1007/s10901-024-10153-2,,Scalable multimodal assessment of the micro-neighborhood using orthogonal visual inputs,2024.0,Journal of Housing and the Built Environment,Miroslav Despotovic; Wolfgang A. Brunauer,"The features of the micro-location and in particular the micro-neighborhood that residents perceive on a daily basis have a considerable influence on the quality of living and also on housing prices. For automated valuation models (AVMs), the use of micro-neighborhood information would be beneficial, as incorporating additional spatial effects into the price estimate could potentially reduce the empirical error. However, measuring related features is difficult, as they must first be defined and then collected, which is extremely challenging at such a small spatial level. In this study, we investigate the extent to which the quality of micro-neighborhoods can be assessed holistically using multiple data modalities. We design a scalable approach using alternative data (images and text), with the potential to expand coverage to other urban regions. To achieve this, we propose a multimodal deep learning architecture that integrates both textual and visual inputs and fuses this information. In addition, we introduce a training strategy that enables a targeted fusion of orthogonal visual representations of the residential area within the model architecture. In our experiments, we test and compare different unimodal models with our multimodal architectures. The results demonstrate that the multimodal model with targeted fusion of the orthogonal visual inputs achieves the best performance and also improves the prediction accuracy for underrepresented location quality classes.",3.0,https://link.springer.com/content/pdf/10.1007/s10901-024-10153-2.pdf,True,JournalArticle,,2024-08-19,b5db3b0406a10459ffc13d01ce60ae766927928b,scalablemultimodalassessmentofthemicroneighborhoodusingorthogonalvisualinputs,despotovic,10.1007/s10901-024-10153-2,scalablemultimodalassessmentofthemicroneighborhoodusingorthogonalvisualinputs|2024,1,2992,5,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"") || automated valuation model residential",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,automated valuation model residential,780edd13060402b09a553ce0bdd0248a2710348c,10.1007/s00168-023-01212-7,,Machine learning approach to residential valuation: a convolutional neural network model for geographic variation,2023.0,The annals of regional science,Ho-Jun Lee; Hoon Han; C. Pettit; Qishuo Gao; Vivien Shi,"Geographic location and neighbourhood attributes are major contributors to residential property values. Automated valuation models (AVM) often use hedonic pricing with location and neighbourhood attributes in the form of numeric and categorical variables. This paper proposed a novel approach to automated property valuation using a machine learning model with a convolutional neural network (CNN), fully connected neural network layers with numeric and categorical variables. In this study we compare the results of a fused model, which treat geographical data as an input with the performance of the baseline neural network model with only numerically or categorically represented data. Furthermore, the residential valuation by the proposed fused model was tested with actual sold price data in Greater Sydney, Australia. The study found that the fused model produced valuations with a significantly lower mean absolute percentage error (MAPE) (8.71%) than the MAPE of the baseline model (11.59%). The results show that the fused model with CNN significantly improves the accuracy for residential valuation, reducing spatial information loss by data manipulation and distance calibration.",30.0,https://link.springer.com/content/pdf/10.1007/s00168-023-01212-7.pdf,True,,,2023-02-27,780edd13060402b09a553ce0bdd0248a2710348c,machinelearningapproachtoresidentialvaluationaconvolutionalneuralnetworkmodelforgeographicvariation,lee,10.1007/s00168-023-01212-7,machinelearningapproachtoresidentialvaluationaconvolutionalneuralnetworkmodelforgeographicvariation|2023,1,2691,5,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || automated valuation model residential || hedonic property valuation",2,1,1,5,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",bd917fbc268f603e65254282c54b6f02bea23e74,10.56578/ataiml010103,,House Price Prediction Using Exploratory Data Analysis and Machine Learning with Feature Selection,2022.0,Acadlore Transactions on AI and Machine Learning,F. M. Basysyar; Gifthera Dwilestari,"In many real-world applications, it is more realistic to predict a price range than to forecast a single value. When the goal is to identify a range of prices, price prediction becomes a classification problem. The House Price Index is a typical instrument for estimating house price discrepancies. This repeat sale index analyzes the mean price variation in repeat sales or refinancing of the same assets. Since it depends on all transactions, the House Price Index is poor at projecting the price of a single house. To forecast house prices effectively, this study investigates the exploratory data analysis based on linear regression, ridge regression, Lasso regression, and Elastic Net regression, with the aid of machine learning with feature selection. The proposed prediction model for house prices was evaluated on a machine learning housing dataset, which covers 1,460 records and 81 features. By comparing the predicted and actual prices, it was learned that our model outputted an acceptable, expected values compared to the actual values. The error margin to actual values was very small. The comparison shows that our model is satisfactory in predicting house prices.",14.0,https://library.acadlore.com/ATAIML/2022/1/1/ATAIML_01.01_03.pdf,True,,,2022-11-20,bd917fbc268f603e65254282c54b6f02bea23e74,housepricepredictionusingexploratorydataanalysisandmachinelearningwithfeatureselection,basysyar,10.56578/ataiml010103,housepricepredictionusingexploratorydataanalysisandmachinelearningwithfeatureselection|2022,1,2680,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",fb5183981e725691995dd9c6147fa08225de7143,10.56294/la2025153,,Revolutionizing Home Price Forecasting Through Machine Learning,2025.0,Land and Architecture,Awais Azam; S. Rai,"Introduction; This study develops a data-driven framework for accurate house price prediction using machine learning techniques. Method; We implement a comprehensive methodology involving rigorous data preprocessing, exploratory visualization through multiple chart types, and comparative evaluation of predictive models. Our approach demonstrates the effectiveness of combining analytical visualization with algorithmic modeling for real estate valuation. Result; The research contributes to both academic discourse and practical applications by establishing robust data cleaning protocols and validating model performance. Results indicate significant improvements in prediction accuracy, offering valuable insights for homeowners, investors, and urban planners. Conclusion; This work advances the field of property analytics while providing a replicable methodology for housing market analysis in different socioeconomic contexts.",0.0,https://la.ageditor.ar/index.php/la/article/download/153/44,True,JournalArticle,,2025-05-04,fb5183981e725691995dd9c6147fa08225de7143,revolutionizinghomepriceforecastingthroughmachinelearning,azam,10.56294/la2025153,revolutionizinghomepriceforecastingthroughmachinelearning|2025,1,2433,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0f0a978a642f206a007b1a44aa36b3ff13fd2f6e,10.52436/1.jutif.2026.7.3.5517,,Hybrid Unsupervised-Supervised Learning for Housing Submarket Segmentation and Price Prediction in Surabaya Urban Areas,2026.0,Jurnal Teknik Informatika (Jutif),Rinabi Tanamal; S. Nugraha; Nathalia Minoque Kusuma Salma Rasyid; Livanty Efatania Dendy; Jessica Theijer,"Surabaya’s rapid population growth, reaching 3.02 million residents, has intensified housing affordability challenges and increased structural variability in residential markets. This study proposes a hybrid machine learning framework that combines unsupervised clustering with supervised classification to identify submarket segments and predict housing price categories. A dataset of 490 properties containing structural, land, ownership, and contextual features was preprocessed and analyzed using K-Means. Cluster quality assessment through elbow inspection and a silhouette score of 0.45 indicated the presence of five meaningful market segments. These segments served as targets for a supervised classification stage that evaluated seven models, optimized via randomized hyperparameter search within a standardized preprocessing pipeline. The RBF-SVM achieved the strongest performance, reaching 97 percent accuracy and a macro-F1 score of 0.97, representing an 8 percent improvement over non-hybrid baselines and outperforming boosted ensembles such as XGBoost. Permutation importance analysis identified number of floors, building orientation, position rank, and ownership status as dominant drivers of segment differentiation. The integration of clustering and classification enhances predictive reliability while improving interpretability, offering a transparent analytical toolkit for housing market assessment. The proposed framework provides actionable insights for developers, appraisers, and policymakers in Surabaya, enabling data-driven identification of submarkets and supporting more equitable housing strategies aligned with SDG 11 on sustainable urban development. The approach is scalable to other Indonesian cities and establishes a foundation for future work incorporating spatial, socioeconomic, or temporal predictors.",0.0,https://jutif.if.unsoed.ac.id/index.php/jurnal/article/download/5517/1249,True,JournalArticle,,2026-06-15,0f0a978a642f206a007b1a44aa36b3ff13fd2f6e,hybridunsupervisedsupervisedlearningforhousingsubmarketsegmentationandpricepredictioninsurabayaurbanareas,tanamal,10.52436/1.jutif.2026.7.3.5517,hybridunsupervisedsupervisedlearningforhousingsubmarketsegmentationandpricepredictioninsurabayaurbanareas|2026,1,3345,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0faf41b3a48225a17edc93450012d54be60cbc48,10.37676/jmcs.v5i3.11579,,Prediksi Harga Kos di Wilayah Jimbaran Menggunakan Algoritma Decision Tree Regression Berbasis Web,2026.0,Jurnal Media Computer Science,I. Putu Krishna Paradipta; P. Prihatini; Ni Nyoman Harini Puspita,"Jimbaran District is one of the strategic areas in Badung Regency, Bali, experiencing rapid growth in the education, tourism, and economic sectors. This condition has increased the demand for temporary housing, especially boarding houses for students and migrant workers. The large number of boarding house options with various facilities and rental prices often makes it difficult for prospective tenants to choose accommodations that match their needs and financial capabilities. This study aims to develop a web-based boarding house price prediction application using the Decision Tree Regression algorithm to assist users in estimating boarding house prices based on the desired facilities. The research applies a quantitative approach using machine learning and data mining methods. The dataset consists of boarding house data in the Jimbaran area, including room size, bathroom type, room facilities, public facilities, distance to campus, and monthly rental price. The research stages include data preprocessing, feature transformation, dataset splitting using the Train-Test Split method, model training, and model evaluation using MAE, RMSE, and R² metrics. The results show that the Decision Tree Regression algorithm achieved good performance with an R² value of 0.8332, an MAE value of IDR 225,462, and an RMSE value of IDR 302,509.",0.0,https://jurnal.unived.ac.id/index.php/jmcs/article/download/11579/8544,True,JournalArticle,,2026-07-22,0faf41b3a48225a17edc93450012d54be60cbc48,prediksihargakosdiwilayahjimbaranmenggunakanalgoritmadecisiontreeregressionberbasisweb,paradipta,10.37676/jmcs.v5i3.11579,prediksihargakosdiwilayahjimbaranmenggunakanalgoritmadecisiontreeregressionberbasisweb|2026,1,2844,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ee55b5018946c3740b08987e6ce1cb0beb52cbd2,10.30871/jaic.v10i1.10718,,The Comparative Analysis of K-Nearest Neighbors Algorithm and Random Forest Regressor for House Price Prediction in Bandung City,2026.0,JOURNAL OF APPLIED INFORMATICS AND COMPUTING,Dimas Yudhistira Ananda; Ferian Fauzi Abdulloh,"The rapid population growth and continuous urban expansion in Bandung have contributed to volatile and escalating housing prices, creating significant challenges for market transparency and affordability. This study aims to develop and evaluate machine-learning models to predict house prices in the Bandung region using a publicly available dataset consisting of 7,609 property records. Following the CRISP-DM methodology, the research includes data exploration, preprocessing (outlier handling using IQR, one-hot encoding, and feature standardization), model training, and performance evaluation. Two regression models K-Nearest Neighbors (KNN) Regressor and Random Forest (RF) Regressor—were compared through systematic hyperparameter tuning using Grid Search and Random Search techniques. The experimental results show that the Random Forest Regressor achieves the best performance with an R² score of 0.7838 and a mean absolute error (MAE) of approximately Rp 399.7 million, outperforming the optimized KNN model. Feature importance analysis also indicates that land area, building area, and location are the most influential predictors of property prices. The findings highlight the effectiveness of ensemble methods in handling complex real-estate data and demonstrate the potential of machine-learning-based predictive tools to support buyers, sellers, and policymakers in making informed and data-driven decisions in the Bandung housing market.",0.0,https://jurnal.polibatam.ac.id/index.php/JAIC/article/download/10718/3427,True,JournalArticle,,2026-02-10,ee55b5018946c3740b08987e6ce1cb0beb52cbd2,thecomparativeanalysisofknearestneighborsalgorithmandrandomforestregressorforhousepricepredictioninbandungcity,ananda,10.30871/jaic.v10i1.10718,thecomparativeanalysisofknearestneighborsalgorithmandrandomforestregressorforhousepricepredictioninbandungcity|2026,1,2953,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",876eae43cf96a7dfa471d309316629469062911d,10.65227/jaia.v1i1.5,,PREDIKSI HARGA REAL ESTATE MENGGUNAKAN METODE REGRESI LINEAR BERBASIS MACHINE LEARNING,2025.0,Journal of Artificial Intelligence Application,Nanda Khalijah Zainal,"Abstrak: Kenaikan dan penurunan harga real estate yang cepat dan tidak stabil dapat berdampak pada pertumbuhan pendapatan masyarakat, akses perumahan, dan menciptakan ketidaksetaraan pembangunan antar wilayah. Diperlukan upaya prediksi untuk meminimalisir fluktuasi harga real estate yang tidak stabil. Kebijakan regulasi, seperti pembatasan spekulasi dan pemantauan ketat terhadap peminjaman berisiko, diperlukan untuk menjaga stabilitas pasar. Prediksi menggunakan metode regresi dimana dalam metode regresi terdapat 7 metode lagi meliputi (1) Linear Regression, (2) Support Vector Regression – Linear, (3) Support Vector Regression – RBF, (4) Decision Tree Regression, (5) Random Forest Regressor, (6) Gradient Boosting Regression, (7) NLP Regressor yang diterapkan dalam penelitian ini. Berdasarkan 7 metode regresi tersebut akan dicari 1 metode terbaik dengan nilai akurasi paling terbaik yang akan digunakan dalam proses pengolahan deploy untuk memprediksi harga rumah per unitnya. Hasilnya metode MLP-Regressor keluar sebagai metode terbaik karena metode tersebut paling banyak menghasilkan nilai akurasi terbaik pada saat pengujian akurasi dengan menggunakan 4 pengujian akurasi dengan rasio 90:10, 80:20, 70:30 dan 60:40. MLP-Regressor pada pengujian akurasi rasio 80:20 menghasilkan 2 nilai akurasi terbaik yaitu nilai estimasi pada MSE sebesar 1.00796350 dan pada RMSE sebesar 1.00397386. Kemudian pada rasio 70:30 MLP-Regressor menghasilkan 1.43239002 dengan nilai pengukuran MSE, 1.19682498 dengan RMSE. Kemudian pada rasio 60:40 MLP-Regressor menghasilkan 1.74633185 dengan nilai pengukuran MSE, 1.32148850 dengan RMSE. Dengan demikian metode regresi yaitu MLP-Regressor digunakan sebagai metode regresi yang akan di implementasikan dalam pembuatan deploy aplikasi untuk memprediksi harga rumah per unitnya. Abstract: Rapid and unstable increases and decreases in real estate prices can impact people's income growth, access to housing, and create development inequality between regions. Predictive efforts are needed to minimize unstable real estate price fluctuations. Regulatory policies, such as restrictions on speculation and strict monitoring of risky lending, are necessary to maintain market stability. Predictions use the regression method where in the regression method there are 7 more methods including (1) Linear Regression, (2) Support Vector Regression – Linear, (3) Support Vector Regression – RBF, (4) Decision Tree Regression, (5) Random Forest Regressor , (6) Gradient Boosting Regression, (7) NLP Regressor applied in this research. Based on the 7 regression methods, we will look for the best method with the best accuracy value which will be used in the deploy processing process to predict house prices per unit. As a result, the MLP-Regressor method came out as the best method because this method produced the best accuracy values when testing accuracy using 4 accuracy tests with ratios of 90:10, 80:20, 70:30 and 60:40. The MLP-Regressor in the 80:20 ratio accuracy test produces the 2 best accuracy values, namely the estimated value for MSE of 1.00796350 and RMSE of 1.00397386. Then at a ratio of 70:30 MLP-Regressor produces 1.43239002 with MSE measurement values, 1.19682498 with RMSE. Then at a ratio of 60:40 MLP-Regressor produces 1.74633185 with MSE measurement values, 1.32148850 with RMSE. Thus, the regression method, namely MLP-Regressor, is used as a regression method that will be implemented in deploying applications to predict house prices per unit. ",0.0,https://jurnal.mutiaraamaliyah.com/index.php/jaia/article/download/5/9,True,JournalArticle,,2025-10-29,876eae43cf96a7dfa471d309316629469062911d,prediksihargarealestatemenggunakanmetoderegresilinearberbasismachinelearning,zainal,10.65227/jaia.v1i1.5,prediksihargarealestatemenggunakanmetoderegresilinearberbasismachinelearning|2025,1,5018,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",09777faa14aafa05fd9004b1e1abf68bf20a7c8a,10.47233/jsit.v4i1.1464,,Rancang Bangun Sistem Prediksi Nilai Jual Properti Berbasis Web Menggunakan Regresi Linear,2024.0,Jurnal Sains dan Teknologi (JSIT),Jordi Septriaznu,"This research was conducted to provide a clearer and more accurate depiction of the property value at Ziva Graha Property based on the physical characteristics and location of the houses. The study utilized PHP for data processing and modeling. The data employed included information on land area, building area, year of construction, number of floors, number of bedrooms, number of bathrooms, number of garages, and selling prices. This data was explored and processed using PHP software to assess the efficacy of linear regression methods in predicting house values. The research findings indicate that variables such as land area, building area, and number of bedrooms significantly influence the house's selling price at Ziva Graha Property. Other variables, such as the number of floors, number of bathrooms, distance to the city center, and road width, have a smaller impact. It is expected that the results of this research can provide a useful guide for prospective buyers and sellers in determining fair and realistic prices based on the physical characteristics of the houses.",3.0,https://jurnal.minartis.com/index.php/jsit/article/download/1464/1269,True,JournalArticle,,2024-01-14,09777faa14aafa05fd9004b1e1abf68bf20a7c8a,rancangbangunsistemprediksinilaijualpropertiberbasiswebmenggunakanregresilinear,septriaznu,10.47233/jsit.v4i1.1464,rancangbangunsistemprediksinilaijualpropertiberbasiswebmenggunakanregresilinear|2024,1,2586,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",549490db5c5b3276e86f1772d9e5a45101a64483,10.59697/jik.v7i2.135,,ANALISIS PREDIKSI HARGA RUMAH DI JABODETABEK MENGGUNAKAN MULTIPLE LINEAR REGRESSION,2023.0,Jurnal Informatika Kaputama (JIK),Inayatul Maula; L. Hasanah; A. Tholib,"The economic and infrastructure development in the Jabodetabek area has increased the rate of population growth in the region continues to increase. This attracted the attention of property agents in the housing price competition in the area, considering that the demand for housing will also continue to increase. There are several kinds of specificities and house prices in the Jabodetabek area, so an appropriate analysis is needed to get the desired house in this study the researcher implements the Multiple Linear method Regression to predict house prices with the desired specifications. This study used a total dataset of 3553 data with 11 variables obtained from the Kaggle.com. website The data will be through the pre-pronce process of data before training the model. Furthermore, at the evaluation stage using a test matrix, namely CC (Coefficient of Correlation), MAE (Met Absolute Error), RMSE (Root Met Square Error) is used to assess the performance of the model. Based on the results of the analysis from the dataset were 965 data, where the data was divided into two parts, namely, 80% as training data and 20% as testing data from the analysis results were obtained at an accuracy rate of 0.85 or 85% with an error rate of MAE of 375428909.51 and the RMSE level of 516385,50. ",4.0,https://jurnal.kaputama.ac.id/index.php/JIK/article/download/135/101,True,JournalArticle,,2023-07-11,549490db5c5b3276e86f1772d9e5a45101a64483,analisisprediksihargarumahdijabodetabekmenggunakanmultiplelinearregression,maula,10.59697/jik.v7i2.135,analisisprediksihargarumahdijabodetabekmenggunakanmultiplelinearregression|2023,1,2796,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",104bd4a8f4a36a1959c10f8c9721691d25a69344,10.29207/resti.v9i4.6619,,Comparing Optimization Algorithms in ANN Models for House Price Prediction in Pekanbaru,2025.0,Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi),Doni Winarso; Edo Arribe; Syahril; Aryanto; Muhardi; S. Musa,"This study evaluates the performance of five optimization algorithms in Artificial Neural Network (ANN) models for predicting house prices in Pekanbaru. The optimizers tested include Adam, AdaDelta, Stochastic Gradient Descent (SGD), Nadam, and Adaptive Sharpness-Aware Minimization (ASAM). A total of 3,149 house sales records were collected from rumah123.com between January and December 2024. After cleaning 148 incomplete entries, 3,001 valid records remained. The dataset included seven features: price, location, number of bedrooms, number of bathrooms, land area, building area, and garage capacity, with the location encoded using one-hot encoding. The research involved a literature review, problem formulation, data acquisition, preprocessing, model development, and evaluation. Model performance was assessed using the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). The results show that SGD consistently achieved the best performance, particularly at a 90:10 train-test split, with the lowest MAPE (1.74%) and MSE (0.3279). Adam and Nadam also performed well, while ASAM had the highest error (MAPE 6.14%). These findings indicate that SGD was the most effective optimizer for this dataset. Future research should explore larger datasets and advanced hyperparameter tuning to improve the generalizability of this model.",1.0,https://jurnal.iaii.or.id/index.php/RESTI/article/download/6619/1109,True,JournalArticle;Review,,2025-08-17,104bd4a8f4a36a1959c10f8c9721691d25a69344,comparingoptimizationalgorithmsinannmodelsforhousepricepredictioninpekanbaru,winarso,10.29207/resti.v9i4.6619,comparingoptimizationalgorithmsinannmodelsforhousepricepredictioninpekanbaru|2025,1,2885,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",dd785bf30fa4e655690ff985a8b4e95f050b6a93,10.52436/1.jpti.846,,Optimasi Prediksi Harga Rumah dengan Random Forest dan Optuna Hyperparameter Tuning,2025.0,Jurnal Pendidikan dan Teknologi Indonesia,Reza Muhammad Fadzryan; Eka Angga Laksana,"Prediksi harga rumah merupakan aspek penting dalam pengambilan keputusan investasi di sektor properti. Penelitian ini bertujuan untuk membandingkan beberapa algoritma Machine Learning dan Deep Learning dalam memprediksi harga rumah, serta mengoptimalkan Random Forest Regressor menggunakan Optuna dengan implementasi Tree-structured Parzen Estimators (TPE). Dataset yang digunakan adalah House Price 2023 Dataset dari Kaggle, yang mencakup 168.000 entri data properti di Pakistan. Metodologi penelitian ini meliputi tahap preprocessing data, rekayasa fitur, serta penerapan beberapa algoritma prediksi, yaitu Artificial Neural Networks (ANN) dengan model Feedforward Neural Network, KNeighborsRegressor, Linear Regression, dan Random Forest Regressor. Model-model ini dievaluasi menggunakan metrik MAE, MSE, RMSE, R-squared, dan Akurasi. Random Forest Regressor memberikan hasil terbaik dengan R-squared 0.91 dan Akurasi 91.33%. Untuk meningkatkan performa model, diterapkan optimasi hyperparameter menggunakan Optuna dengan pendekatan TPE yang berbasis Bayesian Optimization. Hasil model yang dioptimalkan mencapai peningkatan performa dengan R-squared 0.92 dan akurasi 91.75%. Hasil ini menunjukkan bahwa optimasi hyperparameter menggunakan Optuna berbasis Bayesian Optimization dapat meningkatkan akurasi prediksi harga rumah yang dapat diaplikasikan dalam analisis investasi properti.",0.0,https://jpti.journals.id/index.php/jpti/article/download/846/508,True,JournalArticle,,2025-06-26,dd785bf30fa4e655690ff985a8b4e95f050b6a93,optimasiprediksihargarumahdenganrandomforestdanoptunahyperparametertuning,fadzryan,10.52436/1.jpti.846,optimasiprediksihargarumahdenganrandomforestdanoptunahyperparametertuning|2025,1,2888,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c447ce6545a0c5a0b667134b5e77e961a7074a73,10.26577/jpcsit2025338,,Forecast of Housing Prices in Almaty Using Machine Learning Algorithms,2025.0,Journal of Problems in Computer Science and Information Technologies,A. Rakhimzhanov; Jelena V. Caiko,"Precise prediction of housing values is an important task for various stakeholders involved in the housing market, including investors, builders, and city planners. In this research, supervised machine learning models are used to predict the price of apartments in Almaty, Kazakhstan, which is a dynamic urban market in Central Asia. With an openly available dataset of apartments for sale, Linear Regression, Lasso Regression, Random Forest, and XGBoost models are implemented and tested. The data is scaled and encoded with scalable pipelines, and models are evaluated with regards to Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² Score. The best performing model amongst those tested was Random Forest Regressor with an R² of 0.9158, followed by XGBoost with 0.8438. Feature importance visualization identifies district, area, and construction year as primary influencing factors. The research supports that ensembling machine learning models are efficient and scalable predictors for housing forecasts and suggests future improvements with time-series and geospatial features",0.0,https://jpcsit.kaznu.kz/index.php/kaznu/article/download/209/83,True,JournalArticle,,2025-10-02,c447ce6545a0c5a0b667134b5e77e961a7074a73,forecastofhousingpricesinalmatyusingmachinelearningalgorithms,rakhimzhanov,10.26577/jpcsit2025338,forecastofhousingpricesinalmatyusingmachinelearningalgorithms|2025,1,2599,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d2206cc8f09c37c83dc24c28a3073557f17ff68c,10.3846/1648715x.2016.1259190,,Forecasting spatial dynamics of the housing market using Support Vector Machine,2017.0,,Jieh-Haur Chen; Chuan-Fang Ong; Lin-Zi Zheng; Shu-Chien Hsu,"This paper adopts a novel approach of Support Vector Machine (SVM) to forecast residential housing prices. as one type of machine learning algorithm, the proposed SVM encompasses a larger set of variables that are recognized as price-influencing and meanwhile enables recognizing the geographical pattern of housing price dynamics. The analytical framework consists of two steps. The first step is to identify the supporting vectors (SVs) to price variances using the stepwise multi-regression approach; and then it is to forecast the housing price variances by employing the SVs identified by the first step as well as other variables postulated by the hedonic price theory, where the housing prices in Taipei City are empirically examined to verify the designed framework. Results computed by nonparametric estimation confirm that the prediction power of using SVM in housing price forecasting is of high accuracy. Further studies are suggested to extract the geographical weights using kernel density estimates to reflect price responses to local quantiles of hedonic attributes.",82.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/1802/1424,True,,Economics,2017-07-11,d2206cc8f09c37c83dc24c28a3073557f17ff68c,forecastingspatialdynamicsofthehousingmarketusingsupportvectormachine,chen,10.3846/1648715x.2016.1259190,forecastingspatialdynamicsofthehousingmarketusingsupportvectormachine|2017,1,2582,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices || machine learning hedonic pricing housing",2,1,2,4,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0e3ac967c407c31a730846c08adb56ac5403d0e5,10.3846/ijspm.2022.15962,,REAL ESTATE VALUATION MODELS PERFORMANCE IN PRICE PREDICTION,2022.0,International Journal of Strategic Property Management,Adela Deaconu; Anuța Buiga; Helga Tothăzan,"Using a sample of 900 apartments from Cluj-Napoca, Romania, containing selling transactions for the second semester of 2019, and data for 33 locational, physical and neighbourhood-related attributes (socio-cultural, environmental, and urbanism related), our research objective is to test the performance in price prediction, and hence the utility, of the Artificial Neural Networking (ANN), as artificial intelligence model versus the Generalized Linear Model (GLM), as a regression model. By contributing to an ongoing debate, our empirical findings confirm the results of a predominant group of earlier studies, namely the superiority of ANN. Precisely, we found that ANN can better predict selling prices and provides stability of results. Additionally, we addressed the critiques related to the transparency of results, showing that ANN also has the ability to illustrate the significance of the different attributes of real estate, if appropriate statistical indicators are used. These findings can serve the different real estate valuation purposes, including that of the review of valuation reports.",14.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/15962/10893,True,Review,,2022-02-11,0e3ac967c407c31a730846c08adb56ac5403d0e5,realestatevaluationmodelsperformanceinpriceprediction,deaconu,10.3846/ijspm.2022.15962,realestatevaluationmodelsperformanceinpriceprediction|2022,1,2606,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f967745d6170767bb9697ec985dd4f2b299f9376,10.15641/jarer.v11i1.2029,,Comparative Performance of Selected Machine Learning Algorithms in Predicting Residential Property Price Across Sub-Markets in Lagos Metropolis,2026.0,Journal of African Real Estate Research,T. Ashaolu; Amos Adewusi; A. Oyelakin,"Traditional techniques of property valuation are increasingly criticised for their subjectivity, vulnerability to data insufficiency, and other discrepancies, thereby fuelling a methodological shift towards automated and data-driven valuation frameworks. Hence, this study explores three selected machine learning algorithms - Random Forest (RF), Bayesian Ridge (BR), and LASSO Regression – in estimating residential property prices across three distinct sub-markets within Lagos Metropolis. The study seeks to identify the algorithm that achieves the highest predictive precision and to examine the extent to which sub-market heterogeneity affects model reliability. The dataset utilised for the study comprised 469 residential property transactions concluded over the years by thirteen registered Estate Surveying and Valuation firms operating within the Metropolis. The results revealed varied performance across the sub-markets, with Random Forest emerging as the most efficient model, demonstrating the model’s robustness and adaptability to nonlinear property data and spatial variability. The Bayesian Ridge Regression model produced moderate results, performing reliably in sub-markets characterised by stable property structures, while LASSO Regression yielded the least accuracy, owing to its sensitivity to linear assumptions and coefficient shrinkage. By situating algorithmic comparison within a spatially segmented urban framework, this research contributes to the development of location-sensitive, data-driven valuation models that can enhance valuation practice, improve investor confidence, and promote greater transparency and efficiency in Nigeria’s property market.",0.0,https://journals.uct.ac.za/index.php/JARER/article/download/2029/1274,True,JournalArticle;Review,,2026-06-25,f967745d6170767bb9697ec985dd4f2b299f9376,comparativeperformanceofselectedmachinelearningalgorithmsinpredictingresidentialpropertypriceacrosssubmarketsinlagosmetropolis,ashaolu,10.15641/jarer.v11i1.2029,comparativeperformanceofselectedmachinelearningalgorithmsinpredictingresidentialpropertypriceacrosssubmarketsinlagosmetropolis|2026,1,3186,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || automated valuation model residential",0,1,1,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d9076a1590ae075b8ad8d8be3b1df7d65bd269d6,10.1177/21582440221079916,,How on Earth Did Spanish Banking Sell the Housing Stock?,2022.0,SAGE Open,José Torres-Pruñonosa; Pablo García-Estévez; J. Raya; Camilo Prado-Román,"The accumulation of properties by Spanish banks during the crisis of the first decade of the 21st century has definitely changed the housing market. An optimal house price valuation is useful to determine the bank’s actual financial situation. Furthermore, properties valued according to the market can be sold in a shorter span of time and at a better price. Using a sample of 24,781 properties and a simulation exercise, we aim to identify the decision criteria that Spanish banking used to decide which properties were going to be sold and at what price. The results of the comparison among four methods used to value real estate—artificial neural networks, semi log regressions, a combined model by means of weighted least squares regression, and quantile regressions—and the actual situation suggest that banking aimed to maximize the reversal of impairment losses, although this would mean capital losses, selling less properties, and decreasing their revenues. Therefore, the actual combined result was very detrimental to banking and, consequently, to the Spanish society because of its banking bailout.",6.0,https://journals.sagepub.com/doi/pdf/10.1177/21582440221079916,True,JournalArticle,,2022-01-01,d9076a1590ae075b8ad8d8be3b1df7d65bd269d6,howonearthdidspanishbankingsellthehousingstock,torres-pruñonosa,10.1177/21582440221079916,howonearthdidspanishbankingsellthehousingstock|2022,1,2611,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",57a52907519ba9ffcaec92bb4a656bdb885b4c64,10.22363/2312-8143-2025-26-3-258-265,,Regression Neural Networks Advantage over Classical Regression Analysis,2025.0,RUDN Journal of Engineering Researches,O. Saltykova; V. D. Saushkin,"In this study, two analyzing methods are used to predict housing prices in California: neural network forecasting methods and methods based on regression analysis. Using the example of individual forecast indicators produced on the basis of two methods, the forecast results are compared. The purpose of this study is to show that the accuracy of prediction by neural networks is higher than that of the classical method. The assessment is carried out by creating a product in Python, which was chosen for reasons of ease of implementation of this analysis, ease of implementation of the product, as well as ease of constructing a graphical analysis of the results obtained. An open data source consisting of sixteen thousand items, which includes a number of housing criteria and prices based on these criteria, was used as resources for training the neural network. A broad review of studies comparing the predictive performance of artificial neural network-based methods and other forecasting methods is conducted. Much attention is paid to comparing artificial neural network methods and linear regression methods. Based on the results of this study, it was revealed that the accuracy of the neural network model is much higher when predicting results using linear regression methods, depending on the introduction of new forecasting criteria.",0.0,https://journals.rudn.ru/engineering-researches/article/download/47076/25830,True,JournalArticle;Review,,2025-11-11,57a52907519ba9ffcaec92bb4a656bdb885b4c64,regressionneuralnetworksadvantageoverclassicalregressionanalysis,saltykova,10.22363/2312-8143-2025-26-3-258-265,regressionneuralnetworksadvantageoverclassicalregressionanalysis|2025,1,2847,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d811b6629dc28520a63cd5f26f8a571d4f4de886,10.31098/ijmesh.v9i1.3567,,Improving Housing Price Prediction with Machine Learning: Insights from Yogyakarta,2025.0,"International Journal of Management, Entrepreneurship, Social Science and Humanities",Galuh Sudarawerti; Fahmi Arif,"Predicting housing prices accurately remains a major challenge in the real estate industry, especially in fast-urbanizing areas where both structural and locational factors are at play. This study focuses on the Special Region of Yogyakarta, Indonesia—a city with varied land use and dynamic housing market conditions—to explore how machine learning (ML) can support better price forecasting. Using the CRISP-DM framework, we analyzed data from 2,020 residential listings, incorporating variables such as building area, land size, number of rooms, and district. Among the several classification models tested, Random Forest achieved the highest accuracy and F1-score. According to the feature importance analysis, building area, land area, and district emerged as the strongest predictors, while vertical features like the number of floors had relatively little effect. These findings suggest that in Yogyakarta’s market, observable physical features may play a bigger role in price determination than location-specific factors. While the study offers a practical framework that real estate professionals can apply, its reliance on structural data and a single-region focus does limit how broadly the findings can be applied. Future research could expand on this by including socioeconomic or environmental variables to strengthen model performance and relevance across different markets.",0.0,https://journals.researchsynergypress.com/index.php/ijmesh/article/download/3567/1882,True,JournalArticle,,2025-10-11,d811b6629dc28520a63cd5f26f8a571d4f4de886,improvinghousingpricepredictionwithmachinelearninginsightsfromyogyakarta,sudarawerti,10.31098/ijmesh.v9i1.3567,improvinghousingpricepredictionwithmachinelearninginsightsfromyogyakarta|2025,1,2888,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",0cd8fe3e501ff7cd8823d7460093959c04768b5d,10.1371/journal.pone.0356472,,Is a (satellite) image worth a thousand data points? Comparing machine learning approaches to predict environmental and social inequalities in England,2026.0,PLoS ONE,Barbara Metzler; Martin Fleischmann; Dani Arribas-Bel,"Accurately mapping environmental and social inequalities at fine spatial scales is critical for urban policy, yet the data required are often costly and infrequently updated. Vision foundation models can extract information directly from satellite imagery, offering a rapid and scalable alternative. We compare three modelling pipelines for predicting two contrasting indicators of urban inequality – air pollution and house prices – across England on a fine hexagonal grid: regression models trained on structured features of form and function (census, land cover and urban morphology), models trained on 128-dimensional image embeddings from a geospatial foundation model and a hybrid of the two, each evaluated with and without coarse regional context. Structured features achieve the best overall accuracy, but image embeddings become competitive once regional context is added – most clearly for air pollution, where image-only models reach a median R2 of 0.78 (0.85 with regional context), indicating that the embeddings capture genuine image signal. For house prices the picture is more cautious: image-only models achieve a median R2 of around 0.58; however, much of the embeddings’ apparent gain reflects coarse spatial location rather than image content. Off-the-shelf satellite embeddings, while not yet surpassing data-intensive approaches, are a promising low-cost complement, particularly for rapid, large-scale, or repeated analyses and in settings where traditional data are limited.",0.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0356472&type=printable,True,Study;JournalArticle,Medicine,2026-08-21,0cd8fe3e501ff7cd8823d7460093959c04768b5d,isasatelliteimageworthathousanddatapointscomparingmachinelearningapproachestopredictenvironmentalandsocialinequalitiesinengland,metzler,10.1371/journal.pone.0356472,isasatelliteimageworthathousanddatapointscomparingmachinelearningapproachestopredictenvironmentalandsocialinequalitiesinengland|2026,1,2999,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",792de905e26db923cc495f4df0c6cb502f55aaf2,10.1371/journal.pone.0335722,,RETRACTED: Optimization of house price evaluation model based on multi-source geographic big data and deep neural network,2025.0,PLoS ONE,Xuan Wang; Xuan Li; Haiyan Li,"The real estate market requires effective and precise house price prediction, as conventional models often face difficulties in generalization, computational efficiency, and interpretability. The research problem is addressed by introducing the House Price Evaluation Model (HPEM), which utilizes a hybrid deep learning network for analyzing multi-source geographic data. The network integrates the attention mechanism with spatial feature extraction, and a bat optimization algorithm is used to improve explainability and accuracy. The gathered properties are processed using normalized techniques to convert unstructured data into structured data, which directly improves the overall prediction accuracy. The bat-optimized attention mechanism with spatial networks dynamically arranges high-impact features to effectively address unstable feature importances, computation inefficiency, and poor generalization issues. In addition, the echolocation-inspired approach explores optimal solutions by balancing exploration and exploitation, thereby minimizing the deviation between the outputs and reducing training time by 30% compared to existing methods. The efficiency of the system is then evaluated using the Housing Price Dataset information, where HPEM achieves 98.5% feature stability, 1.2 hours of human-in-loop updates, and a 4.2% mean absolute error (MAE) under distribution shifts. The effective exploration of dynamic features through bat optimization integration yields 15% closer convergences, enhancing regulatory compliance and accuracy. Therefore, the developed model is effectively utilized in real estate valuation schemes.",1.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0335722&type=printable,True,JournalArticle,Medicine,2025-11-05,792de905e26db923cc495f4df0c6cb502f55aaf2,retractedoptimizationofhousepriceevaluationmodelbasedonmultisourcegeographicbigdataanddeepneuralnetwork,wang,10.1371/journal.pone.0335722,retractedoptimizationofhousepriceevaluationmodelbasedonmultisourcegeographicbigdataanddeepneuralnetwork|2025,1,3141,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",5cd98ed2ae6029f43c034327d3bd0ff5fdefc976,10.1371/journal.pone.0332439,,An improved artificial gorilla troops optimizer for BP neural network-based housing price prediction,2025.0,PLoS ONE,Yulin Li,"In the context of global economic austerity in the post epidemic era, housing, as one of the basic human needs, has become particularly important for accurate prediction of house prices. BP neural network is widely used in prediction tasks, but their performance is easily affected by weights and biases, and thus metaheuristic algorithms are needed to optimize the network parameters. Firstly, to address the shortcomings of the artificial Gorilla Troops Optimizer (GTO) in such optimization tasks, such as reduced population diversity, easy to fall into local optimal solutions and slow convergence, this paper proposes a fitness allocation strategy, a Cauchy variation strategy, and an elite evolution mechanism to improve the algorithm, which in turn results in an improved artificial Gorilla Troops Optimizer (IGTO). Subsequently, a BP neural network house price prediction model based on IGTO is constructed and experiments are conducted on four datasets, namely, Boston, California-Bay, California-Land and Taiwan. The experiments are first compared with eleven other swarm intelligence algorithms and then with four machine learning models, and the results show that IGTO-BPNN improved 17.66%, 18.27%, 28.10%, 49.35% and 24.83% on five evaluation metrics, namely, MAE, MAPE, R2, RMSE, and SMAPE, respectively. The improvement of these indicators fully proves the superiority and effectiveness of IGTO-BPNN in house price prediction.",1.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0332439&type=printable,True,JournalArticle,Medicine,2025-09-17,5cd98ed2ae6029f43c034327d3bd0ff5fdefc976,animprovedartificialgorillatroopsoptimizerforbpneuralnetworkbasedhousingpriceprediction,li,10.1371/journal.pone.0332439,animprovedartificialgorillatroopsoptimizerforbpneuralnetworkbasedhousingpriceprediction|2025,1,2940,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",064ff96c42ad49a4ee0754cc084e0b760473a622,10.1371/journal.pone.0322821,,Forecasting second-hand house prices in China using the GA-PSO-BP neural network model,2025.0,PLoS ONE,Ji-Ning Wang; Hua-Bin Ji; Lei Wang,"While the traditional genetic algorithms are capable of forecasting house prices, they often suffer from premature convergence, which adversely affects the reliability of the forecasts. To address this issue, the research employs a genetic-particle swarm optimization (GA-PSO) algorithm and develops a GA-PSO-BP neural network model through the integration of the BP neural network. Building upon this foundation, the study considers several pivotal factors affecting housing prices and employs a dataset comprising 1,824 transactions of second-hand homes from 2023 to 2024, gathered from Lianjia.com, to forecast housing prices in China. This work shows that the GA-PSO-BP neural network model demonstrates exceptional forecasting performance when dealing with complex and high-dimensional data, significantly minimizing forecasting errors. The test set achieved an RMSE of 0.786 and a MAPE of 8.9%. Its effectiveness in forecasting prices of second-hand houses notably surpasses that of a BP neural network model optimized by a single algorithm. This research provides more accurate forecasts of second-hand house prices in rapidly growing urban areas such as Guangzhou, thus providing essential insights for investors contemplating real estate investment.",3.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0322821&type=printable,True,JournalArticle,Medicine,2025-05-07,064ff96c42ad49a4ee0754cc084e0b760473a622,forecastingsecondhandhousepricesinchinausingthegapsobpneuralnetworkmodel,wang,10.1371/journal.pone.0322821,forecastingsecondhandhousepricesinchinausingthegapsobpneuralnetworkmodel|2025,1,2758,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,5,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",855f8c9e0508b319a7022588b7485215b722768f,10.31548/energiya5(69).2023.088,,A RECURRENT NEURAL NETWORK FOR FOR REAL ESTATE PRICE ESTIMATION,2023.0,Energy and automation,O. Tymchuk; A. Pylypenko; Артем Кича,"The paper considers the problem of estimating the price of real estate. Automation of the solution to the specified problem is one of the methods of providing an objective assessment, which excludes such subjective factors as an arithmetic calculation error, assessment under the influence of emotions, assessment under the influence of the pursuit of one's own goals. Most often, regression analysis (hedonic regression) and machine learning methods are used to solve the problem of estimating the price of real estate. The purpose of this research is to build a model for estimating the price of real estate not only on the basis of the usual quantitative indicators (for example, area, number of rooms, floor, etc.), but also on the basis of the textual description of the real estate. In this paper we consider a real estate in new residential builings in the Kyiv. To achieve the goal, a mathematical model was developed for the classification of real estate class based on a textual description using a recurrent neural network. The model is developed as a multi-layer feed-forward neural network that accepts textual data describing a new residential building and passes it through a series of hidden layers, where each layer consists of neurons. The simulated categorical variable 'predictedClass' was used as the independent variable of the linear regression to calculate the price of an apartment in a new building. Compared to classical linear regression with quantitative regressors, the new model provided a high R2 with a minimal number of variables.",1.0,https://journals.nubip.edu.ua/index.php/Energiya/article/download/energiya5%2869%29.2023.088/15629,True,JournalArticle,,,855f8c9e0508b319a7022588b7485215b722768f,arecurrentneuralnetworkforforrealestatepriceestimation,tymchuk,10.31548/energiya5(69).2023.088,arecurrentneuralnetworkforforrealestatepriceestimation|2023,1,3064,4,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"") || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression",2,2,2,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1793ab881b5c9b30ef5844197595ca24e88d3035,10.33093/jiwe.2026.5.2.7,,Machine Learning-based Prediction of House Sale Prices in Hulu Langat,2026.0,Journal of Informatics and Web Engineering,S. Ng; Kath Moon Yap Choo; Da An,"House price prediction remains a complex task due to the interplay of structural, locational, and economic factors. This study proposes a machine learning-based predictive framework tailored to the residential real estate market in Hulu Langat, Malaysia, a district undergoing rapid urbanization. Using open-source datasets, three structured variations are developed: one containing only housing and locational attributes, another incorporating regional-level macroeconomic data, and a third combining both regional and national macroeconomic indicators. The proposed model (Ensemble Weighted Average) is trained and evaluated using evaluation metrics, then compared with models such as Random Forest, XGBoost, LightGBM, and Ensemble Stacking. The proposed model trained solely on housing and locational data achieved the highest accuracy, outperforming all other models across most evaluation metrics, while XGBoost achieves the fastest computation time. Models trained with the inclusion of macroeconomic indicators consistently underperforms, suggesting that macroeconomic indicators added noise to model prediction, potentially due to spatial resolution mismatches or multicollinearity. The interpretability of the best-performing model was further enhanced with SHapley Additive exPlanations (SHAP), the resulting SHAP analysis reveals that land parcel area, property type, and local housing supply are the top contributing features to model’s performance. These findings validate the effectiveness of ensemble models for localized price prediction and highlight the importance of house attributes over broader economic trends. The proposed framework yields a practical and interpretable approach to house price prediction and may assist policymakers, developers, and planners in making informed decisions.",0.0,https://journals.mmupress.com/index.php/jiwe/article/download/1991/1363,True,JournalArticle,,2026-06-14,1793ab881b5c9b30ef5844197595ca24e88d3035,machinelearningbasedpredictionofhousesalepricesinhululangat,ng,10.33093/jiwe.2026.5.2.7,machinelearningbasedpredictionofhousesalepricesinhululangat|2026,1,3311,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",dc01044cd8fb661043f38881e350ef4f12772f7b,10.32473/flairs.v35i.130668,,A Comparison of House Price Classification with Structured and Unstructured Text Data,2022.0,The Florida AI Research Society,Erika Cardenas; C. Shorten; T. Khoshgoftaar; B. Furht," Purchasing a home is one of the largest investments most people make. House price prediction allows individuals to be informed about their asset wealth. Transparent pricing on homes allows for a more efficient market and economy. We report the performance of machine learning models trained with structured tabular representations and unstructured text descriptions. We collected a dataset of 200 descriptions of houses which include meta-information, as well as text descriptions. We test logistic regression and multi-layer perceptron (MLP) classifiers on dividing these houses into binary buckets based on fixed price thresholds. We present an exploration into strategies to represent unstructured text descriptions of houses as inputs for machine learning models. This includes a comparison of term frequency-inverse document frequency (TF-IDF), bag-of-words (BoW), and zero-shot inference with large language models. We find the best predictive performance with TF-IDF representations of house descriptions. Readers will gain an understanding of how to use machine learning models optimized with structured and unstructured text data to predict house prices. ",2.0,https://journals.flvc.org/FLAIRS/article/download/130668/133869,True,JournalArticle,Computer Science,2022-05-04,dc01044cd8fb661043f38881e350ef4f12772f7b,acomparisonofhousepriceclassificationwithstructuredandunstructuredtextdata,cardenas,10.32473/flairs.v35i.130668,acomparisonofhousepriceclassificationwithstructuredandunstructuredtextdata|2022,1,2674,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",3b37941a7cbd283073b7d78d9cbe2e0afe700cde,10.1186/s40537-018-0154-3,,A mass-market appraisal of the English housing rental market using a diverse range of modelling techniques,2018.0,Journal of Big Data,Stephen Clark; N. Lomax,"Mass appraisals in the rental housing market are far less common than those in the sales market. However, there is evidence for substantial growth in the rental market and this lack of insight hampers commercial organisations and local and national governments in understanding this market. This case study uses data that are supplied from a property listings web site and are unique in their scale, with over 1.2 million rental property listings available over a 2 year period. The data is analysed in a large data institute using generalised linear regression, machine learning and a pseudo practitioner based approach. The study should be seen as a practical guide for property professionals and academics wishing to undertake such appraisals and looking for guidance on the best methods to use. It also provides insight into the property characteristics which most influence rental listing price. From the regression analysis, attributes that increase the rental listing price are: the number of rooms in the property, proximity to central London and to railway stations, being located in more affluent neighbourhoods and being close to local amenities and better performing schools. Of the machine learning algorithms used, the two tree based approaches were seen to outperform the regression based approaches. In terms of a simple measure of the median appraisal error, a practitioner based approach is seen to outperform the modelling approaches. A practical finding is that the application of sophisticated machine learning algorithms to big data is still a challenge for modern desktop PCs.",15.0,https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-018-0154-3,True,JournalArticle,Computer Science;Medicine,2018-11-12,3b37941a7cbd283073b7d78d9cbe2e0afe700cde,amassmarketappraisaloftheenglishhousingrentalmarketusingadiverserangeofmodellingtechniques,clark,10.1186/s40537-018-0154-3,amassmarketappraisaloftheenglishhousingrentalmarketusingadiverserangeofmodellingtechniques|2018,1,3099,1,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",0,2,2,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6780d7ed10512152f92b6800e6f8062c5d99f2ae,10.9734/ajrcos/2023/v16i2339,,Machine Learning Approach for House Price Prediction,2023.0,Asian Journal of Research in Computer Science,M. J. Chowhaan; D. Nitish; G. Akash; N. Sreevidya; Subhani Shaik,"In our ecosystem, real estate is clearly a distinct industry. Predicting house prices, significant housing characteristics, and many other things is made a lot easier by the capacity to extract data from raw data and extract essential information. Daily fluctuations in housing costs are still present, and they occasionally rise without regard to calculations. According to research, changes in property prices frequently have an impact on both homeowners and the real estate market. To analyze the key elements and the best predictive models for home prices, literature research is conducted. The analyses' findings supported the usage of artificial neural networks, support vector regression, and linear regression as the most effective modeling techniques. Our results also imply that real estate agents and geography play important roles in determining property prices. Finding the most crucial factors affecting housing prices and identifying the best machine learning model to utilize for this research would both be greatly aided by this study, especially for housing developers and researchers.",19.0,https://journalajrcos.com/index.php/AJRCOS/article/download/339/675,True,JournalArticle,,2023-06-15,6780d7ed10512152f92b6800e6f8062c5d99f2ae,machinelearningapproachforhousepriceprediction,chowhaan,10.9734/ajrcos/2023/v16i2339,machinelearningapproachforhousepriceprediction|2023,1,2604,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",227a0c9a9887c8a998a5ac0a6f554f967e2c3347,10.21831/jiety.v3i1.1262,,Information System Prediction of Room for Rent Price in Yogyakarta Region Based on Website Using Linear Regression Algorithm,2025.0,Journal of Information Engineering and Technology (JIETY),C. Cahyana; H. Jati,"This development research was carried out to create a boarding house price prediction information system product that could help the problems of entrepreneurs and boarding house users regarding the determination of boarding prices. This development research was carried out to create a boarding house price prediction information system product that could help the problems of entrepreneurs and boarding house users regarding the determination of boarding prices. The research method used in this development research process is Research and Development with the Waterfall Model method. During the development process, tests were also carried out to determine the quality of the information system developed by referring to the ISO 25010:2011. The results of this research and development process are (1) Linear Regression algorithm as the best Machine Learning model that can make relevant and accurate predictions of boarding house rental prices. (2) A web-based boarding price prediction information system in the Yogyakarta Region was developed using the Waterfall development model and the Flask framework, which can automatically predict the relevant prices of the facilities selected by the user. (3) Test results on the Functional Suitability aspect ensure that all functions in the information system can run properly. The usability aspect produces 86.7% (Very Good category). The Performance Efficiency aspect produces a percentage of 100% (Very Good Performance), and fully loaded web page time is only 0.707 seconds (Good category). The reliability aspect results in a percentage of system resilience of 100% on sessions, pages, and hits in testing.",0.0,https://journal.uny.ac.id/publications/jiety/article/download/1262/390,True,JournalArticle,,2025-04-24,227a0c9a9887c8a998a5ac0a6f554f967e2c3347,informationsystempredictionofroomforrentpriceinyogyakartaregionbasedonwebsiteusinglinearregressionalgorithm,cahyana,10.21831/jiety.v3i1.1262,informationsystempredictionofroomforrentpriceinyogyakartaregionbasedonwebsiteusinglinearregressionalgorithm|2025,1,3161,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e76b723a135c0f89473101662e6fc9fd97d0977d,10.24912/jiksi.v13i1.32863,,PERBANDINGAN KINERJA ALGORITMA EXTREME GRADIENT BOOSTING DAN RANDOM FOREST UNTUK PREDIKSI HARGA RUMAH DI JABODETABEK,2025.0,Jurnal Ilmu Komputer dan Sistem Informasi,Dhiwa Aqsha,"The demand for housing continues to increase along with population growth. Predicting house prices is crucial to assist prospective buyers and investors in making more informed decisions. This study aims to predict house prices in the Jabodetabek area by comparing the performance of two machine learning algorithms, namely Extreme Gradient Boosting and Random Forest, to produce accurate price estimates. The prediction process includes data preprocessing, key variable exploration, and model evaluation using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²). The results show that the Random Forest model performs best, with an MAE of 95,200,513.25, an MSE of 1.47e+19, and an R² of 0.77, outperforming the Extreme Gradient Boosting model with an MAE of 121,836,703.27, an MSE of 3.03e+19, and an R² of 0.52. Thus, this research is expected to serve as an effective tool for stakeholders in mitigating risks in property investment decisions in the Jabodetabek area.",7.0,https://journal.untar.ac.id/index.php/jiksi/article/download/32863/19320,True,JournalArticle,,2025-01-29,e76b723a135c0f89473101662e6fc9fd97d0977d,perbandingankinerjaalgoritmaextremegradientboostingdanrandomforestuntukprediksihargarumahdijabodetabek,aqsha,10.24912/jiksi.v13i1.32863,perbandingankinerjaalgoritmaextremegradientboostingdanrandomforestuntukprediksihargarumahdijabodetabek|2025,1,2506,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",08caed8fe7ead5b595fd0ea3cd4cb5b07bc527f5,10.47002/metik.v7i2.608,,Implementasi Framework Streamlit Sebagai Prediksi Harga Jual Rumah Dengan Linear Regresi,2023.0,Metik jurnal,Gita Ayu Syafarina; Zaenuddin Zaenuddin,"This research aims to develop an Artificial Intelligence (AI)-based application using the Streamlit framework to predict house sale prices in Banjarmasin City using Linear Regression methodology. The increase in demand and supply of properties in Banjarmasin City poses a complex challenge in determining house sale prices. The Linear Regression method was chosen as the primary analytical tool to identify factors influencing house sale prices. This application utilizes historical data of house sale prices and variables such as land area, building area, number of rooms, proximity to public facilities, and geographical location as inputs for the Linear Regression model. Furthermore, the Streamlit framework is employed to create an interactive and user-friendly interface for end-users. The outcome of this research is an AI application that assists potential buyers or sellers in Banjarmasin City in determining competitive prices. By inputting information about the property being evaluated, users can obtain a more accurate estimated sale price based on factors identified by the Linear Regression model. In testing the application, actual house sale price data from Banjarmasin City was used to assess the model's accuracy. The testing results indicate that the application is capable of providing reasonably accurate price estimates, achieving an accuracy level of 67.8%. Thus, this AI application holds the potential to be a valuable tool in the property industry in Banjarmasin City, aiding stakeholders in more informed and data-driven decision-making regarding house sale prices. Additionally, this application could serve as a foundation for further developments in AI research and property price analysis.",17.0,https://journal.universitasmulia.ac.id/index.php/metik/article/download/608/344,True,JournalArticle,,2023-12-31,08caed8fe7ead5b595fd0ea3cd4cb5b07bc527f5,implementasiframeworkstreamlitsebagaiprediksihargajualrumahdenganlinearregresi,syafarina,10.47002/metik.v7i2.608,implementasiframeworkstreamlitsebagaiprediksihargajualrumahdenganlinearregresi|2023,1,3221,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",0726a6cc581fee39cc268e4d32d8292b296bebc9,10.26740/jram.v8n2.p94-104,,THE COMPARISON OF HEDONIC REGRESSION AND ARTIFICIAL NEURAL NETWORK IN THE DEVELOPMENT OF MASS APPRAISAL MODEL,2024.0,Jurnal Riset dan Aplikasi Matematika (JRAM),Mukti Ratna Dewi; B. S. S. Ulama; D. Susilaningrum,"The appraisal of land and building tax objects in Surabaya City has been carried out manually, so it is prone to bias, human error, and inefficiency in terms of time and energy. Therefore, it is necessary to develop a mass appraisal system based on a property price prediction model in accordance with the real estate market structure. The traditional approach to mass appraisal is based on multiple regression analysis, which, despite having a long history in the appraisal world, cannot address interactions between variables such as nonlinearity. This deficiency was then overcome by introducing an Artificial Intelligence approach based on an artificial neural network (ANN), which has the ability to learn on its own and generalize solutions. Specifically, this study aims to build a mass appraisal model for residential property using (i) multiple regression analysis and (ii) artificial neural network, as well as (iii) determining the best mass appraisal model. The result of the study shows that ANN performs better than multiple regression analysis in predicting the value of residential property in Surabaya City.",2.0,https://journal.unesa.ac.id/index.php/jram/article/download/34980/12295,True,JournalArticle,,2024-10-30,0726a6cc581fee39cc268e4d32d8292b296bebc9,thecomparisonofhedonicregressionandartificialneuralnetworkinthedevelopmentofmassappraisalmodel,dewi,10.26740/jram.v8n2.p94-104,thecomparisonofhedonicregressionandartificialneuralnetworkinthedevelopmentofmassappraisalmodel|2024,1,2624,5,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression",2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",daf65dd7c289554c16b459a42025ac91b3e26f79,10.58905/sana.v2i1.265,,Analysis of K-NN Algorithm and Linear Regression to Predict House Prices in Jabodetabek,2024.0,"SaNa: Journal of Blockchain, NFTs and Metaverse Technology",Nadia Putri Ariyanti; Agung Triayudi; Ratih Titi Komala Sari,"Jabodetabek is now the region with the highest average level of citizen satisfaction, so many people migrate to this region in the hope of getting better living conditions, this will make people who want to buy a house question whether the house they want to buy is good value or not. The purpose of this study is to evaluate the effectiveness of multiple linear regression and K-Nearest Neighbors (KNN) algorithm on a dataset of house prices in Jabodetabek. Better results are obtained by using the Multiple Linear Regression model which has lower Mean Absolute Error (MAE) and Mean Squared Error (MSE) values and a fairly good R-squared of around 48.72%. However, the very high MAE and MSE values of the KNN model indicate inaccuracy and significant prediction variance. Although KNN has a relatively high R-squared value, more research is needed to see if the model can adequately explain data fluctuations. Based on the performance evaluation, multiple linear regression is ultimately a better choice",2.0,https://journal.mediadigitalpublikasi.com/index.php/sana/article/download/265/118,True,JournalArticle,,2024-02-22,daf65dd7c289554c16b459a42025ac91b3e26f79,analysisofknnalgorithmandlinearregressiontopredicthousepricesinjabodetabek,ariyanti,10.58905/sana.v2i1.265,analysisofknnalgorithmandlinearregressiontopredicthousepricesinjabodetabek|2024,1,2504,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",cbe22e39a5c675fa0a242d856aed571c08638f33,10.57255/intellect.v4i1.1385,,"Analisis Perbandingan Prediksi Harga Rumah Dengan Random Forest, Gradient Boosting, dan XGBoost",2025.0,Intellect,B. Wulansari; Donni Prabowo,"House price prediction poses a significant challenge in the property sector, especially in the Yogyakarta region, which exhibits a wide range of price variations. This study aims to compare the performance of three regression algorithms such as Random Forest, Gradient Boosting, and XGBoost, in building predictive models based on features such as land area, building area, number of bedrooms, bathrooms, and garage availability. The dataset analyzed consists of 1,642 entries, with house prices ranging from IDR 7 million to IDR 4.37 billion, an average price of IDR 1.14 billion, and a mode of IDR 775 million. Model evaluation was conducted using Mean Squared Error (MSE) and the coefficient of determination (R²), where XGBoost achieved the best performance with an MSE of 1.56 × 10¹⁴ IDR², an R² of 0.7746, and a Root Mean Squared Error (RMSE) of approximately IDR 12.5 million. These results indicate that XGBoost outperforms the other two models in handling complex tabular data and provides more accurate predictions. The predictive model has practical potential to be utilized by property developers, real estate agents, and local governments as a decision-support tool for price estimation, market evaluation, and data-driven urban planning. These findings highlight that selecting the appropriate algorithm can significantly enhance the quality of house price prediction. Abstrak Prediksi harga rumah menjadi tantangan penting dalam bidang properti, khususnya di wilayah Yogyakarta yang memiliki variasi harga cukup ekstrem. Penelitian ini bertujuan untuk membandingkan performa tiga algoritma regresi yaitu Random Forest, Gradient Boosting, dan XGBoost digunakan untuk membangun model prediksi harga rumah berdasarkan fitur seperti luas tanah, luas bangunan, jumlah kamar tidur, kamar mandi, dan garasi. Data yang dianalisis mencakup 1.642 entri dengan harga rumah berkisar antara Rp 7 juta hingga Rp 4,37 miliar, harga rata-rata sebesar Rp 1,14 miliar, dan modus Rp 775 juta. Evaluasi model dilakukan menggunakan metrik Mean Squared Error (MSE) dan koefisien determinasi (R²), di mana XGBoost menghasilkan performa terbaik dengan MSE sebesar 1,56 × 10¹⁴ rupiah², R² sebesar 0,7746, dan Root Mean Squared Error (RMSE) sekitar 12,5 juta rupiah. Hasil ini menunjukkan bahwa XGBoost lebih unggul dalam menangani data tabular kompleks dan memiliki akurasi prediksi yang lebih baik dibanding dua model lainnya. Model prediktif ini berpotensi digunakan oleh pengembang properti, agen real estate, maupun pemerintah daerah sebagai alat bantu dalam penetapan harga, evaluasi pasar, dan perencanaan tata ruang yang berbasis data. Temuan ini memberikan gambaran bahwa pemilihan algoritma yang tepat dapat meningkatkan kualitas prediksi harga properti.",5.0,https://journal.makwafoundation.org/index.php/intellect/article/download/1385/875,True,JournalArticle,,2025-06-30,cbe22e39a5c675fa0a242d856aed571c08638f33,analisisperbandinganprediksihargarumahdenganrandomforestgradientboostingdanxgboost,wulansari,10.57255/intellect.v4i1.1385,analisisperbandinganprediksihargarumahdenganrandomforestgradientboostingdanxgboost|2025,1,4253,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",85e7d045bbf2ae1d56d2757857ccc6cc136f63d1,10.52158/jacost.v4i1.491,,"Analisis Perbandingan Metode Regresi Linier, Random Forest Regression dan Gradient Boosted Trees Regression Method untuk Prediksi Harga Rumah",2023.0,Journal of applied computer science and technology,Evita Fitri,"The need for a place to live is one that many people prepare, both millennials and adults and the elderly. With the continued increase in population growth in Indonesia and increasing public interest in buying a place to live early on, this can make not all groups of people have a place to live or a house that is quite livable. Related to this, the public needs up-to-date information related to predictions of house prices both for housing and second-hand housing prices for planning purposes in the future. The purpose of this study is to carry out a comparative analysis of the prediction results of house prices with several Machine Learning algorithms consist of Linear Regression, Random Forest Regression and Gradient Boosted Trees Regression. Evaluation for all the method applying Cross-Validation. The evaluation is seen from the smallest Root Mean Square Error (RMSE) error rate of each testing method. The results of this study are the Random Forest Regression obtained an RMSE value of 0.440, the Linear Regression model obtained an RMSE value of 0.515 and the RMSE value of Gradient Boosted Trees Regression of 0.508. The results were obtained from testing a dataset of 2011 records with a division of 80% for data training and 20% for data testing, the data has 6 attributes used in testing including house prices, land area, building area, number of bathrooms, number of bedrooms and the number of garages. In this study, prediction results using the Random Forest Regression method yielded the highest accuracy of 81.5% compared to the Linear Regression and Gradient Boosted Trees Regression methods.",33.0,https://journal.isas.or.id/index.php/JACOST/article/download/491/202,True,JournalArticle,,2023-07-01,85e7d045bbf2ae1d56d2757857ccc6cc136f63d1,analisisperbandinganmetoderegresilinierrandomforestregressiondangradientboostedtreesregressionmethoduntukprediksihargarumah,fitri,10.52158/jacost.v4i1.491,analisisperbandinganmetoderegresilinierrandomforestregressiondangradientboostedtreesregressionmethoduntukprediksihargarumah|2023,1,3119,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d84d94c36bcf79ab29cc69177849e3b0f53ec5ac,10.25134/ilkom.v19i2.411,,A Data-Driven Approach to Comparative Evaluation of Regression Models for Accurate House Price Prediction,2025.0,Nuansa Informatika,Tiara Permata Hati; B. Budiman; Imannudin Akbar; Nur Alamsyah,"This study aims to develop and evaluate a property price prediction model in Bandung by applying machine learning (ML) algorithms. The need for more accurate property price predictions is increasing due to fluctuations in the property market. This study analyzes property characteristics, including the number of bedrooms, bathrooms, land area, building area, and location, as well as their impact on house prices. The study evaluates four regression algorithms, including linear regression, K-Nearest Neighbors (KNN), Random Forest, and XGBoost. Finally, this study proposes price_per_m2 and building_land_ratio as new features recommended for improvement in accuracy. The bottleneck method is derived from the data collection area of the Rumah123.com website, encompassing data preprocessing and data exploration. The following metrics will be used to evaluate each model: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). Based on our study, we conclude that both Random Forest Regression and XGBoost Regression achieve the highest accuracy, with R² values of 0.9941 and 0.9955, respectively, after adjustment. Conversely, Linear Regression and KNN Regression have the lowest accuracy, with KNN Regression being the least accurate. The primary contribution of this study is the development of a more accurate house price prediction model that can be applied in cities with similar market characteristics. These findings provide practical insights for property developers and buyers when making investment decisions.",0.0,https://journal.fkom.uniku.ac.id/ilkom/article/download/411/119,True,JournalArticle,,2025-07-10,d84d94c36bcf79ab29cc69177849e3b0f53ec5ac,adatadrivenapproachtocomparativeevaluationofregressionmodelsforaccuratehousepriceprediction,hati,10.25134/ilkom.v19i2.411,adatadrivenapproachtocomparativeevaluationofregressionmodelsforaccuratehousepriceprediction|2025,1,3073,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b4f2581ecafbb85d23f7610cc6131c2a102c92b1,10.37943/21ebbh5859,,DEVELOPMENT OF MACHINE LEARNING METHODS FOR MARKET TRENDS,2025.0,Scientific Journal of Astana IT University,S. Sapakova; Zhansaya Bekaulova; A. Nurlanuly; Duriya Daniyarova; G. Ybytayeva; Kaldybayeva Aizhan,"In the rapidly evolving real estate market, the application of machine learning (ML) is crucial for understanding and predicting price trends. This study evaluates and compares seven ML models, including multiple linear regression, random forest regression, support vector regression (SVR), decision tree regression, and XGBoost, to determine the most effective predictor of real estate prices in Astana, Kazakhstan. The study focuses on the Yesil district, a key area in the city, utilizing a dataset of over 9,000 records extracted from a broader collection of more than 30,000 real estate transactions across Kazakhstan. Through rigorous experimentation, model performance was assessed using statistical metrics such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R-squared). The results indicate that the Random Forest Regressor and XGBRegressor models outperformed others, achieving the highest R-squared values (99.55% and 99.18%, respectively) and the lowest MAE and RMSE values. These findings highlight their robustness in predicting housing prices with high accuracy. The primary objective of this study was to develop a precise ML model capable of accurately forecasting real estate prices in Astana based on key market attributes. The superior predictive performance of the Random Forest and XGBRegressor models justifies their selection for deployment in real-world applications. Their high predictive accuracy suggests their potential utility for real estate professionals, policymakers, and investors seeking data-driven insights into market dynamics. This research expands of knowledge on the applications of ML in the real estate sector, reinforcing the importance of evidence-based decision-making within the industry.",0.0,https://journal.astanait.edu.kz/index.php/ojs/article/download/663/228,True,JournalArticle,,2025-03-30,b4f2581ecafbb85d23f7610cc6131c2a102c92b1,developmentofmachinelearningmethodsformarkettrends,sapakova,10.37943/21ebbh5859,developmentofmachinelearningmethodsformarkettrends|2025,1,3288,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",799db8c3e5db1067f56df44451b9408775675af5,10.33411/ijist/20246310311044,,Real Estate Price Prediction,2024.0,International Journal of Innovative Science & Technology,R. Naz; Bushra Jamil; Humaira Ijaz,"Real estate price predictions are critical for stakeholders, including investors and developers, because they have a considerable impact on investment decisions and market stability. In order to fill in the shortcomings in earlier approaches, this work presents a novel methodology by utilizing Deep Learning(DL) and Machine Learning (ML) techniques to improve real estate price forecast accuracy. We used the ""House Prices 2023 Dataset"" from Kaggle, which contains 168,000 entries of Pakistani property data. Our methodology included extensive data preparation, feature engineering, and the use of various algorithms, including Linear Regression, Gradient Boosting, Random Forest, Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN). The models were tested using MSE, RMSE, R-squared, and accuracy. KNN outperformed the other models, with a lower RMSE of 13.79 and a higher R-squared value of 0.85, indicating improved predictive accuracy. RF also produced impressive results, with an accuracy of 80%. Handling complicated feature interactions, guaranteeing model scalability, and controlling hardware resources were all challenges that suggested possibilities for future improvement. As a result, our research offers a solid foundation for raising forecasting accuracy in fluctuations in the market and emphasizes the possibility of utilizing ML approaches for better real estate price prediction.",0.0,https://journal.50sea.com/index.php/IJIST/article/download/951/1498,True,JournalArticle,,2024-07-25,799db8c3e5db1067f56df44451b9408775675af5,realestatepriceprediction,naz,10.33411/ijist/20246310311044,realestatepriceprediction|2024,1,2914,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",21a30395c73abf85ee06634a1732ff4a03f25e8e,10.33411/ijist/2025718397,,Comparative Evaluation of Machine Learning and Deep Learning Models for Real Estate Price Prediction,2025.0,International Journal of Agriculture & Sustainable Development,Asad Ullah Khan; Muhammad Khateeb Khan; Usama Khan; Faheem Shaukat,"Accurate real estate price prediction is vital in informed decision-making for investors, policymakers, and stakeholders. This study evaluates various machine learning and deep learning models for predicting real estate prices using the House Prices 2023 dataset which contains 168,000 entries of Pakistani property data. In our proposed methodology we performed data preprocessing and features engineering to standardize the data. We performed extensive experiments by using Machine Learning (ML) and Deep Learning (DL) models on our preprocessed data. The model’s performance was evaluated based on the R-squared (R²) score and Mean Squared Error (MSE) metrics. Based on the provided metrics, the Decision Tree achieved the highest performance with an R² of 0.9968 and an MSE of 0.0021, followed by Random Forest with an R² of 0.990 and MSE of 0.0007. Similarly, other ML models like Gradient Boosting and XG Boost also outperformed by achieving (R² 0.9959, MSE 0.0028 R² 0.9747, and MSE 0.0170) respectively. In contrast,models like AdaBoost, Neural Network, and Convolutional Neural Network (CNN) showed comparatively lower performance due to the nature of the data. The study emphasizes that ensemble-based models like Decision Trees and Random Forests are highly effective at identifying patterns in real estate prices. Additionally, applying optimization techniques improves the model's ability to generalize and perform well on unseen data.",0.0,https://journal.50sea.com/index.php/IJIST/article/download/1180/1700,True,JournalArticle,,2025-01-16,21a30395c73abf85ee06634a1732ff4a03f25e8e,comparativeevaluationofmachinelearninganddeeplearningmodelsforrealestatepriceprediction,khan,10.33411/ijist/2025718397,comparativeevaluationofmachinelearninganddeeplearningmodelsforrealestatepriceprediction|2025,1,2948,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",69a8fbb019943e92b1b955ab1de471d61c0df722,10.70864/joae.2025.v13.i7(1).pp247-253,,A SCALABLE HYBRID MACHINE LEARNING FRAMEWORK FOR RENTAL PRICE CLASSIFICATION LEVERAGING SEMANTIC TEXTUAL EMBEDDINGS AND SENTIMENT-DRIVEN FEATURE ENGINEERING,2025.0,Scientific Digest : Journal of Applied Engineering,K. S. Kumar; R. Karthik; B. N. Kumar; B. S. Prakash; Banothu Mani,"In today’s digital era, rental listing platforms host thousands of property ads, making it increasingly difficult for users to assess price categories and for agents to market properties effectively. While structured features like location, number of bedrooms, and square footage are widely used, they often fail to capture the full context provided in descriptions and amenities, which influence viewer interest and pricing perception. Traditional systems primarily rely on rulebased filters or manual interpretation, which are not only time-consuming but also inconsistent and incapable of adapting to the growing scale and diversity of listings. The limitations of these traditional approaches include poor handling of textual data, low scalability, and limited accuracy in predicting appropriate price tiers. This leads to mismatches in user expectations, reduced engagement, and inefficiencies in rental market operations. To overcome these challenges, there is a clear need for a system that intelligently leverages both structured and unstructured data to predict the rental price category more accurately. This research proposes a machine learning-based classification system that incorporates both numerical property features and natural language data (such as descriptions and amenities) using advanced preprocessing techniques and TF-IDF vectorization. Three models—Support Vector Classifier (SVC), KNearest Neighbors (KNN), and Light Gradient Boosting Machine (LGBM)—are implemented and compared. The LGBM model significantly outperforms others, achieving near-perfect accuracy and balanced performance across price categories. The system offers a scalable, accurate, and intelligent solution to predict rental price ranges, making it highly valuable for real estate platforms aiming to optimize pricing strategies and enhance user experience",0.0,https://joae.org/index.php/JOAE/article/download/188/156/351,True,JournalArticle,,2025-07-14,69a8fbb019943e92b1b955ab1de471d61c0df722,ascalablehybridmachinelearningframeworkforrentalpriceclassificationleveragingsemantictextualembeddingsandsentimentdrivenfeatureengineering,kumar,10.70864/joae.2025.v13.i7(1).pp247-253,ascalablehybridmachinelearningframeworkforrentalpriceclassificationleveragingsemantictextualembeddingsandsentimentdrivenfeatureengineering|2025,1,3354,1,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",23d3cce411ca2b2e1081631866285f47f0d62ca3,10.25077/jmua.15.2.163-178.2026,,Locally Weighted KNN-Based Fuzzy Regression for Property Valuation under Market Uncertainty,2026.0,Jurnal Matematika UNAND,H. Yozza; R. Efendi; N. A. Samat; Izzati Rahmi; Ridho Saputra,"Accurate evaluation of house valuation is crucial. Misestimation of house prices creates serious consequences for a variety of stakeholders. House prices can be modeled as a function of their constituent attributes. House prices are fuzzy due to negotiation and unpredictable market conditions. This study aims to develop rules to predict triangular fuzzy numbers of price for a given new house by implementing locally weighted KNN-based fuzzy regression, and to compare its performance with possibilistic fuzzy regression. The dataset used is the house valuation dataset. Data is examined using the modified Cheng and Lee k-nearest neighbor fuzzy regression and Tanaka’s possibilistic fuzzy regression. It is found that the modified Cheng and Lee k-nearest neighbor fuzzy regression outperforms possibilistic fuzzy regression in predicting the triangular fuzzy number of the house prices. The best performance is achieved when data is trained using the modified Cheng and Lee k-nearest neighbor fuzzy regression using: = 29 nearest neighbors, Minkowski distance with exponent parameter = 1.6 and an unequal weighting scheme with r = 1.",0.0,https://jmua.fmipa.unand.ac.id/index.php/jmua/article/download/1653/1051,True,JournalArticle,,2026-05-04,23d3cce411ca2b2e1081631866285f47f0d62ca3,locallyweightedknnbasedfuzzyregressionforpropertyvaluationundermarketuncertainty,yozza,10.25077/jmua.15.2.163-178.2026,locallyweightedknnbasedfuzzyregressionforpropertyvaluationundermarketuncertainty|2026,1,2640,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1ef780452306d3363b15761e5de34008a9ea4839,10.53106/160792642022092305022,,A Study on House Price Prediction Based on Stacking-Sorted-Weighted-Ensemble Model,2022.0,網際網路技術學刊,Zhen-Tao-Li-Zhen-Tao Li; Shi-Yi-Xie-Zhen-Tao Li; Ying-Zhang-Shi-Yi Xie; Jie-Hu-Ying Zhang," The house price prediction problem is a typical regression problem, and most of the common house price prediction models are single prediction algorithms, which are not ideal in terms of accuracy and stability. For solving this problem, this paper proposes a house price forecasting method based on Stacking-Sorted-Weighted-Ensemble (SSWE) model. Considering the characteristics of different algorithms and giving full play to the advantages of each model, multiple individual forecasting models are fused with the Stacking model. The algorithm validation is performed using the data generated by the system of real estate management department in western Guangdong. The prediction results show that the Stacking model is superior to the single model. Compared with the Stacking regression model, the SSWE model has a 13.6% increase in the root mean square error on the training set but a 0.3% decrease on the test set, indicating that the SSWE model prevents overfitting to a some extent and increases the accuracy and stability of the model. ",1.0,https://jit.ndhu.edu.tw/article/download/2774/2797,True,,,2022-09-01,1ef780452306d3363b15761e5de34008a9ea4839,astudyonhousepricepredictionbasedonstackingsortedweightedensemblemodel,li,10.53106/160792642022092305022,astudyonhousepricepredictionbasedonstackingsortedweightedensemblemodel|2022,1,2548,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",056678cc4f500b4e1c8725f9d8280ce0a5af2b64,10.52783/jisem.v10i41s.7980,,Property Price Prediction Using Machine Learning,2025.0,Journal of Information Systems Engineering & Management,Armaan Siddiqui; Gayathri Thekkayil; R. Verma; V. Verma; Dr. Rajni Kaushik,"To make smart investment choices in the rapidly evolving property sector market of today, it's critical to have reliable tools for estimating property prices This study aims to come up with a comprehensive machine learning-based system for forecasting property prices. The proposed solution incorporates historical property data and takes into consideration the future development plans to enhance prediction accuracy. Using crucial factors that effects the property prices such as location, square footage, count of bedrooms, and bathrooms, this study presents a machine learning model that assists in estimating residential sector pricing. The model also accounts for actual developments that may impact property value, such as new highways, train stations, airports, retail centres, and other public infrastructure. The system also integrates financial tools, including an EMI calculator and a rent estimator based on user-defined profit margins. In the advanced phase, a decade’s worth of historical data, combined with categorized property classes and reasons for annual price shifts, was used to further enhance accuracy. A “Future Factors Database” was incorporated to account for upcoming infrastructure projects, enabling future growth predictions. The system is developed using Python and Flask, with plans to transition from CSV to a MySQL database for dynamic data handling.",0.0,https://jisem-journal.com/index.php/journal/article/download/7980/3645,True,JournalArticle,,2025-05-01,056678cc4f500b4e1c8725f9d8280ce0a5af2b64,propertypricepredictionusingmachinelearning,siddiqui,10.52783/jisem.v10i41s.7980,propertypricepredictionusingmachinelearning|2025,1,2888,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d49ad1c1d41b6c3bf498a7409f4d8ffebc27501e,10.54338/27382656-2022.2-012,,Automated Real Estate Valuation With Machine Learning: A Case Study on Apartment Sales in Yerevan,2022.0,Journal of Architectural and Engineering Research,Henrik Sergoyan; Grigor Bezirganyan,"Real estate is one of the major sectors of the Armenian economy and has been developing dynamically since Armenia transitions from planned to market economies in early 1990s. More recently, large online platforms have been developed in Armenia to advertise real estate offerings, thus reducing information asymmetry, and increasing liquidity in both sales and rental markets. Simultaneously, granular geospatial data became increasingly affordable via platforms such as OpenStreetMap, Google Maps and Yandex Maps. With granular data concerning a representative portion of the real estate offering available online, it is increasingly tenable to monitor the real estate market in real time and develop analytical tools that can automatically and accurately estimate the value of real estate assets based on their internal and external features. This paper sets out to analyze Armenia real estate market and assess the performance of a special class of machine learning models while predicting the price of a square meter of apartments in Yerevan. Furthermore, it is presented the way to determine the most decisive factors which have an influence on the price of apartments on sale.",4.0,https://jaer.nuaca.am/index.php/jaer/article/download/92/41,True,,,2022-06-29,d49ad1c1d41b6c3bf498a7409f4d8ffebc27501e,automatedrealestatevaluationwithmachinelearningacasestudyonapartmentsalesinyerevan,sergoyan,10.54338/27382656-2022.2-012,automatedrealestatevaluationwithmachinelearningacasestudyonapartmentsalesinyerevan|2022,1,2681,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2dc7e31185bf1cf3df91fe07e91780ca4e3622f7,10.3837/tiis.2025.04.002,,Predicting Housing Price in Seoul using Explainable AI (XAI) and Machine Learning,2025.0,KSII Transactions on Internet and Information Systems,Hae Jung Chun; Hui U. Lee; B. Lee,,0.0,"https://itiis.org/digital-library/manuscript/file/102442/TIIS Vol 19, No 4-2.pdf",True,JournalArticle,Computer Science,2025-04-30,2dc7e31185bf1cf3df91fe07e91780ca4e3622f7,predictinghousingpriceinseoulusingexplainableaixaiandmachinelearning,chun,10.3837/tiis.2025.04.002,predictinghousingpriceinseoulusingexplainableaixaiandmachinelearning|2025,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",ea0eef073c11f96a51de2a6a9aff888673ce698a,10.47392/irjaeh.2025.0381,,Precision in Real Estate: Hybrid Multi-Stage Predictions for House Prices,2025.0,International Research Journal on Advanced Engineering Hub (IRJAEH),A. Manasa; P. Chandana; Shoaib Ahmed; Kumar Baradur,"In the dynamic world of real estate, proper house price forecasting is extremely important for buyers, sellers, and policymakers. This work presents a hybrid multi-stage machine learning system that integrates diverse data modalities—numerical, textual, and visual—to achieve high-precision property price forecasting. The model is organized in three stages. Firstly, a Neighbourhood Scoring Module exploits geo-spatial and socio-economic features extracted from images with CLIP (Contrastive Language–Image Pre-training), giving contextual knowledge of the surroundings of the property. Secondly, the House Attribute Module exploits structured information—e.g., area, number of rooms, and amenities—coupled with text features such as property descriptions represented using SBERT (Sentence-BERT). The third and last stage, the Fusion Module, integrates predictions from the earlier stages through late fusion methods and XGBoost to provide the final estimate of the house price. The datasets are preprocessed meticulously to eliminate duplicates, manage missing values, and preserve image-text alignment. For training, the system is trained independently on every modality and then combines the predictions for accuracy improvement. Metrics such as MAE, RMSE, R²-score, and classification metrics (in the case of price range prediction) show that the modular strategy outperforms single-modality models quite dramatically. This paper demonstrates the power of integrating deep learning with structured modeling to address challenging real-world problems. The proposed solution is encoded in a modular, scalable, and reproducible manner with Python, rendering it deployable in online real estate websites. Finally, the suggested approach advances the state of accuracy in real estate valuation through multi-modal data fusion.",0.0,https://irjaeh.com/index.php/journal/article/download/890/816,True,JournalArticle,,2025-05-23,ea0eef073c11f96a51de2a6a9aff888673ce698a,precisioninrealestatehybridmultistagepredictionsforhouseprices,manasa,10.47392/irjaeh.2025.0381,precisioninrealestatehybridmultistagepredictionsforhouseprices|2025,1,3326,5,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"") || text real estate valuation machine learning",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_citations,cites:sirmans2006,74dcccd85bf2f164d137e196c56da9391da5222d,10.1088/1757-899x/640/1/012001,,Impact of Security on Rental Price of Residential Properties: Evidence from South Africa,2019.0,IOP Conference Series: Materials Science and Engineering,T. B. Odubiyi; O. Oguntona; O. Oshodi; C. Aigbavboa; W. Thwala,"The property market plays a vital role in the economy through the provision of constructed space for productive activities and employment opportunities. Evidence gleaned from the literature suggests that the level of security within an area affects property prices. In the current study, the distance between a property and the police station was used as a proxy variable for measuring the perceived level of security. The data on rental prices of residential properties and its attributes were retrieved from a reliable property source (www.property24.com). A Neural network model was used for evaluating the impact of the presence of a police station on rental prices of residential properties within Cape Town, South Africa. Experimental results showed that the developed model is 77.27% accurate when used to predict the rental prices of residential properties. Floor area, number of bathroom, number of bedroom and proximity of a police station have the most significant impact on the rental price of residential properties. Greater efforts are needed to provide insights into the effect of sustainability on rental prices of residential properties. This information would serve a justification for embedding sustainability into residential construction projects.",7.0,https://iopscience.iop.org/article/10.1088/1757-899X/640/1/012001/pdf,True,Conference,Physics;Business,2019-11-13,74dcccd85bf2f164d137e196c56da9391da5222d,impactofsecurityonrentalpriceofresidentialpropertiesevidencefromsouthafrica,odubiyi,10.1088/1757-899x/640/1/012001,impactofsecurityonrentalpriceofresidentialpropertiesevidencefromsouthafrica|2019,1,2768,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || cites:sirmans2006",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b8a801f6e2315e48457b64db62faee01bf52caa7,10.1088/1757-899x/263/4/042098,,Real estate value prediction using multivariate regression models,2017.0,,R. Manjula; Shubham Jain; S. Srivastava; Pranav Rajiv Kher,"The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.",34.0,https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042098/pdf,True,,Mathematics;Physics,2017-11-01,b8a801f6e2315e48457b64db62faee01bf52caa7,realestatevaluepredictionusingmultivariateregressionmodels,manjula,10.1088/1757-899x/263/4/042098,realestatevaluepredictionusingmultivariateregressionmodels|2017,1,2502,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",cedcbf4dc68b72010d51562d21f1a97155deb882,10.1088/1757-899x/1022/1/012110,,Empirical analysis of regression techniques by house price and salary prediction,2021.0,IOP Conference Series: Materials Science and Engineering,U. Bansal; A. Narang; A. Sachdeva; I. Kashyap; S. Panda,"Regression analysis is extensively used for prediction and prognostication, and its use has substantial overlap with the domain of machine learning. The main objective of this paper is to compare the performance of two regression techniques namely Simple Linear Regression (SLR) and Multiple Linear Regression (MLR) algorithms by two cases: predicting the salary of employees after certain years and predicting the prices of real estates. An employee’s salary depends on numerous factors, such as total employee experience, certifications, and overall experience as a lead and manager. The factors in predicting house prices are the area of land (sqft_living), condition, waterfront, number of bedrooms, and so on. The dataset used in this experiment is an open-source dataset from KaggleInc. The algorithms were compared using parameters like R-squared value, Mean absolute error (MAE), Mean Squared Error (MSE), Median Absolute Error (MDAE), Variance Score, and Root Mean Square Error (RMSE). Results have shown that MLR provides the better efficiency in comparison to SLR.",9.0,https://iopscience.iop.org/article/10.1088/1757-899X/1022/1/012110/pdf,True,Conference,Physics;Computer Science,2021-01-19,cedcbf4dc68b72010d51562d21f1a97155deb882,empiricalanalysisofregressiontechniquesbyhousepriceandsalaryprediction,bansal,10.1088/1757-899x/1022/1/012110,empiricalanalysisofregressiontechniquesbyhousepriceandsalaryprediction|2021,1,2575,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",99e1fae40792423eaeee4cdcf0e3e315e66576bc,10.1088/1742-6596/2287/1/012019,,"Deep Neural Network As a Tool for Appraising Housing Prices: A Case Study of Busan, South Korea",2022.0,"Journal of Physics, Conference Series",S. An; Y. Song; H. Jang; K. Ahn,"This study examines whether the number of hidden layers in a deep neural network significantly influences the model accuracy and efficiency for appraising housing prices. We provide empirical evidence that the deep neural network can achieve high accuracy with a small number of hidden layers on our dataset, which contains various hedonic variables. Furthermore, we show that adding layers does not necessarily guarantee the model’s accuracy and effectiveness of the computing time.",1.0,https://iopscience.iop.org/article/10.1088/1742-6596/2287/1/012019/pdf,True,Conference,Physics,2022-06-01,99e1fae40792423eaeee4cdcf0e3e315e66576bc,deepneuralnetworkasatoolforappraisinghousingpricesacasestudyofbusansouthkorea,an,10.1088/1742-6596/2287/1/012019,deepneuralnetworkasatoolforappraisinghousingpricesacasestudyofbusansouthkorea|2022,1,1983,2,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices",2,1,1,1,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",539c6241d6f7d7a09aa2c3739422b58b23325dc4,10.1088/1742-6596/1994/1/012023,,Support Vector Regression with Gaussian kernel for Housing Prices Prediction,2021.0,"Journal of Physics, Conference Series",Di Miao; Hongru Tang; Bo-Sheng Wang,"The housing sector is one of the main sources of economic growth in both developing and developed countries. It is reported that nearly half of people buy or sell houses at an inappropriate price. Based on the public data set of Boston housing prices, this essay analyzed the factors affecting house prices and selected the five most important factors based on the decision tree with the ID3 algorithm. Then, this essay developed the support vector regression (SVR) with Gaussian kernel to predict housing prices. Experimental results showed that our method achieves superior accuracy and effectiveness compared with the SVR with linear kernel, KNN, and decision tree. To verify the applicability of our model, this research applied this model in Beijing housing price data, and it also achieved satisfactory fitting results.",0.0,https://iopscience.iop.org/article/10.1088/1742-6596/1994/1/012023/pdf,True,Conference,Physics,2021-08-01,539c6241d6f7d7a09aa2c3739422b58b23325dc4,supportvectorregressionwithgaussiankernelforhousingpricesprediction,miao,10.1088/1742-6596/1994/1/012023,supportvectorregressionwithgaussiankernelforhousingpricesprediction|2021,1,2325,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fe4754dd3b0b5691640f2160331e147b88d77dc9,10.1088/1742-6596/1982/1/012048,,Application of support vector regression in prediction model using genetic algorithm optimized,2021.0,"Journal of Physics, Conference Series",Wenke Du; Ruihan Chen; Zhenglong Cong,"No matter in any period, housing is the most basic demand of people’s life, and it is closely related to people’s daily life. Although many domestic and foreign experts have done research and prediction on housing prices, the causes of housing prices are complex, and the results of housing price research in different regions and different periods are very different, and most forecasting models have limitations in the use of them. Therefore, this article is based on the performance of support vector regression depends on the characteristics of key parameter selection, and uses genetic algorithm to optimize the penalty parameters, kernel function parameters and insensitive loss function of the support vector regression model. The optimized parameters are used to establish a support vector regression prediction model, and the housing price is predicted through the prediction model. The simulation results show that the convergence speed and prediction accuracy of the support vector regression prediction model optimized by genetic algorithm have been greatly improved, and the prediction results verify the feasibility and effectiveness of the model..",4.0,https://iopscience.iop.org/article/10.1088/1742-6596/1982/1/012048/pdf,True,Conference,Physics,2021-07-01,fe4754dd3b0b5691640f2160331e147b88d77dc9,applicationofsupportvectorregressioninpredictionmodelusinggeneticalgorithmoptimized,du,10.1088/1742-6596/1982/1/012048,applicationofsupportvectorregressioninpredictionmodelusinggeneticalgorithmoptimized|2021,1,2662,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d88c6c59b59a9de7f364aec7702dac0e996728e8,10.1088/1742-6596/1802/3/032034,,House price prediction using polynomial regression with Particle Swarm Optimization,2021.0,"Journal of Physics, Conference Series",Chenhao Zhou,"This paper combines the characteristics of the real Washington DC house price estate market and the theoretical model of housing prices to conduct an empirical analysis and comparison of the current mainstream housing price index prediction models. It is found that the current mainstream model only studies the trend of the housing price index itself, and is not sensitive to the characteristics of the house itself. Therefore, this paper uses a multiple regression model to integrate the advantages of external factors and use the improved housing price composition establishing a multiple regression prediction model with the particle swarm optimization. It not only makes up for the disadvantages of poorly determined housing price regression indicators and lack of statistical data in multiple regression prediction, but also enables the model to reflect the inflection point of housing prices in advance. PSO is used for selection of affect variables and regression analysis is used to determine the optimal coefficient in prediction.",15.0,https://iopscience.iop.org/article/10.1088/1742-6596/1802/3/032034/pdf,True,Conference,Mathematics;Physics,2021-03-01,d88c6c59b59a9de7f364aec7702dac0e996728e8,housepricepredictionusingpolynomialregressionwithparticleswarmoptimization,zhou,10.1088/1742-6596/1802/3/032034,housepricepredictionusingpolynomialregressionwithparticleswarmoptimization|2021,1,2540,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",57054c72b0ea2098e688f71f5cddd5b2ad3d85d6,10.1088/1742-6596/1693/1/012062,,House Price Prediction with An Improved Stack Approach,2020.0,"Journal of Physics, Conference Series",Hang Zhang; Kehan Wang; Mengchu Li; Xinchen He; Ruibo Zhang,"As a typical framework in ensemble learning, Stacking creates a multi-layer learner by integrating the learning results of different individual learners. Despite a general improvement in accuracy, multiple layers in stacking results in high computation cost. In addition, some individual learners do not perform better than others in single-model prediction task. To solve the above drawbacks of traditional stacking, this paper proposes an improved Stacking algorithm based on the concept of “weighted attribute”. For each individual learner, a corresponding weight will be calculated based on its prediction performance on training data. Weights will then be used as evaluation criteria for model performance. Models with larger weights will be selected as attributes for meta-learner. When improved Stacking algorithm is applied on the same dataset, experiment results indicate a slight improvement in prediction accuracy and a relatively large reduction in computation cost. This demonstrates practical application value of the improvement.",4.0,https://iopscience.iop.org/article/10.1088/1742-6596/1693/1/012062/pdf,True,Conference,Computer Science;Physics,2020-12-01,57054c72b0ea2098e688f71f5cddd5b2ad3d85d6,housepricepredictionwithanimprovedstackapproach,zhang,10.1088/1742-6596/1693/1/012062,housepricepredictionwithanimprovedstackapproach|2020,1,2544,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",af2c84da3468d951fe0c6f94f076c3b075773ac7,10.59934/jaiea.v4i2.733,,House Price Prediction Analysis Using a Comparison of Machine Learning Algorithms in the Jabodetabek Area,2025.0,Journal of Artificial Intelligence and Engineering Applications (JAIEA),Indah Ratna Ningsih; Ahmad Faqih; Ade Rizki Rinaldi,"Jabodetabek, as the largest metropolitan area in Indonesia, has complex property price dynamics, making it difficult for developers and buyers to determine house prices. This study aims to analyze and compare the performance of the Multiple Linear Regression and Random Forest Regression algorithms in predicting house prices in the region. The data was obtained through scraping techniques from the rumah123.com website in October 2024, covering 999 data points with variables such as price, location, building area, land area, number of bedrooms, bathrooms, and garages. A comparative approach with cross-validation was applied to evaluate the performance of both algorithms using the metrics MAE, MSE, RMSE, MAPE, and R². The research results show that Random Forest Regression using GridsearchCV has better predictive performance, with an MAE value of Rp.645,764,815, MAPE of 28.12%, and R² of 0.864. The main factors influencing house prices in Jabodetabek include building size, land size, number of bedrooms, bathrooms, garages, and location. This finding emphasizes the superiority of Random Forest Regression in capturing complex data patterns and the significant role of these variables in determining house prices.",4.0,https://ioinformatic.org/index.php/JAIEA/article/download/733/516,True,JournalArticle,,2025-02-15,af2c84da3468d951fe0c6f94f076c3b075773ac7,housepricepredictionanalysisusingacomparisonofmachinelearningalgorithmsinthejabodetabekarea,ningsih,10.59934/jaiea.v4i2.733,housepricepredictionanalysisusingacomparisonofmachinelearningalgorithmsinthejabodetabekarea|2025,1,2726,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ecb5a7e047ecc8eaecd0dbae9ebc62bf6d675388,10.59934/jaiea.v5i2.2073,,Predicting House Prices in South Jakarta using Linear Regression and Decision Tree Regressor Methods,2026.0,Journal of Artificial Intelligence and Engineering Applications (JAIEA),Frendy; Neviasary; Nursiah; Laura Vidiani; Siti Nurdiani,"The development of digital technology and data analytics has significantly improved predictive capabilities across various sectors, including the property market. House price prediction is a critical element in decision-making for buyers, sellers, investors, and developers, as prices are influenced by factors such as location, building size, land area, number of rooms, and available facilities. This study aims to build a house price prediction model in South Jakarta using Linear Regression and Decision Tree Regressor machine learning algorithms and compare their performance based on regression evaluation metrics. The dataset consists of 1010 entries, divided into 80% training data and 20% testing data. The experimental results show that Linear Regression produced the best performance with an R² score of 0.7713, meaning that the model can explain 77% of the variance in house prices. The model achieved a prediction error of approximately MAE Rp 1.98 billion and RMSE Rp 3.26 billion. Meanwhile, the Decision Tree Regressor obtained an R² score of 0.5560 with higher prediction errors, indicating a tendency toward overfitting and weaker generalization on testing data. Therefore, Linear Regression is recommended as the most effective approach for predicting property prices in South Jakarta and has the potential to be applied in decision-support systems for real estate market analysis.",0.0,https://ioinformatic.org/index.php/JAIEA/article/download/2073/1401,True,JournalArticle,,2026-02-15,ecb5a7e047ecc8eaecd0dbae9ebc62bf6d675388,predictinghousepricesinsouthjakartausinglinearregressionanddecisiontreeregressormethods,frendy,10.59934/jaiea.v5i2.2073,predictinghousepricesinsouthjakartausinglinearregressionanddecisiontreeregressormethods|2026,1,2900,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",59deef5440599497c8f622abbe85f977745df929,10.59934/jaiea.v4i3.1047,,House Price Prediction Analysis Using Linear Regression and Random Forest Algorithms,2025.0,Journal of Artificial Intelligence and Engineering Applications (JAIEA),Hanspran Limbong; Mufti Alwisyah Lubis; Mhd. Furqan,"This study aims to analyze house price prediction using two machine learning algorithms: Linear Regression and Random Forest. Quantitative evaluation is conducted using four main metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R² Score, and Mean Absolute Percentage Error (MAPE). The experimental results show that the Random Forest model outperforms Linear Regression in all four evaluation metrics. The MAE and RMSE of the Random Forest model are lower, indicating that this model is more effective in minimizing prediction errors. Additionally, the higher R² Score demonstrates the model's better ability to explain house price variance, while the smaller MAPE indicates more accurate prediction errors in the context of real estate. These findings suggest that choosing the right algorithm is crucial for modeling complex house price data, and although Random Forest is more accurate, its black-box nature limits interpretability. Therefore, for future research, more interpretable methods such as XGBoost with SHAP analysis could be considered.",1.0,https://ioinformatic.org/index.php/JAIEA/article/download/1047/758,True,JournalArticle,,2025-06-15,59deef5440599497c8f622abbe85f977745df929,housepricepredictionanalysisusinglinearregressionandrandomforestalgorithms,limbong,10.59934/jaiea.v4i3.1047,housepricepredictionanalysisusinglinearregressionandrandomforestalgorithms|2025,1,2569,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",38e5179e651516fce504171b6e7b28d22ab659c9,10.11113/intrest.v15n1.10,,Developing Real Estate Automated Valuation Models by Learning from Heterogeneous Data Sources,2021.0,International journal of real estate studies,F. Bergadano; Roberto Bertilone; D. Paolotti; G. Ruffo,"In this paper we propose a data acquisition methodology, and a Machine Learning solution for the partially automated evaluation of real estate properties. The novelty and importance of the approach lies in two aspects: (1) when compared to Automated Valuation Models (AVMs) as available to real estate operators, it is highly adaptive and non-parametric, and integrates diverse data sources; (2) when compared to Machine Learning literature that has addressed real estate applications, it is more directly linked to the actual business processes of appraisal companies: in this context prices that are advertised online are normally not the most relevant source of information, while an appraisal document must be proposed by an expert and approved by a validator, possibly with the help of technological tools. We describe a case study using a set of 7988 appraisal documents for residential properties in Turin, Italy. Open data were also used, including location, nearby points of interest, comparable property prices, and the Italian revenue service area code. The observed mean error as measured on an independent test set was around 21 K€, for an average property value of about 190 K€. The AVM described here can help the stakeholders in this process (experts, appraisal company) to provide a reference price to be used by the expert, to allow the appraisal company to validate their evaluations in a faster and cheaper way, to help the expert in listing a set of comparable properties, that need to be included in the appraisal document.",5.0,https://intrest.utm.my/index.php/intrest/article/download/10/7,True,,,2021-06-23,38e5179e651516fce504171b6e7b28d22ab659c9,developingrealestateautomatedvaluationmodelsbylearningfromheterogeneousdatasources,bergadano,10.11113/intrest.v15n1.10,developingrealestateautomatedvaluationmodelsbylearningfromheterogeneousdatasources|2021,1,3045,5,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || automated valuation model || automated valuation model residential || real estate valuation deep learning",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",4d6490850634808b04e25e82bd46f7e11efe2955,10.1007/978-3-031-34107-6_23,,Comparing Machine Learning Techniques for House Price Prediction,2023.0,Artificial Intelligence Applications and Innovations,K. P. Fourkiotis; A. Tsadiras,,8.0,https://inria.hal.science/hal-05498067,True,JournalArticle,Computer Science,,4d6490850634808b04e25e82bd46f7e11efe2955,comparingmachinelearningtechniquesforhousepriceprediction,fourkiotis,10.1007/978-3-031-34107-6_23,comparingmachinelearningtechniquesforhousepriceprediction|2023,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",7acf8629acb2a8f6c499c47a0a8b31bacead1d2d,10.1007/978-3-030-79150-6_47,,Analysis and Prediction for House Sales Prices by Using Hybrid Machine Learning Approaches,2021.0,Artificial Intelligence Applications and Innovations,S. M. Soliman Hossain; Jyoti Rawat; D. Logofătu,". Over the past few years, machine learning has played an increasingly vital role in every aspect of our society. There are count-less applications of machine learning, from tradition topic such as image recognition or spam detection, to advanced areas like automatic customer service or secure automobile systems. This paper analyzes a popular machine learning application, namely housing price prediction, by applying a full machine learning process: feature extraction, data preparation, model selection, model training and optimization, and last, but not least, prediction and evaluation. We experiment with different algorithms: linear regression, random forest, and gradient boosting. This paper demonstrates the comparison of effectiveness of these algorithms that may help sellers and buyers to have a fair deal of their respective businesses.",0.0,https://inria.hal.science/hal-03287678,True,JournalArticle,Computer Science,,7acf8629acb2a8f6c499c47a0a8b31bacead1d2d,analysisandpredictionforhousesalespricesbyusinghybridmachinelearningapproaches,hossain,10.1007/978-3-030-79150-6_47,analysisandpredictionforhousesalespricesbyusinghybridmachinelearningapproaches|2021,1,2349,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,1,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2ff1cc364240166b028fc5361498fbd2d8b3ac28,10.59075/ijss.v3i2.1527,,Enhancing Real Estate Price Forecasting Using Advanced Machine Learning Algorithms: An Empirical Evaluation with Real Market Data,2025.0,Indus Journal of Social Sciences,Jamshaid-ul-Hassan; K. Ullah; Humera Hayat; Shamim Jhatial; Kainat Yousuf,"Predicting real estate prices accurately has become crucial for buyers, investors, and legislators to make wise choices. A viable strategy for identifying the complex patterns and trends in an ever-changing market is to use cutting-edge machine learning algorithms. The purpose of this study is to offer reliable conclusions about the dynamics of the Lahore, Pakistan, real estate market, especially as it relates to inflation and other economic shifts. The goal is to improve forecasting techniques in order to promote a real estate market that is more open and effective. The results are intended to assist in making decisions and to provide guidance for policies that encourage steady, sustained market growth. To forecast housing prices, a number of parametric regression models were used, such as the Extra Trees Regressor, XGBoost, Random Forest, Gradient Boosting, Decision Tree, and CatBoost Regressor. As of June 26, 2023, data set was collected from a public website. Data set comprises on 9,539 listings from 6 districts. Additionally, As of June 26, 2023 in Pakistan real estate market, the average inflation rate in 2024 was 24.76%. The models that performed the best among the ones that were assessed were Gradient Boosting and Extra Trees Regressor, which had the lowest mean squared errors (MSE) and R2 scores of 85%. Additionally, CatBoost shown competitive performance and is emphasized for its usefulness. The study emphasizes the importance of particular property attributes in predicting prices and advances our knowledge of machine learning applications in real estate forecasting.",0.0,https://induspublishers.com/IJSS/article/download/1527/1367,True,JournalArticle,,2025-05-30,2ff1cc364240166b028fc5361498fbd2d8b3ac28,enhancingrealestatepriceforecastingusingadvancedmachinelearningalgorithmsanempiricalevaluationwithrealmarketdata,jamshaid-ul-hassan,10.59075/ijss.v3i2.1527,enhancingrealestatepriceforecastingusingadvancedmachinelearningalgorithmsanempiricalevaluationwithrealmarketdata|2025,1,3106,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bda7e22d4e2610c5e517c48b14c12014a9406cac,10.32628/ijsrst523103134,,A Novel Model for House Price Prediction with Machine Learning Techniques,2023.0,International Journal of Scientific Research in Science and Technology,Harshada S. Belsare; K. Warkar,"In this paper, we are going to use machine learning algorithms for house price prediction. House prices increases drastically every year, so we felt a need for a system that will predict house prices in the future. Due to a lack of knowledge of property assets people cannot guess the accurate price of houses. Therefore, we felt a need for a model that will predict an accurate house price. So, the main aim of our project is to predict the accurate price of the house without any loss. This survey also deals with a comparative analysis of the results of the algorithms used and the model with the highest accuracy and minimum error rate will be implemented. For the choice of prediction ways, we tend to compare and explore numerous prediction ways. We tend to utilize Linear and random forest regression as our model attributable to its liable and probabilistic methodology on model Choice. Our result exhibits that approach to the problem ought to achieve success and has the flexibility to predictions that will be compared to different house price prediction models. We have a proclivity to propose a house price prediction model to hold up a customer to estimate the proper valuation of a house.",1.0,https://ijsrst.com/paper/11261.pdf,True,JournalArticle;Review,,2023-06-01,bda7e22d4e2610c5e517c48b14c12014a9406cac,anovelmodelforhousepricepredictionwithmachinelearningtechniques,belsare,10.32628/ijsrst523103134,anovelmodelforhousepricepredictionwithmachinelearningtechniques|2023,1,2703,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ab0b2680d1ff367332b99dfcf7636dd5f7ee0fad,10.32628/ijsrset2411263,,Enhancing House Price Prediction and Accuracy using Particle Swarm Optimization,2024.0,International Journal of Scientific Research in Science Engineering and Technology,M. Rao; B. Rao; M. Yamuna; G. Revanth Mehatha; G. Bhavya Nagasri,"Predictive models for determining the sale price of houses in cities like Bengaluru is still remaining as more challenging and tricky task. The sale price of properties in cities like Bengaluru depends on a number of interdependent factors. Key factors that might affect the price include area of the property, location of the property and its amenities. In this research work, an analytical study has been carried out by considering the data set that remains open to the public by illustrating the available housing properties in machine hackathon platform. The data set has nine features. In this study, an attempt has been made to construct a predictive model for evaluating the price based on the factors that affect the price. Modeling explorations apply some regression techniques such as multiple linear regression (Least Squares), Lasso and Ridge regression models, support vector regression, and boosting algorithms such as Extreme Gradient Boost Regression (XG Boost). Such models are used to build a predictive model, and to pick the best performing model by performing a comparative analysis on the predictive errors obtained between these models. Here, the attempt is to construct a predictive model for evaluating the price based on factors that affects the price.",0.0,https://ijsrset.com/index.php/home/article/download/IJSRSET2411263/IJSRSET2411263,True,JournalArticle,,2024-04-25,ab0b2680d1ff367332b99dfcf7636dd5f7ee0fad,enhancinghousepricepredictionandaccuracyusingparticleswarmoptimization,rao,10.32628/ijsrset2411263,enhancinghousepricepredictionandaccuracyusingparticleswarmoptimization|2024,1,2778,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",dcebaba2290f2b160bba96f8383d76309062cdb3,10.55041/ijsrem30682,,Machine Learning based Predicting House Prices using Regression Technique,2024.0,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,Kunal Sapkal,"Predicting the price of a house helps for ascertain the house's selling price in a specific area and assist individuals in determining the ideal moment to purchase a home. Our goal in this machine learning task on house price prediction is to use data to develop a machine learning model capable of predicting housing values in the specified area. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to increase the model's efficiency for this project we are using supervised learning, which is a part of machine learning. We have to go through different attributes of the dataset. This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This suggested model is thought to be the most accurate one for estimating home prices and makes the most accurate predictions. This offers a succinct overview, which is necessary in order to forecast the price of the home. This project consists of what and how the house price model works with the assistance of machine learning technique using scikit-learn and which datasets we will be using in our proposed model. Key Words: house price, lasso regression, ridge regression, R-squared.",0.0,https://ijsrem.com/download/machine-learning-based-predicting-house-prices-using-regression-technique/?wpdmdl=29996&refresh=665b4aae3cd611717258926,True,JournalArticle;Review,,2024-04-13,dcebaba2290f2b160bba96f8383d76309062cdb3,machinelearningbasedpredictinghousepricesusingregressiontechnique,sapkal,10.55041/ijsrem30682,machinelearningbasedpredictinghousepricesusingregressiontechnique|2024,1,2885,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0848e26ae9a314de2560b0cf706d70619babaeba,10.55041/ijsrem35291,,House Price Prediction Website,2024.0,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,V. Sharma,"Abstract The project focuses on developing a user-friendly web platform that predicts house prices based on various factors like location, size and historical pricing trends.This project not only demonstrates technical skills but also addresses a real-world problem. This project shows us that the machine learning algorithm based on accuracy, consistency out performs the other in the performance of the housing price prediction. Python is used for writing the Machine Learning Algorithms .We will implement a linear regression algorithm on our dataset. HTML,CSS and JS is used for designing the front end of the system. At last, House Price Prediction Website will be very helpful in detecting the prices of the houses and keeping the record of the high and low of the prices. Predicting the price of a house helps for determine the selling price of the house in a particular region and it help people to find the correct time to buy a home.",0.0,https://ijsrem.com/download/house-price-prediction-website/?wpdmdl=35563&refresh=665d136bd1d361717375851,True,JournalArticle,,2024-06-02,0848e26ae9a314de2560b0cf706d70619babaeba,housepricepredictionwebsite,sharma,10.55041/ijsrem35291,housepricepredictionwebsite|2024,1,2443,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f1023c931d10dcea94362fa5c5fcdcdec0916c51,10.55041/ijsrem26175,,House Price Prediction using R language,2023.0,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,P. Gawali,"The purpose of this project is to use the R language to predict house prices based on various features, such as location, size, number of rooms, and other characteristics, using a dataset from Kaggle. In order to gain a better understanding of the data, we conducted exploratory data analysis, which included assessing the distribution of variables, identifying missing values, and detecting potential outliers. Additionally, we examined the correlation between the features and the target variable After performing data cleaning and pre- processing, we employed multiple machine learning algorithms, such as linear regression, random forests, and XGBoost, to construct predictive models. We evaluated the performance of these models using various metrics, such as mean absolute error, mean squared error, and root mean squared error. Our findings revealed that XGBoost had the best performance, achieving a root mean squared error of 0.13, indicating high predictive accuracy. We also performed a feature importance analysis to identify the most critical variables for predicting house prices. In summary, our project highlights the effectiveness of using R language and machine learning techniques to predict house prices while providing valuable insights into the key factors that influence house prices. Keywords— House price Prediction, Machine Learning , Regression",0.0,https://ijsrem.com/download/house-price-prediction-using-r-language-2/?wpdmdl=24443&refresh=65c20f0e372451707216654,True,JournalArticle,,2023-10-01,f1023c931d10dcea94362fa5c5fcdcdec0916c51,housepricepredictionusingrlanguage,gawali,10.55041/ijsrem26175,housepricepredictionusingrlanguage|2023,1,2871,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f2d895b966896b33294c749c51879082a23846b3,10.70849/ijsci03042639097,,An Intelligent Real Estate Price Prediction Framework Using Geospatial and Socioeconomic Features,2026.0,International Journal of Sciences and Innovation Engineering,E. P. Kumar; S. M. Kumar,": Intelligent price prediction systems have been developed as a result of India's growing need for trustworthy property appraisal. Based on important variables like location, area, number of rooms, infrastructure quality, crime rate, and socioeconomic conditions, this project offers a data-driven method for estimating home values. To determine the connection between property attributes and pricing, a dataset of 5000 real estate records with various qualities was examined. The system achieves great reliability in estimating property values by identifying patterns in the data to produce precise predictions. Using Flask, the solution is developed as an easy-to-use web application that allows users to enter property details and obtain real-time price estimates. For buyers, sellers, and real estate analysts, the program guarantees usability, consistency, and usefulness. This research shows how predictive modeling and data analysis may help make better decisions in the real estate industry. To further increase accuracy and practicality, future improvements might incorporate location-based intelligence and real-time market data.",0.0,https://ijsci.com/ijscipaperm/uploads/113667939097.pdf,True,JournalArticle,,2026-04-03,f2d895b966896b33294c749c51879082a23846b3,anintelligentrealestatepricepredictionframeworkusinggeospatialandsocioeconomicfeatures,kumar,10.70849/ijsci03042639097,anintelligentrealestatepricepredictionframeworkusinggeospatialandsocioeconomicfeatures|2026,1,2639,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b44b6ce256aa963caa5123c0f7d3a96cb91d462a,10.17762/ijritcc.v11i9.8321,,House Price Prediction using Machine Learning Algorithms,2023.0,International Journal on Recent and Innovation Trends in Computing and Communication,Angulakshmi M; D. M; M. I; Thilagavathi M; A. P,"House prices are a major financial decision for everyone involved in the housing market, including potential home buyers. A major part of the real estate industry is housing. An accurate housing price prediction is a valuable tool for buyer and seller as well as real estate agents. The study is done for the purpose of knowledge among the people to understand and estimate the pricing of their houses. The prediction will be made using four machine learning algorithms such as linear regression, polynomial regression, random forest, decision tree. Linear Regression has good interpretability. Decision tree is a graphical representation of all possible solutions. Polynomial regression can be easily fitted to a wide variety of curves. Regression and classification issues are resolved with random forests .Among the given algorithm, Random forest provides better accuracy of about 89% for given dataset.",0.0,https://ijritcc.org/index.php/ijritcc/article/download/8321/6598,True,JournalArticle,,2023-10-27,b44b6ce256aa963caa5123c0f7d3a96cb91d462a,housepricepredictionusingmachinelearningalgorithms,m,10.17762/ijritcc.v11i9.8321,housepricepredictionusingmachinelearningalgorithms|2023,1,2406,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",137e1f51d64b7e08b91c1a5103065a91cf95d765,10.17762/ijritcc.v10i8.5666,,Prediction of Housing Price and Forest Cover Using Mosaics with Uncertain Satellite Imagery,2022.0,International Journal on Recent and Innovation Trends in Computing and Communication,M. Sai; V. Rao; K. Vani; P. Poul,"The growing world is more expensive to estimate land use, road length, and forest cover using a plant-scaled ground monitoring system. Satellite imaging contains a significant amount of detailed uncertain information. Combining this with machine learning aids in the organization of these data and the estimation of each variable separately. The resources necessary to deploy Machine learning technologies for Remote sensing images, on the other hand, restrict their reach ability and application. Based on satellite observations which are notably underutilised in impoverished nations, while practical competence to implement SIML might be restricted. Encoded forms of images are shared across tasks, and they will be calculated and sent to an infinite number of researchers who can achieve top-tier SIML performance by training a regression analysis onto the actual data. By separating the duties, the proposed SIML solution, MOSAIKS, shapes SIML approachable and global. A Featurization stage turns remote sensing data into concise vector representations, and a regression step makes it possible to learn the correlations which are specific to its particular task which link the obtained characteristics to the set of uncertain data.",2.0,https://ijritcc.org/index.php/ijritcc/article/download/5666/5450,True,,,2022-08-31,137e1f51d64b7e08b91c1a5103065a91cf95d765,predictionofhousingpriceandforestcoverusingmosaicswithuncertainsatelliteimagery,sai,10.17762/ijritcc.v10i8.5666,predictionofhousingpriceandforestcoverusingmosaicswithuncertainsatelliteimagery|2022,1,2736,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a96d247980b0840c21f5095c2f6fc9fa55f2e9cc,10.22452/ijps.vol8no1.2,,"Classification Modelling for the Impact of Historical Site on Residential Property Rental Value in Osogbo, Nigeria",2018.0,International Journal of Property Sciences,Oyedeji Oyedeji; Joseph Oyewale,"Previous studies have revealed that historical sites have impact on property value. However, none of the previous studies have forecasted the impact of historical site on residential property value using a classification model. Data for the study were gathered from the record of recent letting in the study area. For the purpose of precision, this study adopted artificial neural network, logistic regression and support vector machine as model of classifying the rental value of residential property in Osogbo, Nigeria. The study considered relevant variables which include distance to cultural site, age of building, state of exterior and state of interior as input variables. Findings from the study revealed that the three adopted forecasting models have over 80% percent of the forecasted properties correctly classified which make the property forecasting reliable.",2.0,https://ijps.um.edu.my/article/download/13166/8333,True,,Computer Science,2018-08-30,a96d247980b0840c21f5095c2f6fc9fa55f2e9cc,classificationmodellingfortheimpactofhistoricalsiteonresidentialpropertyrentalvalueinosogbonigeria,oyedeji,10.22452/ijps.vol8no1.2,classificationmodellingfortheimpactofhistoricalsiteonresidentialpropertyrentalvalueinosogbonigeria|2018,1,2372,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),3d555b719dcd66f5b773ee480ecb47ce9229f3fe,10.28991/esj-2020-01250,,Estimation of Residential Property Market Price: Comparison of Artificial Neural Networks and Hedonic Pricing Model,2020.0,Emerging Science Journal,Michaela Štubňová; Marta Urbaníková; J. Hudáková; V. Papcunová,"The correct real estate property price estimation is significant not only in the real estate market but also in the banking sector for collateral loans and the insurance sector for property insurance. The paper focuses on both traditional and advanced methods for real estate property valuation. Attention is paid to the analysis of the accuracy of valuation models. From traditional methods, a regression model is used for residential property price estimation, which represents the hedonic approach. Modern advanced valuation methods are represented by the artificial neural network, which is one of the soft computing techniques. The results of both methods in residential property market price estimation are compared. The analysis is performed using data on residential properties sold on the real estate market in the city of Nitra in the Slovak Republic. To estimate the residential property prices, artificial neural networks trained with the Levenberg-Marquart learning algorithm, the Bayesian Regularization learning algorithm, and the Scaled Conjugate Gradient learning algorithm, and the regression pricing model are used. Among the constructed neural networks, the best results are achieved with networks trained with the Regularization learning algorithm with two hidden layers. Its performance is compared with the performance of the regression pricing model, and it can state that artificial neural networks can considerably improve prediction accuracy in the estimation of residential property market price. Doi: 10.28991/esj-2020-01250 Full Text: PDF",35.0,https://ijournalse.org/index.php/ESJ/article/download/425/pdf,True,,Computer Science,2020-12-01,3d555b719dcd66f5b773ee480ecb47ce9229f3fe,estimationofresidentialpropertymarketpricecomparisonofartificialneuralnetworksandhedonicpricingmodel,štubňová,10.28991/esj-2020-01250,estimationofresidentialpropertymarketpricecomparisonofartificialneuralnetworksandhedonicpricingmodel|2020,1,3068,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic property valuation || real estate hedonic regression",2,1,1,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1f9ec93ea62e6db567f4e34f08026cd3dfa37e13,10.25125/ijoer-aug-2026-1,,House Price Prediction in Nanjing Based on Machine Learning and SHAP Interpretability,2026.0,International Journal of Engineering Research and Science,Zai-Bin Yang; Xinxin Cui,"Accurate house price prediction is of great significance for homebuyers, developers, and policymakers, yet the complexity and non-linearity of housing markets pose substantial challenges to traditional linear models. This study addresses the dual goals of high predictive accuracy and high interpretability by systematically comparing five regression models—linear regression, random forest, XGBoost, LightGBM, and multilayer perceptron (MLP)—on a large-scale second-hand housing dataset from Nanjing, China. Model performance is evaluated using MAE, RMSE, and R². Results show that ensemble tree models significantly outperform linear regression and MLP, with LightGBM and XGBoost achieving the lowest MAE (6,200 RMB/m²) and RMSE (8,800 RMB/m²), and LightGBM attaining the highest R² (0.92). We further employ SHAP (SHapley Additive exPlanations) to open the ""black box"" of the optimal LightGBM model. Global feature importance reveals that house age, subway distance, and decoration condition are the three most influential drivers of Nanjing house prices. SHAP summary plots demonstrate that newer houses, closer subway proximity, and better decoration consistently raise prices, while area exhibits a non-monotonic effect—small units command higher unit prices under total-price constraints, whereas very large luxury properties show diminishing marginal value. Prediction diagnostics confirm robust performance in the mainstream price range (15,000–35,000 RMB/m²), though extreme high-end properties remain harder to predict due to unobserved idiosyncratic factors. This study not only provides a high-performance predictive tool for the Nanjing market but also offers transparent, actionable insights into the underlying price-formation mechanism. The integrated framework of machine learning plus SHAP interpretability is readily generalisable to other cities and real-estate contexts, supporting evidence-based decision-making and policy design.",0.0,https://ijoer.com/assets/articles_menuscripts/file/IJOER-AUG-2026-1.pdf,True,JournalArticle,,2026-08-31,1f9ec93ea62e6db567f4e34f08026cd3dfa37e13,housepricepredictioninnanjingbasedonmachinelearningandshapinterpretability,yang,10.25125/ijoer-aug-2026-1,housepricepredictioninnanjingbasedonmachinelearningandshapinterpretability|2026,1,3453,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",35974ce2320bc976b6e930c3a9175e75ce25b55e,10.47738/ijiis.v7i4.226,,Enhancing Housing Price Prediction Accuracy Using Decision Tree Regression with Multivariate Real Estate Attributes,2024.0,IJIIS: International Journal of Informatics and Information Systems,Ahmar Dwi Utomo,,2.0,https://ijiis.org/index.php/IJIIS/article/download/226/151,True,JournalArticle,,2024-12-01,35974ce2320bc976b6e930c3a9175e75ce25b55e,enhancinghousingpricepredictionaccuracyusingdecisiontreeregressionwithmultivariaterealestateattributes,utomo,10.47738/ijiis.v7i4.226,enhancinghousingpricepredictionaccuracyusingdecisiontreeregressionwithmultivariaterealestateattributes|2024,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b7797999153edec8212df5ef5955cc5c1ee405b3,10.64764/ijietas.v2.i1.05,,House Price Prediction Using Machine Learning Techniques,2026.0,International Journal of Innovations in Engineering Technology and Applied Sciences,M. Roy; Taufeeq Ahmad,"Prediction of housing market prices is critically important because many sectors of the housing market rely on accurate house price forecasts to make decisions about investments and financing. Therefore, this study presents an extensive analysis of the prediction of residential property prices in Delhi through using a machine-learning approach to connect multiple sources of information from the housing sector, including a curated database of housing sales in Delhi, geographical locations such as locations to metro stations, schools, hospitals, etc., and local environmental quality (i.e., air quality index). Through feature engineering and through the use of a Random Forest regressor trained with 7738 records of housing sales... The Random Forest regressor trained on 7738 records of housing sales has provided good performance with an R² of approximately 0.92 when tested against the test dataset when forecasting sale price for individual residential properties. Res1lts show that including data about location-related amenities (such as subway stations) and air quality significantly increases predictive accuracy of the sale prices of the houses in Delhi. An description of the models' output and analysis is also provided, as well as the implications of the findings for urban planning and housing market analysis.",1.0,https://ijietas.com/index.php/files/article/download/39/17,True,JournalArticle,,2026-06-12,b7797999153edec8212df5ef5955cc5c1ee405b3,housepricepredictionusingmachinelearningtechniques,roy,10.64764/ijietas.v2.i1.05,housepricepredictionusingmachinelearningtechniques|2026,1,2828,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3ac85b564f419ff3023722f763a396d5c461fef1,10.37391/ijeer.100215,,Free Hold Price Predictor Using Machine Learning,2022.0,International journal of electrical and electronics research,S. Singh; Shaurya Singh; S. Yadav; Avneesh Kumar,"People who want to buy a new home tend to save more on their budgets and market strategies. The current system includes real estate calculations without the necessary forecasts for future market trends and inflation. The housing market is one of the most competitive in terms of pricing and the same has varied greatly in terms of many factors. Asset pricing is an important factor in decision- making for both buyers and investors in supporting budget allocation, acquisition strategies and deciding on the best plans as a result, it is one of the most important areas in which machine learning ideas can be used to maximize and accurately anticipate prices. As a result, in this paper, we present the different significant factors that we employ to accurately anticipate property values. To reduce residual errors, we can utilize regression models with a range of characteristics. Some engineering aspects are required when employing features in the regression model for improved prediction. To improve model fit, a set of multi-regression elements or a polynomial regression (with a set of varying strengths in the elements) is frequently utilized. In these models, it is expected to be significantly affected by the slope of the spine used to reduce it. Therefore, it directs the best use of regression models over other strategies to maximize the effect. This paper's goal is to predict free hold prices for free hold consumers based on their budgets and goals. Prospective prices can be forecast by evaluating past market trends and price levels, as well as future developments.",4.0,https://ijeer.forexjournal.co.in/papers-pdf/ijeer-100215.pdf,True,,,2022-06-30,3ac85b564f419ff3023722f763a396d5c461fef1,freeholdpricepredictorusingmachinelearning,singh,10.37391/ijeer.100215,freeholdpricepredictorusingmachinelearning|2022,1,3084,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",40013dcf66e67693970973566144b2b8c645e281,10.11591/ijeecs.v35.i2.pp1110-1121,,"Machine learning for real estate valuation: Astana, Kazakhstan case",2024.0,Indonesian Journal of Electrical Engineering and Computer Science,A. Barlybayev; Arman Sankibayev; R. Niyazova; Gulnara Akimbekova,"Purpose of this research is to investigate the accuracy of machine learning models in forecasting and evaluating house prices, and to understand the key factors that impact pricing. The study involved analyzing data scraped from real estate ads in the “sale of secondary housing” category on the website krisha.kz. The paper emphasizes the importance of understanding the factors that affect house prices, such as quality, location, size, and building materials. It was concluded that these factors have a strong correlation with house price prediction. The information available on krisha.kz was found to be a useful resource for finding good apartments. The data collected by the scraper was analyzed by models: Linear regression (LR), interactions linear regression (ILR), robust linear regression (RLR), fine tree regression (FTR), medium tree regression (MTR), coarse tree regression (CTR), linear support vector machine (LSVM), quadratic SVM (QSVM), medium gaussian SVM (MGSVM), rational quadratic gaussian process regression (RQGPR), boosted trees (BoosT), bagged trees (BagT), neural network based on the bayesian regularization algorithm (BR-BPNN). BR-BPNN showed better results than other models, with an MSE of 32.14 and R of 0.9899.",3.0,https://ijeecs.iaescore.com/index.php/IJEECS/article/download/36338/18498,True,JournalArticle,,2024-08-01,40013dcf66e67693970973566144b2b8c645e281,machinelearningforrealestatevaluationastanakazakhstancase,barlybayev,10.11591/ijeecs.v35.i2.pp1110-1121,machinelearningforrealestatevaluationastanakazakhstancase|2024,1,2744,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",59b5ed19cb2ffcbedb32ba932b2f0e26b3dffd2c,10.11591/ijece.v13i4.pp4516-4525,,The role of neural network for estimating real estate prices value in post COVID-19: a case of the middle east market,2023.0,International Journal of Electrical and Computer Engineering (IJECE),Laith T. Khrais; Osman Shidwan,"The main goal of this paper was to explore the use of an artificial neural network (ANN) model in predicting real estate prices in the Middle East market. Although conventional modeling approaches such as regression can be used in prediction, they have a weakness of a predetermined relationship between input and output. In this regard, using the ANN model was expected to reduce the bias and ensure non-linear relationships are also covered in the prediction process for more accurate results. The ANN model was created using Python v.3.10 program. The model exhibited a high correlation between predicted and actual house price data (R=0.658). In this respect, it was realized that the model could be effectively used in appraising real estate by investors. However, a major limitation of the model was realized to be a limited dataset for large and luxurious houses, which were not accurately predicted as data distribution between actual and predicted values became sparse for high house prices. A key recommendation made is that future research should include more variables related to luxurious houses and macroeconomic factors to increase the ANN model accuracy.",21.0,https://ijece.iaescore.com/index.php/IJECE/article/download/30809/16730,True,JournalArticle,Medicine,2023-08-01,59b5ed19cb2ffcbedb32ba932b2f0e26b3dffd2c,theroleofneuralnetworkforestimatingrealestatepricesvalueinpostcovid19acaseofthemiddleeastmarket,khrais,10.11591/ijece.v13i4.pp4516-4525,theroleofneuralnetworkforestimatingrealestatepricesvalueinpostcovid19acaseofthemiddleeastmarket|2023,1,2670,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ceadd302511bfdcbeebb266937f5d9e55078c990,10.22399/ijcesen.2870,,Evaluating Machine Learning Models for House Price Prediction with Different Sampling Techniques,2025.0,International Journal of Computational and Experimental Science and Engineering,A. Çetin,"This study investigates the interplay between advanced sampling techniques and machine learning models to predict residential property sale prices using a diverse dataset encompassing structural, locational, and economic attributes. Emphasizing Stratified Extreme Ranked Set Sampling (SERSS), the research systematically evaluates the impact of five sampling methods—SERSS, Cluster, Bootstrap, Systematic, and Random Sampling—on various machine learning algorithms, including CatBoost, Random Forest, ElasticNet, and FIkNN. The findings reveal that SERSS significantly enhances the generalizability and robustness of predictive models by capturing both central and extreme data tendencies, outperforming traditional methods in preserving dataset variability. Ensemble methods like CatBoost, Random Forest and similarity algorithm like FIkNN consistently demonstrated superior predictive accuracy, achieving the Mean Absolute Error (MAE) between $85 and $650, and high R² values across structured sampling techniques. Conversely, unstructured methods such as Random Sampling introduced biases, leading to substantial deviations in predictions. These results underscore the critical importance of aligning sampling methodologies with model-specific characteristics to optimize performance. This study provides actionable insights for researchers and practitioners in predictive modeling, offering a framework for integrating sampling strategies with advanced machine learning models to tackle heterogeneous datasets effectively.",0.0,https://ijcesen.com/index.php/ijcesen/article/download/2870/826,True,JournalArticle,,2025-06-01,ceadd302511bfdcbeebb266937f5d9e55078c990,evaluatingmachinelearningmodelsforhousepricepredictionwithdifferentsamplingtechniques,çetin,10.22399/ijcesen.2870,evaluatingmachinelearningmodelsforhousepricepredictionwithdifferentsamplingtechniques|2025,1,3026,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",4542e688786256d39af1cf316b6fd83b6cedae0f,10.15849/ijasca.v18i1.42,,Predicting Apartment Prices in Jordan Using Ensemble Machine Learning Algorithms to Support Investment and Urban Planning Decisions,2026.0,International journal of advances in soft computing and its applications,N.F.S Thalji; Mohammad Alwadi,"The housing market is of great significance to the development and advancement of cities, but customary forms of property valuation are frequently biased, time-consuming, and not always effective. This paper focuses on the city of Irbid in Jordan, aiming to collect all the information on apartments and houses, predict the prices of properties, and clarify the key factors influencing the prices. Following the comprehensive cleaning process of the data and exploratory analysis, three ensemble machine learning models were trained and optimized to achieve accurate price predictions. The performance of all three models demonstrated excellent and consistent predictions, highlighting the efficiency of ensemble methods in predicting property prices. SHAP analysis indicated that the size of the house, the number of bedrooms, the number of lounges as well as the location are the most significant factors influencing the prices in Irbid. This reflects the functioning of the local market.",0.0,https://ijasca.zuj.edu.jo/index.php/IJASCA/article/download/42/28,True,JournalArticle,,2026-03-05,4542e688786256d39af1cf316b6fd83b6cedae0f,predictingapartmentpricesinjordanusingensemblemachinelearningalgorithmstosupportinvestmentandurbanplanningdecisions,thalji,10.15849/ijasca.v18i1.42,predictingapartmentpricesinjordanusingensemblemachinelearningalgorithmstosupportinvestmentandurbanplanningdecisions|2026,1,2490,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d5cf4474cfb10fc567f3625da0991c5d33732aa9,10.15849/ijasca.v18i1.30,,Bitcoin Price Forecasting Leveraging X Data and Sentiment Indicators Via an LSTM-Enhanced Deep Learning Architecture,2026.0,International journal of advances in soft computing and its applications,Y. Özen; Mohammed Amen Azal Alwindawi,"The housing market is of great significance to the development and advancement of cities, but customary forms of property valuation are frequently biased, time-consuming, and not always effective. This paper focuses on the city of Irbid in Jordan, aiming to collect all the information on apartments and houses, predict the prices of properties, and clarify the key factors influencing the prices. Following the comprehensive cleaning process of the data and exploratory analysis, three ensemble machine learning models were trained and optimized to achieve accurate price predictions. The performance of all three models demonstrated excellent and consistent predictions, highlighting the efficiency of ensemble methods in predicting property prices. SHAP analysis indicated that the size of the house, the number of bedrooms, the number of lounges as well as the location are the most significant factors influencing the prices in Irbid. This reflects the functioning of the local market.",1.0,https://ijasca.zuj.edu.jo/index.php/IJASCA/article/download/30/29,True,JournalArticle,,2026-03-05,d5cf4474cfb10fc567f3625da0991c5d33732aa9,bitcoinpriceforecastingleveragingxdataandsentimentindicatorsviaanlstmenhanceddeeplearningarchitecture,özen,10.15849/ijasca.v18i1.30,bitcoinpriceforecastingleveragingxdataandsentimentindicatorsviaanlstmenhanceddeeplearningarchitecture|2026,1,2490,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b9a31e5026237365f502539781380854bd433e8d,10.1109/access.2021.3089198,,A Real Estate Valuation Model Using Boosted Feature Selection,2021.0,IEEE Access,Kankawee Chanasit; E. Chuangsuwanich; A. Suchato; P. Punyabukkana,"To estimate real estate values, a complex valuation model based on artificial neural network (ANN) has been established as a successful means in modern machine learning research, specifically when high-dimensional data are available. Unfortunately, the real estate data in many locations, such as Thailand, are quite limited in terms of features. Hence, it becomes mandatory to reduce the complexity using feature selection techniques. These techniques aim to improve performance by identifying significant factors and help decrease the computational overload and model construction. However, due to the lack of explicability and interpretability in ANNs, the analysis of input factors cannot be explained directly by model composition. In this research, we apply a combination of a boosting strategy and input sensitivity analysis in an improved Garson’s algorithm to perform feature selection that can adjust its selection criteria through each iteration on an ANN model. This proposed technique is then compared with other traditional feature selection techniques using synthetic data and real-world house valuation data. The results show that our model can maintain the sensitivity coefficient for every informative feature. The technique of this study provides a set of features that influences the house price and implies the character of each specific area. It also contributes to statistical improvement in most valuation cases, with a lower error than that obtained without a selected feature dataset, and it placed in the top 24% of the public leaderboard in the Zillow Prize competition.",10.0,https://ieeexplore.ieee.org/ielx7/6287639/9312710/09454508.pdf,True,JournalArticle,Computer Science,,b9a31e5026237365f502539781380854bd433e8d,arealestatevaluationmodelusingboostedfeatureselection,chanasit,10.1109/access.2021.3089198,arealestatevaluationmodelusingboostedfeatureselection|2021,1,3098,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1b30433b8928ba3de190c1ab8ade1d974af466b2,10.1109/access.2021.3071306,,Deep Learning Model for House Price Prediction Using Heterogeneous Data Analysis Along With Joint Self-Attention Mechanism,2021.0,IEEE Access,Pei-Ying Wang; Chiao-Ting Chen; Jain-Wun Su; Ting-Wei Wang; Szu-Hao Huang,"House price prediction is a popular topic, and research teams are increasingly performing related studies by using deep learning or machine learning models. However, because some studies have not considered comprehensive information that affects house prices, prediction results are not always sufficiently precise. Therefore, we propose an end to end joint self-attention model for house prediction. In this model, we import data on public facilities such as parks, schools, and mass rapid transit stations to represent the availability of amenities, and we use satellite maps to analyze the environment surrounding houses. We adopt attention mechanisms, which are widely used in image, speech, and translation tasks, to identify crucial features that are considered by prospective house buyers. The model can automatically assign weights when given transaction data. Our proposed model differs from self-attention models because it considers the interaction between two different features to learn the complicated relationship between features in order to increase prediction precision. We conduct experiments to demonstrate the performance of the model. Experimental data include actual selling prices in real estate transaction data for the period from 2017 to 2018, public facility data acquired from the Taipei and New Taipei governments, and satellite maps crawled using the Google Maps application programming interface. We utilize these datasets to train our proposed and compare its performance with that of other machine learning-based models such as Extreme Gradient Boosting and Light Gradient Boosted Machine, deep learning, and several attention models. The experimental results indicate that the proposed model achieves a low prediction error and outperforms the other models. To the best of our knowledge, we are the first research to incorporate attention mechanism and STN network to conduct house price prediction.",88.0,https://ieeexplore.ieee.org/ielx7/6287639/9312710/09395585.pdf,True,JournalArticle,Computer Science,,1b30433b8928ba3de190c1ab8ade1d974af466b2,deeplearningmodelforhousepricepredictionusingheterogeneousdataanalysisalongwithjointselfattentionmechanism,wang,10.1109/access.2021.3071306,deeplearningmodelforhousepricepredictionusingheterogeneousdataanalysisalongwithjointselfattentionmechanism|2021,1,3434,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",63d0fca33adae59cd1330ba9da2372dd3f5cfe09,10.1109/access.2022.3210281,,Graph Convolutional Network-Based Model for Megacity Real Estate Valuation,2022.0,IEEE Access,Zong-Mou Yang; Z. Hong; Ru-Yan Zhou; Hong Ai,"It is challenging to make precise assessments of real estate prices due to its elevated individual prices, complicated influencing factors, and ambiguous attribute selection. As a result of the high demand for owner-occupied and investment properties, real estate is also a substantial concern for society. A hot topic for research by major institutions has been how to accurately estimate its price. Real-world applications of real estate valuation impose stringent requirements on the acquisition of datasets and the generalizability of models. On the basis of SRGCNN, a spatial regression model with excellent generalizability, this paper introduces an external attention mechanism to construct the A-SRGCNN model and compares it to the benchmark model utilizing data from Shanghai, Melbourne, and San Diego. For spatial regression, A-SRGCNN employs graph convolutional neural networks, and the external attention mechanism implicitly considers the relationship between property data. Experiments indicate that the A-SRGCNN model outperforms the benchmark model and has improved real estate price estimation accuracy. In the meantime, this paper employs the A-SRGCNN model to conduct zonal experiments and time-division experiments on the secondary real estate market in Shanghai to analyze the real estate price linkages between different zones and the real estate price linkages at different times. It is revealed that Shanghai real estate prices exhibit spatial aggregation and price aggregation, with comparable prices within the same zones, and that the A-SRGCNN model is effective at predicting house prices.",8.0,https://ieeexplore.ieee.org/ielx7/6287639/6514899/09904611.pdf,True,JournalArticle,Computer Science,,63d0fca33adae59cd1330ba9da2372dd3f5cfe09,graphconvolutionalnetworkbasedmodelformegacityrealestatevaluation,yang,10.1109/access.2022.3210281,graphconvolutionalnetworkbasedmodelformegacityrealestatevaluation|2022,1,3117,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ef7a5b7e886618eece6be16ddff155ae51bfbffc,10.54097/pqmcfy41,,Urban Housing Price Prediction and Service Level Evaluation Based on Machine Learning,2025.0,Highlights in Science Engineering and Technology,Jiaye Chen; Jia-Rui Huang; Hua-Wei Huang; Ali Zulfiqar,"With the acceleration of urbanization, the sustainable development of cities and the improvement of residents' quality of life have become major challenges facing the world. Taking Wenzhou City and Hohhot City of China as examples, this paper uses machine learning method to build urban housing price prediction and service level evaluation models. First, the stochastic forest regression and ridge regression models are used to predict the housing price, and the forecasting effect of the two models is compared. The results show that the stochastic forest regression model has better performance in capturing nonlinear relationship and forecasting accuracy. Secondly, a multi-index comprehensive evaluation model is constructed to evaluate the urban service level, and the factors affecting the service level are determined by weighted summation method. The results show that optimizing the greening rate and property management fees can effectively enhance the service level and improve the quality of life of residents. The research results of this paper provide a scientific basis for urban planning and decision-making, and provide a reference for the sustainable development of other cities. Future studies can further explore other factors that affect housing prices and service levels, and incorporate more advanced machine learning models to improve the predictive accuracy and practicality of the models.",0.0,https://hsetdata.com/index.php/ojs/article/download/597/557,True,JournalArticle,,2025-05-18,ef7a5b7e886618eece6be16ddff155ae51bfbffc,urbanhousingpricepredictionandservicelevelevaluationbasedonmachinelearning,chen,10.54097/pqmcfy41,urbanhousingpricepredictionandservicelevelevaluationbasedonmachinelearning|2025,1,2915,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",dc35a0cc02a20045ba88b28fb9c28cd847fa4673,10.1109/ubmk55850.2022.9919540,,Reviewing the Effects of Spatial Features on Price Prediction for Real Estate Market: Istanbul Case,2022.0,2022 7th International Conference on Computer Science and Engineering (UBMK),M. İ. Ecevit; Z. Erdem; H. Dağ,"In the real estate market, spatial features play a crucial role in determining property appraisals and prices. When spatial features are considered, classification techniques have been rarely studied compared to regression, which is commonly used for price prediction. This study reviews spatial features' effects on predicting the house price ranges for real estate in Istanbul, Turkey, in the classification context. Spatial features are generated and extracted by geocoding the address information from the original data set. This geocoding and feature extraction is another challenge in this research. The experiments compare the performance of Decision Trees (DT), Random Forests (RF), and Logistic Regression (LR) classifier models on the data set with and without spatial features. The prediction models are evaluated based on classification metrics such as accuracy, precision, recall, and F1-Score. We additionally examine the ROC curve of each classifier. The test results show that the RF model outperforms the DT and LR models. It is observed that spatial features, when incorporated with non-spatial features, significantly improve the prediction performance of the models for the house price ranges. It is considered that the results can contribute to making decisions more accurately for the appraisal in the real estate industry.",1.0,https://hdl.handle.net/11729/5362,True,Conference;Review,,2022-09-14,dc35a0cc02a20045ba88b28fb9c28cd847fa4673,reviewingtheeffectsofspatialfeaturesonpricepredictionforrealestatemarketistanbulcase,ecevit,10.1109/ubmk55850.2022.9919540,reviewingtheeffectsofspatialfeaturesonpricepredictionforrealestatemarketistanbulcase|2022,1,2845,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",183debbb3f2d76550a76f0be9c2c609dc551ed3a,10.54097/xbqqjf85,,A Stable and Interpretable NNLS Ensemble for House Price Prediction on the Ames Dataset,2025.0,"Highlights in Business, Economics and Management",Ze-Yu Xiao,"Predicting house prices has broad real-world impact across valuation, lending, and taxation. This paper proposes a principled stacking method for the Ames Housing dataset that blends eight strong regressors—linear, bagging, and boosting—via a simplex-constrained Non-Negative Least Squares (NNLS) optimizer. By enforcing non-negativity and a sum-to-one constraint, the meta-learner produces interpretable convex weights and mitigates collinearity among base models. A pragmatically tuned clipping policy is introduced to stabilize the conversion from log-space pre- dictions back to prices. In a 10-fold out-of-fold evaluation, the ensemble achieves competitive accuracy (R2 =0.886 in price space; R2 =0.906 in log space), closely tracking the best single model while remaining fully transparent. Beyond accuracy, the method offers operational simplicity: standard, stable hyperparameter ranges; minimal preprocessing; and negligible inference overhead for blending. Taken together, these properties yield a reproducible and reliable blueprint for tabular regression ensembles that balances performance, robustness, and interpretability, and can be adopted with minimal engineering effort in commercial settings.",0.0,https://hbemdata.org/index.php/ojs/article/download/96/82,True,JournalArticle,,2025-12-27,183debbb3f2d76550a76f0be9c2c609dc551ed3a,astableandinterpretablennlsensembleforhousepricepredictionontheamesdataset,xiao,10.54097/xbqqjf85,astableandinterpretablennlsensembleforhousepricepredictionontheamesdataset|2025,1,2712,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3369ad0bf58661d98bc8d4f05508972c69d0f664,10.54097/cjaf8x11,,Comparison Of Linear and Nonlinear Models in Predicting House Prices,2025.0,"Highlights in Business, Economics and Management",Zuyu Lin,"Nowadays, the increase in housing prices has led to a heavier financial burden on ordinary citizens, affecting their quality of life. This research used linear regression, random forest, and Support Vector Machine (SVM) to predict housing prices using the Boston housing price dataset, and compared the advantages and disadvantages of them. The paper compares the performance of linear and nonlinear models in predicting housing prices to reflect the importance and magnitude of various factors that affect housing prices. The overall research demonstrates that nonlinear models have unique advantages over linear models in multi-factor impact problems such as predicting housing prices. This is due to the comprehensive examination of multiple factors by nonlinear models, which linear models do not possess. The project still has many shortcomings in many aspects, such as not tuning the parameters of the SVM. There is also no addition of cross-validation, and a lack of visualization of the results.",0.0,https://hbem.org/index.php/OJS/article/download/546/510,True,JournalArticle,,2025-08-19,3369ad0bf58661d98bc8d4f05508972c69d0f664,comparisonoflinearandnonlinearmodelsinpredictinghouseprices,lin,10.54097/cjaf8x11,comparisonoflinearandnonlinearmodelsinpredictinghouseprices|2025,1,2503,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a362e342d478ec7efe873228840ed32d58bbfec9,10.64149/gjaets.13.2.1-6,,A MACHINE LEARNING-BASED HYBRID FRAMEWORK FOR REAL ESTATE PRICE PREDICTION,2026.0,Global journal of advanced engineering technologies and sciences,Shyam Patidar; Arpit Solanki,"Accurate prediction of real estate prices plays a significant role in financial planning, investment decision-making, urban development, and smart city management. Traditional property valuation approaches mainly rely on statistical regression techniques and expert judgment, which often fail to model nonlinear market behavior and heterogeneous housing datasets. To overcome these limitations, this research proposes a novel Hybrid Adaptive Real Estate Prediction Algorithm (HAREPA) within a machine learning–based hybrid framework. The proposed system integrates Linear Regression, K-Nearest Neighbor, and Support Vector Regression models through adaptive ensemble learning. Feature engineering techniques are employed to extract meaningful attributes and improve learning efficiency. Adaptive weights are automatically computed using inverse error analysis, enabling dynamic contribution of each model during prediction. Experimental evaluation is conducted using a real-world real estate dataset obtained from Kaggle. Performance comparison using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and prediction accuracy demonstrates that the proposed HAREPA model significantly outperforms individual machine learning algorithms. The results confirm improved prediction accuracy, reduced error variance, and enhanced generalization capability.",0.0,https://gjaets.com/index.php/gjaets/article/download/402/383,True,JournalArticle,,2026-02-28,a362e342d478ec7efe873228840ed32d58bbfec9,amachinelearningbasedhybridframeworkforrealestatepriceprediction,patidar,10.64149/gjaets.13.2.1-6,amachinelearningbasedhybridframeworkforrealestatepriceprediction|2026,1,2858,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4a1815db0ff486c6557e5a02db48d96f8e76d0ce,10.1108/ijhma-01-2025-0004,,Real estate decision-making: precision in price prediction through advanced machine learning algorithms,2025.0,International Journal of Housing Markets and Analysis,Mohammad A. Shbool; Rand H. Al-Dmour; B. Al-Shboul; N. Albashabsheh; Najat Almasarwah,"Purpose This study aims to enhance real estate price prediction accuracy using advanced machine learning models, minimizing biases and inconsistencies inherent in traditional appraisal methods. By leveraging support vector regression (SVR) and gradient boosting machine (GBM), this study provides a data-driven approach to property valuation, improving decision-making for buyers, sellers and policymakers. This study also seeks to bridge the gap in machine learning applications for emerging markets like Jordan. This study’s research’s broader goal is to offer a transparent, efficient and reliable tool for property valuation that improves market efficiency and reduces transaction uncertainty. Design/methodology/approach This study uses machine learning techniques – SVR and GBM – to predict real estate prices in Amman, Jordan. Data was collected from the Department of Lands and Survey, covering residential property sales from March 2023 to December 2023. The data set underwent preprocessing, including one-hot encoding for categorical variables and logarithmic normalization for skewed data. Hyperparameter tuning was performed using grid search, and an ensemble approach compared multiple algorithms. Performance was evaluated using root mean squared error (RMSE), mean absolute percentage error (MAPE) and MAE. The findings were implemented into a user-friendly “PRICE IT” application for real-world application. Findings The results demonstrate that SVR outperforms GBM in predicting real estate prices, achieving the lowest RMSE (0.31) and MAPE (25%). The most influential factors in price determination are property area, location and apartment type. The study highlights that machine learning models provide superior accuracy compared to traditional appraisal methods. The findings support the integration of data-driven valuation techniques in real estate markets, reducing reliance on subjective human judgment. A user-friendly application was developed to enable nontechnical users to estimate property prices, making the research practical and impactful. Originality/value This study contributes to the growing field of machine learning applications in real estate by demonstrating the effectiveness of SVR and GBM in an emerging market context. Unlike previous research, it focuses on Amman, Jordan, where limited studies have explored advanced machine-learning models for price prediction. The study offers a practical, user-friendly valuation tool that real estate stakeholders can widely adopt. This research enhances decision-making and market efficiency by providing a transparent and objective alternative to traditional appraisal methods. ",9.0,https://figshare.com/articles/journal_contribution/Real_estate_decision-making_precision_in_price_prediction_through_advanced_machine_learning_algorithms/29161865,True,JournalArticle;Review,,2025-03-07,4a1815db0ff486c6557e5a02db48d96f8e76d0ce,realestatedecisionmakingprecisioninpricepredictionthroughadvancedmachinelearningalgorithms,shbool,10.1108/ijhma-01-2025-0004,realestatedecisionmakingprecisioninpricepredictionthroughadvancedmachinelearningalgorithms|2025,1,4170,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",de9df99097a91a951cee948f2d10c566362746d6,10.1080/17421772.2023.2242897,,Comparing modelling performance and evaluating differences of feature importance on defined geographical appraisal zones for mass real estate appraisal,2023.0,Spatial Economic Analysis,A. Aydinoglu; Süleyman Şişman,"ABSTRACT The features influencing real estate value in different residential areas and cities are important for spatial economic analysis besides high appraisal accuracy. In this study, a methodology was developed for computer-assisted mass real estate appraisal with a case study implemented through the use of big geographical datasets including 121 features and around 200,000 samples of real estate in Istanbul and Kocaeli (Turkey). Prediction models using the random forest technique were developed for five appraisal zones determined with spatially constrained multivariate clustering. With machine learning and mass appraisal metrics, modelling performance improves in appraisal zones with a lower standard deviation expressing real estate value in neighbourhoods. Since importance levels and ranks of features vary in zones, the mass appraisal should be done with a sufficient number of features.",12.0,https://figshare.com/articles/journal_contribution/Comparing_modelling_performance_and_evaluating_differences_of_feature_importance_on_defined_geographical_appraisal_zones_for_mass_real_estate_appraisal/24069594,True,JournalArticle,,2023-08-31,de9df99097a91a951cee948f2d10c566362746d6,comparingmodellingperformanceandevaluatingdifferencesoffeatureimportanceondefinedgeographicalappraisalzonesformassrealestateappraisal,aydinoglu,10.1080/17421772.2023.2242897,comparingmodellingperformanceandevaluatingdifferencesoffeatureimportanceondefinedgeographicalappraisalzonesformassrealestateappraisal|2023,1,2404,1,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",455b3094212a104b5ab70dd047f6bbf3e6e6b475,10.1080/13658816.2024.2343771,,A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: a case study on house price in Wuhan,2024.0,International Journal of Geographical Information Science,Jiale Ding; Wenying Cen; Sen-Sen Wu; Yi-Jun Chen; Jin Qi; Bo Huang; Zhen-Hong Du,"Abstract The estimation of spatial heterogeneity within real estate markets holds significant importance in house price modelling. However, employing a single or straightforward distance to measure spatial proximity is probably insufficient in complex urban areas, thereby resulting in an inadequate modelling of spatial heterogeneity. To address this issue, this paper incorporates multiple distance measures within a neural network framework to achieve an optimized measure of spatial proximity (OSP). Consequently, a geographically neural network weighted regression model with optimized measure of spatial proximity (osp-GNNWR) is devised for the purpose of spatially heterogeneous modeling. Trained as a unified model, osp-GNNWR obviates the need for separate pretraining of OSP. This enables OSP to delineate the modeled spatial process through a post hoc calculated value. Through simulation experiments and a real-world case study on house prices, the proposed model reaches more accurate descriptions of diverse spatial processes and exhibits better overall performance. The interpretable results of the case study in Wuhan demonstrate the efficacy of the osp-GNNWR model in addressing spatial heterogeneity within real estate markets, suggesting its potential for modelling and predicting complex geographical phenomena.",19.0,https://figshare.com/articles/journal_contribution/A_neural_network_model_to_optimize_the_measure_of_spatial_proximity_in_geographically_weighted_regression_approach_a_case_study_on_house_price_in_Wuhan/25689685/1/files/45852016.pdf,True,JournalArticle,Computer Science,2024-04-25,455b3094212a104b5ab70dd047f6bbf3e6e6b475,aneuralnetworkmodeltooptimizethemeasureofspatialproximityingeographicallyweightedregressionapproachacasestudyonhousepriceinwuhan,ding,10.1080/13658816.2024.2343771,aneuralnetworkmodeltooptimizethemeasureofspatialproximityingeographicallyweightedregressionapproachacasestudyonhousepriceinwuhan|2024,1,2830,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,1,1,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",0763e624f2a028a65213d8b4ac33d05ee698d93f,10.1109/wacv.2017.42,1610.04805,Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery,2016.0,IEEE Workshop/Winter Conference on Applications of Computer Vision,Archith J. Bency; S. Rallapalli; R. Ganti; M. Srivatsa; B. S. Manjunath,"When modeling geo-spatial data, it is critical to capture spatial correlations for achieving high accuracy. Spatial Auto-Regression (SAR) is a common tool used to model such data, where the spatial contiguity matrix (W) encodes thespatial correlations. However, the efficacy of SAR is limited by two factors. First, it depends on the choice of contiguity matrix, which is typically not learnt from data, but instead, is assumed to be known apriori. Second, it assumes that the observations can be explained by linear models. In this paper, we propose a Convolutional Neural Network (CNN) framework to model geo-spatial data (specifically housing prices), to learn the spatial correlations automatically. We show that neighborhood information embedded in satellite imagery can be leveraged to achieve the desired spatial smoothing. An additional upside of our framework is the relaxation of linear assumption on the data. Specific challenges we tackle while implementing our framework include, (i) how much of the neighborhood is relevant while estimating housing prices? (ii) what is the right approach to capture multiple resolutions of satellite imagery? and (iii) what other data-sources can help improve the estimation of spatial correlations? We demonstrate a marked improvement of 57% on top of the SAR baseline through the use of features from deep neural networks for the cities of London, Birmingham and Liverpool.",59.0,https://escholarship.org/content/qt77c0438f/qt77c0438f.pdf?t=owuq6w,True,JournalArticle;Conference,Computer Science,2016-10-16,0763e624f2a028a65213d8b4ac33d05ee698d93f,beyondspatialautoregressivemodelspredictinghousingpriceswithsatelliteimagery,bency,10.1109/wacv.2017.42,beyondspatialautoregressivemodelspredictinghousingpriceswithsatelliteimagery|2016,1,2923,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",edd785d2ee888ca78237a0107d2b417fca3fc29c,10.1140/epjds/s13688-023-00424-3,2302.13034,Does noise affect housing prices? A case study in the urban area of Thessaloniki,2023.0,EPJ Data Science,Georgios Kamtziridis; D. Vrakas; Grigorios Tsoumakas,"Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies endeavor to create more accurate machine learning models by utilizing data such as basic property characteristics as well as urban features like distances from amenities and road accessibility. Even though environmental factors like noise pollution can potentially affect prices, the research around this topic is limited. One of the reasons is the lack of data. In this paper, we reconstruct and make publicly available a general purpose noise pollution dataset based on published studies conducted by the Hellenic Ministry of Environment and Energy for the city of Thessaloniki, Greece. Then, we train ensemble machine learning models, like XGBoost, on property data for different areas of Thessaloniki to investigate the way noise influences prices through interpretability evaluation techniques. Our study provides a new noise pollution dataset that not only demonstrates the impact noise has on housing prices, but also indicates that the influence of noise on prices significantly varies among different areas of the same city.",7.0,https://epjdatascience.springeropen.com/counter/pdf/10.1140/epjds/s13688-023-00424-3,True,JournalArticle,Computer Science,2023-02-25,edd785d2ee888ca78237a0107d2b417fca3fc29c,doesnoiseaffecthousingpricesacasestudyintheurbanareaofthessaloniki,kamtziridis,10.1140/epjds/s13688-023-00424-3,doesnoiseaffecthousingpricesacasestudyintheurbanareaofthessaloniki|2023,1,2712,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",db31754132ba4c08b18fbe8b322189a07da4ef46,10.30865/jurikom.v12i6.9369,,"Prediksi Harga Rumah Menggunakan Algoritma Regresi Linier,Random Forest, Dan Gradient Boosting",2025.0,JURIKOM (Jurnal Riset Komputer),Akhmadi Akhmadi; Fikri Budiman,"House price prediction is a crucial issue in the property sector because it is influenced by various interrelated factors, such as building characteristics and environmental conditions. Accurate prediction using conventional approaches is often difficult and can lead to errors in decision-making. Therefore, this study aims to develop and compare the performance of house price prediction models using three machine learning algorithms: Linear Regression, Random Forest, and Gradient Boosting. The dataset used is the Home Value Insights Dataset on Kaggle, which consists of 1,000 houses with eight main attributes. The research stages include data pre-processing, dividing training and test data, model training, parameter optimization using GridSearchCV, and performance evaluation based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) metrics using the 10-Fold Cross Validation method. The test results show that Linear Regression provides the best performance with an R² value of 0.8539 and a lower prediction error rate than Random Forest and Gradient Boosting. Although the ensemble model shows competitive results, increasing model complexity does not result in a significant increase in accuracy, so Linear Regression is considered the simplest, most efficient, and most easily interpreted approach for house price prediction systems on datasets with characteristics that tend to be linear.",1.0,https://ejurnal.stmik-budidarma.ac.id/index.php/jurikom/article/download/9369/4297,True,JournalArticle,,2025-12-31,db31754132ba4c08b18fbe8b322189a07da4ef46,prediksihargarumahmenggunakanalgoritmaregresilinierrandomforestdangradientboosting,akhmadi,10.30865/jurikom.v12i6.9369,prediksihargarumahmenggunakanalgoritmaregresilinierrandomforestdangradientboosting|2025,1,2951,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",164cbfb574c942f5224d4e5678d223fc3c998197,10.31284/j.kernel.2024.v5i2.6806,,PREDIKSI HARGA RUMAH DI JABODETABEK MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK,2025.0,KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika,M. Hafizh; Subairi Subairi; Raditya Dimas Libriawan; Naufal Maulana; A. Rizki,"A house is a fundamental need for humans. Determining house prices is a crucial aspect of property transactions, especially in major areas like Jabodetabek, where property prices are consistently rising. Prediction is a suitable tool to assist in decision-making for determining house prices. There are numerous methods that can be applied for prediction; the author employs the Artificial Neural Network (ANN) method. ANN is known as a highly flexible predictive algorithm capable of accommodating various input features. The results of using the ANN method for predicting house prices in the Jabodetabek area show a Mean Absolute Error (MAE) of 0.209, Mean Squared Error (MSE) of 0.159, and Mean Absolute Percentage Error (MAPE) of 4.951.",2.0,https://ejurnal.itats.ac.id/kernel/article/download/6806/4363,True,JournalArticle,,2025-04-26,164cbfb574c942f5224d4e5678d223fc3c998197,prediksihargarumahdijabodetabekmenggunakanmetodeartificialneuralnetwork,hafizh,10.31284/j.kernel.2024.v5i2.6806,prediksihargarumahdijabodetabekmenggunakanmetodeartificialneuralnetwork|2025,1,2240,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a5479e6447253f914c85e0d7a2e986699903eecd,10.59324/ejtas.2023.1(5).22,,Prediction of House Prices in Lagos-Nigeria Using Machine Learning Models,2023.0,European Journal of Theoretical and Applied Sciences,M. P. Nwankwo; Ndukaku Macdonald Onyeizu; Emmanuel Chibuogu Asogwa; Chukwuogo Okwuchukwu Ejike; Okechukwu J. Obulezi,"This paper considers the relationship between the price of houses and the features namely the number of bedrooms, parking space, and different house types. In this study, a machine learning approach was used to develop prediction models that predicted house prices in Lagos. Different machine learning techniques were used, train-test split to split the data into training sets for training and building the model and test data to test the accuracy of the model, performance metric mean absolute error to set the baseline for the model, Variance Inflation Factor (VIF) to help remove multicollinearity between features and Streamlit interactive dashboards to communicate with the model. Correlation and regression methods were used to examine the relationship and build the model. It is observed that there is a strong positive correlation between the number of bedrooms and the number of toilets, likewise the number of bedrooms and the number of bathrooms. It also shows that there is a moderate positive correlation between the number of bedrooms and price. The model shows that the number of bedrooms, parking spaces, and house types play an important role in determining the price of houses.",3.0,https://ejtas.com/index.php/journal/article/download/281/234,True,JournalArticle,,2023-09-01,a5479e6447253f914c85e0d7a2e986699903eecd,predictionofhousepricesinlagosnigeriausingmachinelearningmodels,nwankwo,10.59324/ejtas.2023.1(5).22,predictionofhousepricesinlagosnigeriausingmachinelearningmodels|2023,1,2696,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a1b7322c5d16c49a86342a6acdce41dc4882c32b,10.37134/jcit.vol13.1.4.2023,,The Prediction of Affordable Housing Prices in Petaling Jaya District Using R Statistical Computing Environment,2023.0,Journal of Contemporary Issues and Thought,Suresh Nodeson; Kesavan Krishnan; Sathis Krishnan,"Affordable housing especially in a city environment is recognized as one of citizen needs among the mid-dle-income groups. This research paper intended to explore the possibilities to use data mining algorithms: Linear Regression, Random Forest and Gradient Boosting algorithms for predicting and analyzing the housing affordability price for middle-income earners in Petaling Jaya district, Malaysia. The dataset from Malaysia House Index by Petaling Jaya district used to evaluate based on the proposed algorithms. The dataset extracted based on residential property sub-sectors with three attributes. Based on the prediction models, as results, the Gradient Boosting algorithm shows higher accuracy of 74% for predicting affordable housing price in Petaling Jaya district, Malaysia compared to other prediction techniques.",1.0,https://ejournal.upsi.edu.my/index.php/JCIT/article/download/7579/4306/36719,True,JournalArticle,,2023-01-25,a1b7322c5d16c49a86342a6acdce41dc4882c32b,thepredictionofaffordablehousingpricesinpetalingjayadistrictusingrstatisticalcomputingenvironment,nodeson,10.37134/jcit.vol13.1.4.2023,thepredictionofaffordablehousingpricesinpetalingjayadistrictusingrstatisticalcomputingenvironment|2023,1,2325,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2ba4dc4628fc2c9cf04c3ffb150a87f3bc52eb18,10.17509/seict.v6i1.86768,,Review of Banjarnese Neural Machine Translation Development With Minimal Resources,2025.0,"Journal of Software Engineering, Information and Communication Technology (SEICT)",Ali Muhammad; Novia Winda; A. Firizkiansah; Dita Setiawan; Siti Herawati Fransiska Dewi; Imron Rizki Maulana; Miri Ardiansyah,"With the advancement of information technology, the application of machine learning in the property industry, particularly for house price prediction, has become increasingly important. Technology plays a crucial role in speeding up and enhancing the accuracy of property buying and selling processes. Therefore, the role of machine learning technology can be utilized to meet the need for improving the accuracy of house price predictions in major cities of developing countries, such as Bandung. This research aims to analyze the effectiveness of the Artificial Neural Network and Random Forest algorithms in predicting house prices in Bandung. The data used includes house sales data in Bandung, covering land area, building area, number of bedrooms, number of bathrooms, number of parking spaces, and the subdistrict location. The analysis of the algorithms is conducted by comparing the performance testing results of both algorithms using performance metrics for regression models such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-Square (R2). Additionally, this research analyzes which data ratio among the training, validation, and test data yields the best results. The research findings indicate that the model with a data ratio of 60:20:20 produces the best performance for both algorithms. The Random Forest algorithm demonstrates superior performance with results of MAE: 0.0470; MSE: 0.0079; RMSE: 0.0888; and R2: 0.7085.",1.0,https://ejournal.upi.edu/index.php/SEICT/article/download/86768/33634,True,JournalArticle;Review,,2025-06-01,2ba4dc4628fc2c9cf04c3ffb150a87f3bc52eb18,reviewofbanjarneseneuralmachinetranslationdevelopmentwithminimalresources,muhammad,10.17509/seict.v6i1.86768,reviewofbanjarneseneuralmachinetranslationdevelopmentwithminimalresources|2025,1,2984,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4fc107fab4a3c979bffb004a94026f88a10bb4a7,10.17509/seict.v5i2.75945,,Comparative Analysis of Artificial Neural Network (ANN) and Random Forest in House Price Prediction,2024.0,"Journal of Software Engineering, Information and Communication Technology (SEICT)",Y. Retnowati; Abid Mafahim; Indira Syawanodna,"With the advancement of information technology, the application of machine learning in the property industry, particularly for house price prediction, has become increasingly important. Technology plays a crucial role in speeding up and enhancing the accuracy of property buying and selling processes. Therefore, the role of machine learning technology can be utilized to meet the need for improving the accuracy of house price predictions in major cities of developing countries, such as Bandung. This research aims to analyze the effectiveness of the Artificial Neural Network and Random Forest algorithms in predicting house prices in Bandung. The data used includes house sales data in Bandung, covering land area, building area, number of bedrooms, number of bathrooms, number of parking spaces, and the subdistrict location. The analysis of the algorithms is conducted by comparing the performance testing results of both algorithms using performance metrics for regression models such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-Square (R2). Additionally, this research analyzes which data ratio among the training, validation, and test data yields the best results. The research findings indicate that the model with a data ratio of 60:20:20 produces the best performance for both algorithms. The Random Forest algorithm demonstrates superior performance with results of MAE: 0.0470; MSE: 0.0079; RMSE: 0.0888; and R2: 0.7085.",2.0,https://ejournal.upi.edu/index.php/SEICT/article/download/75945/30273,True,JournalArticle,,2024-12-15,4fc107fab4a3c979bffb004a94026f88a10bb4a7,comparativeanalysisofartificialneuralnetworkannandrandomforestinhousepriceprediction,retnowati,10.17509/seict.v5i2.75945,comparativeanalysisofartificialneuralnetworkannandrandomforestinhousepriceprediction|2024,1,2984,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8c8bc7e39e4a080c70755cc6826e0eb9ba14b7a6,10.33650/coreai.v7i1.13234,,"Comparative Analysis of Decision Tree, Neural Network, K-NN, GBT, SVR and Random Forest Algorithms for House Price Prediction",2026.0,"COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi",Sahal Abdillah; Mochamad Taufiqurrochman Abdul Aziz Zein; Edy Sulistiyanto,"This study aims to analyze and compare the performance of several machine learning algorithms for predicting house prices. The dataset was obtained from Kaggle with variables of house price, building area, land area, number of bedrooms, number of bathrooms, and garage. The algorithms used include Decision Tree, Neural Network, K-Nearest Neighbor, Gradient Boosted Trees, Support Vector Regression, and Random Forest. The study was conducted using Altair Studio through pre-processing, training, testing, and evaluation stages based on performance, computation time, and model complexity. The results showed that K-NN obtained the best RMSE value of 0.608, followed by Neural Network 0.630, Decision Tree 0.649, Random Forest 0.656, SVR 0.662, and GBT 0.747. Based on MAE, Neural Network obtained the best value of 0.464, followed by Random Forest 0.474, Decision Tree 0.478, K-NN 0.485, SVR 0.501, and GBT 0.589. Overall, K-NN was the best model because it had the lowest RMSE, the fastest training time of 0.004 seconds, and a relatively simple model complexity.",0.0,https://ejournal.unuja.ac.id/index.php/core/article/download/13234/pdf,True,JournalArticle,,2026-06-11,8c8bc7e39e4a080c70755cc6826e0eb9ba14b7a6,comparativeanalysisofdecisiontreeneuralnetworkknngbtsvrandrandomforestalgorithmsforhousepriceprediction,abdillah,10.33650/coreai.v7i1.13234,comparativeanalysisofdecisiontreeneuralnetworkknngbtsvrandrandomforestalgorithmsforhousepriceprediction|2026,1,2565,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e5c1344d955c77d6128aa887bd866305be98cd8e,10.36378/jtos.v8i2.4829,,House Price Prediction in Surabaya Using Backpropagation Neural Network,2025.0,JURNAL TEKNOLOGI DAN OPEN SOURCE,Dimas Fajri Pamungkas; Abdul Rezha Efrat Najaf; Reisa Permatasari,"This research develops a house price prediction system in Surabaya using the Backpropagation Neural Network (BPNN) method. The dataset was obtained through web scraping of property listings, resulting in 3,435 records with 52 attributes. To improve stability, the target variable (house price) was transformed using natural logarithms. Several neural network architectures were tested, and the best configuration [32, 64, 32] achieved Mean Absolute Error (MAE) of 0.3125, Root Mean Squared Error (RMSE) of 0.4201, R² of 0.7138, and Mean Absolute Percentage Error (MAPE) of 1.46%. A multi-run evaluation of 20 iterations confirmed consistency of results. The model was implemented as a web-based application using Flask, allowing users to predict house prices in real-time. This research shows that BPNN is reliable for property price forecasting and can support decision-making in the housing market.",0.0,https://ejournal.uniks.ac.id/index.php/JTOS/article/download/4829/3467,True,JournalArticle,,2025-11-22,e5c1344d955c77d6128aa887bd866305be98cd8e,housepricepredictioninsurabayausingbackpropagationneuralnetwork,pamungkas,10.36378/jtos.v8i2.4829,housepricepredictioninsurabayausingbackpropagationneuralnetwork|2025,1,2400,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",aed0105c242eb4fb49d98a1c421ed357f3b79532,10.22452/mjcs.vol29no1.2,,Application Of Fuzzy Regression Model For Real Estate Price Prediction,2016.0,,A. G. Sarip; Muhammad Burhan Hafez; M. Daud,"Many studies have been conducted for modeling the underlying non-linear relationship between pricing attributes and price of property to forecast the housing sales prices. In recent years, more advanced non-linear modeling techniques such as Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have emerged as effective techniques to predict the house prices. In this paper, we propose a fuzzy least-squares regression-based (FLSR) model to predict the prices of real estates. A comprehensive comparison studies in terms of prediction accuracy and computational complexity of ANN, Adaptive Neuro Fuzzy Inference System (ANFIS) and FLSR has been carried out. ANN has been widely used to forecast the price of real estates for many years while ANFIS has been introduced recently. On the other hand, FLSR is comparatively new. To the best of our knowledge, no property prices prediction using FLSR was developed until recently. Besides, a detailed comparative evaluation on the performance of FLSR with other modeling approaches on property price prediction could not be found in the existing literature. Simulation results show that FLSR provides a superior prediction function as compared to ANN and FIS in capturing the functional relationship between dependent and independent real estate variables and has the lowest computational complexity.",43.0,https://ejournal.um.edu.my/index.php/MJCS/article/download/6889/4551,True,,Computer Science,2016-03-01,aed0105c242eb4fb49d98a1c421ed357f3b79532,applicationoffuzzyregressionmodelforrealestatepriceprediction,sarip,10.22452/mjcs.vol29no1.2,applicationoffuzzyregressionmodelforrealestatepriceprediction|2016,1,2859,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",5e4eb46e0fe34316820d6f29f559a88c59b7d9aa,10.22452/ijps.vol5no1.3,,Fuzzy Logic Application for House Price Prediction,2015.0,International Journal of Property Sciences,A. G. Sarip; Muhammad Burhan Hafez,"Various methods have been used previously to estimate housing sales prices to model the underlying non-linearity relation between housing attributes and the price of property. More advanced non-linear modelling techniques such as Artificial Neural Networks (ANN), Fuzzy Inference System (FIS) and Fuzzy Logic (FL) emerged recently to model the nonlinear relation between the independent variables and the price function. A new structured model for house prices prediction based on Fuzzy Logic is proposed. A fuzzy logic based regression model has proved to be effective to address many prediction problems used in business forecasting, marketing and insurance. This paper highlights the development of a theoretical formulation for sales price prediction through the utilisation of a fuzzy regression model by applying fuzzy logic and fuzzy inference system techniques. The results show favourable outputs which indicate superior prediction function when compared with ANN and FIS as well as indicate the fuzzy functional relationship between dependent and independent variables.",19.0,https://ejournal.um.edu.my/index.php/IJPS/article/download/4368/2175,True,,Engineering,2015-08-28,5e4eb46e0fe34316820d6f29f559a88c59b7d9aa,fuzzylogicapplicationforhousepriceprediction,sarip,10.22452/ijps.vol5no1.3,fuzzylogicapplicationforhousepriceprediction|2015,1,2579,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f49dedbf7b2f17cb72917a83bf82eab9adeeab59,10.24014/sitekin.v22i2.35804,,Property Price Prediction Using the Random Forest Regression Algorithm,2025.0,Jurnal Sains dan Teknologi Industri,P. Utami; Muhamad Jundi; Rahmaddeni Rahmaddeni; L. Sinaga,"This study uses an innovative approach to predicting property prices using machine learning with the Random Forest Regression method. The dataset was obtained from Kaggle and consists of 500 rows with 12 attributes, comprising 10 numerical attributes and 2 categorical attributes. The evaluation results, calculated using the R² score on the test dataset, show strong performance, achieving the highest R² score of 81.88% with a dataset split ratio of 90:10. The scatter plot visualization indicates shows the model's predictions often correspond closely with the actual values, showing strong accuracy, despite a tiny gap between the anticipated and real values. The graph comparing the training data and the actual data shows no significant signs of overfitting or underfitting, demonstrating the Random Forest Regression model's strong accuracy in predicting house prices and its capacity to effectively capture the relationship between independent and dependent variables.",1.0,https://ejournal.uin-suska.ac.id/index.php/sitekin/article/download/35804/12258,True,JournalArticle,,2025-05-23,f49dedbf7b2f17cb72917a83bf82eab9adeeab59,propertypricepredictionusingtherandomforestregressionalgorithm,utami,10.24014/sitekin.v22i2.35804,propertypricepredictionusingtherandomforestregressionalgorithm|2025,1,2476,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5bf64415c2a1c1d6d8f623b2f4511938d549d5e4,10.35970/jinita.v8i1.3010,,"Comparison of Multiple Linear Regression, Random Forest, and Decision Tree Methods for House Price Prediction in West Nusa Tenggara",2026.0,Journal of Innovation Information Technology and Application (JINITA),Mindi Richia Putri; Asmaul Husna Rs; Ahmad Fatoni Dwi Putra; M. Rifqi; Syaidatussalihah,"This study aims to develop an accurate housing price prediction model for West Nusa Tenggara (NTB) by comparing three machine learning approaches: Multiple Linear Regression (MLR), Random Forest (RF), and Decision Tree (DT). The research adopts a quantitative experimental design consisting of data collection through web scraping, preprocessing, model training, and performance evaluation. Secondary data were gathered from Lamudi, Rumah123, and 99.com, containing 641 housing records with key features such as land area, building area, number of bedrooms, number of bathrooms, and price. Data preprocessing involved handling missing values, normalization, and data augmentation to improve model robustness. Model performance was assessed using Root Mean Square Error (RMSE) and the coefficient of determination (R²). The results show that non-linear models outperform linear ones, particularly after data augmentation. Without augmentation, Random Forest achieved the best performance (R² = 0.890, RMSE = 5.6×10⁹), while with augmentation, the Decision Tree model achieved the highest accuracy (R² = 0.999, RMSE = 1.1×10⁹). In contrast, MLR exhibited poor performance and sensitivity to data complexity. These findings conclude that tree-based models, particularly Decision Tree and Random Forest, provide superior prediction accuracy and adaptability for non-linear housing market data. The study contributes to the development of data-driven tools that can support policymakers, developers, and consumers in achieving more transparent and informed decision-making in the regional housing market.",0.0,https://ejournal.pnc.ac.id/index.php/jinita/article/download/3010/1110,True,JournalArticle,,2026-06-30,5bf64415c2a1c1d6d8f623b2f4511938d549d5e4,comparisonofmultiplelinearregressionrandomforestanddecisiontreemethodsforhousepricepredictioninwestnusatenggara,putri,10.35970/jinita.v8i1.3010,comparisonofmultiplelinearregressionrandomforestanddecisiontreemethodsforhousepricepredictioninwestnusatenggara|2026,1,3099,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f9062aba37b68908c676eaa58b80bd5eacac4baf,10.55681/jige.v5i2.2794,,Comparative Analysis of Multiple – Linear Regression Algorithm with Random Forest Regression for Prediction of House Plot Prices,2024.0,JURNAL ILMIAH GLOBAL EDUCATION,Sigit Auliana; B. Permana; Gagah Dwiki Putra Aryono,"Humans basically have a basic need to have a place to live, which can be a house or shelter. Along with the rapid population growth in Indonesia, which continues to increase every year, many people do not have or have a decent place to live. Therefore, careful planning is needed so that every family can have a decent home. One very important aspect in planning investment in the form of property is predicting future house prices. One approach that can be used is to use a Random Forest and Multiple Linear Regression algorithm, which is an algorithm from Machine Learning. There are several factors that can influence the price of a house, including land area, building area, number of bedrooms, bathrooms and garage. In this research, multiple linear regression and random forest regression methods were chosen. The aim of this research is to find the best prediction results between the two methods. To achieve accurate predictions, research was carried out repeatedly by dividing the dataset into 80% for training and 20% for testing. The research results show that the random forest regression algorithm provides the best results, with an accuracy of 81.6%.",0.0,https://ejournal.nusantaraglobal.or.id/index.php/jige/article/download/2794/3006,True,JournalArticle,,2024-06-28,f9062aba37b68908c676eaa58b80bd5eacac4baf,comparativeanalysisofmultiplelinearregressionalgorithmwithrandomforestregressionforpredictionofhouseplotprices,auliana,10.55681/jige.v5i2.2794,comparativeanalysisofmultiplelinearregressionalgorithmwithrandomforestregressionforpredictionofhouseplotprices|2024,1,2664,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",67fe71c05322ce8183b91e3dd920031293f7b18b,10.34288/jri.v6i1.262,,House Price Prediction Using Data Mining with Linear Regression and Neural Network Algorithms,2023.0,Jurnal Riset Informatika,E. Palupi,"The need for housing in big cities is very high because most offices and economic centers are in big cities. Limited land and high demand cause house prices to rise. Many developers build housing on the outskirts of big cities with access to trains and toll roads to make transportation easier. Property developers compete by providing the best prices, various choices of house specifications, ease of the mortgage process, and attractive promotions such as no down payment. A house is a long-term investment whose price increases yearly, so proper analysis is needed to buy a place to live in. Several factors influence the price of a house, including location, land area, building area, building type, and so on. This research aims to create a house price prediction model using the Linear Regression Algorithm and Neural Network so that the results can be useful for property agents in predicting house sales or from the buyer's side in predicting house prices. The results of this research use the Linear Regression Algorithm RMSE 0.775, while the Neural Network Algorithm uses RMSE 0.645. From this research, modeling using the Linear Regression Algorithm has better results. Still, the Linear Regression Algorithm and Neural Network Algorithm have RMSE results that are close to accurate and have small errors.",2.0,https://ejournal.kresnamediapublisher.com/index.php/jri/article/download/262/265,True,JournalArticle,,2023-12-21,67fe71c05322ce8183b91e3dd920031293f7b18b,housepricepredictionusingdataminingwithlinearregressionandneuralnetworkalgorithms,palupi,10.34288/jri.v6i1.262,housepricepredictionusingdataminingwithlinearregressionandneuralnetworkalgorithms|2023,1,2816,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",61ba8cd2b2cf66b68d0c4ee4b1ce30074b2e187e,10.59324/ejaset.2025.3(3).29,,Using Artificial Intelligence-Based Machine Learning Regression Models for Predictions of Home Prices,2025.0,"European Journal of Applied Science, Engineering and Technology",Mukund Sai Vikram Tyagadurgam; Venkataswamy Naidu Gangineni; S. Pabbineedi; Ajay Babu Kakani; Sri Krishna Kireeti Nandiraju; Sandeep Kumar Chundru,"Accurate forecast of home prices is crucial in making informed decisions in real estate, financial plans and investment practices. This work exploits the Volusia County Property Sales Dataset to create a strong machine learning model for predicting home prices. By undertaking extensive data preprocessing, feature engineering, and transformation of the outcome and having an XGBoost regression model as a result, we trained and measured its performance Metrics include R², MSE, and MAE. The XGBoost model outperformed baseline models such as Random Forest (R² = 0.87) and Artificial Neural Networks (ANN) with R² = 0.97, MSE - 8.17, and MAE = 4.03, R² = 0.84. These results show the effectiveness and accuracy of XGBoost when identifying complex, nonlinear relations in the housing data. The proposed pipeline is scalable and can be adapted to fit into live property valuation systems, lending valuable insights to stakeholders in urban planning, real estate investments, and public policy formulations.",3.0,https://ejaset.com/index.php/journal/article/download/293/215,True,JournalArticle,,2025-06-18,61ba8cd2b2cf66b68d0c4ee4b1ce30074b2e187e,usingartificialintelligencebasedmachinelearningregressionmodelsforpredictionsofhomeprices,tyagadurgam,10.59324/ejaset.2025.3(3).29,usingartificialintelligencebasedmachinelearningregressionmodelsforpredictionsofhomeprices|2025,1,2505,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",51a1059b59d4b43da8e865be40b3f370d986a54a,10.32890/jcia2026.5.2.5,,"House price prediction in Ajah, Lagos using a random forest regression model",2026.0,Journal of Computational Innovation and Analytics (JCIA),Ayodeji M. Obaude; E. Oyekanmi; A. Adegboye; Oluwaseyi Abe,"Housing is one of the most essential need to commoner but several factors contribute to acquiring a suitable home, factor such as low income, high material cost and most importantly the presence of middleman in acquiring suitable house posed significant challenges thereby increases the cost, this study addresses this problem by developing a random forest regressor model to predict house price in Ajah Lagos, a dataset of 24,326 instances was sourced from Kaggle and filtered to 2142 instance containing Ajah listing. The data underwent several preprocessing steps; outliers were detected using the interquartile range, and Recursive Feature Elimination was used to identify relevant features that contribute to the model while eliminating less important ones. The RF model demonstrates a robust performance with an R-squared of 68.5%, a Mean Absolute Percentage Error of 18.96%, and a Root Mean Squared Error of ₦11.1 million. Compared with existing models in the literature, the model achieved a mean absolute error of ₦8.09 million, representing a 4.82% improvement and outperforming the ridge regression models. This research contributes to the field by providing stakeholders with a data-driven tool to enhance transparency and investment decision-making in the Nigerian real estate market.",0.0,https://e-journal.uum.edu.my/index.php/jcia/article/download/27168/5244,True,JournalArticle,,2026-07-31,51a1059b59d4b43da8e865be40b3f370d986a54a,housepricepredictioninajahlagosusingarandomforestregressionmodel,obaude,10.32890/jcia2026.5.2.5,housepricepredictioninajahlagosusingarandomforestregressionmodel|2026,1,2798,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cd6056e1d83d70847ef50a9005df6bd3db34d66d,10.54097/ncz9xh08,,Comparative Analysis of Linear and Polynomial Regression Models in Predicting Shanghai Housing Prices,2026.0,Journal of Innovation and Development,Zheng-Nan Wang,"This study presents a comparative analysis of linear and polynomial regression models for predicting housing prices in Shanghai's dynamic real estate market. Utilizing a dataset of 100 second-hand housing listings sourced from Lianjia between 2020 and 2023, with living area as the primary predictive feature, we rigorously train and evaluate both models. Our findings demonstrate that a second-degree polynomial regression model (MSE = 0. 58, R² = 0. 89)significantly outperforms a simple linear regression model (MSE = 0. 97, R² = 0. 78)in predictive accuracy and explanatory power. Residual analysis further reveals that the linear model suffers from heteroscedasticity, indicating its inadequacy in capturing the market's complexity, particularly for high-value properties. The results underscore the importance of selecting appropriately complex models for economic forecasting. We conclude that polynomial regression offers a superior fit for the non- linear trends prevalent in Shanghai's housing market. This study provides practical insights for stakeholders in real estate investment, policy formulation, and urban economics, while also acknowledging limitations related to dataset size and feature selection, suggesting directions for future research involving multivariate approaches and regularization techniques.",0.0,https://drpress.org/ojs/index.php/jid/article/download/33674/32950,True,JournalArticle,,2026-03-13,cd6056e1d83d70847ef50a9005df6bd3db34d66d,comparativeanalysisoflinearandpolynomialregressionmodelsinpredictingshanghaihousingprices,wang,10.54097/ncz9xh08,comparativeanalysisoflinearandpolynomialregressionmodelsinpredictingshanghaihousingprices|2026,1,2826,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",07d959cbc0d2539af973b5bbcc64e09f969db4b4,10.54097/fbem.v10i2.11077,,A House Price Prediction Model Based on K-means Clustering and Random Forest in Guangzhou,2023.0,"Frontiers in Business, Economics and Management",Zhishang Huang; Guanren Lai,"This paper addresses the key issues in house price forecasting from multiple perspectives by establishing a house price forecasting model for Guangzhou city, providing valuable information and decision support for home buyers, developers, and the government. First, this paper employs the Person coefficient, stepwise regression model and t-test to address the problem of quantifying data and exploring house price factors. By analyzing the correlation between the relevant variables and house prices, the key characteristics that have significant and strong correlation effects on house prices are obtained. Second, the K-means clustering model was used to classify houses into three categories: economic houses, comfortable houses and high-end houses. This classification result provides a more detailed data base for the subsequent construction of the house price prediction model. Finally, the random forest house price prediction model was established in this paper, and the model was validated by error analysis and stability analysis. The average absolute value error and goodness-of-fit obtained were 0.08 and 0.92, respectively, indicating that the model has high accuracy and reliability. The research in this paper has important implications for all parties, including home buyers, developers, and the government. For home buyers, the model can help them better understand the market situation; for developers, the model can guide their reasonable pricing and development strategies; for the government, the model can provide a scientific basis for real estate market regulation and policy control to promote market stability and sustainable development.",6.0,https://drpress.org/ojs/index.php/fbem/article/download/11077/10783,True,JournalArticle,,2023-08-14,07d959cbc0d2539af973b5bbcc64e09f969db4b4,ahousepricepredictionmodelbasedonkmeansclusteringandrandomforestinguangzhou,huang,10.54097/fbem.v10i2.11077,ahousepricepredictionmodelbasedonkmeansclusteringandrandomforestinguangzhou|2023,1,3165,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0cb7ddecd4a9daddf8fe86dd7018047ae1181b74,10.54097/k82s4204,,Comparison and Analysis of Boston Housing Price Predictions Using Different Machine Learning Methods,2026.0,Academic Journal of Science and Technology,Rui Liu,"In the contemporary real estate market, housing prices are fiercely competitive and influenced by various factors, such as the criminal activities existing in the community and the quality of the surrounding environment. However, inaccuracies in predicting house prices often lead to problems such as excessively high prices. The application of corresponding machine learning models plays a significant role in improving predictive accuracy in this domain. This study compares multiple models, including Ordinary Least Squares (OLS), Ridge regression, Lasso, Elastic Net, Random Forest, and Multilayer Perceptron (MLP), using a real-world housing price dataset. Model performance is evaluated through the accuracy, R², RMSE, and MSE. The final results indicate that the Random Forest outperforms the other methods, achieving the highest predictive accuracy, while Ordinary Least Squares produces the lowest accuracy. Additionally, the study examines the applicability of these techniques across various scenarios and their potential to enhance the accuracy of housing price predictions.",0.0,https://drpress.org/ojs/index.php/ajst/article/download/33374/32658,True,JournalArticle,,2026-01-29,0cb7ddecd4a9daddf8fe86dd7018047ae1181b74,comparisonandanalysisofbostonhousingpricepredictionsusingdifferentmachinelearningmethods,liu,10.54097/k82s4204,comparisonandanalysisofbostonhousingpricepredictionsusingdifferentmachinelearningmethods|2026,1,2586,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",060ee6f01607a112a298d3118b984f4272b7a082,10.54097/jnq11521,,Machine Learning for House Price Prediction,2026.0,Academic Journal of Science and Technology,Zixian Ning,"Housing stands as a core human necessity, and residential property prices—shaped by elements such as local crime levels and air quality—hold great significance for all stakeholders in the real estate sector. A dependable price prediction system plays a key role in supporting well-informed decisions related to property transactions. This research utilized the Boston housing dataset sourced from Kaggle and carried out all-round data preprocessing: it addressed missing or incorrect values, removed outlier data points, applied Z-score normalization to reduce data complexity, and conducted data transformation as part of the data wrangling process. Three machine learning models were evaluated in the study, namely Support Vector Regression (SVR) with a radial basis function kernel, a five-layer Artificial Neural Network (ANN) consisting of three dense layers and two dropout layers, and Extreme Gradient Boosting (XGBoost). Additionally, five-fold cross-validation was employed to select regularization parameters, and principal component analysis was used to enhance model performance. Experimental results revealed that the Artificial Neural Network outperformed the other two models, achieving the highest R-squared value of 0.86, the lowest Mean Squared Error of 0.0046, and the smallest Mean Absolute Error of 0.047. Support Vector Regression also delivered strong performance, with an R-squared of 0.83 and a Mean Squared Error of 0.0054, while XGBoost exhibited relatively higher errors, with a Mean Squared Error of 0.1214.",1.0,https://drpress.org/ojs/index.php/ajst/article/download/33366/32650,True,JournalArticle,,2026-01-29,060ee6f01607a112a298d3118b984f4272b7a082,machinelearningforhousepriceprediction,ning,10.54097/jnq11521,machinelearningforhousepriceprediction|2026,1,3036,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a482b4f2dafe28ba25f09bae8deabba2d3a41599,10.54097/qywqx033,,"Predicting House Prices with Machine Learning: A Comparative Study of Linear, Lasso, and Ridge Regression",2026.0,Academic Journal of Science and Technology,Han Hu,"Machine Learning has seen rapid advancements this decade, marked by daily innovations in applications and algorithms. One significant application is in the real estate sector, particularly in predicting house prices—a crucial task given the continual rise in property values. This project employs Linear Regression, Lasso Regression, and Ridge Regression, utilizing the Scikit-Learn tool to explore the potential of these models. By considering various factors such as the number of bedrooms, the age of the house, proximity to transportation hubs, local schools, and shopping centers, these models aim to assist potential buyers in identifying homes that best meet their requirements. Among the tested methods, the Lasso Regression emerged as the most effective, boasting the lowest root-mean-square error of 190,124.751, which notably outperformed both Ridge and Linear Regression. This outcome not only underscores the efficacy of Lasso Regression but also highlights the overall utility of regression models in accurately predicting house prices.",0.0,https://drpress.org/ojs/index.php/ajst/article/download/33361/32645,True,JournalArticle,,2026-01-29,a482b4f2dafe28ba25f09bae8deabba2d3a41599,predictinghousepriceswithmachinelearningacomparativestudyoflinearlassoandridgeregression,hu,10.54097/qywqx033,predictinghousepriceswithmachinelearningacomparativestudyoflinearlassoandridgeregression|2026,1,2550,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b46f39db3e5093713cd13e6f7a0ec75031945aef,10.54097/hset.v49i.8521,,Comparison of Real Estate Price Prediction Based on LSTM and LGBM,2023.0,Highlights in Science Engineering and Technology,Song-Yi Shi,"Housing price prediction is one of the widely discussed topics, and the application of machine learning and deep learning models to housing price prediction is a research hotspot. Exploring which models are suitable for predicting real housing price datasets has significant implications for guiding government and homebuyers' decision-making. In this study, the LSTM and LightGBM models were selected as research objects, and the suitability of the models was explored and compared using the second-hand housing price dataset in Beijing. Based on the analysis, in the task of pure time series prediction based on historical housing prices, the LSTM model had a better fit (R2=0.91), while in the task of housing price prediction based on multiple influencing factors, the LightGBM model had better comprehensive evaluation index results (R2=0.53). Both models can be used to predict pure time series, but the LSTM model is not suitable for predicting multi-factor input models that are not time series.",9.0,https://drpress.org/ojs/index.php/HSET/article/download/8521/8294,True,JournalArticle,,2023-05-21,b46f39db3e5093713cd13e6f7a0ec75031945aef,comparisonofrealestatepricepredictionbasedonlstmandlgbm,shi,10.54097/hset.v49i.8521,comparisonofrealestatepricepredictionbasedonlstmandlgbm|2023,1,2503,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e6446985cde76fcd6e8a310458e6c8469fe46372,10.54097/hset.v44i.7355,,Guangzhou Housing Price Prediction Based On Machine Learning Regression Models,2023.0,Highlights in Science Engineering and Technology,Ziwen Huang; Kexin Li; Chuming Wang; Jiazhi Wang,"Nowadays, the fluctuation of housing prices is a key concern of homeowners, the real estate market and the government. The researchers found that it was possible to predict house prices accurately by analyzing relevant attributes and using the most effective models. However, due to the impact of the COVID-19 pandemic, there is still a research gap on which model is more suitable for predicting housing prices after the outbreak of COVID-19. Therefore, this study collected the latest real estate data of Guangzhou in China through the web crawler, trained the random forest regression model with collected data, and obtained a model that could predict the housing price by inputting corresponding attributes. The MSE of the trained regression model is 2802, MAE is 534, and the determination coefficient (R²_score) is 0.89, while the MSE of the trained XGBoost regression model is 3108, MAE is 643, and the determination coefficient (R²_score) is 0.87.The random forest regression model has been shown to be more accurate at predicting Guangzhou house prices after the COVID-19 outbreak. Our paper has great potential in house price prediction under the pandemic situation.",1.0,https://drpress.org/ojs/index.php/HSET/article/download/7355/7162,True,JournalArticle,,2023-04-13,e6446985cde76fcd6e8a310458e6c8469fe46372,guangzhouhousingpricepredictionbasedonmachinelearningregressionmodels,huang,10.54097/hset.v44i.7355,guangzhouhousingpricepredictionbasedonmachinelearningregressionmodels|2023,1,2676,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cf5a7ca657bb720f24d15cb2d4e27beb0cf79607,10.54097/hset.v39i.6680,,Analysis of Various Models for House Price Prediction based on Machine Learning,2023.0,Highlights in Science Engineering and Technology,Yu-Han Wu,"The real estate prices prediction is a relatively popular data analysis problem. With the gradual increase in the competitiveness of the housing trading platform in recent years, and the basic model of house prices predicted is different from the factual transaction price. Therefore, it is necessary to design more accurate house prices prediction models. This article will adopt different linear regression models and machine learning models. By training these models of training Set, through the accuracy algorithm of Kaggle, the accuracy of each model is obtained. According to the analysis, this article finally discovered that the accuracy of the new model is improved than the accuracy of the basic model (i.e., new model predicts house price more precision), but it can be improved in the case of less data in the real environment. The reason may be related to economic and public opinion effects. Overall, these results shed light on guiding further exploration of house price.",0.0,https://drpress.org/ojs/index.php/HSET/article/download/6680/6474,True,JournalArticle,,2023-04-01,cf5a7ca657bb720f24d15cb2d4e27beb0cf79607,analysisofvariousmodelsforhousepricepredictionbasedonmachinelearning,wu,10.54097/hset.v39i.6680,analysisofvariousmodelsforhousepricepredictionbasedonmachinelearning|2023,1,2486,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f59be55ef71dbbb241f895d939bfe981c245fd84,10.54097/hset.v39i.6638,,Comparative Analysis of Machine Learning Performance in House Price Prediction for Ames Iowa,2023.0,Highlights in Science Engineering and Technology,Xingchen Zhou,"The price of housing is closely related to the quality of people's lives, which is reflected in various aspects (e.g., finding a place to live and managing their finances). On this basis, being able to predict house prices would be a great convenience, and this study examines the accuracy of multiple mechanistic learning regression models in predicting future house price changes. To be specific, this paper downloaded the dataset from Kaggle which includes the training and testing csv files. and ran many different regression techniques including lasso and more models on google Colab. Furthermore, a comparison between the results of different methods is carried out by estimating the mean square error. Then, this research combined some of the best yielded results of the model to combine into a final model and got a result with a high score. According to the analysis and evaluations, the study concluded that the hybrid model resulted in a more accurate prediction than the single model. These results shed light on guiding further exploration of real estate prediction.",1.0,https://drpress.org/ojs/index.php/HSET/article/download/6638/6432,True,JournalArticle,,2023-04-01,f59be55ef71dbbb241f895d939bfe981c245fd84,comparativeanalysisofmachinelearningperformanceinhousepricepredictionforamesiowa,zhou,10.54097/hset.v39i.6638,comparativeanalysisofmachinelearningperformanceinhousepricepredictionforamesiowa|2023,1,2579,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",eb64c46c0db04d3715515f4a5dbd72a992adde8b,10.54097/hset.v39i.6637,,Real Estate Price Prediction based on Supervised Machine Learning Scenarios,2023.0,Highlights in Science Engineering and Technology,Zhihang Liu,"House price prediction is one of the most common supervised learning tasks in the machine learning field, which makes it a perfect criterion for the effectiveness of different learning models. From basic regression models to neural networks, countless methods have been proposed to solve the house price prediction problem. In this paper, the focus is the performance of three regression models, linear, LASSO, and ridge. There will be a selected dataset of sold houses from the open-source website. The data will be explored and visualized for a better understanding and then implement the regression models for further testing. According to the analysis, the LASSO regression model can yield the most accurate prediction with 90.15% accuracy but need a specific λ value. The linear and ridge regression yields similar predictions with close to 90% accuracy. Therefore, the most effective model for the house price prediction problem is the LASSO regression model. Overall, these results shed light on guiding further exploration of the performance of different machine learning models.",4.0,https://drpress.org/ojs/index.php/HSET/article/download/6637/6431,True,JournalArticle,,2023-04-01,eb64c46c0db04d3715515f4a5dbd72a992adde8b,realestatepricepredictionbasedonsupervisedmachinelearningscenarios,liu,10.54097/hset.v39i.6637,realestatepricepredictionbasedonsupervisedmachinelearningscenarios|2023,1,2587,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b5a8c3df663b85ec70ba2d9ee5bcb99d011d927e,10.54097/hset.v39i.6549,,"The House Price Prediction Using Machine Learning Algorithm: The Case of Jinan, China",2023.0,Highlights in Science Engineering and Technology,Chengke Zou,"House prices increase substantially in China from 1998. Because of expensive house prices, most Chinese people have only one chance to select suitable houses. Therefore, building a house price prediction model based on housing conditions is significant for customers to make decisions. This paper collects the estate market data of Jinan city from the HomeLink website and performs several feature selection algorithms to get critical features for house price prediction. The paper compares the classical machine learning methods for the problem, including Multiple Linear Regression, Random Forest, and Catboost. After cross-validation tests, the CatBoost, algorithm with the lowest Mean Square Error (MSE) is regarded as the most accurate algorithm to predict house prices. The analytic results show that the house price is dominated by the location features such as area and block.",7.0,https://drpress.org/ojs/index.php/HSET/article/download/6549/6344,True,JournalArticle,,2023-04-01,b5a8c3df663b85ec70ba2d9ee5bcb99d011d927e,thehousepricepredictionusingmachinelearningalgorithmthecaseofjinanchina,zou,10.54097/hset.v39i.6549,thehousepricepredictionusingmachinelearningalgorithmthecaseofjinanchina|2023,1,2384,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0b36f197b797b5d0927f704338eb4f587fa0299b,10.54097/hset.v31i.4828,,Transaction price prediction of second-hand houses in Wuhan based on GA-BP model,2023.0,Highlights in Science Engineering and Technology,Can Peng; Heng Xiao; Kai-Man Ou,"The significance of the used housing market to the stability of people's lives and social development is increasing, and the prediction of its prices is becoming a key concern for society. In this paper, BP neural network model is used to predict the price of second-hand properties. In view of the disadvantages of slow convergence and the tendency to obtain local optimal solutions, the BP neural network model's input layer is optimized by a genetic algorithm to speed up the convergence speed and accuracy of the BP neural network model. The experimental results show that the improved GA-BP neural network has high prediction accuracy, with RMSE and MAE of 398.72 and 170.18, respectively. The difference between the actual and predicted values is small, which can provide a more stable tool for predicting house prices in the second-hand property market and broaden a new channel for the practical application of GA-BP neural network.",2.0,https://drpress.org/ojs/index.php/HSET/article/download/4828/4684,True,,,2023-02-10,0b36f197b797b5d0927f704338eb4f587fa0299b,transactionpricepredictionofsecondhandhousesinwuhanbasedongabpmodel,peng,10.54097/hset.v31i.4828,transactionpricepredictionofsecondhandhousesinwuhanbasedongabpmodel|2023,1,2440,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",09e9bfc3ba67fcb9b1cce6ad30df73a09cc9b657,10.54097/2vq1wq81,,House Price Prediction in Boston Based on BP Neural Network,2025.0,Highlights in Science Engineering and Technology,Zuxin Chen,"House is a basic demand for living. It is directly related to people's happiness. Affected by many factors such as the economy condition and the policy, house prices are always dynamic. House price prediction can provide decision support for investors, so it is important to optimize the prediction model. Many machine learning methods are applied to house price prediction, such as support vector machine and random forest. This paper predicts the house price of Boston. To solve the multicollinearity problem in the dataset, this paper builds a BP neural network model using Bayesian regularization. The fitting analysis of the data samples shows the total R-value is 0.9824. The loss function converges to the global minimum within 100 epochs. The experiment indicates that the model is both efficient and reliable in forecasting house prices. This research provides a method to deal with the multicollinearity problem in the dataset, which improves the generalization ability and predictive accuracy of the model.",0.0,https://drpress.org/ojs/index.php/HSET/article/download/28760/28235,True,JournalArticle,,2025-02-18,09e9bfc3ba67fcb9b1cce6ad30df73a09cc9b657,housepricepredictioninbostonbasedonbpneuralnetwork,chen,10.54097/2vq1wq81,housepricepredictioninbostonbasedonbpneuralnetwork|2025,1,2517,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c5eff9499d055f5e031e7bf0b46aeb4ab8dcdb14,10.54097/bdfe8032,,A Comparative Study of Random Forest Regression for Predicting House Prices Using,2024.0,Highlights in Science Engineering and Technology,M. Mao,"Based on the rapid development of the real estate market, real estate prices in various regions of the world fluctuate greatly and are unstable, and we need to make some predictions for real estate prices. However, in reality, we pay too much attention to the relationship between past property prices and current property prices and often ignore the prediction of future house prices. Research on predictive models is lacking. Therefore, studying real estate forecasting models is one of the best solutions to solve the problems faced by the real estate market based on the thinking of the current situation. In response to this problem, I propose to use a random forest model, gradient boosting, and optional to build a reasonable predictive model. The final results prove that this predictive model can be used to some extent to predict changing real estate prices in the future market. It is hoped that the method in this paper can provide a reference for subsequent research on predictive models.",7.0,https://drpress.org/ojs/index.php/HSET/article/download/18543/18082,True,JournalArticle,,2024-03-13,c5eff9499d055f5e031e7bf0b46aeb4ab8dcdb14,acomparativestudyofrandomforestregressionforpredictinghousepricesusing,mao,10.54097/bdfe8032,acomparativestudyofrandomforestregressionforpredictinghousepricesusing|2024,1,2501,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8e6653e6cabe966c754af3436c0e59c5d6e3da33,10.54097/ftyf9665,,House Price Prediction Based on Machine Learning Models,2024.0,Highlights in Science Engineering and Technology,Xiaoyan Ouyang,"This study aims to provide accurate house price predictions using machine learning algorithms. These predictions can assist decision-makers in making informed property investments and planning. Multiple linear regression and random forest were employed to achieve this goal. First, the acquired data underwent thorough analysis, including preprocessing and visualization. Subsequently, the study employed multiple linear regression and random forest models for house price prediction and evaluated their performance. The multiple linear regression model yielded promising results with an R² score of 0.73, explaining 73% of the target variable's variance. However, it exhibited prediction errors in specific cases, suggesting potential areas for improvement. In contrast, the Random Forest model achieved a slightly lower R² score of 0.69. Nonetheless, it excelled at capturing complex nonlinear relationships. Additionally, it identified the top five key features influencing house prices: house size, number of bathrooms, number of floors, parking spaces, and air conditioning. This study highlights the potential of machine learning models for house price prediction. Future research can further enhance these models and consider other influential factors to explain house price fluctuations comprehensively. The results offer valuable applications for investors, brokers, and government planners in the real estate market.",4.0,https://drpress.org/ojs/index.php/HSET/article/download/18525/18063,True,JournalArticle,,2024-03-13,8e6653e6cabe966c754af3436c0e59c5d6e3da33,housepricepredictionbasedonmachinelearningmodels,ouyang,10.54097/ftyf9665,housepricepredictionbasedonmachinelearningmodels|2024,1,2926,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",022921fccba6721f3a22c5f10e3cbb2feb66119f,10.54097/1qq43384,,Dataset Analysis and House Price Prediction,2024.0,Highlights in Science Engineering and Technology,Junjie Liu,"The prediction of house prices through the analysis of data using machine learning and charts is a crucial and significant topic. Many scholars have conducted research in this area, providing valuable insights for both academic learning and real-world applications. The goal of this study is to predict house prices and thoroughly analyze the dataset. The methodology includes data cleaning techniques to ensure data quality. Additionally, three types of charts are employed to analyze the dataset effectively. Finally, two popular models are utilized to predict house prices, and their accuracy is evaluated. The results indicate that the Random Forest Regressor model is more suitable for the specific dataset, and the impact of each factor on house price prediction varies Looking ahead, future research will involve the utilization of more advanced models to further enhance prediction accuracy. This will enable realistic simulations and contribute to the ongoing development of the society. This study has made preliminary progress in data cleaning, dataset analysis, and predictive modeling. The use of charts provides a more intuitive representation of dataset characteristics. The findings have implications for the fields of data cleaning and machine learning.",2.0,https://drpress.org/ojs/index.php/HSET/article/download/16255/15781,True,JournalArticle,,2024-01-26,022921fccba6721f3a22c5f10e3cbb2feb66119f,datasetanalysisandhousepriceprediction,liu,10.54097/1qq43384,datasetanalysisandhousepriceprediction|2024,1,2770,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d6b5702c59ba607062630d0ae155d78c12fad3fd,10.54097/hbem.v5i.5039,,Model For Predicting London House Prices,2023.0,"Highlights in Business, Economics and Management",Ming-Zhe Yin,"With the growth of population, housing prices have become one of the important indicators to reflect the economic performance. Forecasting prices around the world will be part of social and economic development. Through the analysis of various factors affecting the market housing price, it is helpful to make a more accurate assessment of the future housing price trend. In a broad sense, accurate housing price forecast is helpful to the country's macro-control of the market housing price trend, and the prediction of future housing price is part of the strategic planning of enterprises. For consumers, the housing price forecast has a positive effect on the rational planning of individual economy. Since house prices are related to many factors, and there is a linear relationship between house prices and some factors that affect house prices, there are many linear graphs to study this problem. The research direction of this paper is to obtain data from Kaggle and conduct exploratory analysis on these data sets. The main method used in this article is linear regression, and no other forecasting methods will be added. What this work needs to do is use the right machine learning methods to predict home values to create a complex property model under generally stable conditions, which is this work aims to do in this study. Through many data sets show that the multiple linear regression model does achieve a relatively excellent housing price prediction effect.",3.0,https://drpress.org/ojs/index.php/HBEM/article/download/5039/4882,True,JournalArticle,,2023-02-16,d6b5702c59ba607062630d0ae155d78c12fad3fd,modelforpredictinglondonhouseprices,yin,10.54097/hbem.v5i.5039,modelforpredictinglondonhouseprices|2023,1,2975,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",22aee72e39de274ac7e3d768be69281abe204a50,10.54097/hbem.v3i.4704,,Analysis and Forecasting of California Housing,2023.0,"Highlights in Business, Economics and Management",Yu-Chin Chen,"House prices have significant impact on people’s daily life, and it is essential for people to have fixed abode, to live, work and social prosperity and stability. Hence predicting House price is a meaningful and big challenge. To achieve this goal, we use California Census dataset in this project to how distinctive features (attributes) can make the house price higher or lower. The main idea of this project is to build a Regression Model that can learn from this data and make predictions of the price of a house in any block, given some useful features provided in the datasets. In the regression task, we applied cross-validation and K-Fold method on Ridege Model, Random Forest, Gradient Boosting models to select the optimal hyperparameters. Then we apply the best selected model on test set, the results show decent performance for Random Forest and Gradient Boosting. The Random Forest performs the best with MSE (Mean Squared Error) 0.290, while it takes training time 14.7 seconds. Although the Gradient Boosting takes the result of MSE is 0.295, it took a shorter training time (2.91s).",2.0,https://drpress.org/ojs/index.php/HBEM/article/download/4704/4557,True,,,2023-01-20,22aee72e39de274ac7e3d768be69281abe204a50,analysisandforecastingofcaliforniahousing,chen,10.54097/hbem.v3i.4704,analysisandforecastingofcaliforniahousing|2023,1,2600,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",77d50f4073bf17848214c21c7e7821574a67783c,10.54097/xze3as16,,The Investigation and Prediction of Influencing Factors of Australian Housing Price Based on Machine Learning Models,2024.0,"Highlights in Business, Economics and Management",Chongsen Ma,"With the ever-changing economic environment in recent years, people pay more and more attention to the real estate market, and different house configurations affect house prices. In this paper, house features such as bedroom count and bathroom count are analyzed as factors influencing house prices. Analyze the relationship between influencing factors and real estate prices and predict the trend of housing prices. First, constant values and values that have no impact on the research results should be selected and deleted, and effective influencing factors such as bedroom count and bathroom count should be left. Then, Exploratory Data Analysis (EDA) analysis was carried out on these effective influencing factors, and range, mean, standard deviation and quartiles of the data were read. Then the scatter plot is made to observe whether there is a correlation between the influencing factors and real estate prices. In the part of linear regression, the least squares method was first used to establish Ordinary Least Squares (OLS) model for R square, F-statistic and other calculations, observe the values, and further understand the correlation between influencing factors and housing prices. Through the comprehensive analysis of the above EDA and OLS models, it is concluded that there is a correlation between the influencing factors and real estate prices. Finally, plot is used to plot the linear relationship between them. It can be seen from the image that with the increase of the number of influencing factors, the housing price also increases, and the two are in a positive correlation.",0.0,https://drpress.org/ojs/index.php/HBEM/article/download/24523/24027,True,JournalArticle,,2024-09-01,77d50f4073bf17848214c21c7e7821574a67783c,theinvestigationandpredictionofinfluencingfactorsofaustralianhousingpricebasedonmachinelearningmodels,ma,10.54097/xze3as16,theinvestigationandpredictionofinfluencingfactorsofaustralianhousingpricebasedonmachinelearningmodels|2024,1,3104,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",288d5c5a2e7a478994e143701f4cc724e11c890e,10.54097/bd459218,,Multiple Machine Learning Models in House Price Prediction: Performance Evaluation and Comparison,2024.0,"Highlights in Business, Economics and Management",Jiahao Wu,"House pricing is one of the most critical concerns for people. Accurately predicting house prices is essential for participants and investors in the real estate market. This study delves into the realm of predictive modeling for house prices using a multimodal approach in machine learning. The research methodology spans various stages including data collection, preprocessing, feature engineering, and model selection. Based on a dataset sourced from Kaggle, housing data from Iowa comprising 79 pertinent features influencing property prices is utilized. Performance evaluation is conducted using the root mean square error (RMSE), comparing the efficacy of linear regression models such as Lasso regression, Kernel Ridge regression, and ElasticNet regression against tree-based models including XGBoost, Gradient Boosting, and LightGBM. Additionally, the study implements two stacked ensemble models to identify the most optimal predictive model for house prices. Experimental procedures involve training and evaluating these models on specific datasets to gauge their predictive accuracy. Findings and discussions underscore the comparative analysis of diverse machine learning models within the context of house price prediction. Moreover, the study employs the Gradient Boosting method in machine learning to pinpoint the ten most influential features impacting house prices. Recommendations for future research are provided to enhance the precision and robustness of house price prediction models. This research contributes to the advancement of predictive modeling in the real estate domain, offering insights into the efficacy of various machine learning techniques for forecasting property prices.",2.0,https://drpress.org/ojs/index.php/HBEM/article/download/24522/24026,True,JournalArticle,,2024-09-01,288d5c5a2e7a478994e143701f4cc724e11c890e,multiplemachinelearningmodelsinhousepricepredictionperformanceevaluationandcomparison,wu,10.54097/bd459218,multiplemachinelearningmodelsinhousepricepredictionperformanceevaluationandcomparison|2024,1,3208,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3b5d4a173189d979ea9cc4e605d149a3c6311452,10.54097/y4m3bs57,,House Price Prediction Taking Account of the Influence of COVID-19,2024.0,"Highlights in Business, Economics and Management",Mingyu Bi,"Machine learning algorithms are utilized in various practical applications and academic research. Due to the advent of digital technology, there is now a greater availability of massive, organized, and georeferenced datasets. This has simplified the use of algorithms for trend analysis and detection as well as forecasting, which helps users make decisions. The purpose of this study is to determine which Machine Learning (ML) algorithms are most effective in projecting housing price and to assess how the COVID-19 has affected these prices. The method includes the stages of data preprocessing, feature manipulation, hyperparameter tuning, evaluation, selection, and, in the end, interpretation of the model. Another objective is to assess and compare several ensemble learning strategies, comprising bagging techniques like random forest, and boosting techniques like Gradient Boosting Regressor, using a linear regression model. The results of this study confirm that the real estate sector is very resistant to pandemics, as the decline in prices was not as severe as initially anticipated. Among the several models that were examined, extreme gradient boosting demonstrated the highest level of accuracy, albeit with a minimal difference.",0.0,https://drpress.org/ojs/index.php/HBEM/article/download/24520/24025,True,JournalArticle,,2024-09-01,3b5d4a173189d979ea9cc4e605d149a3c6311452,housepricepredictiontakingaccountoftheinfluenceofcovid19,bi,10.54097/y4m3bs57,housepricepredictiontakingaccountoftheinfluenceofcovid19|2024,1,2746,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3687b2f139e31d2062f5910972986c9829680d87,10.54097/hbem.v21i.14837,,House Price Prediction and Analysis Based on Random Forest and XGBoost Models,2023.0,"Highlights in Business, Economics and Management",Han Li,"Accurate prediction of house price is important in housing market. It’s difficult to forecast housing price because it’s influenced by many factors. There has been many discussions on housing price prediction by kinds of machine learning algorithm. This paper attempt to predict housing price by Random Forest and XGBoost models, and compares the performance between them. In this paper, missing values processing, correlation analysis and standardization of samples are carried on the initial data at first, then two machine learning models are constructed, trained and test on the same dataset. The Kaggle house price dataset (“House Prices-Advanced Regression Techniques”) is used in the paper. The dataset contains 1420 samples with 79 features that represent almost all characteristics of house. The results show that XGBoost algorithm achieve higher R square score of 89% , indicating that XGBoost model can be more efficient and accurate on measurement and prediction of housing sales prices.",5.0,https://drpress.org/ojs/index.php/HBEM/article/download/14837/14385,True,JournalArticle,,2023-12-12,3687b2f139e31d2062f5910972986c9829680d87,housepricepredictionandanalysisbasedonrandomforestandxgboostmodels,li,10.54097/hbem.v21i.14837,housepricepredictionandanalysisbasedonrandomforestandxgboostmodels|2023,1,2501,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c66f5c787afea2e3a2b8b378cc2188e6013ee22f,10.54097/hbem.v21i.14755,,Feature Selection in House Price Prediction,2023.0,"Highlights in Business, Economics and Management",Jia Guo,"This study aims to construct a model to choose effective features and use them to predict the market price of an exact house, which can help people with house pricing and property evaluating. This paper preliminarily constructs several machine learning models like Linear Regression, SVM, KNN and compares their accuracy on this problem to choose the best-fit one for improving. After using parameter tuning to optimize this model, this study tries to use and recursive feature elimination and genetic algorithm to select features to improve simple SVM model. After feature selection, this study re-evaluated the accuracy of the model and compared which features had a greater impact on the predictions. After comparison, this study found that in this particular case, features have closer connections with living condition, traffic condition and sale condition will have a huge impact on the house price.",1.0,https://drpress.org/ojs/index.php/HBEM/article/download/14755/14302,True,JournalArticle,,2023-12-12,c66f5c787afea2e3a2b8b378cc2188e6013ee22f,featureselectioninhousepriceprediction,guo,10.54097/hbem.v21i.14755,featureselectioninhousepriceprediction|2023,1,2405,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",6a14de7af006fde7527e2580027767be030a1245,10.54097/hbem.v21i.14541,,Enhancing Neural Network with Particle Swarm: A House Price Prediction Case,2023.0,"Highlights in Business, Economics and Management",Yaotong Liang,"This study delves into the utilization of the Particle Swarm Optimization (PSO) algorithm to discover the best parameters of Long Short-Term Memory (LSTM) model with the aim of housing price prediction. Employing the Ames Housing dataset, our investigation underscores the remarkable efficacy of PSO in mitigating prediction errors within the LSTM framework, thereby enhancing overall predictive performance. The symbiotic integration of PSO with LSTM facilitates the discernment of intricate data patterns, particularly in scenarios involving high-dimensional and nonlinear optimization conundrums. The findings illuminate PSO's ability to expediently traverse and identify global optima. The outcomes unequivocally underscore the PSO-LSTM model's superior attributes, characterized by accelerated convergence, diminished errors, and exceptional proficiency. In summation, this investigation underscores PSO's promise as a potent technique for optimizing machine learning models, notably exemplified by its integration with the LSTM framework.",0.0,https://drpress.org/ojs/index.php/HBEM/article/download/14541/14095,True,JournalArticle,,2023-12-12,6a14de7af006fde7527e2580027767be030a1245,enhancingneuralnetworkwithparticleswarmahousepricepredictioncase,liang,10.54097/hbem.v21i.14541,enhancingneuralnetworkwithparticleswarmahousepricepredictioncase|2023,1,2544,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",15864a01e27a692b6f52efb3ad56b41b182ece42,10.54097/hbem.v21i.13600,,"Research on the Prediction of Boston House Price Based on Linear Regression, Random Rorest, Xgboost and SVM Models",2023.0,"Highlights in Business, Economics and Management",Yihang Chen,"Regression problems, which make up a significant portion of machine learning research, include the Boston housing price forecast problem. Regression problems are a subset of artificial intelligence. Regression problem-solving algorithms that are more frequently used four different machine learning models—the linear regression model, the Random Forest model based on an integrated learning technique, the XGBoost model, and the SVM model—are utilized for training and testing in this study. These models were chosen based on the real-world Boston house price prediction problem. From various angles, the effectiveness of the various algorithmic models on the regression problem of house price prediction was evaluated, and it was determined that among the many algorithmic models. For this problem, the best model is XGBoost Regression and the worst model was the SVM model. The most typical benefit of the XGBoost model is that It can capture complex non-linear relationships between variables.The disadvantage of the SVM model is its computational complexity.",2.0,https://drpress.org/ojs/index.php/HBEM/article/download/13600/13209,True,JournalArticle,,2023-12-12,15864a01e27a692b6f52efb3ad56b41b182ece42,researchonthepredictionofbostonhousepricebasedonlinearregressionrandomrorestxgboostandsvmmodels,chen,10.54097/hbem.v21i.13600,researchonthepredictionofbostonhousepricebasedonlinearregressionrandomrorestxgboostandsvmmodels|2023,1,2562,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",cde804babf5ddd3dcfebe29346074ab7ded5737f,10.1007/978-3-030-30967-1_9,,Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction,2019.0,,Mohsen Shahhosseini; Gui-Ping Hu; Hieu Pham,,42.0,https://dr.lib.iastate.edu/bitstreams/d117954e-b1a6-4ed2-ad8d-f2bb8eed8e20/download,True,,Computer Science,2019-06-27,cde804babf5ddd3dcfebe29346074ab7ded5737f,optimizingensembleweightsformachinelearningmodelsacasestudyforhousingpriceprediction,shahhosseini,10.1007/978-3-030-30967-1_9,optimizingensembleweightsformachinelearningmodelsacasestudyforhousingpriceprediction|2019,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1090db8fe3e5470de3fd3a674b2e617ebef1bb26,10.1155/2022/1078057,,Modeling and Simulation of Real Estate Valuation Based on Genetic Neural Network Model,2022.0,Journal of Function Spaces,Shu-Li Shen,"China’s real estate market is developing rapidly, but the house price is abnormal. The nonlinear relationship between housing characteristics and real estate value is difficult to calculate, resulting in the difficulty of house price prediction. Based on this, the relationship model between characteristic variables and house prices is constructed by using the machine learning method. At the same time, genetic algorithm is used to screen the specific values. The experimental results show that the optimized model converges in 56 iterations; in the application test, the research model found that in 90% of the test samples, the error between the predicted value and the actual measured value shall not exceed 10%. Experiments show that the genetic algorithm is effective in optimizing the BP house price valuation model and improves the calculation efficiency and valuation accuracy of the valuation model.",1.0,https://downloads.hindawi.com/journals/jfs/2022/1078057.pdf,True,,,2022-08-17,1090db8fe3e5470de3fd3a674b2e617ebef1bb26,modelingandsimulationofrealestatevaluationbasedongeneticneuralnetworkmodel,shen,10.1155/2022/1078057,modelingandsimulationofrealestatevaluationbasedongeneticneuralnetworkmodel|2022,1,2410,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2794b8fa32fc6a91b1a93265f6339ef257dc0981,10.1155/2022/3590224,,Prediction and Analysis of Housing Price Based on the Generalized Linear Regression Model,2022.0,Computational Intelligence and Neuroscience,Xin-Shu Li,"In order to improve the reliability of housing price prediction and analysis, this article combines the generalized linear regression model to build a real estate price prediction model and analyzes the basic knowledge of data mining. On the basis of this prior knowledge, this article investigates the cluster analysis algorithm and selects the generalized linear regression model as the research focus based on its definition and the characteristics of stock data. Moreover, this article analyzes the estimation methods of the generalized linear regression model and the nonparametric regression model, and then gives the estimation method of a partial linear model. In addition, this article verifies the validity of the model proposed in this article by means of simulation research. Through the simulation and comparison experiments, it can be seen that the housing price prediction system based on the generalized regression model proposed in this article has a high housing price prediction accuracy.",9.0,https://downloads.hindawi.com/journals/cin/2022/3590224.pdf,True,JournalArticle,Medicine,2022-09-29,2794b8fa32fc6a91b1a93265f6339ef257dc0981,predictionandanalysisofhousingpricebasedonthegeneralizedlinearregressionmodel,li,10.1155/2022/3590224,predictionandanalysisofhousingpricebasedonthegeneralizedlinearregressionmodel|2022,1,2507,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a907287eba86306b6de0fa7078b9b503d1c5a9b3,10.2991/mcei-18.2018.54,,Forecasting Second-hand Housing Price using Artificial Intelligence and Machine Learning Techniques,2018.0,,Tao Fu,"In this era of information explosion, the development of science and technology changes with each passing day. Therefore, the automatic analysis of scientific and technological trends aims to help scientists extract useful information from a large number of academic conferences and scientific and technological documents, which is of great practical significance. Artificial Intelligence give us an useful techniques, this paper we use using artificial intelligence and machine learning techniques to fit and forecast house price, From the point of view of model, the prediction effect of support vector machine is the best, with strong stability, while neural network is the worst. As a contrast, linear regression and random forest have little difference between them. Introduction Artificial Intelligence is a new science of technology to study, develop and extend human intelligence theory, method, technology and application system. Artificial intelligence is the study of computer to simulate certain thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). Enable the computer to achieve a higher level of application. Artificial intelligence will involve computer science, psychology, philosophy and linguistics. Corinna Cortes and Vapnik (1995) proposed support Vector Machine, which can improve the generalization ability of learning machine and minimize the empirical risk and confidence range by seeking the minimum structural risk[1].It can also be extended to other machine learning problems such as function fitting. Nello Cristianini and John Shawe-Taylor can be applied to support vector machine[2].A random forest is a classifier containing multiple decision trees, and the output categories are determined by the modes of the classes outputted by individual trees.Leo Breiman and Adele Cutler(1995) developed an algorithm to infer random forests[3], which was derived from random decision proposed by Tin Kam Ho of Bell Labs in 1995[4]. It improves the prediction accuracy of the model by summing up a large number of classification trees[5].Instead of traditional machine learning such as neural networks A new model of the. Artificial Neural Network (Ann), a hot research hot spot in artificial intelligence field since 1980s, Rumelhart, Hinton, William's(1986) developed BP algorithm has developed the radial basis function (RBF) basis function neural network for the first time[6].It has strong nonlinear fitting ability, can map any complex nonlinear relation, and the learning rules are simple, easy to realize by computer, and have strong robustness and memory ability. Nonlinear mapping ability and powerful self-learning ability. Methodologies Support Vector Machine. Support Vector Machine is based on the development of statistical learning theory. It systematically studies some fundamental problems in pattern recognition in the case of limited samples. The support Vector Machine regression (SVR) algorithm needs to define a loss function, which can ignore the errors in a range of real values, just like the SVM classification algorithm. This kind of function is insensitive loss. Figure 1shows the one-dimensional linear regression function with insensitive region. Copyright © 2018, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). 269 8th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2018) Advances in Computer Science Research (ACSR), volume 83",16.0,https://download.atlantis-press.com/article/25898502.pdf,True,,Engineering,2018-06-01,a907287eba86306b6de0fa7078b9b503d1c5a9b3,forecastingsecondhandhousingpriceusingartificialintelligenceandmachinelearningtechniques,fu,10.2991/mcei-18.2018.54,forecastingsecondhandhousingpriceusingartificialintelligenceandmachinelearningtechniques|2018,1,5074,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f0dfd97e512aee1bfac9c0c89e88d3f3e332b5c5,10.9734/jerr/2026/v28i51905,,Machine Learning Algorithms for House Price Prediction: A Comparative Study,2026.0,Journal of Engineering Research and Reports,Mirali Mammadzade,"Background: Accurate house price prediction is important for supporting real estate valuation, investment planning, banking decisions, and urban development through data-driven analysis of complex housing factors. Aims: The aim of this study is to compare the predictive performance of selected machine learning algorithms for house price prediction using the Ames Housing dataset. The study also investigates whether target transformation and feature importance analysis can improve both model performance and interpretability. Study Design: This study follows a quantitative experimental research design based on supervised machine learning regression methods. Place and Duration of Study: The study was conducted using the Ames Housing dataset during the period from January to March 2026. Methodology: The dataset contained 2930 residential property records and more than 80 explanatory variables. The target variable was SalePrice. Data preprocessing included removal of identifier columns, missing value imputation, categorical encoding, numerical scaling, and logarithmic transformation of the target variable. Four machine learning models were implemented using Python and scikit-learn: Linear Regression, Ridge Regression, Decision Tree Regressor, and Random Forest Regressor. The dataset was divided into training and testing subsets using an 80:20 ratio. Model performance was evaluated using Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, and coefficient of determination. Results: The results showed that Random Forest achieved the best predictive performance among the selected models. It obtained the lowest error values and the highest R² score, indicating stronger generalization ability. Feature importance analysis revealed that overall quality, above-ground living area, garage capacity, total basement area, and construction year were among the strongest predictors of house prices. This finding aligns with previous research that demonstrates the robustness of ensemble-based methods in predictive modeling tasks. Conclusion: The study concludes that ensemble learning methods, particularly Random Forest, are highly effective for house price prediction. The findings also show that preprocessing and target transformation are important for improving model accuracy.",0.0,https://doi.org/10.9734/jerr/2026/v28i51905,True,JournalArticle,,2026-05-22,f0dfd97e512aee1bfac9c0c89e88d3f3e332b5c5,machinelearningalgorithmsforhousepricepredictionacomparativestudy,mammadzade,10.9734/jerr/2026/v28i51905,machinelearningalgorithmsforhousepricepredictionacomparativestudy|2026,1,3808,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,housing characteristics prices hedonic,b09a7a2ac0d92d6b3fbad3f295c16ad119a0ef36,10.9734/ajeba/2021/v21i130345,,Combining Housing Price Forecasts Generated Separately by Hedonic and Artificial Neural Network Models,2021.0,,S. J. Terregrossa; Mohammed Hussein Ibadi,"Aims: A) To enhance accuracy in forecasting housing unit prices by forming combinations of component forecasts generated separately by hedonic and artificial neural network models; B) To help ascertain whether a constrained or unconstrained linear combining model achieves superior forecasting performance. Place and Duration of the Study: Department of Business Administration, Istanbul Aydin University, Istanbul 34295, Turkey; from 2019 to 2020. Study Design: A cross sectional data set of housing unit prices and corresponding housing unit attributes and characteristics is formed and then randomly divided into two segments: in sample (80%) and out of sample (20%). Three different methods (hedonic, artificial neural network and combining) are then employed to process the same in sample data set, and generate out of sample forecasts. The three forecasting methods are then tested and compared. Original Research Article Terregrossa and Ibadi; AJEBA, 21(1): 130-148, 2021; Article no.AJEBA.65400 131 Methodology: Out of sample combination forecasts are formed with component forecast weights generated by in sample weighted least squares (WLS) regression of realized price against in sample component forecasts. Four types of regressions are run: unconstrained, with and without a constant; constrained, with and without a constant. Then the mean absolute forecast error of each forecasting method is calculated and the mean difference in absolute forecast error between all pairs of models are compared and tested with a nonparametric Wilcoxon sign rank test. Results: The combining model formed with component forecast weights generated by weighted least squares (WLS) regression with the constant term suppressed and the sum-of-the-coefficients constrained to equal one, generally performs the best, in comparison with all other forecasting models (component and combination) examined in the study. Conclusion: The findings represent further evidence regarding the benefits of applying constraints on the linear combining forecast model; and demonstrate that a constrained linear combining model can be a successful technique for enhancing the forecast accuracy of housing unit prices.",31.0,https://doi.org/10.9734/ajeba/2021/v21i130345,True,,Computer Science,2021-02-13,b09a7a2ac0d92d6b3fbad3f295c16ad119a0ef36,combininghousingpriceforecastsgeneratedseparatelybyhedonicandartificialneuralnetworkmodels,terregrossa,10.9734/ajeba/2021/v21i130345,combininghousingpriceforecastsgeneratedseparatelybyhedonicandartificialneuralnetworkmodels|2021,1,3695,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices || housing characteristics prices hedonic",2,2,1,4,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c8005d27e59d7d848f2d85545764c2383d3b971c,10.9728/dcs.2022.23.12.2445,,Development of Rent House Price Prediction Service based on Machine Learning,2022.0,Journal of Digital Contents Society,Kwon-Woo Lee; Soualihou Ngnamsie Njimbouom; Jeong-Dong Kim,,1.0,https://doi.org/10.9728/dcs.2022.23.12.2445,True,,,2022-12-31,c8005d27e59d7d848f2d85545764c2383d3b971c,developmentofrenthousepricepredictionservicebasedonmachinelearning,lee,10.9728/dcs.2022.23.12.2445,developmentofrenthousepricepredictionservicebasedonmachinelearning|2022,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ff2384e4914f2b300a7fb0d9314ce184a151933e,10.1145/2996913.2996960,,"A spatio-temporal, Gaussian process regression, real-estate price predictor",2016.0,SIGSPATIAL/GIS,H. Crosby; Paul Davis; T. Damoulas; S. Jarvis,"This paper introduces a novel four-stage methodology for real-estate valuation. This research shows that space, property, economic, neighbourhood and time features are all contributing factors in producing a house price predictor in which validation shows a 96.6% accuracy on Gaussian Process Regression beating regression-kriging, random forests and an M5P-decision-tree. The output is integrated into a commercial real estate decision engine.",34.0,https://doi.org/10.82444/warw.33729535,True,JournalArticle;Book,Computer Science,2016-10-31,ff2384e4914f2b300a7fb0d9314ce184a151933e,aspatiotemporalgaussianprocessregressionrealestatepricepredictor,crosby,10.1145/2996913.2996960,aspatiotemporalgaussianprocessregressionrealestatepricepredictor|2016,1,1944,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,1,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",97188e4c6e76070966d42d2358a5cd905b29f66f,10.7494/geom.2026.20.4.33,,A Novel Housing Price Estimation Model Integrating GIS and DNN: A Case Study of Istanbul,2026.0,Geomatics and Environmental Engineering,Y. Çoruhlu; M. Dihkan; Okan Yıldız; M. Çelik; Hossein Azadi,"Housing valuation is a concrete reflection of socio-economic inequalities in urban space. Particularly in densely populated, spatially fragmented large cities like Istanbul, the current official mass appraisal system used for property taxation fails to reflect market reality. This situation results in revenue losses in property taxes and spatial injustices. This study develops a Deep Neural Network (DNN)-based model that integrates spatially derived variables from Geographic Information Systems (GIS) and incorporates 24 objective variables related to location, structure, and access in Istanbul. The model, trained on3,757 samples created using open-source big data, estimated housing values with high accuracy (R² = 0.979). The findings show that spatial differences in housing values are strongly related to urban variables such as accessibility and proximity to infrastructure. This approach not only produces housing value estimates but also provides a theoretical and methodological framework for spatial analyses of how value is produced in urban space. The study has the potential to support the development of a fair, transparent, and updatable mass appraisal system, especially for developing cities.",0.0,https://doi.org/10.7494/geom.2026.20.4.33,True,JournalArticle,,2026-05-30,97188e4c6e76070966d42d2358a5cd905b29f66f,anovelhousingpriceestimationmodelintegratinggisanddnnacasestudyofistanbul,çoruhlu,10.7494/geom.2026.20.4.33,anovelhousingpriceestimationmodelintegratinggisanddnnacasestudyofistanbul|2026,1,2715,3,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",87691f8649483df9b96b24df76880b3bae3b1602,10.7256/2454-0714.2026.2.80191,,A Method for Detecting Potentially Undervalued Real Estate Assets Using a CatBoost model,2026.0,Программные системы и вычислительные методы,Daniel Gennadjevich Gnitetskiy; M. L. Rysin,"The article proposes a new approach to residential real estate valuation based on intelligent analysis of listing data using gradient boosting. The method is aimed at identifying presumably undervalued offers based on a proxy-valuation model, that is, a model estimate obtained from the parameters of the object. Unlike traditional methods of real estate data analysis that use static rules or limited regression models, the approach proposed by the authors uses the capabilities of CatBoost gradient boosting to identify complex dependencies between the parameters of a residential real estate object. Using the CatBoost model, it is proposed to calculate an estimated valuation of an object relative to offers in the residential real estate market (proxy estimate) and use its deviation from the listing price as the main metric. The method is implemented in a software system that includes the following modules: a web application, a valuation module, a database, and a geoservice. The software system focuses on building a regression model that includes the calculation of the absolute and relative deviation of the listing price from the expected price of the object. These variables are key indicators. The output is a ranked list of residential real estate objects, where listings are sorted by the degree of possible undervaluation relative to the general state of the market. The architectural solution and features of the software implementation are described, and the practical significance of the approach for residential real estate market analysis is shown. The authors consider the key contribution to be: 1. The formalization of the task of identifying undervalued objects as a ranking task; 2. The development of a feature space that takes into account nonlinear dependencies of parameters; 3. The demonstration that a model trained on real estate listing data provides ranking quality (NDCG@10 = 0.982) comparable to regression approaches. The system was tested on a dataset of 500 thousand residential real estate listings for the period 2018-2021. The results show that the proposed approach makes it possible to improve the efficiency of preliminary market analysis. The results of the work may be of interest to investors, developers of real estate platforms, researchers, and market regulators.",0.0,https://doi.org/10.7256/2454-0714.2026.2.80191,True,JournalArticle,,2026-02-01,87691f8649483df9b96b24df76880b3bae3b1602,amethodfordetectingpotentiallyundervaluedrealestateassetsusingacatboostmodel,gnitetskiy,10.7256/2454-0714.2026.2.80191,amethodfordetectingpotentiallyundervaluedrealestateassetsusingacatboostmodel|2026,1,3817,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ed82ae8547cc5c5974ab6a335ec4b2543486d2c9,10.7172/2449-6634.jmcbem.2017.1.1,,"The Use of Artificial Neural Networks (ANN) in Forecasting Housing Prices in Ankara, Turkey",2017.0,,O. Ki̇tapci; Ömür Tosun; M. Tuna; T. Türk,"The purpose of this paper is to forecast housing prices in Ankara, Turkey using the artifi cial neural networks (ANN) approach. The data set was collected from one of the biggest real estate web pages during April 2013. A three-layer (input layer – one hidden layer – output layer) neural network is designed with 15 different inputs to forecast the future housing prices. The proposed model has a success rate of 78%. The results of this paper would help property investors and real estate agents in developing more effective property pricing management in Ankara. We believe that the artifi cial neural networks (ANN) proposed here will serve as a reference for countries that develop artifi cial neural networks (ANN) method-based housing price determination in future. Applying the artifi cial neural networks (ANN) approach for estimation of housing prices is relatively new in the fi eld of housing economics. Moreover, this is the fi rst study that uses the artifi cial neural networks (ANN) approach for analyzing the housing market in Ankara/Turkey.",20.0,https://doi.org/10.7172/2449-6634.jmcbem.2017.1.1,True,,Computer Science,2017-05-30,ed82ae8547cc5c5974ab6a335ec4b2543486d2c9,theuseofartificialneuralnetworksanninforecastinghousingpricesinankaraturkey,ki̇tapci,10.7172/2449-6634.jmcbem.2017.1.1,theuseofartificialneuralnetworksanninforecastinghousingpricesinankaraturkey|2017,1,2558,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",80eca804dcfd2a6b965c6e79de34885df4a0f86a,10.62441/nano-ntp.v20i7.5028,,Empowering Homebuyers with Advanced House Price Prediction Through Machine Learning,2024.0,Nanotechnology Perceptions,,"This project aims to develop a house price prediction application using Flutter, Firebase, and a machine learning (ML) algorithm, specifically linear regression. The application utilizes Flutter for the user interface, Firebase for data storage and retrieval, and linear regression for predicting house prices based on various features such as the number of bedrooms, square footage, and location. The linear regression model is trained using historical house price data stored in Firebase, which is pre-processed to handle missing values and scaled appropriately. Once trained with CSV files, the model is deployed within the Flutter app using Firebase ML Kit, enabling real-time predictions for users. This integrated solution provides a seamless user experience while harnessing the power of machine learning to deliver accurate house price predictions.",0.0,https://doi.org/10.62441/nano-ntp.v20i7.5028,True,JournalArticle,,2024-12-12,80eca804dcfd2a6b965c6e79de34885df4a0f86a,empoweringhomebuyerswithadvancedhousepricepredictionthroughmachinelearning,,10.62441/nano-ntp.v20i7.5028,empoweringhomebuyerswithadvancedhousepricepredictionthroughmachinelearning|2024,1,2356,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9f6d7c05139d687c1decbbdbc52009951e4301a6,10.62051/qjs7y352,,A Comparative Study of Regression Models for Housing Price Prediction,2024.0,Transactions on Computer Science and Intelligent Systems Research,Zizheng Li,"The property market is closely related to the regional economy. This study focuses on exploring the problem of house price prediction in the property market. This paper aims to reveal the performance of extreme gradient boosted tree regression, ridge regression, decision tree, and random forest regression models in feature selection and data processing, and assess their effectiveness in house price prediction. Aspects compared include prediction accuracy, data required for fitting, and running time. The study shows that the random forest and decision tree regression models perform best. Although these two models require a certain amount of data, they are easily satisfied in the context of big data. Although the fit of the random forest regression model and the decision tree regression model is similar, the training time required for the random forest regression model is too long. Therefore, it is concluded that the decision tree regression model performs well on general real estate datasets and is a widely applicable regression model in the real estate domain. This study can provide a helpful reference for data analysis and forecasting in the real estate field and provide a basis and inspiration for further research in related fields.",4.0,https://doi.org/10.62051/qjs7y352,True,JournalArticle,,2024-08-12,9f6d7c05139d687c1decbbdbc52009951e4301a6,acomparativestudyofregressionmodelsforhousingpriceprediction,li,10.62051/qjs7y352,acomparativestudyofregressionmodelsforhousingpriceprediction|2024,1,2754,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",34556515f99aab69038b49d0e1b14ce6da6c4e5a,10.61799/2216-0388.1885,,Enhancing House Price Prediction Using Ensemble Machine Learning Models: A Comparative Study with U.S. Housing Data,2026.0,Mundo Fesc,A. Gbadebo; Abiola Olaide Ayodele,"Background: Proper prediction of house prices is one of the primary issues in the real estate market, especially under unstable political and economic conditions. Forecasting models must adapt to increasing market complexities, yet such adaptability is difficult to achieve using traditional prediction techniques. Objective: This paper aims to compare the predictive accuracy of five machine learning models, including XGBoost, LightGBM, CatBoost, HistGradientBoosting, and Bayesian Ridge Regression, using a Kaggle U.S. housing dataset to determine which model performs best and which features contribute most to accurate predictions. Methods: A dataset of 1,460 samples and 32 variables from 1991 to 2024 was used. Both regression and classification measures were applied to evaluate model effectiveness. Feature importance analyses were conducted to identify variables that consistently influenced predictions. Results: CatBoost demonstrated the strongest performance, with a training accuracy of 0.94, a test R² of 0.86, and an MCC of 0.81. Variables such as OverallQual and GrLivArea were consistently identified as highly influential. Ensemble models, particularly CatBoost and XGBoost, outperformed Bayesian Ridge in handling non-linearities and categorical variables. Classification metrics, including specificity and Cohen’s Kappa, provided additional insights into model robustness. Conclusions: The proposed models produced competitive or superior results compared to previous studies while improving interpretability. The findings underscore the importance of ensemble learning methods and multidimensional analysis in developing robust, evidence-based real estate prediction models. The U.S. housing dataset used in this project was obtained from Kaggle and contains essential features for accurate house price prediction, representing historical data from 1991 through 2024.",0.0,https://doi.org/10.61799/2216-0388.1885,True,JournalArticle,,2026-01-01,34556515f99aab69038b49d0e1b14ce6da6c4e5a,enhancinghousepricepredictionusingensemblemachinelearningmodelsacomparativestudywithushousingdata,gbadebo,10.61799/2216-0388.1885,enhancinghousepricepredictionusingensemblemachinelearningmodelsacomparativestudywithushousingdata|2026,1,3389,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ba1601dcfd5d633c3e8a4afa2bd043a80598d06f,10.61784/wjebr3076,,A HOUSING PRICE PREDICTION MODEL BASED ON BACKPROPAGATION NEURAL NETWORK,2025.0,World Journal of Economics and Business Research,,,0.0,https://doi.org/10.61784/wjebr3076,True,JournalArticle,,,ba1601dcfd5d633c3e8a4afa2bd043a80598d06f,ahousingpricepredictionmodelbasedonbackpropagationneuralnetwork,,10.61784/wjebr3076,ahousingpricepredictionmodelbasedonbackpropagationneuralnetwork|2025,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",200e2d41943c693a8d5ed31607a33aa7a83fbd37,10.56557/ajpam/2025/v7i1235,,"Comparative Analysis of Linear and Non-Linear Regression Models in Predicting House Prices in Delta State, Nigeria",2025.0,Asian Journal of Pure and Applied Mathematics,Ejiro Stanley Omokoh; Eduiyovwiri Lewis Ejiro; Okogun Augustine Ejiroghene; U. E. Nkemjika,,0.0,https://doi.org/10.56557/ajpam/2025/v7i1235,True,JournalArticle,,2025-10-18,200e2d41943c693a8d5ed31607a33aa7a83fbd37,comparativeanalysisoflinearandnonlinearregressionmodelsinpredictinghousepricesindeltastatenigeria,omokoh,10.56557/ajpam/2025/v7i1235,comparativeanalysisoflinearandnonlinearregressionmodelsinpredictinghousepricesindeltastatenigeria|2025,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",414567c350c28d32edccd04fa8c72f8847644043,10.55927/fjst.v4i2.20,,House Price Prediction Using the Random Forest Algorithm on the Rapidminer Application,2025.0,Formosa Journal of Science and Technology,Fitria Fitria; Muhammad Syahid Pebriadi,"Home price prediction is an important aspect of the property industry, especially for real estate agents and potential buyers. With the advancement of technology, machine learning is used to improve the accuracy of home price estimates. One of the algorithms that is often used is Random Forest, which is able to capture complex patterns in data. However, previous research has shown that although this model has high accuracy (high R²), prediction errors are still significant (RMSE and MAE are high), indicating features that have not been fully modeled well. This research uses property datasets, preprocessing (normalization and feature selection), and applies Random Forest in RapidMiner. The model was evaluated using RMSE, MAE, and R², which showed R² = 0.78, but with RMSE = 4,757,343 and MAE = 3,200,000, indicating a large prediction difference. The most influential features are the area of the building (40%), the quality of the house (25%), and the number of bathrooms (10%).",1.0,https://doi.org/10.55927/fjst.v4i2.20,True,JournalArticle,,2025-02-27,414567c350c28d32edccd04fa8c72f8847644043,housepricepredictionusingtherandomforestalgorithmontherapidminerapplication,fitria,10.55927/fjst.v4i2.20,housepricepredictionusingtherandomforestalgorithmontherapidminerapplication|2025,1,2488,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bb94a1d3a15e06eb47d52178dc4e79b172333d96,10.55632/pwvas.v92i1.737,,Forecasting California Housing Prices Using a Linear Regression Model,2020.0,Proceedings of the West Virginia Academy of Science,Luisely Doza; Jason Rafe Miller,"With the prevalence of big data, we are able to implement simple Machine Learning techniques against datasets to solve many present issues. Utilizing the California Housing Prices dataset, we can apply linear regression models to forecast districts’ future median housing prices. As a result, homebuyers could predict if they are getting a good deal on their home, investors could predict their potential return on investments and capitalize on undervalued properties, and sellers could gauge what they could potentially sell for on the open market. To find a starting base, our research began as an independent study guided by a textbook that provides hands-on machine learning training, including one that uses the California Housing Prices dataset. From here, we are able to choose the Least Square Regression Line as our performance measure in the model—which will result in a linear equation that looks for minimizing the variance between the training set’s predictions and the actual points produced on the regression line. Once we are able to train our model to make accurate predictions from the testing set, we can expand upon this research by moving beyond linear regression and investigating different Machine Learning techniques further within the textbook to model the data in more sophisticated ways.",0.0,https://doi.org/10.55632/pwvas.v92i1.737,True,,,2020-04-29,bb94a1d3a15e06eb47d52178dc4e79b172333d96,forecastingcaliforniahousingpricesusingalinearregressionmodel,doza,10.55632/pwvas.v92i1.737,forecastingcaliforniahousingpricesusingalinearregressionmodel|2020,1,2814,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4fa0e3f6df7de77c0eda332cc9c406d53dcde32f,10.55606/jtmei.v5i2.6237,,Optimasi Random Forest untuk Prediksi Harga Rumah menggunakan Seleksi Fitur dan Penyetelan Hyperparamater,2026.0,"Jurnal Teknik Mesin, Industri, Elektro dan Informatika",Roberto Reyna Wijaya; M. E. Purbaya; Rozzan Zhofrul Islam,"House price prediction has become an important need in the property sector because selling prices are influenced by various factors, such as building quality, living area size, garage capacity, and physical property conditions. The complexity of relationships among these variables makes machine learning approaches, particularly Random Forest Regression, relevant for developing accurate and stable prediction models. This article discusses the optimization of Random Forest Regression for house price prediction through feature selection and hyperparameter tuning. The dataset used in this study is the House Prices: Advanced Regression Techniques dataset from Kaggle, with SalePrice as the target variable. The analysis process includes data exploration, data preprocessing, feature selection, train-test data splitting, Random Forest model development, and hyperparameter optimization using Randomized Search. Model performance was evaluated using R-Squared, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results show that the optimized model achieved an R-Squared value of 0.8927 and an RMSE value of 28,687.08. The most dominant features influencing house prices were OverallQual, GrLivArea, and GarageCars. Although the performance improvement after optimization was relatively modest, Random Forest proved capable of producing stable predictions and can be used as a decision-support tool in the property business. However, the model still has limitations in predicting extreme prices in the luxury housing segment.",0.0,https://doi.org/10.55606/jtmei.v5i2.6237,True,JournalArticle,,2026-06-22,4fa0e3f6df7de77c0eda332cc9c406d53dcde32f,optimasirandomforestuntukprediksihargarumahmenggunakanseleksifiturdanpenyetelanhyperparamater,wijaya,10.55606/jtmei.v5i2.6237,optimasirandomforestuntukprediksihargarumahmenggunakanseleksifiturdanpenyetelanhyperparamater|2026,1,3044,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0ec1e7c629b8d1975f1556930a2ba9be7b8cac5e,10.55041/ijsrem62604,,Prediction of House Prices using Machine Learning Techniques,2026.0,International Journal of Scientific Research in Engineering and Management,Yogesh Yogesh,,0.0,https://doi.org/10.55041/ijsrem62604,True,JournalArticle,,2026-05-01,0ec1e7c629b8d1975f1556930a2ba9be7b8cac5e,predictionofhousepricesusingmachinelearningtechniques,yogesh,10.55041/ijsrem62604,predictionofhousepricesusingmachinelearningtechniques|2026,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d436e3a1b30f49b512bb65b77936bd0dcd943388,10.55041/ijsrem42310,,"Estimation Residential Real Estate Price Considering Spatial Variables using Machine Learning in GIS: A Case Study in Chon Buri, Thailand",2025.0,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,Kamonchanok Tonlo,"This research investigates the effectiveness of machine learning (ML) techniques in predicting residential real estate prices, with a specific focus on integrating spatial variables within a Geographic Information System (GIS) framework. A case study in Chon Buri, Thailand, is utilized to compare the predictive accuracy of the Forest-based Classification and Regression (FBCR) algorithm, a GIS-specific ML tool, against established algorithms such as XGBoost (XGB) and Random Forest (RF). The methodology includes data collection through web scraping, rigorous preprocessing using the Interquartile Range (IQR) method for outlier detection, and spatial data integration via ArcGIS Pro. Model performance is evaluated using R², Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results demonstrate that FBCR outperforms both RF and XGBoost in predicting real estate prices, evidenced by a higher R² value of 0.6412, lower RMSE of 1.529, and lower MAE of 1.131 on the testing dataset. This superior performance highlights FBCR's ability to effectively capture and model the complex spatial relationships that influence property prices. The study underscores the potential of GIS-integrated ML tools in enhancing the accuracy and reliability of real estate valuation, providing valuable insights for urban planning and property market analysis. Key Words: residential real estate, predict price, machine learning, forest-based classification and regression (FBCR), geographic information system (GIS), spatial integration.",0.0,https://doi.org/10.55041/ijsrem42310,True,JournalArticle,,2025-03-14,d436e3a1b30f49b512bb65b77936bd0dcd943388,estimationresidentialrealestatepriceconsideringspatialvariablesusingmachinelearningingisacasestudyinchonburithailand,tonlo,10.55041/ijsrem42310,estimationresidentialrealestatepriceconsideringspatialvariablesusingmachinelearningingisacasestudyinchonburithailand|2025,1,3067,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f985188013feb2551466014b75b5e8b42de0e357,10.55041/ijsrem37950,,Predicting House Prices Using Regression Model,2024.0,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,Rajiv Tulsyan; Pranjal Shukla; Tushar Singh; Anshul Bhardwaj,"This paper shows a study on the development of a predictive model for house prices. The model uses machine learning techniques to analyze a large dataset of of housing market data, including demographic information, real estate trends, and economic indicators. The goal of the model is to accurately predict the sale prices of a house given its features and traits. To achieve this target, the study employs feature selection and engineering methods to determine the most significant predictors of house prices [1]. The results of the model are evaluated using standard metrics, sucha as root mean squared error [RMSE] and mean absolute error [MAE]. The results show that the proposed model outperforms benchmark model and provides a reliable prediction of house prices. The model can be used by real estate professionals, policymakers, and homebuyers to gain insights into the housing market and make the right choice [3]. Keywords- Regression, prediction, house price, Machine Learning.",2.0,https://doi.org/10.55041/ijsrem37950,True,JournalArticle,,2024-10-16,f985188013feb2551466014b75b5e8b42de0e357,predictinghousepricesusingregressionmodel,tulsyan,10.55041/ijsrem37950,predictinghousepricesusingregressionmodel|2024,1,2488,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",60db02d7a8ed6bd914b476c6295ef71f7bdfd000,10.54254/2755-2721/96/20241281,,Application and Performance Comparison of Compound Neural Network Model based on CNN Feature Extraction in House Price Forecast,2024.0,Applied and Computational Engineering,Jia-Yu Zhang,"Abstract. This study used a total of eight machine learning algorithms to forecast property prices, it not only provides a robust comparison of the predictive power of different algorithms but also significantly advances our understanding of the factors that influence property prices. In this paper, four traditional machine learning algorithms and four neural network models are selected for comparative study and analysis, of which the neural network models include fully connected neural networks (FCNN), convolutional fully connected neural networks (FCNN+CNN), generative adversarial fully connected networks (FCNN+GANs) and generative adversarial convolutional fully connected neural networks (FCNN+GANs+CNN). This study applied to a Kaggle's sample. The results reveal that the models based on FCNN+CNN and FCNN+GANs+CNN perform relatively well in house price prediction, with both obtaining an explanatory power of R as high as 0. 96 and 0. 97, respectively and significantly outperforming traditional machine learning algorithms. It is worth mentioning that the FCNN+CNN model is slightly stronger in terms of error minimization, but both perform better in terms of stability and generalization capabilities. The conclusion is that neural network models generally have better results than traditional algorithms in house price prediction, and the neural network model of CNN composite has significantly better prediction performance.",2.0,https://doi.org/10.54254/2755-2721/96/20241281,True,JournalArticle,,2024-11-26,60db02d7a8ed6bd914b476c6295ef71f7bdfd000,applicationandperformancecomparisonofcompoundneuralnetworkmodelbasedoncnnfeatureextractioninhousepriceforecast,zhang,10.54254/2755-2721/96/20241281,applicationandperformancecomparisonofcompoundneuralnetworkmodelbasedoncnnfeatureextractioninhousepriceforecast|2024,1,2943,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9b799df6640a19c01c9d7d8282ed86cd7c37843d,10.54254/2754-1169/94/2024ox0208,,A Study of Property Intrinsic Value Prediction Based on Machine Learning Modeling,2024.0,"Advances in Economics, Management and Political Sciences",Wentao Zhang,"When purchasing goods, buyers consistently prioritize the fairness of the price to make informed decisions, particularly for high-value items. This consideration becomes even more crucial in the real estate market, where verifying the fairness of a property's asking price through conventional methods can be challenging. This study addresses this issue by aiming to develop a functional predictive model that can estimate a property's intrinsic value using various machine learning techniques. By analyzing the California Census Data published by the US Census Bureau, this research investigates the potential for determining a property's intrinsic value based on readily available information about the block in which the property is situated. The study's objective is to create a model that provides accurate and reliable property valuations, helping buyers make more informed decisions and promoting transparency in the real estate market. This model leverages demographic, economic, and housing data from the census to predict property values, potentially transforming how properties are appraised and traded. By offering a method to verify asking prices, the research seeks to enhance fairness and efficiency in real estate transactions, providing a valuable tool for buyers, sellers, and real estate professionals alike.",0.0,https://doi.org/10.54254/2754-1169/94/2024ox0208,True,JournalArticle,,2024-10-12,9b799df6640a19c01c9d7d8282ed86cd7c37843d,astudyofpropertyintrinsicvaluepredictionbasedonmachinelearningmodeling,zhang,10.54254/2754-1169/94/2024ox0208,astudyofpropertyintrinsicvaluepredictionbasedonmachinelearningmodeling|2024,1,2827,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c2f7fc257e72a44a5592fa57114c96f9f84a31ce,10.54254/2753-8818/26/20241019,,Machine learning on USA house price prediction,2023.0,Theoretical and Natural Science,Zirong Jin,"Nowadays, an increasing number of students are opting to study abroad in order to acquire more advanced knowledge and pursue a superior educational environment. In many foreign countries, the option to apply for school dormitories is only available during the first year of university or graduate school. At other times, international students have to search for rented apartments or apply to stay with local host families. However, when studying abroad for an extended period, purchasing a property can potentially result in significant savings compared to renting. Therefore, this study focuses on comparing three types of machine learning techniques: multiple linear regression, Random Forest, and XGboost in predicting house prices in the United States. This research could provide reference for families studying abroad or property investors. Based on the preliminary findings of this study so far, it can be concluded that the XG-boost model demonstrates the highest accuracy and stability among these three methods.",0.0,https://doi.org/10.54254/2753-8818/26/20241019,True,JournalArticle,,2023-12-20,c2f7fc257e72a44a5592fa57114c96f9f84a31ce,machinelearningonusahousepriceprediction,jin,10.54254/2753-8818/26/20241019,machinelearningonusahousepriceprediction|2023,1,2522,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4349e9a268f6cdf9deeb588d578c0bd2a09a96e0,10.54097/vcy5n584,,A Comparative Study of House Price Prediction Using Linear Regression and Random Forest Models,2024.0,Highlights in Science Engineering and Technology,Yahan Fu,"The accurate prediction of house prices is crucial for stakeholders in the real estate sector, financial institutions, and urban planners. It not only informs investment decisions but also aids in policy formulation and market analysis. This research paper delves into the comparison of two prominent predictive analytics techniques-Linear Regression and Random Forest—to ascertain their effectiveness in forecasting house prices. Using a Kaggle dataset, this study analyzes key house price predictors such as building classification, living area size, construction year, and land area. The analysis shows Random Forest outperforms Linear Regression in accuracy, emphasizing the importance of building classification and living area in price prediction. Detailed visualizations, like feature importance graphs and scatter plots, offer clear insights into model performance. This research contributes significantly to real estate predictive analytics, offering insights to guide investment strategies and policy-making. It also opens avenues for exploring alternative machine learning approaches and socio-economic factors for a more comprehensive understanding of housing market dynamics.",4.0,https://doi.org/10.54097/vcy5n584,True,JournalArticle,,2024-08-15,4349e9a268f6cdf9deeb588d578c0bd2a09a96e0,acomparativestudyofhousepricepredictionusinglinearregressionandrandomforestmodels,fu,10.54097/vcy5n584,acomparativestudyofhousepricepredictionusinglinearregressionandrandomforestmodels|2024,1,2688,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,housing characteristics prices hedonic,ec75bb4c4de7b9bf80b4878e4744bc00a5c7c82f,10.54097/3bf94c14,,The Mass Appraisal Study of Taxable Price of Commodity Housing Based on Automated Valuation Models,2025.0,Journal of Innovation and Development,Kaiqing Wang; Yichao Wang; Zhuang Yang,"The purpose of this study is to reveal the construction characteristics and advantages of the random forest automatic assessment model based on the transaction data of new commercial housing in X district of A city, and to introduce a rolling time window to enhance the model performance of random forest and guide its practice in the mass appraisal of taxable price of commodity housing. The methods of this study are GIS spatial information processing, random forest algorithm and rolling time window time series analysis. The research results show that: 1) the number of features in the feature subset at regression tree splitting and the node division rules at regression tree splitting have a large impact on the prediction accuracy of the random forest automatic assessment model. 2) The evaluation accuracy and generalization ability of the random forest automatic evaluation model are significantly better than those of the Hedonic model, with a goodness-of-fit of 98% and an average absolute error percentage of 2.7% in the training and test sets. 3) The introduction of a suitable rolling time window can improve the prediction accuracy and economic explanation ability of the random forest automatic valuation model. In conclusion, the random forest automatic valuation model based on rolling time windows can be widely used for automatic batch valuation of taxable prices of real estate in the study area.",0.0,https://doi.org/10.54097/3bf94c14,True,JournalArticle,,2025-03-27,ec75bb4c4de7b9bf80b4878e4744bc00a5c7c82f,themassappraisalstudyoftaxablepriceofcommodityhousingbasedonautomatedvaluationmodels,wang,10.54097/3bf94c14,themassappraisalstudyoftaxablepriceofcommodityhousingbasedonautomatedvaluationmodels|2025,1,2917,6,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic || real estate hedonic regression || spatial hedonic model housing",2,0,1,5,0,5,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d1f63ff45bdb8672552a8577fe4bd9e702ec174d,10.53894/ijirss.v7i3.3220,,Real estate price forecasting utilizing recurrent neural networks incorporating genetic algorithms,2024.0,International journal of innovative research and scientific studies,Ting-Tin Tin; Cheok Jia Wei; Ong-Tzi Min; B. Feng; Too Chin Xian,"This study aims to develop and examine the effectiveness of the Recurrent Neural Network (RNN) model incorporating Genetic Algorithm (GA) in forecasting real estate prices. Real estate prices have a significant impact on a country’s financial system. Therefore, the ability to accurately forecast its price is valuable. A set of data containing 5.4 million unique records of real estate with their prices is used in the study. The data set, which spans from 2018 to 2021, contains twelve independent variables and one dependent variable. We preprocessed the data set to reduce noise and outliers that could potentially lead to poor model performance. The RNN model was selected because (GA) optimises the hyperparameters in the Recurrent Neural Network (RNN) hidden layer to optimise the performance of the RNN model. Relative Root Mean Squared Error (RRMSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) examine the effectiveness of the RNN-GA model.The results show that the RNN-GA outperforms RNN and the traditional statistical models used in the real estate industry. This study provides an understanding of the effectiveness of incorporating GA in optimizing the RNN model for real estate price forecasting, which could benefit stakeholders in the real estate industry and sustain the financial system. This study uses a novel hybrid RNN-GA model for predicting housing prices with a large dataset.",6.0,https://doi.org/10.53894/ijirss.v7i3.3220,True,JournalArticle,,2024-05-30,d1f63ff45bdb8672552a8577fe4bd9e702ec174d,realestatepriceforecastingutilizingrecurrentneuralnetworksincorporatinggeneticalgorithms,tin,10.53894/ijirss.v7i3.3220,realestatepriceforecastingutilizingrecurrentneuralnetworksincorporatinggeneticalgorithms|2024,1,2959,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f8fecd11695987c1e0f29361763d65871a1272eb,10.52465/josre.v2i1.176,,Optimization house price prediction model using gradient boosted regression trees (GBRT) and xgboost algorithm,2023.0,Journal of Student Research Exploration,Putri Susi Sundari; Mahardika Khafidz Putra,"In this rapidly advancing technological era, the demand for the real estate industry has also increased, including in the field of house price prediction. House prices fluctuate every year due to several factors such as changes in land prices, location, year of construction, infrastructure developments, and other factors. Numerous studies have been conducted on this issue. However, the challenge lies in building a proven accurate and effective model for predicting house prices with the abundance of features present in the dataset. The objective of this research is to develop a predictive model that can accurately estimate house prices based on relevant features or variables. The researcher utilizes ensemble learning techniques, combining the Gradient Boosted Regression Trees (GBRT) and XGBoost algorithms. The dataset used in this article is titled ""Ames Housing dataset"" obtained from Kaggle. The predictive model is then evaluated using the Root Mean Squared Error (RMSE) method. The RMSE result from a previous study that used the combination of Lasso and XGBoost was 0.11260, while the RMSE result from this research is 0.00480. This indicates a decrease in the RMSE value, indicating a lower level of error in the model. It also means that the combination of GBRT and XGBoost algorithms successfully improves the prediction accuracy of the previous research model.",6.0,https://doi.org/10.52465/josre.v2i1.176,True,JournalArticle,,2023-09-04,f8fecd11695987c1e0f29361763d65871a1272eb,optimizationhousepricepredictionmodelusinggradientboostedregressiontreesgbrtandxgboostalgorithm,sundari,10.52465/josre.v2i1.176,optimizationhousepricepredictionmodelusinggradientboostedregressiontreesgbrtandxgboostalgorithm|2023,1,2880,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",02437e50728fb744309d82e321792a0d5d850f58,10.5220/0015031500005056,,The Influencing Factors of Second-Hand Housing Prices in Beijing Based on Machine Learning Models,2026.0,"Proceedings of the 1st International Conference on Applied Mathematics, Physics and Digital Simulation",Si-Cheng Li,,0.0,https://doi.org/10.5220/0015031500005056,True,Conference,,,02437e50728fb744309d82e321792a0d5d850f58,theinfluencingfactorsofsecondhandhousingpricesinbeijingbasedonmachinelearningmodels,li,10.5220/0015031500005056,theinfluencingfactorsofsecondhandhousingpricesinbeijingbasedonmachinelearningmodels|2026,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",5a2df7042187ef3f9805a183d9eb9b2aa7c9a103,10.5220/0014884400005130,,Machine Learning Approaches for Predicting Seattle House Prices,2024.0,Proceedings of the 2nd International Conference on Financial Management and the Digital Economy,Xin-Bei Chen,,0.0,https://doi.org/10.5220/0014884400005130,True,Conference,,,5a2df7042187ef3f9805a183d9eb9b2aa7c9a103,machinelearningapproachesforpredictingseattlehouseprices,chen,10.5220/0014884400005130,machinelearningapproachesforpredictingseattlehouseprices|2024,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",68e1bd94fca50f9d2152f75488046841d80e92fe,10.5220/0014871300005130,,The Comparative Analysis of Five Machine Learning Models for House Price Prediction,2024.0,Proceedings of the 2nd International Conference on Financial Management and the Digital Economy,Yue Wu,,0.0,https://doi.org/10.5220/0014871300005130,True,Conference,,,68e1bd94fca50f9d2152f75488046841d80e92fe,thecomparativeanalysisoffivemachinelearningmodelsforhousepriceprediction,wu,10.5220/0014871300005130,thecomparativeanalysisoffivemachinelearningmodelsforhousepriceprediction|2024,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",66a65afa5298d1d6d0bd053f827221a779eb2a95,10.5220/0014379000004918,,Predicting House Price with multiple LASSO Regression and Linear Regression,2025.0,Proceedings of the 3rd International Conference on Computer Science and Mechatronics,Nuo Cheng,,0.0,https://doi.org/10.5220/0014379000004918,True,Conference,,,66a65afa5298d1d6d0bd053f827221a779eb2a95,predictinghousepricewithmultiplelassoregressionandlinearregression,cheng,10.5220/0014379000004918,predictinghousepricewithmultiplelassoregressionandlinearregression|2025,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",8878a91aeefd2cc17d2956f65192f1f5b7ba07c4,10.5220/0014363000004718,,Predicting Second-Hand Housing Prices in Beijing: A Comparative Study of Machine Learning and Ensemble Models,2025.0,"Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence",Jia He,": In recent decades, the real estate market has always been an imperative force boosting China’s economic development. Accurate housing price prediction plays a key role in policymaking and investment decisions. Nonetheless, the traditional models often fall short when dealing with complex and nonlinear relationships. This study focuses on predicting second-hand housing prices in Beijing using machine learning techniques. The web crawler is used to obtain historical transaction data, then yield a dataset that includes 56,793 records with 23 features after pre-processing in Beijing. Three models-Random Forest, XGBoost, and LightGBM-were trained using grid-search and five-fold cross-validation. A combined ensemble model was also built to improve the overall robustness. Evaluation and visualizations were used to compare performance. The ensemble model outperformed the single model, followed by XGBoost, Random Forest, and LighGBM. This paper aims to provide a new idea for house price prediction research through machine learning methods, hoping to bring some inspiration to theoretical research and practical applications in related fields.",0.0,https://doi.org/10.5220/0014363000004718,True,Conference,,,8878a91aeefd2cc17d2956f65192f1f5b7ba07c4,predictingsecondhandhousingpricesinbeijingacomparativestudyofmachinelearningandensemblemodels,he,10.5220/0014363000004718,predictingsecondhandhousingpricesinbeijingacomparativestudyofmachinelearningandensemblemodels|2025,1,2651,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",4b5606b2af40f4fab74129cf6fff570173518079,10.5220/0013700300004670,,"Using Linear Regression, Ridge Regression, Lasso Regression, and Elastic Net Regression for Predicting Real Estate Price",2025.0,Proceedings of the 2nd International Conference on Data Science and Engineering,Zhaorui Zeng,": In the contemporary economic landscape, the real estate market wields significant influence, with housing prices being a crucial factor affecting individuals, industries, and the overall economy. Fluctuations in housing prices can impact people's living standards, investment decisions, and the stability of related industries. Thus, accurately predicting housing sales prices is of utmost importance. This paper focuses on predicting housing sales prices using linear regression, ridge regression, Lasso regression, and elastic net regression. The principles and applications of the four regression models in price prediction are explored in detail. By calculating evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), the performance of each model is compared. The results show that linear regression has an MAE of 0.092129, indicating normal performance. Ridge regression can address multicollinearity issues with an MAE of 0.091513, but may overfit. Lasso regression and elastic net regression, with MAEs of 0.087802 and 0.087756 respectively, can simplify models through feature selection, reducing overfitting risks and improving the ability of generalization. Future research could expand data sources, incorporate external variables, adopt advanced models, and utilize big data and deep learning technologies. This research provides valuable references for real estate-related decision-making and promotes the development of real estate price prediction research.",2.0,https://doi.org/10.5220/0013700300004670,True,Conference,,,4b5606b2af40f4fab74129cf6fff570173518079,usinglinearregressionridgeregressionlassoregressionandelasticnetregressionforpredictingrealestateprice,zeng,10.5220/0013700300004670,usinglinearregressionridgeregressionlassoregressionandelasticnetregressionforpredictingrealestateprice|2025,1,3035,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",235965f0dd928bdd4752851ad45ce71d20991456,10.5220/0013689900004670,,Machine Learning-Based Approaches to Forecasting Housing Prices in the Canadian Market,2025.0,Proceedings of the 2nd International Conference on Data Science and Engineering,Yu-Hao Wen,": Housing prices are one of the key indicators for measuring the state of the real estate market. Establishing an efficient housing price forecasting model is of considerable importance to consumers, investors, and policymakers. In the context of Canadian research, scholars have constructed several regression models to predict housing prices. However, there remains a paucity of systematic research on model comparison and hybrid models. This study utilizes Canadian housing price data and employs a data preprocessing technique involving least absolute shrinkage and selection operator (LASSO) feature selection. Multiple regression models are then constructed including multiple linear regression (MLR), random forest (RF), extreme gradient boosting (XGBoost), and a hybrid model integrating RF and XGBoost. During the model building process, GridSearchCV method is applied to perform hyperparameter tuning for the machine learning models RF and XGBoost. The models are subsequently compared and analyzed using metrics including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R-squared to identify the most effective model for housing price prediction within the Canadian context. The study revealed that the hybrid model, comprising a linear weighted combination of RF and XGBoost, demonstrated the most efficacy in housing price forecasting.",0.0,https://doi.org/10.5220/0013689900004670,True,Conference,,,235965f0dd928bdd4752851ad45ce71d20991456,machinelearningbasedapproachestoforecastinghousingpricesinthecanadianmarket,wen,10.5220/0013689900004670,machinelearningbasedapproachestoforecastinghousingpricesinthecanadianmarket|2025,1,2862,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",42e67bd03d57a94853543cd5b6faf7d2fc716c1d,10.5220/0013511000004619,,AI in Real Estate: Forecasting House Prices with Advanced Machine Learning Models,2024.0,Proceedings of the 2nd International Conference on Data Analysis and Machine Learning,Jia-Shuo Cui; Zhitong Liu; Yinghan Ma,": Homes are essential to our lives, providing a comfortable space for living, working, and learning. Therefore, everyone needs a place to call home. This article describes how to use different machine learning algorithms (one of the core techniques of artificial intelligence, including linear regression, decision trees, random forests, and XGBoost) to complete the task of predicting house price trends at a time when house price trends are becoming the focus of attention. The performance of each model is compared to evaluate its different advantages and disadvantages in predicting housing prices. The results show that XGBoost model is the most effective in predicting house prices and becomes the best choice, followed by random forest model linear regression model, decision tree model can play a role in some cases. The results of the experiment can not only be convenient for home buyers or real estate developers to get help, but also can be used as a reference for government decision-making.",2.0,https://doi.org/10.5220/0013511000004619,True,Conference,,,42e67bd03d57a94853543cd5b6faf7d2fc716c1d,aiinrealestateforecastinghousepriceswithadvancedmachinelearningmodels,cui,10.5220/0013511000004619,aiinrealestateforecastinghousepriceswithadvancedmachinelearningmodels|2024,1,2504,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bd50a67fe743f42f6f0fb4702231f0844b79f000,10.5220/0006040700620068,1609.08399,House Price Estimation from Visual and Textual Features,2016.0,International Joint Conference on Computational Intelligence,Eman Ahmed; Mohamed N. Moustafa,"Most existing automatic house price estimation systems rely only on some textual data like its neighborhood area and the number of rooms. The final price is estimated by a human agent who visits the house and assesses it visually. In this paper, we propose extracting visual features from house photographs and combining them with the house's textual information. The combined features are fed to a fully connected multilayer Neural Network (NN) that estimates the house price as its single output. To train and evaluate our network, we have collected the first houses dataset (to our knowledge) that combines both images and textual attributes. The dataset is composed of 535 sample houses from the state of California, USA. Our experiments showed that adding the visual features increased the R-value by a factor of 3 and decreased the Mean Square Error (MSE) by one order of magnitude compared with textual-only features. Additionally, when trained on the benchmark textual-only features housing dataset, our proposed NN still outperformed the existing model published results.",84.0,https://doi.org/10.5220/0006040700620068,True,JournalArticle,Computer Science,2016-09-27,bd50a67fe743f42f6f0fb4702231f0844b79f000,housepriceestimationfromvisualandtextualfeatures,ahmed,10.5220/0006040700620068,housepriceestimationfromvisualandtextualfeatures|2016,1,2580,4,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3dc8f771b5c7fa83b9032ce05114aed39787c49b,10.5194/agile-giss-5-41-2024,,Artificial intelligence for greater transparency in housing price estimation,2024.0,AGILE Conf.,Christian Mueller-Kett,"Abstract. This paper investigates the use of machine learning (ML) models to predict housing prices. A well-performing housing price model was trained, which can seamlessly be integrated into public sector processes to increase market transparency and is based on modern ML and feature engineering methods. For these models, particular consideration was given to the spatial component. The research uses the Design Science Research approach, with a case study carried out in the city of Duisburg, Germany. The ML models developed showed better performance than traditional models. The models were embedded in official processes using Shapley values to increase interpretability. The study concludes that ML models can contribute to increased market transparency in the real estate sector. ",4.0,https://doi.org/10.5194/agile-giss-5-41-2024,True,JournalArticle,Computer Science,2024-05-30,3dc8f771b5c7fa83b9032ce05114aed39787c49b,artificialintelligenceforgreatertransparencyinhousingpriceestimation,mueller-kett,10.5194/agile-giss-5-41-2024,artificialintelligenceforgreatertransparencyinhousingpriceestimation|2024,1,2289,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",e47c72c1d4892127c75a5ac6f316ce10266ad34d,10.51201/jusst/21/05280,,House Price Prediction using Machine learning and Image Processing,2021.0,Journal of University of Shanghai for Science and Technology,J. Chougale; A. Shinde; Ninad Deshmukh; Dhananjay Sawant; Vaishali Latke,"We demonstrate that these urban features can be recorded by street views and satellite image data and enhance the estimate of house prices. In order to estimate house prices in London, UK, we recommend a pipeline that uses a deep neural network model to automatically extract visual features from images. In calculating the house price model, we use typical housing characteristics, such as age, size, and accessibility, as well as visual features from Google Street View images and Bing aerial pictures. We see promising outcomes where learning to describe a neighborhood’s urban efficiency facilitates the estimation of house prices, even when generalizing to previously unseen London boroughs. We discuss the use of non-linear vs. linear approaches to combine these signals with traditional house pricing models and explain how the interpretability of linear models helps one to specifically derive the visual desirability of neighborhoods as proxy variables that are both of importance in their own right and can be used as inputs to other econometric methods. This is particularly useful as it can be extended elsewhere after the network has been trained with the training data, enabling us to produce vivid complex maps of the desirability of London streets.",3.0,https://doi.org/10.51201/jusst/21/05280,True,,,2021-06-18,e47c72c1d4892127c75a5ac6f316ce10266ad34d,housepricepredictionusingmachinelearningandimageprocessing,chougale,10.51201/jusst/21/05280,housepricepredictionusingmachinelearningandimageprocessing|2021,1,2764,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",5b2925c4115ae077e3520830b821189ea292873e,10.5120/ijca2026926592,,Integrated Framework for House Price and Price-Zone Prediction with Natural Language Processing Chatbot,2026.0,International Journal of Computer Applications,Rodiah S.; Diana Tri Susetianingtias; Eka Patriya,,0.0,https://doi.org/10.5120/ijca2026926592,True,JournalArticle,,2026-03-20,5b2925c4115ae077e3520830b821189ea292873e,integratedframeworkforhousepriceandpricezonepredictionwithnaturallanguageprocessingchatbot,s.,10.5120/ijca2026926592,integratedframeworkforhousepriceandpricezonepredictionwithnaturallanguageprocessingchatbot|2026,1,1500,1,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9430444155a33bf9bb5a8a2fcdcb82fe2b3aeb1f,10.5120/ijca2020920303,,A Machine learning based Advanced House Price Prediction using Logistic Regression,2020.0,International Journal of Computer Applications,Ravula Bala; K. Surya; Tadiparthi Chandravas; M. J,"Machine learning has played a bigger role than in previous years’ image detection, spam changes, normal conversation orders, product recommendations, medical diagnoses. Current machine learning algorithms help to improve security alarms, public safety and medical improvement and other advantages using machine learning penetration in to several global implementations and their improvement. The machine learning system provides better opportunities customer service and safe car system. Currently A paper on which we talk about future housing price forecasts machine learning algorithms. As for Choose from our comparative and various study hypotheses forecasting methods. We use refined-lasso regression as our model due to the method of adaptation and verification on the model selection. Research shows that we need a problem-solving approach able to succeed and develop assumptions It will be possible to compare the cost of the house with other models. On the other hand, the housing price index and progress forecast of housing costs that tend to make real progress real estate policy schemes. This study uses machine learning The algorithm is used as a research method to develop housing prices predictive models. We create a model to calculate the cost of housing an example of a machine learning algorithm, e.g. gradient-boosting framework, refined-lasso regression and the machine learning based system Execute orders accurately. We recommend building an apartment at that time a cost estimate model to support a home seller or real estate an agent for better information based on house valuations. These tests show that there is a lasso regression algorithm The appearance of accuracy and reliability of other models Implement preliminary housing cost estimates",2.0,https://doi.org/10.5120/ijca2020920303,True,,Computer Science,2020-06-18,9430444155a33bf9bb5a8a2fcdcb82fe2b3aeb1f,amachinelearningbasedadvancedhousepricepredictionusinglogisticregression,bala,10.5120/ijca2020920303,amachinelearningbasedadvancedhousepricepredictionusinglogisticregression|2020,1,3273,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",784e397fc77e1719dd6a26964a4bd0b9c79884e2,10.48175/ijarsct-709,,House Price Forecasting Using Ml and RPA,2021.0,,Pradnya Patil; T. Patil,"In today’s world, every individual wish for a house that suits their lifestyle and provides amenities according to their needs and budget. On today’s world prices of houses are keep on changing very frequently which proves that house prices are often overstated. There are various factors that must be taken into deliberation for predicting house prices such as number of rooms, carpet area, and other basic local amenities. By using XGBoost, Light GBM and CatBoost Boosting algorithm to predict house prices based on real-time data which is extracted using Robotic Process Automation. Automate the tasks of data extraction can be done by Robotic Process Automation. The data generated by RPA is inconsistent, hence data is get cleaned and made consistent before feeding to appropriate Machine Learning model. After data cleaning process model is created and accuracy is get achieved [1].",0.0,https://doi.org/10.48175/ijarsct-709,True,,Economics,2021-01-15,784e397fc77e1719dd6a26964a4bd0b9c79884e2,housepriceforecastingusingmlandrpa,patil,10.48175/ijarsct-709,housepriceforecastingusingmlandrpa|2021,1,2388,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b2367716adbb2c099895d56da225a9e7108de17f,10.48175/ijarsct-4854,,House Price Prediction based on ML using Regression Techniques,2022.0,"International Journal of Advanced Research in Science, Communication and Technology",Archit Sisodia,"Guessing models for determining the sale price of houses in cities like Bengaluru still serve as a challenging and deceptive task. The retail price of buildings in cities such as Bengaluru depends on the number of other items. Important factors that may affect the price include the location, location and location of its facilities. In this research project, analytical research was conducted by considering a data set that is always open to the public by displaying available housing structures in the form of a machine hackathon platform. The data set has nine features. In this study, an effort has been made to develop a predictive model for price analysis based on price factors. Modeling tests use some retraction techniques such as multi-line retrieval (Small Squares), Lasso and Ridge retrieval models, vector retrieval, and reinforcing algorithms such as Extreme Gradient Boost Regression (XG Boost). Such models are used to create a predictive model and to select the most efficient model by performing comparative analysis and predictor errors found between these models. Here, the effort is to build a pricing model for price analysis based on price factors.",0.0,https://doi.org/10.48175/ijarsct-4854,True,,,2022-06-18,b2367716adbb2c099895d56da225a9e7108de17f,housepricepredictionbasedonmlusingregressiontechniques,sisodia,10.48175/ijarsct-4854,housepricepredictionbasedonmlusingregressiontechniques|2022,1,2671,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",eb65b1864e2361e4e5710c497b2965ed0c6df59e,10.48175/ijarsct-4845,,House Price Prediction using a Machine Learning,2022.0,"International Journal of Advanced Research in Science, Communication and Technology",Akshit Kathait,"Data mining is now widely used in the real estate market. The ability to extract data to extract relevant information from raw data makes it very useful to predict house prices, important housing features, and much more. Studies have shown that fluctuations in housing prices often affect homeowners and the housing market. A literature review was conducted to determine the appropriate characteristics and most effective models for real estate forecasting. The findings of this analysis confirmed the use of Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. In addition, our findings also suggest that local agents and real estate agents are key to predicting real estate prices. This research will be of great benefit, especially to housing developers and researchers, to find the most important indicators for determining housing prices and to identify the best machine learning model that will be used to conduct research in this field.",0.0,https://doi.org/10.48175/ijarsct-4845,True,Review,,2022-06-18,eb65b1864e2361e4e5710c497b2965ed0c6df59e,housepricepredictionusingamachinelearning,kathait,10.48175/ijarsct-4845,housepricepredictionusingamachinelearning|2022,1,2504,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2eccc0161d4e54491f7121341f0a924554123dd5,10.48175/ijarsct-29516,,House Price Prediction Using Machine Learning,2025.0,"International Journal of Advanced Research in Science, Communication and Technology",Rohan Bamane; Diganta Gavhane; Paras Igave; M. M. Zade,"Abstract: In today’s real estate market, predicting the accurate price of a house has become an important task, as property rates are continuously changing based on various factors. Every buyer or seller expects to know the correct market value before making any decision, but manual estimation often leads to confusion and uncertainty. To solve this problem, our project titled “House Price Prediction using Machine Learning” is designed to estimate the selling price of a house based on its features and specifications. With the growing demand for real estate data analysis, this project helps users make better financial decisions using Artificial Intelligence. The system takes various input features such as total area (in square feet), number of bedrooms, number of bathrooms, stories, parking spaces, availability of main road, presence of a guest room or basement, hot water heating, air conditioning, furnishing status, and preferred area. These features directly influence the house price, and our model learns their relationships from the dataset. In this project, we used Python as the core programming language along with libraries like Pandas, NumPy, and scikit-learn. The data preprocessing phase involved handling missing values, encoding categorical variables, and normalizing data to improve accuracy. After cleaning and analysing the data, a Linear Regression model was trained to predict continuous house prices. The model’s performance was evaluated using metrics like R² Score and Mean Absolute Error (MAE) to ensure accurate predictions. Once the model gave satisfactory results, it was saved using Pickle for later use. For deployment, a Flask web application was created with an interactive user interface built using HTML, CSS, and JavaScript. Users can enter the property details, and after clicking the “Predict” button, the trained model processes the data in the backend and instantly displays the estimated house price on the screen. Overall, this project demonstrates how Machine Learning and Data Science can be applied in real-life scenarios like real estate valuation. It not only automates the pricing process but also provides users with reliable and data-driven predictions. Hence, House Price Prediction using Machine Learning serves as a smart, efficient, and modern approach to understanding property market trends and making informed buying or selling decisions..",0.0,https://doi.org/10.48175/ijarsct-29516,True,JournalArticle,,2025-11-17,2eccc0161d4e54491f7121341f0a924554123dd5,housepricepredictionusingmachinelearning,bamane,10.48175/ijarsct-29516,housepricepredictionusingmachinelearning|2025,1,3905,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",059420b9faab486c7eff57197c01329304af34b3,10.48175/ijarsct-29500,,Smart House Price Estimator Using Artificial Intelligence,2025.0,"International Journal of Advanced Research in Science, Communication and Technology",A. Harale; Kirti Pujari; Anuja Pawar; P. Pawar,"Abstract: With the rapid expansion of the real estate sector, determining property values accurately has become increasingly important. Conventional methods for estimating house prices are slow and rely largely on expert judgment, which can result in inconsistencies. This project presents an AI-driven approach using machine learning to forecast house prices based on parameters like location, square footage, number of rooms, and additional key attributes. The system applies regression-based models to analyze historical property datasets and uncover the correlation between different features and price values. This predictive model assists homeowners, buyers, and real estate professionals in making better investment decisions while reducing manual effort. The study highlights the effectiveness of artificial intelligence and data science in enhancing accuracy and efficiency in real estate price prediction.",1.0,https://doi.org/10.48175/ijarsct-29500,True,JournalArticle,,2025-11-14,059420b9faab486c7eff57197c01329304af34b3,smarthousepriceestimatorusingartificialintelligence,harale,10.48175/ijarsct-29500,smarthousepriceestimatorusingartificialintelligence|2025,1,2415,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",2af08113f7a8d5fe56293541696538cce5f7e1ab,10.48175/ijarsct-23243,,House Price Prediction,2025.0,"International Journal of Advanced Research in Science, Communication and Technology",Nargis A. Mulla; Harshit Nandkumar Gorad; Sarthak Balaso Patole; Pradnya Adhikrav Gorad; Shweta Jadhav,"House price prediction is a crucial aspect of real estate analytics, impacting buyers, sellers, investors, and policymakers. The real estate industry is a dynamic sector where property values fluctuate based on numerous factors such as economic conditions, location, infrastructure, demand-supply balance, and regulatory policies. Traditional valuation methods often rely on expert opinions and manual assessments, which may introduce biases and inconsistencies. In contrast, data-driven approaches, particularly machine learning (ML) techniques, offer a more accurate and systematic way to estimate property values. This study explores the effectiveness of machine learning models in predicting house prices by analyzing historical real estate data. Various predictive models, including linear regression, decision trees, random forests, gradient boosting machines (XGBoost), and deep learning algorithms, are evaluated in terms of their performance. The research aims to determine which model provides the highest accuracy while ensuring interpretability and efficiency. Data pre-processing plays a vital role in enhancing the accuracy of house price predictions. The dataset used in this study includes essential property attributes such as square footage, the number of bedrooms and bathrooms, locality, age of the property, and market trends. Before applying predictive models, pre-processing techniques such as handling missing values, encoding categorical variables, scaling numerical features, and feature selection are implemented to optimize the dataset. The study also examines the impact of feature engineering, where derived attributes such as neighbourhood crime rates, proximity to schools, and public transportation accessibility are incorporated to improve prediction accuracy. The research adopts multiple evaluation metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²) values, to assess the performance of different models. Initial findings suggest that ensemble learning methods such as Random Forest and XGBoost outperform traditional regression models in terms of predictive accuracy. However, deep learning models, specifically artificial neural networks (ANNs), show promising results when trained on large datasets. The study also highlights the importance of hyper parameter tuning in refining model performance, addressing issues such as over fitting and computational efficiency. One of the key contributions of this research is the comparative analysis of different machine learning algorithms to identify the best-suited approach for house price prediction. Traditional regression techniques, while interpretable, often fail to capture complex nonlinear relationships within the data. Decision tree-based methods, such as Random Forest and XGBoost, demonstrate robust performance by learning intricate patterns and reducing variance through ensemble learning. Deep learning models, on the other hand, excel in capturing high-dimensional interactions but require extensive computational resources and larger training datasets. Beyond predictive accuracy, the study examines the interpretability of machine learning models, which is crucial for real-world applications. Feature importance analysis is conducted to understand which variables have the most significant impact on house prices. Factors such as location, property size, and market demand emerge as primary determinants. The study also investigates the effect of external factors, including economic indicators like interest rates, inflation, and employment rates, on housing prices. Despite the promising results, challenges such as data availability, regional market variations, and the need for real-time data integration remain. Future research can focus on enhancing prediction models by incorporating geospatial analytics, satellite imagery, and deep learning advancements. Additionally, integrating real-time market data and leveraging blockchain technology for transparent property transactions can further improve predictive modelling in real estate.",0.0,https://doi.org/10.48175/ijarsct-23243,True,JournalArticle,,2025-02-10,2af08113f7a8d5fe56293541696538cce5f7e1ab,housepriceprediction,mulla,10.48175/ijarsct-23243,housepriceprediction|2025,1,5591,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,10,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8b99b7c0a65b9b01610eb3d2cf2f54527a64cfe4,10.48175/ijarsct-22501,,Housing Price Prediction using Linear Regression,2024.0,"International Journal of Advanced Research in Science, Communication and Technology",Abhimanyu Singh; U. Sree R,"In today's world, real estate is a major investment, particularly in a city like Mumbai, which is a dream destination for many to work and settle down. Hence, understanding the real-time value of a property is crucial before investing your hard-earned money. The primary The purpose of this essay is to estimate the current market price of homes in Mumbai. Various factors, like the quantity of bedrooms and the availability of different amenities, are considered in this prediction. This estimation is designed to assist customers in finding suitable options that meet their needs. We have utilized the Linear Regression model to forecast the prices of the homes in question. This model minimizes the necessity of consulting a broker, thus providing additional assistance to the customer.",2.0,https://doi.org/10.48175/ijarsct-22501,True,JournalArticle,,2024-12-05,8b99b7c0a65b9b01610eb3d2cf2f54527a64cfe4,housingpricepredictionusinglinearregression,singh,10.48175/ijarsct-22501,housingpricepredictionusinglinearregression|2024,1,2289,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0e16ddb99c7f2a5b95ab6c2bc4993147dc13e677,10.48175/ijarsct-15953,,Housing Price Prediction using Machine Learning Technique,2024.0,"International Journal of Advanced Research in Science, Communication and Technology",R. S; G. D,"As house prices increase every year, so there is a need for implementing machine learning in a real estate to develop a model that predicts the current house prices. House prices prediction can help the developers to determine the estimated selling price of a house and can help the buyers to arrange the budget at the right time to purchase a house. There are many factors that influence the price of a house positively and some impact negatively such as the age of the house and include physical conditions, concept and location. This paper presents a model that can generate predictions for housing prices by training the system with a 2001 data set so that it can learn how to forecast the market price of the house based on certain factors which have already mentioned bellow. The connection between the houses costs and the economy of the nation is a critical factor for predicting house prices as housing prices trends are not only the concern of buyers and sellers, but it also plays major role in the current economic situation. Therefore, it is important to predict housing prices to help all the stack holders such as the buyers and the sellers to make their decisions accordingly. In order to select a prediction method, various regression methods were explored such as a multiple linear regression will be chosen for this research proposed work due to its Flexibility and probabilistic approach to learning and model prediction is very high. Factors affecting the housing prices Including house age, longitude, latitude, number of rooms, number of restrooms, number of schools around the house, carports, parks, shopping centers, condition of the house, total number of floors a house has and other distinctive",0.0,https://doi.org/10.48175/ijarsct-15953,True,JournalArticle,,2024-03-23,0e16ddb99c7f2a5b95ab6c2bc4993147dc13e677,housingpricepredictionusingmachinelearningtechnique,s,10.48175/ijarsct-15953,housingpricepredictionusingmachinelearningtechnique|2024,1,3223,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",74468b5133889de393760ea45645b2dce9a80818,10.47392/irjash.2023.s017,,A Literature Review on Using Machine Learning Algorithm to Predict House Prices,2023.0,International Research Journal on Advanced Science Hub,Tanmoy Dhar; M. P,"In this study, we use a variety of machine-learning methods to forecast the sale prices of residences. The size, location, building type, age, number of bedrooms, garages, and other characteristics of the property all affect how much it is worth when it is sold. Machine-learning algorithms are employed to develop the prediction model for houses in this article. Using machine learning methods, such as call trees, supply regression, support vector regression, and the Lasso Regression methodology, a prognostic model is developed in this case. Also, we have contrasted supported parameters for these algorithms such as MAE, MSE, RMSE, and accuracy. In this research, machine learning algorithms are used as a hunting tool to create models for predicting housing value.",1.0,https://doi.org/10.47392/irjash.2023.s017,True,JournalArticle;Review,,2023-05-28,74468b5133889de393760ea45645b2dce9a80818,aliteraturereviewonusingmachinelearningalgorithmtopredicthouseprices,dhar,10.47392/irjash.2023.s017,aliteraturereviewonusingmachinelearningalgorithmtopredicthouseprices|2023,1,2270,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",be87397676bb4f77c364fea6fd08e8f0307a5c1a,10.47164/ijngc.v13i5.952,,Analysing the Factors Influencing the House Prices and Studying House Price Prediction Methods,2022.0,International Journal of Next-Generation Computing,Purushottam Assudani; Chinmay Wankhede,"Home buyers looking for a new house tend to be very cautious with their budgets and market strategies. Theyalways try to optimise the budget in such a way that it matches their requirements and needs. Therefore predictionof price becomes a very important thing when it comes to planning a budget and there is a need for a predictiontool. This can be achieved by doing data analysis notably Exploratory Data Analysis (EDA) and by developingMachine Learning models. An ideal home that a customer dreams of is something that matches as well as fulfils thecustomer’s requirements but at the same time with an appropriate budget. So instead of going to the real estateagent and paying an additional expense in the form of commission, the same work of suggesting and predictingthe price after analysing large data, is done by various machine learning models in a more efficient manner.Thus the research on house price prediction is of much significance as it caters to two stakeholders in this realestate market, home buyers will have a better understanding of property value and will be helped in the decisionmaking process and hence stand a better stand at negotiating. The other stakeholder i.e. the home seller will geta better estimate to put selling cost on property.",1.0,https://doi.org/10.47164/ijngc.v13i5.952,True,,,2022-11-26,be87397676bb4f77c364fea6fd08e8f0307a5c1a,analysingthefactorsinfluencingthehousepricesandstudyinghousepricepredictionmethods,assudani,10.47164/ijngc.v13i5.952,analysingthefactorsinfluencingthehousepricesandstudyinghousepricepredictionmethods|2022,1,2767,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4d4ccb11f7771ceed4206afb8b103ef4ae4800f5,10.46632/daai/3/1/2,,Residential Property Price Estimation Using Machine Learning,2023.0,Data Analytics and Artificial Intelligence,S. Kavitha; P. Brinda; J. Sowmiya; P. Kishorekumar; S. Kailasanath; T. Jayanthan,": Property sales is not much straightforward in the country. Property pricing kept on changing day by day and at times that are being hyped rather than being based on valuation. Our system's key component is its ability to estimate property prices using actual variables. This article describes multiple regression algorithms that can be used with the Python library to estimate housing expenses. The suggested approach yields a more precise forecast by accounting for the complex factors employed in calculating home prices. The house price model and the datasets utilized in the suggested model are explained by this system using machine learning. In this system, we had used the linear regression algorithm. In cities like Mumbai, predictive models are still used to estimate residential sales prices due to factors more than just challenging and complex tasks. In cities like Mumbai, the cost of real estate for sale is influenced by a number of variables.",0.0,https://doi.org/10.46632/daai/3/1/2,True,,,2023-02-01,4d4ccb11f7771ceed4206afb8b103ef4ae4800f5,residentialpropertypriceestimationusingmachinelearning,kavitha,10.46632/daai/3/1/2,residentialpropertypriceestimationusingmachinelearning|2023,1,2460,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3ac46c8c9a9d1cc3106efaeba23de4aa7a8188ab,10.46632/daai/2/4/2,,Enhancement in House Price Prediction using Advanced Regression Technique,2022.0,4,,,0.0,https://doi.org/10.46632/daai/2/4/2,True,,,2022-12-01,3ac46c8c9a9d1cc3106efaeba23de4aa7a8188ab,enhancementinhousepricepredictionusingadvancedregressiontechnique,,10.46632/daai/2/4/2,enhancementinhousepricepredictionusingadvancedregressiontechnique|2022,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",84ce9a41dc472e601cd5b9950e6ff81bf61668fb,10.46593/ijaera.2021.v07i05.001,,House Price Prediction using Machine Learning in Python,2021.0,International journal of advanced engineering research and applications,N. Kalra; Nidhika Uppal; Prerna Pathak; Muskan Nandkani; Garima Sharma,"The business of buying and selling of house continues to grow every year due to population growth and migration to other cities for their financial purposes. Real estate is a very emerging field in everyone’s day to day life. The prices of houses are regularly changing on daily basis and are sometimes fired rather than based on actual estimates. Foreseeing property costs by actual components is a main criterion of this research paper. Our basic aim is to take all the actual and primary features to determine the result of our system. We have used regression models like decision tree classifier, random forest, and multiple linear regression classifier for prediction to get better results and for upgraded accuracy. This paper will give information that how we will predict the home price with the help of different features and python with its libraries. The main objective of this research paper is the estimation of the market worth of a land, house, property which will help customers to buy and sell property without moving to a specialist.",2.0,https://doi.org/10.46593/ijaera.2021.v07i05.001,True,,,2021-09-30,84ce9a41dc472e601cd5b9950e6ff81bf61668fb,housepricepredictionusingmachinelearninginpython,kalra,10.46593/ijaera.2021.v07i05.001,housepricepredictionusingmachinelearninginpython|2021,1,2551,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d18409fb815d8fc73019639579734ed705b18a92,10.46501/ijmtst061236,,Machine Learning House Price Prediction,2020.0,International Journal for Modern Trends in Science and Technology,"Manu Shahi, Abhay Singh, Amita Goel Vasudha Bahl and Nidhi Sengar","This document present the implementation of Machine Learning algorithms for the prediction of the house and the real estate prices. As the house and real estate prices are subject to change with the market conditions, so it become very difficult to predict the real estate prices with the conventional methods as it may sometimes gives some exaggerated result that may incur losses. To predict the prices more accurately and precisely we predict the prices based on the statics of that particular area which has all the trends and factors on which the price is dependent. To analyse these data , several algorithms are used namely random forest, linear regression , lasso regression etc. Use of these algorithms decreases the margin of error and more precise result are achieved. So,we at this point recommend the real estate agents and house vendors as well as the people to look into the model for better valuation of the house. This model can also be integrated with the real estates websites to give better recommendation based on the prices using Machine Learning Algorithms.",0.0,https://doi.org/10.46501/ijmtst061236,True,,,2020-12-13,d18409fb815d8fc73019639579734ed705b18a92,machinelearninghousepriceprediction,sengar,10.46501/ijmtst061236,machinelearninghousepriceprediction|2020,1,2580,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",445898473dc375739ebc1a3f2eca542de5062854,10.46501/ijmtst061220,,A Comparative Study on House Price Prediction,2020.0,,Akash Kapoor,"Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Due to increase in urbanization, there is an increase in demand for renting houses and purchasing houses. Therefore, to determine a more effective way to calculate house price accurately is the need of the hour. So, an effort has been made to determine the most accurate way of predicting house price by using machine learning algorithms: Multivariable Linear Regression, Decision Tree Regression and Random Forest Regression and it is determined that Multivariable Linear Regression has showed most accuracy and less error.",6.0,https://doi.org/10.46501/ijmtst061220,True,,Economics,2020-12-04,445898473dc375739ebc1a3f2eca542de5062854,acomparativestudyonhousepriceprediction,kapoor,10.46501/ijmtst061220,acomparativestudyonhousepriceprediction|2020,1,2317,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",aae551035d2e6f57a75829f22a35e1c1fcae982f,10.4236/oalib.1108346,,Incorporating Multiple Linear Regression in Predicting the House Prices Using a Big Real Estate Dataset with 80 Independent Variables,2022.0,OALib,A. Abdulhafedh,"This paper uses a multiple linear regression analysis to predict the final price of a house in a big real estate dataset. The data describes the sale of individual properties, various features, and details of each home in Ames, Iowa, USA from 2006 to 2010. The dataset comprises of 80 explanatory variables which include 23 nominal, 23 ordinal, 14 discrete, and 20 continuous variables. The goal was to use the training data to predict the sale prices of the houses in the testing data. The most important predictors were determined by random forest and kept in the analysis. The highly correlated predictors were dropped from the dataset. All assumptions of the linear regression were checked, and an optimal final predictive model was achieved by keeping the most influential predictors only. The model accuracy assessments produced very good results with an adjusted R-squared value of 0.9283, a residual standard error (RSE) of 0.094, and a root squared mean error (RSME) of 0.12792. In addition, the prediction error (Mean Squared Error, MSE) of the final model was found to be very small (12%) by applying different cross validation techniques, including the validation set approach, the K-fold approach and the Leave-One-Out-Cross Validation (LOOCV) approach. Results show that multiple linear regression can precisely predict the house prices with big dataset and large number of both categorical and numerical predictors.",10.0,https://doi.org/10.4236/oalib.1108346,True,,,,aae551035d2e6f57a75829f22a35e1c1fcae982f,incorporatingmultiplelinearregressioninpredictingthehousepricesusingabigrealestatedatasetwith80independentvariables,abdulhafedh,10.4236/oalib.1108346,incorporatingmultiplelinearregressioninpredictingthehousepricesusingabigrealestatedatasetwith80independentvariables|2022,1,2930,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,automated valuation model residential,915cbe20c2bb18373993eae9f59dccc1673f7c29,10.4028/www.scientific.net/amm.462-463.1081,,Optimisation to ANN Inputs in Automated Property Valuation Model with Encog 3 and winGamma,2013.0,,Vo Nguyen; Hao Shi; J. Szajman,"An automated property model for prediction of the sales price of residential properties with optimized inputs was developed. Optimised inputs improve efficiency and speed of an Artificial Neural Network (ANN). Property appraisal ANNs have a great potential not only to save time and money but also help local government authorities to determine the tax revenue. While the criteria for the ANN’s number of hidden layer neurons are well known, there is no theory to support the optimisation to ANN inputs. The proposed optimisation to ANN inputs procedure aims to resolve some of the issues in using ANNs especially in the case of automated property valuation modelling (AVM). A brief review of ANNs and their applications is given, followed by the discussion of the ANN design methodology and optimisation. Details of ANN optimisation using Java based Encog 3 and winGamma are presented in this paper. It is shown that optimisation to ANN inputs can improve the accuracy in residential property evaluation using winGamma and Encog 3.",4.0,https://doi.org/10.4028/www.scientific.net/amm.462-463.1081,True,Review,Engineering,2013-11-01,915cbe20c2bb18373993eae9f59dccc1673f7c29,optimisationtoanninputsinautomatedpropertyvaluationmodelwithencog3andwingamma,nguyen,10.4028/www.scientific.net/amm.462-463.1081,optimisationtoanninputsinautomatedpropertyvaluationmodelwithencog3andwingamma|2013,1,2532,2,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || automated valuation model residential",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a1cf0482877f8f53a4698218224a11df272ce0b5,10.4028/p-pk3iuw,,Applying AI to Predict High-Cost Rental Prices: A Comparative Modelling Approach,2026.0,Advances in Science and Technology,Mohammad Amir Parvez; S. Dadvandipour,"Accurately predicting real estate values remains challenging in volatile and rapidly growing real estate markets, especially for expensive homes worldwide. Traditional statistical and economic models frequently miss the temporal dynamics and nonlinear dependencies affecting changes in property values. This study uses a large-scale, multi-market dataset to anticipate real estate values utilizing sophisticated deep learning architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Dense Neural Networks (DNN). To increase learning efficiency, data pretreatment techniques included time-series sequencing, categorical encoding, and normalization. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2) were used to assess the model's performance. The findings show that the LSTM model obtained the lowest average prediction error (MAE) and produced extremely accurate forecasts by effectively incorporating long-term temporal dependencies. According to the study's findings, deep learning gives a solid, scalable foundation for accurate property price predictions. It also has practical ramifications for urban planners, investors, and legislators in dynamic housing markets.",0.0,https://doi.org/10.4028/p-pk3iuw,True,,,2026-08-03,a1cf0482877f8f53a4698218224a11df272ce0b5,applyingaitopredicthighcostrentalpricesacomparativemodellingapproach,parvez,10.4028/p-pk3iuw,applyingaitopredicthighcostrentalpricesacomparativemodellingapproach|2026,1,2754,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic housing prices,9544f4ac839e433b51f55da090f58729fbbf9919,10.3846/ijspm.2020.11544,,A HOUSE PRICE VALUATION BASED ON THE RANDOM FOREST APPROACH: THE MASS APPRAISAL OF RESIDENTIAL PROPERTY IN SOUTH KOREA,2020.0,,Jengei Hong; Heeyoul Choi; Woo-sung Kim,"Mass appraisal is the standardized procedure of valuing a large number of properties at the same time and is commonly used to compute real estate tax. While a hedonic pricing model based on the ordinary least squares (OLS) linear regression has been employed as the traditional method in this process, the stability and accuracy of the model remain questionable. This paper investigates the features of a house price predictor based on the Random Forest (RF) method by comparing it with that of a conventional hedonic pricing model. We used apartment transaction data from the period of 2006 to 2017 in the district of Gangnam, one of the most developed areas in South Korea. Using a data set covering 40% of all transactions in the sample area, we demonstrate that the accuracy of a machine learning-based predictor can be surprisingly high. The average of percentage deviations between the predicted and the actual market price was found to be only around 5.5% in the RF predictor, whereas it was almost 20% in the OLS-based predictor. With the RF predictor, the probability of the predicted price being within 5% of its actual market price was 72%, while only about 17.5% of the regression-based predictions fell within the same range. These results show that, in the practice of mass appraisal, the RF method may be a useful complement to the hedonic models, as it more adequately captures the complexity or non-linearity of actual housing markets.",154.0,https://doi.org/10.3846/ijspm.2020.11544,True,,Economics,2020-02-03,9544f4ac839e433b51f55da090f58729fbbf9919,ahousepricevaluationbasedontherandomforestapproachthemassappraisalofresidentialpropertyinsouthkorea,hong,10.3846/ijspm.2020.11544,ahousepricevaluationbasedontherandomforestapproachthemassappraisalofresidentialpropertyinsouthkorea|2020,1,2952,10,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || hedonic property valuation || machine learning hedonic pricing housing || real estate hedonic regression",2,1,4,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3257c35310edf02c867d8e91483b790ff8577ed0,10.38177/ajast.2023.7105,,Machine Learning Based House Price Prediction Using Modified Extreme Boosting,2023.0,Asian journal of applied science and technology,N. Ragapriya; T. kumar; R. Parthiban; P. Divya; S. Jayalakshm; D. Raman,"In recent years, machine learning has become increasingly important in everyday voice commands and predictions. Instead, it provides a safer auto system and better customer assistance. As a result of all that has been demonstrated, ML is a technology that is becoming more and more popular in a range of industries. To gauge changes in house values, the House Price Index is frequently employed (HPI). Due to the substantial correlation that exists between property prices and other variables, such as location, region, and population, the HPI on its own is not sufficient to accurately forecast a person's house price. Some studies have successfully predicted house prices using conventional machine learning techniques, but they seldom evaluate the efficacy of different models and ignore the more complicated but less well-known models. We proposed Modified Extreme Gradient Boosting as our model in this study due to its adaptive and probabilistic model selection process. Feature engineering, hyperparameter training and optimization, model interpretation, and model selection and evaluation are all steps in the process. Home price indices, which are frequently used to support real estate policy initiatives and estimate housing costs. In this project, models for forecasting changes in home prices are developed using machine learning methods.",5.0,https://doi.org/10.38177/ajast.2023.7105,True,,,,3257c35310edf02c867d8e91483b790ff8577ed0,machinelearningbasedhousepricepredictionusingmodifiedextremeboosting,ragapriya,10.38177/ajast.2023.7105,machinelearningbasedhousepricepredictionusingmodifiedextremeboosting|2023,1,2851,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a408e1de9351d02b0461b080fc8a88cb83cd15c4,10.37708//em.swu.v17i1.3,,MACHINE LEARNING ALGORITHMS FOR FORECASTING ASSET PRICES: AN APPLICATION TO THE HOUSING MARKET,2020.0,Economics and Management,Anton A. Gerunov,"This article investigates the application of advanced machine learning algorithms for forecasting housing prices. To this end we leverage a dataset of 414 observations of housing deals in Taipei and model it with both traditional econometric and novel machine learning algorithms. An exhaustive search among 107 alternative methods is conducted and their forecast accuracy is reported in detail. Using the root mean squared error (RMSE) as a benchmark metric, we find that implementations of the random forest family have superior performance, far surpassing that of more traditional approaches such as the multiple linear regression. The results have utility for both academics and practitioners and can be easily t ransferred to other forecasting problems in economics and business.",0.0,https://doi.org/10.37708//em.swu.v17i1.3,True,,,2020-11-10,a408e1de9351d02b0461b080fc8a88cb83cd15c4,machinelearningalgorithmsforforecastingassetpricesanapplicationtothehousingmarket,gerunov,10.37708//em.swu.v17i1.3,machinelearningalgorithmsforforecastingassetpricesanapplicationtothehousingmarket|2020,1,2284,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",dbe2948d5376907de536fb31d03bd93789812d82,10.37394/23205.2024.23.17,,"Leveraging Data Mining, Machine Learning, and Web Scraping for Forecasting Rental Housing Prices in Tunisia",2024.0,WSEAS Transactions on Computers,A. Balti; M. Lakhoua; Mounir Sayadi,"The Tunisian real estate market has experienced a notable 13.5% surge in prices since 2018, marking a substantial departure from the preceding five years, during which there was a 9% growth, as indicated by data from the National Institute of Statistics (INS). According to the 2020 Rental Barometer, the average monthly rent for unfurnished apartments stands at 1,360 Tunisian dinars. Our initiative, titled ""Predicting Real Rental Prices,"" employs advanced machine learning techniques to provide accurate predictions for rental prices. Users of this platform can plan moves, organize properties into categories, and customize rental price insights based on their preferences. This project is based on machine learning and uses deep learning algorithms to predict rental prices, thereby meeting the needs of both lessors and tenants. The model ensures a thorough and accurate forecasting approach by accounting for a number of issues, such as the effect of furniture and building conditions on rental prices.",1.0,https://doi.org/10.37394/23205.2024.23.17,True,JournalArticle,,2024-07-22,dbe2948d5376907de536fb31d03bd93789812d82,leveragingdataminingmachinelearningandwebscrapingforforecastingrentalhousingpricesintunisia,balti,10.37394/23205.2024.23.17,leveragingdataminingmachinelearningandwebscrapingforforecastingrentalhousingpricesintunisia|2024,1,2509,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4ef7ce4f14445bf9a33984435f44780b61bbb8ff,10.36948/ijfmr.2023.v05i02.1764,,Real Estate Price Predcition System Using Hybrid Lasso Algorithm,2023.0,International Journal For Multidisciplinary Research,Moganarangan; Kishorre Raj; Ashathulla; Devasanjay; Shalinthraj Adhithyan,"House Price prediction system is a website where the user/ buyer can be able to predict the accurate cost of real estate. To analyse the pertinent characteristics and the best models for predicting the price of homes, a literature review is done.. The model will then use the user's data, and the user will be able to view the predicted price of the property they are selling or looking to buy in a matter of seconds. The feature selection process is done with a type of regression called LASSO regression. A kind of linear regression that makes advantage of shrinkage is called LASSO regression. So we are proposing a system that uses Hybrid LASSO Regression algorithm to do the Feature Selection. Hybrid LASSO Regression is hybrid of both LASSO and Ridge Regression. When models exhibit significant levels of multi-collinearity or when you wish to automate specific steps in the model selection process, such as variable selection and parameter elimination, this specific sort of regression is ideally suited. This would greatly help academics and housing developers identify the most important factors that influence home values and recognize the most effective machine learning model to follow when conducting the field investigation.",1.0,https://doi.org/10.36948/ijfmr.2023.v05i02.1764,True,Review,,2023-03-15,4ef7ce4f14445bf9a33984435f44780b61bbb8ff,realestatepricepredcitionsystemusinghybridlassoalgorithm,moganarangan,10.36948/ijfmr.2023.v05i02.1764,realestatepricepredcitionsystemusinghybridlassoalgorithm|2023,1,2738,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",8bff7327e8542a1f416c0b7b74bd792b21404ba4,10.36937/ben.2023.4768,,The Importance of Digitization in Estimating Housing Fair Value with the Artificial Neural Networks Method: The Case of Yenimahalle/Ankara/Turkey,2023.0,Brilliant Engineering,Orhan Doğan,"With the rapid development in the construction sector in recent years, housing sales, which is one of the economic investments, have accelerated, making an objective valuation difficult and making it impossible to predict the real price due to the fact that there are too many parameters in the valuation stage and there is no definite formula. In addition, in real estate appraisal processes, the use of artificial neural networks (ANN), which is one of the artificial intelligence methods, has made it attractive to perform and adapt machine learning using examples, to provide information about unprecedented examples, to work fast and easy to identify, to provides solutions to complex problems, to work with little information. In this study, ANN models were created by rearranging the quantification values in an existing study by creating ANN models with 14 parameters that are effective in determining the fair value of a total of 220 houses for sale advertised on an e-commerce site in different neighborhoods of Yenimahalle district of Ankara/Turkey, and it was observed that with the use of rearranged quantification values, the ANN architecture selected with mean square error (MSE) 0.000016, regression (R) 95.99% and accuracy rate 91.73% gave more successful results in predicting the house price.",3.0,https://doi.org/10.36937/ben.2023.4768,True,,,2023-02-08,8bff7327e8542a1f416c0b7b74bd792b21404ba4,theimportanceofdigitizationinestimatinghousingfairvaluewiththeartificialneuralnetworksmethodthecaseofyenimahalleankaraturkey,doğan,10.36937/ben.2023.4768,theimportanceofdigitizationinestimatinghousingfairvaluewiththeartificialneuralnetworksmethodthecaseofyenimahalleankaraturkey|2023,1,2811,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3631e6be605720050e42f5648a0b06175daa4181,10.36334/modsim.2023.chan39,,An application of machine learning in real estate economics: What extra benefits could machine learning techniques provide?,2023.0,"MODSIM2023, 25th International Congress on Modelling and Simulation.",F. Chan; R. Schulz; Z. Zhang,": The applications of machine learning (ML) techniques in real estate practices has become popular recently. Specifically, ML techniques are often used to develop Automated valuation models (AVM), which purpose is to provide a price estimate of a particular property at a specified time. The main objective is to minimise human intervention in the process of price estimation. Apart from the users providing a set of inputs which, in the present context, would be a set of property features, the AVM would provide a price estimate without any human intervention in the estimating process. The recent literature show that such prediction process performed much better than the traditional approach. However, the presence of missing values in the data remains a major challenge in developing AVM using ML techniques. Therefore, this paper examines different approaches to handle missing values in the context of developing AVMs using various tree based algorithms. This paper has two main objectives: (i) it examines the performance of Gradient Boosting Machine (GBM) in the presence of missing values. Recent literature suggested that GBM can still provide accurate prediction in the presence of missing values and this paper examines this claim in the context of AVMs; (ii) Using GBM, this paper compares some common strategies in managing missing values as well as a special strategy that only applicable for machine learning methods. This helps to identify any extra benefits in using machine learning techniques. It is worthwhile noting that data can be missing in both training and testing stages. Ideally, a model can be trained with missing values in the training set as well as having the ability to make predictions when some of the inputs (features) are missing in the test set. The results show that the proposed GBMs can predict more accurately than the traditional hedonic model across different forecast criteria. The results are also unaffected by the choice of missing value strategies. This is consistent with the results in recent studies. In addition, the proposed implementation required minimum human intervention in both training and testing stages, even in the presence of missing values. Thus, machine learning methods would appear to be more efficient in addition to being more accurate in predicting house price.",0.0,https://doi.org/10.36334/modsim.2023.chan39,True,,,2023-08-01,3631e6be605720050e42f5648a0b06175daa4181,anapplicationofmachinelearninginrealestateeconomicswhatextrabenefitscouldmachinelearningtechniquesprovide,chan,10.36334/modsim.2023.chan39,anapplicationofmachinelearninginrealestateeconomicswhatextrabenefitscouldmachinelearningtechniquesprovide|2023,1,3837,5,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic property valuation",2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6f70b466e027fa246e430e1843fc722c6f677b3b,10.35957/jatisi.v8i1.701,,Prediksi Harga Rumah Menggunakan Web Scrapping dan Machine Learning Dengan Algoritma Linear Regression,2021.0,,A. Saiful,"A place to live or a house is one of the many primary needs for humans. Therefore it is very important to make a plan so that each family can have a private residence. This planning requires a prediction or forecast of future prices. So, the aim of this study is to create a house price prediction model using machine learning methods while the algorithm is linear regression. By doing web scraping to collect data, through several websites that are involved in the sale and purchase of houses. Meanwhile, according to home developers who were successfully asked in the field related to variables that affect house prices, including land area, standing building area, number of bedrooms, number of bathrooms, and the availability of a car park. To get a high predictive value, research is carried out repeatedly but the largest predictive value is using 80% of the dataset for training and 20% of the dataset is used for testing to produce an output value with an accuracy level of predicting 88%.",32.0,https://doi.org/10.35957/jatisi.v8i1.701,True,,Computer Science,2021-03-19,6f70b466e027fa246e430e1843fc722c6f677b3b,prediksihargarumahmenggunakanwebscrappingdanmachinelearningdenganalgoritmalinearregression,saiful,10.35957/jatisi.v8i1.701,prediksihargarumahmenggunakanwebscrappingdanmachinelearningdenganalgoritmalinearregression|2021,1,2497,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",35bc8db1fa0be4775efa534ceb37ddf6b039e25f,10.35940/ijrte.b1084.0982s1119,,"Machine Learning Housing Price Prediction in Petaling Jaya, Selangor, Malaysia",2019.0,International journal of recent technology and engineering,,"This paper demonstrates the utilization of machine learning algorithms in the prediction of housing selling prices on real dataset collected from the Petaling Jaya area, Selangor, Malaysia. To date, literature about research on machine learning prediction of housing selling price in Malaysia is scarce. This paper provides a brief review of the existing machine learning algorithms for the prediction problem and presents the characteristics of the collected datasets with different groups of feature selection. The findings indicate that using irrelevant features from the dataset can decrease the accuracy of the prediction models.",27.0,https://doi.org/10.35940/ijrte.b1084.0982s1119,True,Review,,2019-11-02,35bc8db1fa0be4775efa534ceb37ddf6b039e25f,machinelearninghousingpricepredictioninpetalingjayaselangormalaysia,,10.35940/ijrte.b1084.0982s1119,machinelearninghousingpricepredictioninpetalingjayaselangormalaysia|2019,1,2134,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",027db22ded1b8db4fd10e5c6f2027455cb327516,10.35940/ijitee.c9741.0111322,,Housing Price Prediction with Machine Learning,2022.0,International Journal of Innovative Technology and Exploring Engineering,Amena Begum; Nishad Jahan Kheya; Md Zahidur Rahman,"For socioeconomic development and the well-being of citizens, developing a precise model for predicting housing prices is always required. So that, a real estate broker or a house seller/buyer can get an intuition in making well-knowledgeable decisions from the model. In this work, a various set of machine learning algorithms such as Linear Regression, Decision Tree, Random Forest are being implemented to predict the housing prices using available datasets. The housing datasets of 506 samples and 13 feature variables from January 2015 to November 2019 were taken from the StatLib library which is maintained at Carnegie Mellon University. Since housing price is emphatically connected to different factors like location, area, the number of rooms; it requires all of this information to predict individual housing prices. This paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models to explore various impacts of features on prediction methods. This paper will also provide an optimistic result for housing price prediction by comprehensively validating multiple techniques in model execution on regression.",7.0,https://doi.org/10.35940/ijitee.c9741.0111322,True,,,2022-01-30,027db22ded1b8db4fd10e5c6f2027455cb327516,housingpricepredictionwithmachinelearning,begum,10.35940/ijitee.c9741.0111322,housingpricepredictionwithmachinelearning|2022,1,2692,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",eef6e7515041e510be00392c2759d3c70dc2801a,10.35671/jmtt.v4i2.104,,Predicting Greater Jakarta Area House Prices Using Random Forest and Linear Regression,2025.0,Journal of Multimedia Trend and Technology,F. Agustin; F. Zaki,"This study focuses on analyzing and predicting house prices in the Greater Jakarta Area using a machine learning approach, specifically comparing the performance of random forest regression and multiple linear regression. the increasing demand for adequate housing in Greater Jakarta Area, coupled with fluctuating house prices influenced by factors like land size, building size, number of bedrooms, bathrooms, and other facilities, necessitates an accurate price prediction system to assist both the public and businesses in decision-making. data was collected from Rumah123.com via Kaggle, followed by pre-processing and exploratory data analysis (EDA). the models were built using both algorithms and evaluated through 10-fold cross-validation, with an 80% training and 20% testing data split. the results demonstrate that random forest regression outperforms multiple linear regression, achieving a correlation coefficient of 0.5043 and a mean absolute error of 157,698,532. in contrast, multiple linear regression (m5p) yielded a correlation coefficient of 0.4895 and a mean absolute error of 209,890,933. therefore, random forest regression is recommended as a superior model for house price prediction in the Greater Jakarta Area region.",0.0,https://doi.org/10.35671/jmtt.v4i2.104,True,JournalArticle,,2025-08-28,eef6e7515041e510be00392c2759d3c70dc2801a,predictinggreaterjakartaareahousepricesusingrandomforestandlinearregression,agustin,10.35671/jmtt.v4i2.104,predictinggreaterjakartaareahousepricesusingrandomforestandlinearregression|2025,1,2745,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ad4880c1c13209bda1dc3f98e5da148188d920fa,10.34229/2707-451x.21.1.7,,Using Machine Learning Methods to Estimate the Cost of Housing,2021.0,Cybernetics and Computer Technologies,V. Tretynyk; А. Voznyak; V. Domrachev,"Introduction. Nowadays, the state has enshrined at the legislative level the definition of appraised value for tax purposes in sales of real estate as mandatory. The comparative approach most often used by appraisers has disadvantages such as the inability to find analogues in some cases and the need to make corrections, which affects the reliability of the results. The module of electronic determination of appraisal value (Module) similar to the object of property appraisal of the Unified database of appraisal reports works on the same approach and quite often overestimates appraisal value that leads to increase in the size of the tax during sales as the real estate cannot be sold for the price less than the estimated cost. Today to determine the price of an automated system correctly, it is necessary to fill the Unified Valuation Database in the State Property Fund with large knowledge bases - a huge IT system. So far, the thoughtless machine still determines the price by the average value. Currently there are often situations when the appraised value of real estate, determined by the Module, exceeds its real market value. Given that the approach used by the Valuation Module does not always give the correct result, there is a need to find a better method to determine the value of housing that could be used by the Module. The purpose of the paper. In this paper, an approach based on fuzzy logic was used to estimate the cost of housing in Kyiv. Fuzzy methods allow to apply a linguistic description of complex processes, to establish fuzzy relationships between concepts, to predict the behavior of the system, to create a set of alternative actions, to formally describe fuzzy decision-making rules. Results. The software implementation of the model in Python programming language was performed. Data for modeling were taken for the period July – October 2020 from a single database of property valuation reports. The sample contained 2133 records, it was filtered, divided into training and testing in the proportion of 85 : 15. To assess the quality of the program, the average relative error of the developed model was calculated. Keywords: fuzzy logic, machine learning, Python programming, linguistic variables, predictive model.",0.0,https://doi.org/10.34229/2707-451x.21.1.7,True,,Computer Science,2021-03-30,ad4880c1c13209bda1dc3f98e5da148188d920fa,usingmachinelearningmethodstoestimatethecostofhousing,tretynyk,10.34229/2707-451x.21.1.7,usingmachinelearningmethodstoestimatethecostofhousing|2021,1,3766,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e1eb5b8de2095cc61265f79d90a2976b58a67664,10.34028/iajit/22/3/1,,Explore the relationship between house prices and crime rate in the UK using machine learning techniques,2025.0,˜The œinternational Arab journal of information technology,Soudabeh Motevali; Hamzeh Aljawawdeh; Sami Abuezhayeh; Eman Qaddoumi,"In the UK, house price estimation is an important topic. It has been discussed in numerous academic research publications and official and business reports. A house’s price can change depending on size, age, and location. One of the main characteristics of a property’s neighbourhood is the crime rate; it is crucial to consider how it may affect the home’s price. If crime rates rise, a neighbourhood may lose appeal to potential purchasers in favour of comparable-priced neighbourhoods. This study employs data from January 2020 to April 2022 to examine the relationship between crime rates and house prices in Bristol, UK. Crime datasets were taken from the UK Crime Stats website, while data on house prices was taken from the Price Paid Data of the HM Land Registry. 34,000 transaction records and criminal statistics are combined in the study to provide a comprehensive dataset for analysis. Two tree-based machine learning algorithms-Decision Tree (DT) and Random Forest (RF)-and Exploratory Data Analysis (EDA) were used to model and evaluate the data. The results show that the Random Forest outperformed the Decision Tree regarding prediction accuracy. According to the results, there is a substantial correlation between crime rates and house prices. While vehicle crimes and bike theft had a positive correlation with property values, violent crimes had the opposite effect. These revelations highlight the intricate relationship between different kinds of crime and the characteristics of the housing market, with considerable consequences for investors, policymakers, and real estate developers.",0.0,https://doi.org/10.34028/iajit/22/3/1,True,JournalArticle,Computer Science,,e1eb5b8de2095cc61265f79d90a2976b58a67664,exploretherelationshipbetweenhousepricesandcrimerateintheukusingmachinelearningtechniques,motevali,10.34028/iajit/22/3/1,exploretherelationshipbetweenhousepricesandcrimerateintheukusingmachinelearningtechniques|2025,1,3109,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",e99c0b6efc74a48816f236b958a375d34da8bbe6,10.3390/su11184896,,Contextualized Property Market Models vs. Generalized Mass Appraisals: An Innovative Approach,2019.0,Sustainability,P. Morano; P. Rosato; F. Tajani; B. Manganelli; F. Di Liddo,"The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016–2017. The ability to generate a “unique” functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies.",25.0,https://doi.org/10.3390/su11184896,True,,Economics,2019-09-06,e99c0b6efc74a48816f236b958a375d34da8bbe6,contextualizedpropertymarketmodelsvsgeneralizedmassappraisalsaninnovativeapproach,morano,10.3390/su11184896,contextualizedpropertymarketmodelsvsgeneralizedmassappraisalsaninnovativeapproach|2019,1,2450,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || automated valuation model residential",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9960c6a38ff73ff0d2c4ef194a52833524b69a57,10.3390/stats8010012,,Smart Renting: Harnessing Urban Data with Statistical and Machine Learning Methods for Predicting Property Rental Prices from a Tenant’s Perspective,2025.0,Stats,F. Louzada; K. J. C. C. de Lacerda; P. H. Ferreira; N. D. Gomes,"The real estate market plays a pivotal role in most nations’ economy, showcasing continuous growth. Particularly noteworthy is the rapid expansion of the digital real estate sector, marked by innovations like 3D visualization and streamlined online contractual processes, a momentum further accelerated by the aftermath of the Coronavirus Disease 2019 (COVID-19) pandemic. Amidst this transformative landscape, artificial intelligence emerges as a vital force, addressing consumer needs by harnessing data analytics for predicting and monitoring rental prices. While studies have demonstrated the efficacy of machine learning (ML) algorithms such as decision trees and neural networks in predicting house prices, there is a lack of research specifically focused on rental property prices, a significant sector in Brazil due to the prohibitive costs associated with property acquisition. This study fills this crucial gap by delving into the intricacies of rental pricing, using data from the city of São Carlos-SP, Brazil. The research aims to analyze, model, and predict rental prices, employing an approach that incorporates diverse ML models. Through this analysis, our work showcases the potential of ML algorithms in accurately predicting rental house prices. Moreover, it envisions the practical application of this research with the development of a user-friendly website. This platform could revolutionize the renting experience, empowering both tenants and real estate agencies with the ability to estimate rental values based on specific property attributes and have access to its statistics.",1.0,https://doi.org/10.3390/stats8010012,True,JournalArticle,,2025-01-27,9960c6a38ff73ff0d2c4ef194a52833524b69a57,smartrentingharnessingurbandatawithstatisticalandmachinelearningmethodsforpredictingpropertyrentalpricesfromatenantsperspective,louzada,10.3390/stats8010012,smartrentingharnessingurbandatawithstatisticalandmachinelearningmethodsforpredictingpropertyrentalpricesfromatenantsperspective|2025,1,3102,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b5b83085c50826574232a8ab7ecdfbd6928feb14,10.3390/realestate3030015,,Beyond Prediction: A Generalized Explainable Hybrid Econometric-Machine Learning Framework for Real Estate Valuation,2026.0,Real Estate,Firangiz Mammadrzayeva; N. Jafarov; I. Aliyev,"Real estate valuation increasingly requires methodologies that combine predictive accuracy, economic interpretability, and model transparency. This study proposes a Generalized Explainable Hybrid Econometric–Machine Learning Framework integrating econometric modelling, machine learning, and Explainable Artificial Intelligence (XAI) within a unified analytical architecture. The framework is organized around a generalized conceptual valuation equation and implemented through a workflow comprising data quality assessment, econometric benchmarking, hyperparameter optimization, nonlinear machine-learning modelling, five-fold cross-validation, and SHAP-based explainability. The methodology is demonstrated using an internally constructed Azerbaijan commercial real estate dataset and independently assessed using the publicly available UCI residential benchmark dataset through an identical Python-based computational pipeline. Under the complete income-capitalization benchmark, the Artificial Neural Network achieved the highest predictive performance for the Azerbaijan dataset, whereas Random Forest performed best for the external benchmark. A leakage-reduced robustness analysis further showed that the framework retained meaningful predictive capability after excluding Net Operating Income and the Capitalization Rate, with XGBoost providing the strongest performance under the reduced specification. SHAP analysis identified economically meaningful valuation drivers, highlighting income generation in the commercial market and transport accessibility in the residential benchmark. The proposed framework provides a transparent, explainable, and transferable decision-support methodology for modern real estate valuation.",0.0,https://doi.org/10.3390/realestate3030015,True,JournalArticle,,2026-09-04,b5b83085c50826574232a8ab7ecdfbd6928feb14,beyondpredictionageneralizedexplainablehybrideconometricmachinelearningframeworkforrealestatevaluation,mammadrzayeva,10.3390/realestate3030015,beyondpredictionageneralizedexplainablehybrideconometricmachinelearningframeworkforrealestatevaluation|2026,1,3233,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,1,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9e584952822304a1a6401260dee9a7bd9e88d9cb,10.3390/realestate1030012,,"Property Valuation in Latvia and Brazil: A Multifaceted Approach Integrating Algorithm, Geographic Information System, Fuzzy Logic, and Civil Engineering Insights",2024.0,Real Estate,Vladimir Surgelas; V. Puķīte; Irina Arhipova,"This study aimed to predict residential apartment prices in Latvia and Brazil using algorithms from machine learning, fuzzy logic, and civil engineering principles, with a focus on overcoming multicollinearity challenges. To explore the market dynamics, we conducted four initial experiments in the central regions of Riga and Jelgava (Latvia), as well as São Paulo and Niterói (Brazil). Data were collected from real estate advertisements, supplemented by civil engineering inspections, and analyzed following international valuation standards. The research integrated human decision-making behavior with machine learning and the Apriori algorithm. Our methodology followed five key stages: data collection, data preparation for association rule mining, the generation of association rules, fuzzy logic analysis, and the interpretation of model accuracy. The proposed method achieved a mean absolute percentage error (MAPE) that ranged from 5% to 7%, indicating strong alignment with market trends. These findings offer valuable insights for decision making in urban development, particularly in optimizing renovation priorities and promoting sustainable growth.",3.0,https://doi.org/10.3390/realestate1030012,True,JournalArticle,,2024-10-21,9e584952822304a1a6401260dee9a7bd9e88d9cb,propertyvaluationinlatviaandbrazilamultifacetedapproachintegratingalgorithmgeographicinformationsystemfuzzylogicandcivilengineeringinsights,surgelas,10.3390/realestate1030012,propertyvaluationinlatviaandbrazilamultifacetedapproachintegratingalgorithmgeographicinformationsystemfuzzylogicandcivilengineeringinsights|2024,1,2663,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a7d7d518e27311e61bc9a8dde2eab286b81e3cde,10.3390/ijfs14050130,,Development of a Predictive Tool for Real Estate Analysis Using Machine Learning Techniques,2026.0,International Journal of Financial Studies,Ricardo Francisco Reier Forradellas; Gregorio Acedo Benítez,"The real estate market is a complex and dynamic sector that plays a key role in economic stability and wealth generation. In many regions, real estate assets represent around 80% of household wealth, while rising housing prices have turned access to housing into a major social and economic challenge. In this context, the availability of accurate and accessible information is essential for decision-making by buyers, investors, and public administrations. This study proposes the development of an advanced technological tool based on Artificial Intelligence and Machine Learning techniques to predict and analyze real estate market dynamics within a specific geographic area. Using the city of Madrid as a case study, the research presents a digital application capable of estimating the market value of a property by analyzing comparable recently sold properties and incorporating key housing characteristics. By entering an address and a set of property features, the system generates a precise and data-driven valuation. The results demonstrate that AI-based approaches can significantly improve the accuracy and accessibility of real estate valuation processes. The proposed methodology enables real-time price estimation, graphical comparisons, and dynamic market analysis. Furthermore, the framework is scalable and can be extended to other geographic areas where relevant data are available, providing valuable insights for both academic research and practical decision-making in the real estate sector.",1.0,https://doi.org/10.3390/ijfs14050130,True,JournalArticle,,2026-05-11,a7d7d518e27311e61bc9a8dde2eab286b81e3cde,developmentofapredictivetoolforrealestateanalysisusingmachinelearningtechniques,forradellas,10.3390/ijfs14050130,developmentofapredictivetoolforrealestateanalysisusingmachinelearningtechniques|2026,1,3013,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",db45b6585af6d91680e9dc8d223253bea52b9702,10.33564/ijeast.2021.v05i11.041,,ANALYSIS OF DIFFERENT REGRESSION MODELS FOR REAL ESTATE PRICE PREDICTION,2021.0,International Journal of Engineering Applied Sciences and Technology,Pranav Kangane; Aadesh Mallya; A. Gawane; V. Joshi; Shivam Gulve,"The housing market is a standout amongst the most engaged with respect to estimating the price and continues to vary. Individuals are cautious when they are endeavoring to purchase another house with their financial plan and market strategies. Consequently, making the housing market one of the incredible fields to apply the ideas of machine learning on how to enhance and anticipate the house prices with precision. The objective of the paper is the prediction of the market value of a real estate property and present a performance comparison between various regression models applied. Nine algorithms were selected to predict the dependent variable in our dataset and then their performance was compared using R2 score, mean absolute error, mean squared error and root mean squared error. Moreover, this study attempts to analyze the correlation between variables to determine the most important factors that are bound to affect the prices of house. ",2.0,https://doi.org/10.33564/ijeast.2021.v05i11.041,True,,Mathematics,2021-03-01,db45b6585af6d91680e9dc8d223253bea52b9702,analysisofdifferentregressionmodelsforrealestatepriceprediction,kangane,10.33564/ijeast.2021.v05i11.041,analysisofdifferentregressionmodelsforrealestatepriceprediction|2021,1,2454,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d1229569c6973d9c15fbf58c5853c868844048e1,10.33480/jitk.v11i2.7256,,COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND RANDOM FOREST ALGORITHM FOR PREDICTING HOUSING PRICES,2025.0,JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer),Dahlan Susilo; Diyah Ruswanti; Supriyanta; Wawan Nugroho,"House price predictions are an important thing in the property industry and are useful for buyers in making decisions. Principal Component Analysis (PCA) and Random Forest (RF) methods were used for accuracy analysis in predicting housing prices. Purpose of this research is to measure the accuracy of both methods also to compare RF method optimized with PCA and the one that has not been optimized. The data used is house prices in Karanganyar city based on data scraping results on the rumah123.com site. The analysis reveals that Jaten has the highest number of house sales, and sales of houses with land ownership certificates are also the highest. Of the 10 variables used, land area and buildings have the most influence on selling prices. The model training results show that the RF and PCA methods combination has more optimal value than only using the RF method. The error rate of the PCA method is smaller, averaging 0.0257, making its value more consistent than using only the RF method, which has a larger error value with an average of 0.0332. The model training time using PCA is faster (5005.75) than only using the RF method (6099.25)",0.0,https://doi.org/10.33480/jitk.v11i2.7256,True,JournalArticle,,2025-11-27,d1229569c6973d9c15fbf58c5853c868844048e1,comparisonofprincipalcomponentanalysisandrandomforestalgorithmforpredictinghousingprices,susilo,10.33480/jitk.v11i2.7256,comparisonofprincipalcomponentanalysisandrandomforestalgorithmforpredictinghousingprices|2025,1,2651,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",136360c272ec89e97a97f38e58993b9b521d7353,10.33050/sensi.v10i2.3480,,Analysis of House Price Using K-Means and Naïve Bayes Methods,2024.0,Journal Sensi,Arman Prasojo Sugiyarto; Nur Hayati; Eri Mardiani,"This study aims to compare the performance of the K-Means and Naïve Bayes algorithms in analyzing house prices. The dataset used is a house price dataset obtained from observational results. The study was conducted for approximately 2 months, focusing on the implementation of the K-Means and Naïve Bayes algorithms. The data was processed and analyzed using Orange software, and the results were presented in tables and graphs. The analysis results showed that the K-Means algorithm outperformed the Naïve Bayes algorithm with an accuracy value of 30% for the variable y distance to public facilities and 22% for the variable y land area and 82% with Naïve Bayes calculation. Therefore, it can be concluded that the K-Means method is a more effective method for analyzing house prices.",0.0,https://doi.org/10.33050/sensi.v10i2.3480,True,JournalArticle,,2024-08-31,136360c272ec89e97a97f38e58993b9b521d7353,analysisofhousepriceusingkmeansandnaivebayesmethods,sugiyarto,10.33050/sensi.v10i2.3480,analysisofhousepriceusingkmeansandnaivebayesmethods|2024,1,2286,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0a18829dfe20685d1048d5a37fe8ee184fff8faa,10.32628/ijsrst229242,,House Pricing Prediction using ML Algorithm - A Comparative Analysis,2022.0,International Journal of Scientific Research in Science and Technology,Pushpa; Shah Ir; Qamar Aziz; Tarique Anwar; Mohammed Arfath,"The real estate industry is the least transparent in our environment. Housing prices fluctuate on a daily basis and are sometimes inflated rather than based on valuation. Since the housing industry is rapidly expanding, forecasting house prices is critical not just for businesses but also for individuals. However, there are other factors that influence house price variations. The purpose of this article is to forecast a real estate property's market value. The goal here is to build a prediction model for evaluating pricing based on characteristics that influence price. We are going to predict data using Machine learning algorithm i.e. linear regression model to predict the house pricing.",0.0,https://doi.org/10.32628/ijsrst229242,True,,,2022-04-01,0a18829dfe20685d1048d5a37fe8ee184fff8faa,housepricingpredictionusingmlalgorithmacomparativeanalysis,pushpa,10.32628/ijsrst229242,housepricingpredictionusingmlalgorithmacomparativeanalysis|2022,1,2196,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",287e461e7c01925f22341155132b3b9b157bf9ab,10.31462/jcemi.2021.02106116,,House price prediction: A data-centric aspect approach on performance of combined principal component analysis with deep neural network model,2021.0,Journal of Construction Engineering Management & Innovation,Fatemeh Mostofi; Vedat Toğan; H. B. Başağa,,7.0,https://doi.org/10.31462/jcemi.2021.02106116,True,,Computer Science,2021-06-30,287e461e7c01925f22341155132b3b9b157bf9ab,housepricepredictionadatacentricaspectapproachonperformanceofcombinedprincipalcomponentanalysiswithdeepneuralnetworkmodel,mostofi,10.31462/jcemi.2021.02106116,housepricepredictionadatacentricaspectapproachonperformanceofcombinedprincipalcomponentanalysiswithdeepneuralnetworkmodel|2021,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1f1e429f9e627f1d6560eabcd2210b3b2c67dbf7,10.1109/icad69378.2026.11608473,,Predicting St. Louis Housing Prices with Machine Learning on Market and Assessor Data,2026.0,International Conference on Auditory Display,Brian Adler; Anne Brown,"Housing markets are more complex than a simple supply-demand relationship. Prices are set by complex market and spatial neighborhood dynamics. Certain cities like St. Louis, MO have experienced dramatic population decline marked by extreme vacancy and abandonment. Amidst its population decline, St. Louis simultaneously demonstrates neighborhoods with sharp housing shortages and competition alongside others with entrenched vacancy and disinvestment mere blocks away from one another. We use supervised machine learning models to predict housing prices with a diverse feature set that incorporates spatial aspects of vacancy among other traditional housing amenities in St. Louis. Our results show how proximity to vacancy may impact a home’s value even more than its number of bedrooms. These findings, we expect, may prompt policymakers to combat vacancy even more urgently to maintain neighborhood market stability.",0.0,https://doi.org/10.31235/osf.io/s9v4u_v1,True,Conference,,2026-06-01,1f1e429f9e627f1d6560eabcd2210b3b2c67dbf7,predictingstlouishousingpriceswithmachinelearningonmarketandassessordata,adler,10.1109/icad69378.2026.11608473,predictingstlouishousingpriceswithmachinelearningonmarketandassessordata|2026,1,2420,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",55c940d832ef8dcdad2835c8fc50397dc9fa7c33,10.30595/juita.v14i1.28582,,Enhancing Geospatial House Price Prediction in Greater Jakarta Using XGBoost and ResNet18 Feature Fusion,2026.0,Jurnal Informatika,Hadi Santoso; Alif Biuti Anastasya,"Precise house price predictions play a vital role in shaping housing policies and informing investment decisions in urban regions such as Greater Jakarta, encompassing Jakarta, Bogor, Depok, Tangerang, and Bekasi. Most models rely exclusively on structured data, ignoring spatial and environmental factors that influence property prices. This study proposes a multimodal machine learning framework that integrates structured property data with Sentinel-2 satellite imagery within a geospatial context. The baseline dataset consists of 17 tabular variables. The ResNet-18 algorithm extracts visual environmental information from satellite imagery. The integration of both modalities through a late fusion strategy results in improved predictive performance. The baseline XGBoost achieved R² scores of 0.81 (log scale) and 0.79 (Rupiah), with an error of about 184 million Rupiah. The image-only model achieved an R² of 0.43, indicating moderate explanatory capability. Late fusion further improved performance, achieving R² values of 0.94 (log scale) and 0.93 (Rupiah), while reducing prediction error by over 40%.",1.0,https://doi.org/10.30595/juita.v14i1.28582,True,JournalArticle,,2026-03-31,55c940d832ef8dcdad2835c8fc50397dc9fa7c33,enhancinggeospatialhousepricepredictioningreaterjakartausingxgboostandresnet18featurefusion,santoso,10.30595/juita.v14i1.28582,enhancinggeospatialhousepricepredictioningreaterjakartausingxgboostandresnet18featurefusion|2026,1,2613,2,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e20cd68bb7ed86c70d9107a624ed0fa631d8d77a,10.30591/jpit.v10i2.8200,,Prediksi Harga Rumah di Bandung 2024 Menggunakan Ensemble Learning: Analisis Komparatif dan Interpretabilitas,2025.0,Jurnal Informatika: Jurnal Pengembangan IT,Muh Naufal Hibatulloh; Gilang Danu Prakoso; Adiestiana Dwi Putri Yunus; Tommy Dwi Putra,"House price prediction plays a crucial role in investment decision-making and financial planning, particularly in developing cities like Bandung with its complex property market dynamics. This study aims to evaluate and compare the performance of various ensemble learning techniques in predicting house prices in Bandung for the year 2024, with a specific focus on model interpretability analysis. The data was collected through web scraping from www.rumah123.com in March 2024, covering attributes such as location, number of rooms, land area, and building area. The evaluated ensemble techniques include Random Forest, Gradient Boosting Machines, Xtreme Gradient Boosting, Linear Regression, and Stacking Ensemble. Model performance was assessed using MAE, RMSE, and R-squared metrics, while interpretability analysis was conducted using SHAP values. The Model Stacking Ensemble shows the most optimal results with R² 0.9076, RMSE 0.311, and MAE 0.216 in experiments involving location features. Features such as land size, building size, and location have proven to have the greatest impact in predicting prices based on SHAP analysis. This model has been successfully integrated into a Flask website for interactive price predictions.",3.0,https://doi.org/10.30591/jpit.v10i2.8200,True,JournalArticle,,2025-04-30,e20cd68bb7ed86c70d9107a624ed0fa631d8d77a,prediksihargarumahdibandung2024menggunakanensemblelearninganalisiskomparatifdaninterpretabilitas,hibatulloh,10.30591/jpit.v10i2.8200,prediksihargarumahdibandung2024menggunakanensemblelearninganalisiskomparatifdaninterpretabilitas|2025,1,2739,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5c44228eab8defddfb00d8e4526f3123e75db18a,10.30534/ijeter/2020/70842020,,House Sale Price Prediction using Advanced Regression Techniques and AutoML (TPOTRegressor),2020.0,International Journal of Emerging Trends in Engineering Research,Tejaswi V. S. B.,"The increasing and decreasing occurrence of house prices changes from time to time. There are more reasons that effect the fluctuation of house prices. Some are build year, location, physical amenities, size of house etc. Predicting the value of house (Sale price) helps the customers to take right choice of buying the house. Machine learning is being adapted for various fields that could build prediction model and estimate the outcomes. In this paper, we are contemplating the issue of rise and fall of house rates as a regression problem. Regression is a process that aims to predict the correlation between target dependent feature and a sequence of other changing independent features. In our experimental analysis, we are using Decision Tree Regression",1.0,https://doi.org/10.30534/ijeter/2020/70842020,True,,Economics,2020-04-25,5c44228eab8defddfb00d8e4526f3123e75db18a,housesalepricepredictionusingadvancedregressiontechniquesandautomltpotregressor,b.,10.30534/ijeter/2020/70842020,housesalepricepredictionusingadvancedregressiontechniquesandautomltpotregressor|2020,1,2260,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2590f2b5251dbc8d9f3c01df87e0e3930d6e21af,10.30534/ijeter/2019/247112019,,Improving Accuracy by applying Z-Score Normalization in Linear Regression and Polynomial Regression Model for Real Estate Data,2019.0,International Journal of Emerging Trends in Engineering Research,D. Anggoro,"This research aimed to prove whether a normalization concept, i.e., z-score normalization was able to improve the accuracy result of algorithms for predicting house prices in Sindian District, New Taipei City, Taiwan. There were specific techniques in data science, e.g., linear regression and polynomial regression, that became the focus of this research. This study used several features that were implemented to model the house price. The features used included the transaction date, the house age, the distance to the nearest MRT, the number of nearby convenience stores, the latitude geographic coordinate, and the longitude geographic coordinate. The data were then preprocessed by splitting it into a training dataset (75%) and a testing dataset (25%) using a simple random sampling method. The subsequent step was to process the dataset with linear regression and polynomial regression model. Based on the result of the data processing, the optimum order befell to quadratic polynomial regression – maximum order was 2. This algorithm was then applied to normalized datasets and earned low scores of Mean Squared Error (MSE) and R-squared score, which were 7.044 x 10 -7 and 0.989, respectively. It was concluded that this algorithm combination was the best-performed algorithm for predicting the real estate dataset in Sindian District, New Taipei City, Taiwan.",26.0,https://doi.org/10.30534/ijeter/2019/247112019,True,,Mathematics,2019-11-15,2590f2b5251dbc8d9f3c01df87e0e3930d6e21af,improvingaccuracybyapplyingzscorenormalizationinlinearregressionandpolynomialregressionmodelforrealestatedata,anggoro,10.30534/ijeter/2019/247112019,improvingaccuracybyapplyingzscorenormalizationinlinearregressionandpolynomialregressionmodelforrealestatedata|2019,1,2870,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",257b53b8439c8135e8306325d592117f951f99af,10.30534/ijatcse/2020/235932020,,Real Estate Market Data Analysis and Prediction Based on Minor Advertisements Data and Locations’ Geo-codes,2020.0,International Journal of Advanced Trends in Computer Science and Engineering,Waleed T. Al-Sit,"Country’s economic status can derived from many complex and branched indicators, one of which is property prices estimation. Working on such indicator changed the state of literature from many perspectives and corners. Whilst the scarcity of such works imposes a need for it, and demonstrates an unutilized aspect of the economy that requires little resources to create some business and academic opportunities. In this work, efforts evolved to address the problem of estimating properties prices accurately, in specific apartment’s prices among The Amman City, The Capital of The Hashemite Kingdom of Jordan. Leading to shed the lights on employing data science different techniques namely data processing, analysis and predictive modeling for adopting and estimating the apartment’s prices based on advertisement data published through the web and its extracted location geocodes. In addition, the work evaluates the final analysis reported results based on selected evaluation measures, and compare them with other five similar works on such problem conducted in other countries. Trying to aim to enrich the literature with valuable insights gained using Machine Learning and Data Mining different predictive techniques mainly, and its related conditions, branches and requirements for other data processing and analysis techniques under the data science umbrella.",7.0,https://doi.org/10.30534/ijatcse/2020/235932020,True,,Business,2020-06-25,257b53b8439c8135e8306325d592117f951f99af,realestatemarketdataanalysisandpredictionbasedonminoradvertisementsdataandlocationsgeocodes,al-sit,10.30534/ijatcse/2020/235932020,realestatemarketdataanalysisandpredictionbasedonminoradvertisementsdataandlocationsgeocodes|2020,1,2867,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b1608fc75ce83165dfc44fc63b06bdb8a470326d,10.2991/assehr.k.201214.522,,A Geographic Feature Integrated Multivariate Linear Regression Method for House Price Prediction,2020.0,Proceedings of the 2020 3rd International Conference on Humanities Education and Social Sciences (ICHESS 2020),Yu-Hang Mao; Rui Yao,"Housing price prediction is of great significance in financial real estate investment and urban construction planning. Multiple linear regression models are commonly used for housing price prediction. However, traditional methods are mostly focused on the characteristics of the houses themselves, without or little considering the features of the surroundings. The features of the surroundings are also important for house price prediction. Motivated by these, we propose a geographic feature integrated multivariate linear regression method for house price prediction. Especially, the Zip Code is chosen as the additional geographic feature for its convenience to obtain. Then the integrated features are used to learn the multivariate linear regression model. We conduct an extensive experiment on the real-world case of the King County area and compare our method linear regressions. The results verified the effectiveness and superiority of our model.",3.0,https://doi.org/10.2991/assehr.k.201214.522,True,Conference,,,b1608fc75ce83165dfc44fc63b06bdb8a470326d,ageographicfeatureintegratedmultivariatelinearregressionmethodforhousepriceprediction,mao,10.2991/assehr.k.201214.522,ageographicfeatureintegratedmultivariatelinearregressionmethodforhousepriceprediction|2020,1,2456,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",758a901297c082af4ca00b813f701fa1fbdeabc7,10.26877/asset.v6i3.602,,Improving the Accuracy of House Price Prediction using Catboost Regression with Random Search Hyperparameter Tuning: A Comparative Analysis,2024.0,"Advance Sustainable Science, Engineering and Technology",Faezal Hartono; Muljono Muljono; A. Fanani,"Achieving a significant improvement over traditional models, this study presents a novel approach to house price prediction through the integration of Catboost Regression and Random Search Hyperparameter Tuning. By applying these advanced machine learning techniques to the King County Dataset, we conducted a thorough regression analysis and predictive modeling that resulted in a marked increase in accuracy. The baseline model, a conventional linear regression, provided a foundation for comparison, evaluating performance metrics such as R-squared and Mean Squared Error (MSE). The meticulous hyperparameter tuning of the Catboost model yielded a remarkable improvement in predictive accuracy, demonstrating the efficacy of sophisticated data science techniques in real estate and property valuation. The percentage increase in accuracy over the baseline model is explicitly stated in the abstract.",7.0,https://doi.org/10.26877/asset.v6i3.602,True,JournalArticle,,2024-07-27,758a901297c082af4ca00b813f701fa1fbdeabc7,improvingtheaccuracyofhousepricepredictionusingcatboostregressionwithrandomsearchhyperparametertuningacomparativeanalysis,hartono,10.26877/asset.v6i3.602,improvingtheaccuracyofhousepricepredictionusingcatboostregressionwithrandomsearchhyperparametertuningacomparativeanalysis|2024,1,2402,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",bef6a612825c7689b0ce8d1af1bbc724e8a7c529,10.26562/irjcs.2023.v1005.29,,Real Estate Price Prediction Using Artificial Intelligence,2023.0,International Research Journal of Computer Science,N. Kalyani; B. Shankar; Bharat Lekkihal; Vinod Kumar,"The sales of houses are influenced by various factors such as location, area, population, and other relevant information. Predicting individual housing prices can be instrumental in estimating future real estate prices. This study employs the Random Forest algorithm, a machine learning technique, as the primary methodology for developing a housing price prediction model. By focusing on the Random Forest algorithm, this project aims to optimize prediction accuracy and consistency, considering it as the best model for price prediction. The implementation of this project involves using Python (AI/ML) for coding the Random Forest algorithm, while HTML, CSS, and JS are employed for designing the system's frontend. Ultimately, the House Price Prediction system proves to be a valuable tool for accurately assessing property prices and maintaining a record of price fluctuations, promoting transparency and discouraging fraudulent activities.",0.0,https://doi.org/10.26562/irjcs.2023.v1005.29,True,JournalArticle,,2023-06-23,bef6a612825c7689b0ce8d1af1bbc724e8a7c529,realestatepricepredictionusingartificialintelligence,kalyani,10.26562/irjcs.2023.v1005.29,realestatepricepredictionusingartificialintelligence|2023,1,2445,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",518ba78f2902bb654a1c69a0482dc1d326ccfae6,10.26438/ijcse/v10i1.4548,,House Price Prediction through Machine Learning Technique,2022.0,International Journal of Computer Science and Engineering,C. Patidar,,0.0,https://doi.org/10.26438/ijcse/v10i1.4548,True,JournalArticle,,2022-01-31,518ba78f2902bb654a1c69a0482dc1d326ccfae6,housepricepredictionthroughmachinelearningtechnique,patidar,10.26438/ijcse/v10i1.4548,housepricepredictionthroughmachinelearningtechnique|2022,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",6844d55ddfac217fce786648d60b3a52ad3526cc,10.22495/rgcv15i1p3,,Traditional or advanced machine learning approaches: Which one is better for housing price prediction and uncertainty risk reduction?,2025.0,Risk Governance and Control: Financial Markets & Institutions,Long Phi Tran; Hoang Duc Le; Ta Thu Phuong; Dung Chi Duy Nguyen,"Predicting housing prices is particularly of interest to many scholars and policymakers. However, housing prices are highly volatile and difficult to predict. This study used both traditional and advanced machine learning (ML) approaches to address the issue of housing price prediction. This study involves and compares the predictive power between advanced ML models, including random forest, gradient boosting, k-nearest neighbors (KNN), bagged classification and regression trees (CART), and traditional ML models based on linear regression and its modifications. Notably, in this study, we employed both performance metrics, including the mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and k-fold cross-validation (CV) procedure in order to investigate the predictive performance of each model. Empirically, based on a dataset comprising 78,704 real estate sales in Hanoi, Vietnam, we find that advanced ML approaches outperform traditional approaches. Specifically, advanced ML models enhance the accuracy of house price prediction and the decision-making process related to housing buying and selling activities. Our findings also reveal that among advanced ML algorithms, the random forest algorithm performs better than the other models in predicting housing prices.",3.0,https://doi.org/10.22495/rgcv15i1p3,True,JournalArticle,,,6844d55ddfac217fce786648d60b3a52ad3526cc,traditionaloradvancedmachinelearningapproacheswhichoneisbetterforhousingpricepredictionanduncertaintyriskreduction,tran,10.22495/rgcv15i1p3,traditionaloradvancedmachinelearningapproacheswhichoneisbetterforhousingpricepredictionanduncertaintyriskreduction|2025,1,2821,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",005bd1896feb17019771cd5721b4ce254162019f,10.22271/maths.2024.v9.i4a.1769,,Optimizing valuation accuracy in Kenya’s real estate market: Application of supervised machine learning models,2024.0,International Journal of Statistics and Applied Mathematics,Ruth Wangari Muchai,"The real estate sector in Kenya plays a pivotal role in economic development yet traditional valuation methods often struggle to accurately reflect dynamic market conditions. This research explores the transformative potential of advanced data-driven techniques to enhance real estate valuation accuracy focusing on residentials within Nairobi metropolis for the period between 2009 to 2024. The research assesses the effectiveness of supervised machine learning models. The models evaluated included linear regression, lasso regression, ridge regression, gradient boosting, and random forest algorithms. Data was sourced from Highlands Valuers firm covering 521 records of residential properties. The research employed exploratory data analysis and model development. Lasso regression emerged as the best model due to its significant improvements in RMSE and R-squared values after hyperparameter tuning and regularization. This demonstrated superior accuracy and predictive capability. The most significant features were comparable sales, socio-economic status of the area, built-up area, number of floors, total number of rooms, rent, building age, residential development type, number of bedrooms, neighborhood characteristics, environmental considerations, and proximity to amenities. The research underscored the potential of machine learning to revolutionize real estate valuation practices in Kenya. The research outcomes are expected to inform stakeholders, policymakers, and practitioners and foster more informed decision-making, improve sector stability, and support sustainable growth in the Kenyan real estate market.",0.0,https://doi.org/10.22271/maths.2024.v9.i4a.1769,True,JournalArticle,,2024-07-01,005bd1896feb17019771cd5721b4ce254162019f,optimizingvaluationaccuracyinkenyasrealestatemarketapplicationofsupervisedmachinelearningmodels,muchai,10.22271/maths.2024.v9.i4a.1769,optimizingvaluationaccuracyinkenyasrealestatemarketapplicationofsupervisedmachinelearningmodels|2024,1,3131,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,7,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",eb20d6eaa9f35ff701a1badb2aa1c88015de9c5d,10.22214/ijraset.2026.80865,,House Price Prediction Using Customer Needs and Financial Analysis,2026.0,International Journal for Research in Applied Science and Engineering Technology,Aman Singh,"The prediction of house prices has become one of the important uses of machine learning in the real estate field. Accurate price predictions based on data help buyers, sellers, real estate agents, and investors to make informed financial choices. In this research project, a model based on machine learning is created to estimate house prices by using various property-specific features. These features include location type, area measured in square feet, number of bedrooms and bathrooms, number of stories, type of road access, availability of amenities such as guestrooms, basements, hot water heating, air conditioning, parking availability, preferred area designation, and the status of furnishing. The dataset is sourced from publicly accessible housing records and goes through a detailed preprocessing pipeline that involves dealing with missing values, eliminating outliers, encoding categorical variables via label encoding and ordinal mapping, and normalizing numerical features to ensure the model trains consistently. Techniques for feature engineering are utilized to create meaningful representations from the original attributes. Several machine learning algorithms are put into practice and compared systematically. These algorithms include Linear Regression, Decision Tree Regressor, Random Forest Regressor, and XGBoost, which stands for Extreme Gradient Boosting.",0.0,https://doi.org/10.22214/ijraset.2026.80865,True,JournalArticle,,2026-04-30,eb20d6eaa9f35ff701a1badb2aa1c88015de9c5d,housepricepredictionusingcustomerneedsandfinancialanalysis,singh,10.22214/ijraset.2026.80865,housepricepredictionusingcustomerneedsandfinancialanalysis|2026,1,2883,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",be4e0de511d9810ff119e486834bdc97d88450b9,10.22214/ijraset.2026.78883,,Intelligent Rental Valuation: A Full-Stack Approach to Multi-Modal Property Price Prediction Using Machine Learning and Deep Learning,2026.0,International Journal for Research in Applied Science and Engineering Technology,Abhishek Singh,"The rapid expansion of digital property rental platforms has intensified the challenge of determining fair and competitive rental prices in highly dynamic markets. Property rental prices are influenced by a combination of structural attributes, geographical location, rental duration, and latent market demand, making manual pricing strategies unreliable and inconsistent. This research presents an intelligent, full-stack rental valuation system that integrates Machine Learning (ML) and Deep Learning (DL) models into a real-world property rental platform to deliver accurate, transparent, and scalable price predictions. The proposed system introduces a three-tier price prediction framework: manual feature-based prediction, where users provide explicit property attributes; geospatial map-based prediction, leveraging Google Maps integration to extract latitude and longitude for location-aware pricing; and CSV-based batch prediction, enabling bulk rental valuation for large property datasets. Ensemble regression models including Random Forest and XGBoost are evaluated alongside a deep neural network architecture to capture nonlinear relationships between property features and rental prices across daily, weekly, monthly, and yearly time horizons. Experimental evaluation using real-world rental datasets demonstrates that ensemble ML models achieve strong predictive performance in moderate data regimes, while DL models offer improved generalization as data volume increases. The bestperforming model achieved an R² score exceeding 0.90 with low Mean Absolute Error (MAE). The results validate the feasibility of deploying ML/DL-driven pricing intelligence within a live full-stack environment, contributing to fair pricing, reduced information asymmetry, and improved decision-making for both hosts and guests",0.0,https://doi.org/10.22214/ijraset.2026.78883,True,JournalArticle,,2026-04-30,be4e0de511d9810ff119e486834bdc97d88450b9,intelligentrentalvaluationafullstackapproachtomultimodalpropertypricepredictionusingmachinelearninganddeeplearning,singh,10.22214/ijraset.2026.78883,intelligentrentalvaluationafullstackapproachtomultimodalpropertypricepredictionusingmachinelearninganddeeplearning|2026,1,3323,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,7,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",de85e492d4c71ede92f719c2ae2ac073f4282233,10.22214/ijraset.2025.72962,,Real Estate Price Prediction Using Machine Learning Models,2025.0,International Journal for Research in Applied Science and Engineering Technology,Sairaj Nagula,"Accurate real estate price prediction is crucial in today’s market to aid buyers, sellers, and investors in making informed decisions. This study employs machine learning algorithms—specifically Linear Regression, Decision Tree Regression, and Random Forest Regression—to model and predict housing prices based on various influential features. The methodology involves data preprocessing, feature engineering, and model evaluation using standard metrics like R² score and Root Mean Squared Error (RMSE). The models are trained on real-world housing datasets, and results demonstrate the efficiency of ensemble learning over traditional linear approaches. This paper establishes that Random Forest offers the most accurate predictions and is suitable for practical applications in real estate.",2.0,https://doi.org/10.22214/ijraset.2025.72962,True,JournalArticle,,2025-07-31,de85e492d4c71ede92f719c2ae2ac073f4282233,realestatepricepredictionusingmachinelearningmodels,nagula,10.22214/ijraset.2025.72962,realestatepricepredictionusingmachinelearningmodels|2025,1,2292,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6e7c4c2a8bba183e6c86e6c8636efbf988f6a69c,10.22214/ijraset.2023.56232,,House Price Prediction: Comparative Analysis of Regression-Based Machine Learning Algorithms,2023.0,International Journal for Research in Applied Science and Engineering Technology,D. E. Aniobi; Chukwuemeka Oluebube Ochuba; Saater Benedicta Nguideen,"Abstract: Advancement in technology has revolutionized the ways of doing things in contemporary time. One of such is Artificial intelligence which has given birth to myriads of methods/techniques employed in solving real life problems. Many Machine learning techniques can be used in predicting house prices with several factors in consideration. House prices rise on annual basis, triggering the need for house price prediction models. Predictive models enable families to acquire a house of their choice when accurately developed. There have been a significant number of articles that adopt traditional machine learning algorithms to successfully estimate house prices, but they rarely compare the performance of individual models. This study will extensively test and compare numerous machine learning technique and present an optimistic model that will be used in developing a house pricing prediction system. Several models were developed and compared using Linear Regression (LR), Least Absolute Shrinkage & Selection Operator Regression (LASSO-R), Ridge Regression (RR), K-Nearest Neighbours Regression (KNN-R), Decision Tree Regression (DTR) and Extra Trees Regression (ETR) algorithms. Implemented using Python programming language. Amongst them ETR outperformed the others with MSE (16233.4), RMSE (128.7), MAE (49.6) and R2 (0.63) while the least performed is KNN-R with MSE (45763.3), RMSE (213.9), MAE (99.2) and R2 (-0.04).",2.0,https://doi.org/10.22214/ijraset.2023.56232,True,JournalArticle,,2023-10-31,6e7c4c2a8bba183e6c86e6c8636efbf988f6a69c,housepricepredictioncomparativeanalysisofregressionbasedmachinelearningalgorithms,aniobi,10.22214/ijraset.2023.56232,housepricepredictioncomparativeanalysisofregressionbasedmachinelearningalgorithms|2023,1,2937,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7fe37a38138af56180f31def64e34e1f61d7de77,10.22214/ijraset.2022.44033,,Prediction of House Prices Using Machine Learning,2022.0,International Journal for Research in Applied Science and Engineering Technology,S. Mysore,"Abstract: Economy of the country is greatly driven by the prices of houses in that country. Both buyers and sellers depend on the pricing strategies. Ask an emptor to explain the factors they think are considered for pricing the house at that price and that they probably start with railways and end with various attributes. Over here it proves that more factors will be applied on the pricing strategies of the house. The aim of the project is to predict the house prices with various regression models. Nowadays Machine Learning is a booming technology. Data is the heart of Machine Learning. AI and Machine Learning holds the key position in the technological market. All industries are moving towards automation. So we have considered ML as a main predicting subject in our project and worked using it. These days everything fluctuates. Starting with crypto and various business models varies day by day which includes real estate as well so in this project house prediction depends on real estate data and ML techniques. Many people want to buy a good house within the budget. But the disadvantage is that the present system doesn’t calculate the house predictions so well and end up in loss of money. So, the goal of our project is to reduce money loss and buy good house. Many factors are there to be considered in order to predict the house price which includes budget factors and fewer house modifications according to the buyer. So, we are considering all of those factors and predicted using various machine learning techniques like SVR, KNN, SGB regression, CatBoost regression, Random forest regression",7.0,https://doi.org/10.22214/ijraset.2022.44033,True,,,2022-06-30,7fe37a38138af56180f31def64e34e1f61d7de77,predictionofhousepricesusingmachinelearning,mysore,10.22214/ijraset.2022.44033,predictionofhousepricesusingmachinelearning|2022,1,3115,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",67c0cc6b53ecb39625d225e0ec44320ea42cc61a,10.22214/ijraset.2022.40272,,House Price Prediction Using Regression,2022.0,International Journal for Research in Applied Science and Engineering Technology,Meha Shukla,"Abstract: The housing sector is the second largest employment provider after agriculture sector in India and is estimated to grow at 30% over the next decade. Housing is one of the major sectors of real estate and is well complemented by the growth of the urban and semi-urban accommodations. Ambiguity among the prices of houses makes it difficult for the buyer to select their dream house. The interest of both buyers and sellers should be satisfied so that they do not overestimate or underestimate price. Our system provides a decisive housing price prediction model to benefit a buyer and seller or a real estate agent to make a better-informed decision system on multiple features. To achieve this, various features are selected as input from feature set and various approaches can be taken such as Regression Models or ANN. Keywords: Machine Learning, Artificial Intelligence, Supervised Machine Learning, Regression, Artificial Neural Network (ANN)",0.0,https://doi.org/10.22214/ijraset.2022.40272,True,,,2022-02-28,67c0cc6b53ecb39625d225e0ec44320ea42cc61a,housepricepredictionusingregression,shukla,10.22214/ijraset.2022.40272,housepricepredictionusingregression|2022,1,2456,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e59a5eb955526110f421fe4dd21582858c71d6ac,10.22214/ijraset.2019.5698,,Crossbreed Regression Technique for House Prices Prediction,2019.0,International Journal for Research in Applied Science and Engineering Technology,Vadaka Keerthi,"-Usually, House rate index represents the summarized charge adjustments of residential housing. While for a single family house charge prediction, it needs more accurate technique based on location, residence type, size, construct year, neighbourhood amenities, and some different elements which could affect house demand and supply. With constrained dataset and information features, a realistic and composite data pre-processing, innovative feature engineering technique is examined in this paper. The paper additionally proposes a hybrid Lasso and Gradient boosting regression model to predict person house price. The proposed strategy has lately been deployed as the key kernel for Kaggle Challenge “House Prices: Advanced Regression Techniques”. The performance is promising as our trendy score was once ranked top 1% out of all opposition groups and individuals.",0.0,https://doi.org/10.22214/ijraset.2019.5698,True,,Mathematics,2019-05-31,e59a5eb955526110f421fe4dd21582858c71d6ac,crossbreedregressiontechniqueforhousepricesprediction,keerthi,10.22214/ijraset.2019.5698,crossbreedregressiontechniqueforhousepricesprediction|2019,1,2368,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",fbe8548c89ee46ca6468c1cc9e57c0c5a6af1a39,10.22214/ijraset.2019.5251,,House Price Prediction Analysis using Machine Learning,2019.0,International Journal for Research in Applied Science and Engineering Technology,S. B.,": Land is the least straightforward industry in our biological system. Lodging costs continue changing all day every day and now and then are advertised instead of being founded on valuation. Anticipating lodging costs with genuine elements is the principle essence of our exploration venture. Here we plan to make our assessments dependent on each fundamental parameter that is considered while deciding the cost. We utilize different relapse systems in this pathway, and our outcomes are not sole assurance of one method rather it is the weighted mean of different procedures to give most exact outcomes. The outcomes demonstrated that this methodology yields least blunder and most extreme precision than individual calculations connected. We additionally propose to utilize ongoing neighbourhood subtleties utilizing Google maps to get accurate genuine valuations. House costs increment consistently, so there is a requirement for a framework to anticipate house costs later on. House value expectation can enable the engineer to decide the selling cost of a house and can assist the client with arranging the correct time to buy a house. There are three factors that impact the cost of a house which include physical conditions, idea and area.",6.0,https://doi.org/10.22214/ijraset.2019.5251,True,,Computer Science,2019-05-31,fbe8548c89ee46ca6468c1cc9e57c0c5a6af1a39,housepricepredictionanalysisusingmachinelearning,b.,10.22214/ijraset.2019.5251,housepricepredictionanalysisusingmachinelearning|2019,1,2748,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",398fa6c4f5f77786a030859c7b7874bc382e598c,10.22214/ijraset.2019.4622,,Housing Prices Prediction using Linear Regression,2019.0,International Journal for Research in Applied Science and Engineering Technology,Dhikhi T,": Individuals hoping to purchase home will in general be increasingly traditionalist with their financial plans and market systems. The present structure incorporates figuring of house costs without the fundamental estimate about future market examples and cost increase. It predict the capable house esteeming for land customers concerning their money related plans and needs. By breaking down past market patterns and value ranges, and furthermore up and coming improvements future costs will be anticipated. The entire work will be done using linear regression algorithm of supervised learning. It includes a site which acknowledges client's determinations and after that joins the utilization of various direct relapse calculation of information mining. This application will assist clients with investing in a home without moving toward a specialist. It likewise diminishes the hazard engaged with the exchange. The cost function equation is expressed as C(x)= FC + V(x), where C equals total production cost, FC is total fixed costs, V is variable cost and x is the number of units. Gradient Descent Algorithm is an advancement calculation used to limit some capacity by iteratively moving toward steepest drop as characterized by the negative of the inclination. In AI, we use angle plunge to refresh the parameters of our model.",0.0,https://doi.org/10.22214/ijraset.2019.4622,True,,Mathematics,2019-04-30,398fa6c4f5f77786a030859c7b7874bc382e598c,housingpricespredictionusinglinearregression,t,10.22214/ijraset.2019.4622,housingpricespredictionusinglinearregression|2019,1,2836,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic house price,30adab40128b6432d5c9664a3ebbfc2ca8a9ca59,10.21837/pm.v17i9.581,,THE ARTIFICIAL NEURAL NETWORK MODEL (ANN) FOR MALAYSIAN HOUSING MARKET ANALYSIS,2019.0,PLANNING MALAYSIA JOURNAL,Siti Norasyikin Abd. Rahman; N. H. Adi Maimun; Muhammad Najib Mohamed Razali; S. Ismail,"The Hedonic Model, a traditional method for forecasting house prices has been criticised due to nonlinearity, multicollinearity and heteroskedasticity problems, which were argued to affect estimation accuracy. Unlike the Hedonic Model, the Artificial Neural Network Model (ANN), permits nonlinear relationships and thus avoids the problems plaguing the Hedonic Model resulting in superior forecasting performance. Despite these advantages, attempts to model house prices using ANN are limited in geography and data thus besetting the usefulness of the results. To address the research gap, this paper aims to establish such a new model using ANN in forecasting house prices. A sample of double-storey terraced houses transacted in Johor Bahru are analysed using ANN. The findings illustrate a superior forecasting performance for ANN through high values of goodness of fit and low values of errors. This paper adds to the house price modelling literature and provides new knowledge to both academics and practitioners.",53.0,https://doi.org/10.21837/pmjournal.v17.i9.581,True,,,2019-05-06,30adab40128b6432d5c9664a3ebbfc2ca8a9ca59,theartificialneuralnetworkmodelannformalaysianhousingmarketanalysis,rahman,10.21837/pm.v17i9.581,theartificialneuralnetworkmodelannformalaysianhousingmarketanalysis|2019,1,2518,3,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic house price || hedonic housing prices",2,1,3,1,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fff8ff2c2b6b74b00fbc6d1895b66c9a5303bbba,10.1108/ijhma-06-2026-0213,,Housing price prediction in Ireland: a machine learning framework integrating geospatial encodings and macroeconomic indicators,2026.0,International Journal of Housing Markets and Analysis,Emmanuel Oluwapelumi Odedele; Oladele Bidemi Ajayi," This study aims to develop and evaluate a machine learning framework for predicting residential property prices in Ireland using the complete Irish Property Price Register, a population-complete data set of 736,002 arm’s-length transactions spanning January 2010 to March 2026. It formally quantifies the marginal contribution of geospatial, structural and macroeconomic data modalities. A multi-modal predictive framework integrates structural transaction data, address-derived geospatial location signals via Bayesian out-of-fold target encoding and macroeconomic indicators from the Central Statistics Office. Five models are trained and evaluated, including Ridge Regression, Random Forest and three HistGradientBoosting variants on an 80/20 train-test split. Geospatial location features increase R² by 0.2552, from 0.3494 to 0.6046, reducing mean absolute error by €28,271 per property. Macroeconomic lag features covering the consumer price index, unemployment and earnings change R² by less than 0.0001. The best model, HistGradientBoosting v3, achieves R² = 0.6046 and MAE = €80,755 on 144,470 held-out observations. The study is constrained by the absence of floor area, bedroom count and BER information for most transactions in the Irish Property Price Register. To the best of the authors’ knowledge, this is the first population-scale machine learning study applied to the complete Irish Property Price Register. It introduces Bayesian out-of-fold target encoding of raw address strings as a geocoding-free geospatial method generalisable to any national transaction register and delivers an ablation study isolating geospatial and macroeconomic modality contributions in the Irish residential market. ",0.0,https://doi.org/10.2139/ssrn.7244318,True,JournalArticle,,2026-09-29,fff8ff2c2b6b74b00fbc6d1895b66c9a5303bbba,housingpricepredictioninirelandamachinelearningframeworkintegratinggeospatialencodingsandmacroeconomicindicators,odedele,10.1108/ijhma-06-2026-0213,housingpricepredictioninirelandamachinelearningframeworkintegratinggeospatialencodingsandmacroeconomicindicators|2026,1,3248,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",218228fbccbd2c1f178ab4b57770aefd836ffea3,10.2139/ssrn.6405858,,Smart Prediction of House Cost Price Using AI and Machine Learning,2026.0,Social Science Research Network,Kartik S. Bhosale; Agrim Nikhra; Lavanya Chaudhary; Parveen Bajaj,"Predicting home costs with accuracy has become a central requirement in the areas of real estate management, investment decision making and policy making. Traditionally, valuation methods include the manual appraisal and linear model-based techniques that, in almost all cases, are not designed to identify the complex nonlinear relationships between variables such as location, property size, amenities and market movements. This study addresses the above issues by generating housing price predictions using a data-driven intelligent framework that takes advantage of Artificial Intelligence (AI) and Machine Learning (ML) techniques, which have the potential to be more accurate and interpretable. The suggested model combines various regression-based and ensemble learning methods—such as Random Forest, XGBoost, and Artificial Neural Networks—to capture the complex interdependencies of housing datasets in multiple dimensions. Feature engineering, outlier identification, and normalization methods were adopted to improve data quality and the model’s performance. The experiments were done using the standard datasets from Kaggle and local real estate data, where the prediction accuracy over 92% with a mean absolute error (MAE) of 12.5% was achieved. Comparison results reveal that ensemble learning models are more powerful than traditional regression techniques in terms of their ability to generalize and be robust. This study paves the way for the evolution of intelligent property valuation systems that facilitate transparent, automated, and evidence-based decision-making in the real estate industry. Upcoming features are the integration of geospatial and temporal dimensions, enabling continuous and adaptive prediction models.",0.0,https://doi.org/10.2139/ssrn.6405858,True,JournalArticle,,,218228fbccbd2c1f178ab4b57770aefd836ffea3,smartpredictionofhousecostpriceusingaiandmachinelearning,bhosale,10.2139/ssrn.6405858,smartpredictionofhousecostpriceusingaiandmachinelearning|2026,1,3244,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",a303f771010075203c591e922cc1f63675cf8818,10.1080/09599916.2023.2206823,,Automated valuation models: improving model performance by choosing the optimal spatial training level,2023.0,Social Science Research Network,Bastian Krämer; Moritz Stang; Vanja Doskoč; W. Schäfers; Tobias Friedrich,"ABSTRACT The academic community has discussed using Automated Valuation Models (AVMs) in the context of traditional real estate valuations and their performance for several decades. Most studies focus on finding the best method for estimating property values. One aspect that has not yet to be studied scientifically is the appropriate choice of the spatial training level. The published research on AVMs usually deals with a manually defined region and fails to test the methods used on different spatial levels. Our research aims to investigate the impact of training AVM algorithms at different spatial levels regarding valuation accuracy. We use a dataset with 1.2 million residential properties from Germany and test four methods: Ordinary Least Square, Generalised Additive Models, eXtreme Gradient Boosting and Deep Neural Network. Our results show that the right choice of spatial training level can significantly impact the model performance, and that this impact varies across the different methods.",12.0,https://doi.org/10.2139/ssrn.4272379,True,JournalArticle,,2023-05-02,a303f771010075203c591e922cc1f63675cf8818,automatedvaluationmodelsimprovingmodelperformancebychoosingtheoptimalspatialtraininglevel,krämer,10.1080/09599916.2023.2206823,automatedvaluationmodelsimprovingmodelperformancebychoosingtheoptimalspatialtraininglevel|2023,1,2509,3,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || automated valuation model residential",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",5fa8b8c8c2cfa9c052abf7601c92fda3abd2ebcd,10.2139/ssrn.4096393,,Valuation of House Prices Using Predictive Machine Learning Techniques,2022.0,Social Science Research Network,Meghana Tribhuwan; S. Godse,,0.0,https://doi.org/10.2139/ssrn.4096393,True,,,,5fa8b8c8c2cfa9c052abf7601c92fda3abd2ebcd,valuationofhousepricesusingpredictivemachinelearningtechniques,tribhuwan,10.2139/ssrn.4096393,valuationofhousepricesusingpredictivemachinelearningtechniques|2022,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a585411f7d48cd2a09a5dd5f912da9f1679ea684,10.2139/ssrn.3983312,2112.06192,Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market,2021.0,Social Science Research Network,Mohammad Mirbagherijam,"This study contributes a house price prediction model selection in Tehran City based on the area between Lorenz curve (LC) and concentration curve (CC) of the predicted price by using 206,556 observed transaction data over the period from March 21, 2018, to February 19, 2021. Several different methods such as generalized linear models (GLM) and recursive partitioning and regression trees (RPART), random forests (RF) regression models, and neural network (NN) models were examined house price prediction. We used 90% of all data samples which were chosen randomly to estimate the parameters of pricing models and 10% of remaining datasets to test the accuracy of prediction. Results showed that the area between the LC and CC curves (which are known as ABC criterion) of real and predicted prices in the test data sample of the random forest regression model was less than by other models under study. The comparison of the calculated ABC criteria leads us to conclude that the nonlinear regression models such as RF regression models give an accurate prediction of house prices in Tehran City.",0.0,https://doi.org/10.2139/ssrn.3983312,True,,Economics,2021-12-12,a585411f7d48cd2a09a5dd5f912da9f1679ea684,housingpricepredictionmodelselectionbasedonlorenzandconcentrationcurvesempiricalevidencefromtehranhousingmarket,mirbagherijam,10.2139/ssrn.3983312,housingpricepredictionmodelselectionbasedonlorenzandconcentrationcurvesempiricalevidencefromtehranhousingmarket|2021,1,2597,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d0e3f2aa78de04bf20436c8f57ef3ae22a9f28ef,10.2139/ssrn.3770895,,"House Price Estimation with Alternative Data and Machine Learning: A Case Study of Residential House Prices in Surrey, Canada",2021.0,Social Science Research Network,Da-Zhai Dong; Dongyi Wang,,0.0,https://doi.org/10.2139/ssrn.3770895,True,,,,d0e3f2aa78de04bf20436c8f57ef3ae22a9f28ef,housepriceestimationwithalternativedataandmachinelearningacasestudyofresidentialhousepricesinsurreycanada,dong,10.2139/ssrn.3770895,housepriceestimationwithalternativedataandmachinelearningacasestudyofresidentialhousepricesinsurreycanada|2021,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",cb989f959c3984fa04267feef406a28d263da44d,10.21307/ijanmc-2020-001,,Research on House Price Prediction Based on Multi-Dimensional Data Fusion,2020.0,"International Journal of Advanced Network, Monitoring, and Controls",Yonghui Yang,"Abstract The price of commercial housing is related to the process of urbanization in China and the living standard of residents, so the prediction of the price of commercial housing is very important. A major difficulty in predicting regression problems is how to handle different attribute types and fuse them. This paper proposes a house price prediction model based on multi-dimensional data fusion and a fully connected neural network. The model building steps are: First, normalize the data involved in the sample; then, interpolate the normalized data to increase the data density; subsequently, the normalized sample data is converted into a pixel matrix; finally, a fully connected neural network model is established from the pixel matrix to the price of the commercial house. After the neural network model has been established, the price of house can be obtained by entering the attributes of the house into the neural network model.",2.0,https://doi.org/10.21307/ijanmc-2020-001,True,,Computer Science,2020-01-01,cb989f959c3984fa04267feef406a28d263da44d,researchonhousepricepredictionbasedonmultidimensionaldatafusion,yang,10.21307/ijanmc-2020-001,researchonhousepricepredictionbasedonmultidimensionaldatafusion|2020,1,2445,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7548ae3e2da742d93482dc814c7cf13fa9d2ec46,10.20852/ntmsci.2018.327,,A new machine learning approach to house price estimation,2018.0,New Trends in Mathematical Sciences,ChangchunWang Wang; Hui Wu,"In this paper, we propose using Random forests, a machine lea rning approach, to build house price estimation model. Compared to benchmark linear regression model, random fore sts model can better capture hidden nonlinear relations bet ween the price and features of houses and gives an overall better estimatio n. Numerical experiments have been done on North Virginia ho use price data, which can strongly demonstrate our findings.",29.0,https://doi.org/10.20852/ntmsci.2018.327,True,,Computer Science,2018-12-29,7548ae3e2da742d93482dc814c7cf13fa9d2ec46,anewmachinelearningapproachtohousepriceestimation,wang,10.20852/ntmsci.2018.327,anewmachinelearningapproachtohousepriceestimation|2018,1,1933,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",4d495eac2da3050de423475cadc63d50355bea75,10.18178/jaai.2025.3.1.1-18,,Let’s Boost House Price Predictions: A Machine Learning Approach for Norwich,2025.0,Journal of Advances in Artificial Intelligence,,,1.0,https://doi.org/10.18178/jaai.2025.3.1.1-18,True,JournalArticle,,,4d495eac2da3050de423475cadc63d50355bea75,letsboosthousepricepredictionsamachinelearningapproachfornorwich,,10.18178/jaai.2025.3.1.1-18,letsboosthousepricepredictionsamachinelearningapproachfornorwich|2025,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",bd81b598fca1e12e2fddebafd5c0fe29c6dccc47,10.17308/sait/1995-5499/2025/1/133-142,,The impact of data preparation techniques on house price prediction task,2025.0,Вестник ВГУ. Серия: Системный анализ и информационные технологии,Ihcene Zitoune,"Accurate house price prediction is considered critical for decision-making in the real estate sector, where datasets are often characterized by missing values, outliers, and skewed distributions. In this study, the impact of various data preprocessing techniques on the performance of the XGBoost algorithm for predicting house prices is investigated. A real estate dataset from Kaggle is used to analyze and compare methods such as missing value imputation, categorical encoding, log transformation, and dimensionality reduction. The results show that preprocessing techniques significantly improve model performance, with certain approaches greatly reducing prediction errors and improving efficiency. Advanced methods, such as PCA with normalization and log transformation, produced the best results, showing the importance of choosing effective preprocessing steps. This study provides practical guidance for using data preprocessing to improve machine learning models, offering insights particularly relevant to real estate price prediction and other structured data applications.",0.0,https://doi.org/10.17308/sait/1995-5499/2025/1/133-142,True,JournalArticle,,2025-05-12,bd81b598fca1e12e2fddebafd5c0fe29c6dccc47,theimpactofdatapreparationtechniquesonhousepricepredictiontask,zitoune,10.17308/sait/1995-5499/2025/1/133-142,theimpactofdatapreparationtechniquesonhousepricepredictiontask|2025,1,2585,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",12d6ee799e126130b2a765b45573c151950677ba,10.17072/1994-9960-2026-2-214-226,,Decision support system with a Telegram bot and machine learning methods for the real estate market,2026.0,Вестник Пермского университета Серия «Экономика» = Perm University Herald ECONOMY,V. Gorelik; T. Zolotova,"Introduction. The problem of managing investments in residential real estate as a tool for generating income is considered; an investment option with mortgage lending and no additional funds is proposed. A Telegram bot was programmed to automate the investment decision-making process. Purpose. The aim of the study is to develop a decision support system as a Telegram bot, aimed at assessing the investment attractiveness of real estate objects with statistical data analysis and forecasting methods. Materials and methods. The data for the study is taken from the CIAN platform. The methodological framework includes methods for statistical data analysis, machine learning, and user interface design in decision support systems. Primary calculations and research are performed with Python programming language functions; the decision support system was implemented in Google Colab with the pyTelegramBotAPI library. Results. Information was collected, cleaned and pre-processed for cities in Russia, and rental and sales housing prices were studied. The CatBoostRegressor machine learning model was applied to forecast rental price for properties put up for sale, which made it possible to calculate their expected profitability. The possibility of using mortgage lending as a tool for higher investment efficiency was analyzed. A decision support system was implemented as a Telegram bot capable of assessing the profitability of real estate and helping the user make decisions defined by the specified parameters and forecast models. The Telegram bot was tested, and examples of use were demonstrated, confirming the accuracy and usefulness of the calculations obtained. Conclusions. The developed decision support system can provide recommendations based on the analysis of statistical data of the real estate market and a forecast model. The system is easy to use, focused on private investors, offers real objects presented on the market, automates the process of selection and evaluation of objects, and compares purchase strategies with a mortgage and no additional funds.",0.0,https://doi.org/10.17072/1994-9960-2026-2-214-226,True,JournalArticle,,,12d6ee799e126130b2a765b45573c151950677ba,decisionsupportsystemwithatelegrambotandmachinelearningmethodsfortherealestatemarket,gorelik,10.17072/1994-9960-2026-2-214-226,decisionsupportsystemwithatelegrambotandmachinelearningmethodsfortherealestatemarket|2026,1,3586,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",aab434f6813b5928621565d90dfc7c68dcafeb02,10.15446/dyna.v87n212.80202,,Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes,2020.0,,Jorge Iván Pérez Rave; Favián González Echavarría; J. C. Correa-Morales,"The objective of this work is to develop a machine learning model for online pricing of apartments in a Colombian context. This article addresses three aspects: i) it compares the predictive capacity of linear regression, regression trees, random forest and bagging; ii) it studies the effect of a group of text attributes on the predictive capability of the models; and iii) it identifies the more stable-important attributes and interprets them from an inferential perspective to better understand the object of study. The sample consists of 15,177 observations of real estate. The methods of assembly (random forest and bagging) show predictive superiority with respect to others. The attributes derived from the text had a significant relationship with the property price (on a log scale). However, their contribution to the predictive capacity was almost nil, since four different attributes achieved highly accurate predictions and remained stable when the sample change.",3.0,https://doi.org/10.15446/dyna.v87n212.80202,True,,Computer Science,,aab434f6813b5928621565d90dfc7c68dcafeb02,modelingofapartmentpricesinacolombiancontextfromamachinelearningapproachwithstableimportantattributes,rave,10.15446/dyna.v87n212.80202,modelingofapartmentpricesinacolombiancontextfromamachinelearningapproachwithstableimportantattributes|2020,1,2477,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",122a34e7d81c2534c504d2bdffac655420114887,10.14744/megaron.2024.22316,,"Determination of the relationship between housing characteristics and housing prices before and after the Kahramanmaraş earthquake using machine learning: A case study of Adana, Türkiye",2024.0,"MEGARON / Yıldız Technical University, Faculty of Architecture E-Journal",Simge Doğan; Levent Genç; Sait Can Yücebaş Metin Uşakli; Cengizhan Dumlu,"Earthquakes have a significant impact on the real estate sector. Damage caused by earthquakes leads to an imbalance in the supply and demand for housing, thus temporarily causing stagnation in the real estate sector. Two earthquakes occurred in the Pazarcık and Elbistan districts of Kahramanmaraş on February 6, 2023, at 04:17 am with a magnitude of 7.7 and at 13:24 pm with a magnitude of 7.6. A machine learning-based model was created to analyze the change in house prices and the variables affecting the price during the earthquake, which is called ""the Disaster of the Century."" After the earthquake, the prices of houses for sale in the central districts of Adana province (Seyhan, Yüreğir, Sarıçam",3.0,https://doi.org/10.14744/megaron.2024.22316,True,JournalArticle,,,122a34e7d81c2534c504d2bdffac655420114887,determinationoftherelationshipbetweenhousingcharacteristicsandhousingpricesbeforeandafterthekahramanmarasearthquakeusingmachinelearningacasestudyofadanaturkiye,doğan,10.14744/megaron.2024.22316,determinationoftherelationshipbetweenhousingcharacteristicsandhousingpricesbeforeandafterthekahramanmarasearthquakeusingmachinelearningacasestudyofadanaturkiye|2024,1,2205,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4d558bc7f0cf44cd263b3ef5e14335da9a7ba5bf,10.14358/pers.79.9.809,,Remote Sensing-based House Value Estimation Using an Optimized Regional Regression Model,2013.0,,Zhen-Yu Lu; J. Im; Lindi J. Quackenbush; Sanglim Yoo,"This study proposed a new method to predict residential property value using remote sensing data as a major data source substitute to traditional inputs in house price estimation models. An optimized regional regression (ORR) approach was proposed in this study. This approach integrated a differential evolution optimization algorithm along with the ordinary least square regression to improve house value prediction accuracy. In addition to ORR, four other regression methods, random forest, Cubist regression trees, geographically weighted regression, and global ordinary least square, were also employed to provide a comparison. Results showed that models using remote sensing data are capable of acquiring accurate house price information. In addition, the volume of residential buildings proved to be an efficient substitute for total living area, the most important variable of the house price estimation model (i.e., a hedonic model). The ORR approach yielded the most accurate predictions followed by the geographically weighted regression. Further investigation indicated that the ORR approach has three major advantages: it is effective, stable, and the results are readily interpretable.",13.0,https://doi.org/10.14358/pers.79.9.809,True,,Geography,2013-09-01,4d558bc7f0cf44cd263b3ef5e14335da9a7ba5bf,remotesensingbasedhousevalueestimationusinganoptimizedregionalregressionmodel,lu,10.14358/pers.79.9.809,remotesensingbasedhousevalueestimationusinganoptimizedregionalregressionmodel|2013,1,2699,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || hedonic house price",2,4,2,2,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f5d851aa205f9a3835454624d12f0b137883e616,10.14293/s2199-1006.1.sor-.pp0tp9i.v1,,Property valuation by machine learning for the Norwegian real estate market,2021.0,,Amandip Sangha,"We train a machine learning model on large data set for predicting property values in the Norwegian real estate market. Our model is a gradient boosted regression tree. The data set is the largest market data set of properties in Norway considered in the research literature. We achieve state of the art accuracy. A large scale market data set of real estate properties is collected from sales and rental ads on publicly accessible internet sites. The property advertisements show property features and appraisal values made by real estate brokers. We train a gradient boosted regression tree model on selected features of the data set. This is a multivariate regression model built with supervised learning. We do 5-fold cross validation to assess the accuracy and robustness of the model. The gradient boosted regression tree models are already known to give the best prediction accuracy on real estate price valuations. We achieve state of the art pre- diction accuracy using a minimal feature set and only publicly and freely available sales advertisement data. The novelty of our work lies in the fact that we use a minimal feature set in our model, and we have the largest data set in the research literature, and moreover we have used only freely and publicly accessible data which are simple to obtain. This shows that useful estimation models with high accuracy can be built with quite simple resources.",0.0,https://doi.org/10.14293/s2199-1006.1.sor-.pp0tp9i.v1,True,,Computer Science,2021-09-13,f5d851aa205f9a3835454624d12f0b137883e616,propertyvaluationbymachinelearningforthenorwegianrealestatemarket,sangha,10.14293/s2199-1006.1.sor-.pp0tp9i.v1,propertyvaluationbymachinelearningforthenorwegianrealestatemarket|2021,1,2915,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f2c7836df75238f5583aeb06d704ed029c7069dc,10.1371/journal.pone.0321951,,Real estate valuation with multi-source image fusion and enhanced machine learning pipeline,2025.0,PLoS ONE,Lin Deng,"The automated valuation model (AVM) has been widely used by real estate stakeholders to provide accurate property value estimations automatically. Traditional valuation models are subjective and inaccurate, and previous studies have shown that machine learning (ML) approaches perform better in real estate valuation. These valuation models are based on structured tabular data, and few consider integrating multi-source unstructured data such as images. Most previous studies use fixed feature space for model training without considering the model performance variation brought by various feature configuration parameters. To fill these gaps, this study uses Hong Kong as a case study and proposes an enhanced ML-based real estate valuation framework with feature configuration and multi-source image data fusion, including exterior housing photos, street view and remote sensing images. Eight ML regressors, namely, Random Forest, Extra Tree, XGBoost, Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Multiple Linear Regression (MLR) are used to formulate ML pipelines for training. The SHapley Additive exPlanations (SHAP) method is used to examine the effects of images on housing prices. The experimental results show that the model performances using different feature configuration parameters are significantly different, indicating the necessity of feature configuration to obtain more accurate and reliable predictions. Extra Tree performs significantly better than other models. Half of the top 10 significant features are image features, and incorporating multi-source image features can improve property valuation accuracy. Nonlinear associations exist between image features and housing prices, and the spatial distribution patterns of image feature values and corresponding SHAP main effects vary significantly from the city centre to the suburbs. These findings contribute to a better understanding of AVM development with image fusion and the nonlinear associations between image features and housing prices for public authorities, urban planners, and real estate developers.",10.0,https://doi.org/10.1371/journal.pone.0321951,True,JournalArticle,Medicine,2025-05-19,f2c7836df75238f5583aeb06d704ed029c7069dc,realestatevaluationwithmultisourceimagefusionandenhancedmachinelearningpipeline,deng,10.1371/journal.pone.0321951,realestatevaluationwithmultisourceimagefusionandenhancedmachinelearningpipeline|2025,1,3680,4,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d7b4864ef0252581d29f8d728e2be6f753505eea,10.1007/s43546-022-00358-4,,Predicting residential property value: a comparison of multiple regression techniques,2022.0,SN Business & Economics,Lee Whieldon; Huthaifa I. Ashqar,,9.0,https://doi.org/10.13016/m2d15w-hqpn,True,,,2022-10-24,d7b4864ef0252581d29f8d728e2be6f753505eea,predictingresidentialpropertyvalueacomparisonofmultipleregressiontechniques,whieldon,10.1007/s43546-022-00358-4,predictingresidentialpropertyvalueacomparisonofmultipleregressiontechniques|2022,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",5df1dc5f6c85f973973e1a5da5ec5159f042daa8,10.12677/sa.2016.53030,,基于增强回归树的房价影响因素分析—以波士顿地区为例 Factor Analysis of Housing Price Based on Boosting Regression Tree—Taking Boston as an Example,2016.0,,Jia Sheng,,0.0,https://doi.org/10.12677/sa.2016.53030,True,,Economics,2016-08-18,5df1dc5f6c85f973973e1a5da5ec5159f042daa8,factoranalysisofhousingpricebasedonboostingregressiontreetakingbostonasanexample,sheng,10.12677/sa.2016.53030,factoranalysisofhousingpricebasedonboostingregressiontreetakingbostonasanexample|2016,1,1500,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f18d727f7ec59f17ff6f8de5a2eb2c8d1e7101b7,10.1145/3821835.3821841,,MIC-BiLSTM: An Interpretable Deep Learning Framework for Real Estate Asset Pricing in High-Dimensional Markets,2026.0,Proceedings of the 2nd International Conference on Smart City and Sustainable Development,Nuerbiye Xiemuxiding; Xing-Long Li; Xiao-Ye Du; Liang Zhao; Maimaitiaili Maimaitimin; Lin Tang,"In recent years, Machine Learning (ML) and its subfield Deep Learning (DL) have flourished, providing powerful tools for complex data modeling. However, high-dimensional data in house price prediction poses significant challenges due to varying feature importance and redundant information. Existing studies often overlook complex nonlinear dependencies among features, temporal evolution patterns in data, and model interpretability. To address these issues, this paper proposes a novel framework integrating Maximal Information Coefficient (MIC) feature selection, Bidirectional Long Short-Term Memory (BiLSTM), and Local Interpretable Model-agnostic Explanations (LIME). The MIC method effectively screens 40 key features from 79 original dimensions, reducing computational complexity while preserving critical nonlinear relationships. BiLSTM captures bidirectional temporal dependencies among construction and remodeling years, improving prediction accuracy. LIME provides fine-grained feature contribution analysis, enhancing model transparency. Experimental results on the Kaggle House Prices dataset demonstrate that the proposed BiLSTM-MIC model achieves R² of 0.8269, representing a 57.41% relative improvement over traditional Multiple Linear Regression (MLR) and a 16.79% improvement over standard BiLSTM. The MSE is reduced by 63.53% compared to MLR, significantly outperforming existing feature selection methods.",0.0,https://doi.org/10.1145/3821835.3821841,True,Book;Conference,,2026-04-17,f18d727f7ec59f17ff6f8de5a2eb2c8d1e7101b7,micbilstmaninterpretabledeeplearningframeworkforrealestateassetpricinginhighdimensionalmarkets,xiemuxiding,10.1145/3821835.3821841,micbilstmaninterpretabledeeplearningframeworkforrealestateassetpricinginhighdimensionalmarkets|2026,1,2926,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d0ff96d37e73ec095d1498279a0ecfe7f53ed3b3,10.1145/3778450.3778468,,House Price Prediction and Interpretability Analysis Based on Automated Machine Learning,2025.0,Proceedings of the 2025 International Symposium on Machine Learning and Social Computing,Wen-Hao Tang,"Purchasing a home is not only a major life decision but also one of the most critical investments in household wealth allocation. In recent years, with the advancement of data science, machine learning (ML) methods have become important tools for studying and predicting housing prices, owing to their strengths in feature processing and pattern recognition. However, automated machine learning (AutoML) techniques, which are designed to automatically generate high-performing models for specific tasks and enable non-experts to conduct ML, have seen limited application in the domain of housing price prediction. Moreover, many ML algorithms do not prioritize interpretability, making it difficult to identify which features drive the predictions of housing prices. This study employed four AutoML tools, including a commercial web-based platform and several open-source libraries. Housing price data from King County, Washington (2014–2015) was used to investigate the predictive performance of AutoML models in housing price prediction and to conduct interpretability analyses of these models. The results suggest that AutoML models are capable of achieving promising predictive performance in this domain, with \({{{\bf R}}}^2 \) exceeding 0.85, and that location-related factors emerge as the most influential determinants of housing prices.",0.0,https://doi.org/10.1145/3778450.3778468,True,Book,,2025-10-16,d0ff96d37e73ec095d1498279a0ecfe7f53ed3b3,housepricepredictionandinterpretabilityanalysisbasedonautomatedmachinelearning,tang,10.1145/3778450.3778468,housepricepredictionandinterpretabilityanalysisbasedonautomatedmachinelearning|2025,1,2846,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic house price,1462a807ace2b4c97b7b8368ffbe3a1c694cd0f7,10.1145/3442381.3449967,2102.08162,Integrating Floor Plans into Hedonic Models for Rent Price Appraisal,2021.0,The Web Conference,K. Solovev; Nicolas Pröllochs,"Online real estate platforms have become significant marketplaces facilitating users’ search for an apartment or a house. Yet it remains challenging to accurately appraise a property’s value. Prior works have primarily studied real estate valuation based on hedonic price models that take structured data into account while accompanying unstructured data is typically ignored. In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal. We propose a tailored two-staged deep learning approach to learn price-relevant designs of floor plans from historical price data. Subsequently, we integrate the floor plan predictions into hedonic rent price models that account for both structural and locational characteristics of an apartment. Our empirical analysis based on a unique dataset of 9,174 real estate listings suggests that current hedonic models underutilize the available data. We find that (1) the visual design of floor plans has significant explanatory power regarding rent prices – even after controlling for structural and locational apartment characteristics, and (2) harnessing floor plans results in an up to 10.56 % lower out-of-sample prediction error. We further find that floor plans yield a particularly high gain in prediction performance for older and smaller apartments. Altogether, our empirical findings contribute to the existing research body by establishing the link between the visual design of floor plans and real estate prices. Moreover, our approach has important implications for online real estate platforms, which can use our findings to enhance user experience in their real estate listings.",19.0,https://doi.org/10.1145/3442381.3449967,True,JournalArticle;Book;Conference,Computer Science;Economics;Mathematics,2021-02-16,1462a807ace2b4c97b7b8368ffbe3a1c694cd0f7,integratingfloorplansintohedonicmodelsforrentpriceappraisal,solovev,10.1145/3442381.3449967,integratingfloorplansintohedonicmodelsforrentpriceappraisal|2021,1,3239,8,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic property valuation || hedonic regression housing || housing price text listing",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",34f822f9ee5baf70913147b3efa58abc566e2a3e,10.1142/s3082841425500078,,Predicting Residential Real Estate Values in China: An Empirical Analysis of Quzhou Utilizing a Novel Hybrid Computational Framework Integrating Gaussian Process Regression with Bayesian Hyperparameter Optimization,2025.0,Economics Open,Bing-Zi Jin; Xiao-Jie Xu,"Over the past decade, China’s real estate sector has experienced a transformative evolution marked by distinct cyclical phases, transitioning from a period of sustained expansion to an abrupt economic slowdown initiated in late 2021. These systemic realignments, driven by dynamic regulatory adjustments and heightened macroeconomic instability, have presented substantial operational challenges for stakeholders — including investors, policymakers and urban development strategists — engaged in forecasting housing price dynamics across metropolitan regions. Addressing gaps in traditional econometric approaches, this study advances a novel analytical framework leveraging Gaussian process regression (GPR) enhanced by multi-dimensional kernel architectures and context-sensitive basis functions. The model interrogates temporal price variations in residential assets within Quzhou, Zhejiang Province, drawing on a longitudinal transactional dataset encompassing nine years of observations (April 2015–July 2024). Bayesian inferential methods for hyperparameter optimization, augmented by stratified cross-validation protocols, achieved significant performance gains during exogenous validation intervals (September 2022–July 2024), attaining a relative root mean square error (RRMSE) of 0.2349% in out-of-sample testing. These results not only validate the model’s proficiency in capturing nonlinear market behaviors but also advance spatial economics discourse by delineating the interconnectedness between regional regulatory mechanisms and valuation dynamics. The framework’s modular architecture facilitates synergistic coupling with complementary econometric systems or independent deployment in heterogeneous urban environments, thereby enhancing predictive capabilities for data-driven policy formulation. Furthermore, the methodology provides actionable insights for designing adaptive governance protocols, bridging theoretical inquiry with practical applications in multi-faceted urban development planning.",3.0,https://doi.org/10.1142/s3082841425500078,True,JournalArticle,,2025-10-17,34f822f9ee5baf70913147b3efa58abc566e2a3e,predictingresidentialrealestatevaluesinchinaanempiricalanalysisofquzhouutilizinganovelhybridcomputationalframeworkintegratinggaussianprocessregressionwithbayesianhyperparameteroptimization,jin,10.1142/s3082841425500078,predictingresidentialrealestatevaluesinchinaanempiricalanalysisofquzhouutilizinganovelhybridcomputationalframeworkintegratinggaussianprocessregressionwithbayesianhyperparameteroptimization|2025,1,3520,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,machine learning hedonic pricing housing,27a0475ade51b3efad8840036dcd4309a4da68c0,10.1137/18s017296,,A Comparison of Machine Learning Approaches to Housing Value Estimation,2019.0,SIAM Undergraduate Research Online,Orton Babb,"Housing value estimation relies on hedonic pricing models whereby price is determined by both internal characteristics (bedrooms, bathrooms, living area, etc.) as well as external characteristics (neighboring houses, ZIP code, etc.). While classical parametric models based on linear regression analysis have been well studied in this application, the theory of hedonic prices places no restrictions on the hedonic price functional form, and hence, more recent research has attempted to apply machine learning (ML) approaches such as K-Nearest Neighbors and Support Vector Machine Regression (SVR). Many of these ML methods are employed on the basis of their flexibility in terms of making less assumptions on the shape or distribution of the data. ML models are therefore used with the expectation of higher accuracy on predicting the final sale price of a house. In this study, we consider the combination of various pre-processing procedures and candidate models on a historical data set of house sales in King County, Washington. Different measures of accuracy are considered in interpreting model performance. The results suggest that while machine learning algorithms like SVR achieve top performance as measured by the adjusted R, classical parametric models can also achieve out-of-sample generalization nearing that of the more sophisticated ML models, with faster training times, no need for feature scaling and more easily interpreted parameters.",4.0,https://doi.org/10.1137/18s017296,True,,Computer Science,,27a0475ade51b3efad8840036dcd4309a4da68c0,acomparisonofmachinelearningapproachestohousingvalueestimation,babb,10.1137/18s017296,acomparisonofmachinelearningapproachestohousingvalueestimation|2019,1,2957,4,hedonic house price || hedonic housing prices || housing characteristics prices hedonic || machine learning hedonic pricing housing,2,3,3,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),cdc2c570d488b25e40ea567da8068ca1ff68558c,10.1111/1540-6229.70030,,Explainable spatial machine learning for hedonic real estate modeling,2026.0,Real Estate Economics,Tim Gyger; Simona Hauri; Simon Bühlmann; Manuel Lehner; Jaron Schlesinger; Fabio Sigrist,"Accurately modeling rents and prices is a key challenge in real estate analysis. Traditional linear models may fail to capture complex non‐linear relationships, and spatial dependencies are often ignored in existing machine‐learning approaches. This article introduces a novel hybrid statistical machine‐learning model for modeling real estate rents and prices. The proposed approach combines a spatial Gaussian process with tree boosting. In so doing, spatial correlations are explicitly accounted for, and the tree‐boosting part can handle complex non‐linear relationships and interactions. We compare the proposed model against established benchmarks using a large‐scale dataset consisting of more than 1.5 million rental apartment listings across Germany and also a smaller condominium price listings dataset. Our findings demonstrate that the proposed model yields superior prediction accuracy due to accounting for both nonlinear patterns and spatial dependencies. We further use machine‐learning explainability techniques to better understand the nonlinear relationships present among rents and predictor variables, and we conduct a detailed analysis of the impact of locational characteristics on rents.",5.0,https://doi.org/10.1111/1540-6229.70030,True,JournalArticle,,2026-01-04,cdc2c570d488b25e40ea567da8068ca1ff68558c,explainablespatialmachinelearningforhedonicrealestatemodeling,gyger,10.1111/1540-6229.70030,explainablespatialmachinelearningforhedonicrealestatemodeling|2026,1,2711,2,apartment prices hedonic || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),2,2,4,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),18ea805d5a2f44f4bacc7ec0c10ee531f31a2a83,10.1109/tkde.2020.3010548,,Modeling Submarket Effect for Real Estate Hedonic Valuation: A Probabilistic Approach,2021.0,IEEE Transactions on Knowledge and Data Engineering,Zhi-Cheng Liu; Jun Cao; Renjie Xie; Jun-Yan Yang; Qiao Wang,"It is critical for urban planners and real estate developers to understand how the built environment and house characteristics are valued in housing market. However, this problem is challenging because of the existence of the submarket effect resulted from the heterogeneity nature of city. In this paper, we propose a probabilistic approach to residential property hedonic valuation problem modeling the full scope of submarket effect based on built environment and house characteristics. Specifically, we introduce a latent variable representing housing submarket and model both of the submarket criteria and hedonic price model(HPM) into a Bayesian network. Utilizing the probabilistic dependencies in the Bayesian network, our model is able to capture the full scope of the submarket effect. Furthermore, to analyze the relationship among the discovered submarkets, we propose a probabilistic hierarchical clustering method to infer the hierarchical structure of housing market. In particular, we perform Bayesian hypothesis testings to find the most similar submarkets and agglomerate submarkets step-by-step, thus revealing the hierarchical structure of housing market. Finally, we conduct comprehensive experiments in the housing market of Nanjing which is a metropolis in eastern China. The experimental results demonstrate the effectiveness of our proposed modeling method.",9.0,https://doi.org/10.1109/tkde.2020.3010548,True,JournalArticle,Computer Science,2021-07-01,18ea805d5a2f44f4bacc7ec0c10ee531f31a2a83,modelingsubmarketeffectforrealestatehedonicvaluationaprobabilisticapproach,liu,10.1109/tkde.2020.3010548,modelingsubmarketeffectforrealestatehedonicvaluationaprobabilisticapproach|2021,1,2882,5,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic,2,0,3,1,0,5,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f4bc80dc0fc2e84dcb6087bbed9633773fafd225,10.1109/access.2025.3568798,,Deep-Learning Approach for an Analysis of Real-Estate Prices and Transactions,2025.0,IEEE Access,Cheng-Hong Yang; Borcy Lee; Yu-Da Lin,"Research on the study of houses, condominiums and buildings in Taiwan’s metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. Data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (DBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results of this study indicate that three key factors—the number of real estate transactions, the average unit price of real estate transactions, and the building material and construction index—significantly affected real estate prices in Taiwan. Our results could help researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors. In addition, the relationships between real estate trends and the aforementioned three key factors were determined to obtain valuable information that can enable the Taiwanese government to regulate the property market and prevent excessive growth. The framework proposed in this paper allows researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors, and provides a new mechanism for approaching real estate-related forecasting.",1.0,https://doi.org/10.1109/access.2025.3568798,True,JournalArticle,Computer Science,,f4bc80dc0fc2e84dcb6087bbed9633773fafd225,deeplearningapproachforananalysisofrealestatepricesandtransactions,yang,10.1109/access.2025.3568798,deeplearningapproachforananalysisofrealestatepricesandtransactions|2025,1,3602,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,5,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",6102a582e5b767e292fa8b423e39a575e103fab4,10.1109/access.2024.3484409,,Explainable and Fair AI: Balancing Performance in Financial and Real Estate Machine Learning Models,2024.0,IEEE Access,D. Acharya; B. Divya; Karthigeyan Kuppan,"This paper introduces a framework that integrates fairness and transparency into advanced machine learning models, specifically LightGBM and XGBoost, applied to loan approval and house price prediction datasets. The key contribution is using fairness-focused techniques, such as Calibrated Equalized Odds and Intersectional Fairness, which are not widely studied in financial and real estate contexts. To improve model transparency, SHAP (SHapley Additive exPlanations) is utilized along with a novel fairness-based interpretability method to measure both model fairness and the importance of individual features. Through comprehensive experiments, we show that LightGBM delivers high accuracy while balancing fairness and performance effectively. The broader relevance of this work is discussed in the context of governance and regulatory requirements, highlighting the importance of responsible practices in high-stakes financial decision-making processes. This research highlights the importance of fairness and transparency in real-world applications, promoting equity, trust, and adherence to evolving legal standards, and provides practical insights for data scientists, machine learning researchers, and professionals in the real estate and financial sectors.",30.0,https://doi.org/10.1109/access.2024.3484409,True,JournalArticle,Computer Science,,6102a582e5b767e292fa8b423e39a575e103fab4,explainableandfairaibalancingperformanceinfinancialandrealestatemachinelearningmodels,acharya,10.1109/access.2024.3484409,explainableandfairaibalancingperformanceinfinancialandrealestatemachinelearningmodels|2024,1,2766,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",00bfecc7b6efdca033a850c011eef8b7197d43b0,10.1109/access.2018.2814065,,Predicting House Price With a Memristor-Based Artificial Neural Network,2018.0,IEEE Access,Jun Wang; S.-G. Hu; Xi-Tong Zhan; Qu Luo; Q. Yu; Zhen Liu; Tu-Pei Chen; You Yin; S. Hosaka; Yong Liu,"Synaptic memristor has attracted much attention for its potential applications in artificial neural networks (ANNs). However useful applications in real life with such memristor-based networks have seldom been reported. In this paper, an ANN based on memristors is designed to learn a multi-variable regression model with a back-propagation algorithm. A weight unit circuit based on memristor, which can be programed as an excitatory synapse or inhibitory synapse, is introduced. The weight of the electronic synapse is determined by the conductance of the memristor, and the current of the synapse follows the charge-dependent relationship. The ANN has the ability to learn from labeled samples and make predictions after online training. As an example, the ANN was used to learn a regression model of the house prices of several Boston towns in the USA and the predicted results are found to be close to the target data.",44.0,https://doi.org/10.1109/access.2018.2814065,True,JournalArticle,Computer Science,2018-03-09,00bfecc7b6efdca033a850c011eef8b7197d43b0,predictinghousepricewithamemristorbasedartificialneuralnetwork,wang,10.1109/access.2018.2814065,predictinghousepricewithamemristorbasedartificialneuralnetwork|2018,1,2422,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",fdcfe87837d0a8bd9109f95937450351010db95e,10.1080/14697688.2025.2468269,,Harnessing uncertainty: a new approach to real estate investment decision support,2025.0,Quantitative finance (Print),A. J. Pollestad; A. Oust,"This paper investigates the efficiency of AI-uncertainty quantification as a decision support tool to mitigate adverse selection issues for home buyers and real estate investors. The study is grounded in the premise that buyers can use uncertainty estimates from AI models to identify properties with uncertain price predictions, thus enabling them to filter out risky investment prospects and place bids closer to the fundamental value. We employ an automated valuation model trained on a dataset of over 50,000 historical apartment transactions in Oslo, Norway, spanning 2016–2022. Through a financial performance simulation, we evaluate three bidding strategies: random selection, human model evaluation, and AI-uncertainty quantification. Our findings reveal that AI-uncertainty quantification outperforms human evaluation in identifying uncertain investment opportunities, yielding higher potential profit margins and purchase ratios. The results are robust across different sample sizes and models. Explainable AI analysis using SHAP values further reveals the AI model’s ability to identify spatial uncertainty patterns, enhancing its effectiveness in mitigating adverse selection. Our study contributes to the literature on information asymmetry and automated valuation, offering insights into the application of AI to address adverse selection challenges in residential real estate investment.",3.0,https://doi.org/10.1080/14697688.2025.2468269,True,JournalArticle,,2025-03-03,fdcfe87837d0a8bd9109f95937450351010db95e,harnessinguncertaintyanewapproachtorealestateinvestmentdecisionsupport,pollestad,10.1080/14697688.2025.2468269,harnessinguncertaintyanewapproachtorealestateinvestmentdecisionsupport|2025,1,2902,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || automated valuation model residential",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",37643b06c7ecb224773fb8206b909413003da2bb,10.1080/09599916.2020.1832558,,Predicting property prices with machine learning algorithms,2021.0,,Winky K.O. Ho; B. Tang; S. Wong,"ABSTRACT This study uses three machine learning algorithms including, support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM) in the appraisal of property prices. It applies these methods to examine a data sample of about 40,000 housing transactions in a period of over 18 years in Hong Kong, and then compares the results of these algorithms. In terms of predictive power, RF and GBM have achieved better performance when compared to SVM. The three performance metrics including mean squared error (MSE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) associated with these two algorithms also unambiguously outperform those of SVM. However, our study has found that SVM is still a useful algorithm in data fitting because it can produce reasonably accurate predictions within a tight time constraint. Our conclusion is that machine learning offers a promising, alternative technique in property valuation and appraisal research especially in relation to property price prediction.",249.0,https://doi.org/10.1080/09599916.2020.1832558,True,,,2021-01-02,37643b06c7ecb224773fb8206b909413003da2bb,predictingpropertypriceswithmachinelearningalgorithms,ho,10.1080/09599916.2020.1832558,predictingpropertypriceswithmachinelearningalgorithms|2021,1,2537,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1d36c57677f1559e8d4f4ddfcf39766d00050f6c,10.1051/itmconf/20269102016,,Machine Learning Approaches to House Price Determinants,2026.0,ITM Web of Conferences,Shi-Yu Dong,"This study takes housing prices in Jinan as an example. Data were collected from official government websites and official housing price platforms. The Hadoop platform was used for distributed storage and preprocessing. Linear regression and recurrent neural network (RNN) models were constructed to compare and analyze the influence of various factors on housing prices. The results show that factors such as interior area, location distance, and regional planning have significant effects on housing prices. Among these, the marginal contribution of interior area is the highest, and communities located 600–900 meters from a subway station have the optimal prices. The RNN model achieves better prediction accuracy than linear regression and effectively captures market dynamics, nonlinearity, and time-dependent characteristics. The comprehensive ranking of influencing factors is: interior area > regional location > surrounding planning development > housing condition > shared area. This study indicates that interior area and regional location are the main factors affecting housing price fluctuations. The findings can provide data references for homebuyers, developers, and policymakers.",0.0,https://doi.org/10.1051/itmconf/20269102016,True,JournalArticle;Conference,,,1d36c57677f1559e8d4f4ddfcf39766d00050f6c,machinelearningapproachestohousepricedeterminants,dong,10.1051/itmconf/20269102016,machinelearningapproachestohousepricedeterminants|2026,1,2697,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ba5888e5375daceadfdec830d1897161de0f895c,10.1051/itmconf/20269102003,,Ranked Factor Contributions: Random Forest Insights Into Beijing’s Housing Prices,2026.0,ITM Web of Conferences,Chi Zhang," This paper is based on Beijing's housing data and uses the random forest regression model to analyze the main influencing factors of housing prices. The USA's housing data is minor and are only compared with those of the Beijing area. The model performance is evaluated by the coefficient of determination (R 2 ) and the mean square error (MSE). The results show that the model has good predictive ability and generalization performance. The feature importance analysis based on the mean decrease in impurity (MDI) indicates that structural factors such as housing area play a dominant role in the formation of housing prices, followed by location factors and educational facilities. The residual analysis results show that the prediction errors are mainly concentrated around zero and follow an approximately symmetrical distribution, indicating that the model does not have obvious systematic deviations. The research results emphasize the significant role of housing physical attributes and urban amenities in the formation of housing prices, and provide empirical support for the urban spatial structure theory. ",0.0,https://doi.org/10.1051/itmconf/20269102003,True,JournalArticle;Conference,,,ba5888e5375daceadfdec830d1897161de0f895c,rankedfactorcontributionsrandomforestinsightsintobeijingshousingprices,zhang,10.1051/itmconf/20269102003,rankedfactorcontributionsrandomforestinsightsintobeijingshousingprices|2026,1,2620,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",37d3e1489386e809f9159a6c381cd6cdbb7794f2,10.1051/itmconf/20246701042,,Prediction of residential property prices using machine learning algorithms,2024.0,ITM Web of Conferences,S. Oh; See Pheng Hang; Jasmine Thye Wei Wang,"Residential property prices prediction is essential for evaluating market value and identifying over-pricing or under-pricing. This study investigates the performance of various machine learning algorithms, including Decision Tree (DT), Random Forest (RF), and Multilayer Perceptron (MLP) in predicting residential property prices. The study performs exploratory data analysis and principal components analysis (PCA) to reduce the dimensionality of the variables and extract the most useful variables affecting terrace house prices in Kuala Lumpur, Malaysia. A publicly available dataset is used for training and testing the algorithms, with a 70:30 proportion after pre-processing procedures. Performance indicators such as Kappa statistics, r-squared, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are used to evaluate the algorithms. The results show that RF outperforms DT and MLP, achieving the highest accuracy score of 85.82%, and highest Kappa statistics of 0.8307. The study also finds that the predicted data by RF algorithm are reliable from the train set. After performing exploratory data analysis and PCA, RF-PCA demonstrated the best performance in residential property price prediction, with an r-squared value of 0.7497, the lowest values of MAE (0.6091), MAPE (19.23%), and RMSE (1.066) compared to DT-PCA and MLP-PCA.",2.0,https://doi.org/10.1051/itmconf/20246701042,True,JournalArticle;Conference,,,37d3e1489386e809f9159a6c381cd6cdbb7794f2,predictionofresidentialpropertypricesusingmachinelearningalgorithms,oh,10.1051/itmconf/20246701042,predictionofresidentialpropertypricesusingmachinelearningalgorithms|2024,1,2891,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3de036dfddcce85a957ed7bf307b09a73c9ba574,10.1051/e3sconf/202560501007,,Enhancing real estate price prediction using optimized least squares moment balanced machine,2025.0,E3S Web of Conferences,Riqi Radian Khasani,"Real estate price prediction is crucial for urban planning and economic forecasting, as property values are influenced by factors such as location and infrastructure. Traditional methods often struggle to capture the complex relationships between these variables, resulting in inefficient land use and suboptimal resource allocation. To address this challenge, this study introduces the Optimized Least Squares Moment Balanced Machine (OLSMBM), an advanced machine learning model designed to enhance the accuracy of real estate price predictions. The model incorporates key features such as transaction date, house age, proximity to MRT stations, number of convenience stores, and geographic location. The OLSMBM was benchmarked against five other machine learning models, including LSSVM, BPNN, ELSIM, Decision Tree, and Linear Regression. The results from a 10-fold cross-validation demonstrate that OLSMBM consistently outperforms other models across five evaluation metrics, including RMSE (6.977), MAE (4.752), MAPE (13.76%), R (0.846), and R² (0.728). The comprehensive evaluation, summarized by the Reference Index (RI), showed that OLSMBM achieved a perfect RI score of 1.000, highlighting its superior performance. These findings underscore the potential of the OLSMBM as a decision-support tool, enhancing the accuracy of real estate price predictions and reinforcing data-driven strategies in urban planning.",2.0,https://doi.org/10.1051/e3sconf/202560501007,True,JournalArticle;Conference,,,3de036dfddcce85a957ed7bf307b09a73c9ba574,enhancingrealestatepricepredictionusingoptimizedleastsquaresmomentbalancedmachine,khasani,10.1051/e3sconf/202560501007,enhancingrealestatepricepredictionusingoptimizedleastsquaresmomentbalancedmachine|2025,1,2919,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a6809c22f61c5adc84d96325ddb94c578ae5671f,10.1016/j.procs.2025.09.230,,Regression models for house price analysis,2025.0,International Conference on Knowledge-Based Intelligent Information & Engineering Systems,Urszula Boryczka; Mariusz Boryczka,,1.0,https://doi.org/10.1016/j.procs.2025.09.230,True,JournalArticle,Computer Science,,a6809c22f61c5adc84d96325ddb94c578ae5671f,regressionmodelsforhousepriceanalysis,boryczka,10.1016/j.procs.2025.09.230,regressionmodelsforhousepriceanalysis|2025,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",394f6db067aec4c732bf9902d02a7c12c9505a05,10.1016/j.dajour.2023.100166,,A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization,2023.0,Decision Analytics Journal,S. Lahmiri; S. Bekiros; Christos Avdoulas,,60.0,https://doi.org/10.1016/j.dajour.2023.100166,True,,,2023-03-01,394f6db067aec4c732bf9902d02a7c12c9505a05,acomparativeassessmentofmachinelearningmethodsforpredictinghousingpricesusingbayesianoptimization,lahmiri,10.1016/j.dajour.2023.100166,acomparativeassessmentofmachinelearningmethodsforpredictinghousingpricesusingbayesianoptimization|2023,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",207e3d65ca15e0f4066b5480307854be13a3ac6a,10.1016/j.cacint.2025.100223,,Exploring housing price dynamics in sustainable cities through a cooperated big data driven machine learning method: Case study on a Typical city in China,2025.0,City and Environment Interactions,Feng Han; Mei-Qu Lu; Donghong Qin; G. Zheng; Guihong Zeng; Yan Tan; Zhongyang Wu; Hai-Jian Lu; Jun Wang; Yirong Deng; Hui He,,6.0,https://doi.org/10.1016/j.cacint.2025.100223,True,JournalArticle,,2025-07-01,207e3d65ca15e0f4066b5480307854be13a3ac6a,exploringhousingpricedynamicsinsustainablecitiesthroughacooperatedbigdatadrivenmachinelearningmethodcasestudyonatypicalcityinchina,han,10.1016/j.cacint.2025.100223,exploringhousingpricedynamicsinsustainablecitiesthroughacooperatedbigdatadrivenmachinelearningmethodcasestudyonatypicalcityinchina|2025,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b1fd694ae631459acfcd5e3cf363cd3d865e6746,10.56294/dm2024.231,,Predicting Housing Sale Prices Using Machine Learning with Various Data Split Ratios,2024.0,Data and Metadata,Awais Azam; Alimul Haque; S. Rai,"Introduction: Recent advancements in technology and data analytics have propelled the rapid growth of artificial intelligence (AI) and machine learning (ML), which are now central to various industries. These technologies have become essential tools in many sectors, especially in predictive modeling for asset pricing.Objective: From stock markets and rental properties to real estate and second-hand goods, AI and ML algorithms are widely applied to estimate values, optimize pricing strategies, and forecast market trends. Method: By analyzing vast amounts of data, these tools enable more accurate predictions and informed decision-making, revolutionizing traditional approaches to pricing and valuation. In this study, the primary goal is to achieve the most accurate price prediction for houses or apartments by experimenting with different data split ratios.Result: RMSE (House Price) 188965.28 is acceptable as best average price for houses. Conclusions: The value of RMSE of this model are relatively low and also the value Squared Correlation is 64% which is above the threshold of 50%, so the predicted price of this model is seems appropriate, so I have presented this model and its predicted house price as final acceptable value for my research outcome",4.0,https://dm.ageditor.ar/index.php/dm/article/download/231/889,True,JournalArticle,,2024-12-15,b1fd694ae631459acfcd5e3cf363cd3d865e6746,predictinghousingsalepricesusingmachinelearningwithvariousdatasplitratios,azam,10.56294/dm2024.231,predictinghousingsalepricesusingmachinelearningwithvariousdatasplitratios|2024,1,2766,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,5,1,1,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9010a7849ddf7dd9531b5932820f4d9bcce4a137,10.1145/3772726.3772869,,Resale House Price Prediction Based on RN-LGBM,2025.0,Proceedings of the 2nd International Conference on Intelligent Computing and Data Analysis,Zi-Long Wang,"In the current economic climate, accurately forecasting resale housing price trends and fluctuations has become a critical research focus in the real estate sector. This study introduces a hybrid prediction model, termed RN-LGBM, which integrates a ResNet-based feature extractor with a LightGBM (LGBM) predictor. In the RN-LGBM framework, the feature extractor first generates refined feature representations from the input data, which are subsequently used by the LGBM module to produce the final price predictions. Experimental results validate the effectiveness of the proposed model for resale housing price forecasting. Specifically, the RN-LGBM model achieves a Mean Absolute Error (MAE) below 0.11 and a Mean Absolute Percentage Error (MAPE) under 8%, outperforming both standalone ResNet and LGBM models. These results highlight the advantage of combining deep learning-based feature extraction with gradient boosting techniques, leading to superior predictive performance compared to conventional machine learning methods. Overall, the RN-LGBM model offers a valuable tool for accurate price prediction in the resale housing market and provides meaningful insights for future research in this domain.",0.0,https://dl.acm.org/doi/pdf/10.1145/3772726.3772869?download=true,True,Book;Conference,,2025-08-22,9010a7849ddf7dd9531b5932820f4d9bcce4a137,resalehousepricepredictionbasedonrnlgbm,wang,10.1145/3772726.3772869,resalehousepricepredictionbasedonrnlgbm|2025,1,2710,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",84ad4582441dc4d4bc35704555200680e2777264,10.1145/3767052.3767068,,Machine Learning Models for Predicting Second-hand House Prices: A Comparative Study,2025.0,"Proceedings of the 2025 International Conference on Big Data, Artificial Intelligence and Digital Economy",Haoran Jiang,"This paper presents a comparative analysis of the application of machine learning models in predicting the prices of second-hand houses. It underscores the importance of the real estate market within the economy and points out the advantages of precise predictions for various stakeholders, including homebuyers, real estate agents, and financial institutions. Traditional methods frequently fail to grasp the intricate interactions that impact house prices, whereas machine learning techniques have garnered attention for their capability to handle such complexities. The study made use of the “Shanghai Second-hand Housing Prices from 2024 Anjuke Website” dataset from Kaggle, which comprises 175,135 records, each having nearly 30 features. After undergoing preprocessing procedures like duplicate removal, feature transformation, outlier removal, missing value imputation, feature encoding, feature engineering, and data standardization, the data was divided into training and testing sets. Four machine learning models were compared: Linear Regression, Ridge Regression, Random Forest, and XGBoost. The findings revealed that Random Forest and XGBoost surpassed the linear models, with Random Forest attaining the lowest Mean Absolute Error (MAE) and the highest R² Score. The study concluded that Random Forest was the most effective model for predicting the prices of second-hand houses in Shanghai. Even though the results were promising, the study acknowledged certain limitations such as the temporal and geographical constraints of the dataset and the reliance on historical data. Future research ought to concentrate on real-time data integration, model updating, incorporating additional data sources, and exploring advanced techniques to improve the performance and interpretability of the model.",2.0,https://dl.acm.org/doi/pdf/10.1145/3767052.3767068,True,Book;Conference,,2025-07-18,84ad4582441dc4d4bc35704555200680e2777264,machinelearningmodelsforpredictingsecondhandhousepricesacomparativestudy,jiang,10.1145/3767052.3767068,machinelearningmodelsforpredictingsecondhandhousepricesacomparativestudy|2025,1,3310,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a731247c2b779e9f70acd9b4763a55ac27db0a8d,10.1145/3767052.3767059,,Machine Learning and Deep Learning-Based Housing Rental Price Prediction in China: Analysis of Endogenous and Market Factors,2025.0,"Proceedings of the 2025 International Conference on Big Data, Artificial Intelligence and Digital Economy",Shangjie Mo,"House price prediction has always been an important topic that attracts researchers from different fields. In this paper, an innovative house price prediction model is proposed, which comprehensively considers the intrinsic connection between various attributes of the house and the market price. This paper aims to accurately predict the reasonable price of a house under specific conditions through a variety of machine learning models, such as XGboost and artificial neural networks (ANN) and Convolutional Neural Networks(CNN), and analyze the important characteristics that determine the house price. Through the development and application of this model, this study provides more scientific and reasonable theoretical support for real estate market pricing, and also provides new research perspectives and methods for researchers in related fields.",0.0,https://dl.acm.org/doi/pdf/10.1145/3767052.3767059,True,Book;Conference,,2025-07-18,a731247c2b779e9f70acd9b4763a55ac27db0a8d,machinelearninganddeeplearningbasedhousingrentalpricepredictioninchinaanalysisofendogenousandmarketfactors,mo,10.1145/3767052.3767059,machinelearninganddeeplearningbasedhousingrentalpricepredictioninchinaanalysisofendogenousandmarketfactors|2025,1,2354,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",fe63dca1260ccb4db00ba7abaef1628b2f2ed67b,10.1145/3745133.3745175,,Prediction of House Price in Boston Based on the Fusion of Crested Porcupine Optimizer Algorithm and BP Neural Network,2025.0,Proceedings of the 2025 International Conference on Digital Economy and Information Systems,Qiao Hong; Hao-Qin Li,"Aiming at the problems that the traditional backpropagation (BP) neural network is prone to fall into local optimum and has a slow convergence speed in house price prediction, this paper proposes a BP neural network fusion model (LCPO-BP) based on the improved crested porcupine optimizer algorithm. By introducing the elite reverse learning strategy and the adaptive weight mechanism, the global search ability is enhanced, and the initial weights and biases of the network are optimized, thereby improving the performance of the model. The experimental results based on the Boston house price dataset show that compared with the traditional BP, the LCPO-BP model reduces the mean absolute error (MAE) and root mean square error (RMSE) by 58.81% and 59.63% respectively, and the coefficient of determination (R²) increases to 0.93269. The effectiveness of the improved algorithm in avoiding local optimum, accelerating convergence and improving prediction accuracy was verified, providing a new reference for intelligent optimization methods for complex nonlinear prediction problems.",3.0,https://dl.acm.org/doi/pdf/10.1145/3745133.3745175,True,Book;Conference,,2025-04-25,fe63dca1260ccb4db00ba7abaef1628b2f2ed67b,predictionofhousepriceinbostonbasedonthefusionofcrestedporcupineoptimizeralgorithmandbpneuralnetwork,hong,10.1145/3745133.3745175,predictionofhousepriceinbostonbasedonthefusionofcrestedporcupineoptimizeralgorithmandbpneuralnetwork|2025,1,2585,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2532531a5396139931802327eeb3c6d55011bf3a,10.1145/3724154.3724162,,A Novel Graph based Deep Learning Model for Real Estate Finance,2024.0,Proceedings of the 2024 5th International Conference on Big Data Economy and Information Management,Linfeng Cao; Size Hou,"We aim to explore how urban green spaces, particularly parks, influence house prices in Guangzhou City by examining the association mechanism. With the advancement of urbanization, green Spaces are becoming increasingly important in urban development, especially due to their significant impact on property values. We collected data on green space and house prices in Tianhe, Guangzhou, including 12 parks and 1082 house price samples. We proposed a graph convolution network-based method for regression analysis and relationship extraction. The experiment shows that our model is the state-of-the-art model in home values regression task. We have shown that the larger the area of green space and the closer it is to green space, the higher the house price. By examining the complex relationship between urban green space and property values, this study provides valuable resources for urban planning and development.",0.0,https://dl.acm.org/doi/pdf/10.1145/3724154.3724162,True,Book;Conference,,2024-12-13,2532531a5396139931802327eeb3c6d55011bf3a,anovelgraphbaseddeeplearningmodelforrealestatefinance,cao,10.1145/3724154.3724162,anovelgraphbaseddeeplearningmodelforrealestatefinance|2024,1,2418,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f949a4cf065b922bd089dc601b1c9c022fd09d08,10.1145/3635059.3635065,,Explainable Graph Neural Networks on Linked Statistical Data for Predicting Scottish House Prices,2023.0,Panhellenic Conference on Informatics,Petros Brimos; Areti Karamanou; E. Kalampokis; M. Mamalis; Konstantinos A. Tarabanis,"In the complex landscape of the real estate market, accurately estimating house prices is of paramount importance for a wide array of stakeholders. While traditional statistical methods have been employed in the past, machine learning and deep learning models are increasingly demonstrating their efficacy in predicting house prices by capturing intricate patterns and relationships between features of the housing market, such as neighborhood characteristics and geographical proximity to amenities. Recently, Graph Neural Networks have shown great performance in various predictive tasks, effectively capturing complex spatial dependencies in graph-strucured data. Towards this direction, this paper utilizes a Graph Convolutional Network and linked Open Government Data from the official Scottish data portal to predict house prices in Scotland. The results showed that the Graph Neural Network model achieved an accuracy of 0.84 on the test set. The GNN’s decisions are also interpreted using the GNNexplainer model, that identified important graph structures as well as node features that are most influential for the prediction. Comparative Illness Factor, an indicator of health conditions, is identified as the feature that primarily influences the model’s decisions for a particular data zone.",5.0,https://dl.acm.org/doi/pdf/10.1145/3635059.3635065,True,JournalArticle;Book,Computer Science,2023-11-24,f949a4cf065b922bd089dc601b1c9c022fd09d08,explainablegraphneuralnetworksonlinkedstatisticaldataforpredictingscottishhouseprices,brimos,10.1145/3635059.3635065,explainablegraphneuralnetworksonlinkedstatisticaldataforpredictingscottishhouseprices|2023,1,2802,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,7,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6c26a3f40ccaee5e15fb0e565c9bfda58b146b8e,10.1145/3589860.3589878,,Prediction and Analysis of House Prices in Boston Based on Regression Model,2022.0,EBEE,Zuo-Han Chen,"In artificial intelligence learning, Boston housing price forecast analysis problem is a classic regression problem. Based on the housing price information collected by the U.S. Census Bureau in Boston, Massachusetts. this paper divides the housing price data set of Boston and builds the regression model linear regression, decision tree regression and support vector machine regression SVR and trains the data set, so as to obtain the relationship between different data related to Boston house price, and use this relationship to connect all data, it can finally predict the future house price trend in Boston and display it through visual operation. Through three regression model prediction value, respectively compared with the actual value, the trend of overall and actual and estimated values of the same, but there is a certain error, especially when spot prices higher or lower, often cannot get accurate forecast, so the data for the selection of the characteristic value still exists space for improvement, future study needs to get more data and the characteristics of abundant data.",0.0,https://dl.acm.org/doi/pdf/10.1145/3589860.3589878,True,JournalArticle;Book,Computer Science,2022-12-14,6c26a3f40ccaee5e15fb0e565c9bfda58b146b8e,predictionandanalysisofhousepricesinbostonbasedonregressionmodel,chen,10.1145/3589860.3589878,predictionandanalysisofhousepricesinbostonbasedonregressionmodel|2022,1,2596,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",443e7fe34292b6f8546b99029a7a08965f8ec810,10.1145/3442536.3442552,,Effective Tuning of Regression Models using an Evolutionary Approach: A Case Study,2020.0,Artificial Intelligence and Cloud Computing Conference,Séamus Lankford,"Hyperparameters enable machine learning algorithms to be customized for specific datasets. Choosing the right hyperparameters is a challenge often faced by machine learning practitioners. With this research, tuning of hyperparameters for regression models was explored. Models predicting house prices in King County were created using a detailed suite of regression algorithms. Traditional approaches, and evolutionary algorithms, for improving model accuracy were evaluated. A variety of feature selection methods and hyperparameter tuning using grid search, random search and pipeline optimization were also studied as part of the traditional approaches. Furthermore, evolutionary algorithms were applied to model optimization. In this paper, it is shown that an evolutionary approach, implemented with TPOT, achieves the highest accuracy for a regression model based on the King County dataset. Regarding metrics, combining the RMSE and metrics is shown to be an effective means of determining model accuracy. Finally, greedy feature selection performed best when a variety of feature selection methods are compared.",5.0,https://dl.acm.org/doi/pdf/10.1145/3442536.3442552,True,JournalArticle;Book,Computer Science,2020-12-18,443e7fe34292b6f8546b99029a7a08965f8ec810,effectivetuningofregressionmodelsusinganevolutionaryapproachacasestudy,lankford,10.1145/3442536.3442552,effectivetuningofregressionmodelsusinganevolutionaryapproachacasestudy|2020,1,2619,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f6fd138d77496bedd9ea96a5c5ef586a67aea9f3,10.36898/001c.22311,,House Price Prediction With Statistical Analysis in Support Vector Machine Learning for Regression Estimation,2021.0,Curiosity: Interdisciplinary Journal of Research and Innovation,Cesar A. Vasquez; V. Chellamuthu,"Support vector learning machines are supervised models that analyze data for classification analysis. However, with modifications these models can be extended for regression estimation. In this paper, we use Dean De Cock's Iowa house price dataset, composed of quantitative and qualitative variables, to build a predictive house pricing model. We employ a systematic approach to preparing the data and processing the features using statistical analysis. To reduce model over-fitting, we use stratified k-fold cross-validation. We also use various metrics for measuring model performance including r-squared (*R*^2^) and mean absolute error (MAE). Moreover, we explore various kernels for hyper-dimensional feature mapping to account for data that is not linearly separable. We compare the kernel performance and find that there is a trade-off between model complexity and performance. Furthermore, there also exists a trade-off between maximum accuracy achievable and general model performance.",3.0,https://curiosity.scholasticahq.com/article/22311.pdf,True,,Computer Science,2021-04-20,f6fd138d77496bedd9ea96a5c5ef586a67aea9f3,housepricepredictionwithstatisticalanalysisinsupportvectormachinelearningforregressionestimation,vasquez,10.36898/001c.22311,housepricepredictionwithstatisticalanalysisinsupportvectormachinelearningforregressionestimation|2021,1,2494,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",329258cc685812b876c11a76ec856edabbf485ca,10.70917/ijcisim-2026-4497,,Predictive Modeling of Real Estate Prices Using Machine Learning Techniques,2026.0,International journal of computer information systems and industrial management applications,Mahesh Mahajan; Makrand Shahade; Aishwarya Nikam; Paresh Wani; Bhagyashri A. Patil; C. Patil,"Accurately estimating residential property prices is a challenging yet essential task in today’s changing market envi-ronment. Conventional manual valuation methods often result in inconsistencies; therefore, data-driven approaches have increased importance. This research focuses on developing a regression model using machine learning that can predict house prices and assist in informed decision-making within the real estate industry. The Ames, Iowa dataset from Kaggle contains 1,460 houses with 81 features that include lot size, neighborhood, construction year, and overall quality. Data pre processing involved handling missing values in numerical fields with median imputation and in categorical fields with mode imputation, followed by the application of one-hot encoding and standardization of features using the z-score.The regression algorithms Random Forest, Linear Regression, Decision Tree, Support Vector Regression, and K-Nearest Neigh-bors were trained on an 80/20 train-test split and evaluated using the metrics R2, RMSE, and MAE. Among these, the Random Forest model performed best with R2 ≈ 0.891, RMSE ≈ 28,872, and MAE ≈ 17,614. On the other hand, the weakest performance was observed for Support Vector Regression. The final Random Forest model was deployed as a web application using Streamlit, allowing users to input features and obtain instant predictions of property prices. Overall, the study showed that advanced regression methods and effective feature engineering significantly contribute to improving real estate price forecasts.",0.0,https://cspub-ijcisim.org/index.php/ijcisim/article/download/4497/3613,True,JournalArticle,,2026-08-08,329258cc685812b876c11a76ec856edabbf485ca,predictivemodelingofrealestatepricesusingmachinelearningtechniques,mahajan,10.70917/ijcisim-2026-4497,predictivemodelingofrealestatepricesusingmachinelearningtechniques|2026,1,3066,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3aa30685de2f57df81e909bf3c0e16720351793c,10.4236/jwarp.2009.14029,,Modelling the Financial Value of the Maroochy River to Property Values: An Application of Neural Networks,2009.0,,A. Higgins; L. Pearson; L. Laredo,"The Maroochy River, which is located on east coast of Australia, provides a variety of uses and values to the community. Changes in structure, function and management of the river will influence the value that the community derives from it. Therefore, critical to the river’s continued management is the development of policy relevant tools based on the community’s value of the river. This paper focuses on estimating the fi-nancial value the local residents derive from living close to the river through investigation of changes in residential property values due to attributes of the Maroochy River. It is a complex analysis since there are several confounding geographical and property variables. Given a large and complete dataset of 28,000 properties for the Maroochy region, Artificial Neural Networks (ANN) was applied to estimate the economic value of the properties. This ANN was then able to simulate scenarios for property values with respect to changes in environmental features. It showed the Maroochy River contributed AU$900,000,000 to the unim-proved capital value of the whole region, a value that could not be estimated previously, and much higher than anticipated. Calculating potential annual payments to the Shire Council through land tax analysis from these property values, provides the council with means to justify expenditure to maintain a standard of water quality and ecosystem health.",3.0,https://content.scirp.org/pdf/jwarp20090400002_60799627.pdf,True,,Geography,2009-10-16,3aa30685de2f57df81e909bf3c0e16720351793c,modellingthefinancialvalueofthemaroochyrivertopropertyvaluesanapplicationofneuralnetworks,higgins,10.4236/jwarp.2009.14029,modellingthefinancialvalueofthemaroochyrivertopropertyvaluesanapplicationofneuralnetworks|2009,1,2914,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",31edffcb761f2438a7209dcebdd15d8bfca12426,10.63503/c.acset.2025.20,,HOUSE PRICE PREDICTION USING LINEAR REGRESSION,2025.0,Engineering & Technology,A. Dataset; Adarsh Sahani; Kundan Paswan,"Prediction of prices of houses is very important for buyers, sellers, and planners in the real estate market. In this paper, it is explained how Linear Regression can be used for estimating and predicting the prices of houses using the Ames Housing Dataset. Ames Housing Dataset includes data of 1460 houses and their 81 different features or attributes about the houses. We first cleaned missing data of the dataset using simple methods like replacing them with median or most common values. Then we trained the Linear Regression Model and tested it. Our model resulted in 0.82 score for R², 22417 for MAE, and an RMSE of about 36,879. From this, we understand that even a simple model like Linear Regression can work well in predicting prices of houses. In the end, we also discussed various studies related to our work and also included how we can improve this system in the future.",0.0,https://conferenceseries.adroidjournals.com/index.php/ACSET/article/download/24/18,True,JournalArticle;Conference,,2025-11-24,31edffcb761f2438a7209dcebdd15d8bfca12426,housepricepredictionusinglinearregression,dataset,10.63503/c.acset.2025.20,housepricepredictionusinglinearregression|2025,1,2385,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",654004961a5a2cbe10a8d52513219e45d59b7258,10.31530/cjnst.2025.1.1.2,,A Newly Proposed Hybrid Model for Predicting Real Estate Stock Prices in Kurdistan Region of Iraq,2025.0,Charmo Journal of Natural Sciences and Technologies,Y. Juma; Bryar Hassan,"Background: Accurate prediction of real estate stock prices plays a significant role in enabling investment choices, econometric forecasting, and strategic investment decisions in emerging markets. The Kurdistan Region of Iraq is among the emerging markets that are marked by non-homogeneity of data, limited representativeness, and non-availability of standardized appraisal methods. Aims: This study aims to develop and test a stable hybrid machine learning model for predicting real estate stock prices in the Kurdistan region of Iraq. The new approach highlights methodological quality, representativeness of the dataset, and replicability in a bid to fill the gaps observed in the existing literature. Methodology: The research makes use of the Kurdistan House Price Prediction (KHPP) data, which has various property features. Preprocessing of the data involved leakage-secure feature encoding, missingness checks, and deduplication verification. A combined model of Random Forest (RF) and Extreme Gradient Boosting (XGB) was used, alongside nested cross-validation for hyperparameter selection and testing. Statistical significance testing, bootstrapped confidence intervals, and feature ablation experiments were also performed for redundancy. Results: The findings further indicate that the hybrid RF+XGB model outclassed individual models according to Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). While performance differences remained minute, the hybrid model always returned stable results per fold. Evaluation also observed challenges interpreting R2 and emphasized careful reporting for the raw target scale. The study also demonstrated an externally available benchmarking plan using the Ames dataset for cross-national comparability. Conclusion: The new hybrid approach provides a systematic, replicable, and efficient framework for real estate stock price prediction within the Kurdistan Region of Iraq. By adding robust validation protocols and statistical testing, this study enhances belief in hybrid ensemble methods for under-represented markets. Future research must move beyond single-modal data and local criteria for greater generalizability and real-world applications.",0.0,https://cjnst.chu.edu.iq/index.php/cjnst/article/download/15/3,True,JournalArticle,,2025-11-23,654004961a5a2cbe10a8d52513219e45d59b7258,anewlyproposedhybridmodelforpredictingrealestatestockpricesinkurdistanregionofiraq,juma,10.31530/cjnst.2025.1.1.2,anewlyproposedhybridmodelforpredictingrealestatestockpricesinkurdistanregionofiraq|2025,1,3712,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a483774defcbadf7676aaa742f594af28562b13a,10.1108/ijhma-11-2022-0172,,Predictability of Belgian residential real estate rents using tree-based ML models and IML techniques,2023.0,International Journal of Housing Markets and Analysis,Ian Lenaers; K. Boudt; L. De Moor," Purpose The purpose is twofold. First, this study aims to establish that black box tree-based machine learning (ML) models have better predictive performance than a standard linear regression (LR) hedonic model for rent prediction. Second, it shows the added value of analyzing tree-based ML models with interpretable machine learning (IML) techniques. Design/methodology/approach Data on Belgian residential rental properties were collected. Tree-based ML models, random forest regression and eXtreme gradient boosting regression were applied to derive rent prediction models to compare predictive performance with a LR model. Interpretations of the tree-based models regarding important factors in predicting rent were made using SHapley Additive exPlanations (SHAP) feature importance (FI) plots and SHAP summary plots. Findings Results indicate that tree-based models perform better than a LR model for Belgian residential rent prediction. The SHAP FI plots agree that asking price, cadastral income, surface livable, number of bedrooms, number of bathrooms and variables measuring the proximity to points of interest are dominant predictors. The direction of relationships between rent and its factors is determined with SHAP summary plots. In addition to linear relationships, it emerges that nonlinear relationships exist. Originality/value Rent prediction using ML is relatively less studied than house price prediction. In addition, studying prediction models using IML techniques is relatively new in real estate economics. Moreover, to the best of the authors’ knowledge, this study is the first to derive insights of driving determinants of predicted rents from SHAP FI and SHAP summary plots. ",16.0,https://biblio.ugent.be/publication/01HHQGWGR372B5AP17MNYSHQ4T/file/01HHQH5547HMY0M017VDZ0QBVV.pdf,True,JournalArticle,,2023-04-13,a483774defcbadf7676aaa742f594af28562b13a,predictabilityofbelgianresidentialrealestaterentsusingtreebasedmlmodelsandimltechniques,lenaers,10.1108/ijhma-11-2022-0172,predictabilityofbelgianresidentialrealestaterentsusingtreebasedmlmodelsandimltechniques|2023,1,3213,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || house prices proximity hedonic || real estate hedonic regression || residential property price determinants",2,3,3,6,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1f7c276c0e09e75740719a9d1d7d1f4118ce73b6,10.56632/bct.2025.4103,,Machine Learning Impact on Modern Business Intelligence,2025.0,"Journal of Business, Communication & Technology",R. Moghaddas; Farinaz Tanhaei; Maryam Almoqbali; S. Safari,"The incorporation of machine learning (ML) approaches into business intelligence (BI) results in a great impact on fields that require predictive analysis, such as the real estate market. An accurate prediction of housing prices can provide benefits to stakeholders such as developers, investors, and policy planners. This study aims to explore the application of ML techniques to property valuation by creating a dataset based on real-world house price data collected from various areas in Muscat, Oman. Several ML models, including Linear Regression, Ridge Regression, Gradient Boosting, Random Forest, and Support Vector Regression, were applied and examined on the created dataset to estimate the house prices. Besides, hyperparameter tuning is used for each model in order to improve their predictive accuracy. Finally, we assessed the performance of each model using standard evaluation metrics, i.e., Mean Absolute Error (MAE), Mean Squared Error (MSE), and the R-squared (R²) score. The findings of this research work provide a comparative analysis of model efficiency that highlights both the capabilities and limitations of each model. This study demonstrates the practical power of ML techniques in real-state analytics and its wider applicability in improving BI systems subsequently.",0.0,https://bctjournal.com/article_461_9b60b778a55f888f512cd0ebaba7c5d4.pdf,True,JournalArticle,,,1f7c276c0e09e75740719a9d1d7d1f4118ce73b6,machinelearningimpactonmodernbusinessintelligence,moghaddas,10.56632/bct.2025.4103,machinelearningimpactonmodernbusinessintelligence|2025,1,2796,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b44b9bfe7022079272b30265c977affe0a9faa60,10.54691/v6hkjm84,,Enhancing Housing Price Prediction Accuracy through Hybrid POI-XGBoost Models: A Case Study of Nanjing,2026.0,Scientific Journal of Economics and Management Research,Lei Sun," [Purpose] This study aims to improve the precision of second-hand housing price prediction by overcoming the limitations of traditional statistical data, such as poor timeliness and authenticity.[Method] Utilizing Python web crawlers, this paper collects housing data from Lianjia and processes neighborhood characteristics using Baidu POI (Points of Interest) data. Three models are constructed and compared: Vector Autoregression (VAR), Random Forest, and the proposed POI-XGBoost model. [Findings] The empirical results based on Nanjing [Purpose] This study aims to improve the precision of second-hand housing price prediction by overcoming the limitations of traditional statistical data, such as poor timeliness and authenticity.[Method] Utilizing Python web crawlers, this paper collects housing data from Lianjia and processes neighborhood characteristics using Baidu POI (Points of Interest) data. Three models are constructed and compared: Vector Autoregression (VAR), Random Forest, and the proposed POI-XGBoost model. [Findings] The empirical results based on Nanjing data demonstrate that the XGBoost model, enhanced with POI features, achieves superior predictive performance (of 0.952), significantly outperforming traditional linear and random forest models. [Originality] This research provides a novel methodological framework for integrating multi-source geospatial data with gradient boosting algorithms to capture the non-linear impact of neighborhood amenities on housing values. data demonstrate that the XGBoost model, enhanced with POI features, achieves superior predictive performance (of 0.952), significantly outperforming traditional linear and random forest models. [Originality] This research provides a novel methodological framework for integrating multi-source geospatial data with gradient boosting algorithms to capture the non-linear impact of neighborhood amenities on housing values.",0.0,https://bcpublication.org/index.php/SJEMR/article/download/9337/9272,True,JournalArticle,,2026-03-31,b44b9bfe7022079272b30265c977affe0a9faa60,enhancinghousingpricepredictionaccuracythroughhybridpoixgboostmodelsacasestudyofnanjing,sun,10.54691/v6hkjm84,enhancinghousingpricepredictionaccuracythroughhybridpoixgboostmodelsacasestudyofnanjing|2026,1,3427,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c11e705a12510f4918740f154d27fe1d0c10d98e,10.54691/bcpbm.v38i.4204,,Boston House Price Prediction Based on Machine Leaning Methods,2023.0,BCP Business & Management,Ze Li,"A significant part of the U.S. economy is real estate and the housing industry. The level of housing prices influences the market's total price level to some extent. Data from Kaggle website is evaluated by using linear regression, random forest regressor and SVM regressor. There are 505 samples and the relationships between aim price with 13 different features. In terms of accuracy of the prediction, the method random forest is best method to predict house price. However, better model like XGboost could be chosen to improve prediction results.",2.0,https://bcpublication.org/index.php/BM/article/download/4204/4095,True,,,2023-03-02,c11e705a12510f4918740f154d27fe1d0c10d98e,bostonhousepricepredictionbasedonmachineleaningmethods,li,10.54691/bcpbm.v38i.4204,bostonhousepricepredictionbasedonmachineleaningmethods|2023,1,2050,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5cd7c4cc4e12a3e5eb5ced3a240c0b684e34f4cd,10.54691/bcpbm.v38i.4014,,Price Prediction of Ames Housing Through Advanced Regression Techniques,2023.0,BCP Business & Management,Yueting Han,"House price fluctuates at all times, and it is necessary to predict the future house prices for the reason that it is crucial for buyers, property investors and the economy as a whole. This article reveals the important features relating to house price and demonstrates the method of predicting house prices in Ames, Iowa through advanced regression techniques, such as correlation, feature engineering, and model building. The prediction is performed through machine learning methods based on the dataset containing almost all the features of residential houses in Ames, Iowa. The dataset was originally splitted into the ‘train set’ and the ‘test set’. Six models were chosen, LASSO Regression, Elastic Net Regression, Gradient Boosting Regression, XBoost, LightGBM, and Stacked Model, for evaluation under the five-fold cross-validation to check the performance of the algorithms and report the root-mean-square logarithmic error. Eventually, Gradient Boosting Regression Model is chosen by the lowest root-mean-square logarithmic error of 0.04615 and a low cross-validated value of 0.1174 to predict the house price to benefit buyers, investors, and the whole society.",2.0,https://bcpublication.org/index.php/BM/article/download/4014/3908,True,,,2023-03-02,5cd7c4cc4e12a3e5eb5ced3a240c0b684e34f4cd,pricepredictionofameshousingthroughadvancedregressiontechniques,han,10.54691/bcpbm.v38i.4014,pricepredictionofameshousingthroughadvancedregressiontechniques|2023,1,2672,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",217a40f1cfff83bcf4b3c22d38b8813122cb031d,10.54691/bcpbm.v38i.3720,,Real Estate Price Prediction Based on Linear Regression and Machine Learning Scenarios,2023.0,BCP Business & Management,Ting Mao,"The changes in the housing market are not only related to human beings’ daily life, but also have an important impact on the national economy. The prediction of housing price is one of the most widely concerned topics, which is linked to the formulation of national real estate policies and the analysis of the economic situation. In this context, this paper takes housing price prediction as the topic, selects the Eames housing price dataset in Iowa, and uses supervised multiple linear regression and machine learning algorithm to train and test the real estate price prediction model. Among them, there are 79 explanatory variables, which are related to housing attributes and the explanatory variable is housing price. 1460 data is included in the training set and 1459 in the test set. In the part of machine learning algorithm, PaddlePaddle deep learning framework is used in this paper to train and test the models with the help of AI Studio platform. The experimental results show that the scatter plots of the real values are clustered and distributed on both sides of the predicted line, and their direct differences are within 30 points. According to the analysis, the real estate price prediction model based on linear regression and machine learning is reliable and stable. This paper aims to provide some suggestions for the housing price prediction. These results shed light on guiding the reference direction for investors, so as to guide the formulation of relevant policies.",3.0,https://bcpublication.org/index.php/BM/article/download/3720/3636,True,,,2023-03-02,217a40f1cfff83bcf4b3c22d38b8813122cb031d,realestatepricepredictionbasedonlinearregressionandmachinelearningscenarios,mao,10.54691/bcpbm.v38i.3720,realestatepricepredictionbasedonlinearregressionandmachinelearningscenarios|2023,1,2993,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",99263269f37367d876d05cc14bef78f47e159749,10.54691/bcpbm.v36i.3378,,The real estate price prediction of US prediction based on multi-factorial linear regression models,2023.0,BCP Business & Management,Xiaoyu Xu,"In every period, housing price prediction has always been a fascinating topic. Fluctuations in housing price are not only relevant to each individual resident but also to the politics and economy of the country. This essence of this research project is the usage of some real influencing factors to predict housing prices. In the Ames Housing dataset from Kaggle.com, five real factors that have a relatively strong correlation with housing prices are the overall material and finish quality, the above ground living area, the size of garage in car capacity, the garage area, and the total basement area. Based on these five real factors, two multiple linear regression models are constructed for predicting residential prices in Ames, Lowa, US. According to the analysis, when two independent variables are closely related, removing one of them does not necessarily reduce the fit of the model significantly, even if both independent variables are closely related to housing price. Therefore, choosing more appropriate variables is very important to increase the fit of the model. These results shed light on guiding further exploration of using more significant variables to find more accurate models to fit actual housing prices.",3.0,https://bcpublication.org/index.php/BM/article/download/3378/3317,True,,,2023-01-13,99263269f37367d876d05cc14bef78f47e159749,therealestatepricepredictionofuspredictionbasedonmultifactoriallinearregressionmodels,xu,10.54691/bcpbm.v36i.3378,therealestatepricepredictionofuspredictionbasedonmultifactoriallinearregressionmodels|2023,1,2732,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e8599af6eb800df4353596ffebf7e076df2aa479,10.54691/bcpbm.v32i.2956,,Research on the Prediction of US House Prices Based on Machine Learning,2022.0,BCP Business & Management,Yun-Ling He,"With the increase in the standard, houses have become an indispensable part of people’s lives. However, purchasing houses needs to consider the prices. Therefore, this paper uses machine learning to predict US house prices. Based on the random forest and linear regression method used in this research. It is found that the use of the former has a good effect on predicting multivariate nonlinear relationships in house prices. After comparing the results of actual prices and predicting prices, residents can select different room structures by contrasting them with experimental types as the numerical results are basically accurate. This research provides theoretical value to the literature on housing price prediction and brings different enlightenment to policymakers, regulators, and investors. As a result, this research plays a key role in maintaining the order of the real estate market and helping people make good choices.",0.0,https://bcpublication.org/index.php/BM/article/download/2956/2918,True,,,2022-11-22,e8599af6eb800df4353596ffebf7e076df2aa479,researchonthepredictionofushousepricesbasedonmachinelearning,he,10.54691/bcpbm.v32i.2956,researchonthepredictionofushousepricesbasedonmachinelearning|2022,1,2434,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b1203923f561909463d777893f5a8b9962437278,10.54691/bcpbm.v32i.2881,,Research on the House Price Forecast Based on machine learning algorithm,2022.0,BCP Business & Management,W.-S. Weng,"House price experiences some fluctuations every year, due to some potential factors such as location, area, facilities and so on. Housing price prediction is a significant topic of real estate, and it is beneficial for buyers to make strategy decisions about house dealing. There are many research on house price forecast, yet the current research cannot comprehensively compare and analyze the popular house price prediction approach. Constructing a model begins with pre-processing data to fill null values or remove data outliers and the categorical attribute can be shifted into required attributes by using one hot encoder methodology. This paper used the following five algorithms decision tree, random forest regression, Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), and extreme gradient boosting (XGBoost) this paper utilized to predict house prices and compared according to the root mean squared error. This paper found GBDT and XGBoost have more accurate prediction results compared with other algorithms. Besides, this paper found which features most affect the price of a house. In real-world applications, machine learning based housing price prediction models are utilized by banks and financial institutions to obtain better house price assessment, risk analysis and lending decisions.",7.0,https://bcpublication.org/index.php/BM/article/download/2881/2847,True,,,2022-11-22,b1203923f561909463d777893f5a8b9962437278,researchonthehousepriceforecastbasedonmachinelearningalgorithm,weng,10.54691/bcpbm.v32i.2881,researchonthehousepriceforecastbasedonmachinelearningalgorithm|2022,1,2825,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic property valuation,d9970188d1f09336dc963fed1a447c899751e7a4,10.1108/jerer-08-2018-0035,,Estimation and updating methods for hedonic valuation,2019.0,Journal of European Real Estate Research,Michael Mayer; Steven C. Bourassa; M. Hoesli; D. Scognamiglio," Purpose The purpose of this paper is to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models. Design/methodology/approach The authors apply six estimation methods (linear least squares, robust regression, mixed-effects regression, random forests, gradient boosting and neural networks) and two updating methods (moving and extending windows). They use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017. Findings The gradient boosting method yields the greatest accuracy, while the robust method provides the least volatile predictions. There is a clear trade-off across methods depending on whether the goal is to improve accuracy or avoid volatility. The choice between moving and extending windows has only a modest effect on the results. Originality/value This paper compares a range of linear and machine learning techniques in the context of moving or extending window scenarios that are used in practice but which have not been considered in prior research. The techniques include robust regression, which has not previously been used in this context. The data updating allows for analysis of the volatility in addition to the accuracy of predictions. The results should prove useful in improving hedonic models used by property tax assessors, mortgage underwriters, valuation firms and regulatory authorities. ",62.0,https://aura.abdn.ac.uk/bitstreams/001f368d-c052-4950-8b1f-709dbded807a/download,True,,Computer Science,2019-05-07,d9970188d1f09336dc963fed1a447c899751e7a4,estimationandupdatingmethodsforhedonicvaluation,mayer,10.1108/jerer-08-2018-0035,estimationandupdatingmethodsforhedonicvaluation|2019,1,2951,1,hedonic property valuation,2,1,3,6,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",e87fc9647ef15c29aa82b112fd85bb656732c47e,10.1080/09599916.2020.1861066,,Automated Valuation Services: A case study for Aberdeen in Scotland,2021.0,Journal of Property Research,Rainer Schulz; Martin Wersing,"ABSTRACT Automated valuation services (AVSs) offered by listings platforms predict market values based on property characteristics supplied by users. We investigate the implementation of such a service for the City of Aberdeen. We fit different market value models with machine learning methods and assess them in a rolling windows procedure that mimics an AVS setting. We also investigate the ease and robustness with which the models can be implemented. We discuss how prediction uncertainty can be measured and reported to users. If implemented in the future, such a service has the potential to improve the transparency of the local housing market.",6.0,https://aura.abdn.ac.uk/bitstream/2164/18150/1/Schulz_et_al_JPR_AutomatedValuationServices_AAM.pdf,True,,Business,2021-03-01,e87fc9647ef15c29aa82b112fd85bb656732c47e,automatedvaluationservicesacasestudyforaberdeeninscotland,schulz,10.1080/09599916.2020.1861066,automatedvaluationservicesacasestudyforaberdeeninscotland|2021,1,2152,1,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 arxiv,real estate valuation deep learning,,10.1007/978-3-032-06118-8_12,2506.11812,On the Performance of LLMs for Real Estate Appraisal,2025.0,arXiv,Margot Geerts; Manon Reusens; Bart Baesens; Seppe vanden Broucke; Jochen De Weerdt,"The real estate market is vital to global economies but suffers from significant information asymmetry. This study examines how Large Language Models (LLMs) can democratize access to real estate insights by generating competitive and interpretable house price estimates through optimized In-Context Learning (ICL) strategies. We systematically evaluate leading LLMs on diverse international housing datasets, comparing zero-shot, few-shot, market report-enhanced, and hybrid prompting techniques. Our results show that LLMs effectively leverage hedonic variables, such as property size and amenities, to produce meaningful estimates. While traditional machine learning models remain strong for pure predictive accuracy, LLMs offer a more accessible, interactive and interpretable alternative. Although self-explanations require cautious interpretation, we find that LLMs explain their predictions in agreement with state-of-the-art models, confirming their trustworthiness. Carefully selected in-context examples based on feature similarity and geographic proximity, significantly enhance LLM performance, yet LLMs struggle with overconfidence in price intervals and limited spatial reasoning. We offer practical guidance for structured prediction tasks through prompt optimization. Our findings highlight LLMs' potential to improve transparency in real estate appraisal and provide actionable insights for stakeholders.",,https://arxiv.org/pdf/2506.11812v1,True,preprint,cs.AI;cs.CL;cs.LG,2025-06-13,doi_10_1007_978_3_032_06118_8_12,ontheperformanceofllmsforrealestateappraisal,geerts,10.1007/978-3-032-06118-8_12,ontheperformanceofllmsforrealestateappraisal|2025,1,2920,9,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || house prices proximity hedonic || machine learning hedonic pricing housing || real estate valuation deep learning || spatial hedonic model housing",2,1,2,4,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d7a3927620ebe5d6fd80dd8ffdc9b49395fc8b9f,10.1016/j.cities.2026.107063,2506.09539,"Disentangling spatial and structural drivers of housing prices through Bayesian networks: A case study of Madrid, Barcelona, and Valencia",2025.0,Cities,Alvaro Garcia Murga; Manuele Leonelli,"Understanding how housing prices respond to spatial accessibility, structural attributes, and typological distinctions is central to contemporary urban research and policy. In cities marked by affordability stress and market segmentation, models that offer both predictive capability and interpretive clarity are increasingly needed. This study applies discrete Bayesian networks to model residential price formation across Madrid, Barcelona, and Valencia using over 180,000 geo-referenced housing listings. The resulting probabilistic structures reveal distinct city-specific logics. Madrid exhibits amenity-driven stratification, Barcelona emphasizes typology and classification, while Valencia is shaped by spatial and structural fundamentals. By enabling joint inference, scenario simulation, and sensitivity analysis within a transparent framework, the approach advances housing analytics toward models that are not only accurate but actionable, interpretable, and aligned with the demands of equitable urban governance.",3.0,https://arxiv.org/pdf/2506.09539,True,JournalArticle,Mathematics,2025-06-11,d7a3927620ebe5d6fd80dd8ffdc9b49395fc8b9f,disentanglingspatialandstructuraldriversofhousingpricesthroughbayesiannetworksacasestudyofmadridbarcelonaandvalencia,murga,10.1016/j.cities.2026.107063,disentanglingspatialandstructuraldriversofhousingpricesthroughbayesiannetworksacasestudyofmadridbarcelonaandvalencia|2025,1,2525,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d8442d8e8df30b62544eab224ca0fde684836add,10.1145/3715335.3735485,2505.01591,Machine Learning Fairness in House Price Prediction: A Case Study of America’s Expanding Metropolises,2025.0,The Compass,Abdalwahab Almajed; Maryam Tabar; Peyman Najafirad,"As a basic human need, housing plays a key role in enhancing health, well-being, and educational outcome in society, and the housing market is a major factor for promoting quality of life and ensuring social equity. To improve the housing conditions, there has been extensive research on building Machine Learning (ML)-driven house price prediction solutions to accurately forecast the future conditions, and help inform actions and policies in the field. In spite of their success in developing high-accuracy models, there is a gap in our understanding of the extent to which various ML-driven house price prediction approaches show ethnic and/or racial bias, which in turn is essential for the responsible use of ML, and ensuring that the ML-driven solutions do not exacerbate inequity. To fill this gap, this paper develops several ML models from a combination of structural and neighborhood-level attributes, and conducts comprehensive assessments on the fairness of ML models under various definitions of privileged groups. As a result, it finds that the ML-driven house price prediction models show various levels of bias towards protected attributes (i.e., race and ethnicity in this study). Then, it investigates the performance of different bias mitigation solutions, and the experimental results show their various levels of effectiveness on different ML-driven methods. However, in general, the in-processing bias mitigation approach tends to be more effective than the pre-processing one in this problem domain. Our code is available at https://github.com/wahab1412/housing_fairness.",3.0,https://arxiv.org/pdf/2505.01591,True,JournalArticle;Book,Computer Science,2025-05-02,d8442d8e8df30b62544eab224ca0fde684836add,machinelearningfairnessinhousepricepredictionacasestudyofamericasexpandingmetropolises,almajed,10.1145/3715335.3735485,machinelearningfairnessinhousepricepredictionacasestudyofamericasexpandingmetropolises|2025,1,3095,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 arxiv,hedonic house price,,10.20944/preprints202412.1927.v1,2501.06241,Predicting House Rental Prices in Ghana Using Machine Learning,2025.0,arXiv,Philip Adzanoukpe,"This study investigates the efficacy of machine learning models for predicting house rental prices in Ghana, addressing the need for accurate and accessible housing market information. Utilising a comprehensive dataset of rental listings, we trained and evaluated various models, including CatBoost, XGBoost, and Random Forest. CatBoost emerged as the best-performing model, achieving an $R^2$ of 0.876, demonstrating its ability to effectively capture complex relationships within the housing market. Feature importance analysis revealed that location-based features, number of bedrooms, bathrooms, and furnishing status are key drivers of rental prices. Our findings provide valuable insights for stakeholders, including real estate professionals, investors, and policymakers, while also highlighting opportunities for future research, such as incorporating temporal data and exploring regional variations.",,https://arxiv.org/pdf/2501.06241v1,True,preprint,cs.LG;stat.AP,2025-01-08,doi_10_20944_preprints202412_1927_v1,predictinghouserentalpricesinghanausingmachinelearning,adzanoukpe,10.20944/preprints202412.1927.v1,predictinghouserentalpricesinghanausingmachinelearning|2025,1,2408,4,hedonic house price || hedonic regression housing || house price prediction machine learning || housing price text listing,0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0b75eaf2598b7c9697584c08a8e7bc9181ab2cb9,10.1145/3357384.3357810,2411.18085,MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data,2019.0,International Conference on Information and Knowledge Management,Miao Fan; Jizhou Huang; An Zhuo; Ying Li; Ping Li; Haifeng Wang,"The value assessment of private properties is an attractive but challenging task which is widely concerned by a majority of people around the world. A prolonged topic among us is ""how much is my house worth?"". To answer this question, most experienced agencies would like to price a property given the factors of its attributes as well as the demographics and the public facilities around it. However, no one knows the exact prices of these factors, especially the values of public facilities which may help assess private properties. In this paper, we introduce our newly launched project ""Monopoly"" (named after a classic board game) in which we propose a distributed approach for revaluing private properties by learning to price public facilities (such as hospitals, schools, and metros) with the large-scale urban data we have accumulated via Baidu Maps. To be specific, our method organizes many points of interest (POIs) into an undirected weighted graph and formulates multiple factors including the virtual prices of surrounding public facilities as adaptive variables to parallelly estimate the housing prices we know. Then the prices of both public facilities and private properties can be iteratively updated according to the loss of prediction until convergence. We have conducted extensive experiments with the large-scale urban data of several metropolises in China. Results show that our approach outperforms several mainstream methods with significant margins. Further insights from more in-depth discussions demonstrate that the ""Monopoly"" is an innovative application in the interdisciplinary field of business intelligence and urban computing, and it will be beneficial to tens of millions of our users for investments and to the governments for urban planning as well as taxation.",4.0,https://arxiv.org/pdf/2411.18085,True,JournalArticle;Book;Conference,Computer Science,2019-11-03,0b75eaf2598b7c9697584c08a8e7bc9181ab2cb9,monopolylearningtopricepublicfacilitiesforrevaluingprivatepropertieswithlargescaleurbandata,fan,10.1145/3357384.3357810,monopolylearningtopricepublicfacilitiesforrevaluingprivatepropertieswithlargescaleurbandata|2019,1,3301,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic house price,ba924ed666dc6e80b91d654bd01646d03c73690f,10.48550/arxiv.2310.08133,2310.08133,Multi Level Dense Layer Neural Network Model for Housing Price Prediction,2023.0,arXiv.org,Robert Wijaya,"Predicting the price of a house remains a challenging issue that needs to be addressed. Research has attempted to establish a model with different methods and algorithms to predict the housing price, from the traditional hedonic model to a neural network algorithm. However, many existing algorithms in the literature are proposed without any finetuning and customization in the model. In this paper, the author attempted to propose a novel neural network-based model to improve the performance of housing price prediction. Inspired by the modular neural network, the proposed model consists of a three-level neural network that is capable to process information in parallel. The author compared several state-of-the-art algorithms available in the literature on the Boston housing dataset to evaluate the effectiveness of the proposed model. The results show that the proposed model provides better accuracy and outperforms existing algorithms in different evaluation metrics. The code for the implementation is available https://github.com/wijayarobert/MultiLevelDenseLayerNN",2.0,https://arxiv.org/pdf/2310.08133,True,JournalArticle,Computer Science,2023-10-12,ba924ed666dc6e80b91d654bd01646d03c73690f,multileveldenselayerneuralnetworkmodelforhousingpriceprediction,wijaya,10.48550/arxiv.2310.08133,multileveldenselayerneuralnetworkmodelforhousingpriceprediction|2023,1,2577,3,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic house price || hedonic housing prices",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 arxiv,hedonic house price,,10.1109/tmm.2020.2966890,2107.07148,What Image Features Boost Housing Market Predictions?,2021.0,arXiv,Zona Kostic; Aleksandar Jevremovic,"The attractiveness of a property is one of the most interesting, yet challenging, categories to model. Image characteristics are used to describe certain attributes, and to examine the influence of visual factors on the price or timeframe of the listing. In this paper, we propose a set of techniques for the extraction of visual features for efficient numerical inclusion in modern-day predictive algorithms. We discuss techniques such as Shannon's entropy, calculating the center of gravity, employing image segmentation, and using Convolutional Neural Networks. After comparing these techniques as applied to a set of property-related images (indoor, outdoor, and satellite), we conclude the following: (i) the entropy is the most efficient single-digit visual measure for housing price prediction; (ii) image segmentation is the most important visual feature for the prediction of housing lifespan; and (iii) deep image features can be used to quantify interior characteristics and contribute to captivation modeling. The set of 40 image features selected here carries a significant amount of predictive power and outperforms some of the strongest metadata predictors. Without any need to replace a human expert in a real-estate appraisal process, we conclude that the techniques presented in this paper can efficiently describe visible characteristics, thus introducing perceived attractiveness as a quantitative measure into the predictive modeling of housing.",,https://arxiv.org/pdf/2107.07148v1,True,preprint,cs.CV;cs.LG,2021-07-15,doi_10_1109_tmm_2020_2966890,whatimagefeaturesboosthousingmarketpredictions,kostic,10.1109/tmm.2020.2966890,whatimagefeaturesboosthousingmarketpredictions|2021,1,2966,6,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic house price || hedonic regression housing || housing price text listing",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d388466b2b927ed1ed1644529b5e2103d9f659ae,10.1007/s10618-021-00789-x,2009.00254,Boosting house price predictions using geo-spatial network embedding,2020.0,Data mining and knowledge discovery,Sarkar Snigdha Sarathi Das; M. E. Ali; Yuan-Fang Li; Yong-Bin Kang; T. Sellis,"Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed for real estate price prediction. Most of the existing techniques rely on different house features to build a variety of prediction models to predict house prices. Perceiving the effect of spatial dependence on house prices, some later works focused on introducing spatial regression models for improving prediction performance. However, they fail to take into account the geo-spatial context of the neighborhood amenities such as how close a house is to a train station, or a highly-ranked school, or a shopping center. Such contextual information may play a vital role in users’ interests in a house and thereby has a direct influence on its price. In this paper, we propose to leverage the concept of graph neural networks to capture the geo-spatial context of the neighborhood of a house. In particular, we present a novel method, the geo-spatial network embedding (GSNE), that learns the embeddings of houses and various types of points of interest (POIs) in the form of multipartite networks, where the houses and the POIs are represented as attributed nodes and the relationships between them as edges. Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model. Relevant source code for GSNE is available at: https://github.com/sarathismg/gsne.",41.0,https://arxiv.org/pdf/2009.00254,True,JournalArticle,Computer Science;Mathematics,2020-09-01,d388466b2b927ed1ed1644529b5e2103d9f659ae,boostinghousepricepredictionsusinggeospatialnetworkembedding,das,10.1007/s10618-021-00789-x,boostinghousepricepredictionsusinggeospatialnetworkembedding|2020,1,3207,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4867bed61e20f9da45708be5f539535b9751acc6,10.1109/tkde.2021.3112749,2008.0588,Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market,2020.0,IEEE Transactions on Knowledge and Data Engineering,Hao Peng; Jianxin Li; Z. Wang; Ren-Yu Yang; Ming-Sheng Liu; Ming-Ming Zhang; Philip S. Yu; Li-Fang He,"We present Luce, the first life-long predictive model for automated property valuation. Luce addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data. It is designed to operate on a limited volume of recent house transaction data. As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation. We employ a Graph Convolutional Network (GCN) to extract the spatial information from the HIN for house-related data like geographical locations, and then use a Long Short Term Memory (LSTM) network to model the temporal dependencies for house transaction data over time. Unlike prior work, Luce can make effective use of the limited house transactions data in the past few months to update valuation information for all house entities within the HIN. By providing a complete and up-to-date house valuation dataset, Luce thus massively simplifies the downstream valuation task for the targeting properties. We demonstrate the benefit of Luce by applying it to large, real-life datasets obtained from the Toronto real estate market. Extensive experimental results show that Luce not only significantly outperforms prior property valuation methods but also often reaches and sometimes exceeds the valuation accuracy given by independent experts when using the actual realization price as the ground truth.",45.0,https://arxiv.org/pdf/2008.05880,True,JournalArticle,Computer Science,2020-08-12,4867bed61e20f9da45708be5f539535b9751acc6,lifelongpropertypricepredictionacasestudyforthetorontorealestatemarket,peng,10.1109/tkde.2021.3112749,lifelongpropertypricepredictionacasestudyforthetorontorealestatemarket|2020,1,2997,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",edc964021ada960a4468e9307b3b58ad128417a6,10.1007/978-981-13-6661-1_13,1812.05486,Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning for Cross-City Property Appraisal Framework,2018.0,Australasian Data Mining Conference,Yi-Han Guo; Shan Lin; Xiao Ma; J. Bal; Chang-Tsun Li,"Most existing real estate appraisal methods focus on building accuracy and reliable models from a given dataset but pay little attention to the extensibility of their trained model. As different cities usually contain a different set of location features (district names, apartment names), most existing mass appraisal methods have to train a new model from scratch for different cities or regions. As a result, these approaches require massive data collection for each city and the total training time for a multi-city property appraisal system will be extremely long. Besides, some small cities may not have enough data for training a robust appraisal model. To overcome these limitations, we develop a novel Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning (HFT+HLF) cross-city property appraisal framework. By transferring partial neural network learning from a source city and fine-tuning on the small amount of location information of a target city, our semi-supervised model can achieve similar or even superior performance compared to a fully supervised Artificial neural network (ANN) method.",5.0,https://arxiv.org/pdf/1812.05486,True,JournalArticle,Computer Science,2018-11-28,edc964021ada960a4468e9307b3b58ad128417a6,homogeneousfeaturetransferandheterogeneouslocationfinetuningforcrosscitypropertyappraisalframework,guo,10.1007/978-981-13-6661-1_13,homogeneousfeaturetransferandheterogeneouslocationfinetuningforcrosscitypropertyappraisalframework|2018,1,2620,1,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c4de482d5c48a35197cf9d23d343a7385b3eb987,10.1109/icdm.2016.0134,1803.00919,House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis,2016.0,Industrial Conference on Data Mining,Bang Liu; B. Mavrin; Di Niu; Linglong Kong,"Online real-estate information systems such as Zillow and Trulia have gained increasing popularity in recent years. One important feature offered by these systems is the online home price estimate through automated data-intensive computation based on housing information and comparative market value analysis. State-of-the-art approaches model house prices as a combination of a latent land desirability surface and a regression from house features. However, by using uniformly damping kernels, they are unable to handle irregularly shaped regions or capture land value discontinuities within the same region due to the existence of implicit sub-communities, which are common in real-world scenarios. In this paper, we explore the novel application of recent advances in spatial functional analysis to house price modeling and propose the Hierarchical Spatial Functional Model (HSFM), which decomposes house values into land desirability at both the global scale and hidden local scales as well as the feature regression component. We propose statistical learning algorithms based on finite-element spatial functional analysis and spatial constrained clustering to train our model. Extensive evaluations based on housing data in a major Canadian city show that our proposed approach can reduce the mean relative house price estimation error down to 6.60%.",15.0,https://arxiv.org/pdf/1803.00919,True,JournalArticle;Conference,Computer Science,2016-12-01,c4de482d5c48a35197cf9d23d343a7385b3eb987,housepricemodelingoverheterogeneousregionswithhierarchicalspatialfunctionalanalysis,liu,10.1109/icdm.2016.0134,housepricemodelingoverheterogeneousregionswithhierarchicalspatialfunctionalanalysis|2016,1,2855,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic regression housing || house price prediction machine learning || housing price text listing",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 arxiv,hedonic house price,,10.1007/s00138-018-0922-2,1707.05489,Vision-based Real Estate Price Estimation,2017.0,arXiv,Omid Poursaeed; Tomas Matera; Serge Belongie,"Since the advent of online real estate database companies like Zillow, Trulia and Redfin, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow's estimates.",,https://arxiv.org/pdf/1707.05489v3,True,preprint,cs.CV;cs.LG,2017-07-18,doi_10_1007_s00138_018_0922_2,visionbasedrealestatepriceestimation,poursaeed,10.1007/s00138-018-0922-2,visionbasedrealestatepriceestimation|2017,1,2660,3,hedonic house price || housing price text listing || real estate valuation deep learning,0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 arxiv,real estate valuation deep learning,,10.1109/tmm.2017.2710804,1611.0918,Image Based Appraisal of Real Estate Properties,2016.0,arXiv,Quanzeng You; Ran Pang; Liangliang Cao; Jiebo Luo,"Real estate appraisal, which is the process of estimating the price for real estate properties, is crucial for both buys and sellers as the basis for negotiation and transaction. Traditionally, the repeat sales model has been widely adopted to estimate real estate price. However, it depends the design and calculation of a complex economic related index, which is challenging to estimate accurately. Today, real estate brokers provide easy access to detailed online information on real estate properties to their clients. We are interested in estimating the real estate price from these large amounts of easily accessed data. In particular, we analyze the prediction power of online house pictures, which is one of the key factors for online users to make a potential visiting decision. The development of robust computer vision algorithms makes the analysis of visual content possible. In this work, we employ a Recurrent Neural Network (RNN) to predict real estate price using the state-of-the-art visual features. The experimental results indicate that our model outperforms several of other state-of-the-art baseline algorithms in terms of both mean absolute error (MAE) and mean absolute percentage error (MAPE).",,https://arxiv.org/pdf/1611.09180v2,True,preprint,cs.CV;cs.LG,2016-11-28,doi_10_1109_tmm_2017_2710804,imagebasedappraisalofrealestateproperties,you,10.1109/tmm.2017.2710804,imagebasedappraisalofrealestateproperties|2016,1,2718,2,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || real estate valuation deep learning",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c1c06879bbd671d63e05c737e134d01c273b3e2b,10.11648/j.sd.20200803.12,,Linear Regression Model of House Price in Boston,2020.0,,Ying Niu,"The change of house price is a common phenomenon. People are eager to grasp the law of house price to become the winner of real estate investment. This paper uses Boston house price data to explore the relationship between Boston house price and which independent variables. This paper uses linear regression model to construct the relationship between housing prices and crime rate in Boston. First, the classical linear model is adopted. Then we do the collinearity test, removing the lever point and other operations, the residual of the model still does not conform to the normal distribution, so the classical linear model cannot describe the data very well. Then, we add the quadratic term and the cross term, and use the method of stepwise regression to get the optimal regression autoregressive quantum set. After removing the leverage point and significance test, we found that the residual distribution was approximately normal. It shows that the improved model has well described the law of data. Finally, according to the data, the main conclusions are as follows: house price and tax rate, index close to the highway and index close to the city center are inversely correlated, which is positively correlated with the number of rooms, the proportion of teachers and students, and whether it is close to the Charles River. In addition, the concentration of nitric oxide, the proportion of low-end population and crime rate also have a certain relationship with housing prices.",1.0,https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20200803.12.pdf,True,,Mathematics,2020-06-30,c1c06879bbd671d63e05c737e134d01c273b3e2b,linearregressionmodelofhousepriceinboston,niu,10.11648/j.sd.20200803.12,linearregressionmodelofhousepriceinboston|2020,1,2988,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",8f96caf1524ec200bf310b65044454a6a4cf27c6,10.15439/2026f2863,,Enhancing Automated Valuation Models with LLM-based Feature Engineering and Spatial Indices and a RAG-Integrated Web Application for Trójmiasto Residential Market,2026.0,Conference on Computer Science and Information Systems,Piotr Kłopotowski; Olgun Aydın,"This paper presents a methodology for building robust Automated Valuation Models (AVMs) for residential properties in the Tricity (Gdańsk, Gdynia, Sopot) metropolitan area. Utilising a dataset of over 54,000 listings scraped over a nine-month period. We proposed an enriched feature engineering pipeline that integrates spatial indices (Points of Interest and public transport proximity) with unstructured data extracted via Large Language Models (LLMs). We evaluated the performance of XGBoost and random forest algorithms for both sale and rental price prediction. Results indicate that while traditional features like size and location remain dominant, the inclusion of custom spatial indices and LLM-extracted variables, such as utility fees for rentals, provides measurable improvements in model precision. Additionally, the paper focuses on a web application for analysing the residential market and outlines a practical application of RAG implementation for enhanced residential listing search based on the description of listings through the web application.",0.0,https://annals-csis.org/Volume_47/drp/pdf/2863.pdf,True,Conference,,2026-08-23,8f96caf1524ec200bf310b65044454a6a4cf27c6,enhancingautomatedvaluationmodelswithllmbasedfeatureengineeringandspatialindicesandaragintegratedwebapplicationfortrojmiastoresidentialmarket,kłopotowski,10.15439/2026f2863,enhancingautomatedvaluationmodelswithllmbasedfeatureengineeringandspatialindicesandaragintegratedwebapplicationfortrojmiastoresidentialmarket|2026,1,2566,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || automated valuation model residential",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d8b7e0af98ec6441abd51af97f512015ea6ae69f,10.32996/jbms.2024.6.5.9,,Explainable AI and Machine Learning Model for California House Price Predictions: Intelligent Model for Homebuyers and Policymakers,2024.0,Journal of business and management studies,Azam Khan; Pravakar Debnath; Abdullah Al Sayeed; Md Fakhrul Islam Sumon; Arifur Rahman; Tushar Khan; Laxmi Pant,"California's housing and real estate market is one of the most valuable markets in the USA. Many shareholders such as individual homebuyers, home sellers, real estate agents, lenders, and policymakers depend on high-volume information regarding the dynamics at work and their correct estimation. The research project aimed at developing an Explainable AI machine-learning model for California house price predictions. Data on house prices were collected from reliable sources such as California home estate websites, land sites, and public datasets. Features of the data included location, size, number of rooms, area type, availability, sale prices, and oceanic proximity. In this research project, credible, proven, and renowned machine learning algorithms were used most notably, Linear Regression analysis, XG-Boost, and Random Forest. The Random Forest came up quite impressively with a superior accuracy score and low MAE and MSE; thus, it was good for learning the underlying best patterns and relationships that may exist within the data for house price predictions. XG-Boost also did relatively well, showcasing moderately high accuracy and relatively low MSE and MAE, compared to the Linear Regression.",11.0,https://al-kindipublisher.com/index.php/jbms/article/download/7929/6651,True,JournalArticle,,2024-09-14,d8b7e0af98ec6441abd51af97f512015ea6ae69f,explainableaiandmachinelearningmodelforcaliforniahousepricepredictionsintelligentmodelforhomebuyersandpolicymakers,khan,10.32996/jbms.2024.6.5.9,explainableaiandmachinelearningmodelforcaliforniahousepricepredictionsintelligentmodelforhomebuyersandpolicymakers|2024,1,2712,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,housing characteristics prices hedonic,3229833cbf66f7437c3ccda763ab4938ba95a151,10.5194/agile-giss-7-15-2026,,The value of where – quantifying location values with tabular transformer models,2026.0,AGILE: GIScience Series,Christian Mueller-Kett,"Abstract. Location is a key feature of urban spaces. It influences housing and land markets, social segregation, and decisions in urban planning and administration. Hedonic price models and, increasingly, machine learning (ML) methods are traditionally used for quantitative location valuation. At the same time, transformer architectures and their more recent adaptations for tabular data mark a methodological milestone. However, to date, tabular transformers have not been widely adopted in spatial analyses and, in particular, have not been systematically evaluated for location value assessments. This study addresses this gap by comparing classic ML methods with two tabular transformer approaches: TabNet and Mitra. In a case study, rents are used as the target variable, with property-specific characteristics being normalised using spatial regressions. Location-describing features include accessibility to POIs and areal variables such as imperviousness, noise, and population density. While a stacked ensemble of classical ML models achieved the lowest RMSE, TabNet and Mitra performed within a narrow margin, demonstrating that they can be used to map spatial patterns.",0.0,https://agile-giss.copernicus.org/articles/7/15/2026/agile-giss-7-15-2026.pdf,True,JournalArticle,,2026-06-10,3229833cbf66f7437c3ccda763ab4938ba95a151,thevalueofwherequantifyinglocationvalueswithtabulartransformermodels,mueller-kett,10.5194/agile-giss-7-15-2026,thevalueofwherequantifyinglocationvalueswithtabulartransformermodels|2026,1,2681,6,hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic || machine learning hedonic pricing housing || rent hedonic regression housing || spatial hedonic model housing,2,1,1,4,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",28a1a5e1453616b480e490c1e1966efca65fe728,10.70382/ajsitr.v13i9.091,,A Hybrid Consensus-Based Feature Selection Framework for Robust Housing Price Prediction,2026.0,Journal of Science Innovation and Technology Research,Josiah Afang Kalat; J. Alhassan; O. Ojerinde,"Accurate predictions are essential for investment planning and risk assessment in real estate markets. Machine learning techniques have increasingly been adopted to improve the accuracy of housing price prediction. However, numerous studies have reported the absence of comprehensive predictor sets, ineffective feature selection practices, and limited model reliability and interpretability. Accordingly, this study develops a hybrid feature selection framework that compiles filter, wrapper, and embedded regularization feature selectors to improve the reliability of predictors. The framework employs the Boston Housing benchmark dataset and applies consensus-based feature selection prior to training four regression models: Linear Regression, Support Vector Regression, Random Forest, and Artificial Neural Networks. Four regression models were evaluated using the Mean Absolute Error (MAE), Mean Squared Error (MSE), the Root Mean Squared Error (RMSE), and the coefficient of determination to assess performance, reliability, and overall soundness of the models. Random Forest Regression achieved the highest predictive accuracy followed by ANNs (Artificial Neural Networks), SVR (Support Vector Regression), and standard Linear Regression. Robustness was achieved through the Random Forest Regression, which also exhibited the lowest error variance. As noted in the empirical evaluation, the models exhibit varying levels of performance. Random Forest Regression achieved maximum predictive performance and minimum error (MAE = 2.11, RMSE = 3.14) and surpassed the performance of the models of Artificial Neural Networks, Support Vector Regression, and Linear Regression. The findings support the assertion that consensus based feature prioritization and ensemble learning provide a consistent and reliable structure for modelling the housing price.",0.0,https://africanscholarpub.com/ajsitr/article/download/1715/1541,True,JournalArticle,,2026-09-25,28a1a5e1453616b480e490c1e1966efca65fe728,ahybridconsensusbasedfeatureselectionframeworkforrobusthousingpriceprediction,kalat,10.70382/ajsitr.v13i9.091,ahybridconsensusbasedfeatureselectionframeworkforrobusthousingpriceprediction|2026,1,3356,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",8a163c9eded939c047511cb8811bb2c54f9defb2,10.54254/2754-1169/20/20230199,,Research on the Influencing Factors and Prediction of Housing Prices Based on Regression Analysis --- Taking California as an Example,2023.0,"Advances in Economics, Management and Political Sciences",Kunlong Zhang,"A house is a necessity for everyone's life. But in today's world, with the continuous reduction of land area, the continuous increase of population, and the continuous maturity of the real estate industry, people have to consider more carefully what factors are the most important in the house. Based on the background of housing prices in California, this paper uses linear regression, random forest and principal component analysis to determine which variables have the greatest impact on housing prices. The reason for using three methods is to obtain more accurate results. According to the results, linear regression shows that income is the most relevant variable to housing prices. Random Forest shows an R square of 75.7%, meaning the predictions fit the data fairly well. Principal component analysis also shows that income is the most important variable. At the same time, house prices will be predicted based on the data obtained, which is about 116598.48588. Some suggestions for those who want to buy a house - which factors of the house are more important. Second, this article will also analyse how to face the rising housing prices.",0.0,https://aemps.ewapublishing.org/media/a02f966289d94d4cb7b2f45cd878fd8c.marked_Z9JVjK9.pdf,True,JournalArticle,,2023-09-13,8a163c9eded939c047511cb8811bb2c54f9defb2,researchontheinfluencingfactorsandpredictionofhousingpricesbasedonregressionanalysistakingcaliforniaasanexample,zhang,10.54254/2754-1169/20/20230199,researchontheinfluencingfactorsandpredictionofhousingpricesbasedonregressionanalysistakingcaliforniaasanexample|2023,1,2648,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",fac0094d8fbb1d7256627687ecb6ff23fb6817f5,10.54254/2754-1169/46/20230317,,Analysis and Comparison of House Price Prediction Based on XGboost and LightGBM,2023.0,"Advances in Economics, Management and Political Sciences",Shen Chen; Huihui Jin; Ling Li,"Real estate price prediction is one of the key research topics contemporarily. Based on the rapid development of Big Data, machine learning has gradually become the mainstream tool for housing price prediction. The XGboost and LightGBM models, as new advanced mod-els in recent years, have received widespread attention in the application in housing price prediction. Therefore, this study identifies the house price prediction based on XGboost model and LightGBM model and compares them with other models in order to obtain an analysis of the advantages and disadvantages of these two models in housing price predic-tion. According to the analysis, both models have ad-vantages such as high accuracy, high efficiency, and fast training speed. However, although XGboost has the smallest error pre-diction, it requires more computational time, thereby increasing computational costs. In ad-dition, LightGBM has disadvantages such as high overfitting risk in small sample sizes and increased sensitivity in noisy datasets. Therefore, besides the model studied in this article, feature selection methods such as Filter and Wrapper can also be introduced in subsequent studies to further improve the prediction accuracy.",4.0,https://aemps.ewapublishing.org/media/6ddf24e7e9fc45f3bdf0101499408af2.marked.pdf,True,JournalArticle,,2023-12-01,fac0094d8fbb1d7256627687ecb6ff23fb6817f5,analysisandcomparisonofhousepricepredictionbasedonxgboostandlightgbm,chen,10.54254/2754-1169/46/20230317,analysisandcomparisonofhousepricepredictionbasedonxgboostandlightgbm|2023,1,2716,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",366afdb36deb552049d1bcb75a35cf4d175384c1,10.54254/2754-1169/2026.nj30809,,A Comparative Analysis of Machine Learning Algorithms for Forecasting Housing Prices in Boston,2025.0,"Advances in Economics, Management and Political Sciences",Yingxi Chen,"Real estate is a crucial and multifaceted field that encompasses property valuation, investment, urban development, and even aspects related to the surrounding regional economy. Accurate predictions of housing prices in Boston are therefore of great significance to its various stakeholders. To address this issue, in this study, after thorough data preprocessing and detailed feature engineering, multiple machine learning models were employed to predict the housing prices in Boston. These models include linear regression (LR), random forest (RF), and extreme gradient boosting (XGBoost). These models cover linear algorithms, ensemble methods, and tree-based techniques, and can comprehensively assess their ability to simulate the relationship between the features and prices. Among these models, XGBoost performed the best, achieving the highest R-squared value (0.920), the lowest root mean square error (RMSE = 2.7831), and excellent cross-validation performance. This indicates that XGBoost is highly effective in predicting housing prices in Boston and can provide feasible and practical prediction solutions for stakeholders.",0.0,https://aemps.ewapub.com/article/view/30809.pdf,True,JournalArticle,,2025-12-24,366afdb36deb552049d1bcb75a35cf4d175384c1,acomparativeanalysisofmachinelearningalgorithmsforforecastinghousingpricesinboston,chen,10.54254/2754-1169/2026.nj30809,acomparativeanalysisofmachinelearningalgorithmsforforecastinghousingpricesinboston|2025,1,2636,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",463632ff33917e97f63707a1c0c219e50bb6c435,10.54254/2754-1169/2025.bl25997,,Singapore House Price Forecasting Using Machine Learning,2025.0,"Advances in Economics, Management and Political Sciences",Yu Hu,"Real estate valuation, particularly predicting house prices, is a critical aspect of the real estate industry. This paper provides a comprehensive overview of the application of various machine learning models for predicting house expenses. Four distinct models are evaluated: Back Propagation neural network (BP), random forest (RF), seagull optimization algorithm (SOA), and lion swarm optimization-based algorithm (SLSO-BP). This research aims to identify the most effective machine learning algorithm for accurately predicting house prices, utilizing the mean square error (MSE) and R-squared (R2) as the evaluation metric. Through a comparative analysis, our findings reveal that the SLSO-BP algorithm demonstrates superiority over other predictive tools, showcasing the lowest MSE. This study contributes to the advancement of predictive modeling in real estate, offering valuable insights for practitioners, researchers, and policymakers involved in housing market analysis and decision-making.",0.0,https://aemps.ewapub.com/article/view/25997.pdf,True,JournalArticle;Review,,2025-08-13,463632ff33917e97f63707a1c0c219e50bb6c435,singaporehousepriceforecastingusingmachinelearning,hu,10.54254/2754-1169/2025.bl25997,singaporehousepriceforecastingusingmachinelearning|2025,1,2501,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2f424ef47bc504de4db37062a0a7df65b6c0552f,10.54254/2754-1169/118/20242043,,Predicting Housing Prices Using Supervised Machine Learning Models,2024.0,"Advances in Economics, Management and Political Sciences",Haiying Zhang,"Abstract: Variations in housing prices exert a profound impact on economic policies and personal financial decisions. This study aims to delve into the factors that affect housing prices and construct a predictive model using machine learning techniques. Machine learning enables computers to acquire knowledge from data and make predictions or decisions. This study forcasts house prices by analyzing the characteristics of data from Seattle, Washington using machine learning multiple linear regression, polynomial regression, and K-nearest neighbors regression (KNN). The findings of this investigation demonstrate that the polynomial regression model is the most accurate in predicting housing prices and can capture nonlinear relationships in the data more effectively than multiple linear regression and K-nearest neighbors regression. The main factors affecting housing prices are interior living space and the construction and design of buildings. These insights hold potential for enhancing government policies, facilitating effective land use decision-making by planners, and enabling investors to make more informed choices.",0.0,https://aemps.ewapub.com/article/view/16015.pdf,True,JournalArticle,,2024-10-25,2f424ef47bc504de4db37062a0a7df65b6c0552f,predictinghousingpricesusingsupervisedmachinelearningmodels,zhang,10.54254/2754-1169/118/20242043,predictinghousingpricesusingsupervisedmachinelearningmodels|2024,1,2635,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",90bf51e1887239db4648cac241aff125d68e070a,10.1108/ijhma-01-2025-0010,,A hybrid machine learning approach for housing price prediction: the stacking regressor method,2025.0,International Journal of Housing Markets and Analysis,Ömer Gökberk Erbulut; Zeynep Çolak,"Purpose This study aims to explore alternative methodologies by comparing popular and effective machine learning models for housing price prediction. The primary objective is to develop a hybrid Stacking Regressor model combining multiple regression algorithms to leverage their strengths through a meta-model, thereby enhancing prediction accuracy. Design/methodology/approach The performance of widely used machine learning algorithms, including CatBoost, XGBoost, Random Forest, Extra Trees, Hist Gradient Boosting and Gradient Boosting, was evaluated using various error metrics for housing price prediction. Feature engineering and parameter optimization were applied to improve model performance, resulting in significant enhancements, particularly for Random Forest and Extra Trees. Furthermore, a Stacking Regressor model was constructed by integrating multiple regression algorithms to capitalize on their collective predictive capabilities. Findings The results indicate that CatBoost achieved the lowest error rates among the evaluated models. Random Forest and XGBoost also performed comparably, whereas Gradient Boosting exhibited higher error rates. The hybrid Stacking Regressor model outperformed all algorithms, demonstrating superior predictive accuracy. These findings underscore the potential of integrating machine learning models to address complex data sets and improve overall model performance. Originality/value This study is the data preprocessing and feature engineering processes, which are often overlooked in prior research but critical to machine learning models’ success. Additionally, the study contributes to the field by proposing a hybrid model – the Stacking Regressor. This model combines multiple regression algorithms and uses a meta-model to integrate the strengths of the base models, thereby aiming to improve prediction accuracy. ",5.0,https://acikerisim.comu.edu.tr/bitstreams/9955b279-fdc9-4fe6-9084-052b063bd0fb/download,True,JournalArticle,,2025-03-28,90bf51e1887239db4648cac241aff125d68e070a,ahybridmachinelearningapproachforhousingpricepredictionthestackingregressormethod,erbulut,10.1108/ijhma-01-2025-0010,ahybridmachinelearningapproachforhousingpricepredictionthestackingregressormethod|2025,1,3378,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fbb61aeb7411f58d93b8bbf6f634cf7c8d88091a,10.54254/2755-2721/2026.gu34304,,A Comparative Study of House Price Prediction Using Multiple Models,2026.0,Applied and Computational Engineering,Yang Chen,"Predicting housing prices, therefore, has important implications for real estate management, financial risk assessment, and urban planning. The rise of big data has further opened up new tools for housing price prediction, enabling the use of more complex and data‑driven approaches alongside traditional methods. This study applies linear regression, decision tree regression, and random forest regression to the Boston housing dataset. Model performance is evaluated using MAE, MSE, and R². The random forest model achieves the best performance (MAE=2.05, MSE=7.93, R²=0.89), outperforming linear regression (R²=0.67) and decision tree regression (R²=0.86).Correlation analysis reveals that the number of rooms (RM) shows the strongest positive correlation (r=0.70), while the lower-status population proportion (LSTAT) exhibits the strongest negative correlation (r=–0.74), together with crime rate (CRIM), these features form a core explanatory framework. The proportion of low-income population (LSTAT), and the distance to employment centers (DIS) are the most significant factors affecting housing prices. This study not only provides effective machine learning methods for housing price prediction but also offers data support for real estate appraisal, credit risk control, and urban policy making.",0.0,https://ace.ewapub.com/article/view/34304.pdf,True,JournalArticle,,2026-06-08,fbb61aeb7411f58d93b8bbf6f634cf7c8d88091a,acomparativestudyofhousepricepredictionusingmultiplemodels,chen,10.54254/2755-2721/2026.gu34304,acomparativestudyofhousepricepredictionusingmultiplemodels|2026,1,2807,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",32efd0ebb04018868b7084b47a6cd49902d56cf6,10.54254/2755-2721/55/20241483,,"House price prediction using machine learning for Ames, Iowa",2024.0,Applied and Computational Engineering,Qiong-Wei Ye,"The real estate sector is pivotal to economic growth and plays a substantial role in the global GDP. In this technologically advanced age, the adoption of machine learning for accurate house price prediction is crucial. These models optimize decision-making for homeowners, sellers, and investors alike. This study represents a comprehensive exploration into the field of house price prediction within the context of Ames, Iowa, United States. The primary objective of this research is to construct a reliable and highly accurate predictive model, empowering individuals to estimate property values with unprecedented precision. The research encompasses three different machine learning algorithms: linear regression, random forest, and XGBoost. The use of the dataset from the reputed website helps improve the reliability of the result. Furthermore, the investigation extends to a detailed examination of the multifaceted determinants exerting a profound influence on house prices in the dynamic Ames real estate landscape, and determined that the factor that will influence the house price most is the total area of the house. Among all models, XGBoost produces the best result, which achieved an R-square of 0.8803. Moreover, the importance of each feature is also analyized using the feature ranking algorithm in random forest, showing that the overall quantity of the house, the living area of the house, and the total area of the basement are the top three factors that influence the house price most.",2.0,https://ace.ewapub.com/article/view/11040.pdf,True,JournalArticle,,2024-04-25,32efd0ebb04018868b7084b47a6cd49902d56cf6,housepricepredictionusingmachinelearningforamesiowa,ye,10.54254/2755-2721/55/20241483,housepricepredictionusingmachinelearningforamesiowa|2024,1,3008,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9956aba8c110f602a88fd2b3743617f75e16f997,10.54254/2755-2721/45/20241031,,Research on predicting Ames housing price based on forward selection regression and principal component regression,2024.0,Applied and Computational Engineering,Ziwen Wang,"This study aimed to predict home prices in Ames, Iowa, using two regression techniques: forward selection regression and principal component regression. The research began with exploratory data analysis and several pre-processing steps. Features are classified, overlapping features are merged, missing values are processed appropriately, and predictors of zero or near-zero variance are removed. The data is then scaled to solve the problem of data fragmentation. The results indicated that the forward selection regression method had higher prediction accuracy and a lower root mean square error (RMSE) value, suggesting its superiority in predicting home prices. The findings of this study have practical implications for various stakeholders in the real estate market. By focusing on the identified areas that contribute significantly to the value of real estate, stakeholders can make informed decisions to enhance their investments. This research provides valuable guidance for homeowners, real estate agents, and property developers in understanding the factors influencing home prices in Ames, Iowa.",1.0,https://ace.ewapub.com/article/view/10752.pdf,True,JournalArticle,,2024-03-15,9956aba8c110f602a88fd2b3743617f75e16f997,researchonpredictingameshousingpricebasedonforwardselectionregressionandprincipalcomponentregression,wang,10.54254/2755-2721/45/20241031,researchonpredictingameshousingpricebasedonforwardselectionregressionandprincipalcomponentregression|2024,1,2607,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",25bbac9ea7797b2fb56ffd521bfbc2203cea2540,10.54517/cd.v5i1.2742,,Prediction of new housing prices in Changsha urban area based on multiple machine learning algorithms: A comparative analysis,2024.0,City Diversity,Jun-Jia Yin; A. Alias; N. A. Haron; Nabilah Abu Bakar,"As China’s pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are used to construct forecasting models, and the best-performing model is selected by conducting a comparative analysis of the forecasting errors obtained between these models. The research found that machine learning is a practical approach to property price prediction, with least squares regression and Lasso regression providing relatively more convincing results.",0.0,https://aber.apacsci.com/index.php/cd/article/download/2742/3415,True,JournalArticle,,2024-08-05,25bbac9ea7797b2fb56ffd521bfbc2203cea2540,predictionofnewhousingpricesinchangshaurbanareabasedonmultiplemachinelearningalgorithmsacomparativeanalysis,yin,10.54517/cd.v5i1.2742,predictionofnewhousingpricesinchangshaurbanareabasedonmultiplemachinelearningalgorithmsacomparativeanalysis|2024,1,3065,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",35d47b5d82e1cdb4d70de7bf35cdfe970d793f35,10.2139/ssrn.1316046,,Machine Learning and the Spatial Structure of House Prices and Housing Returns,2008.0,,A. Caplin; S. Chopra; J. Leahy; Yann LeCun; Trivikraman Thampy,,29.0,http://yann.lecun.com/exdb/publis/pdf/caplin-ssrn-08.pdf,True,,Economics,2008-12-14,35d47b5d82e1cdb4d70de7bf35cdfe970d793f35,machinelearningandthespatialstructureofhousepricesandhousingreturns,caplin,10.2139/ssrn.1316046,machinelearningandthespatialstructureofhousepricesandhousingreturns|2008,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ebb9ed71f15fa99b443bbf874eb355e0d70207b1,10.4236/ijis.2013.34017,,A Data Mining Model by Using ANN for Predicting Real Estate Market: Comparative Study,2013.0,,I. S. H. Bahia,"This paper aims to demonstrate the importance and possible value of housing predictive power which provides independent real estate market forecasts on home prices by using data mining tasks. A (FFBP) network model and (CFBP) network model are one of these tasks used in this research to compare results of them. We estimate the median value of owner occupied homes in Boston suburbs given 13 neighborhood attributes. An estimator can be found by fitting the inputs and targets. This data set has 506 samples. “ousing inputs” is a 13 × 506 matrix. The “housing targets” is a 1 × 506 matrix of median values of owner-occupied homes in $1000’s. The result in this paper concludes that which one of the two networks appears to be a better indicator of the output data to target data network structure than maximizing predict. The CFBP network which is the best result from the Output_network for all samples are found from the equation output = 0.95 * Target + 1.2. The regression value is approximately 1, (R = 0.964). That means the Output_network is matching to the target data set (Median value of owner-occupied homes in $1000’s), and the percent correctly predict in the simulation sample is 96%.",17.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=37583,True,,Mathematics,2013-09-18,ebb9ed71f15fa99b443bbf874eb355e0d70207b1,adataminingmodelbyusingannforpredictingrealestatemarketcomparativestudy,bahia,10.4236/ijis.2013.34017,adataminingmodelbyusingannforpredictingrealestatemarketcomparativestudy|2013,1,2699,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",701c45f31cecbf5d47133602b1615224e335f272,10.5815/ijieeb.2020.02.03,,House Price Prediction Modeling Using Machine Learning,2020.0,International Journal of Information Engineering and Electronic Business,M. Thamarai; S. Malarvizhi,"—Machine Learning is seeing its growth more rapidly in this decade. Many applications and algorithms evolve in Machine Learning day to day. One such application found in journals is house price prediction. House prices are increasing every year which has necessitated the modeling of house price prediction. These models constructed, help the customers to purchase a house suitable for their need. Proposed work makes use of the attributes or features of the houses such as number of bedrooms available in the house, age of the house, travelling facility from the location, school facility available nearby the houses and Shopping malls available nearby the house location. House availability based on desired features of the house and house price prediction are modeled in the proposed work and the model is constructed for a small town in West Godavari district of Andhrapradesh. The work involves decision tree classification, decision tree regression and multiple linear regression and is implemented using Scikit-Learn Machine Learning Tool.",57.0,http://www.mecs-press.org/ijieeb/ijieeb-v12-n2/IJIEEB-V12-N2-3.pdf,True,,Computer Science,2020-04-08,701c45f31cecbf5d47133602b1615224e335f272,housepricepredictionmodelingusingmachinelearning,thamarai,10.5815/ijieeb.2020.02.03,housepricepredictionmodelingusingmachinelearning|2020,1,2546,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",be6ae1fe5f379c1f93d4a901418767ac34145360,10.56975/ijrar.v13i2.334066,,House Price Prediction System Using Linear Regression,2026.0,International journal of research and analytical reviews,Saraswathi Akkapalli; V. S. Kumar; Nagaraju Kongari,,0.0,http://www.ijrar.org/download1.php?file=IJRAR26B2304.pdf,True,JournalArticle,,,be6ae1fe5f379c1f93d4a901418767ac34145360,housepricepredictionsystemusinglinearregression,akkapalli,10.56975/ijrar.v13i2.334066,housepricepredictionsystemusinglinearregression|2026,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",982e7e552461540c7a26ed5dfdcfa2a05c293cfb,10.14807/ijmp.v9i1.682,,A roadmap to determine the important factors of the house value: a case study by using actual price registration data of Taipei housing transactions,2018.0,,Pei-De Wang; Ming-chien Chen,"While many studies have applied data mining techniques to judge housing prices, few have decoded the important attributes or prioritized them simultaneously. This paper aims to utilize five data mining techniques to discover the important attributes for three major types of real estate in Taipei city. The datasets, involving a total of 22,480 transactions, were publicly available from the Taiwan Actual Price Registration from July 2013 to August 2015. The five models are decision trees, random forests, model trees, artificial neural networks and multiple regression. The criteria used to measure the forecasting accuracy are MAPE, R², RMSE, MAE and COR. The model with the best performance for all houses is the Model Tree with a MAPE value of 27.59. As for apartments, the best is Random Forests. Artificial Neural Networks perform best for suites and buildings with elevators. Different housing types need different models. Furthermore, the attributes importance helps us to conclude the really critical attributes, which include the floor area, administrative districts, parking area and land area, and their rankings. This variable ranking and selection procedure proposed by this research can also be adopted to improve the prediction efficiency for most big data applications other than the housing transactions.",4.0,http://www.ijmp.jor.br/index.php/ijmp/article/download/682/787,True,,Computer Science,2018-03-02,982e7e552461540c7a26ed5dfdcfa2a05c293cfb,aroadmaptodeterminetheimportantfactorsofthehousevalueacasestudybyusingactualpriceregistrationdataoftaipeihousingtransactions,wang,10.14807/ijmp.v9i1.682,aroadmaptodeterminetheimportantfactorsofthehousevalueacasestudybyusingactualpriceregistrationdataoftaipeihousingtransactions|2018,1,2824,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",50915bab05b8c9a1e33ae813b9cd868e78344368,10.19139/soic.v7i1.435,,"Predicting Home Value in California, United States via Machine Learning Modeling",2019.0,"Statistics, Optimization and Information Computing",Yitong Huang,"The market value of real estate is difficult to predict with simple regression model due to the diversity and complexity of the real data. In this paper, with the latest real estate data of three counties in Los Angeles, California, United States, both linear and non-linear machine learning methods are employed to predict the log error of the home value. The motivation is to improve the accuracy in home value prediction with advanced methods. The main contribution is that it finds that traditional linear models are not predictive for complex home value data sets, while tree based non-linear models are most accurate with the lowest mean square errors.",40.0,http://www.iapress.org/index.php/soic/article/download/2019-M-5/403,True,,Computer Science,2019-01-07,50915bab05b8c9a1e33ae813b9cd868e78344368,predictinghomevalueincaliforniaunitedstatesviamachinelearningmodeling,huang,10.19139/soic.v7i1.435,predictinghomevalueincaliforniaunitedstatesviamachinelearningmodeling|2019,1,2158,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",990dee291c538b7594ca7ab048bc0f71c4529056,10.23977/autml.2023.040103,,House Price Forecasting in Ames Based on Bayesian Regularized BP Neural Network,2023.0,Automated Machine Learning,Haiqing Bai; Xiaoyong Chen,": Housing has always been an important issue related to the national economy and people's livelihood. House prices not only affect people's welfare, but also have a significant impact on the national economy and social stability. Therefore, the prediction of house prices is also a necessary means. This paper forecasts the housing price data of Ames City in the United States, and establishes a Bayesian regularized BP neural network model to solve the nonlinear mapping relationship between housing prices and indicators. Through curve fitting analysis of samples, the overall R value is 0.9667, the overall result is better, and the error histogram also conforms to normal distribution. The experimental results show that the BP neural network model based on Bayesian regularization is very effective in dealing with the problem of house price prediction, and can better analyze and predict the trend of house price. This study provides a basis for real estate developers to develop and position products, and also provides model support for buyers to better judge the real price of houses.",4.0,http://www.clausiuspress.com/assets/default/article/2023/03/13/article_1678707835.pdf,True,,,,990dee291c538b7594ca7ab048bc0f71c4529056,housepriceforecastinginamesbasedonbayesianregularizedbpneuralnetwork,bai,10.23977/autml.2023.040103,housepriceforecastinginamesbasedonbayesianregularizedbpneuralnetwork|2023,1,2593,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",77ba361f1375441f9d9b928967cfb0dbcd3e8432,10.23977/bgdds.2018.11001,,Research on Ensemble Learning-based Housing Price Prediction Model,2018.0,,Buyang Cao; Bo-Wen Yang,"Housing price is influenced by multiple factors. The existing housing price forecasting model usually belongs to the so called single predictor model, whose prediction accuracy is not ideal and the over-fitting phenomenon often happens due to the data noise. To resolve these issues, this paper proposes an ensembe lerning-based housing price prediction model incorporating various predictors. To evaluate the effectiveness of the proposed model, extra trees, random forest, GBDT and XGB algorithms are selected for the benchmarks. The dataset used is the California housing price available over the web. The results demonstrate that the proposed method can improve the predicting accuracy and stability compared with other four single prediction models.",23.0,http://www.clausiuspress.com/assets/default/article/2018/03/08/article_1520536624.pdf,True,,Computer Science,2018-03-08,77ba361f1375441f9d9b928967cfb0dbcd3e8432,researchonensemblelearningbasedhousingpricepredictionmodel,cao,10.23977/bgdds.2018.11001,researchonensemblelearningbasedhousingpricepredictionmodel|2018,1,2254,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",8802a8ce5b544ffc926bde91241db94e5da9e147,10.3934/math.20251058,,Learning dynamics and convergence of machine learning driven neural ordinary differential equations model for housing price prediction,2025.0,AIMS Mathematics,Zhikun Luo,,1.0,http://www.aimspress.com/aimspress-data/math/2025/10/PDF/math-10-10-1058.pdf,True,JournalArticle,,,8802a8ce5b544ffc926bde91241db94e5da9e147,learningdynamicsandconvergenceofmachinelearningdrivenneuralordinarydifferentialequationsmodelforhousingpriceprediction,luo,10.3934/math.20251058,learningdynamicsandconvergenceofmachinelearningdrivenneuralordinarydifferentialequationsmodelforhousingpriceprediction|2025,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",994920bae87a9dc1df6f28ae713e52c11e5c1a35,10.1007/978-3-642-32172-6_6,,Multivariate Regression Modeling for Home Value Estimates with Evaluation Using Maximum Information Coefficient,2013.0,,Gong-Zhu Hu; Jin-Ping Wang; Wenying Feng,,21.0,http://www.acisinternational.org/springer/samplepaper.pdf,True,,Engineering,,994920bae87a9dc1df6f28ae713e52c11e5c1a35,multivariateregressionmodelingforhomevalueestimateswithevaluationusingmaximuminformationcoefficient,hu,10.1007/978-3-642-32172-6_6,multivariateregressionmodelingforhomevalueestimateswithevaluationusingmaximuminformationcoefficient|2013,1,1500,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",934c228700b544511e5b1406a0a562934dbcfe58,10.14569/ijacsa.2021.0121291,,Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur,2021.0,International Journal of Advanced Computer Science and Applications,Shuzlina Abdul-Rahman; Nor Hamizah Zulkifley; Ismail Ibrahim; S. Mutalib,,23.0,http://thesai.org/Downloads/Volume12No12/Paper_91-Advanced_Machine_Learning_Algorithms.pdf,True,,,,934c228700b544511e5b1406a0a562934dbcfe58,advancedmachinelearningalgorithmsforhousepricepredictioncasestudyinkualalumpur,abdul-rahman,10.14569/ijacsa.2021.0121291,advancedmachinelearningalgorithmsforhousepricepredictioncasestudyinkualalumpur|2021,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9170a7fab9d882152c28fff91ab9215487dc2100,10.52783/cienceng.v11i1.261,,House Price Prediction Using Texture and Visual Features,2023.0,Proceeding International Conference on Science and Engineering,M. Sweety; G. Jachak; Dr. Sayantani Nath,"Real estate sector has been growing at a rate never seen before. For this sector, a key role is played by the pricing of the property. Gone are the days when the price of the property was based on whims and facies of the real estate dealers. Machine learning has numerous applications in the domain of real estate, and one of the most popular ones is predicting house prices. The application of machine learning in house price prediction involves training a model on a dataset that includes a variety of visual and texture features related to the property. The model is then used to predict the price of a new property based on its features. This paper successfully explores machine learning based house price prediction. The methodology followed was to first use data sets to train the model. Later, using correlation-based hybrid GA-reinforcement strategy, a suitable set of features has been selected. In the end, these features are applied to a XG boost regressor to get results. The accuracies are compared with the cases of without feature selection of different regressors. This algorithm, if successfully deployed will be beneficial to both sellers and buyers, because it sets a data-based benchmarking for pricing the property.",0.0,http://seer-ufu-br.online/index.php/journal/article/download/261/191,True,JournalArticle;Conference,,2023-05-15,9170a7fab9d882152c28fff91ab9215487dc2100,housepricepredictionusingtextureandvisualfeatures,sweety,10.52783/cienceng.v11i1.261,housepricepredictionusingtextureandvisualfeatures|2023,1,2737,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0bb03709c510b536f0af6feb5ee893626d47af9e,10.24251/hicss.2020.130,,Improving Prediction Models for Mass Assessment: A Data Stream Approach,2020.0,Hawaii International Conference on System Sciences,Donghui Shi; J. Guan; J. Żurada; Alan S. Levitan,"Mass appraisal is the process of valuing a large collection of properties within a city/municipality usually for tax purposes. The common methodology for mass appraisal is based on multiple regression though this methodology has been found to be deficient. Data mining methods have been proposed and tested as an alternative but the results are very mixed. This study introduces a new approach to building prediction models for assessing residential property values by treating past sales transactions as a data stream. The study used 110,525 sales transaction records from a municipality in the Midwest of the US. Our results show that a data stream based approach outperforms the traditional regression approach, thus showing its potential in improving the performance of prediction models for mass assessment.",1.0,http://scholarspace.manoa.hawaii.edu/bitstream/10125/63869/1/0104.pdf,True,JournalArticle,Computer Science,2020-01-07,0bb03709c510b536f0af6feb5ee893626d47af9e,improvingpredictionmodelsformassassessmentadatastreamapproach,shi,10.24251/hicss.2020.130,improvingpredictionmodelsformassassessmentadatastreamapproach|2020,1,2312,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",88e19a7cf222cd85cb0e6389b23e07e5926f36a0,10.12962/jaree.v7i1.328,,House Price Prediction using Multiple Linear Regression and KNN,2023.0,JAREE (Journal on Advanced Research in Electrical Engineering),F. Febriyanto; E. Endroyono; Yoyon Kusnendar,"The transition of BPHTB management from central taxes to regional taxes is a continuation of the regional autonomy policy. The difference between the market value and the prevailing NJOP poses a challenge for the Sintang District Government in determining the Tax Object Acquisition Value (NPOP) as the basis for imposing BPHTB. Machine learning has been extensively explored for predictions and can be an alternative that can help predict NPOP, especially house prices. This study uses backward elimination and forward selection methods to select the features used in this study and multiple linear regression and K-Nearest Neighbor methods to make house price prediction models. The results of model performance measurement using RMSE, Multiple Linear Regression method with feature selection using backward elimination resulted in a better model with an RMSE value of 44.02 (million rupiahs) and an R2 value of 0.707.",0.0,http://jaree.its.ac.id/index.php/jaree/article/download/328/142,True,JournalArticle,,2023-01-31,88e19a7cf222cd85cb0e6389b23e07e5926f36a0,housepricepredictionusingmultiplelinearregressionandknn,febriyanto,10.12962/jaree.v7i1.328,housepricepredictionusingmultiplelinearregressionandknn|2023,1,2420,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e2af7a0e3d0cf44a6704c4df7427ea1f8f7260aa,10.25008/ijadis.v4i2.1290,,Optimization of the Random Forest Method Using Principal Component Analysis to Predict House Prices,2023.0,International Journal of Advances in Data and Information Systems,Emha Ahdan Fahmi Elmuna; Totok Chamidy; Fresy Nugroho,"Investment is an interesting thing, especially property investment. The developer must also be careful in determining the price of the property. It should be noted that every year, both short-term and long-term, property prices increase and rarely go down. In determining the price, it is often also based on the features of the house such as the concept, location, bedrooms, etc. To predict house prices based on their features, the random forest has a good performance for predicting house prices. However, the random forest method has the disadvantage that if you use too many variables, the training process will take longer and feature selection tends to select features that are not informative. One way to reduce features without removing other features is to use Principal Component Analysis. In this research, the method used is Principal Component Analysis (PCA) and Random Forest. From the results of model training, it can be concluded that the use of model evaluation results using PCA has a smaller error rate and more consistent values, with an average of 0.018. While the results of the evaluation without PCA and using only Random Forest have a higher error value with an average of 0.03125. The training time using the PCA model has a faster time, with an average of 7918 milliseconds, while those using only random forest without PCA have an average time of 8975 milliseconds.",5.0,http://ijadis.org/index.php/ijadis/article/download/1290/58,True,JournalArticle,,2023-10-06,e2af7a0e3d0cf44a6704c4df7427ea1f8f7260aa,optimizationoftherandomforestmethodusingprincipalcomponentanalysistopredicthouseprices,elmuna,10.25008/ijadis.v4i2.1290,optimizationoftherandomforestmethodusingprincipalcomponentanalysistopredicthouseprices|2023,1,2895,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",acd945a225c6801bacec2b1df9418c4067093ec2,10.25236/ajbm.2023.050207,,Machine Learning-Based Analysis of Housing Price Predictors,2023.0,Academic Journal of Business & Management,,,1.0,http://francis-press.com/uploads/papers/l47TQMFSc1bDDJ4GVFsLrVf73NxkWEWVL9HF98us.pdf,True,,,,acd945a225c6801bacec2b1df9418c4067093ec2,machinelearningbasedanalysisofhousingpricepredictors,,10.25236/ajbm.2023.050207,machinelearningbasedanalysisofhousingpricepredictors|2023,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",8fd1a903a1b48fedf4fec2447b29bb6c3dbdab5b,10.25236/ajcis.2024.070401,,The Xgboost-Bagging Approach of Predicting House Prices,2024.0,Academic Journal of Computing & Information Science,Xin-Chao Wu,": In this paper we aim to establish appropriate models to predict housing prices that are affected by multiple factors and we use various machine learning methods to predict house prices. Boosting methods often aim to reduce the bias of a model but can increase its variance. Conversely, bagging methods tend to decrease the variance of a model. Therefore, we attempted to combine these two algorithms to improve the model's capability for predicting house prices. After comparing different methods, we combined the XGBoost and Bagging to construct a novel model for predicting house prices. The experimental results demonstrate that among all the methods, the proposed approach achieves the best prediction performance for housing prices. Furthermore, when compared to using XGBoost alone, our integrated model exhibits improved precision, indicating its efficacy in addressing the challenges associated with housing price prediction.",0.0,http://francis-press.com/uploads/papers/TK3pWobwuoqC0AWINgZl1T7CMpnPUt0yOs1KFjKS.pdf,True,JournalArticle,,,8fd1a903a1b48fedf4fec2447b29bb6c3dbdab5b,thexgboostbaggingapproachofpredictinghouseprices,wu,10.25236/ajcis.2024.070401,thexgboostbaggingapproachofpredictinghouseprices|2024,1,2435,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",44c815ae72d5549d70c5587925f2562528ed0580,10.4108/eai.26-5-2023.2334435,,Machine Learning based Comparative Study on House Price Prediction Task,2023.0,"Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26–28, 2023, Nanjing, China",Ru Hu,,0.0,http://eudl.eu/pdf/10.4108/eai.26-5-2023.2334435,True,Conference,,,44c815ae72d5549d70c5587925f2562528ed0580,machinelearningbasedcomparativestudyonhousepricepredictiontask,hu,10.4108/eai.26-5-2023.2334435,machinelearningbasedcomparativestudyonhousepricepredictiontask|2023,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1193c6e60fafd317276072a4905b639d19d1d8ec,10.4108/eai.22-5-2026.2365118,,Boston Housing Price Prediction and Factor Analysis: A Comparative Study of Linear Regression and Random Forest,2026.0,"Proceedings of the 4th International Conference on Image, Algorithms, and Artificial Intelligence, ICIAAI 2026, 22-24 May 2026, Singapore, Singapore",Ji-Xuan Chen,". Accurate housing price prediction is important for real estate transactions, urban planning, and policy making. This study uses Boston housing data to find key influencing factors through correlation analysis and significance testing. It builds linear regression and random forest models, and evaluates their performance using R², MAE, and RMSE. The results show that the random forest model has better fitting accuracy and generalization ability. Its test set R² is 0.89, which is 15% higher than the linear regression model. Finally, the paper summarizes the findings, discusses limitations, and suggests future work such as integrating multi-source data. This provides practical references for housing price prediction in similar urban contexts.",0.0,http://eudl.eu/pdf/10.4108/eai.22-5-2026.2365118,True,Conference,,,1193c6e60fafd317276072a4905b639d19d1d8ec,bostonhousingpricepredictionandfactoranalysisacomparativestudyoflinearregressionandrandomforest,chen,10.4108/eai.22-5-2026.2365118,bostonhousingpricepredictionandfactoranalysisacomparativestudyoflinearregressionandrandomforest|2026,1,2250,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a090a9e2f62d50d7c4f9b35da3141a7a1e038603,10.4108/eai.18-11-2022.2327138,,Influencing Factors of California Housing Prices in 1990: a Multiple Linear Regression Analysis,2023.0,"Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China",Yitan Hao; Luhan Zhuang; Zitao Ying; Juncheng Zhai,"— In 1990, it was a hot topic to study the housing prices of blocks in California. That was mainly because of the recession period of the U.S. economy in 1990, which affected consumption and personal income and impacted the real estate market. The purpose of the study is to find factors that affect the housing price for blocks in California in 1990 with the consideration of properties of the house itself, the income of buyers, and the geographic surroundings. The paper uses the housing price impact theory and multiple linear regression method to study the influencing factors of the California housing price in 1990. This research concludes that median house value is positively correlated with median income, median house age, total bedrooms, households, and distance to San Diego, while negatively correlated with total rooms, population, distance to the coast, and Los Angeles. In addition, there is no statistically significant relationship between the median house value and distance to San Jose. In fact, it gives very specific results about the impact of each specific factor involved in our model. However, the general ideas like the impact of the whole dwelling structures do not involve in this study.",1.0,http://eudl.eu/pdf/10.4108/eai.18-11-2022.2327138,True,Conference,,,a090a9e2f62d50d7c4f9b35da3141a7a1e038603,influencingfactorsofcaliforniahousingpricesin1990amultiplelinearregressionanalysis,hao,10.4108/eai.18-11-2022.2327138,influencingfactorsofcaliforniahousingpricesin1990amultiplelinearregressionanalysis|2023,1,2717,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ca6ff999d046595f6c5a93bb4d89d22b894c5403,10.4108/eai.17-11-2023.2342672,,House Price Prediction Based on Machine Learning --Taking Beijing House Price Data as an Example,2024.0,"Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17–19, 2023, Beijing, China",Yuhong Xu,,0.0,http://eudl.eu/pdf/10.4108/eai.17-11-2023.2342672,True,Conference,,,ca6ff999d046595f6c5a93bb4d89d22b894c5403,housepricepredictionbasedonmachinelearningtakingbeijinghousepricedataasanexample,xu,10.4108/eai.17-11-2023.2342672,housepricepredictionbasedonmachinelearningtakingbeijinghousepricedataasanexample|2024,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9f95edfd65bd72e9bb2829089b03d5a0ba9a0c20,10.12982/cmjs.2022.102,,Housing Price Prediction by Divided Regression Analysis,2022.0,Chiang Mai Journal of Science,Y. Goh; Y. Goh; Chun-Chieh Yip; K. Ng,"Regression analysis is a statistical methodology to investigate the relationship between the dependent variable and the independent variables. In current era with the trend of big data, we might face some problems when performing statistical analysis for the massive volume of data. For example, the heavy burden of the computing load will cause the computation to be time consuming, the accuracy of the results might be affected in view of the vast volume of data. Hence, divided regression analysis is proposed to reduce the burden of the computing load. This approach performs subdivision of the dataset into several unique subsets, then the multiple linear regression is fi tted into each subset. The results obtained from each subset are then combined to obtain a divided regression model which is treated as the original overall dataset. The dataset used in this paper is KC Housesales Data, obtained from the Kaggle website. The dataset contains statistics information about the housing price, for example, size of lot, size of living area and selling price of the house. The goal of this paper is to predict the selling price of a house from the given attributes. The dataset is partitioned into fi ve subsets. Consequently, multiple linear regression is fi tted for each subset. Then, some model adequacy checking will be applied on the models. The test in determining the existence of multicollinearity in the models is rather important as well because the collinearity among the independent variables will affect the overall results. Hence, the variance infl ation factor (VIF) approach is used to determine the existence of multicollinearity. Finally, the divided regression model is obtained by combining results from all the subsets and the validity of divided regression model is verifi ed.",0.0,http://epg.science.cmu.ac.th/ejournal/dl.php?journal_id=11694,True,,,2022-11-30,9f95edfd65bd72e9bb2829089b03d5a0ba9a0c20,housingpricepredictionbydividedregressionanalysis,goh,10.12982/cmjs.2022.102,housingpricepredictionbydividedregressionanalysis|2022,1,3305,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",79f4aed15e0ac08ea43c3de2d283c604e5a2a5b3,10.62051/7gzntt12,,Utilize the Machine Learning Models to Forecast Home Values.:Seattle U.S.,2024.0,Transactions on Computer Science and Intelligent Systems Research,Yunqi Zhang,"Understanding housing price predictions assists the government in better adjusting and formulating relevant policies to promote economic stability and sustainable social development. In this study, researchers collected a large amount of data on the Seattle real estate market, including housing characteristics (such as area, number of bedrooms, number of bathrooms), geographical location (such as neighborhood, nearby facilities), and historical price data. This paper used this data to train and test linear regression, and KNN prediction models to forecast future housing price trends. The linear regression model, on the other hand, models the linear relationship between a single independent variable and the dependent variable, predicting housing prices by fitting an optimal line. The KNN prediction model, based on the nearest neighbor algorithm, predicts by searching for the K nearest neighbor samples closest to the target sample. Researchers will compare the accuracy and effectiveness of these three methods in predicting Seattle housing prices to determine which method is most suitable for housing price forecasting. Through this analysis, they aim to provide a reliable housing price prediction model for local residents to help them make wiser real estate decisions.",0.0,http://dx.doi.org/10.62051/7gzntt12,True,JournalArticle,,2024-08-12,79f4aed15e0ac08ea43c3de2d283c604e5a2a5b3,utilizethemachinelearningmodelstoforecasthomevaluesseattleus,zhang,10.62051/7gzntt12,utilizethemachinelearningmodelstoforecasthomevaluesseattleus|2024,1,2785,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c948572d6427e96f049bcf44723f628bb1c02918,10.58776/jriti.v1i3.64,,Penerapan Decision Tree Regression dalam Memprediksi Harga Rumah di Provinsi Jawa Barat,2024.0,Jurnal Riset Informatika dan Teknologi Informasi,Nafis Almajid; Y. Ginting; Alif Izzudin Ramadhan,"A house plays an important role as a basic necessity, not only as a shelter and place of rest. Additionally, the selling price of a house is greatly influenced by environmental factors such as proximity to shopping centers, office buildings, land size, and others. To gain a more accurate understanding and predict the selling price of houses, this research proposes the application of machine learning methods. This study involves the use of three machine learning algorithms: multiple linear regression, decision tree regression, and linear support vector regression to predict house prices in West Java Province. By utilizing historical data and various relevant features, such as the number of rooms, bathrooms, garages, land area, and width, the machine learning algorithms will conduct complex analyses and provide more accurate price estimations. The expected outcome of this research is to provide valuable insights for stakeholders in the property industry in West Java Province. The adoption of machine learning approaches is anticipated to enhance the ability to predict house prices and provide useful information for buyers, sellers, and other relevant parties in making better decisions in property transactions.",4.0,http://dx.doi.org/10.58776/jriti.v1i3.64,True,JournalArticle,,2024-08-13,c948572d6427e96f049bcf44723f628bb1c02918,penerapandecisiontreeregressiondalammemprediksihargarumahdiprovinsijawabarat,almajid,10.58776/jriti.v1i3.64,penerapandecisiontreeregressiondalammemprediksihargarumahdiprovinsijawabarat|2024,1,2726,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4bd2eb32f66789b92181e5c9bd7cece6b38999ee,10.56726/irjmets47584,,PRECISION IN PROPERTY: ADVANCED REGRESSION MODELS FOR ENHANCED HOUSE PRICE FORECASTING,2023.0,International Research Journal of Modernization in Engineering Technology and Science,A. Gund; S. Gaikwad,.,0.0,http://dx.doi.org/10.56726/irjmets47584,True,JournalArticle,,2023-12-23,4bd2eb32f66789b92181e5c9bd7cece6b38999ee,precisioninpropertyadvancedregressionmodelsforenhancedhousepriceforecasting,gund,10.56726/irjmets47584,precisioninpropertyadvancedregressionmodelsforenhancedhousepriceforecasting|2023,1,1501,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",087e2c4888cf8abeb34c745891220c0cbe793278,10.56726/irjmets43945,,Forestalling House Prices Using Linear Regression in Machine Learning,2023.0,International Research Journal of Modernization in Engineering Technology and Science,LATIKA KHARB; D. Chahal,"Predicting house prices is a fundamental problem in the field of real estate and economics. The accurate estimation of house prices is crucial for various stakeholders including homeowners, buyers, sellers, and policymakers. In this paper, we explore the application of linear regression, a simple yet effective machine learning technique, to predict house prices. We discuss the methodology, data preprocessing, feature selection, model training, and evaluation metrics. We also highlight the strengths, limitations, and potential improvements of the linear regression approach in the context of house price prediction. In this paper, we focus on using linear regression to predict house prices, discussing the dataset, preprocessing steps, model training, and evaluation.",0.0,http://dx.doi.org/10.56726/irjmets43945,True,JournalArticle,,2023-08-13,087e2c4888cf8abeb34c745891220c0cbe793278,forestallinghousepricesusinglinearregressioninmachinelearning,kharb,10.56726/irjmets43945,forestallinghousepricesusinglinearregressioninmachinelearning|2023,1,2273,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",49afbdfab03ddb471d9fb568d0e110755f9f301d,10.55248/gengpi.5.0324.0798,,Accurate House Price Projections,2024.0,International Journal of Research Publication and Reviews,Ravindra Turpati; S. Krishna; Ajit Kumar Rout,"— This study delves into the dynamics of housing prices in Visakhapatnam, employing a multifaceted approach that combines linear regression modelling, geocoding, and K-means clustering. Leveraging a comprehensive dataset encompassing diverse locations, area (in square feet), and bedroom-bathroom configurations, we construct a robust linear regression model for price estimation. The dataset is further enriched through geocoding, introducing latitude and longitude coordinates to each location. Expanding our analysis, we employ K-means clustering to unveil spatial patterns and groupings within the housing market. Our findings reveal intricate relationships between location, property attributes, and housing prices, providing valuable insights for both prospective buyers and real estate stakeholders. Visualizations on the Visakhapatnam map illustrate the spatial distribution of housing clusters, shedding light on localized market trends. This integrated approach not only refines price predictions but also enhances our understanding of the spatial dynamics shaping the real estate landscape in Visakhapatnam. The outcomes of this study contribute to informed decision-making in the housing market and set the stage for future research exploring the nuances of regional real estate dynamics.",0.0,http://dx.doi.org/10.55248/gengpi.5.0324.0798,True,JournalArticle,,2024-03-09,49afbdfab03ddb471d9fb568d0e110755f9f301d,accuratehousepriceprojections,turpati,10.55248/gengpi.5.0324.0798,accuratehousepriceprojections|2024,1,2800,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",93a959319d751f298301e0b785f4c63907951778,10.55248/gengpi.234.5.39837,,An Approach to Predict House Price Using Machine Learning,2023.0,International Journal of Research Publication and Reviews,Ankit Tripathi; Ankit Rai; V. Agrawal,,0.0,http://dx.doi.org/10.55248/gengpi.234.5.39837,True,JournalArticle,,2023-05-04,93a959319d751f298301e0b785f4c63907951778,anapproachtopredicthousepriceusingmachinelearning,tripathi,10.55248/gengpi.234.5.39837,anapproachtopredicthousepriceusingmachinelearning|2023,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b72781aed2cf6317bb1f29020627545b870f8605,10.54097/q8cwsx22,,The Investigation of Rental Market Rent Models Based on Machine Learning,2024.0,"Highlights in Business, Economics and Management",Shiqi Tang,"In terms of the renting field, landlords often set rental prices subjectively based on market trends, lacking standardized criteria. This can lead to arbitrary pricing and potential market instability. In contrast, machine learning, with its advancements across various industries, offers a solution to model these complex, non-linear relationships. Although numerous studies have investigated the factors that impact rental prices, there is a scarcity of research dedicated to predicting rental prices, in contrast to the extensive body of work centered on forecasting property prices within the real estate sector. Firstly, this study examines key factors influencing rental prices. Utilizing a Kaggle dataset containing statistical information on Italian housing rentals, including architectural and geographical features, this study processes the data, handles missing values, removes outliers, and analyzes feature correlations. Various machine learning models, including Backpropagation (BP) neural networks, decision trees, random forests, gradient boosting regression, and multiple linear regression, are employed for rental price prediction. The study concludes by comparing model performance and discussing their strengths and weaknesses. Key findings reveal that neural networks outperform other methods in rental price prediction, showing the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) errors, while decision trees perform the poorest. Furthermore, bathroom count exhibits the highest correlation with rental prices, followed by property size, with villa status ranking third.",0.0,http://dx.doi.org/10.54097/q8cwsx22,True,JournalArticle,,2024-03-21,b72781aed2cf6317bb1f29020627545b870f8605,theinvestigationofrentalmarketrentmodelsbasedonmachinelearning,tang,10.54097/q8cwsx22,theinvestigationofrentalmarketrentmodelsbasedonmachinelearning|2024,1,3113,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",89320da66f3ce980a47b29c40bdd0d9763125dd3,10.54097/cvn9mr84,,Integrating Machine Learning Techniques for Real Estate Analysis,2024.0,Highlights in Science Engineering and Technology,Ze-Kun Chen,"Machine learning can be applied to prediction models. The study delved into house price prediction, an area of significance to both individual participants and policymakers within and beyond the real estate market. The advanced predictive model is a tool that informs quality decision-making. This study synthesizes and builds on existing research to implement and enhance house price prediction methods, assessing the performance of Convolutional Neural Networks (CNNs), Decision Trees, and K-Nearest Neighbors (KNN) which is of focus on Most Correlated Features (KNN-MCF). Although these techniques have been developed and refined over the years, they still face challenges such as overfitting, noises existing in the dataset, and the complexity of modeling both linear and non-linear relationships. By integrating the resources, the study aims to provide objective guides for addressing the common challenges and predicting house prices, illustrating the appropriate analytical methods to apply based on characteristics of a database, extra space for improvements, and critical concerns of their practical applications. Ultimately, the research strives to bridge the gap between theoretical models and real-world applications to develop practical tools for reliable house price prediction.",1.0,http://dx.doi.org/10.54097/cvn9mr84,True,JournalArticle,,2024-04-10,89320da66f3ce980a47b29c40bdd0d9763125dd3,integratingmachinelearningtechniquesforrealestateanalysis,chen,10.54097/cvn9mr84,integratingmachinelearningtechniquesforrealestateanalysis|2024,1,2792,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",5c6283b04171a3d88a6ed11c99a471946c157118,10.54097/5811g474,,Research on Housing Price Prediction Based on Machine Learning,2024.0,Highlights in Science Engineering and Technology,Zi-Rui Huang,"The stability and long-term growth of the real estate industry depend heavily on the capacity to forecast house price trends with accuracy. The purpose of this research is to evaluate the benefits and drawbacks of several machine learning models for housing price prediction. First, the factors influencing housing prices in various ways are explained. Next, the multiple linear regression, backpropagation neural network, and random forest model, respectively, are introduced. In the meantime, the paper analyzes their forecasting shortcomings and provides examples of other scholars' optimization experiments on the aforementioned three models. The findings indicate that these three types of optimized models can obtain more stable and accurate prediction results when housing prices are predicted. Housing price forecasting can lower investment risks for real estate developers by assisting them in creating more effective project development plans and sales tactics. The government can better grasp the real estate market's development trend, create pertinent regulations and control measures, and support the stable and sustainable growth of the real estate market with the aid of home price prediction. Finding an appropriate way to predict the price of housing is therefore imperative.",0.0,http://dx.doi.org/10.54097/5811g474,True,JournalArticle,,2024-08-15,5c6283b04171a3d88a6ed11c99a471946c157118,researchonhousingpricepredictionbasedonmachinelearning,huang,10.54097/5811g474,researchonhousingpricepredictionbasedonmachinelearning|2024,1,2793,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",edd222f8c89bdf34b560a44b9091d2c544558107,10.5220/0012999000004601,,Predicting New York Housing Prices: A Comparative Analysis of Machine Learning Models,2024.0,"Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy",Jie Yu,": Accurate prediction of housing prices in New York City is crucial for investors, policymakers, and consumers navigating one of the most volatile housing markets. This study explores various machine learning methods to forecast housing prices in New York City. The predictive power of Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), and XGBoost (XGB) was examined using a comprehensive dataset with diverse housing attributes. Our results show that LR and SVR provided less accurate predictions, with LR achieving an RMSE of 4,091,594, a MAPE of 1.2991, and an adjusted R-squared of 0.2642, while SVR had an RMSE of 4,967,168, a MAPE of 0.7753, and an adjusted R-squared of -0.0844. In contrast, ensemble methods, namely RF and XGB, demonstrated superior performance on all accounts. RF achieved an RMSE of 2,145,123, a MAPE of 0.3086, and an adjusted R-squared of 0.7978, while XGB had an RMSE of 2,483,884, a MAPE of 0.4163, and an adjusted R-squared of 0.7288. These results conclude that ensemble methods, which can handle complex datasets with higher dimensionality and noise, are more adept at predicting housing prices in varied markets such as New York City. The findings have implications for stakeholders in the real estate industry seeking to leverage machine learning for investment and policy-making decisions.",1.0,http://dx.doi.org/10.5220/0012999000004601,True,Conference,,,edd222f8c89bdf34b560a44b9091d2c544558107,predictingnewyorkhousingpricesacomparativeanalysisofmachinelearningmodels,yu,10.5220/0012999000004601,predictingnewyorkhousingpricesacomparativeanalysisofmachinelearningmodels|2024,1,2853,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",97c15cfffa40422a5169adda49b90ebfa3545fd6,10.5220/0012990800004601,,Predictive Models for United States House Prices,2024.0,"Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy",Hongyi Yang,": House prices are a crucial indicator affecting citizens' lives, directly impacting individuals' and families' financial situations, as well as the stability and development of entire communities. Therefore, it is imperative to conduct in-depth research on the societal impact of house price prediction models, exploring their effects on housing markets, economic development, social welfare, and potential challenges and issues. This study addresses the issue of accurate house price prediction by conducting extensive analyses on four ensemble learning models: Random Forest, XGBoost, AdaBoost, and Stacking. The selected metrics for assessing model performance in this experiment include RMSE, R-squared, Explained Variance Score, and MAPE. The results demonstrate that the Random Forest model excels across multiple evaluation metrics, outperforming other models with the lowest RMSE and MAPE values. XGBoost shows strong competitiveness, providing accurate predictions and effectively capturing nonlinear relationships in the data, albeit slightly inferior to Random Forest. AdaBoost and Stacking exhibit moderate performance, possibly limited by their ability to handle complex relationships and noisy data.",0.0,http://dx.doi.org/10.5220/0012990800004601,True,Conference,,,97c15cfffa40422a5169adda49b90ebfa3545fd6,predictivemodelsforunitedstateshouseprices,yang,10.5220/0012990800004601,predictivemodelsforunitedstateshouseprices|2024,1,2714,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",88af5986d31944f366ab7070ec51a76e205d1a0f,10.5220/0012973500004508,,Advancing House Price Forecasting: Linear Regression and Deep Learning Models Analysis,2024.0,"Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence",Feng Tian,": The prediction of housing prices has received widespread attention from researchers due to its importance. This study offers a comprehensive analysis of house price forecasting, employing both traditional linear regression models and advanced deep learning techniques to enhance prediction accuracy. Through meticulous comparison and experimentation, deep learning methods, particularly feedforward neural networks, emerged as significantly superior in capturing complex nonlinear relationships and high-dimensional data patterns compared to linear regression models. In order to improve prediction performance, the research integrates data preparation, feature selection, and model evaluation as it methodically investigates different aspects of the dynamics of the housing market. Results highlight the potential of deep learning techniques to offer substantial improvements over conventional models, particularly in recognizing spatial and temporal trends in house pricing data. Future research aims to integrate external factors like economic indicators and urban development parameters to refine and expand predictive capabilities. It is anticipated that this strategic approach will improve the model's accuracy and usefulness in real-world real estate market analysis, enabling better informed decision-making processes.",0.0,http://dx.doi.org/10.5220/0012973500004508,True,Conference,,,88af5986d31944f366ab7070ec51a76e205d1a0f,advancinghousepriceforecastinglinearregressionanddeeplearningmodelsanalysis,tian,10.5220/0012973500004508,advancinghousepriceforecastinglinearregressionanddeeplearningmodelsanalysis|2024,1,2829,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8ec68ee17a6327d0220531303c49f28beae75aa3,10.5220/0012832700004547,,House Price Prediction and Feature Analysis Based on Multilayer Perceptron (MLP),2024.0,Proceedings of the 1st International Conference on Data Science and Engineering,Mingwei Xu,": Within the ever-changing real estate sector, accurately forecasting housing prices is crucial for different entities. This study aims to enhance forecasting models for housing costs by utilizing the benefits of Multilayer Perceptron (MLP). This research aims to improve the precision of real estate price forecasts by utilizing the sophisticated data processing capabilities of MLPs to assess the significance of different attributes. The chosen approach focuses on crafting an MLP model, intricately structured with various layers and activation mechanisms, to decode intricate connections in assorted housing market data. Findings indicate a notable enhancement in forecasting precision, as the MLP model surpassed traditional regression models, attaining more than 90% accuracy. Such results are crucial for the real estate industry, empowering key players such as purchasers, vendors, and evaluators to make better-informed choices. Implementing MLP effectively in this scenario improves understanding of the market and highlights the expertise of neural networks in predictive analytics in diverse fields.",0.0,http://dx.doi.org/10.5220/0012832700004547,True,Conference,,,8ec68ee17a6327d0220531303c49f28beae75aa3,housepricepredictionandfeatureanalysisbasedonmultilayerperceptronmlp,xu,10.5220/0012832700004547,housepricepredictionandfeatureanalysisbasedonmultilayerperceptronmlp|2024,1,2612,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ed537a6a82930e5844cd657f4218d82c34c502bf,10.5220/0012825400004547,,House Price Prediction with Optimistic Machine Learning Methods Using Bayesian Optimization,2024.0,Proceedings of the 1st International Conference on Data Science and Engineering,Haolan Jiang,": Recently, housing has been a fundamental necessity for human survival. However, the challenge lies in the often-inflated housing prices, particularly in high-GDP cities. House price prediction is crucial for citizens as it aids in effective financial planning and contributes to social stability. This study delves into the factors influencing house prices, employing and evaluating four regression models: multiple linear regression, regression decision tree, Random Forest, and XGBoost. The focus is on optimizing the performance of the two most promising models. The study finds that there is no substantial positive or negative association between the prediction label, which is the average house price of a region, and any individual attribute in the dataset. Through model comparisons, it is observed that the decision tree model outperforms the regression model significantly, with the integrated models, specifically Random Forest and XGBoost, outshining the regular regression tree model. In 5-fold cross-validation, the Bayesian optimized XGBoost model yields the best results in this study. The post-optimization R2 value of XGBoost is 0.846, showcasing an improvement of 0.024 compared to the pre-optimization phase. The hybrid model introduced in this study holds significant research potential in the realm of house price prediction. Additionally, it provides valuable insights for individuals, enabling them to make well-informed financial plans, particularly in terms of home purchase decisions. This, in turn, contributes to addressing potential social issues and fostering greater social harmony and stability.",0.0,http://dx.doi.org/10.5220/0012825400004547,True,Conference,,,ed537a6a82930e5844cd657f4218d82c34c502bf,housepricepredictionwithoptimisticmachinelearningmethodsusingbayesianoptimization,jiang,10.5220/0012825400004547,housepricepredictionwithoptimisticmachinelearningmethodsusingbayesianoptimization|2024,1,3130,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c6c5373e7d621a44459cbf98dd26ba0e9250a689,10.5220/0012797700003885,,Analysis and Forecast of Influencing Factors of House Price in Boston,2023.0,Proceedings of the 1st International Conference on Data Analysis and Machine Learning,Boyao Zhang,": The cost of housing is a significant indicator of economic activity. Both buyers and developers of real estate closely monitor fluctuations in home prices. As a result, developing an accurate housing price forecasting model is critical for the financial market and people's livelihood. Boston dataset is used for data analysis in this paper. Firstly, this paper conducted a correlation study using Pearson's r and created scatter plots between each variable and the house price. In the multiple linear regression model, this study employed techniques including outlier removal, collinearity, and variable transformation to optimize the model for a more accurate prediction, finally getting an R-squared of 0.7927. In random forest model, this research finds the optimal parameter value and then compares the importance of predictors using the indicators %IncMSE and %IncNodePurity. Finally, this study determines that the random forest regression model is superior through the investigation of the factors that influence home prices, and this paper also analyze the elements that influence house prices and consider the shortcomings of this paper.",0.0,http://dx.doi.org/10.5220/0012797700003885,True,Conference,,,c6c5373e7d621a44459cbf98dd26ba0e9250a689,analysisandforecastofinfluencingfactorsofhousepriceinboston,zhang,10.5220/0012797700003885,analysisandforecastofinfluencingfactorsofhousepriceinboston|2023,1,2649,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",15fafa249bfb405131b68b8b2bafec4e222337c2,10.30598/ppcst.knmxxiiv5i1p101-116,,Comparison of Multiple Linear Regression and Random Forest Regression Models for House Price Prediction in Semarang City Using the CRISP-DM Method,2024.0,Pattimura Proceeding Conference of Science and Technology,F M Sari; S. Sugiman,"The population density in Semarang City is increasing every year. This requires more potential land to build houses to accommodate the denser population. There are various kinds of house prices based on specifications in Semarang City. This requires the right prediction to get the desired house. This study implements and compares the performance of Multiple Linear Regression (MLR) and Random Forest Regression (RFR) models to predict house prices in Semarang City. The method used in this research is CRISP-DM (Cross-Industry Standard Process for Data Mining) as a data mining process. The data used in this research amounted to 9533 data with 8 variables obtained by web scraping. The data will go through a data preprocessing process then training the model. Next is the evaluation stage, which is carried out to measure the performance of the two models using evaluation metrics, namely R-Squared (prediction accuracy), MSE (Mean Squared Error), and RMSE (Root Mean Squared Error). The results of this study show that the MLR model obtained a prediction accuracy 61.1% with a training and testing data division ratio of 75%: 25%. While the RFR model produces a prediction accuracy 78.4% with a training and testing data division ratio of 90%: 10%. This shows that the RFR model is the best performing model. This research successfully applied the RFR model to the streamlit web framework. The final result of this research is a website that can be used by the public to predict house prices according to criteria in Semarang City.",1.0,http://dx.doi.org/10.30598/ppcst.knmxxiiv5i1p101-116,True,JournalArticle;Conference,,2024-11-22,15fafa249bfb405131b68b8b2bafec4e222337c2,comparisonofmultiplelinearregressionandrandomforestregressionmodelsforhousepricepredictioninsemarangcityusingthecrispdmmethod,sari,10.30598/ppcst.knmxxiiv5i1p101-116,comparisonofmultiplelinearregressionandrandomforestregressionmodelsforhousepricepredictioninsemarangcityusingthecrispdmmethod|2024,1,3036,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",8b4130b30360630b9f6359917bc5a9117459b657,10.22214/ijraset.2023.56935,,Effective Machine Learning Algorithm to Predict House Price,2023.0,International Journal for Research in Applied Science and Engineering Technology,Ashwani Kumar; Hemadri Rawat; Yamini Singh,"Abstract: The real estate industry, known for its dynamic pricing, is a prime candidate for the application of machine learning to enhance and accurately predict property costs. This paper focuses on predicting the market value of real estate properties, specifically in Mumbai, using a Decision Tree Regressor. The system leverages geographical variables, analyzing past market trends and upcoming developments to forecast future prices. The research aims to empower users to make informed investment decisions without relying on intermediaries. Results demonstrate an 89% accuracy with the Decision Tree Regressor. The paper discusses the system's methodology, emphasizing data preprocessing, model training, and integration with Flask for a userfriendly interface.",2.0,http://dx.doi.org/10.22214/ijraset.2023.56935,True,JournalArticle,,2023-11-30,8b4130b30360630b9f6359917bc5a9117459b657,effectivemachinelearningalgorithmtopredicthouseprice,kumar,10.22214/ijraset.2023.56935,effectivemachinelearningalgorithmtopredicthouseprice|2023,1,2267,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0d4ac511415bfe435b351caf7aeb5245033ece68,10.22214/ijraset.2023.55990,,Predicting the Rental Values of Houses in Bangalore City Using the Linear Regression Approach,2023.0,International Journal for Research in Applied Science and Engineering Technology,Ujjwal Jaiswal; Ayush Kumar; D. Das; Arya Chakraborty; Aditya Vikram Sahana; Debmitra Ghosh; N. Mishra,"Abstract: In recent years, Karnataka, one of the hotspots for real estate development, has seen an increase in demand from prospective home buyers and investors and is expected to witness further boom in the sector by 2020. What things should a potential home buyer consider before purchasing a home? Location, property size, proximity to offices, schools, parks, restaurants, hospitals or the stereotypical white picket fence? And the most important factor — price? Now with the lingering impact of COVID-19, enforcement of the Real Estate (Regulation and Development) Act (RERA) and lack of confidence in developers in the city, apartments sold in India fell by 3 percent in 2019. In fact, property prices in Bengaluru fell by nearly 5 percent in the second half of 2019, according to a study published by real estate consultancy Knight Frank. Buying a house, especially in a city like Bengaluru, is a tricky decision. While the main factors are usually the same for all metros, there are more to consider for Silicon Valley in India. With the help of the millennial crowd, vibrant culture, great climate and plenty of job opportunities, finding out the price of a house in Bengaluru is difficult. This post reflects an effort to solve the mentioned problems. In this paper, the authors have tried to develop such a system which in turn will provide a very accurate prediction of house prices in Bengaluru city. The authors have tried to create a user-friendly interface design that will allow users to choose options according to their requirements and get an estimated house price according to their needs.",0.0,http://dx.doi.org/10.22214/ijraset.2023.55990,True,JournalArticle,,2023-10-31,0d4ac511415bfe435b351caf7aeb5245033ece68,predictingtherentalvaluesofhousesinbangalorecityusingthelinearregressionapproach,jaiswal,10.22214/ijraset.2023.55990,predictingtherentalvaluesofhousesinbangalorecityusingthelinearregressionapproach|2023,1,3110,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,5,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9d3c19e8632a684b81cce60be45ee47e8672a3c9,10.22214/ijraset.2023.53273,,House Price Prediction and Recommendation,2023.0,International Journal for Research in Applied Science and Engineering Technology,Poonam Patekar,"Abstract: Determining how much a house will sell for in a city is still a challenging and time-consuming task. This article's goal is to make predictions about the coherence of non-housing prices. A crucial method to ease the challenging design is to use machine learning, which can intelligently optimize the best pipeline fit for a task or dataset. For individuals who will be residing in a home for an extended period of time but not permanently, it is essential to predict the selling price. Real estate forecasting is a crucial part of the industry. From historical real estate market data, the literature seeks to extract pertinent information. Land price bubbles grow as a result of real estate prices, which leads to macroeconomic instability. The government should look into the variables that drive up real estate prices so that it can use them as a guide to assist stabilize the area. There are many economic circumstances that are in play at the time also have an impact on the selling price of a home.",0.0,http://dx.doi.org/10.22214/ijraset.2023.53273,True,JournalArticle,,2023-05-31,9d3c19e8632a684b81cce60be45ee47e8672a3c9,housepricepredictionandrecommendation,patekar,10.22214/ijraset.2023.53273,housepricepredictionandrecommendation|2023,1,2514,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",44d9c9de089a2aa76fb13464c64bfd1a37dd7269,10.22214/ijraset.2023.52578,,House Price Prediction System Using Machine Learning,2023.0,International Journal for Research in Applied Science and Engineering Technology,Amol Dhakne,"Abstract: When individuals are in the market for a new home, they often display a more cautious approach when it comes to their budgets and overall market strategies. However, the current system for determining house prices typically lacks the essential element of predicting future market trends and potential price increases. The existing system Give the functionality for buyers, allowing them to search for houses by features or address. Machine Learning has significantly contributed to various areas such as natural language processing, product recommendations, healthcare, customer service, and automotive safety. Its widespread adoption in these fields highlights its trendiness and its potential for transformative impact. In light of this, we are incorporating Machine Learning into our project. Predicting the selling price of houses in cities such as Bengaluru remains a complex and intricate task. Numerous interrelated factors influence the property prices in these cities. Essential elements that impact the price comprise the property's size, its location, and its characteristics, including the number of rooms and bathrooms.",0.0,http://dx.doi.org/10.22214/ijraset.2023.52578,True,JournalArticle,,2023-05-31,44d9c9de089a2aa76fb13464c64bfd1a37dd7269,housepricepredictionsystemusingmachinelearning,dhakne,10.22214/ijraset.2023.52578,housepricepredictionsystemusingmachinelearning|2023,1,2642,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,4,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",6ac9658b7e8d06a4856baaded7047cf2200da810,10.22214/ijraset.2023.52048,,Image Based Appraisal Real Estate Properties,2023.0,International Journal for Research in Applied Science and Engineering Technology,E. Sankar; M. D. P. Varma; M. M. Varma,"Abstract: Real estate appraisal, which is the process of estimating the price for real estate parcels, is vital for both deals and merchandisers as the base for concession and trade. Traditionally, the duplication deals model has been vastly espoused to estimate real estate price. still, it depends the design and computation of a complex profitable affiliated indicator, which is challenging to estimate directly. moment, real estate brokers give easy access to detailed online information on real estate parcels to their guests. We're interested in estimating the real estate price from these large quantities of fluently entered data. In particular, we dissect the vaticination power of online house cinema, which is one of the vital factors for online addicts to make a implicit visiting decision. In this work, we employ a intermittent Neural Network( RNN) to prognosticate real estate price using the state- of- the- art visual features. The experimental results indicate that our model outperforms several of other state- of- theart birth algorithms in terms of both mean absolute error( MAE) and mean absolute chance error( MAPE).",0.0,http://dx.doi.org/10.22214/ijraset.2023.52048,True,JournalArticle,,2023-05-31,6ac9658b7e8d06a4856baaded7047cf2200da810,imagebasedappraisalrealestateproperties,sankar,10.22214/ijraset.2023.52048,imagebasedappraisalrealestateproperties|2023,1,2639,1,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f7b72366e648e4477be61d38a2eab9263211f43a,10.22214/ijraset.2023.50307,,Dream House Price Predictor,2023.0,International Journal for Research in Applied Science and Engineering Technology,Abhiraj Singh; K. Chand; Susheela Singh; Kamal Soni,"Abstract: The dream house price predictor project aims to build a machine learning model that can predict the selling price of a house based on various features such as location, number of bedrooms, square footage, and other relevant factors. The model will be trained on a dataset of historical housing prices and features, and will use regression techniques to make predictions on new, unseen data. The project will also explore the impact of different features on house prices and provide insights into which factors are the most important in determining the value of a property. The goal of the project is to provide a tool for homebuyers, sellers, and real estate professionals to better understand the market and make informed decisions. The Dream House Price Predictor project is aimed at predicting the prices of residential properties based on various features such as location, size, number of bedrooms, and other amenities. The project uses a dataset of real estate transactions and employs machine learning algorithms to build a predictive model. The model is trained on the historical data and tested on a validation set to ensure its accuracy. The results of the project can be used by real estate agents, property buyers, and sellers to make informed decisions about pricing and investment opportunities. This project demonstrates the potential of machine learning to assist in the real estate market and provides a valuable tool for predicting property prices.",0.0,http://dx.doi.org/10.22214/ijraset.2023.50307,True,JournalArticle,,2023-04-30,f7b72366e648e4477be61d38a2eab9263211f43a,dreamhousepricepredictor,singh,10.22214/ijraset.2023.50307,dreamhousepricepredictor|2023,1,2976,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e85a70d80c8be7df5bb231edecac21f80be1f8eb,10.1063/5.0239036,,House price prediction with advanced machine learning techniques,2024.0,AIP Conference Proceedings,P. Saraswathi; M. Prabha; N. A. Jhoshitha; J. G. Prathiksaa,,1.0,http://dx.doi.org/10.1063/5.0239036,True,Conference,,,e85a70d80c8be7df5bb231edecac21f80be1f8eb,housepricepredictionwithadvancedmachinelearningtechniques,saraswathi,10.1063/5.0239036,housepricepredictionwithadvancedmachinelearningtechniques|2024,1,1500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",8583e9cfae17f0160faf43729d21d6d3618746d9,10.1155/2020/5287263,,A Fully Automated Adjustment of Ensemble Methods in Machine Learning for Modeling Complex Real Estate Systems,2020.0,Complex,J. Alfaro-Navarro; E. Cano; Esteban Alfaro-Cortés; N. García; Matías Gámez; Beatriz Larraz,"The close relationship between collateral value and bank stability has led to a considerable need to a rapid and economical appraisal of real estate. The greater availability of information related to housing stock has prompted to the use of so-called big data and machine learning in the estimation of property prices. Although this methodology has already been applied to the real estate market to identify which variables influence dwelling prices, its use for estimating the price of properties is not so frequent. The application of this methodology has become more sophisticated over time, from applying simple methods to using the so-called ensemble methods and, while the estimation capacity has improved, it has only been applied to specific geographical areas. The main contribution of this article lies in developing an application for the entire Spanish market that fully automatically provides the best model for each municipality. Real estate property prices in 433 municipalities are estimated from a sample of 790,631 dwellings, using different ensemble methods based on decision trees such as bagging, boosting, and random forest. The results for estimating the price of dwellings show a good performance of the techniques developed, in terms of the error measures, with the best results being achieved using the techniques of bagging and random forest.",46.0,http://downloads.hindawi.com/journals/complexity/2020/5287263.pdf,True,JournalArticle,Computer Science,2020-04-14,8583e9cfae17f0160faf43729d21d6d3618746d9,afullyautomatedadjustmentofensemblemethodsinmachinelearningformodelingcomplexrealestatesystems,alfaro-navarro,10.1155/2020/5287263,afullyautomatedadjustmentofensemblemethodsinmachinelearningformodelingcomplexrealestatesystems|2020,1,2870,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",56d3855be100922ffd29e919f1825f3d73093375,10.54691/bcpbm.v24i.1525,,An empirical analysis of second-hand house transaction prices based on machine learning,2022.0,BCP Business & Management,Yingshi Cai,"The Beijing real estate market is one of the most developed and representative real estate markets in China. As of May 25, 2016, Beijing accounted for 88.2% of the market residential transactions, and the Beijing property market has fully entered the era of second-hand houses. Therefore, the price of second-hand houses in Beijing can be judged by studying the factors influencing the price of second-hand houses in Beijing, and the study has theoretical significance. Taking Beijing second-hand houses as the research object, the data source is mainly designed as a crawler to mine the data and obtain the transaction attribute information of chain house network, and the data sample size is 2000 items in total [1]. In this paper, we obtained the important variables affecting the house price through multiple model analysis and predicted the data of 100 unknown house prices.",0.0,http://bcpublication.org/index.php/BM/article/download/1525/1534,True,,,2022-08-10,56d3855be100922ffd29e919f1825f3d73093375,anempiricalanalysisofsecondhandhousetransactionpricesbasedonmachinelearning,cai,10.54691/bcpbm.v24i.1525,anempiricalanalysisofsecondhandhousetransactionpricesbasedonmachinelearning|2022,1,2379,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",01ea132f4898d955e0b682ac4aeb09ec7c778707,10.48550/arxiv.2302.00117,2302.00117,Real Estate Property Valuation using Self-Supervised Vision Transformers,2023.0,arXiv.org,Mahdieh Yazdani; M. Raissi,"The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self-supervised vision transformers, a recent breakthrough in computer vision and deep learning. Our proposed algorithm uses a combination of machine learning, computer vision and hedonic pricing models trained on real estate data to estimate the value of a given property. We collected and pre-processed a data set of real estate properties in the city of Boulder, Colorado and used it to train, validate and test our algorithm. Our data set consisted of qualitative images (including house interiors, exteriors, and street views) as well as quantitative features such as the number of bedrooms, bathrooms, square footage, lot square footage, property age, crime rates, and proximity to amenities. We evaluated the performance of our model using metrics such as Root Mean Squared Error (RMSE). Our findings indicate that these techniques are able to accurately predict the value of properties, with a low RMSE. The proposed algorithm outperforms traditional appraisal methods that do not leverage property images and has the potential to be used in real-world applications.",3.0,http://arxiv.org/pdf/2302.00117,True,JournalArticle,Computer Science;Economics,2023-01-31,01ea132f4898d955e0b682ac4aeb09ec7c778707,realestatepropertyvaluationusingselfsupervisedvisiontransformers,yazdani,10.48550/arxiv.2302.00117,realestatepropertyvaluationusingselfsupervisedvisiontransformers|2023,1,2750,7,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || computer vision property valuation || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic property valuation || house prices proximity hedonic",2,0,1,5,0,5,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1fe0020ebbc9cb9f5fb434984db4a44f6789e96e,10.48550/arxiv.2210.06261,2210.06261,Predicting housing prices and analyzing real estate market in the Chicago suburbs using Machine Learning,2022.0,Journal of student-scientists' research,Kevin Xu; Hieu Nguyen,"The pricing of housing properties is determined by a variety of factors. However, post-pandemic markets have experienced volatility in the Chicago suburb area, which have affected house prices greatly. In this study, analysis was done on the Naperville/Bolingbrook real estate market to predict property prices based on these housing attributes through machine learning models, and to evaluate the effectiveness of such models in a volatile market space. Gathering data from Redfin, a real estate website, sales data from 2018 up until the summer season of 2022 were collected for research. By analyzing these sales in this range of time, we can also look at the state of the housing market and identify trends in price. For modeling the data, the models used were linear regression, support vector regression, decision tree regression, random forest regression, and XGBoost regression. To analyze results, comparison was made on the MAE, RMSE, and R-squared values for each model. It was found that the XGBoost model performs the best in predicting house prices despite the additional volatility sponsored by post-pandemic conditions. After modeling, Shapley Values (SHAP) were used to evaluate the weights of the variables in constructing models. The code and data files can be found at https://github.com/ GeometricBison/HousePriceML.",5.0,http://arxiv.org/pdf/2210.06261,True,JournalArticle,Computer Science,2022-10-12,1fe0020ebbc9cb9f5fb434984db4a44f6789e96e,predictinghousingpricesandanalyzingrealestatemarketinthechicagosuburbsusingmachinelearning,xu,10.48550/arxiv.2210.06261,predictinghousingpricesandanalyzingrealestatemarketinthechicagosuburbsusingmachinelearning|2022,1,2838,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",639ac23c5f13dafd945cdd40a5f60f7978c08db0,10.48550/arxiv.2205.01180,2205.0118,Using Machine Learning to Evaluate Real Estate Prices Using Location Big Data,2022.0,Systems and Information Engineering Design Symposium,W. Coleman; Ben Johann; N. Pasternak; Jaya Vellayan; N. Foutz; Heman Shakeri,"With everyone trying to enter the real estate market nowadays, knowing the proper valuations for residential and commercial properties has become crucial. Past researchers have been known to utilize static real estate data (e.g, number of beds, baths, square footage) or even a combination of real estate and demographic information to predict property prices. In this investigation, we attempted to improve upon past research. So we decided to explore a unique approach - we wanted to determine if mobile location data could be used to improve the predictive power of popular regression and tree-based models. To prepare our data for our models, we processed the mobility data by attaching it to individual properties from the real estate data that aggregated users within 500 meters of the property for each day of the week. We removed people that lived within 500 meters of each property, so each property's aggregated mobility data only contained non-resident census features. On top of these dynamic census features, we also included static census features, including the number of people in the area, the average proportion of people commuting, and the number of residents in the area. Finally, we tested multiple models to predict real estate prices. Our proposed model is two stacked random forest modules combined using a ridge regression that uses the random forest outputs as predictors. The first random forest model used static features only and the second random forest model used dynamic features only. Comparing our models with and without the dynamic mobile location features concludes the model with dynamic mobile location features achieves 3 % lower mean squared error than the same model but without dynamic mobile location features.",3.0,http://arxiv.org/pdf/2205.01180,True,JournalArticle,Computer Science,2022-04-28,639ac23c5f13dafd945cdd40a5f60f7978c08db0,usingmachinelearningtoevaluaterealestatepricesusinglocationbigdata,coleman,10.48550/arxiv.2205.01180,usingmachinelearningtoevaluaterealestatepricesusinglocationbigdata|2022,1,3254,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d9de0ae75242c3ae0a2c1b2f102935a41fd6249c,10.48550/arxiv.2204.09050,2204.0905,House Price Prediction Based On Deep Learning,2022.0,arXiv.org,Yuying Wu; Youshan Zhang,"Since ancient times, what Chinese people have been pursuing is very simple, which is nothing more than ""to live and work happily, to eat and dress comfortable"". Today, more than 40 years after the reform and opening, people have basically solved the problem of food and clothing, and the urgent problem is housing. Nowadays, due to the storm of long-term rental apartment intermediary platforms such as eggshell, increasing the sense of insecurity of renters, as well as the urbanization in recent years and the scramble for people in major cities, this will make the future real estate market competition more intense. In order to better grasp the real estate price, let consumers buy a house reasonably, and provide a reference for the government to formulate policies, this paper summarizes the existing methods of house price prediction and proposes a house price prediction method based on mixed depth vision and text features. real estate related analyzes the development status the real estate at home M2, GRP, commonly existing resnet50 are used to preprocess the index attributes. The flow chart of the MVTs model is constructed, and the implementation steps of the proposed model are described in detail. Finally, we select 75% of housing samples as training, and the remaining 25% of the samples as test. We compare the prediction results of our model with other five models: autoregressive integrated moving average mode (ARIMA), grey prediction model (GM(1,1)), support vector regression (SVR), BP neural network and artificial neural network (ANN), the results show that the proposed novel MVTs model has higher prediction accuracy. Therefore, MVTs is more suitable for the house price prediction.",1.0,http://arxiv.org/pdf/2204.09050,True,JournalArticle,Computer Science,2022-04-19,d9de0ae75242c3ae0a2c1b2f102935a41fd6249c,housepricepredictionbasedondeeplearning,wu,10.48550/arxiv.2204.09050,housepricepredictionbasedondeeplearning|2022,1,3211,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,5,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1b1ce25ba43987a53746ab26ca5e28722e019c82,10.11648/j.sjbm.20261403.15,,A Comparative Study of Machine Learning-Based Predictive Models for House Price Prediction,2026.0,Science Journal of Business and Management,Praveen Singh; Rachna Jain; Shikha Sharma," Predicting house prices is vital in helping homebuyers, investors, real estate agencies, and policymakers make informed decisions. But it's hard to precisely value a house when there are a number of factors at play – from property details to location to market conditions. The present study accounts for the comparative analysis of four machine learning techniques Multiple Linear Regression (MLR), Support Vector Regression (SVR), Feed Forward Neural Network (FFNN) and Extreme Gradient Boosting (XGBoost)) on predicting house prices by applying the Boston Housing dataset. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Coefficient of Determination (R 2 ) were used to assess the models. Experimental results showed that XGBoost had the lowest RMSE (3.9147), MAE (2.8454), MSE (15.3252) and the highest R 2 value (0.7910) among all the models. The second best result was obtained by SVR while FFNN gave an accurate prediction and MLR gave the least accurate result. The analysis results show that the XGBoost model can better capture the complex nonlinear relationship between house attributes and makes it significantly better than the traditional regression model and neural network model. In conclusion, XGBoost is a powerful and stable model that can be used to accurately predict house prices and value real estate. ",0.0,http://article.sciencepg.com/pdf/j.sjbm.20261403.15,True,JournalArticle,,2026-09-08,1b1ce25ba43987a53746ab26ca5e28722e019c82,acomparativestudyofmachinelearningbasedpredictivemodelsforhousepriceprediction,singh,10.11648/j.sjbm.20261403.15,acomparativestudyofmachinelearningbasedpredictivemodelsforhousepriceprediction|2026,1,2870,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",766d53dbf6ae79a1ef460d6815ef54de34afa9bd,10.11648/j.sjams.20261401.12,,Unveiling the Impact of Nonlinear Modeling in Housing Price Prediction: An Empirical Comparative Study,2026.0,Science Journal of Applied Mathematics and Statistics,Jie Wang; Qiu-Tong Yu; Xin-Tong Liu; Hongli Zhu; Qiuyu Ming; Jia-Tong Dai; Guan-Yu Sha; Hanyu Xu; Yan Zhong; Shan-Cheng Yu,"Regression analysis is a core analytical tool widely employed across diverse domains for predicting continuous outcomes, serving as a cornerstone of statistical inference and machine learning applications ranging from economic trend forecasting to healthcare risk assessment and real estate valuation. Choosing an effective regression technique is critical for accurate predictions, yet a daunting challenge for non-experts due to the wide variety of methods, each with distinct assumptions, tuning requirements and applicability boundaries. To address this dilemma, this study conducts a rigorous empirical comparison of five popular regression techniques—Ordinary Least Squares (OLS), Ridge regression, Lasso regression, Elastic Net, and Polynomial regression—applied to house price prediction using two benchmark datasets: the classic Boston Housing dataset and the comprehensive California Housing dataset. A multi-dimensional evaluation framework was adopted, including quantitative metrics (Mean Squared Error (MSE) and coefficient of determination () and qualitative diagnostics (residual analysis and Quantile-Quantile (QQ) plots) to assess prediction accuracy and error distribution. Results indicate that Polynomial regression consistently achieves superior performance across both datasets, highlighting its effectiveness in capturing the complex nonlinear relationships inherent in housing data. Ridge, Lasso, and Elastic Net provide comparable but lower performance, with strengths in mitigating multicollinearity rather than enhancing nonlinear fitting. OLS yields acceptable baseline results but less robust performance when confronted with real-world nonlinearities. These findings offer clear practical guidance for non-experts seeking reliable “out-of-the-box” regression techniques, and contribute valuable insights to assist practitioners in model selection for real-world predictive tasks without extensive tuning.",0.0,http://article.sciencepg.com/pdf/j.sjams.20261401.12,True,JournalArticle,,2026-01-09,766d53dbf6ae79a1ef460d6815ef54de34afa9bd,unveilingtheimpactofnonlinearmodelinginhousingpricepredictionanempiricalcomparativestudy,wang,10.11648/j.sjams.20261401.12,unveilingtheimpactofnonlinearmodelinginhousingpricepredictionanempiricalcomparativestudy|2026,1,3435,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",528f0ce730b0607cc6107d672aec344aaaae1b24,10.1016/j.eswa.2014.11.040,,"Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data",2015.0,Expert systems with applications,Byeonghwa Park; J. Bae,,367.0,,False,JournalArticle,Computer Science,2015-04-15,528f0ce730b0607cc6107d672aec344aaaae1b24,usingmachinelearningalgorithmsforhousingpricepredictionthecaseoffairfaxcountyvirginiahousingdata,park,10.1016/j.eswa.2014.11.040,usingmachinelearningalgorithmsforhousingpricepredictionthecaseoffairfaxcountyvirginiahousingdata|2015,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ec63c3fbb71c48fb41af79adb6e35a856b60e736,10.1080/00420980600990928,,Determinants of House Price: A Decision Tree Approach,2006.0,,Gang-Zhi Fan; S. Ong; H. Koh,,156.0,,False,JournalArticle,Economics,2006-11-01,ec63c3fbb71c48fb41af79adb6e35a856b60e736,determinantsofhousepriceadecisiontreeapproach,fan,10.1080/00420980600990928,determinantsofhousepriceadecisiontreeapproach|2006,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",cc667ff1c6266ef85aa48678b7c69b3d4f0aa9c9,10.1109/icmlde.2018.00017,,"Housing Price Prediction Using Machine Learning Algorithms: The Case of Melbourne City, Australia",2018.0,2018 International Conference on Machine Learning and Data Engineering (iCMLDE),The Danh Phan,"House price forecasting is an important topic of real estate. The literature attempts to derive useful knowledge from historical data of property markets. Machine learning techniques are applied to analyze historical property transactions in Australia to discover useful models for house buyers and sellers. Revealed is the high discrepancy between house prices in the most expensive and most affordable suburbs in the city of Melbourne. Moreover, experiments demonstrate that the combination of Stepwise and Support Vector Machine that is based on mean squared error measurement is a competitive approach.",113.0,,False,Conference,,2018-12-01,cc667ff1c6266ef85aa48678b7c69b3d4f0aa9c9,housingpricepredictionusingmachinelearningalgorithmsthecaseofmelbournecityaustralia,phan,10.1109/icmlde.2018.00017,housingpricepredictionusingmachinelearningalgorithmsthecaseofmelbournecityaustralia|2018,1,1606,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d7da46c7cf311d72cf9bf91cd870dfe73509d542,10.1016/j.cities.2022.103941,,Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms,2022.0,Cities,A. Soltani; M. Heydari; F. Aghaei; C. Pettit,,110.0,,False,,,,d7da46c7cf311d72cf9bf91cd870dfe73509d542,housingpricepredictionincorporatingspatiotemporaldependencyintomachinelearningalgorithms,soltani,10.1016/j.cities.2022.103941,housingpricepredictionincorporatingspatiotemporaldependencyintomachinelearningalgorithms|2022,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",252045b616ef9311fe7438d6ff02978aa1888f62,10.1109/icsss.2019.8882834,,House Price Prediction Using Regression Techniques: A Comparative Study,2019.0,2019 International Conference on Smart Structures and Systems (ICSSS),C. Madhuri; G. Anuradha; M. Pujitha,"People are careful when they are trying to buy a new house with their budgets and market strategies. The objective of the paper is to forecast the coherent house prices for non-house holders based on their financial provisions and their aspirations. By analyzing the foregoing merchandise, fare ranges and also forewarns developments, speculated prices will be estimated. The paper involves predictions using different Regression techniques like Multiple linear, Ridge, LASSO, Elastic Net, Gradient boosting and Ada Boost Regression. House price prediction on a data set has been done by using all the above mentioned techniques to find out the best among them. The motive of this paper is to help the seller to estimate the selling cost of a house perfectly and to help people to predict the exact time slap to accumulate a house. Some of the related factors that impact the cost were also taken into considerations such as physical conditions, concept and location etc.",106.0,,False,Conference,Computer Science,2019-03-01,252045b616ef9311fe7438d6ff02978aa1888f62,housepricepredictionusingregressiontechniquesacomparativestudy,madhuri,10.1109/icsss.2019.8882834,housepricepredictionusingregressiontechniquesacomparativestudy|2019,1,1971,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c1d9d50faf0276a3d26ba46e3d70fcbc88d3f5d4,10.1109/icicct.2018.8473231,,House Price Prediction Using Machine Learning and Neural Networks,2018.0,International Conference Inventive Communication and Computational Technologies,Ayush Varma; Abhijit Sarma; Sagar Doshi; Rohini Nair,,103.0,,False,Conference,Computer Science,2018-04-20,c1d9d50faf0276a3d26ba46e3d70fcbc88d3f5d4,housepricepredictionusingmachinelearningandneuralnetworks,varma,10.1109/icicct.2018.8473231,housepricepredictionusingmachinelearningandneuralnetworks|2018,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",cf2ceb976c9b804633f11d3a52d477f348c07eeb,10.1109/ieem.2017.8289904,,A hybrid regression technique for house prices prediction,2017.0,IEEE International Conference on Industrial Engineering and Engineering Management,Sifei Lu; Zeng-Xiang Li; Zheng Qin; Xulei Yang; R. Goh,,98.0,,False,Conference,Computer Science,2017-12-01,cf2ceb976c9b804633f11d3a52d477f348c07eeb,ahybridregressiontechniqueforhousepricesprediction,lu,10.1109/ieem.2017.8289904,ahybridregressiontechniqueforhousepricesprediction|2017,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,real estate hedonic regression,27f607f5625634531b0f4d7b0bff5ac0eef6f1cf,10.1002/cpe.7342,,House price prediction using hedonic pricing model and machine learning techniques,2022.0,Concurrency and Computation,John F. W. Zaki; Anand Nayyar; Surjeet Dalal; Z. H. Ali,"The problem with property valuation is that it is extremely complex. It is difficult to objectively model the pricing process or fairly estimate a property value. Many factors can contribute to this complexity such as spatial and time factors. Evaluators and researchers have been trying to model the process for centuries. Up until recently, when computer‐aided valuation systems provided better solutions in the data evaluation and real estate valuation. Nevertheless, they may suffer from low transparency, inaccuracy, and inefficiency. This work explores the ability of machine learning techniques (MLTs) in enhancing economic activities by increasing the accuracy of house price prediction. In this article, XGBoost algorithm has been integrated with outlier sum‐statistic (OS) approach. In the real estate industry, the price of property plays a crucial role in economic growth. The research attempts to predict the price of a house using MLTs. Here, the price of the property is predicted using Extreme Gradient (XG) boosting algorithm and hedonic regression pricing. Both XGBoost and hedonic pricing models use 13 variables as inputs to predict house prices. The contribution of this research lies in the practicality of using XGboost technique to predict house prices. Finally, the accuracy of the prediction algorithms is reported with XGBoosting showing the highest accuracy of 84.1% while the accuracy of the hedonic regression algorithm is 42%.",64.0,,False,JournalArticle,Computer Science,2022-09-22,27f607f5625634531b0f4d7b0bff5ac0eef6f1cf,housepricepredictionusinghedonicpricingmodelandmachinelearningtechniques,zaki,10.1002/cpe.7342,housepricepredictionusinghedonicpricingmodelandmachinelearningtechniques|2022,1,2457,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic property valuation || real estate hedonic regression",2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",20dd3e477e0e5f33792b85692f8e002c8ebbff9f,10.1109/icimia48430.2020.9074952,,Machine Learning based Predicting House Prices using Regression Techniques,2020.0,International Conference on Innovative Mechanisms for Industry Applications,J. Manasa; Radha Gupta; N. S. Narahari,"Predictive models for determining the sale price of houses in cities like Bengaluru is still remaining as more challenging and tricky task. The sale price of properties in cities like Bengaluru depends on a number of interdependent factors. Key factors that might affect the price include area of the property, location of the property and its amenities. In this research work, an analytical study has been carried out by considering the data set that remains open to the public by illustrating the available housing properties in machine hackathon platform. The data set has nine features. In this study, an attempt has been made to construct a predictive model for evaluating the price based on the factors that affect the price. Modeling explorations apply some regression techniques such as multiple linear regression (Least Squares), Lasso and Ridge regression models, support vector regression, and boosting algorithms such as Extreme Gradient Boost Regression (XG Boost). Such models are used to build a predictive model, and to pick the best performing model by performing a comparative analysis on the predictive errors obtained between these models. Here, the attempt is to construct a predictive model for evaluating the price based on factors that affects the price.",63.0,,False,Conference,Computer Science,2020-03-01,20dd3e477e0e5f33792b85692f8e002c8ebbff9f,machinelearningbasedpredictinghousepricesusingregressiontechniques,manasa,10.1109/icimia48430.2020.9074952,machinelearningbasedpredictinghousepricesusingregressiontechniques|2020,1,2278,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",dd1db5adbc20b1f1cb9ebbdc8e79f7c4e4bd3725,10.1109/fskd.2016.7603227,,Housing price prediction using neural networks,2016.0,"International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery",W. T. Lim; Lipo Wang; Yaoli Wang; Qing Chang,,59.0,,False,JournalArticle;Conference,Computer Science,2016-05-24,dd1db5adbc20b1f1cb9ebbdc8e79f7c4e4bd3725,housingpricepredictionusingneuralnetworks,lim,10.1109/fskd.2016.7603227,housingpricepredictionusingneuralnetworks|2016,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",493645a0fe1cde4bb62cfe299ca0eb4e8d73913f,10.1016/j.neucom.2020.05.013,,Multi-source urban data fusion for property value assessment: A case study in Philadelphia,2020.0,Neurocomputing,Junchi Bin; Bryan Gardiner; Eric Li; Zheng Liu,"Abstract The property value assessment in the real estate market still remains as a challenges due to incomplete and insufficient information, as well as the lack of efficient algorithms. House attributes, such as size and number of bedrooms, are currently being employed to perform the estimation by professional appraisers and researchers. Numerous algorithms have been proposed; however, a better assessment performance is still expected by the market. Nowadays, there are more available relevant data from various sources in urban areas, which have a potential impact on the house value. In this paper, we propose to fuse urban data, i.e., metadata and imagery data, with house attributes to unveil the market value of the property in Philadelphia. Specifically, two deep neural networks, i.e., metadata fusion network and image appraiser, are proposed to extract the representations, i.e., expected levels, from metadata and street-view images, respectively. A boosted regression tree (BRT) is adapted to estimate the market values of houses with the fused metadata and expected levels. The experimental results with the data collected from the city of Philadelphia demonstrate the effectiveness of the proposed model. The research presented in this paper also provides the real estate industry a new reference to the property value assessment with the data fusion methodology.",51.0,,False,JournalArticle,Computer Science,2020-09-03,493645a0fe1cde4bb62cfe299ca0eb4e8d73913f,multisourceurbandatafusionforpropertyvalueassessmentacasestudyinphiladelphia,bin,10.1016/j.neucom.2020.05.013,multisourceurbandatafusionforpropertyvalueassessmentacasestudyinphiladelphia|2020,1,2382,2,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",20cf5ab8d1aaf597cd6cd639c46cd1d2c459cde9,10.1007/s10614-020-09973-5,,A New Appraisal Model of Second-Hand Housing Prices in China’s First-Tier Cities Based on Machine Learning Algorithms,2020.0,Computational Economics,Lu-guang Xu; Zhong-Wu Li,,51.0,,False,,Computer Science,2020-03-04,20cf5ab8d1aaf597cd6cd639c46cd1d2c459cde9,anewappraisalmodelofsecondhandhousingpricesinchinasfirsttiercitiesbasedonmachinelearningalgorithms,xu,10.1007/s10614-020-09973-5,anewappraisalmodelofsecondhandhousingpricesinchinasfirsttiercitiesbasedonmachinelearningalgorithms|2020,1,1000,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property) || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",74da7d0584f54f7463f565a726cbfdddc677be74,10.1109/ssci44817.2019.9002790,,Deep Learning with XGBoost for Real Estate Appraisal,2019.0,IEEE Symposium Series on Computational Intelligence,Yun Zhao; Girija Chetty; D. Tran,"Real estate appraisal is an important tool for evaluating property values when purchasing, selling, insuring, lending or taxing on residency properties. The traditional way to do real estate appraisal is to analyse recent sales data with basic information, such as bedrooms, bathrooms, land-size, and suburb to estimate house price prediction by comparing and selecting similar properties to the property that is being evaluated. Real estate agents are widely using this method for their clients to help them determine a price to list when selling a home or a price to offer when buying a home. However, the traditional method is very subjective and depends heavily on the experience and knowledge of real estate agents and two different agents might come up with different estimate prices. Today, machine learning and deep learning techniques provide an objective, advanced and robust option, making the joint analysis of tabular data combined with visual content possible. We are interested in estimating the real estate prices like real estate agents. In this paper, we examine to use deep learning combining with extreme Gradient Boosting (XGBoost) for real estate appraisal, by analysing historical sale records together with visual content, with online house pictures, and by scoring each image from an aesthetic point of view to make a house price prediction. Our experiment shows that an improvement in performance of house price prediction accuracy, with replacing the last output layer with XGBoost.",50.0,,False,JournalArticle,Computer Science,2019-12-01,74da7d0584f54f7463f565a726cbfdddc677be74,deeplearningwithxgboostforrealestateappraisal,zhao,10.1109/ssci44817.2019.9002790,deeplearningwithxgboostforrealestateappraisal|2019,1,2509,2,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e90c1618193e23838606cbca3759d3e6518426eb,10.1145/3195106.3195133,,House Prices Prediction with Machine Learning Algorithms,2018.0,International Conference on Machine Learning and Computing,Chenchen Fan; Z. Cui; Xiao-Feng Zhong,,48.0,,False,Book;JournalArticle,Computer Science,2018-02-26,e90c1618193e23838606cbca3759d3e6518426eb,housepricespredictionwithmachinelearningalgorithms,fan,10.1145/3195106.3195133,housepricespredictionwithmachinelearningalgorithms|2018,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",196ea01e8cc6728291e96bbf0320240535ff68f9,10.1109/icait.2019.8935894,,Model Research on Forecast of Second-Hand House Price in Chengdu Based on XGboost Algorithm,2019.0,International Conference on Advanced Infocomm Technology,Zhen Peng; Qiang Huang; Yincheng Han,"In order to better and more accurately study the housing price of second-hand houses, this paper analyzed and studied 35417 pieces of data captured by Chengdu HOME LINK network. Firstly, the captured data were cleaned and the characteristics were selected. Then, multiple linear regression, decision tree and XGboost models were used to fit the predicted housing price score curve for these ten factors, and finally, the optimal prediction model was selected through parameter adjustment. The experimental results show that the accuracy of XGboost prediction is the highest, and the prediction accuracy score reaches 0.9251. Compared with linear regression and decision tree model, XGboost algorithm has better generalization ability and robustness in data prediction, and also prevents overfitting phenomenon, laying a solid foundation for the subsequent second-hand house price prediction.",44.0,,False,JournalArticle;Conference,Computer Science;Mathematics,2019-10-01,196ea01e8cc6728291e96bbf0320240535ff68f9,modelresearchonforecastofsecondhandhousepriceinchengdubasedonxgboostalgorithm,peng,10.1109/icait.2019.8935894,modelresearchonforecastofsecondhandhousepriceinchengdubasedonxgboostalgorithm|2019,1,1891,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1a32e3b85cd2babeea2bf250605dcaaa578f5530,10.2139/ssrn.3565512,,House Price Forecasting Using Machine Learning,2020.0,Social Science Research Network,Alisha Kuvalekar; Shivani Manchewar; Sidhika Mahadik; Shila Jawale,"The real estate market is a standout amongst the most focused regarding pricing and keeps fluctuating. It is one of the prime fields to apply the ideas of machine learning on how to enhance and foresee the costs with high accuracy. The objective of the paper is the prediction of the market value of a real estate property. This system helps find a starting price for a property based on the geographical variables. By breaking down past market patterns and value ranges, and coming advancements future costs will be anticipated. This examination means to predict house prices in Mumbai city with Decision tree regressor. It will help clients to put resources into a bequest without moving towards a broker. The result of this research proved that the Decision tree regressor gives an accuracy of 89%.",43.0,,False,,Economics,2020-04-08,1a32e3b85cd2babeea2bf250605dcaaa578f5530,housepriceforecastingusingmachinelearning,kuvalekar,10.2139/ssrn.3565512,housepriceforecastingusingmachinelearning|2020,1,1801,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a30fd51dbaa3270aa960796ead177febad313dee,10.1109/dsaa.2018.00043,1808.02547,The Economic Value of Neighborhoods: Predicting Real Estate Prices from the Urban Environment,2018.0,International Conference on Data Science and Advanced Analytics,Marco De Nadai; B. Lepri,"Housing costs have a significant impact on individuals, families, businesses, and governments. Recently, online companies such as Zillow have developed proprietary systems that provide automated estimates of housing prices without the immediate need of professional appraisers. Yet, our understanding of what drives the value of houses is very limited. In this paper, we use multiple sources of data to entangle the economic contribution of the neighborhood's characteristics such as walkability and security perception. We also develop and release a framework able to now-cast housing prices from Open data, without the need for historical transactions. Experiments involving 70,000 houses in 8 Italian cities highlight that the neighborhood's vitality and walkability seem to drive more than 20% of the housing value. Moreover, the use of this information improves the nowcast by 60%. Hence, the use of property's surroundings' characteristics can be an invaluable resource to appraise the economic and social value of houses after neighborhood changes and, potentially, anticipate gentrification.",41.0,,False,JournalArticle;Conference,Computer Science;Business,2018-08-07,a30fd51dbaa3270aa960796ead177febad313dee,theeconomicvalueofneighborhoodspredictingrealestatepricesfromtheurbanenvironment,nadai,10.1109/dsaa.2018.00043,theeconomicvalueofneighborhoodspredictingrealestatepricesfromtheurbanenvironment|2018,1,2099,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",95ed7cf5b17bda8e6fed7d46057d17eb49f6b738,10.1109/iccsnt47585.2019.8962443,,House Price Prediction Approach based on Deep Learning and ARIMA Model,2019.0,International Conference on Computer Science and Network Technology,Feng Wang; Yang Zou; Haoyu Zhang; Haodong Shi,"The nonlinear relationship between influential factors and house price and inadequate number of sample size could be the cause of the poor performance of the traditional models. Meanwhile, the daily data of the real estate market is very huge and it is increasing rapidly. The traditional house price prediction approaches are lack of capacity for massive data analysis, causing low utilization of data. To address these concerns, a house price prediction model based on deep learning is proposed in this paper, implemented on the TensorFlow framework. Adam optimizer is used to train the model, where Relu function is adopted to be the activation function. Then house price trend is predicted based on the ARIMA model. By using Scrapy, housing data are obtained from Internet to be the experimental dataset. Comparative experiments were conducted between the proposed approach and SVR method. The experimental results show that individual house price predicted by the proposed approach is better than that of SVR method. And the predicted house price trend is mainly agreement with the real situation.",39.0,,False,Conference,Computer Science,2019-10-01,95ed7cf5b17bda8e6fed7d46057d17eb49f6b738,housepricepredictionapproachbasedondeeplearningandarimamodel,wang,10.1109/iccsnt47585.2019.8962443,housepricepredictionapproachbasedondeeplearningandarimamodel|2019,1,2102,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f2308f0a4f0981801b518b9ca2152bcb4c797ad7,10.1109/iccubea.2018.8697639,,Real Estate Value Prediction Using Linear Regression,2018.0,International Conference on Computing Communication Control and automation,Nehal Ghosalkar; Sudhir N. Dhage,"The real estate market is a standout amongst the most focused regarding pricing and keeps fluctuating. It is one of the prime fields to apply the ideas of machine learning on how to enhance and foresee the costs with high accuracy. There are three factors that influence the price of a house which includes physical conditions, concepts and location. The current framework includes estimating the price of houses without any expectations of market prices and cost increment. The objective of the paper is prediction of residential prices for the customers considering their financial plans and needs. By breaking down past market patterns and value ranges, and coming advancements future costs will be anticipated. This examination means to predict house prices in Mumbai city with Linear Regression. It will help clients to put resources into a bequest without moving toward a broker. The result from this research proved linear regression gives minimum prediction error which is 0.3713.",39.0,,False,Conference,Computer Science,2018-08-01,f2308f0a4f0981801b518b9ca2152bcb4c797ad7,realestatevaluepredictionusinglinearregression,ghosalkar,10.1109/iccubea.2018.8697639,realestatevaluepredictionusinglinearregression|2018,1,1988,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",865fcba893598d78106e2540974f1e301b3d2c75,10.1145/3318299.3318377,,Ensemble Learning Based Rental Apartment Price Prediction Model by Categorical Features Factoring,2019.0,International Conference on Machine Learning and Computing,Asif Ahmed Neloy; H. M. Sadman Haque; Md. Mahmud Ul Islam,"Apartment rental prices are influenced by various factors. The aim of this study is to analyze the different features of an apartment and predict the rental price of it based on multiple factors. An ensemble learning based prediction model is created to reach the goal. We have used a dataset from bProperty.com which includes the rental price and different features of apartments in the city of Dhaka, Bangladesh. The results show the accuracy and prediction of the rent of an apartment, also indicates the different types of categorical values that affect the machine learning models. Another purpose of the study is to find out the factors that signify the apartment rental price in Dhaka. To help our prediction we take on the Advance Regression Techniques (ART) and compare to different features of an apartment for establishing an acceptable model. The following algorithms are selected as the base predictors -- Advance Linear Regression, Neural Network, Random Forest, Support Vector Machine (SVM) and Decision Tree Regressor. The Ensemble learning is stacked of following algorithms -- Ensemble AdaBoosting Regressor, Ensemble Gradient Boosting Regressor, Ensemble XGBoost. Also, Ridge Regression, Lasso Regression, and Elastic Net Regression has been used to combine the advance regression techniques. Tree-based algorithms generate a decision tree from categorical 'YES' and 'NO' values, Ensemble methods to boosting up the learning and prediction accuracy, Support Vector Machine to extend the model for both classification and regression approach and lastly advance linear regression to predict the house price with different features values.",38.0,,False,JournalArticle,Computer Science,2019-02-22,865fcba893598d78106e2540974f1e301b3d2c75,ensemblelearningbasedrentalapartmentpricepredictionmodelbycategoricalfeaturesfactoring,neloy,10.1145/3318299.3318377,ensemblelearningbasedrentalapartmentpricepredictionmodelbycategoricalfeaturesfactoring|2019,1,2655,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,3,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",32a0805b769229ccee166d6c0009ef7c08ddad98,10.1109/nana56854.2022.00082,,An optimized prediction algorithm based on XGBoost,2022.0,International Conference on Networking and Network Applications,Cheng Sheng; Hai-Zheng Yu,"The real estate market is closely related to people's life. It is very important to accurately predict the future real estate price. Traditional methods are difficult to describe the nonlinear characteristics of house price prediction. XGBoost algorithm can effectively represent the nonlinear relationship in house price prediction. However, the selection of parameters determines the learning and generalization ability of XGBoost, and it is very important to determine the parameters of XGBoost. Particle swarm optimization algorithm can select the training parameters of XGBoost more quickly and accurately. Therefore, this paper studies the house price prediction based on the hybrid model of particle swarm optimization XGBoost algorithm, namely PSO-XGBoost model. Using the collected sample data of houses in Ames, Iowa, five different machine learning algorithms including PSO-XGBoost are used to predict house prices. Finally, the results of five algorithms are compared and analyzed. The experimental results show that PSO-XGBoost model has the highest prediction accuracy and the best effect, and the prediction effect of integrated learning algorithm is better than that of linear regression model.",35.0,,False,JournalArticle;Conference,Computer Science,2022-12-01,32a0805b769229ccee166d6c0009ef7c08ddad98,anoptimizedpredictionalgorithmbasedonxgboost,sheng,10.1109/nana56854.2022.00082,anoptimizedpredictionalgorithmbasedonxgboost|2022,1,2210,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,machine learning hedonic pricing housing,892b455253708392fa1d374a50e37bf898d2c0ed,10.1145/3409501.3409543,,Spatial Prediction of Housing Prices in Beijing Using Machine Learning Algorithms,2020.0,HPCCT/BDAI,Ziyue Yan; Lu Zong,"The real estate industry places key influence on almost every aspect of social economy given its great financing capacity and prolonged upstream and downstream industry chain. Therefore, predicting housing prices is regarded as an emerging topic in the recent decades. Hedonic Regression and Machine Learning Algorithms are two main methods in this field. This study aims to explore the important explanatory features and determine an accurate mechanism to implement spatial prediction of housing prices in Beijing by incorporating a list of machine learning techniques, including XGBoost, linear regression, Random Forest Regression, Ridge and Lasso Model, bagging and boosting, based on the housing price and features data in Beijing, China. Our result shows that compared to traditional hedonic method, machine learning methods demonstrate significant improvements on the accuracy of estimation despite that they are more time-costly. Moreover, it is found that XGBoost is the most accurate model in explaining and prediciting the spatial dynamics of housing prices in Beijing.",34.0,,False,JournalArticle;Book,Computer Science,2020-07-03,892b455253708392fa1d374a50e37bf898d2c0ed,spatialpredictionofhousingpricesinbeijingusingmachinelearningalgorithms,yan,10.1145/3409501.3409543,spatialpredictionofhousingpricesinbeijingusingmachinelearningalgorithms|2020,1,2080,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || machine learning hedonic pricing housing || real estate hedonic regression || spatial hedonic model housing",2,1,1,4,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",555d702cde574ff00a5e1a9ed0511e4ab00f6e08,10.1109/icsai48974.2019.9010071,,Prediction of House Price Based on The Back Propagation Neural Network in The Keras Deep Learning Framework,2019.0,International Conference on Systems and Informatics,Zhong-Yun Jiang; Guo-Xin Shen,"This paper uses the housing data of the chain home network to predict the price of second-hand housing in Shanghai. Firstly, this paper use the crawler technology to parse the URL text information through the j son request address and the BeautifulSoup parser. Then a multi-layer feedforward neural network model trained by error inverse propagation algorithm is established based on the deep learning library Keras. Finally, to enter standardized data to predict the price. The experimental results show that for the model with Gaussian noise, the sample with an absolute value of the relative error between the predicted value and the actual value is 95.59%. The model has a good effect in the house price forecast.",33.0,,False,JournalArticle;Conference,Computer Science,2019-11-01,555d702cde574ff00a5e1a9ed0511e4ab00f6e08,predictionofhousepricebasedonthebackpropagationneuralnetworkinthekerasdeeplearningframework,jiang,10.1109/icsai48974.2019.9010071,predictionofhousepricebasedonthebackpropagationneuralnetworkinthekerasdeeplearningframework|2019,1,1717,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),7c573ca029c19fd342b7e4b81a53031c69bd303c,10.1109/icmla.2016.0092,,Hedonic Housing Theory — A Machine Learning Investigation,2016.0,International Conference on Machine Learning and Applications,T. Oladunni; Sharad Sharma,,32.0,,False,JournalArticle;Conference,Computer Science,2016-12-01,7c573ca029c19fd342b7e4b81a53031c69bd303c,hedonichousingtheoryamachinelearninginvestigation,oladunni,10.1109/icmla.2016.0092,hedonichousingtheoryamachinelearninginvestigation|2016,1,1000,6,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || machine learning hedonic pricing housing || real estate hedonic regression,2,0,1,1,0,5,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9c4bc8b56ec39ddc9c64518ff5ba94a0c504e862,10.1109/tencon.2016.7848726,,Predicting public housing prices using delayed neural networks,2016.0,IEEE Region 10 Conference,Lipo Wang; F. Chan; Yaoli Wang; Qing Chang,,28.0,,False,Conference,Economics,2016-11-01,9c4bc8b56ec39ddc9c64518ff5ba94a0c504e862,predictingpublichousingpricesusingdelayedneuralnetworks,wang,10.1109/tencon.2016.7848726,predictingpublichousingpricesusingdelayedneuralnetworks|2016,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",ccde446e06e8cf48e16b4088c2f68701ad42f921,10.63642/1357-1419.1047,,Performance comparison of automated valuation models,2006.0,Journal of Property Tax Assessment & Administration,J. W. Moore,"Because it is necessary for assessors to prepare value estimates for huge numbers of properties, all at a specific point in time each year, a process called computer assisted mass appraisal (CAMA), which uses Automated Valuation Models (AVMs), has evolved during the past 35 years to handle the logistic challenge presented by the task. Several CAMA methodologies using different AVMs exist for determining the assessed value of residential properties. A large research study and controlled experiment that statistically tested the results of the several CAMA methodologies when applied under the same circumstances has not been reported until now. This research used all valid market sale transactions for the five years of 1999 through 2003 to estimate selling prices of an actual residential property population in an assessing jurisdiction by using four different CAMA methodologies. The model specification, calibration and value estimation work was done blindly by nine independent CAMA practitioners from across North America without knowledge of the source of the data, the actual 2004 sale prices, or even names of other participants. Six of the participants used a generic AVM market model specification approach and were free to select software tools. Three used a pre-specified transportable AVM. Actual sale prices for 2004 valid market transactions for properties from the same population were used to compare the predictive accuracy of each of the CAMA methods to determine if any statistical significance in predictive accuracy existed between the methods. This paper reports the results of the controlled experiment conducted under the supervision of an academic mentor. Performance Comparison of Automated Valuation Models 3 Introduction Six methodologies are used for performing computer assisted mass appraisal (CAMA) to estimate assessed value of residential properties for local property taxation. The first method is the direct sales comparison approach, which is widely used by fee appraisers to produce mortgage appraisals for home purchases. This method is used by property assessors, less frequently for the mass appraisal process, but is widely used to both challenge and defend individual property assessments. A second method, multiple regression analysis (MRA) using software such as SPSS, is a statistical extension of direct sales comparison, emerging in the past 30 years as the power of the computer became available to assessors. The third method is adaptive estimation procedure (AEP), also called feedback, which has its roots in numerical analysis and has also been available for about 30 years. The fourth and most commonly used method is the cost approach that relies upon local market analysis to provide an estimate of depreciation from all causes. The fifth is a hybrid approach referred to in this paper as the transportable cost-specified market (TCM) approach. These methods are used to varying degrees by local property assessors throughout the world. A sixth method exists, based upon artificial neural networks, but is not widely used. The International Association of Assessing Officers (IAAO) is a professional organization that helps establish best practices and standards for performing tax assessment, including statistical measures for evaluating the quality of assessment work. Assessing professionals have presented numerous case study reports on AVM methodology, but a research study and controlled experiment has not been reported that statistically compares the results of the main AVM methodologies when applied to the same jurisdiction. A competition among vendors to select a computer-assisted mass appraisal system was conducted by the Board of County Commissioners of the County of Allegheny, Pennsylvania, in 1976-1977, has been reported and described (Carbone 1980). The Allegheny competition was patterned after one that had been conducted at Harvard and reported upon in 1977. Richard Ward and Lorraine Steiner presented a paper describing a comparison of feedback and nonlinear regression. At the time of their research, nonlinear regression was just beginning to appear and the stated purpose of the study was “to clarify for assessors some of the issues raised by these techniques with the hope that the comparison of these techniques will contribute to assessor education in the CAMA area” Performance Comparison of Automated Valuation Models 4 (Ward and Steiner 1988, 43). Consistent with its educational purpose, the paper provided an overview of software available at the time and summarized statistical results from four different tests, but its main purpose was description of new CAMA techniques rather than performance comparison. Charles Calhoun discussed the lack of independent testing of AVMs in his article in Housing Finance International, which reported upon property valuation methods and data in the United States, stating in footnote 38: While there is increasing competition among various commercial models, independent evaluations are practically nonexistent given the proprietary nature of the data and models. Whether market forces will ultimately identify the most successful methodologies depends in part on the ability of consumers of these models to undertake their own validations (Calhoun 2001, 21) The controlled experiment reported in this paper fills the research gap that Calhoun comments upon. The research used five years of sales data (1999-2003), property descriptive data and nine highly qualified CAMA practitioners from across the country to estimate the selling prices of an actual residential property population in an actual jurisdiction, with the practitioners using different CAMA methods as treatments. The model specification, calibration and value estimation work was done blindly by the nine independent CAMA practitioners without knowledge of the source of the data, the actual 2004 sale prices, or even names of other participants. Six of the participants used a generic AVM market model specification approach and were free to select software tools. Four of these six participants are listed as contributors or reviewers for the IAAO’s Standard on Automated Valuation Models. The three other participants used a pre-specified transportable AVM. Articles cited in this paper that were published by the research participants in this experiment are noted with an asterisk (*). Standard IAAO statistical quality measures were applied to actual 2004 sales compared to calculated values of properties from the same population for each participant to determine if differences of any statistical significance existed between the methods. Literature Review The first attempts at using multiple regression analysis (MRA) to estimate property market value occurred around 1970 (Gouldemans* and Miller 1976). Prior to that time the most widely used Performance Comparison of Automated Valuation Models 5 method was the traditional cost approach, primarily doing the work by hand with minimal market analysis. The formal description of the adaptive estimation procedure (AEP), also called feedback, first appeared in the late 1970’s (Carbone 1980). Carbone’s 1976 PhD dissertation provided a rigorous academic definition of the technique (Carbone 1976). The procedure tests and systematically adjusts model coefficients, converging upon the set of coefficients that minimize an error term (International Association of Assessing Officers [IAAO] 2003, 12). Schultz* (2001) makes a case for use of feedback in his 2001 Research & Technology Update article. There have been numerous conference papers, case studies and journal articles on the application of both MRA and AEP in the past 20 years. Typical of these is a 1995 paper describing the Denver County, Colorado, revaluation using multiple regression analysis presented by the jurisdiction’s Chief Appraiser (White 1995). White’s paper provides a very good description of the actual revaluation process as used throughout North America. The third method considered is the cost approach. The traditional cost approach is by definition not a market approach, even though in theory all three approaches to value (cost, market and income) should yield similar final values. The cost approach, with locally developed depreciation schedules, or with depreciation individually determined by appraisers, is widely used by assessors. The fundamentals of the cost approach have been well documented for more than 70 years in books such as The Valuation of Property (Bonbright 1937). Cost theoretically sets an upper limit on market value (assuming reasonable supply and time factors) and it is generally acknowledged that the main difficulty in using the cost approach is estimation of depreciation from all causes (physical, functional and economic) and the rapidly changing dynamics of the real estate market (Clapp 1977). Nevertheless, a number of states such as Alabama, Illinois, Indiana, Iowa, Nevada, and Michigan publish a state cost manual with a depreciation schedule and require or encourage its use by assessors in their respective states. Assessors were seeking ways to improve the fundamental assessment process as early as 1966. Franklin Graham, the Assessor of the City of Wisconsin Dells, published an article that proposed a new approach, beginning his paper by stating, “This method is a combination of the cost approach and the market data approach” (Graham 1966, 42). An article 14 years later, after the introduction of MRA into the assessment process, discussed a simplifying base home approach that was hinted at in Graham’s article (Gloudemans* 1981). In 1986 Eckert published a paper suggesting methods for calibrating the cost model to market that provided insight into what I have defined Performance Comparison of Automated Valuation Models 6 as the fourth method considered in this research, the transportable cost-specified market (TCM) approach. “Much of the process of determining depreciation and fine tuning fo",26.0,,False,JournalArticle;Review,Economics,2006-03-01,ccde446e06e8cf48e16b4088c2f68701ad42f921,performancecomparisonofautomatedvaluationmodels,moore,10.63642/1357-1419.1047,performancecomparisonofautomatedvaluationmodels|2006,1,11000,3,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || automated valuation model residential",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",8d2c1a851fa02d756efeb74af73165a5d9e44998,10.1109/istas50296.2020.9462170,,Bidirectional Transformer based on online Text-based information to Implement Convolutional Neural Network Model For Secure Business Investment,2020.0,International Symposium on Technology and Society,Maryam Heidari; Setareh Rafatirad,"Real estate investment decisions are critical for low-income people who have just one home as their life-time investment option. So during the COVID-19 pandemic, unemployment causes many homeowners with a low income to lose their homes because of two major factors: one, they could not pay their mortgages without a job, and second, their house could not be rented easily. Rent prediction in real-estate can guarantee the success of an investment. Online information from real estate websites plays a significant role in making a business decision to buy a home. This paper applies natural language processing models to introduce a new model for safe real estate investment based on online information. For the first time, we use a transfer learning model based on online information from various online resources to detect a profitable rental property. Bidirectional Encoder Representations from Transformers(BERT) are used to implement a semantic convolutional neural network model to predict real estate investment safety. This work introduces a new model for rent prediction based on the United States housing market. Our contribution is three-fold: (1) using natural language processing approach to use the semantics of online information on Airbnb, Zillow, Schools, Public transportation, and crime rate websites for rent prediction (2) We perform a comprehensive analysis of eager and lazy machine learning models as a traditional Machine learning models with our proposed new transfer learning model for rent prediction. (3) Creating a new public data set of semantic analysis for more than 5 million houses in the United States based on online information. This data set will be available for public research in natural language processing research for people analytic applications. This work introduces a new machine learning model to guarantee safe investment in the real estate market using a transfer learning approach based on online information.",25.0,,False,JournalArticle,Computer Science,2020-11-12,8d2c1a851fa02d756efeb74af73165a5d9e44998,bidirectionaltransformerbasedononlinetextbasedinformationtoimplementconvolutionalneuralnetworkmodelforsecurebusinessinvestment,heidari,10.1109/istas50296.2020.9462170,bidirectionaltransformerbasedononlinetextbasedinformationtoimplementconvolutionalneuralnetworkmodelforsecurebusinessinvestment|2020,1,2960,1,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,1,6,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",35335d6e7f690b2dc2dd3ab4bfbb3e36b0f3a18b,10.1109/acit50332.2020.9300074,,"House Price Prediction using Machine Learning Algorithm - The Case of Karachi City, Pakistan",2020.0,"Automation, Control, and Information Technology",Maida Ahtesham; N. Bawany; Kiran Fatima,"House prices are a significant impression of the economy, and its value ranges are of great concerns for the clients and property dealers. Housing price escalate every year that eventually reinforced the need of strategy or technique that could predict house prices in future. There are certain factors that influence house prices including physical conditions, locations, number of bedrooms and others. Traditionally predictions are made on the basis of these factors. However such prediction methods require an appropriate knowledge and experience regarding this domain. Machine Learning techniques have been a significant source of advanced opportunities to analyze, predict and visualize housing prices. In this paper, Gradient Boosting Model XGBoost is utilized to predict housing prices. Publicly available dataset containing 38,961 records of Karachi city is attained from an Open Real Estate Portal of Pakistan. Lot of work has been done in predicting house prices across many countries, however very limited amount of work has been done for predicting house prices in Pakistan. Our proposed house price prediction model is able to predict 98% accuracy.",25.0,,False,JournalArticle;Conference,Computer Science,2020-11-28,35335d6e7f690b2dc2dd3ab4bfbb3e36b0f3a18b,housepricepredictionusingmachinelearningalgorithmthecaseofkarachicitypakistan,ahtesham,10.1109/acit50332.2020.9300074,housepricepredictionusingmachinelearningalgorithmthecaseofkarachicitypakistan|2020,1,2161,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",82c7b324fecd20c1cea390847e95e61572ffe06c,10.1109/iccmc56507.2023.10084197,,House Price Prediction using Machine Learning Algorithm,2023.0,International Conference Computing Methodologies and Communication,Shailendra Sharma; Deepti Arora; Gori Shankar; Priyanka Sharma; Vihaan Motwani,"Houses are one of the fundamental needs in human society. Good and peaceful surroundings where people feel comfortable are called houses. Hence, to have a good and happy living, people need to choose a good house model. This article focuses on accurately predicting house prices by using machine learning algorithms. This model helps people in selecting the house that is suitable for their living. The main parameter people look for will be the surrounding area, house type, price, location, and some other amenities. All these factors are considered to find out the required house for living. As people are very concerned about their budgets for buying a house, the prediction of house prices should be very precise. It also helps people in choosing houses based on their budgets, which do not affect their financial state in the future. The main outcome of this model is to predict the price of a house accurately as per the user requirements. This study has attempted to implement various machine learning algorithms like Linear Regression (LR), Gradient Boosting Regressor (GBR), Histogram Gradient Boosting Regressor, and Random Forest (RF) Regressor algorithms. Finally, the algorithm that generates high accuracy is considered for predicting the house price.",24.0,,False,Conference,,2023-02-23,82c7b324fecd20c1cea390847e95e61572ffe06c,housepricepredictionusingmachinelearningalgorithm,sharma,10.1109/iccmc56507.2023.10084197,housepricepredictionusingmachinelearningalgorithm|2023,1,2266,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c147cda8bf83b3142649ae6e60fef7e1b53baf59,10.1109/icesc48915.2020.9155839,,Prediction of House Pricing using Machine Learning with Python,2020.0,"International Conference Electronic Systems, Signal Processing and Computing Technologies [ICESC-]",Mansi Jain; Himani Rajput; Neha Garg; Pronika Chawla,This paper provides an overview about how to predict house costs utilizing different regression methods with the assistance of python libraries. The proposed technique considered the more refined aspects used for the calculation of house price and provide the more accurate prediction. It also provides a brief about various graphical and numerical techniques which will be required to predict the price of a house. This paper contains what and how the house pricing model works with the help of machine learning and which dataset is used in our proposed model.,24.0,,False,Conference;Review,Computer Science,2020-07-01,c147cda8bf83b3142649ae6e60fef7e1b53baf59,predictionofhousepricingusingmachinelearningwithpython,jain,10.1109/icesc48915.2020.9155839,predictionofhousepricingusingmachinelearningwithpython|2020,1,1561,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",98e296c6406bf1c0e9d784c52731c92d28c56ce1,10.1108/17538270910963090,,"Housing price determinants in Istanbul, Turkey: An application of the classification and regression tree model",2009.0,,Onur Özsoy; H. Sahin,,24.0,,False,,Geography,2009-05-29,98e296c6406bf1c0e9d784c52731c92d28c56ce1,housingpricedeterminantsinistanbulturkeyanapplicationoftheclassificationandregressiontreemodel,özsoy,10.1108/17538270910963090,housingpricedeterminantsinistanbulturkeyanapplicationoftheclassificationandregressiontreemodel|2009,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,real estate hedonic regression,fb4a9d346ea610ca53c0dfb682b8000a6c7b0f50,10.1108/bepam-07-2018-0100,,Driving forces for the US residential housing price: a predictive analysis,2019.0,Built Environment Project and Asset Management,A. Jafari; Reza Akhavian," Purpose The purpose of this paper is to determine the key characteristics that determine housing prices in the USA. Data analytical models capable of predicting the driving forces of housing prices can be extremely useful in the built environment and real estate decision-making processes. Design/methodology/approach A data set of 13,771 houses is extracted from the 2013 American Housing Survey (AHS) data and used to develop a Hedonic Pricing Method (HPM). Besides, a data set of 22 houses in the city of San Francisco, CA is extracted from Redfin real estate brokerage database and used to test and validate the model. A correlation analysis is performed and a stepwise regression model is developed. Also, the best subsets regression model is selected to be used in HPM and a semi-log HPM is proposed to reduce the problem of heteroscedasticity. Findings Results show that the main driving force for housing transaction price in the USA is the square footage of the unit, followed by its location, and its number of bathrooms and bedrooms. The results also show that the impact of neighborhood characteristics (such as distance to open spaces and business centers) on the housing prices is not as strong as the impact of housing unit characteristics and location characteristics. Research limitations/implications An important limitation of this study is the lack of detailed housing attribute variables in the AHS data set. The accuracy of the prediction model could be increased by having a greater number of information regarding neighborhood and regional characteristics. Also, considering the macro business environment such as the inflation rate, the interest rates, the supply and demand for housing, and the unemployment rates, among others could increase the accuracy of the model. The authors hope that the presented study spurs additional research into this topic for further investigation. Practical implications The developed framework which is capable of predicting the driving forces of housing prices and predict the market values based on those factors could be useful in the built environment and real estate decision-making processes. Researchers can also build upon the developed framework to develop more sophisticated predictive models that benefit from a more diverse set of factors. Social implications Finally, predictive models of housing price can help develop user-friendly interfaces and mobile applications for home buyers to better evaluate their purchase choices. Originality/value Identification of the key driving forces that determine housing prices on real-world data from the 2013 AHS, and development of a prediction model for housing prices based on the studied data have made the presented research original and unique. ",22.0,,False,Review,Computer Science,2019-09-09,fb4a9d346ea610ca53c0dfb682b8000a6c7b0f50,drivingforcesfortheusresidentialhousingpriceapredictiveanalysis,jafari,10.1108/bepam-07-2018-0100,drivingforcesfortheusresidentialhousingpriceapredictiveanalysis|2019,1,3778,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || housing characteristics prices hedonic || real estate hedonic regression",2,2,5,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ce263f41bf2280c0b7021c66e88a17ddcc6d4fb8,10.1109/indin45582.2020.9442244,,Housing prices prediction with deep learning: an application for the real estate market in Taiwan,2020.0,International Conference on Industrial Informatics,Choujun Zhan; Zeqiong Wu; Yonglin Liu; Zefeng Xie; Wan-I Chen,"The housing market is increasing huge, predicting housing prices is not only important for a business issue, but also for people. However, housing price fluctuations have a lot of influencing factors. Also, there is a non-linear relationship between housing prices and housing factors. Most econometric or statistical models cannot capture non-linear relationships yet. Therefore, we propose housing price prediction models based on deep learning methods, which can capture non-linear relationships. In this work, we construct a dataset, including the housing attributes data and macroeconomic data in Taiwan from January 2013 to December 2018. The housing attributes data includes two types of housing transactions, which are “land + building” (Type1) and “land + building + park” (Type2). Macroeconomic data includes housing investment demand ratio, owner-occupier housing ratio, housing price to income ratio, housing loan burden ratio, and housing bargaining space ratio. Then, this dataset is utilized to evaluate the prediction methods based on deep learning algorithms BPNN and CNN to predict housing prices. Experimental results show that CNN with housing features has the best prediction effect. This study can be used to develop targetted interventions aimed at the housing market.",22.0,,False,JournalArticle;Conference,Computer Science;Business,2020-07-20,ce263f41bf2280c0b7021c66e88a17ddcc6d4fb8,housingpricespredictionwithdeeplearninganapplicationfortherealestatemarketintaiwan,zhan,10.1109/indin45582.2020.9442244,housingpricespredictionwithdeeplearninganapplicationfortherealestatemarketintaiwan|2020,1,2291,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",401c2aaf392cfea1b8314f2c2124d596adfc5d39,10.1109/conit55038.2022.9848309,,Boston House Price Prediction Using Regression Models,2022.0,2022 2nd International Conference on Intelligent Technologies (CONIT),Saptarsi Sanyal; Saroj Kumar Biswas; Dolly Das; Manomita Chakraborty; Biswajit Purkayastha,"Everyone wishes to buy and live in a house which suits their lifestyle and which provides amenities according to their needs. There are many factors that are to be taken into consideration like area, location, view etc. for prediction of house price. It is very difficult to predict house price as it is constantly changing and quite often the prices are exaggerated for which people who want to buy houses, and various real estate agencies who want to invest in properties, find it difficult to buy or sell houses. For this reason, in this paper the author creates an advanced automated Machine Learning model using Simple Linear Regression, Polynomial Regression, Ridge Regression and Lasso Regression using the Boston house dataset to predict house price in future accurately, and to measure the accuracy of these models various measuring metrics like R-Squared, Root Mean Square Error (RMSE) and Cross-Validation are used. This paper also studies the correlation of various attributes of the Boston dataset using the heat map to see which attributes actually impact the prediction of the models. It removes the outliers which are present in the dataset to achieve good accuracy. In this paper it is observed that Lasso Regression performs better in all the measuring matrices whereas Simple Linear Regression performs poor in all of the measuring matrices.",21.0,,False,Conference,,2022-06-24,401c2aaf392cfea1b8314f2c2124d596adfc5d39,bostonhousepricepredictionusingregressionmodels,sanyal,10.1109/conit55038.2022.9848309,bostonhousepricepredictionusingregressionmodels|2022,1,2360,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2c9b2681340257ea34844a734bdd36c311dbc237,10.1109/iccubea.2018.8697402,,Comprehensive Analysis of Housing Price Prediction in Pune Using Multi-Featured Random Forest Approach,2018.0,International Conference on Computing Communication Control and automation,Rushab Sawant; Yashwant Jangid; Tushar Tiwari; Saurabh Jain; Ankita Gupta,"The housing sector in India has been predicted to grow at 30-35% over the next decade. In terms of employment provided, it is second only to the agricultural sector. Housing is one of the major domain of real estate. Pune is emerging as one of the major metropolitan cities of India and has many prestigious Educational institutions and IT parks. This makes it an ideal place to buy homes. Vagueness among the prices of houses makes it challenging for the buyer to select their dream house. The interests of both buyer and seller should be satisfied so that they do not overestimate or underestimate price. This housing price prediction model acts as a helping hand for buyer and seller or a real estate agent to make a better-informed decision. To achieve this, diverse features are selected as input from feature set and various algorithms are applied such as Random Forest and Decision Tree.",21.0,,False,Conference,Computer Science,2018-08-01,2c9b2681340257ea34844a734bdd36c311dbc237,comprehensiveanalysisofhousingpricepredictioninpuneusingmultifeaturedrandomforestapproach,sawant,10.1109/iccubea.2018.8697402,comprehensiveanalysisofhousingpricepredictioninpuneusingmultifeaturedrandomforestapproach|2018,1,1894,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,5,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,real estate hedonic regression,545cf326ccb3a54be5fbf23c4d0d594ed51df62f,10.1145/3567430,,Tackling the Accuracy-Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal,2022.0,ACM Transactions on Management Information Systems,Jan-Peter Kucklick; Oliver Müller,"Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows extending the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this article, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning, which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.",20.0,,False,JournalArticle,Computer Science,2022-10-10,545cf326ccb3a54be5fbf23c4d0d594ed51df62f,tacklingtheaccuracyinterpretabilitytradeoffinterpretabledeeplearningmodelsforsatelliteimagebasedrealestateappraisal,kucklick,10.1145/3567430,tacklingtheaccuracyinterpretabilitytradeoffinterpretabledeeplearningmodelsforsatelliteimagebasedrealestateappraisal|2022,1,2654,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || real estate hedonic regression",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7cc080e6035f5ca684924692878aa4dbf9699930,10.1108/ijhma-09-2023-0120,,Comparative analysis of machine learning models in predicting housing prices: a case study of Prishtina's real estate market,2024.0,International Journal of Housing Markets and Analysis,Visar Hoxha," Purpose The purpose of this study is to carry out a comparative analysis of four machine learning models such as linear regression, decision trees, k-nearest neighbors and support vector regression in predicting housing prices in Prishtina. Design/methodology/approach Using Python, the models were assessed on a data set of 1,512 property transactions with mean squared error, coefficient of determination, mean absolute error and root mean squared error as metrics. The study also conducts variable importance test. Findings Upon preprocessing and standardization of the data, the models were trained and tested, with the decision tree model producing the best performance. The variable importance test found the distance from central business district and distance to the road leading to central business district as the most relevant drivers of housing prices across all models, with the exception of support vector machine model, which showed minimal importance for all variables. Originality/value To the best of the author’s knowledge, the originality of this research rests in its methodological approach and emphasis on Prishtina's real estate market, which has never been studied in this context, and its findings may be generalizable to comparable transitional economies with booming real estate sector like Kosovo. ",20.0,,False,JournalArticle,,2024-01-09,7cc080e6035f5ca684924692878aa4dbf9699930,comparativeanalysisofmachinelearningmodelsinpredictinghousingpricesacasestudyofprishtinasrealestatemarket,hoxha,10.1108/ijhma-09-2023-0120,comparativeanalysisofmachinelearningmodelsinpredictinghousingpricesacasestudyofprishtinasrealestatemarket|2024,1,2334,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0d6bc73ac5588c938604152251b9d2b09cf958d6,10.1109/taai51410.2020.00047,,A Gradient Boosting Method for Effective Prediction of Housing Prices in Complex Real Estate Systems,2020.0,International Conference on Technologies and Applications of Artificial Intelligence,B. Almaslukh,"Analyzing real estate market changes by different parties and agencies that have a significant effect on real estate health and trends. In complex real estate systems, the prediction of housing prices plays an important role in mitigating the impacts of property valuation and economic growth. Several works have proposed the use of various machine learning models for predicting housing prices of real estate markets. However, developing an effective machine learning models to predict the housing prices is still a challenge and needs to be investigated. Therefore, this paper proposes an optimized model based on the gradient boosting (GB) method for improving the prediction of housing prices in complex real estate systems. To evaluate the proposed method, a set of experiments is conducted on a public real estate dataset. The experimental results show that the optimized GB (OGB) method can be used effectively for housing price prediction of real estate and achieves 0.01167 of the root mean square error; the lowest result compared to the other baseline machine learning models.",20.0,,False,JournalArticle;Conference,Computer Science,2020-12-01,0d6bc73ac5588c938604152251b9d2b09cf958d6,agradientboostingmethodforeffectivepredictionofhousingpricesincomplexrealestatesystems,almaslukh,10.1109/taai51410.2020.00047,agradientboostingmethodforeffectivepredictionofhousingpricesincomplexrealestatesystems|2020,1,2087,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic property valuation,ed9e429c2a6b69b09652fe87f2c32e25308c408b,10.1109/bigdata.2018.8622422,,A Bayesian Approach to Residential Property Valuation Based on Built Environment and House Characteristics,2018.0,2018 IEEE International Conference on Big Data (Big Data),Zhi-Cheng Liu; Shuai Yan; Jun Cao; Tan-Hua Jin; Jiabo Tang; Jun-Yan Yang; Qiao Wang,"Housing market receives a broad attention from society. Understanding how built environment and house characteristics are valued in housing market is critical to investment and city development. However, this problem is challenging because of the existence of submarket resulted from the heterogeneous urban form and physical barriers. Traditionally, residential property valuation is carried out by Hedonic Price Model(HPM). However, traditional HPM based on linear model has limitation in valuation accuracy and suffers from submarket effect. In this paper, we propose a Bayesian approach to residential property valuation based on built environment and house characteristics. Specifically, we introduce a latent variable representing housing submarket and model corresponding factors and HPM into a Bayesian network. Utilizing the dependencies modeled in the Bayesian network, our model is able to capture the characteristics of submarket in location proximity, house attribute similarity and substitutability. Meanwhile, our model leverages the mutual enhancement of clustering and regression to build HPMs for each submarket. We conduct empirical evaluations quantitatively and qualitatively in housing market of Nanjing, China. The result shows that our method outperforms all baseline methods in residential property valuation accuracy. Besides, using our model, we are able to interpret the submarkets in Nanjing and quantify the effect of house features on housing price in each submarket.",16.0,,False,JournalArticle;Conference,Computer Science,2018-12-01,ed9e429c2a6b69b09652fe87f2c32e25308c408b,abayesianapproachtoresidentialpropertyvaluationbasedonbuiltenvironmentandhousecharacteristics,liu,10.1109/bigdata.2018.8622422,abayesianapproachtoresidentialpropertyvaluationbasedonbuiltenvironmentandhousecharacteristics|2018,1,2498,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic house price || hedonic housing prices || hedonic property valuation || house prices proximity hedonic || housing characteristics prices hedonic",2,1,3,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2adaf0a9c21f1fe4b1addbe9aa420bf9fcf05e64,10.1145/3371425.3371454,,An intelligent automatic valuation system for real estate based on machine learning,2019.0,"International Conferences on Artificial Intelligence, Information Processing and Cloud Computing",Jiafei Niu; Pei-Qing Niu,"The demand for intelligent automatic real estate evaluation system is growing rapidly with the growth of the economy and the promotion of real estate marketization. House price is affected by a lot of factors from every aspect, how to deal with the insufficient multi-dimensional data is a great challenge. Besides, with the development of the artificial intelligence, applying machine learning and deep learning technology in the real estate valuation field is becoming more and more a tendency. This paper proposes a high-accuracy real estate valuation system based on machine learning. Concretely, the system is composed of three parts: 1) the repeated record recognition model to solve the problem of repeated house records collected from different web crawlers. 2) feature extraction and quantification module to deal with various types of characteristic factors. 3) automated evaluation module based on machine learning. Specially, we use the ensemble learning to integrate different machine learning models in order to improve the model performance further. And the above three parts are integrated into the intelligent automatic valuation system for real estate. The experiments on our self-constructed real estate dataset of Hangzhou, China has shown the effectiveness and superiority of our model.",16.0,,False,JournalArticle,Computer Science,2019-12-19,2adaf0a9c21f1fe4b1addbe9aa420bf9fcf05e64,anintelligentautomaticvaluationsystemforrealestatebasedonmachinelearning,niu,10.1145/3371425.3371454,anintelligentautomaticvaluationsystemforrealestatebasedonmachinelearning|2019,1,2307,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",51f59f7550a548af2dfd544956d7f8b479b5c26e,10.1108/ijhma-02-2022-0033,,Longitudinal modelling of housing prices with machine learning and temporal regression,2022.0,International Journal of Housing Markets and Analysis,Yu Zhang; A. Rahman; Eric W. Miller," Purpose The purpose of this paper is to model housing price temporal variations and to predict price trends within the context of land use–transportation interactions using machine learning methods based on longitudinal observation of housing transaction prices. Design/methodology/approach This paper examines three machine learning algorithms (linear regression machine learning (ML), random forest and decision trees) applied to housing price trends from 2001 to 2016 in the Greater Toronto and Hamilton Area, with particular interests in the role of accessibility in modelling housing price. It compares the performance of the ML algorithms with traditional temporal lagged regression models. Findings The empirical results show that the ML algorithms achieve good accuracy (R2 of 0.873 after cross-validation), and the temporal regression produces competitive results (R2 of 0.876). Temporal lag effects are found to play a key role in housing price modelling, along with physical conditions and socio-economic factors. Differences in accessibility effects on housing prices differ by mode and activity type. Originality/value Housing prices have been extensively modelled through hedonic-based spatio-temporal regression and ML approaches. However, the mutually dependent relationship between transportation and land use makes price determination a complex process, and the comparison of different longitudinal analysis methods is rarely considered. The finding presents the longitudinal dynamics of housing market variation to housing planners. ",15.0,,False,,,2022-04-25,51f59f7550a548af2dfd544956d7f8b479b5c26e,longitudinalmodellingofhousingpriceswithmachinelearningandtemporalregression,zhang,10.1108/ijhma-02-2022-0033,longitudinalmodellingofhousingpriceswithmachinelearningandtemporalregression|2022,1,2559,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices || machine learning hedonic pricing housing",2,3,1,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a221cbb983800ef86a32fffe6a9b793fd1a1cef1,10.1109/icoei51242.2021.9452820,,Prediction of House Price Using Machine Learning Algorithms,2021.0,2021 5th International Conference on Trends in Electronics and Informatics (ICOEI),G. Kumar; D. M. Rani; Neeraja Koppula; S. Ashraf,"People are very careful when they want to buy a new house with market strategies and their budgets. The objective of this paper is to predict the house prices for non-house holders based on their aspirations and financial provisions. By analyzing different parameters like area of the house, square feet of the house, no of floors in the house etc. This research work has utilized the dataset from Kaggle.. An analysis is performed by applying advanced machine learning regression techniques such as Linear regression, KNN Regression, Random Forest Regression, Decision Tree Regression, Extra Trees Regression etc. to attain the most efficient and least error driven regression technique. From the analysis performed, an observation has been made that Catboost Regression Algorithm has outperformed other algorithms. The model predicts the final output with respect to correlated attributes in the dataset.",14.0,,False,Conference,,2021-06-03,a221cbb983800ef86a32fffe6a9b793fd1a1cef1,predictionofhousepriceusingmachinelearningalgorithms,kumar,10.1109/icoei51242.2021.9452820,predictionofhousepriceusingmachinelearningalgorithms|2021,1,1906,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4cce35e2462aa6adb0b180455af1e248a840a902,10.1109/eiecs53707.2021.9587907,,House price prediction based on machine learning and deep learning methods,2021.0,2021 International Conference on Electronic Information Engineering and Computer Science (EIECS),Yihao Chen; Run-Qian Xue; Yu Zhang,"House price is closely related to everyone's life, and it is affected by many factors. House price prediction is an important research point for that it can help people to make strategies about house dealing. There are many existing researches about house price prediction. However, existing studies cannot make a comprehensive comparison and analysis of popular housing price forecasting methods. This paper uses five popular machine learning and in-depth learning approaches to predict house prices and evaluate the models. Our methods include Linear Regression (LR), Bayesian, Backpropagation neural network (BP neural network), Support Vector Machine (SVM), Deep Neural Network (DNN). The dataset we used is from the Kaggle platform. Experimental results show that Bayesian, Backpropagation neural networks, and SVM are more suitable for house price prediction.",14.0,,False,Conference,,2021-09-23,4cce35e2462aa6adb0b180455af1e248a840a902,housepricepredictionbasedonmachinelearninganddeeplearningmethods,chen,10.1109/eiecs53707.2021.9587907,housepricepredictionbasedonmachinelearninganddeeplearningmethods|2021,1,1865,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",182f1b80e010899e69f3cd5b79c8406a971aa002,10.1016/j.jhe.2020.101734,,Measuring aggregate housing wealth: New insights from machine learning ☆,2021.0,Journal of Housing Economics,J. Gallin; Raven S. Molloy; E. Nielsen; Paul A. Smith; K. Sommer,"Abstract We construct a new measure of aggregate housing wealth for the U.S. based on (1) home-value estimates derived from machine learning algorithms applied to detailed information on property characteristics and recent transaction prices, and (2) Census housing unit counts. According to our new measure, the timing and amplitude of the recent house-price cycle differs materially but plausibly from commonly-used measures, which are based on survey data or repeat-sales price indexes. Thus, our methodology generates estimates that should be of considerable value to researchers and policymakers interested in the dynamics of aggregate housing wealth.",14.0,,False,Review,Economics,2021-03-01,182f1b80e010899e69f3cd5b79c8406a971aa002,measuringaggregatehousingwealthnewinsightsfrommachinelearning,gallin,10.1016/j.jhe.2020.101734,measuringaggregatehousingwealthnewinsightsfrommachinelearning|2021,1,1656,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",44a87d523306e4f5fc9f3cb023781fee5e10a220,10.1109/icadeis58666.2023.10270916,,"The Comparison Study of Regression Models (Multiple Linear Regression, Ridge, Lasso, Random Forest, and Polynomial Regression) for House Price Prediction in West Nusa Tenggara",2023.0,"International Conference Advancement Data Science, E-learning and Information Systems",Mindi Richia Putri; I. G. P. S. Wijaya; Fritzie Primananda Adi Praja; Abdul Hadi; Faqih Hamami,"Predicting house prices is essential in the real estate industry as it enables stakeholders to make informed decisions. Accurately predicting house prices is paramount in facilitating buying and selling transactions, aiding property valuation, and providing valuable insights for investors and homeowners. This research compares five regression models, namely Multiple Linear Regression, Ridge, Lasso, Random Forest, and Polynomial Regression, to predict house prices in West Nusa Tenggara Province. These considered building area, land area, number of bedrooms, and bathrooms. The data was taken from the public website, namely Lamudi Website, and was collected using a web scraping method and then processed using Machine Learning. By considering factors such as building area, land area, number of bedrooms, and bathrooms, the study aimed to determine which model delivered the most accurate predictions and exhibited a low error rate. This research used various metrics such as R-Squared, Root Mean Square Error (RMSE), and Cross-Validation to measure the accuracy of these models. In this study, the R-Squared and RMSE methods showed that Multiple Linear Regression and Lasso Regression were the best models with the same R-Squared and RMSE values, namely R-Squared = 0.6947 (69.47%) and RMSE = 2863760831. Meanwhile, the RMSE (Cross-Validation) method shows that Random Forest Regression is the best model with a value of 3343572297. These research outcomes have the potential to provide valuable guidance to the local community in making informed decisions regarding property transactions in West Nusa Tenggara.",13.0,,False,Conference,,2023-08-02,44a87d523306e4f5fc9f3cb023781fee5e10a220,thecomparisonstudyofregressionmodelsmultiplelinearregressionridgelassorandomforestandpolynomialregressionforhousepricepredictioninwestnusatenggara,putri,10.1109/icadeis58666.2023.10270916,thecomparisonstudyofregressionmodelsmultiplelinearregressionridgelassorandomforestandpolynomialregressionforhousepricepredictioninwestnusatenggara|2023,1,2618,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",acb2aa00a1957b93d67befab7ff24d3bce05c99a,10.1080/09599916.2021.1890187,,Predicting owner-occupied housing values using machine learning: an empirical investigation of California census tracts data,2021.0,Journal of Property Research,Prodosh E. Simlai,"ABSTRACT In this paper, we introduce machine-learning (ML) methods to evaluate one of the key concepts of real estate analysis – the prediction of housing prices in the presence of a large number of covariates. We use several supervised ML tools that are based on regularisation methods – notably Ridge, LASSO, and Elastic Net regressions – and discuss their relative performance in comparison to conventional OLS-based methods. Our empirical results show that the supervised ML methods provide a comprehensive description of the determinants of owner-occupied housing values in the census tracts of California. We find that, compared to the familiar worlds of OLS and WLS, the Ridge, LASSO, and Elastic Net regressions provide relatively better out-of-sample predictions. Among the benefits of shrinkage-based ML methods are their ability to resolve such issues as variable selection and overfitting.",13.0,,False,,Computer Science,2021-04-13,acb2aa00a1957b93d67befab7ff24d3bce05c99a,predictingowneroccupiedhousingvaluesusingmachinelearninganempiricalinvestigationofcaliforniacensustractsdata,simlai,10.1080/09599916.2021.1890187,predictingowneroccupiedhousingvaluesusingmachinelearninganempiricalinvestigationofcaliforniacensustractsdata|2021,1,1901,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",abdcace94f2704496eeea64c035cd4e01ff45963,10.1109/mlbdbi54094.2021.00059,,Decision Trees for Objective House Price Prediction,2021.0,"2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)",Zhishuo Zhang,"Different people buy houses with the same value at different prices, which usually leads to dissatisfaction with housing prices and unfair housing prices. To solve this problem, we designed an objective housing price prediction scheme based on a decision tree. First, we selected 5 important features based on the decision tree for subsequent modeling. Then we designed a housing price prediction model based on a decision tree. To obtain the optimal parameters, we used grid search. The results showed that the number of houses is the most important factor affecting housing prices, followed by the local population's quality, geographic location, education, and crime rate. To verify the effectiveness of the decision tree scheme, we compared it with some other advanced machine learning models. The implementation results show that our scheme achieves the best results.",13.0,,False,Conference,,2021-12-01,abdcace94f2704496eeea64c035cd4e01ff45963,decisiontreesforobjectivehousepriceprediction,zhang,10.1109/mlbdbi54094.2021.00059,decisiontreesforobjectivehousepriceprediction|2021,1,1872,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0b0a26f8891fa7516a6b0a7e234002eed48217d4,10.1007/s12652-019-01616-4,,The usage of artificial intelligence in the commodity house price evaluation model,2020.0,Journal of Ambient Intelligence and Humanized Computing,Xiang Zhou,,13.0,,False,,Computer Science,2020-01-02,0b0a26f8891fa7516a6b0a7e234002eed48217d4,theusageofartificialintelligenceinthecommodityhousepriceevaluationmodel,zhou,10.1007/s12652-019-01616-4,theusageofartificialintelligenceinthecommodityhousepriceevaluationmodel|2020,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9357477a8421c36420b4fbb6be89a50b36cce0a8,10.48550/arxiv.2409.05335,2409.05335,A Multi-Modal Deep Learning Based Approach for House Price Prediction,2024.0,arXiv.org,Md Hasebul Hasan; Maknun Jahan; Mohammed Eunus Ali; Yuan-Fang Li; T. Sellis,"Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to predict the house price from this jointly learned embedding vector. Our experimental results with a real-world dataset show that the text embedding of the house advertisement description and image embedding of the house pictures in addition to raw attributes and geo-spatial embedding, can significantly improve the house price prediction accuracy. The relevant source code and dataset are publicly accessible at the following URL: https://github.com/4P0N/mhpp",12.0,,False,JournalArticle,Computer Science,2024-09-09,9357477a8421c36420b4fbb6be89a50b36cce0a8,amultimodaldeeplearningbasedapproachforhousepriceprediction,hasan,10.48550/arxiv.2409.05335,amultimodaldeeplearningbasedapproachforhousepriceprediction|2024,1,2656,4,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2fbb3a58c956f382827e6bbb2455f777f5f01d19,10.2139/ssrn.3833734,,House Price Prediction Using Multiple Linear Regression,2021.0,,Anirudh Kaushal; Achyut Shankar,"There comes a point in everyone’s life when the person wishes to buy or sell a house. First consider a scenario where a person needs to buy a house. The person will look for his/her desired house for a sensible price tag. The person will have some features decided what he/she wants to have in the house. The person will be able to decide whether the type of house he/she is looking for is worth of the price or not. Similarly, consider a scenario where a person needs to sell a house. By making use of the house price prediction system, the seller would be able to decide what all features he/she could add in the house so that the house can be sold for a higher price. Hence, from both the above scenarios we can confirm that house price prediction is useful both for the buyer and seller. This paper will help to predict the house prices based on various parameters. The users will be able to input the type of house they desire to buy and with the help of machine learning the house price predictor will display the estimated price of the desired house.",12.0,,False,,Computer Science,2021-04-25,2fbb3a58c956f382827e6bbb2455f777f5f01d19,housepricepredictionusingmultiplelinearregression,kaushal,10.2139/ssrn.3833734,housepricepredictionusingmultiplelinearregression|2021,1,2059,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",89e297cc6f8d3eccaad4811d5d584356d8e26414,10.1109/icmnwc56175.2022.10031728,,House Prices Prediction Using Machine Learning Regression Models,2022.0,2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC),Amit Gupta; S. K. Dargar; Abha Dargar,"Understanding request drifts is critical in today’s booming civilization and ever-changing request settings. The current vaticination of house prices corresponds to the study’s top substance patterns. Property values are of tremendous relevance to consumers as merchandisers, and casing prices are an essential representation of frugality. Property is one of our ecosystem’s lowest quantum transparent diligences. The major goal of this investigation is to predict housing prices using real-time factors. The results showed that this strategy produces less inaccuracy and delicacy than individual algorithms. We also propose leveraging actual-time locality details from Google maps to obtain precise actual-world estimates. The former model, with its reduced treatment and information over-fitting, reduces.",12.0,,False,Conference,,2022-12-02,89e297cc6f8d3eccaad4811d5d584356d8e26414,housepricespredictionusingmachinelearningregressionmodels,gupta,10.1109/icmnwc56175.2022.10031728,housepricespredictionusingmachinelearningregressionmodels|2022,1,1807,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",eb701cf86026ddf09cc2f6fa6112efb905086050,10.1109/icacccn51052.2020.9362864,,House Price Prediction Using Optimal Regression Techniques,2020.0,"2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)",V. S. Rana; Jayanto Mondal; Annu Sharma; Indu Kashyap,"Humans are very thoughtful when they want to make some investments, especially when it is a house. When people need a house, they focus on various factors but the most important is the budget. The motivation of this research work is to predict the house prices for lower- and middle-class people based on their financial parameters. This paper compared the results of different regression techniques like Support vector regression, XGBoost, Decision tree regression, and Random forest regression, and based on their performances, we have taken the best among them for optimally predicting the house prices. To train the models, we have focused on various parameters that affect the costs of a house like physical condition, location, amenities, interest rates, etc.",12.0,,False,Conference,,2020-12-18,eb701cf86026ddf09cc2f6fa6112efb905086050,housepricepredictionusingoptimalregressiontechniques,rana,10.1109/icacccn51052.2020.9362864,housepricepredictionusingoptimalregressiontechniques|2020,1,1765,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 crossref,apartment rent hedonic,,10.1002/isaf.287,,Hedonic price models and indices based on boosting applied to the Dutch housing market,2007.0,"Intelligent Systems in Accounting, Finance and Management",Martijn Kagie; Michiel Van Wezel,"Abstract We create a hedonic price model for house prices for six geographical submarkets in the Netherlands. Our model is based on a recent data‐mining technique called boosting. Boosting is an ensemble technique that combines multiple models, in our case decision trees, into a combined prediction. Boosting enables capturing of complex nonlinear relationships and interaction effects between input variables. We report mean relative errors and mean absolute error for all regions and compare our models with a standard linear regression approach. Our model improves prediction performance by up to 39% compared with linear regression and by up to 20% compared with a log‐linear regression model. Next, we interpret the boosted models: we determine the most influential characteristics and graphically depict the relationship between the most important input variables and the house price. We find the size of the house to be the most important input for all but one region, and find some interesting nonlinear relationships between inputs and price. Finally, we construct hedonic price indices and compare these with the mean and median index and find that these indices differ notably in the urban regions of Amsterdam and Rotterdam. Copyright © 2008 John Wiley & Sons, Ltd.",11.0,,,journal-article,,,doi_10_1002_isaf_287,hedonicpricemodelsandindicesbasedonboostingappliedtothedutchhousingmarket,kagie,10.1002/isaf.287,hedonicpricemodelsandindicesbasedonboostingappliedtothedutchhousingmarket|2007,1,2282,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || apartment rent hedonic || hedonic house price || hedonic housing prices || housing characteristics prices hedonic",2,1,2,2,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",556ed929f42725e3cfee065d7013ded790b2bc60,10.1145/3449726.3463141,,House price prediction using clustering and genetic programming along with conducting a comparative study,2021.0,GECCO Companion,Fateme Azimlu; S. Rahnamayan; Masoud Makrehchi,"One of the most important tasks in machine learning is prediction. Data scientists use different regression methods to find the most appropriate and accurate model for each type of datasets. This study proposes a method to improve accuracy in regression and prediction. In common methods, different models are applied to the whole data to find the best model with higher accuracy. In our proposed approach, first, we cluster data using different methods such as K-means, DBSCAN, and agglomerative hierarchical clustering algorithms. Then, for each clustering method and for each generated cluster we apply various regression models including linear and polynomial regressions, SVR, neural network, and symbolic regression in order to find the most accurate model and study the genetic programming potential in improving the prediction accuracy. This model is a combination of clustering and regression. After clustering, the number of samples in each created cluster, compared to the number of samples in the whole dataset is reduced, and consequently by decreasing the number of samples in each group, we lose accuracy. On the other hand, specifying data and setting similar samples in one group enhances the accuracy and decreases the computational cost. As a case study, we used real estate data with 20 features to improve house price estimation; however, this approach is applicable to other large datasets.",11.0,,False,JournalArticle;Book,Computer Science,2021-07-07,556ed929f42725e3cfee065d7013ded790b2bc60,housepricepredictionusingclusteringandgeneticprogrammingalongwithconductingacomparativestudy,azimlu,10.1145/3449726.3463141,housepricepredictionusingclusteringandgeneticprogrammingalongwithconductingacomparativestudy|2021,1,2412,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6f0e952fb55a010a0ffdc91e3472469a178aec38,10.1109/icscss57650.2023.10169290,,House and Rent Price Prediction System using Regression,2023.0,2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS),Sujata Khandaskar; Chirag Panjwani; V. Patil; Deanna Fernandes; Prerna Bajaj,"Real estate market analysis plays a crucial role in predicting house prices and rental trends. In recent years, several techniques have been developed to address this challenge, including machine learning (ML) algorithms, data mining approaches, and statistical models. There were a few issues which needed to be solved, like data quality and availability pose significant obstacles. Housing datasets often exhibit missing values, outliers, and inconsistent formats, which can hinder the performance of prediction models. Addressing these challenges requires careful data preprocessing techniques and collaboration with data providers and government agencies to ensure data accuracy and coverage. Existing systems face significant challenges that hinder their accuracy and reliability. Some of these challenges include limited availability of comprehensive and up-to-date data, complex market dynamics, and the need for efficient feature selection and model optimization. The proposed objectives includes Data collection and preprocessing, feature selection and engineering, Model selection and development along with evaluation, usability, comparison with existing systems, etc. By addressing these challenges, proposed system aims to provide more accurate and reliable predictions for real estate market analysis, aiding homeowners, tenants, and real estate professionals in making informed decisions.",11.0,,False,Conference,,2023-06-14,6f0e952fb55a010a0ffdc91e3472469a178aec38,houseandrentpricepredictionsystemusingregression,khandaskar,10.1109/icscss57650.2023.10169290,houseandrentpricepredictionsystemusingregression|2023,1,2403,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",be30188e84b262c124b8904f8d6cf566ae05fde0,10.1080/00167223.2018.1498364,,Artificial neural networks and geostatistical models for housing valuations in urban residential areas,2018.0,Danish journal of geography,M. Carmen Morillo Balsera; S. Martínez-Cuevas; Iñigo Molina Sánchez; César García-Aranda; M. Izquierdo,"ABSTRACT Property valuation studies often use classical statistics techniques. Among these techniques, the Artificial Neural Networks are the most applied, overcoming the inflexibility and the linearity of the hedonic models. Other researchers have used Geostatistics techniques, specifically the Kriging Method, for interpreting spatial-temporal variability and to predict housing unit prices. The innovation of this study is to highlight how the Kriging Method can help to better understand the urban environment, improving the results obtained by classical statistics. This study presents two different methods that share the general objective of extracting information regarding a city’s housing from datasets. The procedures applied are Ordinary Kriging (Geostatistics) and Multi-Layer Perceptron algorithm (Artificial Neural Networks). These methods were used to predict housing unit prices in the municipality of Pozuelo de Alarcon (Madrid). The implementation of both methods provides us with the urban characteristics of the study area and the most significant variables related to price. The main conclusion is that the Ordinary Kriging models and the Neural Networks models, applied to predicting housing unit prices are necessary methodologies to improve the information obtained in classical statistical techniques. Abbreviations: ANN: Artificial Neural Networks; OK: ordinary Kriging; MLP: multi-layer perceptron",10.0,,False,,Computer Science,2018-07-03,be30188e84b262c124b8904f8d6cf566ae05fde0,artificialneuralnetworksandgeostatisticalmodelsforhousingvaluationsinurbanresidentialareas,balsera,10.1080/00167223.2018.1498364,artificialneuralnetworksandgeostatisticalmodelsforhousingvaluationsinurbanresidentialareas|2018,1,2426,5,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices || hedonic property valuation || housing characteristics prices hedonic || spatial hedonic model housing",2,1,2,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7745824548c1ccfa21c0b9386d950bb794481e58,10.1109/icmi55296.2022.9873784,,Artificial Intelligence Approach for Modeling House Price Prediction,2022.0,International Conference on Multimodal Interaction,Melihsah Cekic; Kübra Nur Korkmaz; Habib Müküs; A. Hameed; Akhtar Jamil; Faezeh Soleimani,"Real estate has a vast market volume across the globe. This domain has been growing significantly in the past few decades. An accurate prediction can help buyers, and other decision-makers make better decisions. However, developing a model that can effectively predict house prices in complex environments is still a challenging task. This paper proposes machine learning models for the accurate prediction of real estate house prices. Furthermore, we investigated the feature importance and various data analysis methods to improve the prediction accuracy. Linear Regression, Decision Tree, XGBoost, Extra Trees, and Random Forest were used in this study. For all models, hyperparameters were first calculated using k-fold cross-validation, and then they were trained to apply to test data. The models were tested on the Boston housing dataset. The proposed method was evaluated using Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics.",10.0,,False,Conference,,2022-04-15,7745824548c1ccfa21c0b9386d950bb794481e58,artificialintelligenceapproachformodelinghousepriceprediction,cekic,10.1109/icmi55296.2022.9873784,artificialintelligenceapproachformodelinghousepriceprediction|2022,1,1990,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f1706ff5c106c27f641f1693db1c4e0afced7dbc,10.1109/icecet52533.2021.9698641,,"Explainable, Multi-Region Price Prediction",2021.0,"2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)",Atharva Ghatnekar; A. Shanbhag,"Today, Machine Learning has risen to prominence in a variety of fields, from medical diagnosis to stock market trading. Working in the same line, we have focused our paper on one such application of machine learning. In this paper, we have implemented machine learning algorithms to develop prediction forms for house prices based on actual data of real estate in Boston and California. A comparative study of house prices, this paper first introduces the learning algorithms used. Then, multiple regression models and their optimized parameter values are discussed. Basis the prediction results, the paper first compares the performance of Machine Learning models through quantitative metrics, evaluates the importance of attributes influencing the valuation of houses in Boston and California through SHAP values, and finally draws a conclusion on the real estate prices using economic reasoning.",10.0,,False,Conference,,2021-12-09,f1706ff5c106c27f641f1693db1c4e0afced7dbc,explainablemultiregionpriceprediction,ghatnekar,10.1109/icecet52533.2021.9698641,explainablemultiregionpriceprediction|2021,1,1898,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,1,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",025963448000777b738b2cb405afeec0b78230e6,10.1109/mdm.2019.00-15,,A LSTM and Graph CNN Combined Network for Community House Price Forecasting,2019.0,International Conference on Mobile Data Management,Chuancai Ge,"Community house price forecasting has been a livelihood issue for the governments and the residents, and accurate forecast of real estimate price is so important to urban planning as well as house-purchase suggestions. However, the price of residential communities involving many aspects including economic factors, community attributes and time series trend. What's more, in this paper, we take spatial dependence among communities into account, which is hard to capture in city-level. Finally, we propose a novel deep network framework to integrate all the aspects and model the spatial-temporal patterns for community house price forecasting.",10.0,,False,JournalArticle;Conference,Computer Science,2019-06-01,025963448000777b738b2cb405afeec0b78230e6,alstmandgraphcnncombinednetworkforcommunityhousepriceforecasting,ge,10.1109/mdm.2019.00-15,alstmandgraphcnncombinednetworkforcommunityhousepriceforecasting|2019,1,1645,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9f78b302b1ac7f326ade807408fa0224e2e3c9d3,10.1109/icseiet58677.2023.10303359,,Housing Price Prediction Model Using Machine Learning,2023.0,2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology (ICSEIET),A. Chaurasia; Inam UI Haa,"Housing price prediction is a challenging task due to the complexity of huge data variance with changes in location points. In this research paper, we propose a machine learning- based house pricing prediction model that can predict the prices of houses more accurately. The proposed model uses a combination of data pre-processing techniques and machine learning algorithms simultaneously. The efficiency of the proposed model is evaluated using real-time house price data, and the results show significant improvement over the existing techniques.",10.0,,False,Conference,,2023-09-14,9f78b302b1ac7f326ade807408fa0224e2e3c9d3,housingpricepredictionmodelusingmachinelearning,chaurasia,10.1109/icseiet58677.2023.10303359,housingpricepredictionmodelusingmachinelearning|2023,1,1549,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e80ce8d9b22d671aa3ae4dbc3b6dcebd405cca2f,10.1109/ams.2010.29,,Estimation of Housing Prices by Fuzzy Regression and Artificial Neural Network,2010.0,2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation,R. Ghodsi; A. Boostani; F. Faghihi,,10.0,,False,Conference,Mathematics;Computer Science,2010-05-26,e80ce8d9b22d671aa3ae4dbc3b6dcebd405cca2f,estimationofhousingpricesbyfuzzyregressionandartificialneuralnetwork,ghodsi,10.1109/ams.2010.29,estimationofhousingpricesbyfuzzyregressionandartificialneuralnetwork|2010,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,spatial hedonic model housing,0f9d81a090cc6c05b9318940b15f970e70b93622,10.1016/j.neucom.2013.07.035,,Semiparametric spatial effects kernel minimum squared error model for predicting housing sales prices,2014.0,Neurocomputing,J. Shim; Okmyung Bin; Changha Hwang,,10.0,,False,JournalArticle,Mathematics;Computer Science,,0f9d81a090cc6c05b9318940b15f970e70b93622,semiparametricspatialeffectskernelminimumsquarederrormodelforpredictinghousingsalesprices,shim,10.1016/j.neucom.2013.07.035,semiparametricspatialeffectskernelminimumsquarederrormodelforpredictinghousingsalesprices|2014,1,1000,2,hedonic housing prices || spatial hedonic model housing,0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",4b326dc94801b2703a3bef85158e9ca150e8d47f,10.1109/punecon55413.2022.10014818,,Real Estate Price Prediction using Supervised Learning,2022.0,2022 IEEE Pune Section International Conference (PuneCon),Vedang Matey; Nikita Chauhan; Aditi Mahale; Vidya Bhistannavar; Ajitkumar Shitole,"The least transparent sector of our economy is real estate. Housing prices change daily and are occasionally inflated rather than based on an appraisal. The central focus of our approach is using fundamental factors to forecast house values. Here, we strive to establish our assessments on each essential aspect when deciding the house's price. In our project, three elements affect a house's price: its physical attributes, design, and location. There have been a lot of studies utilizing typical machine learning techniques to estimate house prices effectively. Still, they need to pay more attention to how well each model performs and ignore the less well-known but more sophisticated models. Our project involves predictions using different Regression techniques like Linear Regression, Lasso Regression, and Decision Tree. Our project includes estimating the price of houses without any expectations of market prices and cost increments. The project aims to predict residential prices for customers considering their financial plans and needs. This project means to predict house prices in Pune city with various regression techniques. The project aims to predict cogent housing prices for those who do not own homes depending on their financial capabilities and desires. Estimating pricing will be possible by examining the mentioned goods, fare ranges, and advancements. This initiative aims to enable individuals to pinpoint the specific timeline for home acquisition and sellers in assessing the cost of a home sale. Spending resources on web-based apps without consulting a broker will benefit clients. Additionally, it provides a brief explanation of the various graphical and numerical techniques that are required to calculate the price of a home. Our study explains the goal of machine learning, the workings of the house pricing model, and the datasets that went into developing the model we suggest. Lasso, Decision Tree, and Linear Regression were among the models looked at in the study (accuracy: 83.54 percent) (accuracy -77.88 percent).",9.0,,False,Conference,,2022-12-15,4b326dc94801b2703a3bef85158e9ca150e8d47f,realestatepricepredictionusingsupervisedlearning,matey,10.1109/punecon55413.2022.10014818,realestatepricepredictionusingsupervisedlearning|2022,1,3058,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,6,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",150995021d6de7030847639847d9172115f9cead,10.1145/3474717.3484257,,Attention-Based Spatial Interpolation for House Price Prediction,2021.0,SIGSPATIAL/GIS,Darniton Viana; Luciano Barbosa,"Estimating the market price of a house is important for many businesses such as real estate and mortgage lending companies. The price of a house depends not only on its structural features (e.g. area and number of bedrooms) but also on the spatial context where it is located. In this work we estimate the price of a house based solely on its structural features and the characteristics and price of its neighbors. For that, we propose a hybrid attention mechanism that weights neighbors based on their similarity to the house in terms of structural features and geographic location. For the structural features, we apply an euclidean-based attention and, for the geographic location, we propose an attention layer based on a radial basis function kernel. Those attention mechanisms are then used by a neural network regressor to predict the price of a house and to generate a vector representation of the house based on its implicit context: the house embedding, which can be used as a feature set by any regressor to perform house price prediction. We have performed an extensive experimental evaluation on real-world datasets that shows that: (1) regressors using house embedding obtained the best results on all 4 datasets, outperforming baseline models; (2) the learned house embedding improves the performance of the evaluated regressors in almost all scenarios in comparison to raw features; and (3) simple regressor models such as Linear Regression using house embedding achieved comparable results to more competitive algorithms (e.g. Random Forest and Xgboost).",9.0,,False,Book;JournalArticle,Computer Science,2021-11-02,150995021d6de7030847639847d9172115f9cead,attentionbasedspatialinterpolationforhousepriceprediction,viana,10.1145/3474717.3484257,attentionbasedspatialinterpolationforhousepriceprediction|2021,1,2571,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ec674e9f69f5e6c26fae8058d895f759bd808c0b,10.1109/icced56140.2022.10010684,,House Price Prediction Using Linier Regression,2022.0,International Conference Computing Engineering and Design,N. T. M. Sagala; Laura Hestia Cendriawan,"Property entrepreneurs will compete to build properties, especially houses for investment facilities. This will make house prices increase day by day with a high purchasing power of the people. Consumers will think in buying a house whether the house they buy will have a good profit value or not. The aim of the study is to build a model that may predict house price for company and become a business decision for consumers. The methodology used seven major steps namely, business understanding, data understanding, data cleaning, data standardization, modelling, and evaluation. This study develop a linear regression model for home price prediction and tests it using data from the Maribelajar company. These are carried out on the Azure Platform by creating two pipelines. One pipeline for training and another for testing. The results are then visualized using Power Business Intelligence for providing a proper business performance analysis. From the experimental results, the model achieved RMSE= 0.0334 and R coefficient = 0.7. The data analysis and testing in this study show that the multiple linear regression model can forecast and evaluate housing prices to some extent, but the algorithm may still be improved using more advanced machine learning approaches.",9.0,,False,Conference,,2022-07-28,ec674e9f69f5e6c26fae8058d895f759bd808c0b,housepricepredictionusinglinierregression,sagala,10.1109/icced56140.2022.10010684,housepricepredictionusinglinierregression|2022,1,2272,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6213dd2736adf021b6b4d4e17854e9b115c1d349,10.1109/kst51265.2021.9415771,,Stacking Ensemble Learning for Housing Price Prediction: a Case Study in Thailand,2021.0,International Conference on Knowledge and Smart Technology,Gan Srirutchataboon; Saranpat Prasertthum; E. Chuangsuwanich; Ploy N. Pratanwanich; Chotirat Ratanamahatana,"In this paper, we analyze the housing price data obtained from a leading Thai real estate website and Open Street Maps (OSM) to identify the features that affect the housing price in Thailand from 2015 to 2019. Moreover, we propose a model based on a stacking ensemble learning framework, where the predictions are generated by stacking three base learning models consisting of a convolutional neural network (CNN), an ensemble model (such as random forests (RF), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost)) and a simple linear regression technique. The CNN is used to extract features from house images which are then combined with traditional features to estimate the initial price. The prediction is then calibrated using linear regression. Compared to individual models, the proposed model achieves a Mean Absolute Percentage Error (MAPE) of 17.83%, significantly outperforming other baselines.",9.0,,False,JournalArticle;Conference,Computer Science,2021-01-21,6213dd2736adf021b6b4d4e17854e9b115c1d349,stackingensemblelearningforhousingpricepredictionacasestudyinthailand,srirutchataboon,10.1109/kst51265.2021.9415771,stackingensemblelearningforhousingpricepredictionacasestudyinthailand|2021,1,1924,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e9b23209ba31dae8d87bf9d7577d6230db2e2799,10.1109/icecaa55415.2022.9936336,,Machine Learning based House Price Prediction Model,2022.0,2022 International Conference on Edge Computing and Applications (ICECAA),Chee-Kin Chen; Zailan Arabee Bin Abdul Salam; Kadhar Batcha Nowshath,"In this digital era, People have become more aware on purchasing a new property. Many digital tools have been developed to analyze the property marketing strategies and the buyers' budget constraints. The goal of this paper is to predict house prices for non-home owners based on their financial resources and aspirations. Estimated prices will be calculated by using different tools such as Machine Learning (ML), Artificial Neural Network (ANN) and Chatbot. All of the above-mentioned techniques were used here to determine the most effective house price from the collected dataset. This research project will particularly conduct multiple researches on the affordability of houses present within Malaysia. The motive of this work is to build a prediction model to help in the process of house price prediction and assist both buyers and seller to have a general view on the current market price and trend.",9.0,,False,Conference,,2022-10-13,e9b23209ba31dae8d87bf9d7577d6230db2e2799,machinelearningbasedhousepricepredictionmodel,chen,10.1109/icecaa55415.2022.9936336,machinelearningbasedhousepricepredictionmodel|2022,1,1908,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",90048d6d48c69e36fe82b7c2f62d837956e9144d,10.1109/idap.2018.8620781,,Predicting Housing Price Based on Ensemble Learning Algorithm,2018.0,2018 International Conference on Artificial Intelligence and Data Processing (IDAP),Ya-Juan Tang; Shuang Qiu; Pengcheng Gui,"The housing prices are affected by many factors. Finding a practical prediction algorithm is becoming an important issue. Because the effect of a single classifier is limited. In this paper, we select a real estate transaction instance of a city in the United States, and employ the bagging algorithm and random forest to construct a model to predict housing price. Ensemble learning which combines multiple base learners, which can obtain excellent generalization performance and improves the prediction accuracy. The results show that ensemble learning has better prediction accuracy than the decision tree model.",9.0,,False,Conference,Computer Science,2018-09-01,90048d6d48c69e36fe82b7c2f62d837956e9144d,predictinghousingpricebasedonensemblelearningalgorithm,tang,10.1109/idap.2018.8620781,predictinghousingpricebasedonensemblelearningalgorithm|2018,1,1615,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fed7f3cb0648a7182a0bb7a5ddd8bb95551f8a7d,10.1109/rpc.2018.8482191,,Predicting Sales Prices of the Houses Using Regression Methods of Machine Learning,2018.0,2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC),Parasich Andrey Viktorovich; Parasich Viktor Aleksandrovich; Kaftannikov Igor Leopoldovich; Parasich Irina Vasilevna,"In this article we will describe our solution for “House Prices: Advanced Regression Techniques” machine learning competition, which was held on Kaggle platform. The competitor's goal was to predict house's sale price by their attributes like house area, year of building, etc. In our solution, we use classic machine learning algorithms, and our original methods, which will be described here. At the end of the competition, we took 18th place among 2124 participants from whole world (top 1%).",9.0,,False,Conference,Computer Science,2018-08-01,fed7f3cb0648a7182a0bb7a5ddd8bb95551f8a7d,predictingsalespricesofthehousesusingregressionmethodsofmachinelearning,viktorovich,10.1109/rpc.2018.8482191,predictingsalespricesofthehousesusingregressionmethodsofmachinelearning|2018,1,1495,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",19697f0b89c27ff27c7548e65e3748a6d53bc5f7,10.1109/icoco53166.2021.9673533,,Housing Price Analysis Using Linear Regression and Logistic Regression: A Comprehensive Explanation Using Melbourne Real Estate Data,2021.0,2021 IEEE International Conference on Computing (ICOCO),Keren He; Cui-Wei He,,9.0,,False,Conference;Review,,2021-11-17,19697f0b89c27ff27c7548e65e3748a6d53bc5f7,housingpriceanalysisusinglinearregressionandlogisticregressionacomprehensiveexplanationusingmelbournerealestatedata,he,10.1109/icoco53166.2021.9673533,housingpriceanalysisusinglinearregressionandlogisticregressionacomprehensiveexplanationusingmelbournerealestatedata|2021,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",207221def10a8ad1d4b5869ab5582235019678fe,10.1108/ijhma-01-2021-0003,,Comparing predictive performance of general regression neural network (GRNN) and hedonic regression model for factors affecting housing prices in “Pune-India”,2021.0,,A. Abhyankar; H. Singla," Purpose The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.” Design/methodology/approach Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016). Findings While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%). Research limitations/implications The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices. Practical implications The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence. Originality/value To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India. ",8.0,,False,,Mathematics,2021-06-10,207221def10a8ad1d4b5869ab5582235019678fe,comparingpredictiveperformanceofgeneralregressionneuralnetworkgrnnandhedonicregressionmodelforfactorsaffectinghousingpricesinpuneindia,abhyankar,10.1108/ijhma-01-2021-0003,comparingpredictiveperformanceofgeneralregressionneuralnetworkgrnnandhedonicregressionmodelforfactorsaffectinghousingpricesinpuneindia|2021,1,4223,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic housing prices || hedonic property valuation",2,1,1,4,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d8d2c984693866420298617808b5cba27b0ff952,10.1145/3645110,,Improved Regression Analysis with Ensemble Pipeline Approach for Applications across Multiple Domains,2024.0,ACM Trans. Asian Low Resour. Lang. Inf. Process.,Debajyoty Banik; R. Paul; Rajkumar Singh Rathore; Rutvij H. Jhaveri,"In this research, we introduce two new machine learning regression methods: the Ensemble Average and the Pipelined Model. These methods aim to enhance traditional regression analysis for predictive tasks and have undergone thorough evaluation across three datasets, Kaggle House Price, Boston House Price, and California Housing, using various performance metrics. The results consistently show that our models outperform existing methods in terms of accuracy and reliability across all three datasets. The Pipelined Model, in particular, is notable for its ability to combine predictions from multiple models, leading to higher accuracy and impressive scalability. This scalability allows for their application in diverse fields like technology, finance, and healthcare. Furthermore, these models can be adapted for real-time and streaming data analysis, making them valuable for applications such as fraud detection, stock market prediction, and IoT sensor data analysis. Enhancements to the models also make them suitable for big data applications, ensuring their relevance for large datasets and distributed computing environments. It is important to acknowledge some limitations of our models, including potential data biases, specific assumptions, increased complexity, and challenges related to interpretability when using them in practical scenarios. Nevertheless, these innovations advance predictive modeling, and our comprehensive evaluation underscores their potential to provide increased accuracy and reliability across a wide range of applications. The results indicate that the proposed models outperform existing models in terms of accuracy and robustness for all three datasets. The source code can be found at https://huggingface.co/DebajyotyBanik/Ensemble-Pipelined-Regression/tree/main",8.0,,False,JournalArticle,Computer Science,2024-02-08,d8d2c984693866420298617808b5cba27b0ff952,improvedregressionanalysiswithensemblepipelineapproachforapplicationsacrossmultipledomains,banik,10.1145/3645110,improvedregressionanalysiswithensemblepipelineapproachforapplicationsacrossmultipledomains|2024,1,2806,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,1,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3cc62df1c7d0ec4fa5a7c9163e0abe59d2cea643,10.1109/icetet-sip58143.2023.10151561,,Real Estate Prediction System Using ML,2023.0,2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP),Abhinav Wandhe; Lakshya Sehgal; Hardik Sumra; Aryaman Choudhary; M. Dhone,"In recent years, machine learning has played a significant role in many aspects of our lives, including medical diagnosis, natural speech command, picture detection, product suggestion, spam recognition, and price prediction, etc. The desire to receive a profit on an investment property is a typical justification for home buying. They frequently want to know when and where to buy a property, thus they frequently ask the same questions. Current Real Estate Management System does not provide the prediction of the property price for users. In this project we offer the facility to the users to look for properties. This will provide the facility to view the system as an admin or user. The unique feature of this system is that it can predict the chance of a rise in the price of a property in the coming future. Prediction is done with the help of Machine Learning Algorithms such as Convolutional Neural Network (CNN) and Natural Language Processing (NLP), which helps to generate an output that is either positive (1) or negative (0). The predicted value depends upon various factors of the property such as property area, surrounding area, age of construction, Floors, Available rooms and carpet area. This will help the user to predict the chance of the property price rise in the future, thus making a better deal.",8.0,,False,Conference,,2023-04-28,3cc62df1c7d0ec4fa5a7c9163e0abe59d2cea643,realestatepredictionsystemusingml,wandhe,10.1109/icetet-sip58143.2023.10151561,realestatepredictionsystemusingml|2023,1,2323,2,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c59c73d4433eae0b582bc5cf0dcf71c493e1941c,10.48550/arxiv.2405.07456,2405.07456,Boosting House Price Estimations with Multi-Head Gated Attention,2024.0,Expert systems with applications,A. Sellam; C. Distante; A. Taleb-Ahmed; P. L. Mazzeo,"Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relationships that affect property values. To address these challenges, we have developed a new method called Multi-Head Gated Attention for spatial interpolation. Our approach builds upon attention-based interpolation models and incorporates multiple attention heads and gating mechanisms to capture spatial dependencies and contextual information better. Importantly, our model produces embeddings that reduce the dimensionality of the data, enabling simpler models like linear regression to outperform complex ensembling models. We conducted extensive experiments to compare our model with baseline methods and the original attention-based interpolation model. The results show a significant improvement in the accuracy of house price predictions, validating the effectiveness of our approach. This research advances the field of spatial interpolation and provides a robust tool for more precise house price evaluation. Our GitHub repository.contains the data and code for all datasets, which are available for researchers and practitioners interested in replicating or building upon our work.",8.0,,False,JournalArticle,Computer Science,2024-05-13,c59c73d4433eae0b582bc5cf0dcf71c493e1941c,boostinghousepriceestimationswithmultiheadgatedattention,sellam,10.48550/arxiv.2405.07456,boostinghousepriceestimationswithmultiheadgatedattention|2024,1,2309,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4fbe553cfb0b2600eaf110898ac72714b198292d,10.1108/dta-05-2020-0111,,Using photographs and metadata to estimate house prices in South Korea,2020.0,Data Technologies and Applications,Changro Lee; Key-Ho Park,"PurposeMost prior attempts at real estate valuation have focused on the use of metadata such as size and property age, neglecting the fact that the building workmanship in the construction of a house is also a key factor for the estimation of house prices. Building workmanship, such as exterior walls and floor tiling correspond to the visual attributes of a house, and it is difficult to capture and evaluate such attributes efficiently through classical models like regression analysis. Deep learning approach is taken in the valuation process to utilize this visual information.Design/methodology/approachThe authors propose a two-input neural network comprising a multilayer perceptron and a convolutional neural network that can utilize both metadata and the visual information from images of the front view of the house.FindingsThe authors applied the two-input neural network to Guri City in Gyeonggi Province, South Korea, as a case study and found that the accuracy of house price estimations can be improved by employing image information along with metadata.Originality/valueFew studies considered the impact of the building workmanship in the valuation process. The authors revealed that it is useful to use both photographs and metadata for enhancing the accuracy of house price estimation.",8.0,,False,JournalArticle,Computer Science;Geography,2020-11-24,4fbe553cfb0b2600eaf110898ac72714b198292d,usingphotographsandmetadatatoestimatehousepricesinsouthkorea,lee,10.1108/dta-05-2020-0111,usingphotographsandmetadatatoestimatehousepricesinsouthkorea|2020,1,2304,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e217681a0c6a91107aa1d6f31181952f096a4892,10.1109/globconpt57482.2022.9938255,,Selection of Data Preprocessing Techniques and Its Emergence Towards Machine Learning Algorithms using HPI Dataset,2022.0,"2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT)",G. V. Sai Krishna; Kundrapu Supriya; K. Rao,"To compute the frequent metamorphosis of the housing price, the House Price Index (HPI) is one of the effective indicators. Various methodologies are involved in the data processing of the current house prices, which are affected by multiple factors like house configuration, building class, air conditioning quality, etc. Remarkably, a greater number of research papers adopting classical machine learning approaches are introduced to estimate house sale prices accurately. Still, they barely regard the data processing techniques that make the data suitable for modelling more accurate house price forecasting models. This research contributes to a wide variety of adequate data preprocessing. It extensively highlights mechanisms like missingness of data, missing data handling, outliers, and feature scaling extensively to build efficient predictive models. Compre-hensive arguments have been broadly presented to portray the advantages and disadvantages of prevailed data preprocessing techniques at various distribution scenarios of variables in the house price data. The current research conclusions oblige the evolution of modern data-driven research in machine learning.",8.0,,False,Conference,,2022-09-23,e217681a0c6a91107aa1d6f31181952f096a4892,selectionofdatapreprocessingtechniquesanditsemergencetowardsmachinelearningalgorithmsusinghpidataset,krishna,10.1109/globconpt57482.2022.9938255,selectionofdatapreprocessingtechniquesanditsemergencetowardsmachinelearningalgorithmsusinghpidataset|2022,1,2179,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),fbc4053765389f000d3a39c7276d07d83039c716,10.1007/s12145-021-00589-3,,Learning with self-attention for rental market spatial dynamics in the Atlanta metropolitan area,2021.0,Earth Science Informatics,Xiao-Lu Zhou; Wei-Tian Tong,,8.0,,False,JournalArticle,Computer Science,2021-03-01,fbc4053765389f000d3a39c7276d07d83039c716,learningwithselfattentionforrentalmarketspatialdynamicsintheatlantametropolitanarea,zhou,10.1007/s12145-021-00589-3,learningwithselfattentionforrentalmarketspatialdynamicsintheatlantametropolitanarea|2021,1,1000,5,"(""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || machine learning hedonic pricing housing || spatial hedonic model housing",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c4ee95a98b9526e6c775066ad12a5e8dcc31fa2e,10.1109/cscloud-edgecom58631.2023.00025,,A Housing Price Prediction Method Based on Stacking Ensemble Learning Optimization Method,2023.0,International Conference on Cyber Security and Cloud Computing,Zhenyu Yang; Xinghui Zhu; Yangcong Zhang; Peng Nie; Xin-Bo Liu,"The growth of real estate sector has been significantly influenced in recent years by ongoing regulation of real estate acquisition policy and the effects of the COVID-19 epidemic on economy. The fluctuation in housing prices is one of the most concerning factors for prospective homeowners. Whether property prices can sustain a comparatively constant level for an extended period of time is a crucial factor for prospective homeowners. Numerous modeling and application techniques for prediction algorithms, together with the promotion of machine learning algorithms, offer fresh approaches to forecast residential real estate values. This work proposes a novel stacking ensemble learning method (DStacking) which is based on the diversity of learners including XGBoost and BP neural network. Through the application of ensemble learning algorithm, D-Stacking method can successfully predict the possible promotion of housing price. Housing price datasets from China and the USA were used in the experiments to guarantee the generalizability of findings. Experimental findings indicate that the diversity of base learners significantly affects the predictive power of D-Stacking method. Furthermore, the more diverse the models are, the more precise the predictions can be with proposed D-Stacking method. Compared with classical Stacking ensemble learning models, the proposed D-Stacking method demonstrates an excellent feasibility in reducing the RMSE to 0.869 and 1.029 across various datasets.",7.0,,False,JournalArticle;Conference,Computer Science,2023-07-01,c4ee95a98b9526e6c775066ad12a5e8dcc31fa2e,ahousingpricepredictionmethodbasedonstackingensemblelearningoptimizationmethod,yang,10.1109/cscloud-edgecom58631.2023.00025,ahousingpricepredictionmethodbasedonstackingensemblelearningoptimizationmethod|2023,1,2500,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",faa1d688377647cbb692f2d2d7e64eee396ff6d5,10.35741/issn.0258-2724.57.5.45,,POST-PANDEMIC ANALYSIS OF HOUSE PRICE PREDICTION IN SURABAYA: A MACHINE LEARNING APPROACH,2022.0,Journal of Southwest Jiaotong University,Trianggoro Wiradinata; Felicia Graciella; Rinabi Tanamal; Y. Soekamto; Theresia Ratih Dewi Saputri,"The COVID-19 outbreak caused a slowdown in the Indonesian economy, as it did in many other impacted nations. Consequently, the housing market in Indonesia, along with other industries, deteriorated. Other post-pandemic issues displace the property industry's priorities in Indonesia. Determining a fair property price is a problem occurring because of the economic slowdown. Property sellers expected their property selling prices to be the same before the pandemic or even increase, but property agents hoped the properties would be selling fast, creating a sense of distrust between the seller and the property agents. This work aims to develop a machine learning-based prediction model for real estate agents to use in determining property prices, with the expectation that the resulting predictions will be more accurate and supported by the data, increasing seller and buyer confidence. Following the suggestion from previous studies, several supervised algorithms such as Linear Regression, Decision Tree, and Random Forest were used to develop the model. Training data were collected from five property agents in Surabaya and as well as web scraping from the online home sales portals. Findings from the study show that Random Forest performs best in predicting with the highest coefficient of determination and lowest error. Using evaluation measures such as Mean Absolute Percent Error (MAPE), the error was calculated to be 23%, which is acceptable for prediction.",7.0,,False,,Medicine,2022-10-30,faa1d688377647cbb692f2d2d7e64eee396ff6d5,postpandemicanalysisofhousepricepredictioninsurabayaamachinelearningapproach,wiradinata,10.35741/issn.0258-2724.57.5.45,postpandemicanalysisofhousepricepredictioninsurabayaamachinelearningapproach|2022,1,2474,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c975137190a72c5375447530819a02db2a0d6fbe,10.1109/csde56538.2022.10089222,,Deep Learning for Real Estate Trading,2022.0,2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE),Yun Zhao; Girija Chetty; Dat Tran,"Artificial intelligence (AI) and Machine Learning techniques have been making impact on the real estate industry recently. Increasingly, several real estate companies have started to use a variety of AI techniques to optimize their property business. Ma-chine learning (ML) technology for providing support on real es-tate investment decisions, allows investigation of historical property sales data by computer algorithms to automatically predict house prices. Real estate professionals can leverage sophisticate ML techniques to analyse sales data as benchmarks and make appraisals for their home selling clients and customers. ML technologies not only make predictions or classifications, but also can assistant real estate professionals for investment purposes by providing a trading strategy. In this paper, we propose a novel machine learning model, based on a standard deep reinforcement learning (DRL) model, enhanced with a combination of two popular time series algorithms, the Gramian Angular Field (GAF) and long short-term memory (LSTM) algorithms for providing decision support on real estate trading strategy. Our goal is to explore if the proposed enhanced DRL model can make profitable trading strategy for a long-time investment, such as the real estate markets.",7.0,,False,Conference,,2022-12-18,c975137190a72c5375447530819a02db2a0d6fbe,deeplearningforrealestatetrading,zhao,10.1109/csde56538.2022.10089222,deeplearningforrealestatetrading|2022,1,2280,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",cf13f86bf0a960f63bcc18e02e9eaa08bc2c5b73,10.1109/iciscois56541.2023.10100566,,Optimization Techniques for Deep Learning Based House Price Prediction,2023.0,"2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS)",J. Vijava; Pangoth Santhosh Kumar; Meetiksha Sorgile; Murukuri SV Sv Vasanth,"With the persisting increment in the rates of houses, the cost of homes is escalating every year. So it is necessary to design a system that can predict future housing prices. This intelligent system undertakes the task to play a crucial role between learning and optimization. But because these systems are dynamic, complex, and multidisciplinary, optimizing them is particularly challenging. The House Price Index (HPI) is one of the most useful indicators for calculating frequent changes in home prices. The data processing of the current home pricing involves several approaches and takes into account several variables, including housing design, layouts, climate control conditions, etc. To incorporate the benefits of external factors and utilize the increased home price, this approach makes use of a multiple regression model and optimization approaches. Using enhanced housing price composition and multiple regression prediction models, such as particle swarm optimization, genetic, firefly, and mayfly algorithms, etc., it is possible to include the benefits of outside influences. The purpose of this work is to provide an overview of the latest and most popular deep learning optimization techniques which predicts the more precise and accurate prices of houses.",7.0,,False,Conference;Review,,2023-02-09,cf13f86bf0a960f63bcc18e02e9eaa08bc2c5b73,optimizationtechniquesfordeeplearningbasedhousepriceprediction,vijava,10.1109/iciscois56541.2023.10100566,optimizationtechniquesfordeeplearningbasedhousepriceprediction|2023,1,2276,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,5,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ee29e8f8dc538ea83411050af0d9177cdbad9b4d,10.1109/icmsci62561.2025.10893972,,Unveiling House Price with Machine Learning Algorithm,2025.0,2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI),Rambabu Kusuma; Kalagadda Kiran Kumar; Siva Ramakrishna Pillutla; A. Saikumar,"A frequently overlooked and yet very critical factor in having a high quality of life is the perfecting of one's living environment. A house should be quiet, unique, and instill peace in the soul of its residents, attaining individual needs across many aspects. Therefore, finding such a house with great architecture and making the right choice is essential. Beyond location, the structure and cost of a house are key elements that significantly influence the suitability of a place to live. Accurate price predictions are crucial to address potential future concerns. This study presents a model designed to help individuals select an ideal home based on their financial circumstances. It evaluates various machine learning models, including the Random Forest Regressor and Gradient Boosting Regressor, to ensure reliable predictions. The proposed model aims to present estimates as closely as possible aligned with ideal conditions, considering individual preferences and demands.",7.0,,False,Conference,,2025-01-20,ee29e8f8dc538ea83411050af0d9177cdbad9b4d,unveilinghousepricewithmachinelearningalgorithm,kusuma,10.1109/icmsci62561.2025.10893972,unveilinghousepricewithmachinelearningalgorithm|2025,1,1983,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b92ac1251e29013dc2fbf276d3317cad843dd278,10.1109/aisc56616.2023.10085264,,Using Machine Learning to Predict Housing Prices,2023.0,Artificial Intelligence and Symbolic Computation,H. Prakash; Kshitij Kanaujia; Sapna Juneja,"Apartment prices are projected based on various apartment attributes such as location, square footage, population, latitude, and longitude. Real estate is generally seen as an investment vehicle for the modern consumer, which is reflected in the volatility of real estate prices and the impact of real estate markets on the global economy. A literature review is conducted to analyze relevant properties and the most efficient models for predicting property prices. The results of this paper demonstrate the effectiveness of machine learning techniques such as linear regression in predicting accurate prices. Our results also suggest that location and structural characteristics are important factors in predicting house prices. The study identifies key attributes used by property developers and researchers to determine property prices, and is particularly helpful in identifying the best machine learning models for conducting research in this area.",7.0,,False,Conference;Review,,2023-01-27,b92ac1251e29013dc2fbf276d3317cad843dd278,usingmachinelearningtopredicthousingprices,prakash,10.1109/aisc56616.2023.10085264,usingmachinelearningtopredicthousingprices|2023,1,1953,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e7de23fdbf68fe5930da5eac02236f86018d72c6,10.1109/icscde54196.2021.00012,,A Novel Deep Neural Network based Method for House Price Prediction,2021.0,2021 International Conference of Social Computing and Digital Economy (ICSCDE),Jiayi Xu,"Nowadays, house price is extremely essential for human living and plays an important part in the financial market, especially in the real estate industry. In this paper, we proposed a deep learning based network for house price prediction. Firstly, a brief introduction and related work are discussed. Then, Linear Regression, Random Forest Regression, XGBoost, and SVM are all detailed explained, developed and tested. In addition, several types of densely connected based neural network are proposed and developed. Finally, all methods are evaluated on a publicly available dataset, Boston house price dataset. Our method performs competitively performance compared with other classical methods including Linear Regression, Random Forest Regression, XGBoost, and SVM, and evaluated with a variety of evaluation methods including R2, Adjustable R2, MAE, MSE, RMSE.",7.0,,False,Conference,,2021-08-01,e7de23fdbf68fe5930da5eac02236f86018d72c6,anoveldeepneuralnetworkbasedmethodforhousepriceprediction,xu,10.1109/icscde54196.2021.00012,anoveldeepneuralnetworkbasedmethodforhousepriceprediction|2021,1,1865,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9506ae1fbe9151332d35795cdaa05259db376dcc,10.1109/aidas47888.2019.8970916,,Automated Machine Learning based on Genetic Programming: a case study on a real house pricing dataset,2019.0,2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS),S. Masrom; T. Mohd; Nur Syafiqah Jamil; Abdullah Sani Abdul Rahman; Norhayati Baharun,"Designing an effective machine learning model for prediction or classification problem is a tedious endeavor. Significant time and expertise are needed to customize the model for a specific problem. A significant way to reduce the complicated design is by using Automated Machine Learning (AML) that can intelligently optimize the best pipeline suitable for a problem or dataset. This paper demonstrates the utilization of an AML that has been developed with a meta-heuristic algorithm namely Genetic Programming (GP). Empirical experiment has been conducted to test the performances of AML on a real dataset of house prices in the area of Petaling Jaya, Selangor. The results show that the AML with GP able to produce the best pipeline of machine learning with high score of accuracy and minimal error. (Abstract)",7.0,,False,Conference,Computer Science,2019-09-01,9506ae1fbe9151332d35795cdaa05259db376dcc,automatedmachinelearningbasedongeneticprogrammingacasestudyonarealhousepricingdataset,masrom,10.1109/aidas47888.2019.8970916,automatedmachinelearningbasedongeneticprogrammingacasestudyonarealhousepricingdataset|2019,1,1814,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),88ad98a57373aa3b16881de2e01f9dd1700559b2,10.1109/icspcre62303.2024.10675142,,Real Estate Price Prediction Using PySpark MLlib,2024.0,2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE),Kadam Prajwal Dharmaraj; Pradeep Kumar Gupta; K. Ajith; Mahi Kolli; Sangita Khare; Niharika Panda,"Prices of real estates in metropolitan landscapes harbour immense importance in steering the complexities of dynamic city environments. In India, the real estate sector contributes around 6–7% to India's GDP. Therefore, accurate forecasting of real estate prices is a factor in making informed decisions, affecting various stakeholders. Many techniques were used for this in past several years, like Hedonic models, Repeat-Sales models, Rule-Based Systems and Heuristics, etc. This study leverages the power of machine learning and big data to develop a robust framework for predicting housing prices in metropolitan landscapes. We employ PySpark, a powerful big data processing framework with built-in MLlib library (machine learning library), to analyse large-scale housing data encompassing various cities in India & to predict the prices for the same. By implementing a comparative analysis of prominent regression models - Random Forest, Linear Regression, Decision Tree, and Gradient-Boosted Tree - our approach identifies the most effective algorithms for real estate price prediction using MLlib library. Also, this study highlights the need for scalable solutions to manage an increasing number of data sources and emphasizes the PySpark library which will simplify big data handling and enable parallel computing. This study paves the way for utilizing advanced machine learning techniques and big data platforms to gain valuable insights into real estate markets. Our findings emphasize the critical role of combining these powerful tools to navigate the complex dynamics of urban housing and predict prices with greater accuracy.",6.0,,False,Conference,,2024-07-19,88ad98a57373aa3b16881de2e01f9dd1700559b2,realestatepricepredictionusingpysparkmllib,dharmaraj,10.1109/icspcre62303.2024.10675142,realestatepricepredictionusingpysparkmllib|2024,1,2641,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || machine learning hedonic pricing housing || real estate hedonic regression",2,2,1,3,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",50b770be3980d88d8f40641c8ff78c06bf2317b1,10.1142/s2737599425500355,,An empirical investigation of Lijiang’s residential property valuation in China using an integrated framework with Gaussian process regression and Bayesian hyperparameter tuning,2025.0,Innovation and Emerging Technologies,Bing-Zi Jin; Xiao-Jie Xu,"Over the last decade, China’s property market has undergone pronounced cyclical shifts, transitioning from sustained growth to a marked slowdown following macroeconomic shocks in late 2021. These structural adjustments—shaped by evolving regulatory interventions and growing fiscal uncertainty—have posed complex forecasting challenges for stakeholders such as institutional investors, governmental regulators, and urban development planners charged with anticipating metropolitan housing value trends. To address the shortcomings of traditional prediction techniques, this research introduces an advanced framework based on Gaussian process regression, enhanced by adaptable kernel configurations and tailored feature engineering underpinned by domain expertise. The framework analyzes the temporal evolution of housing prices in Lijiang, Yunnan Province, utilizing a 9-year panel dataset spanning October 2015 to July 2024. By integrating Bayesian hyperparameter tuning with nested cross-validation, the model achieved substantial out-of-sample accuracy gains for October 2022 to July 2024, registering a relative root mean square error of 0.1209%. These results confirm the method’s ability to capture complex nonlinear dynamics and enrich spatial economic theory by revealing how local governance structures interact with price-setting mechanisms. Its modular architecture enables seamless coupling with additional econometric models or standalone applications across diverse urban contexts, offering scalable, evidence-based tools for policy decision-makers. Moreover, the findings provide strategic insights for designing adaptive regulatory frameworks, thereby linking this work to practical urban governance.",6.0,,False,JournalArticle,,2025-01-01,50b770be3980d88d8f40641c8ff78c06bf2317b1,anempiricalinvestigationoflijiangsresidentialpropertyvaluationinchinausinganintegratedframeworkwithgaussianprocessregressionandbayesianhyperparametertuning,jin,10.1142/s2737599425500355,anempiricalinvestigationoflijiangsresidentialpropertyvaluationinchinausinganintegratedframeworkwithgaussianprocessregressionandbayesianhyperparametertuning|2025,1,2716,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",354284b564059f8232fd974f92ea9958c463f7fd,10.48550/arxiv.2510.13774,2510.13774,UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations,2025.0,arXiv.org,Dominik J. Mühlematter; Che Lin; Ye Hong; M. Raubal; Nina Wiedemann,"Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data. Current methods primarily utilize task-specific models, while recent generic models for spatial representations often support only limited modalities and lack multimodal fusion capabilities. To overcome these challenges, we present UrbanFusion, a spatial representation model that features Stochastic Multimodal Fusion (SMF). The framework employs modality-specific encoders to process different types of inputs, including street view imagery, remote sensing data, cartographic maps, and points of interest (POIs) data. These multimodal inputs are integrated via a Transformer-based fusion module that learns unified representations. An extensive evaluation across 41 tasks in 56 cities worldwide demonstrates UrbanFusion's strong generalization and predictive performance compared to state-of-the-art GeoAI models. Specifically, it 1) outperforms prior models on location-encoding, 2) allows multimodal input during inference, and 3) generalizes well to regions unseen during training. UrbanFusion can flexibly utilize any subset of available modalities for a given location during both pretraining and inference, enabling broad applicability across diverse data availability scenarios.",6.0,,False,JournalArticle,Computer Science,2025-10-15,354284b564059f8232fd974f92ea9958c463f7fd,urbanfusionstochasticmultimodalfusionforcontrastivelearningofrobustspatialrepresentations,mühlematter,10.48550/arxiv.2510.13774,urbanfusionstochasticmultimodalfusionforcontrastivelearningofrobustspatialrepresentations|2025,1,2330,1,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2b1d3717efdab1f6e8e9081464737bc38060da5d,10.1109/ic3i59117.2023.10398134,,Real Estate Forecasting System Using Machine Learning Algorithms,2023.0,International Conferences on Contemporary Computing and Informatics,Harshpreet Kaur; Kushwanth A; S. M; Akshay Y; K. Y; S. B.; Ganesh Kamal Reddy G,"The idea of this paper is to predict the value of houses and properties of the market value. The aim of the presented paper is to estimate the market value of the asset being sold. This application assists in determining a location's beginning pricing based on location-specific factors. The below mentioned idea to find the price of a location based on location variables. The below discussed models helps to find the market value of any property in India through different databases depending upon the locations. The presented paper suggests the buyer in determining the prices of different houses with the similarity of characteristics and properties. According to the survey made the prices of properties always rise every year, so a buyer needs a real estate forecasting system. This research paper can help estimate the price of a house which helps both seller and buyer for a reasonable price to buy a property. The result showed that machine learning algorithms in linear regressions model can accurately predict area price with high accuracy rates. However, some challenges such as property value based on area, characteristics, locality, facilities provided. It highlights the importance of machine learning algorithms for price predictions to research and address the challenges.",6.0,,False,JournalArticle;Conference;Review,Computer Science,2023-09-14,2b1d3717efdab1f6e8e9081464737bc38060da5d,realestateforecastingsystemusingmachinelearningalgorithms,kaur,10.1109/ic3i59117.2023.10398134,realestateforecastingsystemusingmachinelearningalgorithms|2023,1,2290,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cb863c5bc8e9b488ebd40ea4cd9dd4ed10a895df,10.1109/iconstem56934.2023.10142280,,Improved Prediction Accuracy of House Price Using Decision Tree Algorithm over Linear Regression Algorithm,2023.0,2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM),Pammi Chandu; N. Devi,"As a direct outcome of this research, it is planned that the accuracy of house price projections will be enhanced by using a novel decision tree algorithm rather than linear regression. This will be done in order to achieve the desired result (LR). The N=10 iteration of the Decision Tree Algorithm is put to use in order to generate the prediction. The size of the sample is figured out with the use of a G power Calculator, and a cutoff of 80% is decided upon as the minimum need for sufficient analytical power. The Linear Regression Method can be found in Group 1, whereas the New Decision Tree Algorithm can be found in Group 2. The confidence interval for the pre-test power is from 95% to 80%, the alpha value is 0.05, the beta value is 0.2, and the total number of participants in the study is twenty. In contrast, the accuracy of the New Decision Tree (DT) Algorithm was 90%, while the accuracy of the Linear Regression Algorithm was 80%. The findings of the statistical analysis that was carried out with the assistance of SPSS showed that the value of accuracy was insignificant: p=0.618 (p>0.05). The Innovative Decision Tree Algorithm outperforms the Linear Regression approach when it comes to estimating the value of real estate in the future.",6.0,,False,Conference,,2023-04-06,cb863c5bc8e9b488ebd40ea4cd9dd4ed10a895df,improvedpredictionaccuracyofhousepriceusingdecisiontreealgorithmoverlinearregressionalgorithm,chandu,10.1109/iconstem56934.2023.10142280,improvedpredictionaccuracyofhousepriceusingdecisiontreealgorithmoverlinearregressionalgorithm|2023,1,2258,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e8bae31e062b39fcfae928507d80a388b7fd2bc1,10.1109/iccosite57641.2023.10127705,,Comparative Models of Price Estimation Using Multiple Linear Regression and Random Forest Methods,2023.0,"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)",Denny Jean Crosss Sihombing; Desi C. Othernima; Jonson Manurung; J. Sagala,"The house is one of the essential humans needs as a place to gather and do activities with family, and shelter, as well as a means of investment. The growth rate of people's demand for housing, especially houses in an area, is influenced by the rate of population growth in that area. Some regions in Indonesia with a reasonably high population growth rate are Jakarta, Bogor, Depok, Tangerang, and Bekasi (Jabodetabek). On the other hand, property entrepreneurs must be able to project house prices because businesses engaged in the property sector are currently very competitive. This study aims to model and compare several machine learning methods to estimate house prices in Jabodetabek based on facilities, year of construction, location, land and building area, number of rooms, condition of house construction, and legality documents. This modeling uses Multiple Linear Regression and Random Forest methods. The results of the modeling evaluation where the Random Forest model has an accuracy rate of 95.6%, while the Multiple Linear Regression model has an accuracy rate of 75%.",6.0,,False,Conference,,2023-02-16,e8bae31e062b39fcfae928507d80a388b7fd2bc1,comparativemodelsofpriceestimationusingmultiplelinearregressionandrandomforestmethods,sihombing,10.1109/iccosite57641.2023.10127705,comparativemodelsofpriceestimationusingmultiplelinearregressionandrandomforestmethods|2023,1,2087,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",dbc22646dd6ae067fa0471b51335dee3a86eb54a,10.1109/iciibms46890.2019.8991474,,Prediction of House Price Based on RBF Neural Network Algorithms of Principal Component Analysis,2019.0,2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS),Li Xiao; T. Yan,"When the traditional BP neural network is used for prediction, the convergence speed is too slow, the prediction accuracy is low, and it is easy to fall into the local optimal solution. Aiming at these problems, a PCA-based RBF neural network prediction algorithm is proposed and verified. Firstly, PCA is used to recombine the influencing factors of housing prices to generate new comprehensive indicators. Then use the RBF neural network algorithm with strong approximation ability to model and predict the house price. The experimental results show that the fitting result of the predicted value and the real value is 97%, which can be used as an effective method for forecasting house prices.",6.0,,False,Conference,Computer Science,2019-11-01,dbc22646dd6ae067fa0471b51335dee3a86eb54a,predictionofhousepricebasedonrbfneuralnetworkalgorithmsofprincipalcomponentanalysis,xiao,10.1109/iciibms46890.2019.8991474,predictionofhousepricebasedonrbfneuralnetworkalgorithmsofprincipalcomponentanalysis|2019,1,1696,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2176f4fe2b9a4d0e27f5ea1864450f3d2ae2bf5b,10.1145/3456126.3456139,,House-price Prediction Based on OLS Linear Regression and Random Forest,2021.0,Asia Service Sciences and Software Engineering Conference,Yige Wang,"Based on a simple dataset with 20 features of houses, this paper builds two forecasting models to predict house prices. It primarily takes a series of data processes, including missing value process, outlier removal, new variables creation, and correlation analysis. Then this paper conducts the first theoretical model using OLS method. Next, it uses secondary cross-validation to optimize the parameters of random forest and gets a respectively realistic model. Finally based on the results, this paper analyzes and compares the advantages and disadvantages of the two models.",6.0,,False,JournalArticle;Book,Computer Science,2021-02-24,2176f4fe2b9a4d0e27f5ea1864450f3d2ae2bf5b,housepricepredictionbasedonolslinearregressionandrandomforest,wang,10.1145/3456126.3456139,housepricepredictionbasedonolslinearregressionandrandomforest|2021,1,1578,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),7eac765766a4833adcc8dbfc5ac301a2170ac93f,10.1109/wids-psu54548.2022.00015,,Analysis of the Dimensionality Issues in House Price Forecasting Modeling,2022.0,2022 Fifth International Conference of Women in Data Science at Prince Sultan University (WiDS PSU),S. Sakri; Zaiton Ali,"House price fluctuations in the real estate market occur due to the effects of other variables that are correlated to housing prices. Some prices cannot be controlled or it is not known when they will increase or decrease. These fluctuations accord with the hedonic pricing theory, which suggests that house pricing depends on the composite features. Our study explores this econometric concept using machine learning algorithms in the effort to accurately forecast house prices. To build the forecasted models, we use the Ames housing dataset, which include 82 explanatory features and 2930 entries on housing sales in Ames, Iowa, USA. We use particle swarm optimization feature selection to address the dataset dimensionality issues for both classification-based models and regression-based models. With dimension reduction, the experimental results demonstrate that the house price prediction accuracy increased from 81.4% to 84.4% for classification-based models, and the price prediction error decreased from 13.3% to 6.9% for regression-based models. The experimental results demonstrate that there is a substantial difference between using and not using feature selection in terms of the performance in predicting housing prices. The main benefit of applying feature selection is that it facilitates the selection of the more relevant and correlated features to prevent overfitting of the model with too many irrelevant features.",5.0,,False,Conference,,2022-03-01,7eac765766a4833adcc8dbfc5ac301a2170ac93f,analysisofthedimensionalityissuesinhousepriceforecastingmodeling,sakri,10.1109/wids-psu54548.2022.00015,analysisofthedimensionalityissuesinhousepriceforecastingmodeling|2022,1,2436,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || machine learning hedonic pricing housing || real estate hedonic regression",2,2,2,2,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2e6c87d5a40e61e68977c9ed3a874f56c005d51e,10.1109/ubmk59864.2023.10286673,,"A New Hybrid Approach for Real Estate Price Prediction Using Outlier Detection, Feature Selection, and Clustering Techniques",2023.0,2023 8th International Conference on Computer Science and Engineering (UBMK),F. Abut; H. Arlı; M. Akay; Yildirim Adigüzel,"Real estate price prediction is vital for property valuation, investment analysis, and market trends forecasting. Accurate prediction models offer valuable insights to real estate professionals, investors, and homeowners. However, developing effective models in the real estate domain is challenging due to outliers, feature relevance, and local market dynamics. This study proposes a hybrid approach that combines outlier detection, feature selection, and clustering techniques to enhance real estate price prediction accuracy. The approach integrates Support Vector Regressor (SVR) and LightGBM, leveraging their strengths. The self-created dataset used includes comprehensive real estate attributes like county, building age, floor count, room count, and more. The hybrid approach's performance is evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results demonstrate that the hybrid approach outperforms other traditional experiments with normal prediction, feature selection, and outlier detection, achieving lower MAE, RMSE, and MAPE values, indicating improved accuracy in real estate price prediction. The study highlights the effectiveness of the hybrid approach in addressing challenges related to outliers, feature relevance, and local market dynamics, providing a robust and efficient framework for realistic price prediction. This contributes to advancing machine learning-based methodologies in real estate, supporting informed decisionmaking and providing valuable guidance to stakeholders.",5.0,,False,Conference,,2023-09-13,2e6c87d5a40e61e68977c9ed3a874f56c005d51e,anewhybridapproachforrealestatepricepredictionusingoutlierdetectionfeatureselectionandclusteringtechniques,abut,10.1109/ubmk59864.2023.10286673,anewhybridapproachforrealestatepricepredictionusingoutlierdetectionfeatureselectionandclusteringtechniques|2023,1,2589,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",936f098d39da4ba2c40de13448578384b4d75ba2,10.1109/dsaa49011.2020.00070,,Urban and socio-economic correlates of property prices in Dublin’s area,2020.0,International Conference on Data Science and Advanced Analytics,D. Dupré,"Understanding the characteristics of the housing market is essential for both sellers and buyers. However, the housing market is influenced by multiple factors. In this paper, the urban and socio-economic structure of an area is used to predict the price of 10387 properties sold in 2018 in the city of Dublin. More precisely, the direct distance from each property to 160 urban features taken from OpenStreetMap is calculated, and an extreme gradient boosting linear regression performed. Using these features, the model explains 45% of the housing price variance. The most important features in this model are the proximity to an embassy and to a grassland. In addition, the results of a population census from 2016 are also used to correlate with the price of properties. From this census, 48 features are used as the input of a gradient boost linear regression model. In all, the socio-economic features are explaining 43% of the housing price variance as well. The density of individuals reporting that they are not providing unpaid personal help for a friend or family member as well as individuals reporting that they have no religion are the most important socio-economic feratures. By taking into account either urban or socio-economic features, it is possible to accurately estimate housing prices and to predict their evolution.",5.0,,False,JournalArticle;Conference,Computer Science,2020-10-01,936f098d39da4ba2c40de13448578384b4d75ba2,urbanandsocioeconomiccorrelatesofpropertypricesindublinsarea,dupré,10.1109/dsaa49011.2020.00070,urbanandsocioeconomiccorrelatesofpropertypricesindublinsarea|2020,1,2339,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fe183352fb321feaa73bf58b4d917f7f6c3bfe53,10.1109/isemantic55962.2022.9920395,,Residential Property Price Prediction Using Machine Learning: MakanSETU,2022.0,2022 International Seminar on Application for Technology of Information and Communication (iSemantic),Yash Y Panchal; Manan Mer; Abhiroop Ghosh,"MakanSETU is an emerging and advanced solution in the Real Estate industry. Real Estate Industry is at boom in the 21st century and trading Real Estate has become a great opportunity for Real Estate owners as well as others. The projection of Real Estate industry in business acquisitions is expected to reach 11 trillion USD. However, there is no proper solution to deal with inaccurate prices of properties online. The system proposed in this paper uses Native and new age Machine learning algorithms to predict and validate value of residential properties. Supervised learning is used in the system along with multiple Regressors to obtain the best result. Some of the regression algorithms used are Simple Linear regression, Decision tree regression, Random Forest regression (100 n-trees, 200 n-trees, and 500 n-trees), and Extreme Gradient Boost regression algorithm. The development of this system has followed a series of Data Collection, data handling, data processing, EDA, Feature engineering and Feature selection. The system enables investors to get a fair value of a property. The system is considered successful and ready to implement in the real work.",5.0,,False,,,2022-09-17,fe183352fb321feaa73bf58b4d917f7f6c3bfe53,residentialpropertypricepredictionusingmachinelearningmakansetu,panchal,10.1109/isemantic55962.2022.9920395,residentialpropertypricepredictionusingmachinelearningmakansetu|2022,1,2167,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",83256fd137f6ef8c2ef6cd8993a867c93eaa3877,10.1109/idciot64235.2025.10915014,,Enhancing Real Estate Market Predictions: A Machine Learning Approach to House Valuation,2025.0,2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT),Pradeep Jha; Manju Mathur; Abhay Purohit; Apoorva Joshi; Anantika Johari; Suhani Mathur,"Machine learning (ML) has emerged as a versatile technology, transforming various domains such as speech recognition, medical diagnostics, and real estate analytics. This research addresses the critical challenge of accurately predicting house prices in dynamic and volatile real estate markets. Traditional valuation methods often fail to capture complex relationships between features, leading to suboptimal predictions. To mitigate these limitations, this study proposes a predictive model leveraging multiple ML algorithms, including Linear Regression, Decision Tree Regression, K-Means Clustering, and Random Forest Regression. Among these, Random Forest Regression demonstrated superior accuracy, achieving a root mean square error (RMSE) of 2.913. The findings indicate that integrating advanced ML techniques can streamline the home-buying process by enhancing price prediction accuracy, reducing dependency on intermediaries, and enabling informed decision-making for stakeholders. This study provides a robust framework for applying ML in real estate and highlights opportunities for further advancements in predictive modeling.",5.0,,False,Conference,,2025-02-05,83256fd137f6ef8c2ef6cd8993a867c93eaa3877,enhancingrealestatemarketpredictionsamachinelearningapproachtohousevaluation,jha,10.1109/idciot64235.2025.10915014,enhancingrealestatemarketpredictionsamachinelearningapproachtohousevaluation|2025,1,2138,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d38981179925ad5482beb66234833edc1b9e1463,10.1109/isc2.2018.8656965,,Using Geographic Information and Point of Interest to Estimate Missing Second-Hand Housing Price of Residential Area in Urban Space,2018.0,2018 IEEE International Smart Cities Conference (ISC2),Jiabo Tang; Zhi-Cheng Liu; Yu-Ran Wang; Jun-Yan Yang; Qiao Wang,"The real estate market including second-hand real estate market plays an important role in Chinese economy. However, it is not easy to acquire price of the each pieces of residential area in a city, and for instance the data acquired from Internet in our paper can only cover 56% of residential area in Nanjing. To this end, our paper proposed an model to fill the missing price data by using price and locations of second-hand real estates and Point of Interest (POI) information which were acquired from Internet. Our experiment was conducted in Nanjing and Chongqing, and demonstrates that our model is able to perform better than traditional Geographic Information System (GIS) method, such as Kriging interpolation, and general machine learning model, such as K-Nearest Neighbour (KNN). Also, our proposed model can be more interpretable than traditional methods, and able to reveal how the POI information can influence the second-hand real estate price. Our proposed model can help domain experts, e.g. city planners and economists, to better research the second-hand real estate market in the future.",5.0,,False,JournalArticle;Conference,Computer Science,2018-09-01,d38981179925ad5482beb66234833edc1b9e1463,usinggeographicinformationandpointofinteresttoestimatemissingsecondhandhousingpriceofresidentialareainurbanspace,tang,10.1109/isc2.2018.8656965,usinggeographicinformationandpointofinteresttoestimatemissingsecondhandhousingpriceofresidentialareainurbanspace|2018,1,2108,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",263bf1c9283573e3d39aa8835463976e8203edda,10.1109/icsca57840.2023.10087486,,Evaluating machine learning algorithms for predicting house prices in Saudi Arabia,2023.0,International Conference on Software and Computer Applications,T. Alshammari,"With real estate projects increasingly growing, it has a critical economic role in countries and a significant effect on economic and investment domains. There is limitation of researches have contributed the inclusive evaluation of machine learning algorithms in real estate price prediction in this field. Machine learning algorithms could assist in forecasting real estate prices; thus, this paper presents a comparative assessment of machine learning algorithms to predict housing prices in Saudi Arabia. A real estate transaction datasets is used to evaluate the accuracy of machine learning algorithms. The datasets contains 13 columns and 59,846 instances (records). The results considerably vary in performance across the evaluated algorithms: random forest (RF), decision tree (DT) and linear regression (LR). After extensive examination the three proposed algorithm, Random Forest obtained R2-score 0.65, the decision tree obtained R2- score 0.30 and linear regression obtained R2-score 0.06. Random Forest performed better than the decision tree or linear regression methods.",5.0,,False,Conference,,2023-02-05,263bf1c9283573e3d39aa8835463976e8203edda,evaluatingmachinelearningalgorithmsforpredictinghousepricesinsaudiarabia,alshammari,10.1109/icsca57840.2023.10087486,evaluatingmachinelearningalgorithmsforpredictinghousepricesinsaudiarabia|2023,1,2086,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",6b785cf500c1f34b8adeb9435d8b668659290e81,10.1109/mlise57402.2022.00095,,The Comparison of Six Prediction Models in Machine Learning: Based on the House prices Prediction,2022.0,2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE),Yi-Zhi Wang,"There are many different kinds of prediction models that have different performances when faced with different kinds of data. This essay focus on the comparison of the performance of multiple regressions, SVM with RBF kernel function, and Random Forest when predicting the house prices of Boston and using score function, k-fold cross-validation and shuffle cross-validation to evaluate their performance respectively. Finally, parameter adjustment, grid search, and forward selection are introduced to improve their performance. By combining the result given by three evaluating methods, SVM with RBF kernel function is the better model and the Random Forest is the worst one, whose scores are higher than 0.7 and lower than 0.1 respectively. And all of these three functions can slightly improve the performance, especially, the effect of the grid search is the best one, which can improve the score by 0.023 higher than the original score.",5.0,,False,Conference,,2022-08-01,6b785cf500c1f34b8adeb9435d8b668659290e81,thecomparisonofsixpredictionmodelsinmachinelearningbasedonthehousepricesprediction,wang,10.1109/mlise57402.2022.00095,thecomparisonofsixpredictionmodelsinmachinelearningbasedonthehousepricesprediction|2022,1,1942,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",32699b5d41da22f1f55e5daad6addd938aacac04,10.1109/ictai59109.2023.00105,,A reinforcement learning-based weight fusion algorithm for house price prediction,2023.0,IEEE International Conference on Tools with Artificial Intelligence,Yige Zhang; Zong-Wen Fan; Jin Gou,"To address the limitations of existing house price prediction methods that struggle with handling mixed data types, we developed a novel model utilizing a reinforcement learning-based weight fusion algorithm. First, we reconstruct the feature fusion vectors from categorical feature variables. Then, we create individual house price prediction models using a variety of algorithms: Multiple Linear Regression, Regression Decision Tree, Extreme Random Regression Tree, and Multilayer Perceptron. These models' weights are dynamically adjusted using Q-Learning to minimize the Root Mean Square Error (RMSE). Our experiments demonstrate substantial gains in RMSE performance using our weight fusion approach. Specifically, on the small-scale Shenzhen dataset, RMSE improvements vary from 8.65% to 64.61%. On the large-scale Boston dataset, the improvements range between 53.06% and 76.40% in comparison to the standalone prediction algorithms.",5.0,,False,JournalArticle;Conference,Computer Science,2023-11-06,32699b5d41da22f1f55e5daad6addd938aacac04,areinforcementlearningbasedweightfusionalgorithmforhousepriceprediction,zhang,10.1109/ictai59109.2023.00105,areinforcementlearningbasedweightfusionalgorithmforhousepriceprediction|2023,1,1940,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,rent hedonic regression housing,2893d55696c2d2bdd65206093b5ae75859620e50,10.1007/s43071-021-00009-0,,Automated valuation model for residential rental markets: evidence from Japan,2021.0,Journal of Spatial Econometrics,W. Cheung; Lewen Guo; Yuichiro Kawaguchi,,5.0,,False,,,2021-03-08,2893d55696c2d2bdd65206093b5ae75859620e50,automatedvaluationmodelforresidentialrentalmarketsevidencefromjapan,cheung,10.1007/s43071-021-00009-0,automatedvaluationmodelforresidentialrentalmarketsevidencefromjapan|2021,1,1000,6,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || automated valuation model residential || hedonic housing prices || hedonic property valuation || rent hedonic regression housing || spatial hedonic model housing",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d6dccddca6043fb77c96ffd2b1b65d5fe5becb71,10.2139/ssrn.4108744,,Predicting House Prices using Machine Learning and LightGBM.,2022.0,Social Science Research Network,Linda M. John; Rakshit Shinde; Shoaib M Shaikh; Devanshu Ashar,,5.0,,False,,,,d6dccddca6043fb77c96ffd2b1b65d5fe5becb71,predictinghousepricesusingmachinelearningandlightgbm,john,10.2139/ssrn.4108744,predictinghousepricesusingmachinelearningandlightgbm|2022,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ef7f7e28c562f9880fe9f1fb339650d782d7fc30,10.19172/kreaa.24.1.5,,"Estimation of the Apartment Housing Price Using the Machine Learning Methods: The Case of Gangnam-gu, Seoul",2018.0,,Seongwan Bae; Jung-Suk Yu,,5.0,,False,,Computer Science,2018-03-31,ef7f7e28c562f9880fe9f1fb339650d782d7fc30,estimationoftheapartmenthousingpriceusingthemachinelearningmethodsthecaseofgangnamguseoul,bae,10.19172/kreaa.24.1.5,estimationoftheapartmenthousingpriceusingthemachinelearningmethodsthecaseofgangnamguseoul|2018,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8d4b2c6db271f945ac0c89c62b812c147103fcdd,10.25236/ajets.2020.030702,,Prediction of California House Price Based on Multiple Linear Regression,2020.0,,Zixuan Wu,,5.0,,False,,Economics,2020-11-05,8d4b2c6db271f945ac0c89c62b812c147103fcdd,predictionofcaliforniahousepricebasedonmultiplelinearregression,wu,10.25236/ajets.2020.030702,predictionofcaliforniahousepricebasedonmultiplelinearregression|2020,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9954c72790f31af4a213fd51ab8c37694b0c0ce8,10.1109/pdgc64653.2024.10984370,,House Price Prediction System Using Ensemble Learning Approach,2024.0,"2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC)",Abhishek Kumar; Paras Jain; Priyank Pandey; Shyamalendu Das; V. Gupta; Dhiraj Kumar Gupta,"The real estate industry is among the most active and critically important industries for any economy. An accurate forecast of the prices of houses is very important to the buyers, sellers, investors and the policy makers. In years gone by, other methods of valuation have been used, but new advancements have been made in machine learning meaning the prediction speeds and the level of accuracy have been boosted. In this article, I would like to know how the development of various sophisticated models of ML can be used to forecast house prices: the most used models, the most favorite datasets and informative features, as well as the appropriate evaluation measures. Real estate is a continually expanding business that is prominently involved in the world economy. To precisely estimate house prices is always a daunting task due to various factors that vary from physical to non-physical factors include size, location, market, and economic conditions. Digital advancements especially, the incorporation of machine learning in the analysis of financial markets, housing and other sectors has enabled real estate specialists, banks and other institutions and government bodies analyze trends and develop sophisticated models to predict housing prices more effectively than conventional analytical models. In the context of this article’s expanse, we provide a deeper look into the components of house price prediction, with additional information on datasets, feature engineering, specific algorithms, optimization tactics, known difficulties, and future developments.",4.0,,False,Conference,,2024-12-18,9954c72790f31af4a213fd51ab8c37694b0c0ce8,housepricepredictionsystemusingensemblelearningapproach,kumar,10.1109/pdgc64653.2024.10984370,housepricepredictionsystemusingensemblelearningapproach|2024,1,2574,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",eda85afed1edd56a447e19b4926cd3ad82665e46,10.1109/iccams60113.2023.10526190,,Modeling House Price Prediction Model using XG Boost and Machine Learning Algorithms,2023.0,"2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS)",Ajmeera Kiran; Mudassir Khan; J. Babu; B. S. Santosh Kumar; S. Ahmed,"Every year, the cost of homes rises, necessitating the development of a system that can forecast future home prices. The ability to estimate house prices can be useful to both the developer and the customer in planning the best time to buy a home. The physical state, design, and location of a house are the three variables that affect its cost. This paper discusses using Python modules to estimate housing prices using regression approaches. The proposed method utilized more precise house price calculations and provided more accurate predictions. It also discusses graphical and numerical methods for housing price prediction. This paper describes our machine learning-based house price model and its dataset. Real estate data mining is frequent. Data mining can anticipate house values, essential property features, and more by extracting meaningful information from raw data. Research shows that property price variations worry homeowners and the real estate market. To find the best house price prediction models, a literature review is done. This investigation showed that Artificial Neural Networks, Support Linear Regression, and were the most efficient models. We also found that locational and structural features predict property prices. This study will help housing developers and academics identify the most important factors affecting house prices and identify the best machine learning model for this subject. Machine Learning Model, Linear Regression, House Price Prediction.",4.0,,False,Conference;Review,,2023-10-27,eda85afed1edd56a447e19b4926cd3ad82665e46,modelinghousepricepredictionmodelusingxgboostandmachinelearningalgorithms,kiran,10.1109/iccams60113.2023.10526190,modelinghousepricepredictionmodelusingxgboostandmachinelearningalgorithms|2023,1,2493,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1a42693f6b206c4a916bb9d8fee60dae4215f18c,10.1109/bdeim55082.2021.00047,,Optimizations of Training Dataset on House Price Estimation,2021.0,2021 2nd International Conference on Big Data Economy and Information Management (BDEIM),Xiangqin Cheri,"House price in many cases can strongly reflect local economy, predicting the real estate price can help consumer and the developers. In this way, consumers can inquiry price by giving a series of attributes which describes their ideal house and sellers can get the recommended price by uploading the property description. Thus, a fast, 24-hour responsive auto-inquiry system can greatly improve customers' and sellers' experience. To establish similar system, the dataset which is used to describe the sale of individual residential property in Ames is set as an experiment material. This paper discusses the influence from the data-preprocessing stage on the establishment of later machine learning model using different data groups. Later in the accuracy comparison, controlled experiment is implemented to show the accuracy among different organization of the algorithms. This paper is aimed at how much influence from the model choosing and data preprocessing stage will be made on the prediction accuracy and interpret methods used in preprocessing stage. Furthermore, the experiments show that the single model prediction's accuracy has on average 5% less than the stacked model. In addition, the results represented in this paper have the potential to be integrated into the housing sales system which can be applied to price delivery systems to benefit both consumers and sellers.",4.0,,False,Conference,,2021-12-01,1a42693f6b206c4a916bb9d8fee60dae4215f18c,optimizationsoftrainingdatasetonhousepriceestimation,cheri,10.1109/bdeim55082.2021.00047,optimizationsoftrainingdatasetonhousepriceestimation|2021,1,2388,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b6ac12925a426e22a37df67ba8959db6586468d6,10.1109/icac3n56670.2022.10074128,,House Price Prediction using regression techniques,2022.0,"2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)",R. Dwivedi; Rishabh Gupta; P. Pal,"Real estate companies keep special care when they purchase or sell a new house. A significant amount of expertise and market awareness is required for making accurate price predictions of houses so as to turn it out into a profitable investment. Also, the setting of prices manually is quite difficult and tedious task and the accuracy of prediction done by the real estate experts is also not good in that case due to the probability of human errors. The primary goal of this project is to provide the best and most profitable house price predictions to real estate investors with the help of a machine learning model so that they can get best returns in their deals as per the market scenario. It can also provide a lot of convenience to buyers who want to purchase a house at best market prices depending upon the desired house features. It consists of a ML-based model which mainly involves the use of three machine learning algorithms namely linear regression, decision tree regression and random forest regression. Three individual models are created on the basis of each of these algorithms and the most suitable model with maximum accuracy is chosen. With the analysis of various features and specifications of the houses such as number of rooms, locality, population status, physical conditions etc., prices will be estimated.",4.0,,False,Conference,,2022-12-16,b6ac12925a426e22a37df67ba8959db6586468d6,housepricepredictionusingregressiontechniques,dwivedi,10.1109/icac3n56670.2022.10074128,housepricepredictionusingregressiontechniques|2022,1,2335,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c3b38d5a05145cea9c21b0ec762415d96341430c,10.1109/icemis56295.2022.9914345,,Applying C atboost Regression Model for Prediction of House Prices,2022.0,2022 International Conference on Engineering & MIS (ICEMIS),Rafea. M. Almejrb; Omar M. Sallabi; Abdelhafid Ali Mohamed,"Every year, the cost of houses rises, demanding the development of a method to forecast home prices in the future. This paper aims to apply the Catboost regression model by changing the iteration and learning rate. A feature and house value dataset for King County, Washington, is used. During the pre-processing of the data, extreme values are winsorized, and features with a high degree of correlation are removed. There are twelve possible models, including Catboost, RandomForestRegressor, and KNeighborsRegressor. Several metrics are used to assess them, such as Mean Squared Error (MSE), R-squared score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) (MSE). The model with a low RMSE of 0.013166 performs well compared to other research, especially in the test set where R2 is 0.915256. In this study, Catboost is the model that performs the best overall and can be used to estimate home prices. The most significant factors affecting property prices are location, living area, and house condition. It is confirmed that the conclusions in this research are consistent with real-world experience after comparing and contrasting with other works.",4.0,,False,Conference,,2022-07-04,c3b38d5a05145cea9c21b0ec762415d96341430c,applyingcatboostregressionmodelforpredictionofhouseprices,almejrb,10.1109/icemis56295.2022.9914345,applyingcatboostregressionmodelforpredictionofhouseprices|2022,1,2166,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c761750a9fa77dab0a7a706faaf68325f3907fb5,10.1109/iicaiet51634.2021.9573830,,Using Machine Learning to Forecast Residential Property Prices in Overcoming the Property Overhang Issue,2021.0,International Conference on Artificial Intelligence in Engineering and Technology,Wan-Yee Lim; N. A. A. Bakar; Noor Hafizah Hassan; N. M. M. Zainuddin; R. Yusoff; N. Rahim,"Overhang property issue has sustained over the past ten years in Malaysia. Major overhang property issue was contributed from the unsold residential property. Though the government announced to build a data system and provide the housing data to prevent a mismatch of supply-demand in the property market, there are still not many relevant studies or research on predicting residential property prices. Hence, it is essential to understand the factors that influence the price of residential properties. The study aims to predict the price of a residential property by using a machine learning algorithm. Three algorithms were selected, namely Decision Tree, Linear Regression, and Random Forest, tested against the training and testing datasets obtained from the Malaysian Valuation and Property Services Department. Results show that the Random Forest model produced high accuracy with lower r_squared (R2), RMSE, and MAE values. Significantly, the study has contributed a new insight into essential property features that primarily influence the property price, which will be useful for property developers and buyers who wish to invest in the property market.",4.0,,False,JournalArticle;Conference,Computer Science,2021-09-13,c761750a9fa77dab0a7a706faaf68325f3907fb5,usingmachinelearningtoforecastresidentialpropertypricesinovercomingthepropertyoverhangissue,lim,10.1109/iicaiet51634.2021.9573830,usingmachinelearningtoforecastresidentialpropertypricesinovercomingthepropertyoverhangissue|2021,1,2163,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",bc742e135a391a0b1a116483f4afe71e55b67192,10.1109/icosec58147.2023.10275956,,Investigating the Effectiveness of Machine Learning Techniques for Real Estate Price Prediction,2023.0,2023 4th International Conference on Smart Electronics and Communication (ICOSEC),Baljap Singh; Jaspreet Singh; Anubhav Kumar; Shikha Gupta,"In the real-estate industry, the capacity to extract significant information from raw data makes it extremely valuable for predicting home values, housing attributes, and much more. The cost of housing varies from day to day and is frequently increased arbitrarily rather than using calculations. The housing market and homeowners are routinely impacted by changes in property values. A literature study is done to evaluate the most important factors and reliable models for predicting housing values. The results of this analysis confirmed the use of Support Vector Regression, Linear Regression, and Artificial Neural Networks as the most effective models when compared to others. Furthermore, our data imply that geographical and real estate agents are important determinants in forecasting property values. This research will be very helpful in determining the most important variables for estimating housing costs and the best machine learning model to employ when doing this type of research, especially for academics and housing developers.",4.0,,False,Conference,,2023-09-20,bc742e135a391a0b1a116483f4afe71e55b67192,investigatingtheeffectivenessofmachinelearningtechniquesforrealestatepriceprediction,singh,10.1109/icosec58147.2023.10275956,investigatingtheeffectivenessofmachinelearningtechniquesforrealestatepriceprediction|2023,1,2047,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,2,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",641dfca1c027153bda3546bb7e3dae4f79037650,10.1109/ietacs68750.2025.11385743,,An Optimized Feature Based House Price Prediction Model Using Machine Learning,2025.0,2025 International Conference on Innovations and Emerging Technologies In AI & Communication Systems (IETACS),Deepak Rawat; Ankita Aggarwal; Saurabh; Geetanjali Sharma,"Household accord is directly impacted with price which further has constraints on choices. This paper focuses on the house price prediction considering all the attributes which impact the property price. Machine learning along with approaches plays crucial role in predictions and leads to the decision to hold the house or not. Traditional algorithms predict the house price and have the variations in performance and show the scope of improvement. Identification of prominent features and optimization improves the accuracy upto 90.8% in the training phase and the accuracy to 98.8% in the testing phase. Predictions of house price helps the buyers to decide the locations, area, faces and other important attributes about the property before buying. Optimization techniques are employed to build a robust structure of the model. Robustness of the model implies how well the model performance in the best and worst situation. The real estate analysis is really very important aspect for the house price prediction.",4.0,,False,Conference,,2025-11-06,641dfca1c027153bda3546bb7e3dae4f79037650,anoptimizedfeaturebasedhousepricepredictionmodelusingmachinelearning,rawat,10.1109/ietacs68750.2025.11385743,anoptimizedfeaturebasedhousepricepredictionmodelusingmachinelearning|2025,1,2016,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",6545a91ed3d84413425e2c4bd9de5224f96850d5,10.1109/icite50838.2020.9231460,,Prediction Of Housing Price Along The Urban Rail Transit Line Based On GA-BP Model And Accessibility,2020.0,International Conferences on Intelligent Transportation Engineering,Ruo-Qi Li; Jun Hu,"In order to study the relationship between the accessibility along the urban rail transit line and the change in housing price, GA-BP model, which combining the genetic algorithm(GA) and back propagation neural network(BPNN), was used to predict the housing price along the urban rail transit line. The reliability of the model was verified by taking the price of residential areas along Nanjing Metro Line 10 as an example, and the performance between GA-BP model and the BP model was compared. It was found that the average absolute error rate of GA-BP model is 6.91%, which is 6.45% lower than that of BP model, and the mean square error of prediction results is significantly reduced. It can be seen that the price prediction algorithm based on GA-BP model for those residential areas along the urban rail transit line is reliable and can fully tap the potential relationship between the accessibility of urban transport network and the housing price.",4.0,,False,JournalArticle;Conference,Computer Science,2020-09-01,6545a91ed3d84413425e2c4bd9de5224f96850d5,predictionofhousingpricealongtheurbanrailtransitlinebasedongabpmodelandaccessibility,li,10.1109/icite50838.2020.9231460,predictionofhousingpricealongtheurbanrailtransitlinebasedongabpmodelandaccessibility|2020,1,1955,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",bb74dac121e0a8c5bfd867cdf074b59bdb68d8e5,10.1109/apcit62007.2024.10673571,,Predicting Real-Time House Prices: A Machine Learning Approach Using XGBoost Algorithm,2024.0,2024 Asia Pacific Conference on Innovation in Technology (APCIT),L. M. I. Leo Joseph; Edunuri Harini Reddy; M. Srinidhi; Pamu Venkata Saketh; D. Mohan; Ch.Rajendra Prasad,"The housing market involves consideration of numerous factors by prospective buyers that may change over time. As a result, the market for real estate prediction will constantly fluctuate. Machine Learning is used to analyze and predict real estate property prices. The XGBoost algorithm is used to achieve a high accuracy in forecasting the market values. The Primary Objective is to assist users in finding a suitable price according to their preferred geographical factors. The model provides accurate predictions for future real estate prices by analyzing the previous trend and market. This study sought to predict prices in Bengaluru City using the XGBoost Algorithm. The results of this study showed that the XGBoost Model achieved a prediction accuracy of 91.77%. Such an accurate valuation tool could benefit homeowners and prospective buyers when pricing properties, without requiring the help of a real estate agent.",4.0,,False,Conference,,2024-07-26,bb74dac121e0a8c5bfd867cdf074b59bdb68d8e5,predictingrealtimehousepricesamachinelearningapproachusingxgboostalgorithm,joseph,10.1109/apcit62007.2024.10673571,predictingrealtimehousepricesamachinelearningapproachusingxgboostalgorithm|2024,1,1927,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",11ba67c8cc609b3303f3ddfbebe9be62851529d0,10.1109/icsd60021.2024.10751593,,A Comprehensive review on House Prediction System,2024.0,IEEE International Conference on Solid Dielectrics,Parveen Bajaj; Lalit Kumar; Kamlesh Lohani; Sudhanshu Mehta,"Predicting the prices of the houses accurately is a very difficult task in the real estate industry. In the last few years, due to the readiness of huge datasets and progress in the machine learning techniques, researchers have attempted to the develop models that can accurately predict house prices. In this paper, we want to build a house price prediction system that utilizes a dataset of residential housing prices to train and evaluate the predictive model. There is a lot of research which is being done in this field from the past few years such as the number of bedrooms, square footage, bathroom, location, and other factors. Overall, the research in this field contributes to the development of more accurate and efficient house price prediction models which builds the substructure for the future researches in this field.",4.0,,False,Conference;Review,,2024-05-02,11ba67c8cc609b3303f3ddfbebe9be62851529d0,acomprehensivereviewonhousepredictionsystem,bajaj,10.1109/icsd60021.2024.10751593,acomprehensivereviewonhousepredictionsystem|2024,1,1834,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",60b98c6ec83d8ecfed4d0426b98a179793bdfb3e,10.1109/icoris60118.2023.10352211,,Machine Learning-Based Classification of House Prices: A Comparative Study,2023.0,2023 5th International Conference on Cybernetics and Intelligent System (ICORIS),B. Budiman; Nur Alamsyah; T. P. Yoga; R. Alamsyah; Chairul Habibi,"This research investigates the problem of house price classification using various machine learning methods, including Random Forest, Gradient Boosting Machines (GBM), XGBoost, Logistic Regression, and K-Nearest Neighbors (KNN). The best performing method for house price classification is Random Forest, achieving an R2 value of 85.32% and an AUC of 0.93 according to the ROC evaluation. The results demonstrate Random Forest’s effectiveness in handling non-linear and complex datasets, while o ther methods such as GBM, XGBoost, Logistic Regression, and KNN also show competitive performance. This study guides house price classification modeling and supports accurate decision-making in the house market context.",4.0,,False,Conference,,2023-10-06,60b98c6ec83d8ecfed4d0426b98a179793bdfb3e,machinelearningbasedclassificationofhousepricesacomparativestudy,budiman,10.1109/icoris60118.2023.10352211,machinelearningbasedclassificationofhousepricesacomparativestudy|2023,1,1715,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1529e86345168d24217580773a14def0af239db5,10.1109/isemantic55962.2022.9920454,,Performance Analysis of Multiple Linear Regression and Random Forest for an Estimate of the Price of a House,2022.0,2022 International Seminar on Application for Technology of Information and Communication (iSemantic),Sukma Ayu Septianingrum; M. A. Dzikri; M. Soeleman; P. Pujiono; Muslih Muslih,"The house is a human need for boards. House prices that continue to rise every year make it difficult for some people to buy a house according to their respective financial capabilities. Many property developers in big cities continue to build housing, including the South Jakarta area with many new arrivals. In this study, we will predict house prices using a comparison of 2 methods, multiple linear regression and random forest which produces a better RMSE value at an 8:2 comparison between training data and testing data, and the multiple linear regression method produces fewer errors. The 8:2 experiment produces an RMSE 3673441811.575 of Linear Regression and 3693111743.726 of Random Forest.",4.0,,False,,,2022-09-17,1529e86345168d24217580773a14def0af239db5,performanceanalysisofmultiplelinearregressionandrandomforestforanestimateofthepriceofahouse,septianingrum,10.1109/isemantic55962.2022.9920454,performanceanalysisofmultiplelinearregressionandrandomforestforanestimateofthepriceofahouse|2022,1,1701,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e4dbc46325b3c6498198f0dba5f49ca932002202,10.1109/icsp54964.2022.9778372,,Boston house price prediction: machine learning,2022.0,International Conference on the Software Process,Songyi Bai,"The Boston housing problem has been studied by many data scientists for over 50 years. The problem is proven to be extremely profitable, and it is considered as one of the most classical machine learning problems. Using machine learning techniques, computer scientists have already reduced the error of their estimation to around 4%. To solve this problem, computer scientists have developed a number of machine learning algorithms in recent years. Some are simple, while others are more complex; some offer a rough estimate of house value, while others offer more precise estimates In this essay we will see how they approach the problem using machine learning regression methods.",4.0,,False,Conference,,2022-04-15,e4dbc46325b3c6498198f0dba5f49ca932002202,bostonhousepricepredictionmachinelearning,bai,10.1109/icsp54964.2022.9778372,bostonhousepricepredictionmachinelearning|2022,1,1681,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",be00e04665be8385b80f457fa2196d5c436fbbb8,10.1007/978-3-030-96308-8_90,,A Machine Learning Framework for House Price Estimation,2021.0,International Conference on Intelligent Systems Design and Applications,Adebayosoye Awonaike; S. Ghorashi; Rawad Hammaad,,4.0,,False,JournalArticle,Computer Science,,be00e04665be8385b80f457fa2196d5c436fbbb8,amachinelearningframeworkforhousepriceestimation,awonaike,10.1007/978-3-030-96308-8_90,amachinelearningframeworkforhousepriceestimation|2021,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a826bfed66a1684bfdc2c9368d186f467204ac8d,10.2139/ssrn.2866115,,Determinants of Residential Property Value in New Zealand: A Neural Network Approach,2016.0,,M. Abraham,,4.0,,False,,Economics,2016-11-07,a826bfed66a1684bfdc2c9368d186f467204ac8d,determinantsofresidentialpropertyvalueinnewzealandaneuralnetworkapproach,abraham,10.2139/ssrn.2866115,determinantsofresidentialpropertyvalueinnewzealandaneuralnetworkapproach|2016,1,1000,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || residential property price determinants",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",aec9179686f613a5ed9bd3b9350a9a681f9f09e3,10.4018/978-1-7998-7685-4.ch005,,An Improved Model for House Price/Land Price Prediction using Deep Learning,2022.0,,B. Mallikarjuna; M. S. Ram; Supriya Addanke; Munish Sabharwal,,4.0,,False,,Economics,,aec9179686f613a5ed9bd3b9350a9a681f9f09e3,animprovedmodelforhousepricelandpricepredictionusingdeeplearning,mallikarjuna,10.4018/978-1-7998-7685-4.ch005,animprovedmodelforhousepricelandpricepredictionusingdeeplearning|2022,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c989ad31c3ab6153621e449484ae7aefaaf516ba,10.1109/seb4sdg60871.2024.10629723,,Housing Price Prediction Using Machine Learning Techniques,2024.0,"2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)",I. Obagbuwa; Samantha Danster,"This research employs advanced machine learning algorithms to predict housing prices, specifically Linear Regression, Random Forest, Gradient Boost Machine, Multilayer Perceptron (MLPs), and Long Short-Term Memory (LSTM). The study aims to address the oversight of crucial house factors such as front area, location, size, and others, to provide effective housing pricing predictions. The methodology involves structured processes, including data collection, preprocessing, feature extraction, dataset splitting, and the implementation of machine learning models. Using a housing prices dataset from Kaggle, the study explores attributes like sale price, lot area, street type, and more. The dataset, comprising four files (Data description, sample submission, test, and train), consists of 163 columns, each record encompassing 80 attributes that describe specific houses. The study emphasizes the significance of data preprocessing involves handling missing values, outlier removal, and log transformation for improved results. The models' performance is evaluated using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) on the test dataset. The results indicate that the models achieve promising outcomes in predicting housing prices, with Linear Regression leading with a remarkable accuracy of 91%, followed closely by Gradient Boost Machine (0.911) and Random Forest (0.905). While MLPs score lower at 0.584, LSTM provides a commendable R2 score of 0.863. The research contributes valuable insights for accurate predictions in real estate, considering factors influencing house prices.",3.0,,False,Conference,,2024-04-02,c989ad31c3ab6153621e449484ae7aefaaf516ba,housingpricepredictionusingmachinelearningtechniques,obagbuwa,10.1109/seb4sdg60871.2024.10629723,housingpricepredictionusingmachinelearningtechniques|2024,1,2631,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",387931cda44f6a153341aafdd3ec67794f0de328,10.1109/icisc62624.2024.00067,,Machine Learning based Real Estate Price Prediction,2024.0,International Conference on Information Security and Cryptology,A. Mukherjee; Rtwik Nambiar; Deepjyoti Choudhury,"The dynamic nature of real estate markets gives rise to huge challenges for buyers and sellers in accurately predicting property prices. This paper presents a detailed study on real estate price predictions, focusing on using machine learning techniques to forecast property prices effectively. Using a dataset from Bengaluru, as a case study, it explored the intricacies of Real Estate markets and developed a predictive model to estimate the property prices. Our studies consist of data preprocessing, featuring engineering, model selection, and evaluations, giving insights into the factors that affect property prices and the performances of various regression algorithms. After performing multiple experiments, we showcase the efficiency of our approaches in predicting property prices. Existing techniques that have tried to predict house prices have often struggled to deal with nonlinear relationships present among the factors. Recent techniques such as Linear regression, Ridge and Lasso Regression and Support Vector Machines(SVM) have come up short of being perfect when it comes to dealing with computational intensity and these techniques require extensive hyperparameter tuning. However, our predictive model is able to deal with these challenges by leveraging the strengths of Random Forest regression. This is done with high accuracy and the implications for real estate stakeholders are discussed, subsequently. This paper also highlights the various avenues through which the performance of real estate estimation models can be improved in the future.",3.0,,False,Conference,,2024-07-29,387931cda44f6a153341aafdd3ec67794f0de328,machinelearningbasedrealestatepriceprediction,mukherjee,10.1109/icisc62624.2024.00067,machinelearningbasedrealestatepriceprediction|2024,1,2570,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",cb26d6490b2572154b54d4788e5ce513c5f4a26d,10.1109/icac3n60023.2023.10541614,,Machine learning-Based Prediction of Home Prices,2023.0,"2023 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)",Dimpal Kumar; Aman Rawat; S. Jha; D. Yadav,"In the past few years, machine learning has played an effective part in product orders and recommendations in the medical industry and every day. In return, we offer great customer support and safer automotive systems. As a result of all that has been illustrated, ML may be a innovation that’s getting to be increasingly prevalent over a wide range of businesses. The genuine domain advertise is one of the foremost competitive and price-focused in presence nowadays. People are endeavouring to buy a modern domestic by considering their budgets and showcase procedure. However, the essential blemish within the existing framework is that it decides the cost of a house without making the fundamental expectations around future advertise patterns, which leads to an increment in cost. Our project’s essential objective is to precisely expect the cost of a house without causing any misfortunes. A few components must be taken into consideration in arrange to accurately anticipate domestic costs and to supply customers with compelling house estimating that’s in line with their inclinations and budget. So, we are creating a show to assess lodging costs. using machine learning advances such as K-Means, Irregular Woodland, Choice Tree, and Direct Relapse. This strategy allows consumers to finance real estate without going through a broker. Research results show that random forest regression has the highest accuracy.",3.0,,False,Conference,,2023-12-15,cb26d6490b2572154b54d4788e5ce513c5f4a26d,machinelearningbasedpredictionofhomeprices,kumar,10.1109/icac3n60023.2023.10541614,machinelearningbasedpredictionofhomeprices|2023,1,2422,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fb1e85a16f197c70dd622edca3d1d6262aaca531,10.1109/inocon60754.2024.10511732,,Predicting House Price Model : A Comprehensive Analysis with Random Forest and Decision Tree Method,2024.0,2024 3rd International Conference for Innovation in Technology (INOCON),Mansi Sharma; Deepak Sharma; Rushikesh Burle; Pawan Patil; Isha Joge; Chetan G. Puri,"This paper explores the field of predicting property prices through a thorough comparison of several regression techniques. Linear regression, decision tree regression, random forest regression, K-neighbors regression, gradient boosting regression, and XGBoost regression are the regression models that are being examined. Finding the best model to reliably estimate house prices based on a set of features is the aim.The dataset used in this study includes location, bedroom count, square footage, and other relevant parameters that are known to affect property values. Each regression model is trained and tested on this dataset using a methodical evaluation procedure, and its performance is evaluated using important metrics including R-squared, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and overall correctness. predicting house prices mainly use harlfoxem (Owner) repository of housing dataset will contain the homes sold between May 2014 and May 2015 with 21613 entries and 21 attribute were used to evaluate the performance of machine learning prediction model. For practitioners and scholars working in real estate, housing market analysis, and related sectors, this study offers insightful information. It emphasizes how crucial it is to properly choose regression techniques depending on the particulars of the prediction task and the distinct qualities of the dataset.",3.0,,False,Conference,,2024-03-01,fb1e85a16f197c70dd622edca3d1d6262aaca531,predictinghousepricemodelacomprehensiveanalysiswithrandomforestanddecisiontreemethod,sharma,10.1109/inocon60754.2024.10511732,predictinghousepricemodelacomprehensiveanalysiswithrandomforestanddecisiontreemethod|2024,1,2397,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a6fabb111ec3ad1ef9d3b80000bfc2c0ef6f0d72,10.48550/arxiv.2405.17505,2405.17505,Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models,2024.0,arXiv.org,Ting-Ting Chen; Shijing Si,"Housing has emerged as a crucial concern among young individuals residing in major cities, including Shanghai. Given the unprecedented surge in property prices in this metropolis, young people have increasingly resorted to the rental market to address their housing needs. This study utilizes five traditional machine learning methods: multiple linear regression (MLR), ridge regression (RR), lasso regression (LR), decision tree (DT), and random forest (RF), along with a Large Language Model (LLM) approach using ChatGPT, for predicting the rental prices of lane houses in Shanghai. It applies these methods to examine a public data sample of about 2,609 lane house rental transactions in 2021 in Shanghai, and then compares the results of these methods. In terms of predictive power, RF has achieved the best performance among the traditional methods. However, the LLM approach, particularly in the 10-shot scenario, shows promising results that surpass traditional methods in terms of R-Squared value. The three performance metrics: mean squared error (MSE), mean absolute error (MAE), and R-Squared, are used to evaluate the models. Our conclusion is that while traditional machine learning models offer robust techniques for rental price prediction, the integration of LLM such as ChatGPT holds significant potential for enhancing predictive accuracy.",3.0,,False,JournalArticle,Computer Science,2024-05-26,a6fabb111ec3ad1ef9d3b80000bfc2c0ef6f0d72,predictingrentalpriceoflanehousesinshanghaiwithmachinelearningmethodsandlargelanguagemodels,chen,10.48550/arxiv.2405.17505,predictingrentalpriceoflanehousesinshanghaiwithmachinelearningmethodsandlargelanguagemodels|2024,1,2357,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",504bd6092a7aefba2fe4323a56e0a9f5efa63538,10.1109/ic457434.2024.10486255,,Real Estate Cost Prediction Through Web Application Using Random Forest Machine Learning Algorithm and Python Flask,2024.0,Journées Francophones d'Ingénierie des Connaissances,Mohit Kumar; Priyash Kumar,"Machine learning has become progressively sophisticated and sturdy due to the development of new algorithms. A compelling application is observed in general is the prediction of the house price. The model which can be created using ML can help the customers to get an accurate price of the house. These Price Prediction models make use of attributes of the house under consideration namely location, count of bedrooms, tax rates, Number of years the house has existed, and commercial complexes in a nearby location. Price prediction forecasting for houses is a critical characteristic of real estate. The objective of this project is to uncover Crucial patterns and trends from historical property records. Historical property data from Bengaluru is subjected to machine learning scrutiny to extract valuable insights. The diverse factors influencing house prices have stimulated the curiosity of researchers across various domains. The research utilizes numerous regression models to investigate the determinants of house price changes. This research leverages various feature selection techniques, and feature scaling techniques to augment the efficacy of the prognostic model. The effectiveness of machine learning models is evaluated based on various metrics, including precision, correctness, and susceptibility.",3.0,,False,Conference,,2024-02-08,504bd6092a7aefba2fe4323a56e0a9f5efa63538,realestatecostpredictionthroughwebapplicationusingrandomforestmachinelearningalgorithmandpythonflask,kumar,10.1109/ic457434.2024.10486255,realestatecostpredictionthroughwebapplicationusingrandomforestmachinelearningalgorithmandpythonflask|2024,1,2317,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",a094e3d2ea94420ed4f781fa527ea93c05ec994a,10.1109/iitcee57236.2023.10091037,,Performance Estimation of Real Estate Business Price Using the Regression Analysis of Machine Learning,2023.0,"2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)",S. Geetha; Pretty Diana; P. S,"Domain ontology construction is an important task in Numerous researches have been done for demonstrating the fundamental non-straight connection between estimating characteristics and cost of property to conjecture the lodging deals costs. Lately, further developed non-direct demonstrating methods like Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have arisen as viable strategies to foresee the house costs. This current undertaking's motivation is to foster a system for anticipating land property costs using an assortment of regression techniques. The objective of this task is to give an approach to foreseeing property costs for land using different regression procedures. The entirety of the strategies is run on a formerly made dataset. To think about regression draws near, the Root Mean Square Error is utilized as a presentation pointer. The motivation behind this task is to utilize a scope of regression procedures to foster state property valuing. We utilize different regression methods in this pathway, and our outcomes are not the sole assurance of one strategy rather it is the weighted mean of different strategies to give the most exact outcomes. The outcomes demonstrated that this methodology yields the best accuracy and greatest precision.",3.0,,False,Conference,,2023-01-27,a094e3d2ea94420ed4f781fa527ea93c05ec994a,performanceestimationofrealestatebusinesspriceusingtheregressionanalysisofmachinelearning,geetha,10.1109/iitcee57236.2023.10091037,performanceestimationofrealestatebusinesspriceusingtheregressionanalysisofmachinelearning|2023,1,2287,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",206f97e2f1b267e7cbc35faacfacc3691888a6cb,10.1109/rusautocon61949.2024.10694522,,Comparative Analysis of Ensemble and Linear Machine Learning Models in the Task of House Price Prediction,2024.0,2024 International Russian Automation Conference (RusAutoCon),Ihcene Zitoune; M. Arabov,"In today's evolving real estate market, accurately predicting real estate prices is important given the crucial role real estate plays in any country's economy. With numerous methods and techniques available, navigating the complexities of data types and the multitude of factors influencing prices requires careful consideration. This research paper presents a comparative analysis of ensemble and linear machine learning models in predicting real estate prices using a wide range of predictive models, including linear regression, ridge regression, lasso regression, random forest, decision trees, XGBoost and LightGBM. The study evaluates their effectiveness using metrics such as MAE (mean absolute error), RMSE (RMS error), RMSLE (RMS logarithmic error) and R2 (coefficient of determination) and evaluates their training time. The experimental results showed that ensemble models outperformed linear models with a higher training time. These results can be useful both for researchers in the field of real estate and finance, as well as for investors interested in predicting and optimizing real estate investment strategies in a volatile market.",3.0,,False,Conference,,2024-09-08,206f97e2f1b267e7cbc35faacfacc3691888a6cb,comparativeanalysisofensembleandlinearmachinelearningmodelsinthetaskofhousepriceprediction,zitoune,10.1109/rusautocon61949.2024.10694522,comparativeanalysisofensembleandlinearmachinelearningmodelsinthetaskofhousepriceprediction|2024,1,2151,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8f2d132ac38de32acc3bf7dd0562a074822f89bf,10.1109/icssas57918.2023.10331796,,Metro Cities House Price Prediction Using ML,2023.0,2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS),Sonal G. Naik; Aarti Puthran,"Due to changes in local infrastructure as well as real estate values, home prices fluctuate every single year. Customers can figure out the value of the house with the aid of a centralized system that predicts house prices and has infrastructure. It aids the consumer in making decisions on where and when to buy a home. When forecasting a property's value, several factors are taken into account, such as location, neighborhood, size, etc. The intention of this study is to predicate house prices for individuals depending on financial situation. This study matched the outcomes of many machine learning methods including linear, XGB, decision tree, lasso regression and random forest regression, due to its outstanding accuracy. House value forecast on a data set will be performed by utilizing all of aforementioned methodologies and selecting the best among them. This study establishes the application of machine learning algorithms in the prediction of house values using datasets collected from Kaggle. This study can construct a regression model that can precisely estimate the cost of a property created on its characteristics.",3.0,,False,Conference,,2023-10-18,8f2d132ac38de32acc3bf7dd0562a074822f89bf,metrocitieshousepricepredictionusingml,naik,10.1109/icssas57918.2023.10331796,metrocitieshousepricepredictionusingml|2023,1,2136,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2ff905c52d134ca4c521ebb2eb4fb7bf5103e7ba,10.1109/raics61201.2024.10689962,,Real-Time House Price Predictions with Regression Analysis,2024.0,IEEE Recent Advances in Intelligent Computational Systems,K. Purushotham; Bangarappa; Ashwini Kodipalli; Trupthi Rao,"Accurate property assessment is essential in the dynamic and ever-changing world of real estate so that both buyers and sellers can make well-informed choices. We are aware that setting the appropriate asking price for a house can be a difficult undertaking. Property values are greatly influenced by factors including location, size, amenities, market trends, and numerous economic indicators. This system supports in determining a property's price based on geographic regions factors, the number of bathrooms, bedrooms, and area (square feet). Forecasting future costs relies on examining historical market trends and value ranges. The primary focus of this analysis is to efficiently utilize regression models in combination with other methodologies to achieve the highest possible accuracy in predictions. This study aims to forecast housing values in Bangalore. Our website is designed to give you reliable and data-driven insights on property values when you're a potential homebuyer looking to identify the best property.",3.0,,False,,,2024-05-16,2ff905c52d134ca4c521ebb2eb4fb7bf5103e7ba,realtimehousepricepredictionswithregressionanalysis,purushotham,10.1109/raics61201.2024.10689962,realtimehousepricepredictionswithregressionanalysis|2024,1,2028,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",64e3af50f7bb76a98e350d2819494b6012f55f61,10.1109/iceccme57830.2023.10253192,,Analysis of Housing Market to Predict Home Price,2023.0,"2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)",M. L. Ali; David Magrini; Michael J. Cangelosi; D. Ribeiro,"Predicting the house market and home prices is still a challenging task. The price of a house depends on several factors and it is important to find the key factors that affect the price of a house in a particular location. In this study, we conducted an experiment to observe how certain variables affect housing prices. Principal component analysis (PCA) and a correlation heatmap were developed to document which variables had the highest correlation with price. At first, these variables were isolated for further development. Using the scikit-learn and XGBoost libraries for Python several models were developed for predicting housing prices within the area of Kings County, California, United States. From these models it was determined that using the full range of variables, rather than the most highly correlated variables, yielded the most accurate results. Out of the three regression models developed, the XGBoost model yielded the most accurate predictions compared to linear regression and random forest.",3.0,,False,Conference,,2023-07-19,64e3af50f7bb76a98e350d2819494b6012f55f61,analysisofhousingmarkettopredicthomeprice,ali,10.1109/iceccme57830.2023.10253192,analysisofhousingmarkettopredicthomeprice|2023,1,2018,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c9974a81588d96825ff05507e6df8d723c5e4764,10.1109/icac3n60023.2023.10541549,,Prediction of House Pricing Using Machine Learning,2023.0,"2023 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)",Kavita Saini; Ramvachan; Piyush Kashyap; Praveen Yadav,"Data mining is increasingly frequently used in the housing market. In order to estimate house prices, important housing qualities, and many other things, data mining is quite helpful since it can extract pertinent knowledge from raw data.According to research, the real estate market and home owners are frequently concerned about price variations in housing. a study analysis of the pertinent characteristics and the best models for predicting property values have been developed. The analysis’s findings demonstrated that, when compared to other models, ANN, can SVM, and XGBoost. After going through the data in this paper we came across that characteristics structures are the deciding factors for predicting house prices. This research will be Finding the most important factors that influence housing prices and identifying the most effective machine learning model to utilise in this project area will be of enormous help, particularly to housing developers and academics.",3.0,,False,Conference,,2023-12-15,c9974a81588d96825ff05507e6df8d723c5e4764,predictionofhousepricingusingmachinelearning,saini,10.1109/icac3n60023.2023.10541549,predictionofhousepricingusingmachinelearning|2023,1,1979,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",23e3bd0a83117858737aabfe67362f511cf7319d,10.1109/sceecs61402.2024.10481902,,Real estate price prognostication through machine learning models,2024.0,"2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)",Doddamani John Vestly; Neelima Thangallapelly; Gandra Sai Manish; Shanmugasundaram Hariharan; Vinay Kukreja; H. Reddy,"In the dynamic realm of real estate, house prices became the crucial part to encounter the set of hurdles to make lives better. Through meticulous feature engineering, which includes advanced data cleansing and feature manipulation, coupled with robust machine learning techniques, our model offers a nuanced understanding of valuation dynamics. The optimization process involves hyper parameter tuning and cross-validation, employing cutting-edge methodologies to extract latent patterns and yield meaningful insights from the underlying data. Leveraging algorithms of supervised learning algorithms like linear regression and K-fold, chosen for their ability to discern intricate patterns within diverse datasets, our research pioneers a transformative approach to real estate valuation. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE) and R-squared were used to ensure a robust and accurate predictive framework which were promising",3.0,,False,Conference,,2024-02-24,23e3bd0a83117858737aabfe67362f511cf7319d,realestatepriceprognosticationthroughmachinelearningmodels,vestly,10.1109/sceecs61402.2024.10481902,realestatepriceprognosticationthroughmachinelearningmodels|2024,1,1971,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",4875877da6ff707e89b8f26cc7798a6c3d473445,10.1109/inocon60754.2024.10511592,,House Price Prediction Using Linear and Lasso Regression,2024.0,2024 3rd International Conference for Innovation in Technology (INOCON),Mayank Sharma; Rahul Chauhan; Swati Devliyal; K. R. Chythanya,"This research paper aims to predict house prices with more efficiency and accuracy. The research paper involves three machine learning algorithms, ""linear regression, lasso regression, and ridge regression,"" that are applied to the data set of Bangalore and have various information about different places in Bangalore, like BHK, number of bathrooms, area, etc. The house price prediction is quite a difficult task, as predicting the price of the house involves many factors like area, number of bathrooms, climate condition, age of the property, and many other factors. To make predictions according to all the factors, the author has applied three algorithms, one by one. From these machine learning algorithms, we have developed a model that helps predict the prices of houses with more accuracy and a lower error percentage. This model can be very useful for both buyers and sellers, as it can justify the price of the house for both of them.",3.0,,False,Conference,,2024-03-01,4875877da6ff707e89b8f26cc7798a6c3d473445,housepricepredictionusinglinearandlassoregression,sharma,10.1109/inocon60754.2024.10511592,housepricepredictionusinglinearandlassoregression|2024,1,1946,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",dc152e6248c388ae7eb58b017cbc929aeb6050bc,10.48550/arxiv.2405.06553,2405.06553,Scalable Property Valuation Models via Graph-based Deep Learning,2024.0,arXiv.org,E. Riveros; Carla Vairetti; C. Wegmann; Santiago Truffa; Sebastián Maldonado,"This paper aims to enrich the capabilities of existing deep learning-based automated valuation models through an efficient graph representation of peer dependencies, thus capturing intricate spatial relationships. In particular, we develop two novel graph neural network models that effectively identify sequences of neighboring houses with similar features, employing different message passing algorithms. The first strategy consider standard spatial graph convolutions, while the second one utilizes transformer graph convolutions. This approach confers scalability to the modeling process. The experimental evaluation is conducted using a proprietary dataset comprising approximately 200,000 houses located in Santiago, Chile. We show that employing tailored graph neural networks significantly improves the accuracy of house price prediction, especially when utilizing transformer convolutional message passing layers.",3.0,,False,JournalArticle,Computer Science,2024-05-10,dc152e6248c388ae7eb58b017cbc929aeb6050bc,scalablepropertyvaluationmodelsviagraphbaseddeeplearning,riveros,10.48550/arxiv.2405.06553,scalablepropertyvaluationmodelsviagraphbaseddeeplearning|2024,1,1922,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8725f2f9ffd3e231100c66aaf901fb41db7ec22d,10.1109/i4tech55392.2022.9952397,,Linear Regression Models based Housing Price Forecasting,2022.0,2022 International Conference on Industry 4.0 Technology (I4Tech),Vulligadla Amaresh; R. R. Singh; Rajeev Kamal; Anant Kulkarni,"Investigation for the house price forecasting is necessary to explore the machine learning based mode of precise estimation for housing units. The data available in the literature is adopted for this investigation. In connection to this, the data analysis is an important aspect. The Linear regression (LR) is one of the preferred techniques due to its superior capability for the data analysis. In this paper, subsequently, the data visualization is also deployed. The results obtained show that the forecasted prices of houses based on various parameters such as average area house income, average area house age, average area of number of rooms, and average area of number of bed rooms. The Linear regression model presented in this paper facilitates accurate estimation of the housing cost with ease. The model can be extrapolated for housing schemes across the globe by customizing it to the regional or local needs.",3.0,,False,Conference,,2022-09-23,8725f2f9ffd3e231100c66aaf901fb41db7ec22d,linearregressionmodelsbasedhousingpriceforecasting,amaresh,10.1109/i4tech55392.2022.9952397,linearregressionmodelsbasedhousingpriceforecasting|2022,1,1921,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",555364da2d7d2e111677de1f9cac779f8e248bc4,10.1109/icdabi60145.2023.10629399,,A Comparative Study For Predicting House Price Based on Machine Learning,2023.0,2023 4th International Conference on Data Analytics for Business and Industry (ICDABI),E. Eze; S. Sujith; M. S. Sharif; W. Elmedany,"This study presents the development and comparison of four machine learning approaches, namely, Random Forest, Decision Tree, Linear Regression and k-Nearest Neighbours (k-NN), to perform house prices prediction based employing the Boston Housing Dataset. The approaches performance was evaluated using root mean square error as well as R2, with the aim of identifying the model that best predicts housing prices. The dataset was thoroughly analyzed for features, correlations, multicollinearity, and overfitting. Results indicate that the RF model outperformed the other models in predicting house prices, as it has ability to address non-linearity and complex interactions among variables and reduce the impact of outliers. The DT model also performed well but may have been more prone to overfitting. LR, on the other hand, may have been limited by its assumptions of linearity and independence among variables.",3.0,,False,Conference,,2023-10-25,555364da2d7d2e111677de1f9cac779f8e248bc4,acomparativestudyforpredictinghousepricebasedonmachinelearning,eze,10.1109/icdabi60145.2023.10629399,acomparativestudyforpredictinghousepricebasedonmachinelearning|2023,1,1914,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",97b2d86c76ce9d0b7273c873c0269e7531a71f81,10.1109/iccrd54409.2022.9730190,,Random Forest Model for the House Price Forecasting,2022.0,International Conference on Computer Research and Development,Yeqi Fang; Tian-Teng Li; Hongyuan Zhao,"Accurately predicting housing prices is of great significance to improving housing equity. In this work, we first analyzed the factors that affect housing prices. The experimental results show that income is the most important influencing factor, and its correlation with housing prices is as high as 0.69. In addition, room distribution and geographical location are the remaining important image factors. In order to achieve accurate housing price prediction, we designed a random forest to achieve housing price prediction. Compared with support vector regression (SVR), linear regression (LR) and decision tree (DT), our method achieves the best root mean square error (RMSE) and goodness of fit. In contrast, the predicted values of LR and SVR tend to be higher than the true values, while the values of DT tend to be lower than the true values.",3.0,,False,Conference,,2022-01-07,97b2d86c76ce9d0b7273c873c0269e7531a71f81,randomforestmodelforthehousepriceforecasting,fang,10.1109/iccrd54409.2022.9730190,randomforestmodelforthehousepriceforecasting|2022,1,1850,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",ba5b8f99e8a03e99fcf8aba7bfd02eb17206f4d4,10.1109/icac3n53548.2021.9725772,,Real Estate Price Prediction Model,2021.0,"2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)",S. Anand; Prince Yadav; Adarsh Gaur; Indu Kashyap,"Machine learning has been a major driving force in the past years in several aspects of our lives namely medical diagnosis, normal speech command, detection of an image, product recommendation, spam recognition, and Price prediction, etc. There has been a steep increase in House prices every year, hence an automated system is required to predict future house prices. This system of house price prediction will help the owner to determine the selling price of a house and can help the buyer to arrange the appropriate time to purchase a house. Supervised Learning algorithm namely Linear Regression has been applied in this paper for prediction and analysis of house prices which depends on various factors such as BHK, locality, and bath, etc, and these factors are considered as the independent variables.",3.0,,False,Conference,,2021-12-17,ba5b8f99e8a03e99fcf8aba7bfd02eb17206f4d4,realestatepricepredictionmodel,anand,10.1109/icac3n53548.2021.9725772,realestatepricepredictionmodel|2021,1,1808,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5577d4bd84f0ce436a5fe76d8264e6d182c27a29,10.1109/icesc57686.2023.10193369,,An Innovative Method for Housing Price Prediction using Least Square - SVM,2023.0,"International Conference Electronic Systems, Signal Processing and Computing Technologies [ICESC-]",Yasha Goel; A. N. Swaminathen; Rishika Yadav; B. Kanthamma; Ravi Kant; Amit Chauhan,"The House Price Prediction is often employed to forecast housing market shifts. Individual house prices cannot be predicted using HPI alone due to the substantial correlation between housing price and other characteristics like location, area, and population. While several articles have used conventional machine learning methods to predict housing prices, these methods tend to focus on the market as a whole rather than on the performance of individual models. In addition, good data pretreatment methods are intended to be established to boost the precision of machine learning algorithms. The data is normalized and put to use. Features are selected using the correlation coefficient, and LSSVM is employed for model training. The proposed approach outperforms other models such as CNN and SVM.",3.0,,False,Conference,,2023-07-06,5577d4bd84f0ce436a5fe76d8264e6d182c27a29,aninnovativemethodforhousingpricepredictionusingleastsquaresvm,goel,10.1109/icesc57686.2023.10193369,aninnovativemethodforhousingpricepredictionusingleastsquaresvm|2023,1,1799,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",806dd2fbc3ad91cda22fd4d7fd18c6f32ee2acba,10.1109/icseiet58677.2023.10303034,,House Price Prediction Using Gradient Boosting and Linear Regression,2023.0,2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology (ICSEIET),Utkarsh Gupta; Himadri Vaidya; Rahul Chauhan; Chandradeep Bhatt,"Everyone dreams of buying and living in their own house, which is suitable for their lifestyle and suits their personality. The main objective for which people look at a house is the square footage of the house, the number of bedrooms and bathrooms, the location of the house, and the year it was built. This qualitative research helps to get an accurate price for the house that is suitable for their budget and will not affect them financially. It also helps a person buy a house according to his or her requirements. There are many machine learning algorithms out there, like Gradient boosting regression, linear regression, polynomial regression, and random forest regression. After researching on every algorithm, the best accuracy algorithm will be chosen to predict the house price.",3.0,,False,Conference,,2023-09-14,806dd2fbc3ad91cda22fd4d7fd18c6f32ee2acba,housepricepredictionusinggradientboostingandlinearregression,gupta,10.1109/icseiet58677.2023.10303034,housepricepredictionusinggradientboostingandlinearregression|2023,1,1789,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fcabe7d239a0aab3a1cae687e93e1bda2c3b67bf,10.1109/icscds53736.2022.9760832,,Price Prediction of House using KNN based Lasso and Ridge Model,2022.0,2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS),N. H; P. R.; R. R; M. P.,"Getting a house of our wishes within our budget in a residential area of our customization is quite a tedious process. In order to overcome this, we have developed a model to get a house of our interest with religious belief and budget this Implemented model is of linear regression and k nearest neighbor's algorithm with gradient descent optimization to make an optimal model for predicting house prices using the dataset. Performed feature engineering and selection using lasso and ridge penalties to eliminate features that had little or no impact on the residual sum of squares error. Then exposes Jupyter notebook cells as REST Endpoints to make predictions with new information. Finally, we are trying to send an email alert to the concerned user to give an alert of the house price",3.0,,False,Conference,,2022-04-07,fcabe7d239a0aab3a1cae687e93e1bda2c3b67bf,pricepredictionofhouseusingknnbasedlassoandridgemodel,h,10.1109/icscds53736.2022.9760832,pricepredictionofhouseusingknnbasedlassoandridgemodel|2022,1,1789,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b711d76963a5c536028ff275f212f25c22ed047a,10.1109/icecaa55415.2022.9936419,,An Efficient System for the Prediction of House Prices using a Neural Network Algorithm,2022.0,2022 International Conference on Edge Computing and Applications (ICECAA),N. Patankar; K. Vengatesan; Y. Deshmukh; R. Chintamani; N. L. Shelake,"The process of projecting house prices involves making estimations regarding how house prices will vary in the future. This is an essential aspect of the process. The price of a home is determined by a variety of different aspects, some of which include the location of the property, the overall land area, and the population of the surrounding area. In order to estimate the range of possible costs associated with purchasing a house, the Deep Learning methodology is used. In addition to this, alternative approaches are used for estimating, such as linear regression, key-value network analysis, and random forest. In the final stage of this process, the results obtained from the models utilised are evaluated and contrasted.",3.0,,False,Conference,,2022-10-13,b711d76963a5c536028ff275f212f25c22ed047a,anefficientsystemforthepredictionofhousepricesusinganeuralnetworkalgorithm,patankar,10.1109/icecaa55415.2022.9936419,anefficientsystemforthepredictionofhousepricesusinganeuralnetworkalgorithm|2022,1,1729,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",19353d1fc066f88950bba89737a52fde52f547a3,10.1109/prai53619.2021.9551047,,Research and Application of Second-Hand Housing Price Prediction Model Based on LSTM,2021.0,2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI),Shunshun Ma; Gang-Wu Jiang; Jian-Hui Liu; Xin Wang,"The price of second-hand houses has always been a matter of great concern to people. This paper collects the sales data of second-hand houses in Jinshui District of Zhengzhou City, assigns spatial sequence to the data through spatial autocorrelation analysis and design algorithm, constructs LSTM model combining spatial sequence and attention mechanism, and forecasts the price of second-hand houses in Jinshui District. Finally, three schemes are designed and compared based on LSTM neural network. Experimental results verify that the LSTM model combining spatial sequence and attention mechanism constructed in this paper has better prediction effect.",3.0,,False,Conference,,2021-08-20,19353d1fc066f88950bba89737a52fde52f547a3,researchandapplicationofsecondhandhousingpricepredictionmodelbasedonlstm,ma,10.1109/prai53619.2021.9551047,researchandapplicationofsecondhandhousingpricepredictionmodelbasedonlstm|2021,1,1655,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",fa4b7cc67d8ba4b82555aae05355069bdc18f6df,10.1109/icemme49371.2019.00046,,Residential Asset Pricing Prediction using Machine Learning,2019.0,2019 International Conference on Economic Management and Model Engineering (ICEMME),Yi-Yang Luo,"Residential asset price prediction and analysis are prevalent research topics in economy. Most researches focus on macroeconomy perspectives to explain the factors affecting residential asset prices. In this paper we examine some micro factors, like lot area, pool area, that can be used as features to predict house price. We fit a rather simple regression model which contains a few characteristics of a residential asset, and we are able to reach a fairly good result. Some machine learning algorithms such as random forest and support vector machine are also implemented to predict asset pricing. All regression models have a R squared over 0.9.",3.0,,False,Conference,Computer Science,2019-12-01,fa4b7cc67d8ba4b82555aae05355069bdc18f6df,residentialassetpricingpredictionusingmachinelearning,luo,10.1109/icemme49371.2019.00046,residentialassetpricingpredictionusingmachinelearning|2019,1,1649,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a1288e8cf65012ea03579489b9345c74c09afe37,10.1109/iccst53801.2021.00090,,A Price Prediction Method Based on CatBoost,2021.0,International Carnahan Conference on Security Technology,Tieping Wang; Yutian Wang; Mingzhe Liu,"We have studied a price prediction method based on CatBoost, which can evaluate and predict the value of objects. In our approach, we applied many advanced and popular techniques of feature engineering. Also, we compared and analyzed different models, including linear models, tree-based models, and deep learning in the modelling part. Specifically, we have applied it to the housing price prediction, which has verified the effectiveness of our method. Our method can not only be used to predict housing prices, but also to evaluate or predict the value of other objects, such as cultural objects or artworks.",3.0,,False,Conference,,2021-11-01,a1288e8cf65012ea03579489b9345c74c09afe37,apricepredictionmethodbasedoncatboost,wang,10.1109/iccst53801.2021.00090,apricepredictionmethodbasedoncatboost|2021,1,1611,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1aa681861940d9ca058ee66aa19a0f1a02d1b0c0,10.1142/s2424922x2550007x,,Predictions of Residential Property Prices for Ningbo City of Zhejiang Province in China Using Machine Learning,2025.0,Advances in Data Science and Adaptive Analysis,Bing-Zi Jin; Xiao-Jie Xu,,3.0,,False,JournalArticle,Computer Science,2025-08-25,1aa681861940d9ca058ee66aa19a0f1a02d1b0c0,predictionsofresidentialpropertypricesforningbocityofzhejiangprovinceinchinausingmachinelearning,jin,10.1142/s2424922x2550007x,predictionsofresidentialpropertypricesforningbocityofzhejiangprovinceinchinausingmachinelearning|2025,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3257364313645698b1556b7f2e9600d4526402cd,10.1007/978-3-031-20716-7_8,,House Price Prediction via Visual Cues and Estate Attributes,2022.0,International Symposium on Visual Computing,S. Vaddi; Amira Yousif; S. Baraheem; Ju Shen; Tam V. Nguyen,,3.0,,False,JournalArticle,Computer Science,,3257364313645698b1556b7f2e9600d4526402cd,housepricepredictionviavisualcuesandestateattributes,vaddi,10.1007/978-3-031-20716-7_8,housepricepredictionviavisualcuesandestateattributes|2022,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3ea725c94fee00d743a2424653d254a479f75657,10.1109/wids-psu61003.2024.00038,,Assessment of the Hybrid Deep Learning Models and Hedonic Pricing Model for House Price Prediction,2024.0,2024 Seventh International Women in Data Science Conference at Prince Sultan University (WiDS PSU),S. Sakri; Zaiton Ali; Nurul Halimatul Asmak Ismail,"Modeling the pricing process or estimating a property’s fair market value in an objective manner is highly complicated. Several variables, including geographical and temporal ones, can heighten this intricacy. Researchers and real estate appraisers have been attempting to simulate the process for ages. Prior until recently, real estate appraisal and data evaluation were both improved by computer-aided valuation systems. However, they are not very efficient, accurate, or transparent and require continuous improvements. In this study, we investigate how hybrid deep learning (DL) algorithms can improve economic activity by making better predictions of house prices. The research employs the DL algorithms and the hedonic regression modeling for the house price prediction. DL and hedonic pricing models were trained using the Ames housing dataset containing 82 variables and 2,930 samples. The research contributes to the feasibility of employing hybrid DL algorithms to predict home values. Finally, the prediction model’s goodness of fit is reported, with CNN+LSTM+FCL (the proposed hybrid DL model) achieving the highest R-squared score of 96.15%, while the hedonic regression model acquired only 52.03%.",2.0,,False,Conference,,2024-03-03,3ea725c94fee00d743a2424653d254a479f75657,assessmentofthehybriddeeplearningmodelsandhedonicpricingmodelforhousepriceprediction,sakri,10.1109/wids-psu61003.2024.00038,assessmentofthehybriddeeplearningmodelsandhedonicpricingmodelforhousepriceprediction|2024,1,2212,7,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price || hedonic housing prices || hedonic property valuation || real estate hedonic regression",2,3,3,2,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),9f82fcff9aa5609e5707d7253a3c9869f58f283b,10.1145/3469213.3470371,,Research on Real Estate Appraisal Method Based on Feature Engineering and BP Neural Network,2021.0,International Conference on Artificial Intelligence and Information Systems,Xiaoman Zhao; Xin-Wei Liu; Xu Cao,"With the rapid development and maturity of China’s real estate industry, the financial risk of the real estate industry is crucial as the banking financial risk and the real estate mort-gage evaluation system of the real estate market. Based on the GIS theme crawler framework, this paper collects data on related websites and combines the price theory of residential real estate to establish a Hedonic commodity housing price influencing factor model. The results of goodness-of-fit test and analysis of variance are performed on the various indicators of commodity housing. In order to study the influence of different factors on the price of commercial housing, and scientifically select the influence indicators that affect the price of commercial housing. According to the implementation steps of BP(Back Propagation) neural network prediction, explore the application of BP neural network in predicting the price of commercial housing in Hefei, obtain the price prediction value of commercial housing, and achieve high fitting accuracy. It has a certain guiding effect on the research of commercial housing prices in Hefei.",2.0,,False,Book;JournalArticle,Computer Science,2021-05-28,9f82fcff9aa5609e5707d7253a3c9869f58f283b,researchonrealestateappraisalmethodbasedonfeatureengineeringandbpneuralnetwork,zhao,10.1145/3469213.3470371,researchonrealestateappraisalmethodbasedonfeatureengineeringandbpneuralnetwork|2021,1,2129,3,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices",2,2,2,1,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5db24a0cf95c50ad2386ce5d531ac7806525cdab,10.1109/iconstem56934.2023.10142407,,Leveraging Affordable Business Innovations to Tackle House Price Rising: Applications of Machine Learning Algorithms,2023.0,2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM),P. P. Sri; Vaddi Naga Padma Prasuna; R. Murugesan; ShilpaManjunath Nilugal,"Affordable housing as the most significant Sustainable Development Goal of the United Nations, is a burgeoning social problem across various nations globally. A lack of reasonable lodging to purchase or lease is fuelling the worldwide accommodation emergency. By 2025, 1.6 billion people are supposed to be unfavorably impacted by this. Starting from the beginning in the historical landmark, the housing market has grown quickly and land costs have been soaring. The land business has ended up being the support point industry of our general economy. Nonetheless, verifiable experience has repetitively demonstrated that, as financial advancement depends too significantly on land, these costs proceed to upsurge quickly there by outstripping demand over supply of land. Covid Pandemic is considered to have made the crisis much worse. The Indian housing market remains to skirmish with supply-side management for housing. Scarcity of technologically advanced and encumbrance-free urban land, the amplified cost of construction, absence of a feasible rental market and master-plan restraints have decreased the latent growth of the formal housing market in India.Taking cue from this, the research paper highlights the classic example of predicting the magnitude of Boston, USA and Indian Metropolitan cities housing prices by using regression techniques and Machine Learning Algorithms. The library packages used for this purpose are numpy, pandas, Scikit Learn and Seaborn. Housing prices are a basic picture of the economy, and its cost ranges are of remarkable interest to the purchasers and venders. Request a home consumer to define their dream house, and they possibly won’t initiate with the elevation of the basement ceiling or the nearness to an east-west railroad. In any case, this jungle gym rivalry’s informational index demonstrates more substantially cost of dealings.Keeping in view of the Contemporary Challenge of the housing crisis, the research paper forecasts the spiraling prices of houses dwellings and its association with various variables. The paper highlights accurate Regression Models and innovative Business Models in mainstream planning and development of cities.",2.0,,False,Conference,,2023-04-06,5db24a0cf95c50ad2386ce5d531ac7806525cdab,leveragingaffordablebusinessinnovationstotacklehousepricerisingapplicationsofmachinelearningalgorithms,sri,10.1109/iconstem56934.2023.10142407,leveragingaffordablebusinessinnovationstotacklehousepricerisingapplicationsofmachinelearningalgorithms|2023,1,3196,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,6,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ec0abab86782559bebc60e4610aa10431c1fe804,10.1109/icoeca66273.2025.00118,,Optimizing House Price Prediction Models: A Hybrid Approach using GWO-Feature Selection and CatBoost Tuning,2025.0,2025 5th International Conference on Expert Clouds and Applications (ICOECA),V. P. Pavan Kumar; B. K; V. S. Divya Sundar; N. S. K. M. K. Tirumanadham; Praveen Kumar Vadapally; K. V. Sunil Varma,"This function is a very important function in real estate, as stakeholders such as investors, buyers, and sellers make the right decisions. Correct property values are thus required for understanding market dynamics and optimizing investment strategies, especially for fair pricing. However, remains a critical challenge because of the multiplicity of housing data-the location, attributes of a property, and economic conditions among others. This study aims to fill these gaps by developing an advanced predictive model for house price estimation. Here, the focus is on improving the feature selection process so that more impactful variables are identified and optimized for more accurate predictions. In this section, it makes use of advanced computational techniques, such as the Grey Wolf Optimizer (GWO) for feature selection and CatBoost with RandomizedSearchCV for model building. The key findings include the efficiency of GWO in selecting the most relevant features, which enhance model interpretability and decrease overfitting. Besides, CatBoost with the RandomizedSearchCV yielded a considerable improvement in accuracy of prediction, thus giving proof of the model’s ability to be robust enough in handling complex and large data. The results therefore point to the fact that when feature selection is integrated with optimized modeling techniques, the predictions for house prices will be more reliable and accurate. It concludes that the proposed method can help deliver useful information regarding the real estate decision-making process and offer an efficient solution to predict the house price across different markets. The findings indicate that integrating optimized feature selection with advanced machine learning models leads to more reliable and accurate house price predictions. This research provides a practical solution for real estate valuation and offers valuable insights to stakeholders for market analysis and investment decision-making.",2.0,,False,Conference,,2025-03-06,ec0abab86782559bebc60e4610aa10431c1fe804,optimizinghousepricepredictionmodelsahybridapproachusinggwofeatureselectionandcatboosttuning,kumar,10.1109/icoeca66273.2025.00118,optimizinghousepricepredictionmodelsahybridapproachusinggwofeatureselectionandcatboosttuning|2025,1,2973,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",372cb799c1e39c41831d74ee1908fbc8fc1183bb,10.1109/comnetsat63286.2024.10862860,,Significant Features for House Price Prediction Using Machine Learning,2024.0,"2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)",M. Saefudin; Mindi Richia Putri; Abdul Hadi; H. Wijayanto; Budi Irmawati,"Predicting house prices is a creative way to comprehend actual home costs. Therefore, changes in house prices affect both buyers and sellers. It also shows the state of the housing economy at the moment. The purpose of this study is to identify the features that have the largest impact on house prices. The methods used to produce the most relevant feature combinations are Chi-Square and Mutual Information. To determine which combination of features has the greatest impact, we will feed the feature selection results into several machine learning algorithms—including Linear Regression, Logistic Regression, and Random Forest Regression—and evaluate them using Mean Absolute Error (MAE). The Linear Regression model shows that the combination of features based on the Mutual Information method with $K=20$ produces the smallest MAE, namely $23,251.43$. The Logistic Regression model shows that the combination of features based on the Mutual Information method with $K=10$ produces the smallest MAE, namely $\mathbf{3 3, 6 5 4 . 8 5}$. The Random Forest Regression model shows that the combination of features based on the Mutual Information method with $\text{K}=15$ produces the smallest MAE, namely $18,648.54$. This research shows that overall, the feature combination based on the Random Forest Regression model produces the smallest MAE with a value $18,648.54$. The 5 best features of the combination are OverallQual (Overall material and finish quality), Neighborhood (Physical locations within Ames city limits), GrLivArea (Above grade (ground) living area square feet), GarageCars (Size of garage in car capacity), and GarageArea (Size of garage in square feet).",2.0,,False,Conference,,2024-11-28,372cb799c1e39c41831d74ee1908fbc8fc1183bb,significantfeaturesforhousepricepredictionusingmachinelearning,saefudin,10.1109/comnetsat63286.2024.10862860,significantfeaturesforhousepricepredictionusingmachinelearning|2024,1,2676,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",8b43b5be5e40f59bf55fded710b3e8aa7b8fd20c,10.1109/icisaa62385.2024.10828645,,"Evaluating the Effectiveness of Random Forest versus Decision Tree Algorithms in Predictive Analytics for Enhancing Sustainable Development Strategies in Real Estate, Infrastructure, and Construction: A Machine Learning Perspective",2024.0,2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA),D. M. Gala; Bhakti Pawar; Gayathri Band; A. Dalal; Arnab Chakraborty; Utpal Das; B. Patil,"This study explores the application of machine learning algorithms, specifically decision tree and random forest models, to predict house prices within the Indian real estate market. Utilizing a comprehensive dataset obtained from Kaggle, this research encompasses extensive data preprocessing, feature engineering, and exploratory data analysis to identify key predictors of house prices. The comparative analysis aims to assess the accuracy, efficiency, and applicability of both models in the context of predictive analytics for sustainable development goals (SDGs) in real estate. The findings reveal that the random forest model outperforms the decision tree model, demonstrating superior predictive accuracy with a lower Root Mean Squared Error (RMSE). Key predictors identified include living area, number of bathrooms, and the condition of the property, with the random forest model providing deeper insights into feature interactions and their collective impact on house prices. This research highlights the potential of advanced machine learning techniques in enhancing real estate market analysis and strategic decision-making. The superior performance of the random forest model underscores its value in predictive analytics, offering stakeholders in the real estate sector a robust tool for achieving greater accuracy in property valuation and market analysis. Future research directions include the integration of additional data sources and the exploration of other machine learning algorithms to further refine predictive models and extend their applicability to sustainable development within the real estate, infrastructure, and construction industries.",2.0,,False,Conference,,2024-10-25,8b43b5be5e40f59bf55fded710b3e8aa7b8fd20c,evaluatingtheeffectivenessofrandomforestversusdecisiontreealgorithmsinpredictiveanalyticsforenhancingsustainabledevelopmentstrategiesinrealestateinfrastructureandconstructionamachinelearningperspective,gala,10.1109/icisaa62385.2024.10828645,evaluatingtheeffectivenessofrandomforestversusdecisiontreealgorithmsinpredictiveanalyticsforenhancingsustainabledevelopmentstrategiesinrealestateinfrastructureandconstructionamachinelearningperspective|2024,1,2671,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e07cc591c6fea66920e0c868c9d079e9c7f8d24c,10.1109/smartgencon60755.2023.10442517,,Revolutionizing House Price Prediction: Harnessing the Power of Data Science and Augmented Reality,2023.0,"2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)",B. Kumar; Manisha Kumari; Pallab Banerjee; Pooja Jha,"Data science and augmented reality (AR) technology convergence in recent years has cleared the door for creative applications in many different fields. To revolutionize the process of predicting property prices, this study investigates the merging of data science approaches with AR interfaces. This research proposes an innovative strategy to empower homebuyers and real estate agents by utilizing machine learning algorithms and real-time data visualization through AR. The study is concerned with gathering and analyzing huge datasets about property characteristics, neighborhood characteristics, and market movements. To determine the important variables influencing house prices, sophisticated data analysis approaches are used. Users can experience real estate in an immersive, engaging way by using these insights, which are neatly integrated into an AR application. Users of the AR interface can view prices, property details, and comparative studies in real-time as forecasted housing data is superimposed over it. Prediction accuracy is ensured by including user preferences and dynamic updates, which improve personalization. Through user feedback loops, the system's efficiency is verified, allowing the prediction models to be continuously improved. The outcomes show the potential for data science and augmented reality to work together to change the home buying, real estate agent, and development processes. This study not only advances housing market analysis but also establishes a precedent for future data-driven real estate decisions, which will ultimately change how homes are bought and sold.",2.0,,False,Conference,,2023-12-29,e07cc591c6fea66920e0c868c9d079e9c7f8d24c,revolutionizinghousepricepredictionharnessingthepowerofdatascienceandaugmentedreality,kumar,10.1109/smartgencon60755.2023.10442517,revolutionizinghousepricepredictionharnessingthepowerofdatascienceandaugmentedreality|2023,1,2615,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b2ca5b926880112da91799e693ad70169f0a4e00,10.1109/icaaic60222.2024.10575745,,An Effective Machine Learning Algorithm for Forecasting the Market Value of a House,2024.0,2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC),S. G. Ancy; J. Praveenchandar,"Machine learning plays a virtual role in everyday speech commands, product recommendation, and even medical fields. But instead of providing better customer service, it provides safer autonomous vehicle systems. House price prediction using machine learning is a hot topic in data science and AI. With the increasing availability of data and the advancement of machine learning algorithms, accurately predicting house prices has never been easier. This study will not only assist housing developers and researchers to determine the most significant factors affecting housing prices but will also help determine the best machine learning model to use in this research. This model will allow people to allocate resources to a request without relying on a broker. According to the results of this study, the random forest regression is the most accurate. Real estate market analysis is one of the most important tools for predicting changes in house prices and rental income. Several techniques have been developed in recent years to solve this problem, including statistical models and data mining methodologies and machine learning algorithms. However, a few issues, including data quality and availability, needed to be fixed. Missing numbers, outliers, and inconsistent formats are common in housing databases. These issues could impair the performance of the prediction models. To solve these issues, thorough data preprocessing is required. Collaboration with data providers and governmental agencies is also required to ensure data coverage and accuracy.",2.0,,False,Conference,,2024-06-05,b2ca5b926880112da91799e693ad70169f0a4e00,aneffectivemachinelearningalgorithmforforecastingthemarketvalueofahouse,ancy,10.1109/icaaic60222.2024.10575745,aneffectivemachinelearningalgorithmforforecastingthemarketvalueofahouse|2024,1,2558,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7137e61015db51053262ac181b480d4924ce46bf,10.1109/icaaic60222.2024.10575519,,Data-Driven House Price Prediction,2024.0,2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC),N. Shalini; A. S. Kiran; B. Sathwik; G. T. Reddy; K. Aswini; M. Yadav,"This research study analyzes the complex and dynamic field of real estate, aiming to reveal the numerous factors influencing home values. The primary objective of this research study is to develop a prediction system using Random Forest (RF). Leveraging a comprehensive dataset encompassing historical real estate data, this study intends to forecast house values by considering a wide array of variables. Key attributes such as the number of bedrooms and bathrooms, property size, location, and proximity to amenities like transportation, hospitals, and schools form the cornerstone of the proposed model. Drawing insights from past price fluctuations, the proposed algorithm enhances forecast accuracy by analyzing previous value trends. Additionally, for comparative analysis, the proposed approach incorporates multiple regression and linear regression models alongside the Random Forests approach, enabling to assess the performance of our primary model against industry-standard techniques. The resultant system offers users detailed insights into the local real estate landscape alongside precise property price projections. Users can compare specific properties with nearby alternatives, empowering them to make informed decisions. Tailored for real estate agents, investors, and homeowners, our system serves as an invaluable resource for navigating the complexities of real estate transactions. The proposed solution aims to facilitate wise and successful real estate decisions by furnishing precise forecasts and revealing market intricacies.",2.0,,False,Conference,,2024-06-05,7137e61015db51053262ac181b480d4924ce46bf,datadrivenhousepriceprediction,shalini,10.1109/icaaic60222.2024.10575519,datadrivenhousepriceprediction|2024,1,2553,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",3939936e88c9a1c978f871703b3aabf3f2f8b666,10.1109/icpct64145.2025.10939118,,Integrating Random Forest and XGBoost For Accurate House Rent Prediction,2025.0,2025 International Conference on Pervasive Computational Technologies (ICPCT),H. Singh; S. Srivastava; N. Pandey; Nitesh Kumar,"Predicting house rents accurately is essential for tenants, landlords, and investors to make informed decisions in the real estate market, particularly given the rising trends in rental prices. This study employs machine learning techniques, including Random Forest and XGBoost, to predict rental values based on factors such as location, area, square footage, rent, and house size. The dataset was preprocessed using Python libraries such as NumPy, pandas, and scikit-learn, with steps including data cleaning, normalization, and feature engineering. Features such as house age, floor details, and distance from city centers were derived to enhance model performance. Evaluation metrics like Root Mean Squared Error (RMSE) were used to measure accuracy. The Random Forest algorithm achieved superior results with its robustness against overfitting and its ability to model complex relationships. XGBoost, with its scalability and speed, also delivered competitive performance. The ensemble approach of these models further improved prediction accuracy by leveraging their individual strengths.The developed system achieved exceptional performance metrics, including a Mean Squared Error (MSE) of 0.0001, Root Mean Squared Error (RMSE) of 0.01, Mean Absolute Error (MAE) of 0.01, and an R-squared value (R2) of 0.9998, indicating high accuracy and robustness in predicting house prices This research highlights the utility of advanced machine learning algorithms and proper preprocessing techniques in real estate analytics.",2.0,,False,Conference,,2025-02-08,3939936e88c9a1c978f871703b3aabf3f2f8b666,integratingrandomforestandxgboostforaccuratehouserentprediction,singh,10.1109/icpct64145.2025.10939118,integratingrandomforestandxgboostforaccuratehouserentprediction|2025,1,2524,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ef26ddd2d4618a26277b45bba61d442685fed9cf,10.1109/cises58720.2023.10183626,,Real Estate Property Price Estimator Using Machine Learning,2023.0,2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES),Disha Ub; Surabhi Saxena,"Property price estimator is a mobile application by creating a model using machine learning to forecast the sale price of homes in a Bangalore. This model will be built using data on a variety of house features, such as location, size, age, and condition, as well as the sale prices of the houses. In order to commence, we have gathered and prepared the required data so that it is available for analysis. Managing missing or inaccurate numbers and making sure the data is consistent and appropriately reflects the properties and their sale prices may be involved in this. Next, we will perform exploratory data analysis (EDA) to better understand the relationships between the various house features and the sale prices. This involves visualizing the data and identifying patterns and trends that may be useful for building the prediction model. After having a good understanding of the data, next comes building and training the prediction model. This involves selecting and optimizing an appropriate machine learning algorithm, such as linear regression or a decision tree, as well as selecting and engineering relevant features for the model. The model will then be put through its paces using suitable metrics such as R squared and any necessary corrections will be made to increase its accuracy. Users can inquiry price by giving a series of features which describes their ideal house and can get the recommended price. This will be a responsive inquiry system that can greatly improve user's experience.",2.0,,False,Conference,,2023-04-28,ef26ddd2d4618a26277b45bba61d442685fed9cf,realestatepropertypriceestimatorusingmachinelearning,ub,10.1109/cises58720.2023.10183626,realestatepropertypriceestimatorusingmachinelearning|2023,1,2510,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",727e65cb483ccea23927180957db6e0c4e7f3a29,10.1109/i-smac61858.2024.10714734,,Enhancing Housing Price Prediction Using AI and Machine Learning: A Stacked Regression Meta-Modeling Approach,2024.0,"2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)",Malgi Nikitha Vivekananda; Prashant Ashok Shidlyali,"Predicting housing prices is crucial in real estate markets, enabling informed decision-making for investors, buyers, and policymakers. Many studies have applied various machine learning models to this task, either focusing on individual models or overlooking the benefits of combining multiple models through ensemble techniques. Additionally, the neglect of optimizing model performance through feature selection and hyperparameter tuning often results in suboptimal outcomes. To address these gaps, we propose a meta-modeling and optimization workflow that integrates multiple regression models—such as Random Forest, Ridge, Lasso, and Support Vector Regression (SVR)—using a stacked regressor to improve prediction accuracy. Our workflow includes systematic hyperparameter tuning and feature selection to refine model performance. The stacked model outperforms individual models, achieving an R² score of 0.9321 and an RMSE of 1.5017, representing a 6.5% improvement in R² and a 39.2% reduction in RMSE compared to the best individual model, Random Forest. This demonstrates the effectiveness of ensemble methods in capturing complex relationships in housing data. Furthermore, our proposed workflow is flexible, allowing adaptation to various datasets and market conditions. This research highlights the importance of advanced model optimization and ensemble learning in housing price prediction, providing a robust solution that significantly improves accuracy over traditional models.",2.0,,False,Conference,,2024-10-03,727e65cb483ccea23927180957db6e0c4e7f3a29,enhancinghousingpricepredictionusingaiandmachinelearningastackedregressionmetamodelingapproach,vivekananda,10.1109/i-smac61858.2024.10714734,enhancinghousingpricepredictionusingaiandmachinelearningastackedregressionmetamodelingapproach|2024,1,2491,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b740022e42110567bf68fd05ede8cc53af8f9469,10.1109/raeeucci61380.2024.10547965,,House Price Precision Assessor,2024.0,"2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI)",Siddarth Daga; Vishnu Priya Kulukuri; Teja sri Kosuru; M. Srilekha,"House price prediction using machine learning entails the development of a model capable of estimating the market value of residential properties by considering various features, including size, location, number of bedrooms, and more. This technology offers practical applications in the real estate industry, assisting buyers, sellers, and investors in making well-informed decisions. Machine learning algorithms analyze historical data to provide accurate price predictions, thereby facilitating property valuation and informed investment strategies. For a number of stakeholders, including investors, legislators, real estate brokers, and homeowners, the ability to anticipate property values accurately is crucial. A thorough summary of the methods and strategies used to forecast home values is provided in this abstract, along with important conclusions and new information from recent research. The study examines the wide variety of variables that affect home values, such as market trends, economic indicators, size, amenities, and location. Many machine learning approaches, such as neural networks, decision trees, random forests, and linear regression, have been utilized to build prediction models. Everyone has pros and cons of their own. There have been uses for random forests, trees, and linear regression. Everyone has pros and cons of their own.",2.0,,False,Conference,,2024-04-17,b740022e42110567bf68fd05ede8cc53af8f9469,housepriceprecisionassessor,daga,10.1109/raeeucci61380.2024.10547965,housepriceprecisionassessor|2024,1,2364,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",917671a3b29d3cfb8a684377ac6726fb693468fd,10.1109/itc-cscc58803.2023.10212833,,Predict Condominium Prices in Bangkok Based on Ensemble Learning Algorithm with various factors,2023.0,"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)",Thanit Anchaleechamaikorn; Taninnuch Lamjiak; Thagoon Thongpe; Lapis Thiralertpanit; Jumpol Polvichai,"Real estate valuation is a critical process that determines the monetary value of a property. Three common approaches to property valuation are the sales comparison approach, the income approach, and the cost approach. However, these methods can be time-consuming, costly, and may suffer from human bias. This paper proposes an Artificial Intelligence (AI)-based system to evaluate the price of condominiums faster, more cost-effectively, and without human bias. We present a comprehensive methodology that utilizes web scraping and machine learning techniques to predict condominium prices accurately. The dataset is obtained through Python scripts that extract information on all condominiums listed on a popular Thai property website. The collected data covers appraisal years from 2008 to 2019. The AI models used in this study include linear regression, Random Forest Regressor, Gradient Boosting Regressor, and XGBoost Regressor models. We evaluated the performance of these models using the MAPE metric. Our results show that machine learning models can predict condominium prices accurately, providing valuable insights into the real estate market in Bangkok. This research contributes to the existing literature by highlighting the importance of incorporating various features and using machine learning models to predict condominium prices accurately.",2.0,,False,Conference,,2023-06-25,917671a3b29d3cfb8a684377ac6726fb693468fd,predictcondominiumpricesinbangkokbasedonensemblelearningalgorithmwithvariousfactors,anchaleechamaikorn,10.1109/itc-cscc58803.2023.10212833,predictcondominiumpricesinbangkokbasedonensemblelearningalgorithmwithvariousfactors|2023,1,2361,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",cb6b998db6d3c4290c51ee5d68cd4d1e2691f197,10.1109/icaiss58487.2023.10250452,,Random Forest-based House Price Prediction,2023.0,2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS),B. Sri; C. R. K. Reddy; Chiluveri Rithish Kumar; Akula Vyshnavi; Buttagalla Vinod; B. K. Reddy,"The precise prediction of house prices is highly significant for professionals in the real estate industry, homeowners, and potential buyers. This study primarily focuses on utilizing the random forest algorithm to accurately predict house prices. A comprehensive dataset containing various features such as location, size, rooms, amenities, and historical transaction data was collected. The investigation included steps of data conditioning, designing features, instruction of models, and oversight. To assess the outcome of the model along with avoiding the situation a random-forest stance was employed, and validated cross-valid techniques were employed. Lasso regression is employed to identify the key features that significantly influence house prices. Results showed promising predictive accuracy, surpassing other baseline algorithms. Feature importance analysis highlighted the significant influence of location, size, and the number of rooms on house prices, aligning with domain knowledge. This study illustrates the precision of random forests and Lasso-regression-based ML assertions, assisting housing specialists, homeowners, and potential purchasers in making accurate choices. Future research can explore integrating additional data sources and advanced techniques to enhance prediction accuracy and address housing market challenges.",2.0,,False,Conference,,2023-08-23,cb6b998db6d3c4290c51ee5d68cd4d1e2691f197,randomforestbasedhousepriceprediction,sri,10.1109/icaiss58487.2023.10250452,randomforestbasedhousepriceprediction|2023,1,2353,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8329139cbfeb35556cbf7eefe4b80e381a9627f5,10.1109/icccnt61001.2024.10726166,,Enhanced House Price Prediction Using Machine Learning Techniques,2024.0,International Conference on Computing Communication and Networking Technologies,A. Verma; Durgesh Singh; Netra S. Patil; Sagar G. Mohite; Sakshi Ranjan; M. Raj,"Real estate is a crucial part of the global economy, contributing significantly to financial stability. A person’s decision to purchase or sell a home is a critical financial matter. Consequently, accurate house price predictions are essential. This research paper utilizes data and advanced machine-learning techniques to develop a robust and precise model for predicting house prices. The model provides valuable insights into the dynamic real estate market by examining numerous relevant attributes, including bathroom size and real-time economic data. Techniques such as Linear Regression, Decision Tree regression, and Lasso Regression were employed to make accurate price predictions-the proposed research utilized outlier removal, dimensional reduction, and feature engineering to enhance model performance. The novelty of this work lies in integrating detailed features and real-time scenario analysis, addressing gaps in existing studies. Finally, we deployed the model as a user-friendly web application on an AWS EC2 instance, allowing users to estimate property prices and make informed decisions conveniently. This comprehensive approach builds on previous research and significantly improves prediction accuracy and practical applicability.",2.0,,False,JournalArticle;Conference,Computer Science,2024-06-24,8329139cbfeb35556cbf7eefe4b80e381a9627f5,enhancedhousepricepredictionusingmachinelearningtechniques,verma,10.1109/icccnt61001.2024.10726166,enhancedhousepricepredictionusingmachinelearningtechniques|2024,1,2254,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2f38af04afe4319f348d3789afbeb7c5b267c6e9,10.1117/12.2656904,,Housing price forecast based on XGBoost algorithm house price: advanced regression techniques,2023.0,"Conference on Statistics, Data Science, and Computational Intelligence",Chutong Xu,"House prices have always been a major concern in people's daily lives, not only for individuals, but also for the stability of society as a whole. Accurate prediction of house prices can help people to buy houses with a great degree of reference and help the state and society to control the overall consumer prices. With the development of machine learning, we are able to make greater use of machine learning tools to obtain accurate forecasting of house prices in recent years, in order to attempt to predict the house price, this article utilize the machine learning algorithms. The house price problem is a typical regression problem in machine learning. In this paper, we obtain data from Kaggle, look at the data and find that there are many factors affecting house prices. By pre-processing the original data, engineering and standardizing the features, finally, machine learning algorithm XGBoost is selected to establish a prediction model for housing price prediction. We selected the root mean square error (RMSE) ranging from the logarithm of the expected value to the logarithm of the real selling price as the final result of the forecast. The final results we obtained can help one to judge the accuracy of the housing price forecast.",2.0,,False,Conference,Engineering,2023-01-13,2f38af04afe4319f348d3789afbeb7c5b267c6e9,housingpriceforecastbasedonxgboostalgorithmhousepriceadvancedregressiontechniques,xu,10.1117/12.2656904,housingpriceforecastbasedonxgboostalgorithmhousepriceadvancedregressiontechniques|2023,1,2250,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",10f2ce55a94a4420983b1a53810c88f2a191a4c8,10.1109/iccmc65190.2025.11140743,,A Comparative Performance Analysis of Linear Regression Models for House Price Prediction,2025.0,International Conference Computing Methodologies and Communication,K.Vengatesan; S.Pragadeeswaran; Jayaprakash Chinnadurai; J.Preetha; T. Ravishankar; M. Rajesh,"A comparative performance analysis of linear regression techniques for predicting housing prices using the Boston Housing dataset. The algorithms evaluated include Ordinary Least Squares (OLS), Ridge Regression, Lasso Regression, and Elastic Net. These models were assessed using key performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R2). A comprehensive methodology was followed, including data preprocessing, train-test splitting, and hyper parameter tuning for regularized models (Ridge, Lasso, and Elastic Net) using cross-validation. The results reveal that while OLS serves as a baseline, regularized techniques like Ridge and Elastic Net outperform in terms of generalization and robustness, especially in the presence of multicollinearity. Lasso’s feature selection capability further highlights its utility in sparse settings. Elastic Net strikes a balance between Ridge and Lasso by combining their strengths. This analysis provides valuable insights into the suitability of various linear regression techniques for real-world predictive modeling tasks in housing markets.",2.0,,False,Conference,,2025-07-23,10f2ce55a94a4420983b1a53810c88f2a191a4c8,acomparativeperformanceanalysisoflinearregressionmodelsforhousepriceprediction,k.vengatesan,10.1109/iccmc65190.2025.11140743,acomparativeperformanceanalysisoflinearregressionmodelsforhousepriceprediction|2025,1,2160,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",91045fe72abab4e0956f750f2832fd3c113a3325,10.54941/ahfe1005725,,A hybrid Regression method for Predicting Housing Prices,2024.0,AHFE International,Gaurab Baral; Junxiu Zhou,"Accurate house price prediction is crucial for accommodating the diverse needs of stakeholders in the home-buying process. House prices can be affected by various factors, such as location, construction date, exterior, etc. This work proposes a hybrid regression method that leverages the strengths of different regression techniques to improve prediction accuracy. Specifically, this work looks at conventional linear regression and other machine learning techniques such as support vector regression (SVR), and XGBoost regression. Then we compare these models with our proposed hybrid regression model that leverages ridge regression and lasso regression to capture hidden relationships between house properties and sale prices to reveal the different predictive power of these models. In addition, this work also highlights feature engineering to address potential issues in the data and improve prediction performance. The dataset used in this study is obtained from the Kaggle Competition “House Prices: Advanced Regression Techniques.” Different model results are submitted to Kaggle, and the scores are illustrated in the paper.",2.0,,False,,,,91045fe72abab4e0956f750f2832fd3c113a3325,ahybridregressionmethodforpredictinghousingprices,baral,10.54941/ahfe1005725,ahybridregressionmethodforpredictinghousingprices|2024,1,2135,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",cf589823ddd33522fd7b92a02e139297739e4036,10.2139/ssrn.3605798,,Decision Tree and Boosting Techniques in Artificial Intelligence Based Automated Valuation Models (AI-AVM),2020.0,Social Science Research Network,T. Sing; Jing-Ye Yang; Shi-Ming Yu,"This paper develops an artificial intelligence-based automated valuation model (AI-AVM) using the decision tree and the boosting techniques to predict residential property prices in Singapore. We use more than 300,000 property transaction data from Singapore’s private residential property market for the period from 1995 to 2017 for the training of the AI-AVM models. The two tree-based AI-AVM models show superior performance over the traditional multiple regression analysis (MRA) model when predicting the property prices. We also extend the application of the AI-AVM to more homogenous public housing prices in Singapore, and the predictive performance remains robust. The boosting AI-AVM models that allow for inter-dependence structure in the decision trees is the best model that explains more than 88% of the variance in both private and public housing prices and keep the prediction errors to less than 6% for HDB and 9% for the private market. When subject the AI-AVM to the out-of-sample forecasting using the 2017-2019 testing property sale samples, the prediction errors remain within a narrow range of between 5% and 9%.",2.0,,False,,Computer Science,2020-05-19,cf589823ddd33522fd7b92a02e139297739e4036,decisiontreeandboostingtechniquesinartificialintelligencebasedautomatedvaluationmodelsaiavm,sing,10.2139/ssrn.3605798,decisiontreeandboostingtechniquesinartificialintelligencebasedautomatedvaluationmodelsaiavm|2020,1,2135,3,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || automated valuation model residential",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",068536806271ff0eb91065900cf4fb4bc2f4b233,10.1109/icpcsn62568.2024.00055,,Exploring House Price Forecasting through Machine Learning and Data Preprocessing,2024.0,2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN),A. Vaishnavi; G. Raghavendra; Mohammed M. Jilan; A. P. Chowdary; Romen Singh; K. C,"Predicting house prices accurately is crucial in real estate, influencing decisions for buyers, sellers, and investors. Machine learning has emerged as a potent tool in this domain, leveraging historical sales data, property features, and economic indicators to forecast future prices. However, the efficacy of machine learning models hinges on data quality, necessitating meticulous preprocessing steps such as cleansing, normalization, and feature engineering. Through techniques like data filtration and normalization, preprocessing refines data suitability for algorithms, enhancing predictive accuracy. In this study, we illustrate the transformative impact of preprocessing on predictive models' reliability. By contrasting accuracy tables for preprocessed and non-preprocessed datasets, we demonstrate the tangible benefits of preprocessing in refining predictive outcomes. The findings highlight the symbiotic relationship between machine learning algorithms and preprocessing techniques, emphasizing their crucial role in enhancing predictive capabilities in the dynamic real estate market landscape.",2.0,,False,Conference,,2024-05-03,068536806271ff0eb91065900cf4fb4bc2f4b233,exploringhousepriceforecastingthroughmachinelearninganddatapreprocessing,vaishnavi,10.1109/icpcsn62568.2024.00055,exploringhousepriceforecastingthroughmachinelearninganddatapreprocessing|2024,1,2109,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f0b7b5770ed252d0ad4fd1852ee58f35fe4fb88e,10.1109/conecct57959.2023.10234749,,House Price Prediction System using Machine Learning Algorithms and Visualization,2023.0,"IEEE International Conference on Electronics, Computing and Communication Technologies",M. S. Supriya; G. Vinayak; Vinod Ramesh Patgar; V. Mahajan,"This paper introduces a real estate price prediction system that uses machine learning algorithms. The system is intended to forecast the price of a home grounded on its various features such as location, square footage, number of bedrooms and bathrooms, and other related factors. The system uses a variety of machine learning algorithms, including linear regression, decision tree regression, random forest regression, and artificial neural networks to make accurate predictions. Algorithms are trained on a large dataset of house prices and their associated characteristics to learn the relationships between these factors and the corresponding prices. The system is measured against various performance metrics, such as: B. root-mean-square and r-squared score, to measure its accuracy and effectiveness. The results show that the system can predict house prices with great accuracy, making it a valuable tool for real estate agents, home buyers and sellers.The implementation of this system could lead to better informed and more efficient decisions in the housing market.",2.0,,False,Conference,,2023-07-14,f0b7b5770ed252d0ad4fd1852ee58f35fe4fb88e,housepricepredictionsystemusingmachinelearningalgorithmsandvisualization,supriya,10.1109/conecct57959.2023.10234749,housepricepredictionsystemusingmachinelearningalgorithmsandvisualization|2023,1,2077,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8bd8f36fe3f09fb3771c727514bb55dd49d767f9,10.1109/idicaiei58380.2023.10406621,,Value Prediction of Real Estate Using Machine Learning,2023.0,2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0 (IDICAIEI),Ishan Danona; L. Rani; Durgesh Srivastava,"Real estate is property in the form of land and houses. Its prices depend on location, number of bedrooms, society, area, etc. India is second in the world regarding population growth, so affordable housing becomes very necessary. The real estate business is very vast. So, customers or sellers need to know the estimated price of the houses. This research covers that area and predicts housing prices. This work uses k-fold cross-validation and linear regression model. In K-fold cross-validation, the data is divided into k different subsets which are also called fold. A linear regression model is a machine learning model that describes the relations between a dependent variable and one or more independent variables. The data used in this work is for Bangalore state. The results obtained from this linear regression model are compared to real data. The results from this study prove that this model can accurately predict the prices of buildings and houses. Furthermore, this accurate prediction will help customers and sellers, and directly impact the economy.",2.0,,False,Conference,,2023-11-27,8bd8f36fe3f09fb3771c727514bb55dd49d767f9,valuepredictionofrealestateusingmachinelearning,danona,10.1109/idicaiei58380.2023.10406621,valuepredictionofrealestateusingmachinelearning|2023,1,2068,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7ec177405264ea4fe9a1d925a7a0f33f4d1c6131,10.1109/aemcse55572.2022.00095,,"Ensemble Learning Based Models for House Price Prediction, Case Study: Miami, U.S",2022.0,"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)",Xiuyuan Wu; Bohan Yang,"Predicting housing prices is beneficial for both investors and households especially for a large housing market like Miami. Although many scholars have already performed multifarious researches in various locations, no vigorous research on Miami’s housing market is conducted to our knowledge. The purpose of this research is to construct a satisfying predicting model to forecast Miami housing prices that provide reliable references for people in Miami. A number of machine learning methods and deep learning models are implemented in this paper, including SVR, Linear Regression, Random Forest, Neural Network, and XGBoost. Among all models, ensemble learning methods Random Forest and XGBoost produce the best results, the former of which achieved an adjusted R2 of 0.9234 and XGBoost obtained an adjusted R2 of 0.9254. Moreover, we also analyze the factors resulting in high housing prices in Miami with the acquired insights from these powerful ensemble learning models.",2.0,,False,Conference,,2022-04-01,7ec177405264ea4fe9a1d925a7a0f33f4d1c6131,ensemblelearningbasedmodelsforhousepricepredictioncasestudymiamius,wu,10.1109/aemcse55572.2022.00095,ensemblelearningbasedmodelsforhousepricepredictioncasestudymiamius|2022,1,1976,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8ea47f50b206f614f8714fb240bb0073eee242a5,10.1109/icaiss58487.2023.10250587,,A Comparative Study of Regression algorithms on House Sales Price Prediction,2023.0,2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS),K. Kalaivani; C. Kanimozhiselvi; Z. H. Mohamed Bilal; G. Sukesh; S. Yokeswaran,"Accurate prediction of house sales prices plays a pivotal role in various real estate and financial applications. As the real estate market continues to grow and evolve, it becomes increasingly important to employ advanced machine learning techniques to model and forecast housing prices. This research study presents a comprehensive study that investigates the performance of various regression algorithms like Linear Regression, Random Forest, Gradient Boosting and Extra-Trees for house sales price prediction. By examining a wide range of regression techniques, this study intends to identify the most effective algorithm for accurately predicting the household prices. Experiments are conducted to assess the models using Kaggle house prediction dataset, which has 1460 instances and 81 features. Feature extraction is done to select the most informative features. The results reveal that Extra-Trees outperform other regression methods with top 30 informative features.",2.0,,False,Conference,,2023-08-23,8ea47f50b206f614f8714fb240bb0073eee242a5,acomparativestudyofregressionalgorithmsonhousesalespriceprediction,kalaivani,10.1109/icaiss58487.2023.10250587,acomparativestudyofregressionalgorithmsonhousesalespriceprediction|2023,1,1975,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d7bef759011b7bcd7012fa6f38d2d87b48efb3ff,10.1109/icirdc62824.2023.00154,,Research on the Design and Application of House Price Prediction Algorithms and Model Based on Machine Learning,2023.0,"2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC)",Kexin Chen; Jianhui Huang,"With the development of the socio-economy, the real estate industry has grown rapidly worldwide. Housing prices, as an important indicator for measuring a city's economic development level, have significant impacts on people's lives, work, investments, and other aspects. Therefore, how to accurately predict housing prices has become a highly researched area of interest. In recent years, the continuous development and application of machine learning technology have provided new solutions for predicting housing prices. This article aims to study the data processing flow for housing price prediction. Through comparing and analyzing different algorithms, we will derive the optimal data processing flow and housing price prediction model, validate its prediction accuracy and stability, provide a reliable tool for the real estate industry, help make more scientific and precise decisions, and promote the continuous development of the industry.",2.0,,False,Conference,,2023-12-29,d7bef759011b7bcd7012fa6f38d2d87b48efb3ff,researchonthedesignandapplicationofhousepricepredictionalgorithmsandmodelbasedonmachinelearning,chen,10.1109/icirdc62824.2023.00154,researchonthedesignandapplicationofhousepricepredictionalgorithmsandmodelbasedonmachinelearning|2023,1,1949,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",cf6cf0b367fdf26096325e0e2631a761b678f8eb,10.1145/3575882.3575939,,Housing Price Prediction Using a Hybrid Genetic Algorithm with Extreme Gradient Boosting,2022.0,"International Conference on Computer, Control, Informatics and its Applications",M. Siregar; Pahlevi Wahyu Hardjita; Farhan Armawan Asdin; D. Wardani; Ardhi Wijayanto; Yessi Yunitasari; Muhammad Anshari,"Predicting property prices provides a better service for customers to evaluate and estimate price movement before their purchases. Some features including OverallQual and GrLivArea, which were selected when applying GA, become important features that can influence property prices. This research proposes a hybrid Genetic algorithm combined with the Extreme Gradient Boosting algorithm to predict real estate housing prices. The proposed scheme is evaluated by Root Mean Square Error, processing time, and the number of deleted features. The proposed scheme has been compared with the sole Extreme Gradient Boosting. The experimental results show that the proposed scheme produces the smallest root mean square error value of 0.129 compared to 0.133 of the sole Extreme Gradient Boosting. Furthermore, the predicted time of the proposed scheme is much better than the sole method.",2.0,,False,JournalArticle;Book,Computer Science,2022-11-22,cf6cf0b367fdf26096325e0e2631a761b678f8eb,housingpricepredictionusingahybridgeneticalgorithmwithextremegradientboosting,siregar,10.1145/3575882.3575939,housingpricepredictionusingahybridgeneticalgorithmwithextremegradientboosting|2022,1,1880,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",17dc729925bee104f49fd611ac1f080f27094aab,10.1109/tocs56154.2022.10015915,,Mass Appraisal of Real Estate Prices Using Improved BP Neural Network with Policy Evaluation,2022.0,"2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)",Yi Zheng; Bohan Yang; Ruichen Zhang; Zhi-Xin Bai; Yu Sun,"This paper proposes a new policy index neural network (NN), which uses the back propagation (BP) NN model optimized by genetic algorithm (GA) and particle swarm algorithm and the policy modeling consistency (PMC) index to quantify the relevant real estate policies. This study also trains and validates the big data of primary housing transactions of 33 properties in the main urban area of Weihai City, Shandong Province and evaluates the model performance using mean squared error, mean absolute percentage error, and R2 indexes. Subsequently, it compares the model performance under the GA and particle swarm algorithm optimization, respectively. The results show that the BPNN model with policies as independent variables is more accurate in predicting house prices in Weihai City.",2.0,,False,Conference,,2022-12-11,17dc729925bee104f49fd611ac1f080f27094aab,massappraisalofrealestatepricesusingimprovedbpneuralnetworkwithpolicyevaluation,zheng,10.1109/tocs56154.2022.10015915,massappraisalofrealestatepricesusingimprovedbpneuralnetworkwithpolicyevaluation|2022,1,1785,2,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3dbd8cf65cd7a15c852c612df4bcfcda23e7f329,10.1109/ainit54228.2021.00082,,House Price Prediction Based on a Machine Learning Model,2021.0,"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)",Dinglong Yu; Zhenxi Wang; Wei Wei,"Using the prediction on the dataset, the three authors modelled and predicted the Iowa house price dataset using support vector machines, random forests, and XGBoost, respectively, and finally compared the model accuracy using mean square error scores. Eighty percent of the data was used for model training, and the remaining twenty percent was used for model testing. In addition, the authors performed the necessary cleaning of the data and data. Finally, mixing hyperparameters to make the model more accurate is one of the main focuses of the discussion.",2.0,,False,,,2021-10-01,3dbd8cf65cd7a15c852c612df4bcfcda23e7f329,housepricepredictionbasedonamachinelearningmodel,yu,10.1109/ainit54228.2021.00082,housepricepredictionbasedonamachinelearningmodel|2021,1,1559,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4379213f1fc2bfce59d0edb44817356863a3a1f4,10.47363/jaicc/2024(3)357,,House Price Prediction Using Machine Learning and Artificial Intelligence,2024.0,Journal of Artificial Intelligence & Cloud Computing,Fatbardha Maloku,"The escalating annual rise in housing prices introduces volatility and uncertainty into the real estate market, underscoring the critical need for accurate price forecasting systems. Predicting house prices accurately remains challenging due to the multitude of influencing factors. This study aims to identify and analyze key determinants affecting house prices, employing two established machine learning models. Through comparative analysis, the research will recommend the most effective model for enhancing the accuracy of house price predictions.",2.0,,False,JournalArticle,,2024-08-31,4379213f1fc2bfce59d0edb44817356863a3a1f4,housepricepredictionusingmachinelearningandartificialintelligence,maloku,10.47363/jaicc/2024(3)357,housepricepredictionusingmachinelearningandartificialintelligence|2024,1,1552,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f6e92a5904d5c9f8e402bd0c9db5cd10c4e894fe,10.1145/3508072.3508091,,Developing a stable house price estimator using regression analysis,2021.0,International Conference on Future Networks and Distributed Systems,O. Aljohani,"Buying a house of their own is a goal of most people, regardless of where they come from and their purchasing power. In order to help people realize this goal within their means, regression techniques such as Multiple linear, Ridge and Lasso regressions and other methods may be applied to determine a fair price. To that end, this paper is based on analysis done by applying the three regression models on a dataset obtained from the King County, WA, United States, to understand which model is most effective to achieve the above goal.",2.0,,False,JournalArticle;Book,Computer Science,2021-12-15,f6e92a5904d5c9f8e402bd0c9db5cd10c4e894fe,developingastablehousepriceestimatorusingregressionanalysis,aljohani,10.1145/3508072.3508091,developingastablehousepriceestimatorusingregressionanalysis|2021,1,1537,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",385044684fadff9c88fa5f3f6cf4597efc5ba4f7,10.1109/tocs53301.2021.9688839,,Housing Price Mathematical Prediction Method through Big Data Analysis and Improved Linear Regression Model,2021.0,"2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)",Xinyu Yang; Ze-Sheng Yin; Jiayi Li,"Housing price prediction is a typical case of exploratory data analysis. This article takes housing prices in a certain city in the United States as an example, and uses a variety of linear models to predict and analyze the trend of housing prices. Based on Spark, we have implemented a distributed housing price prediction analysis model. Through the comparison of multiple linear models, we find out the best linear analysis model with the best prediction results.",2.0,,False,Conference,,2021-12-10,385044684fadff9c88fa5f3f6cf4597efc5ba4f7,housingpricemathematicalpredictionmethodthroughbigdataanalysisandimprovedlinearregressionmodel,yang,10.1109/tocs53301.2021.9688839,housingpricemathematicalpredictionmethodthroughbigdataanalysisandimprovedlinearregressionmodel|2021,1,1466,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e12c7df1216b75ad39d2fe6c8fa156d04537b756,10.1063/5.0196051,,Machine learning based approach for predicting house price in real estate,2024.0,7TH INTERNATIONAL CONFERENCE ON NANOSCIENCE AND NANOTECHNOLOGY,Mohammed Ali Shaik; P. Praveen; T. S. Kumar; M. Parveen; Swetha Mucha,,2.0,,False,Conference,,,e12c7df1216b75ad39d2fe6c8fa156d04537b756,machinelearningbasedapproachforpredictinghousepriceinrealestate,shaik,10.1063/5.0196051,machinelearningbasedapproachforpredictinghousepriceinrealestate|2024,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",90e06b7bb6ecebf1bd06b390d994fc5f50d684a9,10.12677/sa.2020.93036,,Analysis of the Correlation between Housing Price Data in Boston Based on the Regression Method,2020.0,Statistics and Application,Ran Zhao,,2.0,,False,,,,90e06b7bb6ecebf1bd06b390d994fc5f50d684a9,analysisofthecorrelationbetweenhousingpricedatainbostonbasedontheregressionmethod,zhao,10.12677/sa.2020.93036,analysisofthecorrelationbetweenhousingpricedatainbostonbasedontheregressionmethod|2020,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b0023bd4fb0f3efa2e9a6c3cd1959ac8c9924b3f,10.22313/reik.2017.15.3.35,,A Study on the Determinants of Housing Price Using Data Mining,2017.0,,Hae-jung Chun,,2.0,,False,,Business,2017-09-30,b0023bd4fb0f3efa2e9a6c3cd1959ac8c9924b3f,astudyonthedeterminantsofhousingpriceusingdatamining,chun,10.22313/reik.2017.15.3.35,astudyonthedeterminantsofhousingpriceusingdatamining|2017,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2046cade490e25506aded107d8e6e3d5890c2a48,10.22313/reik.2019.17.2.37,,A Study on Prediction of Housing Price Using Deep Learning,2019.0,Residential Environment Institute Of Korea,Hae Jung Chun; Hye-Seon Yang,,2.0,,False,,Computer Science,2019-06-30,2046cade490e25506aded107d8e6e3d5890c2a48,astudyonpredictionofhousingpriceusingdeeplearning,chun,10.22313/reik.2019.17.2.37,astudyonpredictionofhousingpriceusingdeeplearning|2019,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a8bd68467b333bc91b55c4121279e53c5ab1ee8c,10.1063/5.0222464,,Boston housing price prediction with different machine learning methods,2024.0,AIP Conference Proceedings,Zhangjie Xia,,2.0,,False,Conference,,,a8bd68467b333bc91b55c4121279e53c5ab1ee8c,bostonhousingpricepredictionwithdifferentmachinelearningmethods,xia,10.1063/5.0222464,bostonhousingpricepredictionwithdifferentmachinelearningmethods|2024,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,real estate hedonic regression,7ba8bd6b03de5fed857cf21637fa470b5340bb7f,10.1108/ijhma-04-2026-0096,,"Machine learning-based property price prediction in an emerging market: evidence from Islamabad, Pakistan",2026.0,International Journal of Housing Markets and Analysis,H. Raza," This study develops and evaluates machine-learning (ML) models to predict residential property prices in Islamabad, Pakistan, addressing the acute information asymmetry and valuation opacity that characterize real estate markets in emerging economies. The purpose of this paper is to also offer a theoretical refinement of the hedonic pricing framework for markets characterized by within-neighborhood quality heterogeneity. A data set of 116,033 property listings was collected from an online real-estate portal. After preprocessing by including standardization of locally formatted price notations (Crore/Lakh) and heterogeneous area units (Kanal/Marla), a final sample of 59,035 records was analyzed. Six regression algorithms were trained and evaluated in Python (scikit-learn and the XGBoost package): Linear Regression, Support Vector Regression (SVR), Decision Tree, Random Forest, Gradient Boosting and Extreme Gradient Boosting (XGBoost). The best-performing model was further refined through grid-search hyperparameter optimization, and feature attribution was analyzed using both tree-split importance and Shapley Additive exPlanations (SHAP). Predictive accuracy was assessed using mean absolute error (MAE) and root mean squared error (RMSE) on a held-out test set; goodness-of-fit was summarized using the coefficient of determination (R²). Tuned XGBoost delivered the lowest test-set error (MAE = PKR 6.54m; RMSE = PKR 23.37m; R² = 0.698, cross-validated R² = 0.711 ± 0.028) and substantially outperformed the linear baseline (test MAE = PKR 19.03m; R² = 0.251). Tuning improved MAE by approximately 13% over the untuned XGBoost benchmark. Feature-attribution analysis yielded a more nuanced picture than the tree-split importance measure alone. Under tree-split importance, bathroom count (31.9%) appears to dominate; under SHAP, listing purpose (25.6%), property area (24.1%) and bathroom count (20.6%) emerge as three roughly comparable joint drivers, with location (14.2%) and bedrooms (10.4%) playing meaningful secondary roles. The authors interpret the bathroom result as one of three coequal price drivers, consistent with a quality-proxy effect in a market where construction quality varies sharply within neighborhoods, rather than as a dominant predictor. The model uses six features available from online listings; the absence of property age, construction quality and precise geospatial coordinates constrains explanatory power. Label encoding of the location variable does not preserve spatial structure, so the estimated importance of location is a lower bound. The 49.1% attrition because of missing values may introduce selection bias. The analysis is cross-sectional and confined to Islamabad. With a tuned MAE of approximately 22% of the mean listing price, the model is positioned as a screening-grade automated valuation tool rather than as a stand-alone appraisal. It is suitable for flagging mispriced listings, supporting buyer due diligence and as a starting point for human appraisers. Three feasible policy steps are proposed: a standardized listing schema at the portal level; linking Federal Board of Revenue valuation tables to a regularly updated ML reference; and opening anonymized transaction data from provincial property registries to qualified researchers. To the best of the authors’ knowledge, this paper provides the first large-scale, six-algorithm benchmark for property valuation in Pakistan. Its theoretical contribution is to refine the Rosen (1974) hedonic framework for quality-heterogeneous emerging markets, where quality-proxy attributes assume a role normally absorbed into the location premium. ",1.0,,False,JournalArticle,,2026-07-06,7ba8bd6b03de5fed857cf21637fa470b5340bb7f,machinelearningbasedpropertypricepredictioninanemergingmarketevidencefromislamabadpakistan,raza,10.1108/ijhma-04-2026-0096,machinelearningbasedpropertypricepredictioninanemergingmarketevidencefromislamabadpakistan|2026,1,4701,7,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || automated valuation model residential || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic property valuation || real estate hedonic regression",2,2,1,7,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",17888f2589eb5129df9ff4e8efce7499a0a8b09b,10.1109/iciem54221.2022.9853185,,Predicting House Price in India Using Linear Regression Machine Learning Algorithms,2022.0,2022 3rd International Conference on Intelligent Engineering and Management (ICIEM),Ankush Verma; C. Nagar; NikitaVikas Singhi; Nupur Dongariya; Nidhi Sethi,"The price of house is increased every year according to the location and it indicates the current economic situation so there is a need for a system to predict house sales in the future for both buyer and the seller. Here we use dataset of India across different cities and having more than 68,613 entries of train data and 28000 test data of housing sales in whole, India. This analysis includes the effect of markdowns on the sales and the extent of effects on the sales by size, price, area etc. has been analyzed using different machine learning algorithms. Estimating home sales can help the developer determine the selling price of the home and the best time for the buyer to purchase the home. The output values of the algorithms are estimated based on the input characteristics from the data presented in the system and the analysis is a process. Physical conditions, concept and location are the three factors that determine the selling price of a home.An exact forecast of forthcoming development market interest, particularly the private market, is central imperative to strategy producers, as it could assist with forming procedures to develop/balance out the economy and fulfill the social requirements. In spite of that, a sensible forecast of future private interest is never a simple assignment, as it is represented by various social and monetary elements. In this paper, four proactive factor models are created and thought about for straightforwardly anticipating India private area a wide range of interest. In giving a scope of potential estimates, the model additionally gives a chance to the chief to practice judgment in choosing the most fitting figures. We depict the continuous internet based customer conduct expectation framework which predicts the clients buying aim when the client visits the site. To achieve the assignment, we rely upon meeting and guest data and we utilize gullible Bayes classifier, Logistic Regression, Neural Network, GBC, choice tree and arbitrary timberland research the dataset for furcating the buyer choice. Also, we use oversampling to work on the exhibition and the adaptability of the classifier. The outcomes show that irregular backwoods creates the higher precision and utilized different Algorithms. We study about the algorithm linear Regression taking a data set.",1.0,,False,Conference,,2022-04-27,17888f2589eb5129df9ff4e8efce7499a0a8b09b,predictinghousepriceinindiausinglinearregressionmachinelearningalgorithms,verma,10.1109/iciem54221.2022.9853185,predictinghousepriceinindiausinglinearregressionmachinelearningalgorithms|2022,1,3331,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",73900be1d7b3db59ac68b02075d7cd86b6d23740,10.1108/ijhma-01-2025-0022,,"Artificial intelligence algorithms to predict housing market prices in Surabaya, Indonesia",2025.0,International Journal of Housing Markets and Analysis,A. Njo; H. Sugiarto; Doddy Prayogo," Purpose As the second largest city in Indonesia, Surabaya characterized by rapid economic growth and dynamic real estate markets, faces challenges in property valuation including data limitations, subjectivity and traditional methods that lead to inaccurate property pricing. To address these issues, machine learning (ML)-based methods offer the potential to provide more accurate predictions by leveraging historical data and identifying complex patterns. This study aims to analyze and evaluate the accuracy of various ML algorithms in assessing residential property prices in Surabaya. Design/methodology/approach An extensive data set about house prices is collected using hypertext preprocessor language (PHP) to scrap (Web scraping) from a property marketplace called Rumah123 in the capital of East Java Island, namely, Surabaya. This data is used to train and test multiple linear regression model and three popular ML models, i.e. artificial neural network (ANN), support vector machine (SVM) and classification and regression tree (CART), to predict house prices with 16 different features. Findings The model’s performance was evaluated using the linear correlation, mean absolute error, mean absolute percentage error and root mean squared error. The results showed that the ANN performed better than the others, both in bigger and smaller clusters. On the other hand, SVM is not recommended for predicting house prices in Surabaya due to its poor accuracy. Research limitations/implications Predictor importance of ANN in both clusters shows that subdistricts have less impact on the house prices, which makes some data that have the same price guessed differently by ANN, probably due to a lack of data. Practical implications The easiness of the proposed model will allow future users to predict house prices with different models and data sets. Alternatively, further research may implement a different model using neural network, knowing that this model works better for this kind of task. Originality/value To the best of the authors’ knowledge, this is the first comparison of the three ML models (ANN, SVM and CART) and linear regression when predicting house prices, and all parameters are tuned with the grid search method. ",1.0,,False,JournalArticle,,2025-04-17,73900be1d7b3db59ac68b02075d7cd86b6d23740,artificialintelligencealgorithmstopredicthousingmarketpricesinsurabayaindonesia,njo,10.1108/ijhma-01-2025-0022,artificialintelligencealgorithmstopredicthousingmarketpricesinsurabayaindonesia|2025,1,3258,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",812fa5e0891b51b647184994ecedbf2306ac51ea,10.46793/ebm24.417m,,HOUSING PRICE PREDICTION USING XGBOOST AND RANDOM FOREST METHODS,2025.0,Zbornik radova,Ljiljana Matic; Zoran Kalinić,"Real estate prices often experience significant variations due to various reasons such as shifts in land value and local infrastructure development. An application for predicting housing prices, taking into account the property and neighborhood characteristics and infrastructure, is essential for providing customers with accurate insights into where and when to invest in property. This research focuses on apartment price prediction in real estate market of Belgrade, employing advanced machine learning algorithms, XGBoost and Random Forest, to estimate property values. These algorithms are highly effective at processing large datasets and uncovering complex relationships among key predictors. Data preprocessing plays a crucial role in ensuring model accuracy. This process includes data cleaning, handling missing values, and addressing outliers, while categorical features are transformed through one-hot encoding to enhance model performance. Both XGBoost and Random Forest models are applied to predict apartment prices, providing valuable insights into the most influential factors in the market. A comparative analysis of these methods not only highlights their respective strengths and effectiveness within the context of the Serbian real estate market but also demonstrates their utility in improving prediction accuracy. The results of this study contribute to a more transparent and efficient real estate market in Belgrade, offering valuable tools for all stakeholders: sellers, prospective buyers, policymakers, local administration, etc. By integrating these advanced predictive models, this research also lays the groundwork for future advancements in real estate analytics, presenting a robust framework for market forecasting and strategic decision-making. Although applied to apartment price prediction, the models can be, with minor modifications, applied to price predictions of other types of real estate (houses, commercial, industrial, land plots, etc.). Additionally, the findings suggest potential areas for further research to refine prediction models and adapt them to evolving market conditions.",1.0,,False,,,,812fa5e0891b51b647184994ecedbf2306ac51ea,housingpricepredictionusingxgboostandrandomforestmethods,matic,10.46793/ebm24.417m,housingpricepredictionusingxgboostandrandomforestmethods|2025,1,3130,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,4,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2978e64c8b75224a83ddd496a45e744aa32769d7,10.1109/icwite64848.2025.11307001,,House Price Prediction: a Comparative Study of Machine Learning Ensemble Techniques and Deep Learning Model,2025.0,"2025 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)",Melissa Dsilva; Vineet Kadam; H. Kadam; Himani Deshpande,"House price prediction using machine learning has gained substantial attention in recent years due to advancements in algorithms, as well as the increasing availability of real estate data. The conventional approaches of valuation cannot properly reflect the dynamic and complex character of real estate markets, thus the implementation of machine learning (ML) models for higher precision. This study revolves around house price prediction: Random Forest, XGBoost, LightGBM, and ensemble learning approaches. The dataset contains 29,451 properties and has considered 12 vital features like location, area, number of bedrooms, various accessibility factors, etc. The Stacked Model (Random Forest + XGBoost + LightGBM) gives the best accuracy score with an MAE of 2.9511 lakhs and the $\mathbf{R}^{2}$ score of 0.9785. In addition to this, an Enhanced Stacked Model with SVR, MLP, and KNN was developed but performed worse than the simpler stacked model producing MAE of 3.1673 Lakhs and $\mathbf{R}^{\mathbf{2}}$ of 0.9768. A Feedforward Neural Network (FNN) was implemented, yielding a mean absolute error (MAE) of 16.7667 lakhs and an $\mathbf{R}^{\mathbf{2}}$ score of 0.9371. This suggests that its accuracy is considerably lower than that of the ensemble-based methods. Outlier removal, hyperparameter tuning, and cross-validation techniques were employed for better model performance. Our findings reveal that ensemble learning, which yields extremely accurate predictions, is better than standalone models and classical regression techniques. This obviously helps buyers and sellers, as well as real estate professionals, to make decisions based on their data in the housing market.",1.0,,False,Conference,,2025-09-26,2978e64c8b75224a83ddd496a45e744aa32769d7,housepricepredictionacomparativestudyofmachinelearningensembletechniquesanddeeplearningmodel,dsilva,10.1109/icwite64848.2025.11307001,housepricepredictionacomparativestudyofmachinelearningensembletechniquesanddeeplearningmodel|2025,1,2689,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c73f2d05039f1812b77b6d6ab09f1fc36d1217f6,10.1109/iatmsi64286.2025.10985703,,An Automated Property Pricing System using Machine Learning,2025.0,2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI),A. Dubey; Arvind Lovewanshi; Manikant Thakur; Namrata Dhanda; K. Gupta,"The use of data mining in today's real estate industry is increasingly widespread, particularly for predicting property values, identifying key housing features, and extracting insights from raw data. This technology plays a critical role by providing information that aids various stakeholders in the real estate market. Fluctuations in property values can cause anxiety for homeowners and industry professionals alike, and the rising demand for buying and renting homes, driven by urbanization, further intensifies the need for accurate property valuations. As a result, developing more efficient methods to estimate home prices that reflect current market trends has become crucial. This study focuses on evaluating various supervised machine learning regressors for house price prediction. Algorithms such as Extra Trees Regressor, K-Nearest Neighbors (KNN), Decision Tree Regressor, Bagging Regressor, and Linear and Logistic Regression are tested to determine their efficacy in predicting home values. A comparative analysis is performed among these algorithms to identify which one best fits the dataset and model. Ultimately, the most effective algorithm is selected to create a prototype for forecasting house prices. Additionally, a website has been developed, using the selected model, to provide users with real-time predictions based on the algorithm that demonstrated the highest accuracy. This research highlights the importance of employing advanced machine learning techniques to address challenges in real estate, ensuring that property values are predicted more accurately to benefit both consumers and industry professionals.",1.0,,False,Conference,,2025-03-06,c73f2d05039f1812b77b6d6ab09f1fc36d1217f6,anautomatedpropertypricingsystemusingmachinelearning,dubey,10.1109/iatmsi64286.2025.10985703,anautomatedpropertypricingsystemusingmachinelearning|2025,1,2645,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",579b29185c6a16af482e706d2a82a2f2d79819e5,10.1109/iciip61524.2023.10537786,,Enhancing House Price Prediction Accuracy using Deep Learning and Hyperparameter Tuning with AEDA,2023.0,International Conference on Intelligent Information Processing,Ramesh Priya; K. Raghuveer; K. Srinivas; B. A; P. J. Shriidhar; N. Shirisha,"Traditionally, real estate agents and appraisers used their expertise and market knowledge to estimate house prices. However, due to the expansion of data sources and developments in data science, there was a growing interest in developing predictive models to automate and enhance the house price prediction process. This research introduces the Adaptive Exploratory Data Analysis (AEDA) tool, specifically the AEDA by frequency for categorical values, implemented in Python. The tool provides an API that maps usual AEDA tasks in statistical modeling for generating plots and statistics. It automatically generates insights and establishes standards and procedures. The frequency of users in various variables highlights the challenges in data processing pipelines for predicting house prices using Machine Learning (ML) and Deep Learning (DL) techniques. The chapter also discusses the evaluation of the hyperparameter tuning model for house pricing in the subsequent chapter. Model building, in the context of ML, involves creating a model that can learn from data without explicit instructions. Regression analysis is utilized to observe and resolve prediction issues. The suggested house price prediction model combines a joint self-attention mechanism with a specific feature parameter representation. While buyers and sellers focus on external features like building area, location, and construction year, the proposed model emphasizes internal features. It emphasizes the importance of hyperparameter tuning in ML and DL algorithms to improve model accuracy by minimizing residual errors during validation.",1.0,,False,Conference,,2023-11-22,579b29185c6a16af482e706d2a82a2f2d79819e5,enhancinghousepricepredictionaccuracyusingdeeplearningandhyperparametertuningwithaeda,priya,10.1109/iciip61524.2023.10537786,enhancinghousepricepredictionaccuracyusingdeeplearningandhyperparametertuningwithaeda|2023,1,2615,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0225308182c70763f3fd97aa79ec2fc36391fd24,10.1109/sist58284.2023.10223463,,A Case Study of Machine Learning Comparisons for Predicting Apartment Prices in Astana,2023.0,2023 IEEE International Conference on Smart Information Systems and Technologies (SIST),E. Zhantileuov; A. Smaiyl; A. Aibatbek; Samat Kassymkhanov,"Astana, the capital city of Kazakhstan, has undergone rapid urbanization and economic development in recent years. With this growth comes an increased demand for housing, leading to the need for more accurate and efficient methods for predicting house prices. In this paper, we aim to compare the performance of five popular regression algorithms for predicting house prices in Astana: linear regression, decision trees, random forest, XGBoosting, and CatBoosting. We collected a dataset of housing prices in Astana, including information on the location, size, age, and other relevant features of the properties. We then applied each of the six algorithms to the dataset and evaluated their performance using various metrics such as mean squared error (MSE), mean absolute error (MAE), and R2 score. Our results showed that XGBoosting and CatBoosting algorithms outperformed the other methods in terms of prediction score but it overfits a data. The decision trees and random forest algorithms performed well on training set but there did not generalize the data. Linear regression performed the lowest score comparing with other algorithms, but it generalizes the data. This study provides valuable insights for policymakers and real estate developers in Astana, as well as for researchers and practitioners in the field of machine learning. Our findings suggest that advanced machine learning techniques, such as XGBoosting and CatBoosting, can significantly improve the accuracy of house price prediction and aid in informed decision-making.",1.0,,False,Conference,,2023-05-04,0225308182c70763f3fd97aa79ec2fc36391fd24,acasestudyofmachinelearningcomparisonsforpredictingapartmentpricesinastana,zhantileuov,10.1109/sist58284.2023.10223463,acasestudyofmachinelearningcomparisonsforpredictingapartmentpricesinastana|2023,1,2545,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",10f59485f7108f7bbae9b8b2d614ff6cf596247e,10.1109/conmedia67140.2025.11290600,,"Predicting Property Prices Using MLR, Gradient Boosting, and Random Forest: A Case Study in South Tangerang, Indonesia",2025.0,2025 8th International Conference on New Media Studies (CONMEDIA),A. Saputro; Ririn Ikana Desanti,"Accurate property price prediction is crucial for supporting property market stakeholders (buyers, sellers, and developers) in making informed decisions in the real estate market. The study explores the utilization of three machine learning algorithms—Multiple Linear Regression (MLR), Gradient Boosting (GB), and Random Forest (RF)—to predict residential property prices in South Tangerang, Indonesia. A dataset comprising 17,454 listings was gathered through web scraping from Lamudi.co.id. The listings included important housing information such as land area, building size, number of rooms, and location. The study adheres to the CRISP-DM methodology, covering data preparation, modeling, and evaluation phases. Model performance was assessed using the coefficient of determination (R2) and Root Mean Squared Error (RMSE) metrics. The Random Forest model did better than the others, with a R2 of 0.8011 and an RMSE of 536 million IDR. This means that it was able to accurately capture complex nonlinear interactions in the data. The Gradient Boosting model came next, with a good level of accuracy. The Multiple Linear Regression model did the worst. The best-performing model was used in a web app so that people could estimate property prices in real time. This study introduces a robust, data-driven property valuation system employing ensemble learning techniques, functioning as a practical reference for stakeholders and demonstrating the potential of machine learning in the Indonesian real estate market.",1.0,,False,Conference,,2025-10-14,10f59485f7108f7bbae9b8b2d614ff6cf596247e,predictingpropertypricesusingmlrgradientboostingandrandomforestacasestudyinsouthtangerangindonesia,saputro,10.1109/conmedia67140.2025.11290600,predictingpropertypricesusingmlrgradientboostingandrandomforestacasestudyinsouthtangerangindonesia|2025,1,2517,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ce1c455ab32efd54f544601631d21401dc955aff,10.1080/01605682.2025.2513449,,Enhancing house price forecasting with hybrid data driven feature extraction model,2025.0,Journal of the Operational Research Society,Kian Lun Soon; Lam Tatt Soon; Jia-Yi Teo,"Abstract Accurate house price estimation is crucial for stakeholders such as homeowners, investors, and policymakers. While current models excel at capturing spatial dependencies, they often overlook the influence of contextual factors such as macroeconomic conditions and property-specific attributes. To address this gap, the Variational Mode Decomposition and Random Forest (VMD-RF) feature extraction model is proposed to consider both spatial dependency factors (i.e. area, Gross Domestic Product (GDP), Consumer Price Index (CPI), and population) and contextual factors (i.e., tenure, age, and interest rate) in generating a unified housing market health index. Specifically, VMD decomposes each feature into intrinsic mode functions (IMFs), revealing underlying data patterns. The IMFs from all features are concatenated, and Random Forest performs dimensionality reduction, consolidating them into a single feature. This feature improves the model’s predictive power, as evidenced by a higher Spearman’s correlation coefficient (0.9652) with house prices. The contribution of VMD-RF lies in its enhanced prediction accuracy within a simpler machine learning framework, avoiding the complexity of ensemble methods. Validated using the Selangor house price dataset, both VMD-RF-SVR and VMD-RF-LSTM achieve competitive coefficients of determination (R2) of 0.97. Furthermore, the VMD-RF-SVR model demonstrates superior performance compared to VMD-RF-LSTM, as evidenced by its lower median residual distribution.",1.0,,False,JournalArticle,Computer Science,2025-06-02,ce1c455ab32efd54f544601631d21401dc955aff,enhancinghousepriceforecastingwithhybriddatadrivenfeatureextractionmodel,soon,10.1080/01605682.2025.2513449,enhancinghousepriceforecastingwithhybriddatadrivenfeatureextractionmodel|2025,1,2516,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",93190686f11cdfca6207ea210add39da132cb244,10.1109/icacc63692.2024.10845250,,Leveraging Hybrid Machine Learning Technique For Effective House Rental Price Forecasting,2024.0,2024 11th International Conference on Advances in Computing and Communications (ICACC),K. D; Naveenkumar Anbalagan; M. M; Najma N; K. K; M. P,"These days, figuring out the house's sale price is crucial because both the land and the house's prices rise annually. Therefore, the next generation needs a simple approach for forecasting future rents for houses. Accurate and data-driven strategies are crucial for estimating house rental prices in the rapidly shifting real estate market. This study investigates the application of machine learning to accurate rental price forecasting. Conventional methods of rental estimation tend to rely on intuition and knowledge of the marketplace in the area, but they are incapable of taking into consideration the complex network of variables which influence rental rates. This study explores how rental prices are influenced by factors including the age of the property, whether it is furnished, the flat area, the size of the house etc. It also shows how machine learning models may be used to find hidden connections and get precise rental price predictions. The dearth of highly accurate models currently in use, the need to reduce overpricing, adjust to changing conditions in the market, maximize the return on investments, support data-driven decision-making, advance market transparency, and create a simple to use interface are among the reasons behind this research. The main goal is to create a reliable and accurate model for predicting the price of a rental home so that tenants and property owners can benefit from data-driven insights.",1.0,,False,Conference,,2024-11-06,93190686f11cdfca6207ea210add39da132cb244,leveraginghybridmachinelearningtechniqueforeffectivehouserentalpriceforecasting,d,10.1109/icacc63692.2024.10845250,leveraginghybridmachinelearningtechniqueforeffectivehouserentalpriceforecasting|2024,1,2446,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0c30cbad9086dfc4f0e219e80a80bfe73b1dfd2f,10.55041/isjem03420,,Enhancing House Price Prediction Using Hybrid Model Approach,2025.0,International Scientific Journal of Engineering and Management,P. Sumalatha,"Abstract—Accurate prediction of real estate house prices is a difficult task due to the non-linear interactions among different property attributes and market factors. This paper proposes a hybrid model integrating Extreme Gradient Boosting (XG- Boost), Artificial Neural Networks (ANN), and Deep & Cross Networks (DCN) to predict house prices with higher accuracy. All the components contribute differently: XGBoost performs well on structured tabular data, ANN identifies complicated nonlinear patterns, and DCN captures feature interactions well. The ensemble approach combines the best features of these models to reach a prediction accuracy of 96%,which is much better than conventional models. In addition, the model uses SHAP (SHapley Additive exPlanations) to offer interpretable explanations of feature contributions, improving the transparency and credibility of the model. The hybrid model is tested using R-squared, RMSE, MSE, MAPE, and visualizations of actual vs. predicted prices. SHAP summary and dependence plots provide detailed explainability by measuring the effect of each feature on predictions. The improved model can be used as a solid decision- making tool for buyers, investors, and real estate experts. Keywords— House Price Prediction, Machine Learning, XG- Boost, Artificial Neural Network, Deep Cross Network, SHAP, Hybrid model.",1.0,,False,JournalArticle,,2025-05-07,0c30cbad9086dfc4f0e219e80a80bfe73b1dfd2f,enhancinghousepricepredictionusinghybridmodelapproach,sumalatha,10.55041/isjem03420,enhancinghousepricepredictionusinghybridmodelapproach|2025,1,2360,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",313dfd95a7e0ead61e66948f327799f756cfc245,10.1109/icimcis60089.2023.10349077,,House Base-Price Prediction with Machine Learning Methods,2023.0,"2023 International Conference on Informatics, Multimedia, Cyber and Informations System (ICIMCIS)",David Gabriel Immanuel Simanungkalit; Belinda Meylia; Julius Salim; I. S. Edbert; Derwin Suhartono,"Various factors like location, size, age, and condition must be considered to predict house prices accurately. This study utilizes historical sales data and demographic information in conjunction with machine learning algorithms, such as Random Forest, XGBoost, Support Vector Regression, and Artificial Neural Networks, to forecast home prices. The Kaggle dataset ""House Sales in King County, USA"" was used to test the proposed models. This research aims to help buyers find affordable properties and make informed decisions. Previous studies have shown that machine learning algorithms are more effective than traditional regression models in predicting house values. By utilizing artificial intelligence and machine learning, this study aims to improve the performance and effectiveness of predicting home prices. Our findings indicate that the ANN model has the highest performance results compared to the other models, with an performance rate of 0.785 before tuning and 0.887 after tuning. XGBoost comes in second with an performance rate of 0.886 after tuning, followed by Random Forest with an performance rate 0.855 after tuning. Finally, SVR is in fourth place with an performance rate 0.849 after tuning. Based on these results, we recommend using the ANN approach for estimating property prices in future research.",1.0,,False,Conference,,2023-11-07,313dfd95a7e0ead61e66948f327799f756cfc245,housebasepricepredictionwithmachinelearningmethods,simanungkalit,10.1109/icimcis60089.2023.10349077,housebasepricepredictionwithmachinelearningmethods|2023,1,2326,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",92718bc7b3b8f25d68058f82ee2b19a8000630f2,10.55041/isjem04918,,House Price Prediction App,2025.0,International Scientific Journal of Engineering and Management,P. Abhishek; R. B. Sankar,"Predicting housing prices is a vital task in real estate and urban planning, influencing decisions by buyers, sellers, developers, and policymakers. This project presents a machine learning-based prediction system for estimating residential property prices using classification algorithms. Historical housing data across multiple cities in India was utilized, containing attributes such as area, number of bedrooms and bathrooms, year built, location, garage, and property condition. The system employs supervised machine learning models Random Forest, Gradient Boosting, and Linear Regression to forecast house prices. Among these, the Gradient Boosting model achieved the highest accuracy. The selected model is integrated into an interactive web application built using Streamlit, providing users with instant price predictions and model comparisons. Through intuitive visualizations and real-time predictions, this system aids individuals and professionals in making informed property decisions. Index Terms: House Price Prediction, Machine Learning, Regression Models, Gradient Boosting, Random Forest, Linear Regression, Real Estate Analytics, Streamlit Application, Housing Data, Supervised Learning, Property Valuation, Data-Driven Decision Making, Feature Engineering, Predictive Modeling, Urban Planning.",1.0,,False,JournalArticle,,2025-07-31,92718bc7b3b8f25d68058f82ee2b19a8000630f2,housepricepredictionapp,abhishek,10.55041/isjem04918,housepricepredictionapp|2025,1,2315,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9e03cca3c14bd64b56e858d50129d7cf526a6d64,10.1109/ic3i56241.2022.10072476,,An Enhanced Regression Technique for House Price Prediction,2022.0,International Conferences on Contemporary Computing and Informatics,K. Lakshmi.; P. Narayana; P. Bhavani; V. V. S. Madhavacharyulu; N. Lavanya; S. Pousia,"The real estate market is one of the least transparent sectors of society as real estate prices change daily and are often overvalued rather than valued. Homebuyers use budget and market methods to find new homes. However, a fundamental problem with the current approach is the inability to predict future market trends that will lead to price spikes. It is very important for researchers to base their house price proposals on empirical studies. In order to accurately predict the price of a home, customers need to carefully evaluatefactors related to the home, which is very difficult. Using machine learning (ML) to solve this problem seems like a viable solution. To address this problem, ML models such as Linear Regression (LR), K Nearest Neighbors (KNN), Random Forests (RF); Ensembles (LR, KNN, RF) are used. A number of error metrics are used to select the best model, including mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The results in this disclosure show that a model combining linear regression (LR), random forest (RF) and K Nearest Neighbors (KNN) yields the lowest inaccuracies. A successful regression model should have a minimal error value. This eliminates the need to rely on realtors to determine a fair price for a home based on key features.",1.0,,False,JournalArticle;Conference,Computer Science,2022-12-14,9e03cca3c14bd64b56e858d50129d7cf526a6d64,anenhancedregressiontechniqueforhousepriceprediction,lakshmi.,10.1109/ic3i56241.2022.10072476,anenhancedregressiontechniqueforhousepriceprediction|2022,1,2311,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1b74d33204ff11e1e5ed0c2bbdaef13ca70a5fe9,10.1117/12.2658679,,City’s house pricing prediction based on machine learning algorithms,2022.0,"International Symposium on Robotics, Artificial Intelligence, and Information Engineering",Zi-Hao Liu; Yuxuan Wang,"According to a related report, housing prices in New York will have similar price changes in the future as they do now, with minor swings and a modest increase overall. This study hopes to take a more original view, which means, less popular sides would be considered, and through machine learning to predict the trend of house prices in New York. The information of data was about house prices and real estate in New York. The first step is called conversion for data type, converting the string data in the document to the data format called INT. The second step is normalization, the values are mapped from 0-1. The proposed prediction of this article study uses three algorithm methods: Support Vector Machine (SVM), Linear regression, Neural network. And then the article will go for the hyperparameter configuration analysis. The paper concludes that practically every aspect of the data affects house prices through the comparison of the three forecasting models. Multiple linear regression models provide unstable outcomes, and the outcomes always vary from time to time. The neural network model's prediction results are relatively stable, and when the hyperparameters are kept constant, the prediction results are roughly the same. The most stable outcomes are predicted by the SVM model.",1.0,,False,,Engineering,2022-11-23,1b74d33204ff11e1e5ed0c2bbdaef13ca70a5fe9,cityshousepricingpredictionbasedonmachinelearningalgorithms,liu,10.1117/12.2658679,cityshousepricingpredictionbasedonmachinelearningalgorithms|2022,1,2298,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9ca358b99e4f5465a0aeaa938741b17ddb56dbd2,10.1145/3795546.3795587,,Predicting Real Estate Prices: A Gradient Boosting Tree Approach,2025.0,Proceedings of the 2025 International Conference on Digital Transformation and Management,Yanjie Jiang; Linlin Yue; Shuting Yang; B. Yan,"This study applies a Gradient Boosting Tree (GBT) model to predict housing prices in a Chinese secondary city, utilizing comprehensive real estate transaction data. The optimized model identifies historical community transaction volume (19.8% contribution), total floors, and unit area as key pricing determinants. Under rigorous temporal validation, it achieves a robust RMSE of 1,611 CNY/m² and R² of 0.54, reflecting true forecasting capability without data leakage. The analysis provides multi-stakeholder insights: developers should prioritize location-based valuations; homebuyers require model-driven tools for price assessments; policymakers need dynamic monitoring systems to regulate market volatility. Methodological innovations include integrating geospatial data and consumer preference metrics, enhancing model applicability across diverse urban contexts. While the GBT framework shows promise for housing market analysis, challenges persist in model interpretability and cross-regional adaptation. Future directions emphasize hybrid AI approaches combining tree-based models with explainable neural networks, alongside expanded data fusion strategies. This research establishes a scalable paradigm for data-driven real estate decision-making in China's evolving urban landscapes.",1.0,,False,Book;Conference,,2025-12-26,9ca358b99e4f5465a0aeaa938741b17ddb56dbd2,predictingrealestatepricesagradientboostingtreeapproach,jiang,10.1145/3795546.3795587,predictingrealestatepricesagradientboostingtreeapproach|2025,1,2294,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d28c93f15091e564b3897e561c0652d8c5ea7635,10.1109/aic61668.2024.10730866,,Extensive Data Preprocessing and Exploratory Data Analysis for House Price Prediction using Machine Learning and Deep Learning with an Introduction to AutoML Technologies,2024.0,International Workshop on Artificial Intelligence and Cognition,A. Ved; Archana Gupta,"Houses contribute to one of the most essential needs of every human and house prices play a very important role in everyone’s life. House Price prediction has been one of the crucial topics in the real estate industry. This paper focuses on data preprocessing techniques which are carried out using dataset containing the house prices of the Ames City in United States. It covers the entire journey right from Data collection to Data Preprocessing and Data Analysis to Prediction using various Models. In this paper, Machine learning, Deep Learning and Auto ML techniques are applied to study historical property transactions in Ames to find useful models for both house buyers and sellers. The Models we test as a part of this paper are Random Forest Regression, Ridge Regression, Lasso Regression, (SVR) Support Vector Regression, XGBoost (which is an optimized distributed gradient boosting library) and ANN (Artificial Neural Networks). While ANN and Automated Machine Learning (AutoML) represent the forefront of machine learning, our exploration demonstrates that classical ML models remain indispensable across various domains. Our findings also demonstrate that the effectiveness of any machine learning model is highly dependent on comprehensive and rigorous data preprocessing.",1.0,,False,Conference,,2024-07-27,d28c93f15091e564b3897e561c0652d8c5ea7635,extensivedatapreprocessingandexploratorydataanalysisforhousepricepredictionusingmachinelearninganddeeplearningwithanintroductiontoautomltechnologies,ved,10.1109/aic61668.2024.10730866,extensivedatapreprocessingandexploratorydataanalysisforhousepricepredictionusingmachinelearninganddeeplearningwithanintroductiontoautomltechnologies|2024,1,2287,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7f50f7507d0816864e029da4808c78f120116ba6,10.1109/icsintesa62455.2024.10747910,,Regression Modeling for House Price Prediction in Java Island,2024.0,2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA),Faqih Hamami; I. A. Dahlan,"Houses have become significant commodity for many parties. For developers and buyers, houses prices are crucial considerations for property investment. Typically, house price predictions are made by experts. Automating predictions would ease various parties, including the government, in controlling price stability. House price predictions are extensively researched in various countries Like in China, Australia, Taiwan, Pakistan, India, Canada, Indonesia, and others. Generally, features for price prediction revolve around the size and furniture of the house without considering its location. House price in larger cities tend to be more expensive. Given this situation, this research focuses on the impact of city and province attribute on house price prediction. This research proposes to build a predictive model based on home size, furniture, and location. The model is built using several machine learning algorithms for regression measured by Mean Squared Error (MSE) metrics. Testing results show that predictions tend to improve when using location attributes for almost all algorithms. Additionally, simulations are conducted using other feature engineering and categorical encoding, increasing the MSE value of Random Forest up to $\mathbf{0. 0 0 0 6 8 1}$.",1.0,,False,Conference,,2024-07-12,7f50f7507d0816864e029da4808c78f120116ba6,regressionmodelingforhousepricepredictioninjavaisland,hamami,10.1109/icsintesa62455.2024.10747910,regressionmodelingforhousepricepredictioninjavaisland|2024,1,2271,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",63b8d283c7979bfd3be047e085b03a7f5e27f190,10.1108/pm-12-2021-0106,,Designing an optimal neural network architecture: an application to property valuation,2022.0,Property Management,Changro Lee,"PurposeThe success of a neural network depends on, among others, an architecture that is appropriate for the task at hand. This study attempts to identify an optimal architecture of a neural network in the context of property valuation, and aims to test the ability of connecting related neural networks to reduce the property valuation error.Design/methodology/approachThis study explores efficient network architectures to estimate land and house prices in Seoul, South Korea. The input is structured data, and the embedding technique is used to process high-cardinality categorical variables.FindingsThe shared architecture of a network for simultaneous estimation of both land and houses was revealed to be the best performing network. Through weight sharing between relevant layers in networks, the root-mean-square error (RMSE) for land price estimation was reduced significantly, from 0.55–0.68 using the baseline architecture, to 0.44–0.47 using the shared architecture.Originality/valueThe study results are expected to encourage active investigation of efficient architectures by using domain knowledge, and to promote interest in using structured data, which is still the dominant type in most industries.",1.0,,False,,,2022-06-03,63b8d283c7979bfd3be047e085b03a7f5e27f190,designinganoptimalneuralnetworkarchitectureanapplicationtopropertyvaluation,lee,10.1108/pm-12-2021-0106,designinganoptimalneuralnetworkarchitectureanapplicationtopropertyvaluation|2022,1,2216,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f2ccd05869601ee7ac8f142e70e6ac429b9431cc,10.1117/12.2655963,,Multi-feature house price trend prediction based on machine learning,2023.0,Conference on Intelligent Computing and Human-Computer Interaction,Long-Yang Geng; Qihang Ran; Gengyuan Zhang; De-Hua Zhou,"As a pillar industry in China, real estate has provided great help for economic development. At the same time, buying a house has become a topic everyone cannot avoid. Until now, many scientific literatures and materials have proved that gradient boosting is a basic strategy. In this paper, the authors first clean the captured data and select features. Then, three methods eXtreme Gradient Boosting (XGBoost), Random Forest, and Bi-directional Long Short-Term Memory (BiLSTM), are used to predict housing prices in Beijing, one of the most representative cities in China, to investigate and compare the efficiency gradient methods of the three methods. The above three models process and predict 23 factors that affect housing prices by collecting information on houses sold and sold in Beijing in recent years. The score curve is obtained by fitting, finally, the best model of prediction is selected by adjusting the parameters. The influence of different characteristics on housing price prediction is studied. The experimental results show that compared with XGBoost and random forest, BiLSTM has a greater advantage in forecast speed and accuracy and is the most suitable model for predicting housing prices.",1.0,,False,Conference,Engineering,2023-01-12,f2ccd05869601ee7ac8f142e70e6ac429b9431cc,multifeaturehousepricetrendpredictionbasedonmachinelearning,geng,10.1117/12.2655963,multifeaturehousepricetrendpredictionbasedonmachinelearning|2023,1,2215,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7567f31bec28fb34f0e2b7adb02c5cb497803344,10.1109/ocit65031.2024.00075,,Real Estate Price Prediction: Optimized Ridge and Lasso Regression Analysis,2024.0,2024 OITS International Conference on Information Technology (OCIT),Kausik Challapalli; Prashanthi Thota; Harsha Garikapati; Veera Manikanta Sai Adusumilli; Satish Anamalamudi; ReddyPriya Madupuri,"Our second application, developing a reliable house price prediction model, kept housing as a core part of the data world. Data munging including handling of missing values, outliers and categorical variables is a first step of this process. Using these steps, data is explored intuitively, new features like price per square meter are created and further visualization and statistical analysis is conducted. For pattern selection, we use Ridge and Lasso regression, with GridSearchCV being run to tune and optimize parameters. We assess model performance, model assumptions by examining residuals using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R squared metrics. Finally built, the model can be used by real estate professionals, investors and policymakers to accurately predict housing prices based on factors such as location, area type, square footage, total number of buckets and offers a highly valuable statistical analysis of these values. A number of future work directions could include further expanding the model by adding new variables, using more sophisticated algorithms, or carrying out similar analyses in other regions of the developing world to achieve higher accuracy.",1.0,,False,JournalArticle;Conference,Computer Science,2024-12-12,7567f31bec28fb34f0e2b7adb02c5cb497803344,realestatepricepredictionoptimizedridgeandlassoregressionanalysis,challapalli,10.1109/ocit65031.2024.00075,realestatepricepredictionoptimizedridgeandlassoregressionanalysis|2024,1,2209,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,3,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",cfd025983e48b065823ded15da792750d8e54d71,10.55041/ijsrem54127,,Machine Learning Based House Price Forecasting,2025.0,International Journal of Scientific Research in Engineering and Management,P. Kumari; Bharti Verma; Princy Kumari; Abhishek Sharma,"Abstract House price prediction is one of the most important applications of machine learning in the realestate sector. With increasing urbanization and rapid growth of housing markets, accurate prediction of property prices has become crucial for buyers, sellers, investors, and financial institutions. This research focuses on developing a machine learning model using Linear Regression, Decision Tree, and Random Forest algorithms to estimate housing prices based on features such as location, area, number of bedrooms, bathrooms, and other structural parameters. The system integrates a web-based front-end with a Python Flask backend and utilizes a trained machine learning model serialized using a pickle file. The proposed model aims to improve decision-making, reduce valuation errors, and automate price estimation. Results show that the system achieves more than 95% prediction accuracy, demonstrating the effectiveness of the model. This paper also discusses the applications, challenges, and future scope of the system. Keywords: Machine Learning, House Price Prediction, Random Forest, Linear Regression, Decision Tree, Data Preprocessing, Flask Web Application, Real Estate Analytics.",1.0,,False,JournalArticle,,2025-11-20,cfd025983e48b065823ded15da792750d8e54d71,machinelearningbasedhousepriceforecasting,kumari,10.55041/ijsrem54127,machinelearningbasedhousepriceforecasting|2025,1,2200,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",af624359e2e44f3855a502c7fd34dd0039180b6e,10.1109/otcon65728.2025.11070863,,A Comparative Regression Analysis of House Price Prediction using Machine Learning Models,2025.0,2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0,S. Khattar; Akshra Ahuja; Vishesh Kansugia,"The ability to accurately predict the house price is fundamental within the real estate sector as it provides useful information to potential buyers, sellers, investors, and policymakers. This paper provides a complete comparative study on the machine learning models used in prediction of real estate prices by describing the linear models such as Linear Regression, Ridge and Lasso Regression models, as well as ensemble models like Random Forest, Gradient Boosting, XGBoost and Light GBM. The set of these models is evaluated on different datasets, specifically Indian real estate data by MAE, RMSE, RMSLE, R2 score, and even time for training. It is established in this study that ensemble models have been found to be more precise than the linear models, where XGBoost and LightGBM are the most productive, but need more time to execute. This study also highlights model and feature selection, and location information’s impact on productivity. It is factual that the above mentioned results are useful for all the stakeholders as they always try to adapt and make the best decisions in a fast changing and competitive real estate environment.",1.0,,False,Conference,,2025-04-09,af624359e2e44f3855a502c7fd34dd0039180b6e,acomparativeregressionanalysisofhousepricepredictionusingmachinelearningmodels,khattar,10.1109/otcon65728.2025.11070863,acomparativeregressionanalysisofhousepricepredictionusingmachinelearningmodels|2025,1,2148,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",6aa259d69df8435f746df77bb63443ce66398e25,10.1109/iccwamtip51612.2020.9317395,,Analysis and Evaluation of Housing Price for Chengdu Urban,2020.0,2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP),Mu-Jie Li; Ming-Cang Zhu; Yong He; Peng-Shan Li; P. Zhang; Zhanyong He; Jibao Shi; Kai Chen; Tao Weng; Zezhong Zheng; Linghai Jiang,"House prices have received unusual attention in recent years, and evaluation of housing price has also become a research hotpot. Value of a house is considered as a mixture of various features, such as house type, orientation and location. Accurate assessment of houses thus faces a large number of challenges. Several methods have been applied to assess the value of a house, including geographically weighted regression and machine learning. However, every method has some limitations. The accuracy of traditional regression methods needs to be improved. Machine learning methods are weak in interpretability. In this paper, to analyze the housing price, we compared XBGoost, Random Forest, Support vector regression, back-propagation network and geographically weighted regression to model the second-hand housing price dataset, and analyzed the influenced of each feature. The result shows Random Forest reaches a lowest mean absolute error of 1517.29 and best r2 of 0.83. According to the constructed models, the importance values are given, and the area of house shows a strongest influence on housing price in the study area.",1.0,,False,Conference,,2020-12-18,6aa259d69df8435f746df77bb63443ce66398e25,analysisandevaluationofhousingpriceforchengduurban,li,10.1109/iccwamtip51612.2020.9317395,analysisandevaluationofhousingpriceforchengduurban|2020,1,2132,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""spatial hedonic"" | ""spatial econometric"" | ""spatial lag"" | ""spatial error"" | ""geographically weighted regression"") + (housing | house | residential | property)",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",095237a9bb9d5b7ec7b0835c9762968643434a53,10.1109/iceeict61591.2024.10718610,,Optimizing House Price Prediction: Comparative Analysis of Machine Learning Techniques,2024.0,International Conference on Electrical Engineering and Information Communication Technology,Astha Sul; Vaishnavi Jagtap; Parantap Jesalpura; Anushka Nema; R. R,"This research presents a comparative analysis of machine learning models for house price prediction across four distinct metropolitan areas: Pune, Bangalore, Mumbai, and Delhi. Leveraging historical data encompassing multidimensional attributes such as location, size, and amenities, we systematically assess the performance of diverse models, including Linear Regression, Decision Regression Tree, Random Forest, K-Nearest Neighbors (KNN), Decision Tree Classifier, and Gaussian Naïve Bayes. Through rigorous examination, we establish the Decision Regression Tree model as the optimal choice, demonstrating the highest average accuracy score of 96.27%. This model's capacity to capture intricate non-linear relationships within the dataset contributes to enhanced predictive efficacy, while its interpretability facilitates stakeholders' comprehension of underlying price determinants. By shedding light on model performance variations across different urban landscapes, this research furnishes valuable insights for informed decision-making in house price prediction within diverse real estate markets.",1.0,,False,Conference,,2024-07-24,095237a9bb9d5b7ec7b0835c9762968643434a53,optimizinghousepricepredictioncomparativeanalysisofmachinelearningtechniques,sul,10.1109/iceeict61591.2024.10718610,optimizinghousepricepredictioncomparativeanalysisofmachinelearningtechniques|2024,1,2104,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ffbb197247ac9a8dd59ae39b894d0327e0a28e2b,10.1109/icstsn61422.2024.10671079,,A Comparative Study of House Price Prediction Using Machine learning and Deep learning Techniques,2024.0,2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN),Hamsavathi V; D. B; Subiksha R; Venkatesan R; Rekha D,"It is always a dream for everyone to own a house or flat., House prices increase every year, so there is a need for a system to, predict house prices in the future. House price prediction system will, help the developer to determine the selling price of a house and also, help the customer to make correct decision to look for a suitable, property to purchase. Most of the buyers expect the house based on, budget, prime location, property type, size etc. This paper presents, a comparative study on house price prediction using three distinct, models that is Artificial Neural Network (ANN), Naive Bayes, and, Random Forest (RF). Through comparative analysis, we evaluate the, performance of these models on a real-world housing dataset. The, outcome of this research contributes to advancing the field of real, estate prediction models that utilize the advantages of models. These, methods improved accuracy and efficiency in predicting house, prices, aiding house owners for reselling, real estate professionals, and investors facilitating informed decision in the ever-changing, real estate market.",1.0,,False,Conference,,2024-07-18,ffbb197247ac9a8dd59ae39b894d0327e0a28e2b,acomparativestudyofhousepricepredictionusingmachinelearninganddeeplearningtechniques,v,10.1109/icstsn61422.2024.10671079,acomparativestudyofhousepricepredictionusingmachinelearninganddeeplearningtechniques|2024,1,2102,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",cdbc248aa205bf4aed3f640817782cd591d8cfb9,10.1117/12.3030415,,Estimation of House Property Value with Hybrid Machine Learning,2024.0,2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies,S. S. Mallika; Ch. Sarada; G. Swetha,"Numerous variables contribute to wide swings in the price of homes. Size, proximity to services, and other conveniences all play a role. However, in certain instances, these costs are not based on accurate value and are, instead, inflated. Many of us have probably come into situations in which the price of a home seemed excessively high for reasons that ultimately didn't matter to us. The goal of this work is to establish home values by considering a wide range of tangible characteristics. We need a model that takes into account all relevant factors to arrive at a fair pricing that works for both the buyer and the seller. For this reason, rather of depending on just one Machine Learning algorithm to make home price predictions, a combined approach is proposed. We can make sense of the various algorithmic outputs and formulate a rule that makes use of all these numbers. The cost of a home will be settled according to this guideline. The proposed approach would reduce errors and house price prediction precision also increases.",1.0,,False,Conference,Engineering,2024-03-22,cdbc248aa205bf4aed3f640817782cd591d8cfb9,estimationofhousepropertyvaluewithhybridmachinelearning,mallika,10.1117/12.3030415,estimationofhousepropertyvaluewithhybridmachinelearning|2024,1,2040,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",474b2dd56d422c5715b06eb053e7391e307246db,10.1109/icrpset64863.2024.10955925,,Predicting Apartment Rental Prices in Bangladesh: A Machine Learning Approach,2024.0,"2024 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET)",Hamim Reza; Nazrul Islam Tareq; Taseen Alam Dehan; Utsho Majumder Joy,"Bangladesh is a dynamically changing real estate market, and in such a world, property owners as well as tenants want to be able to predict rent prices as accurately as possible. This paper attempts to provide an inclusive machine learning solution to the prediction of apartment rental prices. Using the publicly available “8K + Property Listing Data in Bangladesh” dataset, which includes 8,000 entries with 11 unique features, we use predictive modeling to infer rental prices. The dataset provides numerous property characteristics, including location, size, and the number of beds and baths. Our findings reveal that the Extra Trees Regression model outperforms others, achieving an R2 score of 96.57%. Following closely, the XGBoost model attained an R2 score of 95.40 %, while the General Regression Neural Network (GRNN) also showed strong performance with an R2 score of 96.53 %. These results demonstrate the effectiveness of machine learning in accurately forecasting housing prices in the region.",1.0,,False,Conference,,2024-12-07,474b2dd56d422c5715b06eb053e7391e307246db,predictingapartmentrentalpricesinbangladeshamachinelearningapproach,reza,10.1109/icrpset64863.2024.10955925,predictingapartmentrentalpricesinbangladeshamachinelearningapproach|2024,1,2008,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d20c31b1c855b9712f843362c88385e7178e263c,10.1109/iccect60629.2024.10546182,,"Forecasting Housing Price Using GRU, LSTM and Bi-LSTM for California",2024.0,"2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT)",Jun Liu; Zihan Ma,"This study aims to address the challenge of predicting housing prices in California using various neural network models, including LSTM, Bi-LSTM, GRU, and a hybrid GRU-LSTM. To enhance predictive accuracy, an innovative approach involving spatial sequences based on Haversine is introduced. By considering the spatial correlation of housing prices, the data is spatialized, and the results demonstrate that the collaborative integration of spatial sequences and neural networks significantly improves model prediction accuracy. This not only provides a novel approach for the precise forecasting of individual housing prices but also holds potential to offer additional support for government oversight and regulation, benefiting homebuyers in understanding the complexity of the home-buying process. The findings emphasize the importance of considering spatial factors in housing price predictions, offering a promising direction for future research and practical applications in the real estate domain.",1.0,,False,Conference,,2024-04-26,d20c31b1c855b9712f843362c88385e7178e263c,forecastinghousingpriceusinggrulstmandbilstmforcalifornia,liu,10.1109/iccect60629.2024.10546182,forecastinghousingpriceusinggrulstmandbilstmforcalifornia|2024,1,2004,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",38adcc87b867b6537719185fd94cb53c5ceeca55,10.1109/icet59753.2023.10374881,,Predictive Modeling of House Prices; A Regression Approach,2023.0,International Conference on Emerging Technologies,Natasha Saeed; Ahsan Saeed; Maria Bashir; Lismer Andres Caceres-Najarro,"Housing prices have always been an important issue among the business community. The sudden increase in property prices has an impact on economic cycles as well as our means of living. However, understanding several features that affect residential real estate prices is still challenging. Traditional techniques have been unable to choose essential features for precise predictions, which frequently leads to reduced prediction accuracy. In this research work, we utilized machine learning techniques to increase prediction accuracy and get data-driven insights into property evaluation. This study compares Random Forest Regressor and the Decision Tree Regressor using an extensive dataset of Bangalore that includes nine property features. The findings show that our models achieved an optimal accuracy level and provide insights into the variances in model performance, assisting policymakers in choosing the most suitable approaches to get accurate house prices across various market circumstances.",1.0,,False,Conference,,2023-11-06,38adcc87b867b6537719185fd94cb53c5ceeca55,predictivemodelingofhousepricesaregressionapproach,saeed,10.1109/icet59753.2023.10374881,predictivemodelingofhousepricesaregressionapproach|2023,1,2003,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",04212ec06b4bf4e7affd140b80489edf27c94800,10.1109/seeda-cecnsm68644.2025.11329623,,Evaluating Machine Learning Approaches for Residential Property Price Estimation,2025.0,"SouthEast European Design Automation, Computer Engineering, Computer Networks and Social Media Conference",Elias Dritsas; M. Trigka; Phivos Mylonas,"This study presents a comparative evaluation of six supervised machine learning (ML) models, namely Linear Regression (LinR), Ridge Regression (RidgeR), Random Forest (RF), XGBoost, Support Vector Regression (SVR), and MultiLayer Perceptron (MLP), for house-price prediction on a structured tabular dataset. Performance was assessed on a hold-out test set using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination ($\mathbf{R}^{\mathbf{2}}$). After extensive hyperparameter tuning with grid search and 3-fold cross-validation, XGBoost emerged as the top-performing model, achieving MAE $=16,347$, RMSE $=21,329$, and $\mathrm{R}^{2}=0.932$. The results confirm that ensemble-based models, particularly XGBoost, offer a favorable balance between predictive accuracy and practical deployability in real-estate valuation tasks. Future work will explore the integration of macroeconomic indicators and multimodal property features to enhance generalization.",1.0,,False,JournalArticle;Conference,Computer Science,2025-09-19,04212ec06b4bf4e7affd140b80489edf27c94800,evaluatingmachinelearningapproachesforresidentialpropertypriceestimation,dritsas,10.1109/seeda-cecnsm68644.2025.11329623,evaluatingmachinelearningapproachesforresidentialpropertypriceestimation|2025,1,1992,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b6c37cc2179253126198a3a00409f3c3ac156ea2,10.1109/bdicn58493.2023.00029,,Forecasting House Resale Prices using Ensemble learning,2023.0,"2023 2nd International Conference on Big Data, Information and Computer Network (BDICN)",Zhenyu Li; Zhaoji Li,"Forecasting House sales prices is a critical issue for individual investors and government. prediction system has high accuracy and flexibility and plays an important role in decision making. However, previous studies have focused only on limited datasets, as well as nontime series or feature prediction. This paper proposes a blending model based on Python and its xgboost, DF21, and Geatpy packages to predict resale prices in Singapore. First, the high-cardinality categorical attributes are preprocessed by means of mean coding. Then, we propose a linear blending method that includes GA-HL-Xg-Boost, GARandom Forest (GA-RF), deep-Random Forest (DRF) and lightGBM, with Gini impurity to determine the importance of features. Finally, the result shows that it can obtain a mean absolute percentage error (MAPE) of 7.36% in a stationary overall trend of house prices trend. Our research can provide a powerful predictor of house resale prices in different economic environments.",1.0,,False,Conference,,2023-01-01,b6c37cc2179253126198a3a00409f3c3ac156ea2,forecastinghouseresalepricesusingensemblelearning,li,10.1109/bdicn58493.2023.00029,forecastinghouseresalepricesusingensemblelearning|2023,1,1981,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1a5a200dbc6d4af47cb6e8d5c7885680aada9563,10.1109/otcon60325.2024.10687534,,House Price Prediction using Machine Learning Approaches,2024.0,2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0,Reshma; Tushar Semwal; Shashank Pareek; Shruti Kuhar,"Machine learning has grown in significance as a field of technology throughout time. This study focuses on demonstrating how machine learning and its algorithms may be used to estimate price changes in the real estate business, which is a non-direct sector of the economy of our country. In terms of pricing, real estate has evolved into one of the markets with the highest levels of competition. Our method helps determine a property’s starting price based on local criteria. Analyzing past market patterns, pricing ranges, and technical advancements will help forecast future costs. This study seeks to construct this prediction engine for actual user usage by reviewing the application of existing machine-learning techniques on two drastically different datasets. The goals of our initiative are to reduce financial loss and buy high-quality homes. The cost of a home is determined by a variety of factors, including the buyer’s budget and the number of home improvements.",1.0,,False,Conference;Review,,2024-06-05,1a5a200dbc6d4af47cb6e8d5c7885680aada9563,housepricepredictionusingmachinelearningapproaches,reshma,10.1109/otcon60325.2024.10687534,housepricepredictionusingmachinelearningapproaches|2024,1,1976,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",fadb017374e641cd4843bba0cc36b31469581842,10.1109/iciip61524.2023.10537685,,Linear Regression-Based Prediction Model For Real Estate Properties in India,2023.0,International Conference on Intelligent Information Processing,Harsh Verma; Amrit Singh; S. Avasthi,"The real estate market is one of the most competitive in terms of price, and it is also one of the few that is subject to change on a consistent basis. It is one of the most significant areas where the principles of machine learning are applied to improve and increase the accuracy of cost predictions. The price of a home depends on three factors: its physical condition, its design, and its location. The current method of estimating property prices does not account for fluctuations in market prices or the rate of inflation. The objective of this paper is to provide Indian customers with a forecast of residential prices that takes their needs and budgets into account. To enhance the overall performance of the prediction models, the mean target encoding is also incorporated into the methodology. According to the findings of this study, the estimation error that is produced by linear regression is the lowest and accuracy of the implemented system is 98.6%.",1.0,,False,Conference,,2023-11-22,fadb017374e641cd4843bba0cc36b31469581842,linearregressionbasedpredictionmodelforrealestatepropertiesinindia,verma,10.1109/iciip61524.2023.10537685,linearregressionbasedpredictionmodelforrealestatepropertiesinindia|2023,1,1966,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",bd4437daf3848828ea0bb1b6e0b2bbcf9a2ea862,10.23939/iw_itpm2024.077,,A machine learning method for real estate operation projects forecasting,2024.0,International Workshop IT Project Management,S. Bushuyev; D. Bushuiev; Nikolay Poletaev; Mykola Malaksiano; Dmitriy Kravtsov,"The study examined the impact of heterogeneous urban night lighting on the possibilities of improving the forecast accuracy for real estate operations projects in a large city. By transforming high-resolution night satellite images into light clusters, new dataset features were obtained after calculating the centers of light clusters and the distances between them. The study used a dataset on real estate rentals in Houston, Texas, USA. The light clusters were linked to the terrain based on their geometric coordinates obtained using QGIS. These new features were integrated into a machine learning model based on the LightGBM regressor. Calculations showed that the reduction in forecast error (in the mean squared error metric, MSE) for our dataset was 11.8%, significantly exceeding the influence of other features investigated in the study. The results suggest that the “light” feature can be considered highly promising for real estate operations projects.",1.0,,False,JournalArticle,Computer Science,2024-06-21,bd4437daf3848828ea0bb1b6e0b2bbcf9a2ea862,amachinelearningmethodforrealestateoperationprojectsforecasting,bushuyev,10.23939/iw_itpm2024.077,amachinelearningmethodforrealestateoperationprojectsforecasting|2024,1,1965,1,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",752564cdb552ae2068b7197cd7e2fe2ef503224f,10.1109/icears56392.2023.10085090,,Machine Learning Algorithms for Predicting House Prices in a Smart city using its Real-Time Data,2023.0,2023 Second International Conference on Electronics and Renewable Systems (ICEARS),M. Joel; M. Navaneethakrishnan; K. Sriram; S. Manonmani,"Housing prices are an important economic indicator, wherein the property values are important to both buyers and sellers. One of the least transparent industries across the world is real estate. In order to conduct the research, real-time housing data has been collected from Chennai City. Sine this study deals with the continuous outcome variable, regression techniques are highly preferred. The major goal of this research study is to predict housing values by using real-time information. This study performs various house price evaluation process based on various factors. Several regression models like Lasso and Ridge are utilized and their performances are compared in order to select the best model for resolving a specific issue. This study intends to develop an effective machine learning model to predict the price of a property based on input parameters and display the information in a website to reach out to people.",1.0,,False,Conference,,2023-03-02,752564cdb552ae2068b7197cd7e2fe2ef503224f,machinelearningalgorithmsforpredictinghousepricesinasmartcityusingitsrealtimedata,joel,10.1109/icears56392.2023.10085090,machinelearningalgorithmsforpredictinghousepricesinasmartcityusingitsrealtimedata|2023,1,1931,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d8bb5ab5f11f3763d0620e3c189547e89a43ab74,10.1109/iccigst60741.2024.10717472,,Going the distance: Application of Machine Learning Algorithms in predicting Housing Price Dynamics,2024.0,2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST),Babita Sonare; Sudeep D. Thepade; Pratiksha Gangane; Esha Govardhan; Tisha Nawani; P. Gadkar,"In this paper, we have used various algorithms such as Gradient Boost Regression, Catboost Regression, Decision Tree Regression and K-neighbors Regression by categorizing predictions based on threshold. To address the problems of data quality and other complexities, we performed validation on all the algorithms mentioned above. Predicting house prices often involves many barriers such as taking all the important factors into account, being aware of the demand of a particular area, government policies, spatial locations and houses with different sizes. We overcame these problems with the ML models we built. Among all the algorithms, Catboost Regression gave great results with mean absolute percentage error of 0.018 and r-squared value of 0.904. This paper provides insights in more detail about the strong positive correlation between area and price.",1.0,,False,Conference,,2024-07-18,d8bb5ab5f11f3763d0620e3c189547e89a43ab74,goingthedistanceapplicationofmachinelearningalgorithmsinpredictinghousingpricedynamics,sonare,10.1109/iccigst60741.2024.10717472,goingthedistanceapplicationofmachinelearningalgorithmsinpredictinghousingpricedynamics|2024,1,1859,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5604257b0816bf097e65107bc1857e085e62449e,10.1109/idciot56793.2023.10053480,,ML-based Java UI for Residence Predictor,2023.0,2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT),Saleha Saudagar; Mukund Kulkarni; Isha Raghvani; Haider Hirkani; Inderdeep Bassan; Pranav Hole,"When people consider buying a home or property, they first tend to go online to try trends and other related research. The proposed system presents a console application in Java that can predict house prices using machine learning algorithms based on data mining concepts that extract relevant knowledge from raw data and perform practical analysis. The system proposed here analyzes related properties for predicting the prices. Using the wide varieties of libraries available in Python, the proposed system attempts to implement a way to create user interfaces in Java by interacting with Java and Python machine learning model. Random Forest Regression model has been used to predict the prices with an R-squared score of 86.72%. The proposed system implements a JAVA graphical user interface using Swing technology which is simple and lightweight.",1.0,,False,Conference,,2023-01-05,5604257b0816bf097e65107bc1857e085e62449e,mlbasedjavauiforresidencepredictor,saudagar,10.1109/idciot56793.2023.10053480,mlbasedjavauiforresidencepredictor|2023,1,1851,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",019dcbed2177b0821fa32b9e0be094225713210f,10.1109/sceecs64059.2025.10940122,,Property Price and Rent Prediction System,2025.0,"2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)",A. Singh; Abhinav Rai; Devansh Karnwal; Anupam Vats; Tripti Pandey,"Machine Learning (ML) has revolutionized various sectors including speech recognition, product recommendations, and healthcare. This research focuses on applying ML techniques to address challenges in real estate market prediction. The unpredictable nature of property prices creates significant difficulties for buyers in making informed decisions. This research proposes a multi-algorithm housing price prediction model, integrating SVM, Random Forest, and GBM approaches, with Random Forest demonstrating superior predictive performance. The model considers various factors influencing property prices, enabling buyers to make data-driven decisions without excessive reliance on intermediaries. This paper presents the methodology, implementation, and comparative results of these algorithms in the context of real estate price prediction.",1.0,,False,Conference,,2025-01-18,019dcbed2177b0821fa32b9e0be094225713210f,propertypriceandrentpredictionsystem,singh,10.1109/sceecs64059.2025.10940122,propertypriceandrentpredictionsystem|2025,1,1842,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",4fd2f155d8b84454913ebc2404ff2a757ad176ac,10.1109/iccc54389.2021.9674495,,An Evaluation Model of Demolished Houses based on Improved Deep Learning,2021.0,2021 7th International Conference on Computer and Communications (ICCC),Haifeng Lv; Bao-Ming Pu,"In view of the problem of housing price evaluation, the traditional methods are not objective and efficient, which often stimulate the contradictions of various stakeholders, and even cause social chaos. This paper proposes an evaluation model of demolished houses based on improved deep learning, applies the theory of deep learning to the evaluation of demolished houses, optimizes and improves it, adds forgetting unit to it, and constructs regional factor index system, individual factor index system and transaction based index system. In this paper, a residential area in Shenyang is taken as the evaluation object, and the experiment shows that the proposed method improves the evaluation efficiency of demolished houses and reduces the subjective factors in the evaluation process.",1.0,,False,Conference,,2021-12-10,4fd2f155d8b84454913ebc2404ff2a757ad176ac,anevaluationmodelofdemolishedhousesbasedonimproveddeeplearning,lv,10.1109/iccc54389.2021.9674495,anevaluationmodelofdemolishedhousesbasedonimproveddeeplearning|2021,1,1789,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3816f0b120aa18a8235b6f88c91dc5595fca3e5d,10.1109/mlbdbi54094.2021.00046,,Guangzhou House Price Analysis based on Machine Learning Methods,2021.0,"2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)",Weizheng Liang; Le-Yu Pan; Jia Sun; Jia-Nuo Wang,"With the continuous development of China's economy, Guangzhou, one of the four most powerful cities in China, has attracted many people to go and settle down there. This makes housing demand in Guangzhou continue to rise and has led to higher housing prices in recent years. In this paper, we aim to exploit the main factor, which affects the house price. Due to that, we are not able to obtain the new-house transaction. We start with second-hand housing to find the relationship. We utilize TF-IDF and K-means to analyze the data, and we conclude that the housing type is mainly related to its price, which two or three bedroom houses are with high demand and price. In contrast, houses with only one bedroom are not popular in the housing market at a low price.",1.0,,False,Conference,,2021-12-01,3816f0b120aa18a8235b6f88c91dc5595fca3e5d,guangzhouhousepriceanalysisbasedonmachinelearningmethods,liang,10.1109/mlbdbi54094.2021.00046,guangzhouhousepriceanalysisbasedonmachinelearningmethods|2021,1,1764,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c335e16627a261fa9397ed6c2e942f91c71d8948,10.1109/arace56528.2022.00035,,Research on Submarket Effects of Real Estate Valuation Based on Bayesian Probability Model. A Comparison Between Cities,2022.0,"2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)",Xinjing Qin; Ping Zhang; Xinyang Zhang; Bin Cheng; Xianglin Bao,"Submarket effects are essential for real estate valuation since they could be used to increase both the prediction accuracy of housing prices and the interpretability of the machine learning model. In this paper, a Bayesian probability model that divides the housing market based on the housing location is proposed to forecast house prices, and discover key factors in house prices. A comparison of the key influencing factors affecting the real estate market in Hangzhou and in Chengdu is provided. The experimental results show that the key influencing factors in corresponding functional areas of different cities are similar, which sheds a light on creating a unified model for the real estate valuation.",1.0,,False,Conference,,2022-08-01,c335e16627a261fa9397ed6c2e942f91c71d8948,researchonsubmarketeffectsofrealestatevaluationbasedonbayesianprobabilitymodelacomparisonbetweencities,qin,10.1109/arace56528.2022.00035,researchonsubmarketeffectsofrealestatevaluationbasedonbayesianprobabilitymodelacomparisonbetweencities|2022,1,1709,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",357a9063d77609f7e7c5a7eedd6c092a06c711cc,10.1109/sces61914.2024.10652318,,Enhancing Zillow Zestimates: Leveraging Machine-Learning for Precise Property Valuation Predictions,2024.0,Students Conference on Engineering and Systems,Avirup Banerjee; Aditya Pandey; B. Prakash; Saptorshi Banerjee; Atri Bandyopadhyay; Prasun Chakraborty,"A variety of machine learning techniques, such as Linear Regression, Lasso Regression, Ridge Regression, Elastic Net, Decision Tree, Random Forest, and Gradient Boosting, are used in this work to evaluate the assessment of house worth. We want to improve the precision and dependability of house price forecasts by utilizing a variety of approaches. We compare how well these algorithms capture intricate correlations between different variables and home prices. By doing extensive testing and analysis, we aim to determine the best real estate value model, advancing predictive analytics in the housing industry. Our results will help buyers, sellers, and real estate agents make better-informed decisions.",1.0,,False,Conference,,2024-06-21,357a9063d77609f7e7c5a7eedd6c092a06c711cc,enhancingzillowzestimatesleveragingmachinelearningforprecisepropertyvaluationpredictions,banerjee,10.1109/sces61914.2024.10652318,enhancingzillowzestimatesleveragingmachinelearningforprecisepropertyvaluationpredictions|2024,1,1707,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3e02f40124714e937f79164ce91d5de7a5359b45,10.1109/icot51877.2020.9468726,,Research on House Price Forecast Based on Hyper Parameter Optimization Gradient Boosting Regression Model,2020.0,International Conference on Orange Technologies,Xiaogang Wang; Shichen Zhai; Jui-Le Chen,"In order to study the prediction accuracy of different regression models, this paper selects housing price prediction as a typical research object, and proposes a hyper parameter optimization gradient boosting regression model (HPOGBR). Firstly, the data is preprocessed and the key features are extracted through visualization and basic model pretraining. Then, multiple machine learning regression models are used for comparison with our HPOGBR model base on evaluation indicators. Finally, the optimal model and parameters are selected by grid search. The experimental results show that our proposed HPOGBR model has better accuracy and convergence over existing machine learning regression models.",1.0,,False,Conference,,2020-12-18,3e02f40124714e937f79164ce91d5de7a5359b45,researchonhousepriceforecastbasedonhyperparameteroptimizationgradientboostingregressionmodel,wang,10.1109/icot51877.2020.9468726,researchonhousepriceforecastbasedonhyperparameteroptimizationgradientboostingregressionmodel|2020,1,1701,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d4b82e34d799044cfc3abbe13a0e5718709300f2,10.1109/ssaic61213.2024.00092,,Model Research on Forecast of Second-Hand House Price in Shenyang Based on XGBoost Algorithm,2024.0,"2024 Asia-Pacific Conference on Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC)",Kan Lin,"In order to have a deep understanding of second-hand house prices in Shenyang, this paper studied transaction data of second-hand housing from the platform ke.com. First, the qualified data was determined and collected, then processed and data parameters were set. Second, by comparing three models, Ridge regression, Lasso Regression, and XGBoot model to find which is the best model to be used in the second-hand housing market. By implementing the data and analysis with visualization graphics, the study results indicate that the XGBoost model offers more accurate predictions in understanding second-hand housing prices in Shenyang.",1.0,,False,Conference,,2024-01-10,d4b82e34d799044cfc3abbe13a0e5718709300f2,modelresearchonforecastofsecondhandhousepriceinshenyangbasedonxgboostalgorithm,lin,10.1109/ssaic61213.2024.00092,modelresearchonforecastofsecondhandhousepriceinshenyangbasedonxgboostalgorithm|2024,1,1637,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_citations,cites:sirmans2006,bc308b21c8dd70acbe73301e4fd2377b6dee99e6,10.1007/978-3-030-48465-1_56,,A Model Validation and Predicting the Rental Values of Residential Properties Using Logistic Regression Model,2020.0,"Collaboration and Integration in Construction, Engineering, Management and Technology",T. B. Odubiyi; A. I. Ugulu; O. Oshodi; C. Aigbavboa; W. Thwala,,1.0,,False,,Economics,2020-12-22,bc308b21c8dd70acbe73301e4fd2377b6dee99e6,amodelvalidationandpredictingtherentalvaluesofresidentialpropertiesusinglogisticregressionmodel,odubiyi,10.1007/978-3-030-48465-1_56,amodelvalidationandpredictingtherentalvaluesofresidentialpropertiesusinglogisticregressionmodel|2020,1,1000,1,cites:sirmans2006,0,1,2,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d9fa106183aded621bf9bd1360b0c270a09b8388,10.1049/icp.2023.1820,,House price prediction using random forest techniques: a comparative study,2023.0,"International Conference on Green Energy, Computing and Intelligent Technology (GEn-CITy 2023)",S. S. Jamil; S. Bansal; L. Vinjamuri,,1.0,,False,Conference,,,d9fa106183aded621bf9bd1360b0c270a09b8388,housepricepredictionusingrandomforesttechniquesacomparativestudy,jamil,10.1049/icp.2023.1820,housepricepredictionusingrandomforesttechniquesacomparativestudy|2023,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e1c7e9d11d1d6278657df06ec19b746641e91d07,10.53383/100412,,Using Machine Learning Regression Algorithms to Predict House Prices in Vietnam,2025.0,International Real Estate Review,,,1.0,,False,JournalArticle,,2025-12-31,e1c7e9d11d1d6278657df06ec19b746641e91d07,usingmachinelearningregressionalgorithmstopredicthousepricesinvietnam,,10.53383/100412,usingmachinelearningregressionalgorithmstopredicthousepricesinvietnam|2025,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",99bc73e746c604b97c47dc731713e4829edc4e0a,10.1063/5.0229736,,House price prediction and regression evaluation metrics score using Machine Learning and Deep Learning model,2024.0,AIP Conference Proceedings,G. Venkatesh; B. Rajashekar; Ashwini Kodipalli; Trupthi Rao; Kumaraswamy Siddagangaiah,,1.0,,False,Conference,,,99bc73e746c604b97c47dc731713e4829edc4e0a,housepricepredictionandregressionevaluationmetricsscoreusingmachinelearninganddeeplearningmodel,venkatesh,10.1063/5.0229736,housepricepredictionandregressionevaluationmetricsscoreusingmachinelearninganddeeplearningmodel|2024,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",8a10afbba0fb0c5e4ef5144fe0adff886780ea8b,10.1142/s1752890925500205,,"Forecasting Residential Property Prices in Jiaxing City, China: A Hybrid Machine Learning Framework Integrating Gaussian Process Regressions and Bayesian Optimization",2025.0,Journal of Uncertain Systems,Bing-Zi Jin; Xiao-Jie Xu,,1.0,,False,JournalArticle,Computer Science,2025-09-05,8a10afbba0fb0c5e4ef5144fe0adff886780ea8b,forecastingresidentialpropertypricesinjiaxingcitychinaahybridmachinelearningframeworkintegratinggaussianprocessregressionsandbayesianoptimization,jin,10.1142/s1752890925500205,forecastingresidentialpropertypricesinjiaxingcitychinaahybridmachinelearningframeworkintegratinggaussianprocessregressionsandbayesianoptimization|2025,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",18921a9e183f4d7b7210f68b64dbcfe65924e692,10.1007/978-981-16-0115-6_136,,Gaussian Process Regression for Forecasting of Boston Housing Prices,2021.0,Lecture Notes in Electrical Engineering,Yan-Ran Xu; Haoyang Zhang; Haoxuan Li,,1.0,,False,,,,18921a9e183f4d7b7210f68b64dbcfe65924e692,gaussianprocessregressionforforecastingofbostonhousingprices,xu,10.1007/978-981-16-0115-6_136,gaussianprocessregressionforforecastingofbostonhousingprices|2021,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,apartment prices hedonic,fabd38e3d684b3496fdc9802ac35009ea99845f2,10.37407/kres.2026.44.3.159,,Reliability Assessment of Machine-Learning Automated Valuation Models : Apartment-Complex Holdout Validation in the Seoul Metropolitan Area,2026.0,Korea Real Estate Society,Jae H. Kim,"This study evaluates the reliability of machine-learning automated valuation models (AVMs) when repeated apartment-complex observations are explicitly controlled. AI-based AVMs refer here to random forest, LightGBM, and XGBoost, which are compared with an unpenalized hedonic OLS benchmark using 129,237 apartment transactions assigned to 2,371 composite complex groups. The source file combines 2021-2023 transactions with 2023 management-fee records, but transaction dates are unavailable; the temporal validity of some management-fee inputs therefore cannot be verified, and related results are interpreted as retrospective and conditional. Row-wise random splitting and apartment-complex holdout testing are treated as separate operational evaluation regimes with different test samples, not as a paired estimate of the pure effect of the split rule. Under row-wise random splitting, random forest yields the lowest log-price MAE (0.1026). Under complex holdout testing, OLS has the lowest point estimate (0.1839), but the difference from LightGBM (0.1960) is not statistically significant (Delta=0.0121, 95% CI [-0.0036, 0.0282], p=0.135); XGBoost and random forest have significantly larger errors than OLS. OLS calibration indicators are closer to their ideal values, although no direct between-model calibration test is conducted. Excluding management-fee variables reverses the OLS-LightGBM point ranking; this auxiliary scenario does not establish general LightGBM superiority, but shows that relative performance is conditional on temporally valid feature availability. SHAP is used only as an interpretive diagnostic, not as evidence of predictive superiority. The findings are conditional on the specified data, validation regimes, and limited hyperparameter search space.",0.0,,False,JournalArticle,,2026-09-30,fabd38e3d684b3496fdc9802ac35009ea99845f2,reliabilityassessmentofmachinelearningautomatedvaluationmodelsapartmentcomplexholdoutvalidationintheseoulmetropolitanarea,kim,10.37407/kres.2026.44.3.159,reliabilityassessmentofmachinelearningautomatedvaluationmodelsapartmentcomplexholdoutvalidationintheseoulmetropolitanarea|2026,1,2785,1,apartment prices hedonic,2,1,1,2,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,machine learning hedonic pricing housing,06b68c6ed1012b0432cc7c7a49dd32273dc0f262,10.1109/robothia68364.2026.11507118,,Urban House Price Forecasting in Malaysia Using Tree-Based Machine Learning Models,2026.0,2026 IEEE 2nd International Conference on Robotics and Technologies for Industrial Automation (ROBOTHIA),Ying Zheng; Hui Shen Choo; Yoke Leng Yong; Umi Najiah Ahmad Razimi; Muhammed Basheer Jasser; Sina Abdipoor,"Nowadays, it is essential to perform accurate predictions of urban house prices for housing market analysis and policy planning. Malaysia has traditionally used conventional valuation methods such as ARIMA models, Hedonic Price Model, and Linear Regression, but these methods often struggle to represent complex market dynamics. Now, recent work increasingly explores tree-based or neural machine learning (ML) models. This study seeks to implement tree-based models such as Random Forest, Gradient Boosting, and Extreme Gradient Boosting for urban house price forecasting in Malaysia, to examine the effectiveness of ML on house price predictions. In terms of data, structural, locational, and socioeconomic features were compiled from governmental portals, preprocessed, and explored before modelling. During evaluation, metrics such as Root Mean Squared Error (RMSE), Root Mean Squared Percentage Error (RMSPE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were applied. Out of the three models, Random Forest outperformed the other models when predicting house prices, achieving a MAE value of 0.2189 after optimization and cross-validation. Furthermore, tree-based models also provide additional feature importance insights to stakeholders. As a result, the main influential features affecting house price prediction included house type, population and state. Overall, this study solidifies the effectiveness of tree-based models for urban house price forecasting in terms of accuracy and interpretability.",0.0,,False,Conference,,2026-04-11,06b68c6ed1012b0432cc7c7a49dd32273dc0f262,urbanhousepriceforecastinginmalaysiausingtreebasedmachinelearningmodels,zheng,10.1109/robothia68364.2026.11507118,urbanhousepriceforecastinginmalaysiausingtreebasedmachinelearningmodels|2026,1,2534,5,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic house price || hedonic housing prices || machine learning hedonic pricing housing",2,1,2,4,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic housing prices,2fd3774a9613d11d776a1e9aef35c47157e0ce6b,10.1504/ijaitg.2019.10024550,,Pricing Model for Real Estate Market in Taiwan - Adjusted Matching Approach,2019.0,International Journal of Agriculture Innovation Technology and Globalisation,Jing-Tang Tsay; Jerry T. Yang; Che-Chun Lin,"We propose an adjusted matching model to predict housing prices with actual transaction data for the real estate market in Taiwan. The lack of transparency in Taiwan's real estate market has resulted in information asymmetry between consumers and housing agencies for a long time until the 'actual price registration' online system took effect in August 2012. Utilising APR database, our pricing model encompasses the advantages of three commonly-used pricing methods: hedonic pricing, repeat sales, and matching methods, while mitigating their shortcomings. Numerical results show that our method performs better than hedonic pricing model in terms of prediction precision. Since the sampling period we select overlaps with the planning stage of 'consolidated housing and land taxes', our empirical results can reflect and quantify the impact of the proposed property taxation system on housing price before its implementation. Our study contributes to providing for real estate market a suitable pricing model in accordance with transaction time, place, and housing characteristics. The results of our study can also shed light on how to value the real estate properties included in an urban renewal program or the agricultural lands considered a change of land usage.",0.0,,False,,Business,,2fd3774a9613d11d776a1e9aef35c47157e0ce6b,pricingmodelforrealestatemarketintaiwanadjustedmatchingapproach,tsay,10.1504/ijaitg.2019.10024550,pricingmodelforrealestatemarketintaiwanadjustedmatchingapproach|2019,1,2270,4,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic",2,1,1,1,1,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,0 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",eacd60cb1c5b00e152afea75235716090cb73279,10.1109/icecet63943.2025.11472487,,Harnessing Machine Learning to Evaluate the Green Value of Housing: Random Forest and XGBoost Insights,2025.0,"2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET)",E. Logosha; M. Maumy; F. Bertrand,"In the context of increasingly stringent thermal and environmental regulations, the transition to sustainable real estate has become a significant challenge in France. Green value, which captures the benefits of a property’s energy efficiency and environmental performance, is emerging as a key criterion in buying and selling decisions. To address the limitations of traditional hedonic models, often constrained by linear assumptions, modern machine learning approaches such as random forests and XGBoost provide a greater capacity to uncover complex interactions between property prices, characteristics (e.g., surface area, number of rooms, construction period), socio-economic conditions, and energy labels. These tools deliver more precise and detailed estimations, enhancing the understanding of green value determinants and fostering sustainable practices in the real estate sector.",0.0,,False,JournalArticle;Conference,Computer Science,2025-07-03,eacd60cb1c5b00e152afea75235716090cb73279,harnessingmachinelearningtoevaluatethegreenvalueofhousingrandomforestandxgboostinsights,logosha,10.1109/icecet63943.2025.11472487,harnessingmachinelearningtoevaluatethegreenvalueofhousingrandomforestandxgboostinsights|2025,1,1890,6,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic housing prices || housing characteristics prices hedonic || machine learning hedonic pricing housing",2,1,1,4,0,7,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate),7511153ea51e557f923dfd6800b79eef910eed2d,10.15396/eres2018_114,,"Same difference: A unifying framework for house value predictions through hedonic pricing, nearest neighbours, and comparable sales",2018.0,,Sotirios Thanos; Jean Dubé; Diègo Legros,,0.0,,False,,Economics,,7511153ea51e557f923dfd6800b79eef910eed2d,samedifferenceaunifyingframeworkforhousevaluepredictionsthroughhedonicpricingnearestneighboursandcomparablesales,thanos,10.15396/eres2018_114,samedifferenceaunifyingframeworkforhousevaluepredictionsthroughhedonicpricingnearestneighboursandcomparablesales|2018,1,1000,2,hedonic + (housing | house | houses | residential | apartment | apartments | condominium | dwelling | rent | rental | property | real estate) || hedonic house price,2,1,1,0,0,6,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 crossref,preços hedônicos imóveis,,10.22167/2675-6528-202602220,,Precificação Hedônica de Imóveis na Grande Florianópolis: Uso e Comparação de Modelos de “Machine Learning”,2026.0,Revista E&S,Gabriel Galvan de Cesaro; Lilian Cristina Da Silveira,,0.0,,,journal-article,,,doi_10_22167_2675_6528_202602220,precificacaohedonicadeimoveisnagrandeflorianopolisusoecomparacaodemodelosdemachinelearning,cesaro,10.22167/2675-6528-202602220,precificacaohedonicadeimoveisnagrandeflorianopolisusoecomparacaodemodelosdemachinelearning|2026,1,1000,1,preços hedônicos imóveis,1,0,1,1,0,5,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",03ce431f5186393a8baffca24805ff462dd689db,10.35473/jamastika.v5i1.4703,,Prediksi Harga Rumah Menggunakan Algoritma Regresi Linier,2026.0,Jurnal Mahasiswa Teknik Informatika,Muwasatil Muhtajin; Zaehol Fatah,"The property market, particularly the housing sector, frequently experiences price fluctuations influenced by various complex factors such as location, building size, number of rooms, and economic and demographic conditions. This price uncertainty often makes it difficult for sellers to set competitive selling prices and makes it difficult for buyers to estimate the fair value of a residence. This study aims to develop a house price prediction model using a Machine Learning algorithm, namely Multiple Linear Regression. The research data was obtained from the Kaggle public repository (USA Housing Dataset), which contains 5,000 transaction data with independent variables being the average regional income, building age, number of rooms, number of bedrooms, and regional population. The research methodology follows the stages of Knowledge Discovery in Database (KDD), starting from data collection, cleaning, transformation, modeling, and evaluation. The model was built using the Python programming language with the help of the Scikit-Learn library. The test results show that linear regression is able to provide house price predictions with very good performance, indicated by an R-Squared (R²) value of 0.91, a Mean Absolute Error (MAE) of 81,250, and a Root Mean Squared Error (RMSE) of 100,500. These findings indicate that demographic characteristics and physical attributes of buildings have a strong linear relationship to house selling prices. Keywords: Data Mining, Machine Learning, House Price Prediction, Python, Linear Regression. Pasar properti, khususnya sektor perumahan, sering mengalami perubahan harga yang dipengaruhi oleh berbagai faktor kompleks seperti letak wilayah, luas bangunan, jumlah ruangan, serta kondisi ekonomi dan demografi. Ketidakpastian harga tersebut kerap menyulitkan penjual dalam menetapkan harga jual yang kompetitif dan membuat pembeli kesulitan memperkirakan nilai wajar suatu hunian. Penelitian ini bertujuan mengembangkan model prediksi harga rumah dengan memanfaatkan algoritma Machine Learning, yaitu Multiple Linear Regression. Data penelitian diperoleh dari repositori publik Kaggle (USA Housing Dataset) yang berisi 5.000 data transaksi dengan variabel bebas berupa rata-rata pendapatan kawasan, usia bangunan, jumlah ruangan, jumlah kamar tidur, dan populasi wilayah. Metodologi penelitian mengikuti tahapan Knowledge Discovery in Database (KDD), mulai dari pengumpulan data, pembersihan, transformasi, pemodelan, hingga evaluasi. Model dibangun menggunakan bahasa pemrograman Python dengan bantuan pustaka Scikit-Learn. Hasil pengujian menunjukkan bahwa regresi linear mampu memberikan prediksi harga rumah dengan performa yang sangat baik, ditunjukkan oleh nilai R-Squared (R²) sebesar 0.91, Mean Absolute Error (MAE) sebesar 81.250, dan Root Mean Squared Error (RMSE) sebesar 100.500. Temuan ini mengindikasikan bahwa karakteristik demografi dan atribut fisik bangunan memiliki hubungan linear yang kuat terhadap harga jual rumah. Kata Kunci: Data Mining, Machine Learning, Prediksi Harga Rumah, Python, Regresi Linier.",0.0,,False,JournalArticle,,2026-04-16,03ce431f5186393a8baffca24805ff462dd689db,prediksihargarumahmenggunakanalgoritmaregresilinier,muhtajin,10.35473/jamastika.v5i1.4703,prediksihargarumahmenggunakanalgoritmaregresilinier|2026,1,4087,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e10a51c0af5621a45541cc721eb7399865b8f1d3,10.1109/gtss68743.2026.11679646,,AI-Based Real Estate Price Prediction,2026.0,2026 International Conference on Green Technologies and Sustainable Solutions (GTSS),Ankit Nishad; Ankleshwar Singh; Mayank Srivastav; Nikhil Choudhary; A. Srivastava,"The real estate business has a global presence as the second largest sector in the country and this will continue to grow however currently the major hurdle in this industry is determining how much a property actually costs to purchase or sell at fair market value. Various aspects dictate what determines the value of a property including; the size and location of a property, the age of a building, if there are any necessary amenities nearby, current sales trends in the market, and also social infrastructure available to citizens who may want to purchase or rent a real estate property. Many traditional methods of valuating real estate rely solely on human expertise, performing market research and using basic or simplistic statistical models for calculating the value of a property therefore it takes time to find an accurate assessment of a property's value and it does not take into account how many different variables interact with each other in a non-linear manner. With advances made in both Artificial Intelligence (AI) and Machine Learning (ML), new methods for forecasting and deriving pricing models have become widely accepted as powerful means of optimizing and streamlining the entire process of real estate valuation. The ASN will rely on statistical analysis of existing real estate transactions and appraisal records to create models that will assist in determining the fair market value of residential properties in the United States through the use of AI methods (e.g., linear regression, random forest, gradient boosting - XG boost, and light GBM, ANNs). The project will involve the following steps: data preprocessing; feature engineering; model training; hyperparameter tuning; performance evaluation with metrics (MAE, RMSE, $\mathrm{R}^{\wedge} 2$). Based on our findings, AI models outperformed traditional regression models; and therefore, gradient boosting models and ANNs are the most accurate methods used for estimating property values. Thus, this project will provide an objective and transparent tool to help real estate professionals with the valuation of residential properties in order to help buyers and sellers to make their decisions about when and how to sell or purchase property.",0.0,,False,Conference,,2026-02-01,e10a51c0af5621a45541cc721eb7399865b8f1d3,aibasedrealestatepriceprediction,nishad,10.1109/gtss68743.2026.11679646,aibasedrealestatepriceprediction|2026,1,3228,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",2b0c246599edab8527ee37110e0c2856c6e8bc7a,10.1109/i-coste60462.2023.10500769,,A Data-Driven Approach to Predicting Melbourne Housing Prices Using Advanced Machine Learning Models,2023.0,2023 International Conference on Sustainable Technology and Engineering (i-COSTE),Jesmin Nahar; C. Yun,"This research rigorously explores the intricate correlations between property attributes and housing prices in Melbourne, aiming to refine price prediction accuracy. Positive correlations observed with ‘Rooms,’ ‘Bedroom2,’ ‘Bathroom,’ ‘Landsize,’ and ‘BuildingArea’ underscore the intuitive link between property size and price, while negative associations with ‘Distance,’ ‘Y earBuilt,’ and ‘PropertyCount’ emphasize the influential role of location, age, and market demand. Multiple modeling approaches were employed and compared for their predictive prowess. Among these, the Random Forest model (Approach 5) stands out with an exceptional R-squared value of 0.81, showcasing its adeptness in capturing complex data relationships effectively. Notably, the inclusion of categorical variables and leveraging localized data in conjunction with Random Forest (Approach 6) also yielded an impressive R-squared value of 0.81, underlining the pivotal role of nuanced information in precise predictions. Furthermore, XGBoost (Approach 7) demonstrated commendable performance, achieving an R-squared value of 0.80, showcasing the versatility of boosting techniques. The comparison of these approaches emphasizes the significance of methodical analysis and model selection in predicting housing prices accurately. The findings underscore the superiority of ensemble methods, especially Random Forest, and the importance of incorporating categorical and localized information for precise price estimations in Melbourne's dynamic real estate market. This comprehensive investigation highlights the exceptional performance of Random Forest and underscores the significance of leveraging nuanced data for superior predictive accuracy. Recommendations stemming from this study advocate for the adoption of ensemble methods, specifically Random Forest and XGBoost, while emphasizing the critical role of categorical variables and localized insights for refining housing price predictions in Melbourne. These insights offer a robust roadmap for enhancing predictive accuracy and guiding informed decisions in real estate.",0.0,,False,Conference,,2023-12-04,2b0c246599edab8527ee37110e0c2856c6e8bc7a,adatadrivenapproachtopredictingmelbournehousingpricesusingadvancedmachinelearningmodels,nahar,10.1109/i-coste60462.2023.10500769,adatadrivenapproachtopredictingmelbournehousingpricesusingadvancedmachinelearningmodels|2023,1,3107,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f1f0d3a4671db42dea5d9c7857013733da29881d,10.1109/icmsci67830.2026.11469614,,A Data-Driven Computational Approach for Property Valuation and Market Trend Analysis,2026.0,2026 Second International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI),R. Varaprasad; G. Khatri; Khan Farhan Ali Khan; Kattela Prashanth; Mallela Suresh,"Studying homes, apartments, and other structures in Taiwan's cities is still a significant field of study. Techniques like statistical analysis and questionnaire data collecting are frequently employed in real estate forecasting and analysis. However, these approaches are insufficient and time-consuming when multidimensional features are taken into account. Building a real estate forecasting model that can adjust to a changing environment was the goal of this project. Public government databases provided the data, which were then standardized for precise grouping. A suitable data clustering technique was then used to the standardized data, and cross-statistical analysis was carried out to confirm the chosen algorithm. To improve the clustering analysis's accuracy, we employed an autoencoder-based deep learning system. The best clustering solution was then found using a double-bottom map particle swarm optimization (DBM-PSO) clustering technique. Data gathered from public websites was subjected to cluster analysis and deep learning in order to identify the causes of Taiwan's house prices' steady rise over the last ten years. The study's findings show that three important variables had a major impact on Taiwanese real estate prices: the quantity of real estate transactions, the average unit price of real estate transactions, and the building material and construction index. Our findings may enable governments and scholars to concentrate on particular facets of real estate development without being impacted by other relevant issues. Furthermore, the connections between real estate trends and the three previously stated crucial elements were identified in order to gather important data that would allow the Taiwanese government to control the real estate market and avoid uncontrollably rapid expansion. The paradigm presented in this study offers a novel approach to real estate-related forecasting and enables academics and governments to concentrate on certain elements of real estate growth without being impacted by other related issues.",0.0,,False,Conference,,2026-03-02,f1f0d3a4671db42dea5d9c7857013733da29881d,adatadrivencomputationalapproachforpropertyvaluationandmarkettrendanalysis,varaprasad,10.1109/icmsci67830.2026.11469614,adatadrivencomputationalapproachforpropertyvaluationandmarkettrendanalysis|2026,1,3067,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",48e2f0585e181a915613f0662c44f24ce334187b,10.1109/icet66147.2025.11321431,,"House Price Prediction using Linear Regression, Random Forest, and Gradient Boosting",2025.0,International Conference on Emerging Technologies,Sabeel Ahmed; S. Bazai; Paras Pervaiz; U. Bhatti; Muhammad Imran Ghafoor; Laveeza Rehman,"Accurate house price prediction is essential in real estate planning, investment analysis, and housing policy. This work investigates three machine learning models: Linear Regression, Random Forest, and Gradient Boosting. It uses an extended feature set comprised of property age, spatial location proxy, renovation index, and amenities score. Structured preprocessing, feature engineering, and a subsequent 80/20 traintest split were performed on the dataset. Experimental results indicate that Gradient Boosting yields the lowest prediction error and offers more stable generalization compared to the other benchmarked models. An ablation study showed that the engineered features significantly improve performance, while permutation importance underlined the most influential predictors. Although R2 values remain modest, findings highlight the potential of incorporating richer spatial and socioeconomic attributes. The study contributes a reproducible, improved pipeline for house price prediction and provides practical insights relevant to real estate stakeholders. The study makes use of a dataset containing both categorical and numerical housing attributes from Quetta, Pakistan. Prior to model training, data preprocessing techniques such as feature encoding, normalization, and outlier removal were used. Model performance was compared using evaluation metrics like R2 and RMSE. The results indicate that although Gradient Boosting offered the most precise. his paper aims to investigate the effectiveness of linear regression models in house price forecasting. This paper will first introduce the basic theory of linear regression model, and discuss the factors that affect the housing price, then build and evaluate the housing price prediction model, and then verify the constructed data model through real data. The accuracy of the prediction is analyze using the results of the passing model, linear regression prediction still has some defects, and there are more effective and general methods for housing price prediction..",0.0,,False,Conference,,2025-11-18,48e2f0585e181a915613f0662c44f24ce334187b,housepricepredictionusinglinearregressionrandomforestandgradientboosting,ahmed,10.1109/icet66147.2025.11321431,housepricepredictionusinglinearregressionrandomforestandgradientboosting|2025,1,3042,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,7,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a62c916bb9855e16442c5e11e7fbe5462554a4c4,10.1109/icccnt61001.2024.10723986,,Harnessing Artificial Neural Network Model to Anticipate Housing Market Rates,2024.0,International Conference on Computing Communication and Networking Technologies,K. Karthika; P. Balasubramanie; K. Dharshini; P. Shanmugapriya; T. Ramya,"Predicting house prices is a vital issue in real estate market analysis, having important ramifications for homeowners, buyers, and investors. Amidst this exploration, we utilize an innovative artificial neural network (ANN) approach for predicting house prices, applied to the housing sales data specifically gathered from Chennai. The information includes many parameters, including area, the number of lavatories and bedrooms, the house’s quality rating, and other features. Our initial steps involve data preparation, encompassing tasks such as managing missing data, encoding categorical variables, and standardizing numerical features. Next, we use a sequential architecture with multiple dense layers and dropout regularization to build a deep learning model. Metrics like mean absolute error, mean squared error, root mean square error, mean absolute percentage error, R-squared, and root mean squared log error are used to assess the algorithm’s efficacy after it has been built with the Adam optimizer with mean squared error loss. The results of our experiments underscore the efficacy of the proposed methodology in precisely forecasting house prices. We achieve superior performance levels, and the capacity of the model to effectively discern new data is demonstrated by low error metrics values. Furthermore, epoch-by-epoch preparation and evaluation loss visualizations shed light on the model’s learning dynamics. Overall, this research contributes to the field of real estate market analysis by providing a reliable and efficient method for house price prediction. The discoveries hold tangible significance for homeowners, purchasers, and investors aiming to make decisions in the housing market. Future research directions include exploring alternative model techniques, incorporating additional features, and investigating the consequences of external factors on property prices.",0.0,,False,JournalArticle;Conference,Computer Science,2024-06-24,a62c916bb9855e16442c5e11e7fbe5462554a4c4,harnessingartificialneuralnetworkmodeltoanticipatehousingmarketrates,karthika,10.1109/icccnt61001.2024.10723986,harnessingartificialneuralnetworkmodeltoanticipatehousingmarketrates|2024,1,2900,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",908cb1f7de6418955aafb03a45211fce8ae7cf06,10.15680/ijmrset.2025.0808003,,"Entrepreneur and Broker at Sotheby’sInternational Realty, Kirkland, USA",2025.0,"International Journal of Multidisciplinary Research in Science, Engineering and Technology",Aditya Kasturi,"Predicting the housing prices correctly is one of the major problems in urban analytics, especially when the area experiences high micro-geographic heterogeneity. The spatial, structural, and socioeconomic relationship is often nonlinear and is complex and traditional models do not give an accurate picture of the relationships of variables at the level of the neighborhood. In this paper, a deep learning architecture is proposed to accommodate hyper-local data sources and multi-dimensional sets of features to predict the prices of housing in the Tri-County Region of Seattle (which includes the counties of King, Pierce, and Snohomish). The goal is to build a data driven model which could advance the accuracy of price (forecast) with higher precision at the sub regional level to provide finer granularity of valuations. We use deep neural network (DNN) model that is trained on property-level data presented by Zillow and Redfin which are supplemented with demographic, locational and temporal features provided by the U.S. Census Bureau and OpenStreetMap. The locational resolution is ensured with the help of data normalization (date normalization and feature encode nodes), and geospatial tagging. Empirical evidence shows that the model suggested performs much better than the classical regression and ensemble methods, as it has lesser root mean square error (RMSE) and mean absolute error (MAE) in all the three counties. It entails impressive spatial generalisation and resistance to changes of the neighborhood properties of the model. Paper contributes to the growing research of urban AI by the innovation of ,strokes projective approach for hyperlocal housing markets forecasting. These results have real-life application on real estate investors, urban planners, and policymakers who would like to encourage equitable and practice-based housing policy.",0.0,,False,JournalArticle,,2025-08-15,908cb1f7de6418955aafb03a45211fce8ae7cf06,entrepreneurandbrokeratsothebysinternationalrealtykirklandusa,kasturi,10.15680/ijmrset.2025.0808003,entrepreneurandbrokeratsothebysinternationalrealtykirklandusa|2025,1,2872,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",31a75c18fb9a7bbb19f45217cecf82672bb5bca0,10.48550/arxiv.2605.08660,2605.0866,Optimised Support Vector Regression for California Housing Price Prediction: The Critical Role of Feature Engineering and Hyperparameter Tuning,2026.0,arXiv.org,Emmanuel Adutwum,"In the recent literature, Support Vector Regression (SVR) has been cited as one of the weakest performers on the California Housing benchmark dataset, with Preethi et al. (2025)specifically ranking it last among the algorithms they tested, reporting an R2 of only 0.60. This paper examines whether the previously reported performance reflects experimental configuration choices rather than an inherent algorithmic limitation. A structured experimental workflow is applied: ten domain-motivated derived features are constructed from the eight raw inputs, an exploratory ensemble feature importance analysis identifies the most predictive candidates, and a randomised search over hyperparameter combinations with three-fold cross-validation selects the optimal SVR configuration within a leakage-safe scikit-learn Pipeline. A formal four-stage ablation study isolates the contribution of each component: scaling alone accounts for +0.744 in R2 (from -0.054 to 0.690), feature engineering adds +0.026 (to 0.716), and hyperparameter tuning contributes +0.008 (to 0.723). The resulting tuned SVR achieves a test R2 of 0.723, a 0.123-point absolute improvement over the previously reported SVR result (from 0.60 to 0.723, approximately 20% relative gain). In the ten-model comparison, the tuned SVR ranks fourth with R2 = 0.723, below XGBoost (0.832), Random Forest (0.814) and Gradient Boosting (0.783), while substantially outperforming simpler baselines. Ten-fold cross-validation yields a mean R2 of 0.703 (95% CI: [0.630, 0.775]), confirming robust generalisation. The observed improvement from R2 = 0.60 to R2 = 0.723 is associated primarily with proper feature scaling within a unified preprocessing pipeline, with domain-motivated feature engineering and systematic hyperparameter tuning, providing further incremental gains.",0.0,,False,JournalArticle,Computer Science,2026-05-09,31a75c18fb9a7bbb19f45217cecf82672bb5bca0,optimisedsupportvectorregressionforcaliforniahousingpricepredictionthecriticalroleoffeatureengineeringandhyperparametertuning,adutwum,10.48550/arxiv.2605.08660,optimisedsupportvectorregressionforcaliforniahousingpricepredictionthecriticalroleoffeatureengineeringandhyperparametertuning|2026,1,2827,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property)",8926a379d8bc0c969d50dcdc80b7b3976b6a6a55,10.5220/0013883100004919,,AI-Powered House Price Estimation Using Machine Learning,2025.0,"Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies",P. Kumar; C. Reddy; S. Basha; U. Siva; R. Mukesh,": The exact estimation of house prices plays a vital role in property decision-making to benefit all real estate market parties including buyers and sellers as well as investors. This research investigates the applicability of data-driven methods in property value estimation, with the support of artificial intelligence and machine learning. Sophisticated predictive models analyze different factors including geographic location and size of property structure and economic condition and market trends within a broad range of variables. This method depends on regression models as well as decision trees among ensemble learning techniques and deep neural networks to achieve better price estimation results. Research shows that price forecast accuracy success depends on selecting the right features which encompass property characteristics together with neighborhood variables and financial variables. Predictions generated from analyzing real estate data using deep learning combined with ensemble learning outperform conventional statistical methods in accuracy levels. The research explores additional approaches to improve accuracy which combine the analysis of external economic facts and sentiment evaluation of property marketing content and geospatial data assessment. Research confirms that property market analysis benefits significantly from AI-powered automated valuation models which distribute essential information throughout industry professionals and financial institutions and public policy institutions. The study enhances knowledge about AI-based property valuation while suggesting developments for machine-based property valuation models. This work expands AI-driven real estate valuation knowledge through its proposals for machine-based property valuation method development.",0.0,,False,Conference,,,8926a379d8bc0c969d50dcdc80b7b3976b6a6a55,aipoweredhousepriceestimationusingmachinelearning,kumar,10.5220/0013883100004919,aipoweredhousepriceestimationusingmachinelearning|2025,1,2801,3,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,7,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",098f019656ba0591a949c228ea100966eaaf17e9,10.55041/isjem05364,,Machine Learning Based Smart House Price Prediction System,2026.0,International Scientific Journal of Engineering and Management,V. Mageswari; S. Sanjai,"Abstract Accurate house price estimation is essential for real estate decision-making, loan approval, investment planning, and market analysis. Traditional valuation methods are often subjective and depend on human experience, which may lead to inconsistent pricing and delayed decisions. Machine learning provides a reliable and data-driven solution for house price prediction by learning patterns from historical datasets and producing accurate price estimates for new houses [1], [2]. This paper presents a Machine Learning Based Smart House Price Prediction System that predicts house prices using real estate attributes such as location, total area (sqft), number of bedrooms (BHK), bathrooms, balconies, area type, and availability. Data preprocessing methods including missing value handling, categorical encoding, feature scaling, and outlier removal were performed to improve model performance [1]. Multiple regression models including Linear Regression, Ridge, Lasso, Support Vector Regression (SVR), and XGBoost Regression were trained and evaluated using standard metrics such as RMSE and R² score [7], [8]. Recent studies show that ensemble boosting models such as XGBoost often produce superior prediction accuracy in house price estimation due to their ability to capture complex non-linear relationships [3], [4]. The final trained model was deployed using a Flask-based web application, enabling users to enter house details and obtain real-time predicted prices. This system supports users by providing transparent, automated, and consistent house price estimation. Keywords House price prediction, machine learning, regression models, real estate analytics, data preprocessing, feature engineering, Linear Regression, SVR, XGBoost, RMSE, Flask web application [1], [3], [7]",0.0,,False,JournalArticle,,2026-01-22,098f019656ba0591a949c228ea100966eaaf17e9,machinelearningbasedsmarthousepricepredictionsystem,mageswari,10.55041/isjem05364,machinelearningbasedsmarthousepricepredictionsystem|2026,1,2795,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",dcde1eba0618c187cba1d25ee4d291721a6c03bb,10.55041/isjem06368,,Leveraging Deep Learning Intelligence for Real Estate Analytics,2026.0,International Scientific Journal of Engineering and Management,Ratna Kumari S.; Pinninti Pujita,"Predicting the price of a house is a hard problem. Many things affect the final number at the same time, such as location, the size and condition of the building, nearby amenities, the state of the local economy, and how buyers happen to feel about the market that month. Older valuation methods, which are mostly built on straight-line regression, tend to miss the bends and jumps that show up in real data. In the last few years, deep learning has given researchers new tools to deal with this kind of messy, high-dimensional information. In this paper, we develop a price prediction model around a Convolutional Neural Network (CNN). CNNs were first made famous by image tasks, but they also work well on well-organised tabular data once the columns are arranged with some thought. Our system pulls together property attributes, basic location details, and market signals, and learns the hidden patterns that push prices up or down. Structured real estate records are reshaped into small feature maps, and the CNN then extracts layered representations that a simple regressor cannot easily pick up. The goal of this work is to help buyers, sellers, investors, and local authorities by giving them more trustworthy price estimates and lowering the guesswork that usually comes with a house purchase. In our experiments the CNN-based approach beats standard machine learning baselines on both accuracy and stability, which makes it a practical option for smarter real estate analytics.Index Terms—Deep Learning (DL), Convolutional Neural Network (CNN), 1D-CNN, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Structured Deep Convolutional Neural Network (SDCNN), real estate analytics, price prediction.",0.0,,False,JournalArticle,,2026-04-16,dcde1eba0618c187cba1d25ee4d291721a6c03bb,leveragingdeeplearningintelligenceforrealestateanalytics,s.,10.55041/isjem06368,leveragingdeeplearningintelligenceforrealestateanalytics|2026,1,2747,1,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",0,1,1,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",beadaee0db99b04cf1fc57de6aea4662413c88cd,10.1108/jfmpc-03-2024-0017,,Predicting apartment prices in Prishtina: insights from gradient boosting modeling,2025.0,Journal of Financial Management of Property and Construction,Visar Hoxha; Dhurata Hoxha," This paper aims to apply gradient boosting (GB) to forecast apartment prices in Prishtina, evaluating its effectiveness through established evaluation metrics and identifying key price determinants via variable importance analysis. The research method comprises analyzing a dataset of 1,468 property transactions from 2019 to 2023, attained from the Kosovo Department of Property Taxes. The data were split into training and testing subsets, with normalization applied to provide consistency, and the GB model's performance was evaluated using Mean Squared Error, Coefficient of Determination, Mean Absolute Error and Root Mean Squared Error. Findings point out that the GB model shows noteworthy predictive accuracy, with the distance from the Central Business District identified as the primary influencer of apartment prices in Prishtina. These findings also point out the model's capability to effectively capture and reflect the nuances of the local real estate market. Implications of this study advise the practical utility of machine learning, specifically GB, in enhancing real estate valuation and decision-making processes. By providing accurate and efficient market analyses, such models can enable more informed investment decisions, contribute to market transparency and help in stabilizing property prices. Originality of this research lays in its focused application of Gradient Boosting within an emerging market context, enriching the empirical discourse on real estate valuation. By conducting a comprehensive variable importance analysis, the study offers fresh understanding into the dynamics influencing property prices, thereby advancing the field of real estate research. ",0.0,,False,JournalArticle,,2025-11-12,beadaee0db99b04cf1fc57de6aea4662413c88cd,predictingapartmentpricesinprishtinainsightsfromgradientboostingmodeling,hoxha,10.1108/jfmpc-03-2024-0017,predictingapartmentpricesinprishtinainsightsfromgradientboostingmodeling|2025,1,2728,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,automated valuation model residential,7d2894011a7c40caa92c799e64f5bdf4b1f8f3a6,10.1109/aic70896.2026.11717331,,PropInsight: A Machine Learning and Visual Analytics Framework for Real Estate Price Prediction,2026.0,International Workshop on Artificial Intelligence and Cognition,Shresth Srivastav; Priya Mathur; Amit Kumar Gupta,"Estimating the prices of a homes is a very hard problem because there are so many different factors that an effect it. These factors include the home's location, size, furnishings, floor level, and a luxury grade. Standard valuation techniques that rely on a manual expert evaluation and subjective market comparison often produce inconsistent results, especially in a rapidly changing urban markets. This paper presents the PropInsight, a comprehensive machine learning pipeline and an interactive visual analytics dashboard designed to an automate and enhance real estate valuation for the Gurugram residential market. The system that scrapes property listings from the 99acres goes through a long preprocessing pipeline that gets rid of the duplicates, logically fills in a missing value, handles outliers, encodes features, and extracts useful engineered features. Then, the five regression models are trained and the tested: the Linear Regression, Random Forest, XGBoost, Gradient Boosting, and AdaBoost. The R2 score and the Mean Absolute Error (MAE) are used to measure performance. The experimental results show that the XGBoost has the best predictive accuracy (R2 = 0.908) followed by the linear baseline, which has a very different performance. The trained pipeline is stored in a Streamlit web app that can predict prices in real time and do exploratory analytics at the sector level. The app is called pipeline.pkl. This study demonstrates that the systematic integration of data engineering with comparative model evaluation and user-facing visualization constitutes a pipeline for a practically deployable, interpretable decision-support tool for stakeholders in the real estate sector.",0.0,,False,Conference,,2026-08-01,7d2894011a7c40caa92c799e64f5bdf4b1f8f3a6,propinsightamachinelearningandvisualanalyticsframeworkforrealestatepriceprediction,srivastav,10.1109/aic70896.2026.11717331,propinsightamachinelearningandvisualanalyticsframeworkforrealestatepriceprediction|2026,1,2701,2,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || automated valuation model residential",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",75dc231dde5373a533a4cbad0a39ada6bf28babb,10.1109/isssc66652.2025.11388391,,Accurate House Price Prediction using Ensemble Machine Learning Model,2025.0,"2025 IEEE 3rd International Symposium on Sustainable Energy, Signal Processing and Cybersecurity",Bharti Thakur; A. Bisht,"In today's dynamic real estate industry, accurately predicting home prices is more important than ever. In this study, we developed a machine-learning-based system for estimating home prices using the Ames Housing Dataset. The system is capable of providing predictions from regression models (predicting exact price) and classification models (grouped into price ranges) comprising multiple models from both regression and classification categories, including single regression techniques: linear regression, lasso, and ridge regression, and advanced techniques such as support vector machines (SVM) and random forest. A key contribution of this study is the development of a unified system that leverages Principal Component Analysis (PCA) for efficient dimensionality reduction and simultaneously addresses both the exact price prediction (regression) and price categorization (classification) tasks to provide comprehensive real estate insights. Overall, the random forest provided the best estimates for price across all of the models tested, and SVM was classified into the best price groups. Multiple graphs were employed, including heatmaps, scree plots, and price prediction graphs, to visualize the model's performance and identify key features. There are substantial potential applications for this system because it can provide a buyer, seller, or investor with explicit price estimates. Exploring potential future work and improvements with that system included options such as adding time trends, adding location-based variables, deep learning models (and future versions), and a real-time app that can increase the value of price prediction and be used in multiple cities or markets",0.0,,False,,,2025-11-06,75dc231dde5373a533a4cbad0a39ada6bf28babb,accuratehousepricepredictionusingensemblemachinelearningmodel,thakur,10.1109/isssc66652.2025.11388391,accuratehousepricepredictionusingensemblemachinelearningmodel|2025,1,2697,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4d21a0015220409cff549696893bcfc72c3d66da,10.59994/ajbtme.2026.3.29,,California Housing Price Prediction Using Machine Learning: A Comparative Study Using Feature Engineering and Ensemble Methods,2026.0,Ahliya Journal of Business Technology and MEAN Economies,Mohammad Siwad; Mutaz Rasmi Abu Sara; Ahlam Awwad,"This study aimed to develop an accurate housing price prediction model for California by comparing multiple machines learning algorithms and evaluating the impact of feature engineering and ensemble techniques on predictive performance. The study utilized the California Housing Dataset, comprising 20,433 observations after data cleaning and preprocessing. A five-stage methodology was implemented, including the evaluation of seven baseline regression models, generation of engineered features, feature selection using the F-statistic method, hyperparameter optimization of the best-performing models through GridSearchCV, and the construction of Voting and Stacking ensemble models. The findings revealed that linear models achieved limited performance due to the nonlinear relationships among variables, whereas tree-based and ensemble methods demonstrated superior predictive capabilities. The Stacking Ensemble model achieved the highest performance with an R² value of 0.8431, an RMSE of $46,317, and an MAE of $30,150. Furthermore, the results confirmed that engineered features, particularly rooms per household, played a significant role in enhancing prediction accuracy. The scientific contribution of this study lies in proposing an integrated framework that combines feature engineering, feature selection, hyperparameter optimization, and advanced ensemble learning within a unified comparative environment. This approach improved predictive performance and surpassed the widely recognized benchmark by 3.21 percentage points in terms of R², while highlighting the importance of household-level feature normalization in housing price prediction.",0.0,,False,JournalArticle,,2026-05-31,4d21a0015220409cff549696893bcfc72c3d66da,californiahousingpricepredictionusingmachinelearningacomparativestudyusingfeatureengineeringandensemblemethods,siwad,10.59994/ajbtme.2026.3.29,californiahousingpricepredictionusingmachinelearningacomparativestudyusingfeatureengineeringandensemblemethods|2026,1,2660,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c755a9400eb0b2ef80c9292e669331ffeb078501,10.1109/tqcebt59414.2024.10545253,,Predictive Machine Learning Approaches for Estimating Residential Rental Rates in India,2024.0,2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies,P. Jayadharshini; R. Ragunath; Prabhakaran D; Lalitha Krishnasamy; N. Abinaya; S. Priyanka,"As urban areas like Chennai and Bangalore witness a continuous surge in land and housing prices, accurately estimating the market value of houses has become increasingly crucial. This presents a formidable challenge, prompting a growing demand for an accessible and efficient method to predict house rental prices, ensuring dependable forecasts for future generations. In response to this need, this study delves into the core factors influencing rental prices, with a keen focus on location and area. Leveraging a dataset comprising ten essential features tailored for detecting Rental Price in Metropolitan cities, the research meticulously preprocesses the data using a Python library to ensure data cleanliness, laying a robust foundation for constructing the predictive model. Employing a diverse range of Machine Learning algorithms, including Random Forest, Linear Regression, Decision Tree Regression, and Gradient Boosting, the study evaluates their efficacy in forecasting rental prices. Notably, feature extraction underscores the significance of area and property type in shaping rental prices. In comparison with existing methodologies, this research adopts gradient boosting as its preferred approach, achieving the most satisfactory predictive outcomes. Evaluation metrics are meticulously analyzed to validate the model's performance. Through this comprehensive analysis, the study not only offers valuable insights into rental price prediction but also ensures a rigorous comparison with existing approaches, maintaining originality and relevance in addressing the pressing challenges of housing market dynamics.",0.0,,False,Conference,,2024-03-22,c755a9400eb0b2ef80c9292e669331ffeb078501,predictivemachinelearningapproachesforestimatingresidentialrentalratesinindia,jayadharshini,10.1109/tqcebt59414.2024.10545253,predictivemachinelearningapproachesforestimatingresidentialrentalratesinindia|2024,1,2629,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,5,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",5dba62e533b62357cc2b8976a5a95cf8748fdfa2,10.1109/iscon65210.2025.11341107,,Predictive Analytics of Housing Markets Using Random Forests and Multidimensional Urban Indicators,2025.0,2025 7th International Conference on Information Systems and Computer Networks (ISCON),Sanjay Oli; Z. Mamadiyarov; Somesh Sharma; J. Yuldoshev; Temur Eshchanov; Siddharth Joshi,"In the past few years, the housing market in rapidly urbanizing regions such as Uttarakhand has seen significant change due to increased infrastructure spending and shifts in urban development policies. Accurate forecasting of house prices matters not only for future homebuyers and investors but also for city planners and government authorities attempting to provide sustainable development. This research study sets forth an integrated machine learning-based model based on Random Forest Regression in order to predict and model the prices of housing in four key cities of Uttarakhand, namely Dehradun, Haridwar, Haldwani, and Rudrapur. The study begins with the collection of relevant datasets that include variables such as historical housing prices, investment in infrastructure, and urban development indicators. Next is data preprocessing and feature selection, followed by training and validation of Random Forest models using MATLAB. The simulation results indicate that the models perform optimally in all the cities, with the lowest Root Mean Squared Error (RMSE) in Dehradun (10.869), implying a higher degree of uniformity in the housing market of the capital city. Visual inspection of trends in urbanization development and infrastructure investment also validates the model prediction results. The study demonstrates the capability of Random Forest Regression to capture complex, nonlinear patterns in real estate data. The study presents a reliable and scalable model for urban housing price estimation, which provides an entry point to more strategic and knowledgeable urban development programs",0.0,,False,Conference,,2025-09-05,5dba62e533b62357cc2b8976a5a95cf8748fdfa2,predictiveanalyticsofhousingmarketsusingrandomforestsandmultidimensionalurbanindicators,oli,10.1109/iscon65210.2025.11341107,predictiveanalyticsofhousingmarketsusingrandomforestsandmultidimensionalurbanindicators|2025,1,2614,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",e16a7003a1d7985221f97c7d51e03384677d23aa,10.1109/iccams65118.2025.11234545,,A Neural Network approach for predicting house prices using Boston Housing Price dataset,2025.0,"2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS)",A. Sowmya; B. S. Sindhu; M. B. Sri; Y. Sri Lalitha,"Predicting house prices is a crucial challenge in real estate, helping buyers, sellers, and investors make informed decisions. In this project, we build a neural network to estimate housing prices using the Boston Housing Price dataset. Our approach leverages a Multilayer Perceptron (MLP), a type of artificial neural network, to learn the complex relationships between various housing features— such as crime rates, property tax, and the number of rooms—and their market value.To ensure accuracy, we preprocess the data using Min-Max Scaling, making it easier for the model to learn meaningful patterns. The MLP consists of multiple layers of neurons with ReLU activation functions, allowing it to capture intricate trends in housing prices. We train the model using backpropagation, optimizing with RMSprop and evaluating performance using metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE).The results highlight the potential of deep learning in real estate forecasting, showing how neural networks can provide valuable insights into price estimation. While this project focuses on MLPs, it opens the door to exploring even more advanced techniques, like Convolutional Neural Networks (CNNs) and Transformers, to further improve prediction accuracy. By applying AI to housing price prediction, we take a step toward smarter, data-driven decision-making in the real estate industry.By integrating AI into real estate forecasting, we move closer to smarter, more precise property valuation, helping individuals and businesses make better financial decisions based on data-driven insights.",0.0,,False,Conference,,2025-07-11,e16a7003a1d7985221f97c7d51e03384677d23aa,aneuralnetworkapproachforpredictinghousepricesusingbostonhousingpricedataset,sowmya,10.1109/iccams65118.2025.11234545,aneuralnetworkapproachforpredictinghousepricesusingbostonhousingpricedataset|2025,1,2607,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ea1a8e344a91774c9b30fd0acc72ec4791567e4e,10.38124/ijisrt/25jul971,,Machine-Learning Valuation of Residential Real Estate in Bishkek,2025.0,International Journal of Innovative Science and Research Technology,Meerim Kuldzhanova; Gulshat Muhametjanova,"This paper outlines the development of a machine learning model for accurate and interpretable forecasting of Bishkek housing prices, Kyrgyzstan. Leveraging a collection of 9,664 apartment posts from Kaggle, the model takes into account major real estate attributes such as square footage, building, rooms, and condition. One-hot encoding was applied to encode categorical values, and a new ""preferred floor"" attribute is included to capture buying preference as per floor level. In order to model the data, the Light GBM gradient boosting algorithm was chosen due to its excellent performance on heterogeneous tabular data and its capability to effectively handle missing values and feature interactions. The model produced an R2 score of 0.958 and a mean squared error (MSE) of 0.0419, reflecting very high predictive accuracy. The analysis of the important characteristics showed that the area, the number of rooms, and the type of building are the most important things that influence apartment prices. These results further reveal the dynamics of the urban housing market and can be used to better fine-tune housing affordability strategies. The model can also be used as a foundation for web- based price systems intended to enhance market transparency and reduce housing misinformation. Moreover, the model is scalable and can be adapted to other urban centers throughout Kyrgyzstan and Central Asia, supporting wider application in public planning and real estate investment. Real-time integration into open dashboards will enable dynamic monitoring and improve decision-making by stakeholders.",0.0,,False,JournalArticle,,2025-07-22,ea1a8e344a91774c9b30fd0acc72ec4791567e4e,machinelearningvaluationofresidentialrealestateinbishkek,kuldzhanova,10.38124/ijisrt/25jul971,machinelearningvaluationofresidentialrealestateinbishkek|2025,1,2604,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1af6d482f136f3670644241e9cf4d95410ccb37c,10.1109/icscss64956.2025.11501097,,House Price Prediction Algorithms and Model based on Machine Learning,2025.0,2025 3rd International Conference on Sustainable Computing and Smart Systems (ICSCSS),B. N. Kumar; Kunta Mallik Raj; Podishetti Uday Kumar; Chanda Gopi Reddy; Mohammed Azeem,"This project presents a machine learning-based solution for predicting house prices with high accuracy. It utilizes advanced algorithms such as Linear Regression, Decision Trees, and Random Forest to build a robust and intelligent system capable of estimating property values based on a range of input features. These features include key elements such as the property's location, total area, number of bedrooms and bathrooms, and amenities like parking space, gardens, proximity to schools, and hospitals. The model is trained using historical real estate data, which helps it learn trends in the housing market and understand how different variables affect property prices. By analysing this data, the machine learning algorithms can uncover complex patterns and relationships that might be missed using conventional methods. Each algorithm adds value in its own way: Linear Regression provides a straightforward and interpretable model, while Decision Trees and Random Forests are effective in modelling non-linear relationships and feature interactions. This predictive system proves beneficial to a wide range of users in the real estate industry. Potential homebuyers can assess whether a property's asking price is fair, and sellers can identify optimal pricing strategies. Real estate agents and professionals also gain from this data-driven approach, which supports accurate valuations and enhances client services. In essence, this system brings more transparency and efficiency to property transactions, allowing stakeholders to make informed decisions backed by data analytics.",0.0,,False,Conference,,2025-08-20,1af6d482f136f3670644241e9cf4d95410ccb37c,housepricepredictionalgorithmsandmodelbasedonmachinelearning,kumar,10.1109/icscss64956.2025.11501097,housepricepredictionalgorithmsandmodelbasedonmachinelearning|2025,1,2589,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,3,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 crossref,preços hedônicos imóveis,,10.29327/2290393.1.1-7,,Regressão polinomial e redes neurais artificiais na avaliação de imóveis: um estudo de caso,2022.0,Revista Valorem,Carlos Zilli; Luiz Fernando Palin Droubi; Norberto Hochheim,"A NBR 14.653-2 prevê o uso de diferentes metodologias para avaliação de um imóvel. Dentre elas, destaca-se o uso de regressão linear e, alternativamente, o uso de redes neurais artificias. A regressão linear é um método científico bastante difundido entre os engenheiros avaliadores, enquanto a utilização de redes neurais apresenta aplicação limitada, pois, apesar do seu desempenho extraordinário, ainda não está completamente explícito o que ocorre durante a aprendizagem de uma rede neural e porque elas funcionam. Uma alternativa aos modelos de redes neurais artificiais é a modelagem através de regressão polinomial, que nada mais é que um modelo de regressão linear em que são adicionados os termos quadráticos, cúbicos etc, assim como os termos de todas as possíveis interações entre as variáveis. Nesse estudo procura-se fazer um comparativo acerca da precisão obtida por modelos de regressão polinomial e por redes neurais artificiais em dados imobiliários. Essa precisão é aferida através do cálculo da raiz do erro médio quadrático - RMSE para as duas modelagens. Os resultados obtidos convergem para aqueles obtidos por Matloff et al. (2018) em seu artigo “polynomial regression as an alternative to neural nets”, que pondera que as redes neurais artificiais, quando utilizadas funções de ativação lineares, são modelos de regressão polinomial em que o grau do polinômio aumenta a medida que aumenta o número de camadas ocultas da rede neural. Constatou-se, ainda, que o RMSE da regressão polinomial foi menor e com menor dispersão que o da respectiva rede neural artificial.",0.0,,,journal-article,,,doi_10_29327_2290393_1_1_7,regressaopolinomialeredesneuraisartificiaisnaavaliacaodeimoveisumestudodecaso,zilli,10.29327/2290393.1.1-7,regressaopolinomialeredesneuraisartificiaisnaavaliacaodeimoveisumestudodecaso|2022,1,2588,1,preços hedônicos imóveis,0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6c8ab7d65de723a6251815cc009c423e1d770405,10.1109/i3ceet61722.2024.10994003,,Data-Driven Home Price Prediction: Leveraging Machine Learning for Accurate Market Valuation,2024.0,"2024 International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET)",Jaswinder Singh; S. Mishra,"This study combines machine learning algorithms with several real-count implementations to accurately anticipate the market value of residential properties. The dynamic nature of the freehold market and the complexity of variables influencing property values need sophisticated computational tools for educated, data-driven projections. The sophisticated method integrates a varied array of characteristics, including property qualities, location-based elements, economic metrics, and historical sales information, to provide a conceptual statistic for training and evaluation. Feature engineering techniques are used to derive significant insights from unprocessed data, improving the predictive efficacy of the model. Techniques for forecasting residential property values using machine learning methodologies, including linear regression, decision trees, random forests, gradient boosting, and neural networks, are examined and contrasted to choose the most effective model for precise forecasts. A stringent assessment procedure is used to appraise the models' efficacy, using measures such as mean absolute error (MAE), mean squared error (MSE), and R-squared (R2) to guarantee dependable outcomes. An interactive user interface is built to improve system usability, enabling real estate agents, prospective buyers, and sellers to enter property data and get immediate price estimates. The interface offers visuals and insights into the most critical elements affecting the forecasted pricing, enabling users to make educated choices.",0.0,,False,Conference,,2024-09-20,6c8ab7d65de723a6251815cc009c423e1d770405,datadrivenhomepricepredictionleveragingmachinelearningforaccuratemarketvaluation,singh,10.1109/i3ceet61722.2024.10994003,datadrivenhomepricepredictionleveragingmachinelearningforaccuratemarketvaluation|2024,1,2539,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c570370f1a5b898b91d3be6cfb8286287a39dded,10.1109/iccnct68477.2026.11589953,,"Learning from Numbers, Text, and Place to Improve House Price Prediction",2026.0,International conference Computer Networks and Inventive Communication Technologies,Suvarna Sunil Kumar; Varun Vemulapalli; V. S. N. Murthy; K. Bhavani; K. Saranya; Sriharsha Vikruthi,"Accurate real estate price estimation is critically important for financial stability, urban planning, and individual investment decisions. However, property values depend on a diverse set of factors, and while machine learning has advanced the field, most existing models treat numerical data, listing descriptions, and location information as homogeneous inputs, thereby losing the unique value hidden in each modality. This paper introduces the Heterogeneous Data Fusion Network (HDFNet) a deep learning system designed to explicitly recognize and exploit the differences between data types when predicting house prices. HDFNet employs separate neural pathways for each input type: one processes structured data (e.g., square footage, number of bedrooms), another extracts semantic features from textual property listings, and a third encodes spatial information (coordinates, points of interest). These representations are then fused into a unified prediction. Evaluated on a real-world dataset, HDFNet outperforms strong baselines such as Random Forest and XGBoost, reducing Root Mean Squared Error (RMSE) by 13% and achieving an R2 score of 0.914. Ablation experiments confirm that spatial and textual features contribute substantially to performance. The results demonstrate that respecting the heterogeneity of real-world data not merely using a more sophisticated algorithm is essential for accurate automated valuation. HDFNet offers a practical, deployable solution for complex, data-rich real estate analytics.",0.0,,False,Conference,,2026-05-11,c570370f1a5b898b91d3be6cfb8286287a39dded,learningfromnumberstextandplacetoimprovehousepriceprediction,kumar,10.1109/iccnct68477.2026.11589953,learningfromnumberstextandplacetoimprovehousepriceprediction|2026,1,2522,4,"(""automated valuation"" | ""mass appraisal"") + (residential | housing | house | property) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"") || text real estate valuation machine learning",0,3,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",72f43fd553dee8862ef9637601670e194c8819c3,10.1109/iciics63763.2024.10859514,,Assessing Machine Learning Models for Predictive Analytics in Urban Rental Markets: Rent Price Forecasting in India,2024.0,2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS),Abhishek Maheshwari; Monica Ranka; Amit Malhotra; R. Mishra; M. Basha,"This research marks a groundbreaking application of a diverse array of machine learning techniques to predict rental house prices specifically in the major Indian cities of Pune, Delhi, and Mumbai. These urban areas are known for their rapid economic growth and complex demographic shifts, which introduce significant variability into the rental market. Moving beyond the conventional focus on property sales, this study explores the rental market using advanced machine learning models such as Linear Regression, Ridge, Lasso, ElasticNet, Random Forest, Gradient Boosting, K-Neighbors, Decision Tree, Support Vector Machines, and Polynomial Regression. This novel approach is tailored to meet the unique challenges of the Indian rental landscape, offering a detailed examination that transcends traditional analytic methods. By meticulously adapting these models to the nuanced dynamics of rental prices in Pune, Delhi, and Mumbai, the study fills a crucial gap in existing real estate research. Our results highlight the varied performance of these models, with non-linear methods like K-Neighbors and Random Forest emerging as particularly effective in capturing the complex interplays affecting rental prices. This research not only deepens the understanding of machine learning's utility in real estate analytics but also provides vital data-driven insights to stakeholders involved in policymaking, investing, and managing within India's vibrant and evolving rental markets.",0.0,,False,Conference,,2024-11-22,72f43fd553dee8862ef9637601670e194c8819c3,assessingmachinelearningmodelsforpredictiveanalyticsinurbanrentalmarketsrentpriceforecastinginindia,maheshwari,10.1109/iciics63763.2024.10859514,assessingmachinelearningmodelsforpredictiveanalyticsinurbanrentalmarketsrentpriceforecastinginindia|2024,1,2480,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",271d398b056cc47386cb005650534bc50687c3cc,10.1109/intech64186.2025.11198475,,House Rent Prediction Using Machine Learning Approach,2025.0,InTech,K. Balaji; G. Anurag; M. Nawaz; R. S.; Shanmugasundaram Hariharan; Karuppiah Natarajan,"This research explores the use of machine learning techniques to deliver precise predictions of house prices. The objective is to aid stakeholders in making well-informed property investment decisions and planning strategies. The methodologies utilized include multiple linear regression and random forest algorithms. Initially, the data underwent extensive preprocessing and visualization to ensure reliability and clarity. Subsequently, predictive modeling was performed using the aforementioned algorithms, and their effectiveness was evaluated. The multiple linear regression model achieved an R2 score of 0.73, indicating that it explained 73% of the variance in house prices, though it demonstrated errors in certain scenarios, pointing to areas for improvement. Random forest had a somewhat poorer R2 equal to 0.69, but it performed better when it comes to recognizing the non-linear associations between the variables. The factors that affect the prices of houses put into consideration include size of the house, number of bathrooms, number of floors, parking space, and air conditioning. This paper provides a good foundation for the use of machine learning in modelling the price of houses and it is recommended that further studies be done to incorporate more features and chained variable. The implications of the study are as follows: The results of estimation have general significance for investors, brokers, and policy makers working the field of real estate.",0.0,,False,Conference,,2025-02-27,271d398b056cc47386cb005650534bc50687c3cc,houserentpredictionusingmachinelearningapproach,balaji,10.1109/intech64186.2025.11198475,houserentpredictionusingmachinelearningapproach|2025,1,2476,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",be97b7f99a884fa95708ab89a2924d42dc1034ff,10.1109/icadcs70036.2026.11583665,,"An Empirical Analysis of Regression and Ensemble Learning Models (LR, DTR, RFC, GBR) for Robust and Accurate House Price Prediction Using Structured Data",2026.0,"2026 1st International Conference on AI, Data Science, Cyber Security and Smart Manufacturing for Sustainable Development (ICADCS)",Juhi Kamakoty; Sita Ram Seervi; Anshita Shukla; Jyoti; K. Kiran; Ravi Kumar,"Housing price prediction has become more important to buyers, sellers and investors in the new age of the real estate market. Over the past few years, the accessibility of large-scale datasets, combined with major innovations in machine learning (ML) methods, has become accessible, allowing the creation of extremely accurate predictive models. This paper introduces a house price prediction model that uses a data set of residential houses in the United States to train and test various machine learning algorithms. The suggested framework combines the data preprocessing techniques with the sophisticated regression algorithms to predict the prices of the property basing on different determining characteristics, including the number of bedrooms and bathrooms, location, kitchen specifications, garden presence, and other appropriate characteristics. Various models, such as Linear Regression (LR), Decision Tree Regression (DTR), Random Forest Classifier (RFC) and Gradient Boosting Regression (GBR), are applied and compared to each other to identify the best model. The results prove that the techniques related to ensembles are more efficient at prediction compared to the traditional ones. The study contributes to the efficiency and reliability of the house price prediction systems, and offers a good platform on which more advancements in the artificial intelligence of real estate can be reached with the assistance of the machine learning techniques.",0.0,,False,Conference,,2026-05-23,be97b7f99a884fa95708ab89a2924d42dc1034ff,anempiricalanalysisofregressionandensemblelearningmodelslrdtrrfcgbrforrobustandaccuratehousepricepredictionusingstructureddata,kamakoty,10.1109/icadcs70036.2026.11583665,anempiricalanalysisofregressionandensemblelearningmodelslrdtrrfcgbrforrobustandaccuratehousepricepredictionusingstructureddata|2026,1,2464,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",570aaf50b40f8aa4b41c1b73f3e257712bfc09cf,10.1109/icfts62006.2025.11031771,,House Price Prediction Using Regression in Machine Learning,2025.0,2025 International Conference on Frontier Technologies and Solutions (ICFTS),G. Madhuri; A. Kumar; Mahmoud A. Hussain; N S S S Girish Kumar; M. Jabbar,"The house pricing projection is crucial to the financial services industry and real estate sector. People are researching market techniques and attempting to purchase a new property within their means. However, the primary drawback of the current method is that it does not determine a home's price based on the essential forecast of future market trends, which results in an increase in price. Therefore, our paper's goal is to accurately forecast a home's price with minimal loss. For predicting house prices several factors need to be considered including location, size, amenities and economic indicators. There are very great techniques that machine learning offers for modelling and house price prediction. Regression algorithms are really valuable for predicting continuous values, like house prices by computing the association between data features and target values. The base tool in this direction is simply a basic linear approach, which fits best when discovering simple, linear trends. Decision trees and random forests add flexibility for modelling much more complicated and non-linear relationships. More advanced techniques like gradient boosting and XGBoost improve the accuracy in computing multiple models together to correct each other's errors iteratively. All these methods together provide a set of approaches for solving tasks in the price prediction problem under various accuracy and complexity requirements.",0.0,,False,Conference,,2025-03-27,570aaf50b40f8aa4b41c1b73f3e257712bfc09cf,housepricepredictionusingregressioninmachinelearning,madhuri,10.1109/icfts62006.2025.11031771,housepricepredictionusingregressioninmachinelearning|2025,1,2435,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8b6c495557f66917d5732408eb392d503d05091f,10.1109/incet64471.2025.11140248,,Ensemble Machine Learning Models Used for Forecasting in Rental Apartment Price Prediction of Metro City in India,2025.0,2025 6th International Conference for Emerging Technology (INCET),J. M. Shanthi; P. S. Rao; V. R. Kumar; G. Nithya; J. Kartigeyan; Amit Gupta,"In order to help stakeholders make well-informed real estate decisions, this study assesses two classes of machine learning algorithms to forecast rental home prices in India. The goal is to determine which models are the most accurate. The study examines a large dataset with characteristics including home type, size, location, and cost across major Indian cities using a quantitative research approach. A great deal of preprocessing was done, which included feature selection, dimensionality reduction, encoding categorical variables, and managing missing data.Its thorough assessment of a variety of machine learning models using a specially assembled dataset of rental properties in India is what makes the study distinctive. By concentrating exclusively on rental price prediction, this paper fills a need in the Indian market and adds to the body of previous material. The results show that the models' performance varied significantly, with the K-Neighbors Regressor surpassing the others by a little margin in terms of MAE. By demonstrating how machine learning may improve real estate market analysis and decision-making, this study makes a theoretical and practical contribution. A vital tool for investors, legislators, and prospective tenants, the comparison analysis provides insightful advice on how to choose models that strike a compromise between computational economy and prediction accuracy.",0.0,,False,Conference,,2025-05-23,8b6c495557f66917d5732408eb392d503d05091f,ensemblemachinelearningmodelsusedforforecastinginrentalapartmentpricepredictionofmetrocityinindia,shanthi,10.1109/incet64471.2025.11140248,ensemblemachinelearningmodelsusedforforecastinginrentalapartmentpricepredictionofmetrocityinindia|2025,1,2411,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",486034dcf3047d4284291ae47f309fff740ead97,10.1109/isas66241.2025.11101915,,Interpretable House Price Prediction Using Ensemble Learning and Explainable AI Techniques,2025.0,International Service Availability Symposium,Burçak Asal,"This study presents a ensemble learning based machine learning approach for predicting house prices on a structured tabular dataset. Within the scope of the study, four regression models are investigated, which are Bayesian Ridge Regression, Elastic Net Regression, Stochastic Gradient Descent (SGD) Regression and a Voting Regression model combining the previous three models. Experimental results demonstrate that Bayesian Ridge and SGD Regression achieved the highest predictive accuracy, while the Voting Regression model provides robust yet slightly lower performance. Additionally two Explainable Artificial Intelligence (XAI) techniques, SHAP and LIME, are employed to interpret the Voting Regression model’s predictions, revealing that Square_Footage is the most influential feature, followed by Lot_Size and Year_Built features and with minimal contributions from Neighborhood_Quality feature. The alignment between global and local interpretability methods further validates the model’s decision-making transparency and robustness. Consequently, this study indicates the effectiveness of combining traditional regression models with ensemble strategies and explainability techniques for building accurate and interpretable predictive systems for real estate price estimation, which provides a foundation for future research into incorporating broader contextual and market-driven features.",0.0,,False,,,2025-06-27,486034dcf3047d4284291ae47f309fff740ead97,interpretablehousepricepredictionusingensemblelearningandexplainableaitechniques,asal,10.1109/isas66241.2025.11101915,interpretablehousepricepredictionusingensemblelearningandexplainableaitechniques|2025,1,2399,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",307fcece1735a69f376a239262adb7ce91789e69,10.1109/iconat61936.2024.10774967,,Property Price Prediction,2024.0,2024 3rd International Conference for Advancement in Technology (ICONAT),Aleena Anna Robin; G. Prasad; Jyothsna Jiji; Sanjay Raajesh; K. V. Sheena,"This data analytics project is designed for the real estate industry, with the primary aim of predicting property prices, including lands, buildings, and houses linear regression model and also by carefully analyzing and interpreting current market trends. Although numerous existing systems share the common objective of property price prediction, our proposed system brings distinct advantages. One of them is its ability to explore rental options for rooms and buildings. What truly sets our system apart is its comprehensive approach to property valuation. It achieves this by incorporating a user-specific data analysis component, which considers critical factors like user loan availability. Essentially, it bridges the gap between the realm of property investment and the realm of financial planning, equipping users with essential insights to make sound financial decisions in the context of real estate. This project has been meticulously designed with the unique needs of the real estate industry in mind, offering a transformative tool that assists individuals in making smarter choices concerning property investments, rentals, and loans. By providing a comprehensive, user-focused approach, it paves the way for more informed decision-making in the ever-evolving landscape of the real estate market, ultimately promoting financial prudence and responsible financial planning.",0.0,,False,Conference,,2024-09-06,307fcece1735a69f376a239262adb7ce91789e69,propertypriceprediction,robin,10.1109/iconat61936.2024.10774967,propertypriceprediction|2024,1,2388,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",f2bc891ab586492021d40355a0aa440e9bbc170b,10.1109/icadeis71120.2026.11644565,,Comparing Random Forest and LightGBM for Residential Property Price Prediction Using Multi-Platform Online Listings in Indonesia,2026.0,"International Conference Advancement Data Science, E-learning and Information Systems",Tristanto Wijaya; Gian Fiorentino Kapoh; Bagas Adnan Putra; E. Banjarnahor,"Predicting residential property prices is challenging due to the heterogeneous and non-linear nature of housing markets. This study conducts a comparative analysis of machine learning models for house price prediction in Indonesia using multi-platform online real estate data. Property listings from Lamudi and OLX Indonesia were used to construct a dataset consisting of 3,347 records. Three regression models were evaluated: Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Linear Regression as a baseline model. The models were assessed using multiple train-test split scenarios and cross-validation. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination $\left(\mathrm{R}^{\mathrm{2}}\right)$. The results show that the ensemble models achieve high predictive accuracy across all configurations, with Random Forest consistently outperforming LightGBM. In contrast, Linear Regression demonstrates significantly lower performance, indicating its limitation in capturing complex and non-linear relationships. The best performance was obtained with a 90: 10 train-test split, achieving $\mathrm{R}^{{2}}=0.9983$ and MAPE = 2.26%. These findings confirm the effectiveness of ensemble learning for multi-platform property price prediction.",0.0,,False,Conference,,2026-06-01,f2bc891ab586492021d40355a0aa440e9bbc170b,comparingrandomforestandlightgbmforresidentialpropertypricepredictionusingmultiplatformonlinelistingsinindonesia,wijaya,10.1109/icadeis71120.2026.11644565,comparingrandomforestandlightgbmforresidentialpropertypricepredictionusingmultiplatformonlinelistingsinindonesia|2026,1,2375,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0cbd0dc9e83099456998376b2af563dfd440d27c,10.1145/3583133.3590643,,Evolution Enhancing Property Price Prediction by Generating Artificial Transaction Data,2023.0,GECCO Companion,Zhe Yu Liu; A. Song; Nasser R. Sabar; Takero Izuhara; Kai-Huai Qin,"Property price prediction is not only of great importance for home-buyers but also vital for economic studies. The common approach in price prediction is analysing historical market prices and relevant economic data by a predictive model, such as ARIMA model, linear regression, and artificial neural networks. Regardless of the model, a successful prediction using this approach relies on an abundant amount of data, e.g. more than one million property transactions over the last few decades. However, such a large data set might not be readily available, and also expensive to obtain. Hence we propose evolutionary generative Generative Adversarial Networks (GAN), to learn the distribution of these transaction data and generate samples to enrich the data set. This generated data set can help achieve better prediction. In our experiments, we compare not only the performance of several GAN varieties including evolutionary GAN (E-GAN) and knowledge distillation evolutionary GAN (KDE-GAN), but also the differences in prediction results before and after using GAN enriched data set. Our study shows that evolutionary GAN can improve prediction performance without requiring a large amount of data. In addition, evolution does provide extra benefits to the GAN process and enhances its value not only in methodological advancement but also in commercial potential.",0.0,,False,Book;JournalArticle,Computer Science,2023-07-15,0cbd0dc9e83099456998376b2af563dfd440d27c,evolutionenhancingpropertypricepredictionbygeneratingartificialtransactiondata,liu,10.1145/3583133.3590643,evolutionenhancingpropertypricepredictionbygeneratingartificialtransactiondata|2023,1,2368,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",82a5228270eeb78556e15dacb919d275b0f57963,10.1109/icccnt56998.2023.10307946,,Advanced Regression Models for Accurate House Price Prediction: An Analysis of Performance and Interpretability,2023.0,International Conference on Computing Communication and Networking Technologies,Abhigya Verma; Pooja Gera; S. Singhal; A. Mohapatra,"The accurate prediction of house prices has important implications for the real estate industry, investors, and policymakers. In this study, we aimed to compare the performance of various regression models, including basic and advanced techniques, in predicting house prices using a public dataset containing 79 features. Our analysis included extensive feature engineering and data preprocessing to ensure the best possible performance of each model. Our findings suggest that advanced models, such as Bayesian regression and kernel regression having mean cross_val_score 0.9046 and 0.9058 respectively, outperformed basic models, such as multiple linear regression and polynomial regression. We also identified the importance of addressing issues such as multi-collinearity and outliers in the data to ensure the accuracy of the models. Our study contributes to the development of more precise and reliable methods for predicting house prices, providing insights into the strengths and limitations of different regression models. The implications of our work extend to the real estate industry, policymakers, and investors who rely on accurate real estate valuations for informed decision-making. Our results can be used to guide the selection of appropriate regression models and improve the accuracy of house price predictions.",0.0,,False,JournalArticle;Conference,Computer Science,2023-07-06,82a5228270eeb78556e15dacb919d275b0f57963,advancedregressionmodelsforaccuratehousepricepredictionananalysisofperformanceandinterpretability,verma,10.1109/icccnt56998.2023.10307946,advancedregressionmodelsforaccuratehousepricepredictionananalysisofperformanceandinterpretability|2023,1,2331,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",702cae1a310df2978c8f8431f2b10b8f083488bd,10.1109/iconat66879.2025.11362756,,An Innovative Framework on Predicting House Price Using Machine Learning Approaches,2025.0,2025 IEEE 4th International Conference for Advancement in Technology (ICONAT),D. Das; Ritam Dutta; Shilpa Singh; Shipra Sharma; Navin Kumar Goyal; Sanjay Kumar Sinha,"As house prices are increasing day by day therefore, it becomes a real necessity to create a smart mechanism to predict house prices around your area of choice. House Price Index (HPI) is mainly used to detect variations in house prices. House price is connected with some factors viz. the geographical location of the house, the population density around that area etc. Based on the specific location and population density, HPI detects the accurate price of the selected house. This technique can assist the buyer or seller in determining the accurate price of a house according to geographical location, and number of rooms availability. Our proposed machine learning (ML) approach i.e. Multiple Logistic Regression (LR) is applied to diagnose market trends and give more accurate result. In our proposed framework, we have collected two data sets from Kaggle. One is individual state dataset named “Bangalore house price dataset” and another one is “Dataset of India”. After an extensive literature survey on developing smart house price prediction framework, we have trained the existing model with Multiple LR, Decision Tree (DT) and Lasso Regression. Our simulator recorded the accuracy of 90.35 % for “Bangalore House Price Dataset” and whereas “Dataset of India” resulted the accuracy of 95.14% with Multiple LR model.",0.0,,False,Conference;Review,,2025-09-19,702cae1a310df2978c8f8431f2b10b8f083488bd,aninnovativeframeworkonpredictinghousepriceusingmachinelearningapproaches,das,10.1109/iconat66879.2025.11362756,aninnovativeframeworkonpredictinghousepriceusingmachinelearningapproaches|2025,1,2327,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",1362a388aaa49c104fc63f2f104e177950830ad9,10.1109/icdabi63787.2024.10800514,,"Comparing Linear Regression, Random Forest and Support Vector Machine for Rental House Price Forecasting in Bahrain",2024.0,2024 5th International Conference on Data Analytics for Business and Industry (ICDABI),Hooreya A. Khunji; A. Zeki,"The real estate sector in Bahrain plays a pivotal role in its economy, with factors such as land price and house price predictions being crucial. Previous studies have addressed the importance of land price prediction in Bahrain [1]. In this study, data was collected from three famous real estate websites in Bahrain for a single day in March 2024. The dataset contains 14 attributes (such as property type, size, number of bedrooms, number of bathrooms, etc.) and 3084 instances, with 80% used for training and 20% for testing. “A comparative analysis of three data mining methods, namely Linear Regression, Random Forest, and Support Vector Machine”, for rental house price forecasting in Bahrain was conducted. Unlike previous studies, this research focuses on rental house prices in Bahrain using a diverse set of attributes. “The performance of each model was evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)”. Linear Regression (LR) had the highest MAE value of 0.1746, while SMOreg had a lower MAE of 0.1681. The lowest MAE was 0.1385, found with Random Forest (RF). The RMSE value for SMOreg was 0.2976, which was higher than LR’s 0.2935. In contrast, the lowest RMSE value was for RF with 0.2394. This paper found the Random Forest data mining method to perform the best.",0.0,,False,Conference,,2024-10-23,1362a388aaa49c104fc63f2f104e177950830ad9,comparinglinearregressionrandomforestandsupportvectormachineforrentalhousepriceforecastinginbahrain,khunji,10.1109/icdabi63787.2024.10800514,comparinglinearregressionrandomforestandsupportvectormachineforrentalhousepriceforecastinginbahrain|2024,1,2324,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",155cdb782ffa7fba53354dbcbe02741bf0ca615e,10.1109/isaect68904.2025.11318703,,Innovative Predictive Framework for King County Housing Prices: A Comparative Study of Machine Learning and Ensemble Approaches,2025.0,International Symposium Advanced Electrical and Communication Technologies,M. Sidhu; Sugandh Singh; Shilpa Jain; Karthik Kovuri; Anshu Tomar,"Effective prediction of the residential property values is crucial to the stakeholders in the fluctuating real estate markets. This paper creates a powerful machine learning pipeline to forecast house prices on the basis of the King County data that include 21,613 records. Preprocessing of data consisted of the removal of outliers by interquartile range method and feature engineering, which added a composite attribute, total-sqft. Feature selection using correlation and principal component analysis were used to reduce dimensionality and retain 95 percent variance. Linear Regression, Decision tree, Random Forest, XGBoost, LightGBM and a Stacking Ensemble with 5fold cross-validation were tested on each using grid search. The best performance was the Stacking Ensemble, and it has the highest R2 of 0.92 and the lowest MSE followed by LightGBM (R2 = 0.91). Model efficacy and contributions made by features were clarified through the use of visualization such as correlation heatmap, MSE / R2 bar plot, scatter plot, residual histogram, and feature importance chart. Important results emphasize the effectiveness of the ensemble approach in the preservation of nonlinear trends and the importance of the manipulated properties such as the feature of the total in the form of the total_sqft.",0.0,,False,,,2025-12-18,155cdb782ffa7fba53354dbcbe02741bf0ca615e,innovativepredictiveframeworkforkingcountyhousingpricesacomparativestudyofmachinelearningandensembleapproaches,sidhu,10.1109/isaect68904.2025.11318703,innovativepredictiveframeworkforkingcountyhousingpricesacomparativestudyofmachinelearningandensembleapproaches|2025,1,2297,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,3,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",c88d4b568109010aa6900a74b688291b55b7122b,10.1109/swc65939.2025.00055,,Advanced Machine Learning for House Price Prediction,2025.0,2025 IEEE Smart World Congress (SWC),Juan Pang; Rossitza S. Marinova,"This research uses machine learning to improve house price prediction, a key concern for buyers, sellers, and investors. Traditional valuation methods often overlook complex relationships between property features – machine learning models can better capture these dynamics. Using the Kaggle dataset ""House Prices: Advanced Regression Techniques,"" which includes 79 variables on homes in Ames, Iowa, we apply data cleaning, normalization, and feature engineering to prepare the data. The work evaluates a wide range of property features, including physical attributes, location specifics, and neighborhood characteristics, to increase the accuracy of house price predictions. Regression models including Linear Regression, Random Forest, and Gradient Boosting are evaluated, along with hyperparameter tuning and cross-validation to optimize performance. The goal is to develop a robust predictive model that accurately estimates house prices and identifies key predictors such as physical attributes, location, and neighborhood features. By enhancing prediction accuracy, this study supports smarter, data-driven decisions in real estate and contributes to the broader application of AI in smart urban development. It also encourages academic collaboration and innovation in real- world AI use.",0.0,,False,,,2025-08-18,c88d4b568109010aa6900a74b688291b55b7122b,advancedmachinelearningforhousepriceprediction,pang,10.1109/swc65939.2025.00055,advancedmachinelearningforhousepriceprediction|2025,1,2294,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,6,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",18fe9238aa258567492d025d82cef7701360966f,10.1109/icca66035.2025.11430799,,Beyond Econometrics with a Spatial-Temporal Learning Framework for Predicting and Forecasting Housing Market Responses to Crime,2025.0,International Conferences on Computing Advancements,Jerome Mawuena Klu; I. Osunmakinde; M. Abdulgader; Cheryl Hinds,"Understanding the impact of crime on housing markets is essential for urban planning, real estate investment, and public policy. However, predicting housing price resilience is complex due to spatial and temporal crime dynamics. This study introduces the Model-Aided Resilient Forecasting Architecture (MARFA), a spatial-temporal framework combining machine learning models (Random Forest, XGBoost, Neural Networks) with an LSTM-based deep learning model to assess Virginia’s housing markets (2014–2023). The framework integrates categorized crime rates (property, person, and society) with housing price data to predict and forecast housing prices under three crime scenarios: baseline, +10% annual increase, and −10% annual decrease. XGBoost delivered the highest predictive accuracy (R2 = 0.9021; MA = 95.89%), while LSTM enabled multi-year forecasting. Compared to prior studies relying on econometric methods with limited forecasting, MARFA offers improved accuracy and broader spatial-temporal insights. Results show urban centers like Fairfax and Virginia Beach exhibit strong resilience, whereas smaller high-crime areas such as Galax are more vulnerable. This data-driven approach provides a robust tool for evaluating housing market stability amid evolving safety threats.",0.0,,False,Conference,,2025-12-22,18fe9238aa258567492d025d82cef7701360966f,beyondeconometricswithaspatialtemporallearningframeworkforpredictingandforecastinghousingmarketresponsestocrime,klu,10.1109/icca66035.2025.11430799,beyondeconometricswithaspatialtemporallearningframeworkforpredictingandforecastinghousingmarketresponsestocrime|2025,1,2282,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",07c037af001c700907a158cf30fce23fc4070ab4,10.1109/ice-smartech59237.2023.10461965,,A Deep Learning Approach to Predict Housing Market Price Trends in Bandung,2023.0,2023 1st IEEE International Conference on Smart Technology (ICE-SMARTec),Intan Rahmatillah; I. D. Sudirman,"The purpose of this study is to investigate whether deep learning can be used to accurately forecast Bandung area property values. The dataset was obtained from a well-known website for selling houses and then processed with three fully connected layers with eight neurons each to predict the label. The study shows that the deep learning model used to predict house prices has a high accuracy, with a small root-mean-squared error and a high correlation between predicted and actual prices. This model uses features such as square footage (area), bedrooms, bathrooms, and district to make predictions. House size is the most important factor, followed by the number of bathrooms and the Cibeunying Kaler district. The relative importance of each factor is also provided. These results add to the growing body of knowledge on the topic of predicting future housing prices and suggest that deep learning is a useful tool for homeowners, investors, and other players in the Bandung real estate market. This study demonstrates how deep learning has the potential to provide reliable forecasts of future housing price trends, which can aid in making sound choices. Additional features and datasets may be investigated in subsequent research to enhance prediction precision.",0.0,,False,Conference,,2023-07-17,07c037af001c700907a158cf30fce23fc4070ab4,adeeplearningapproachtopredicthousingmarketpricetrendsinbandung,rahmatillah,10.1109/ice-smartech59237.2023.10461965,adeeplearningapproachtopredicthousingmarketpricetrendsinbandung|2023,1,2269,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,4,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",8278d4bc6386f7850d62584a98831dc2b7c6f23d,10.67825/lrjstm.2026.8021631,,Explainable Machine Learning-Based Decision Support Framework for House Price Prediction in Kathmandu Valley: A Comparative Study of Five Modern Regression Models,2026.0,"LBEF research journal of science, technology and management",Shambhu Gautam; Om Prakash Panjiyar; Sujal Shrestha,"Property valuation in the Kathmandu Valley is still largely informal, based on the heuristics of the brokers rather than on any systematic analyses. The present study builds and tests an explainable machine learning model for predicting the house price in Kathmandu, Lalitpur and Bhaktapur. After attempting to clean a raw data set of 2211 listing records (crawled 27 April 2020), a number of data-quality issues were identified and rectified, resulting in 1547 records for analysis. Five regression models—Random Forest, XGBoost, GBM, SVR, and KNN—were optimized using 5-fold cross validated grid search and trained and evaluated on ten engineered features, such as the target-encoded location score and a composite road accessibility score. Random Forest achieved the best test-set performance (RMSE = NPR 22,586,299; R² = 0.494; MAE = NPR 10,614,911), ahead of KNN, GBM, XGBoost, and SVR. In the explainability analysis, location score and amenities count were the top two predictors of the price across all methods, in contrast to the typical structural characteristics of the properties. The framework provides a transparent and reproducible decision support tool for the Kathmandu Valley market for buyers, sellers, investors, lenders and policy makers.",0.0,,False,JournalArticle,,2026-06-30,8278d4bc6386f7850d62584a98831dc2b7c6f23d,explainablemachinelearningbaseddecisionsupportframeworkforhousepricepredictioninkathmanduvalleyacomparativestudyoffivemodernregressionmodels,gautam,10.67825/lrjstm.2026.8021631,explainablemachinelearningbaseddecisionsupportframeworkforhousepricepredictioninkathmanduvalleyacomparativestudyoffivemodernregressionmodels|2026,1,2259,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,5,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7cf866f4ddcd337aa3f16c16941535eb12c2c174,10.1109/icsit65336.2025.11294987,,Hybrid Real Estate Intelligence: Personalized Recommendations Using Machine Learning,2025.0,"2025 International Conference on Sustainability, Innovation & Technology (ICSIT)",M. Sridevi; V. Biksham; Salar Mohammad; Vangapally Raju; Y. Suguna; V. Reddy,"Nowadays growing the multiple property listings on the web, consumers rapidly struggle to search the important apartments properly. This article begins with a hybrid recommendation mechanism proposes to increase the apartment searching expertise by combining feature-based model similarity steps with machine learning methods for cost prediction. The proposed framework contains algorithms such as cosine similarity and K-Nearest Neighbors to recommend apartments which are similar with location, cost range, and model with type. Latest regression methods and techniques are applied apart from Random Forest, Gradient Boosting and XGBoost are exists to forecast property prices depends on the primary features. With the hybrid approach, proposes scoring approach with these two features are integrated to consider both the anticipated very less with affordability and the resemblance property for physical concern. The most suitable importance of regression models is predicting the metrics like MRR, Precision@5, NDCG@5, Recall@5 that are includes to calculate the quality of recommendation outputs. A more sophisticated effective system can be delivered accurate and property with tailored suggestions, user preferences including available listings.",0.0,,False,Conference,,2025-08-22,7cf866f4ddcd337aa3f16c16941535eb12c2c174,hybridrealestateintelligencepersonalizedrecommendationsusingmachinelearning,sridevi,10.1109/icsit65336.2025.11294987,hybridrealestateintelligencepersonalizedrecommendationsusingmachinelearning|2025,1,2251,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",32c2050a0ecf00c11014993bd07444b10c8a6951,10.1109/iccbd-ai62252.2023.00106,,Price Prediction and Analysis Based on the LightGBM Regression Model,2023.0,"2023 4th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI)",Sheng Huang,"Price forecasting is widely used in many fields.lt is of great significance for economic development to find out the model and method of predicting price accurately.Traditional methods of predicting prices include market comparison, income restoration, and cost estimation, but these methods require a significant amount of human and material resources and usually fail to predict housing prices accurately due to the influence of numerous factors. Therefore, this paper aims to use the LightGBM regression model and LGBMRegressor algorithm for the prediction and analysis of housing prices. We collected a large amount of data on second-hand housing prices and preprocessed the data to eliminate entries with missing values. Subsequently, we performed a general descriptive statistical analysis of the data and established feature data and label data based on different characteristics such as region, age of the house, nearby geographical conditions, cultural and traffic factors. Finally, we divided the dataset and made model predictions. The experimental results show that the predicted values of the regression model are basically consistent with the actual fluctuations in housing prices, allowing for relatively accurate predictions.",0.0,,False,Conference,,2023-12-15,32c2050a0ecf00c11014993bd07444b10c8a6951,pricepredictionandanalysisbasedonthelightgbmregressionmodel,huang,10.1109/iccbd-ai62252.2023.00106,pricepredictionandanalysisbasedonthelightgbmregressionmodel|2023,1,2241,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",b11c02b14e465e192bff0d31c43600c7871af71b,10.1109/iccubea65967.2025.11283836,,Integrating Weather Data to Improve House Price Prediction Accuracy of Machine Learning Models: Evidence and Implications,2025.0,International Conference on Computing Communication Control and automation,Indranath Chatterjee; Swapnajit Chakraborti; J. K,"House price prediction has traditionally relied on textual features like the number of bedrooms, bathrooms, and square footage, but this approach often overlooks crucial qualitative aspects. Integrating house images allows models to evaluate visual appeal, architectural style, and property condition, including curb appeal, neighborhood aesthetics, and surrounding area demographics. Incorporating weather data enables analysis of both direct and indirect effects of weather on property values. The primary goal of including imagery data is to enhance prediction accuracy, while weather data serves to assess the impact of weather events on house prices. This research demonstrates that combining these diverse data sources, especially long-term weather data, enhances prediction accuracy. With different machine learning algorithms, this research has established that incorporation of weather data has led to increase the prediction power of house price models whether it is only textual or textual and imagery dataset. However, the improvement in prediction accuracy due to weather data varies depending on the region. These findings may be used for smart city initiatives, namely, urban planning, fair pricing and investment decisions.",0.0,,False,Conference,,2025-08-22,b11c02b14e465e192bff0d31c43600c7871af71b,integratingweatherdatatoimprovehousepricepredictionaccuracyofmachinelearningmodelsevidenceandimplications,chatterjee,10.1109/iccubea65967.2025.11283836,integratingweatherdatatoimprovehousepricepredictionaccuracyofmachinelearningmodelsevidenceandimplications|2025,1,2239,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || (""text mining"" | ""natural language processing"" | ""textual"" | ""listing descriptions"") + (""house price"" | ""housing price"" | ""real estate"" | ""property price"")",0,3,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",71338b846833a334c9e4bdb0e8c028ab9d715dfe,10.1109/iscs69371.2025.11386147,,AI-Driven House Pricing System using Machine Learning,2025.0,IEEE International Symposium on Compound Semiconductors,Meghna Luthra; Sangeeta Kumari; Rajshri; Jagriti Singh; Ayashi,"Accurately forecasting property prices in today’s dynamic market remains a crucial task, as it directly influences the decisions of buyers, sellers, investors, and policymakers. The older evaluation methods often failed to consider many important and diverse features like environmental and demographic variables, often resulting in less reliable predictions in the housing markets. In this research, Ensemble-based learning models like LightGBM and XGBoost outperformed other traditional models like Random Forest, Linear Regression, and Neural Networks. The gradient boosting algorithm was successfully applied to process large datasets and proved effective in capturing complex feature interactions. When used for processing large datasets, Gradient boosting models, showed high efficiency in capturing even the intricate feature connections. Features including socioeconomic indicators, such as flood-prone areas, population density, and crime rates, and proximity metrics, like the distance from schools, hospitals, and public transportation, were incorporated using spatial feature engineering. When purchasing or selling a home, this approach will help the people in the real estate market in making more knowledgeable decisions.",0.0,,False,Conference,,2025-11-14,71338b846833a334c9e4bdb0e8c028ab9d715dfe,aidrivenhousepricingsystemusingmachinelearning,luthra,10.1109/iscs69371.2025.11386147,aidrivenhousepricingsystemusingmachinelearning|2025,1,2236,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,7,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",df63fc964971d3a5e46b409ac941cbaa7498effe,10.1117/12.3107708,,Research and implementation of housing price analysis based on multiple regression algorithms,2026.0,"International Conference on Telecommunications, Optics and Computer Science",Yang Gong,"With the continuous development and progress of the times, people's living standards are rapidly improving. At this time, people put forward higher requirements for ""housing"" in the basic necessities of life. For example, if you want to live in a better house, the relative house price will also be higher. In order to help those who need to buy a house, this paper proposes a housing price research and analysis method based on regression analysis, which first obtains the public historical data of housing prices; then cleans and analyzes the data; then visualizes the general laws of housing price data; The obtained data is modeled by different regression algorithms (K-nearest neighbor regression, polynomial regression, random forest regression (RFR), and support vector regression (SVR)) models; finally, four regression models are used to separately predict the newly arrived data, and then comprehensively compare different data. The regression algorithm model predicts the value. After a large number of experimental tests, the method proposed in this paper is simple to use and has high analysis efficiency. It can provide analysis and reference for people who need to buy a house, and it is worth popularizing and using.",0.0,,False,Conference,Engineering,2026-05-13,df63fc964971d3a5e46b409ac941cbaa7498effe,researchandimplementationofhousingpriceanalysisbasedonmultipleregressionalgorithms,gong,10.1117/12.3107708,researchandimplementationofhousingpriceanalysisbasedonmultipleregressionalgorithms|2026,1,2232,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5aa0c59ec559223e2cc27a4234f041368d70bc74,10.1109/intech64186.2025.11198471,,"Economic Impact of Location, Size, and Amenities on House Price Prediction in Tashkent",2025.0,InTech,Saidkhuja Farrux Ogli Yashinkhujaev; Akobir Mirzaakhmadov; Abdulaziz Miralim; T. Z.; Siddhartha Dutta Chanakya; Zahira Tabassum,"The aim of this study is to examine how machine learning tools can be applied to predict real estate prices. The majority of the current methods used to evaluate the prices of real estate properties tend to become rigid as the real estate market becomes more dynamic. In this aspect, this research beforehand is to construct a better and an adaptive model for estimating house prices using recent machine learning models especially with respect to certain issues such as market trends, property attributes and geographical position of the property. This study has tried various predictive modeling techniques, such as Decision trees, linear regression, ensemble approaches, among others, to identify the best suited approach for predicting price. It also brings forward the notion that to achieve the expected results, the data has to be well prepared with the appropriate features picked out and the model assessed. Results exhibit how machine learning improves efficacy in the price predictive analytics, which benefits buyers and sellers in the real estate industry. Lastly, we take a step forward in this innovative domain of artificial intelligence within properties by addressing future price prediction issues cohesively.",0.0,,False,Conference,,2025-02-27,5aa0c59ec559223e2cc27a4234f041368d70bc74,economicimpactoflocationsizeandamenitiesonhousepricepredictionintashkent,yashinkhujaev,10.1109/intech64186.2025.11198471,economicimpactoflocationsizeandamenitiesonhousepricepredictionintashkent|2025,1,2228,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a8e2e0c0bf8eb32585c21fc6a2c788b810e86ea7,10.53383/100397,,Using an Asymmetric Loss Function to Alleviate the Risk of Loan Collateral Overvaluation,2025.0,International Real Estate Review,Changro Lee,"Financial institutions are increasingly adopting machine learning-based valuation models to evaluate loan collaterals. However, most machine learning algorithms do not differentiate between the risks associated with the undervaluation and overvaluation of such assets. From the perspective of a lender, the risks of overvaluing loan collateral are more critical than those that arise from undervaluing them. In this study, we alleviate this risk of overvaluation by explicitly considering an asymmetric loss function. We customize a gradient boosting machine (GBM) by specifying an asymmetric loss function, and assigning a higher penalty for overvaluation. This customized GBM is then used to predict house prices in Gimhae, South Korea. The results show that the GBM effectively reduces overvaluation while maintaining prediction accuracy. Researchers and practitioners need to intentionally bias their machine learning algorithms to incorporate the asymmetric risks associated with their businesses. The approach proposed in this study can help stakeholders make informed decisions in the lending process, thereby mitigating the risk of default by borrowers, and ultimately promoting sustainable lending practices.",0.0,,False,JournalArticle,,2025-03-31,a8e2e0c0bf8eb32585c21fc6a2c788b810e86ea7,usinganasymmetriclossfunctiontoalleviatetheriskofloancollateralovervaluation,lee,10.53383/100397,usinganasymmetriclossfunctiontoalleviatetheriskofloancollateralovervaluation|2025,1,2217,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ee209d77c2ef5cd7007d0aa380d8946ca3401458,10.1109/csitss67709.2025.11294013,,Assessment and Prediction of House Price Using Machine Learning,2025.0,2025 9th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS),D. S; V. S; K. N.; Jose de Jusus Agustin Flores Cuautle,"The Assessment and Prediction of house price is a difficult area in the investigation. Traditional statistical methods are used to predict the prices, but they failed due to the complexity of real estate data and non-linear relationships among features. Various machine learning methods have become more popular for predicting home prices. because their ability to learn and solve the complex problems also relationships between the variables. The present investigation analyzes the house pricing data sets of Haryana state with two categories such as, Data collection based on parameters like, no of bedrooms, bathrooms, living area, number of floors, condition of the house, Postal Code, Number of schools nearby, Distance from the airport, based on these parameters 14000 datasets are collected, and the machine learning algorithms that are being compared are decision trees, random forests, and linear regression. Finally results reveals that, for Linear Regression the accuracy R-square obtained is 0.581, Decision Tree the accuracy R-square obtained is 0.631 and Random Forest the accuracy R-square obtained is 0.783. Random Forest is getting more accuracy than decision tree and linear regression.",0.0,,False,Conference,,2025-11-20,ee209d77c2ef5cd7007d0aa380d8946ca3401458,assessmentandpredictionofhousepriceusingmachinelearning,s,10.1109/csitss67709.2025.11294013,assessmentandpredictionofhousepriceusingmachinelearning|2025,1,2204,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c89071ce7fe7a6f97ec7ffa5339b9616c41a47a0,10.71465/fias374,,A Comparative Study of Machine Learning Algorithms for Predicting Housing Prices in Chinese Cities,2025.0,Frontiers in Interdisciplinary Applied Science,Jian-Wei Wang; Xiao-Ping Chen; Lin Li,"This study conducts a comparative analysis of machine learning algorithms for predicting housing prices in major Chinese cities, addressing the growing complexity and economic significance of the real estate market. With housing affordability becoming a critical socioeconomic issue, accurate price prediction models are essential for policymakers, investors, and urban planners. The research evaluates the performance of four algorithms—Linear Regression, Decision Trees, Random Forests, and Gradient Boosting Machines—using a dataset comprising historical transaction records, macroeconomic indicators, and location-based features from 10 major Chinese cities. Results indicate that ensemble methods, particularly Gradient Boosting Machines, achieve the highest predictive accuracy, with a mean absolute error reduction of up to 18% compared to traditional linear models. The findings underscore the importance of incorporating non-linear relationships and feature interactions in housing price modeling. This study not only provides a practical framework for real estate valuation but also highlights the potential of advanced machine learning techniques in addressing urban economic challenges.",0.0,,False,JournalArticle,,2025-10-04,c89071ce7fe7a6f97ec7ffa5339b9616c41a47a0,acomparativestudyofmachinelearningalgorithmsforpredictinghousingpricesinchinesecities,wang,10.71465/fias374,acomparativestudyofmachinelearningalgorithmsforpredictinghousingpricesinchinesecities|2025,1,2198,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",010d6fd80cddcfdce46afdd3b6375a1c8ce381ba,10.55041/ijsrem53246,,Multimodel House Price Prediction Using Machine Learning,2025.0,INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,S. M; M. Sengaliappan,"Abstract The rapid urbanization and escalating property prices in metropolitan cities have made housing affordability a critical concern, especially for middle-class families. Accurate prediction of house prices is essential for buyers, sellers, and real estate agents to make informed decisions and minimize financial risks. This paper presents a machine learning-based system for predicting house prices using a Linear Regression model trained on a comprehensive dataset of residential properties. The system incorporates data preprocessing, feature extraction, and model evaluation to ensure reliable and interpretable predictions. A web-based interface built with Flask allows users to input property details and receive instant price estimates. Experimental results demonstrate the model’s effectiveness in capturing key factors influencing house prices, providing a valuable tool for the real estate market. Future enhancements include integrating advanced algorithms and real-time market data to improve prediction accuracy and usability. Keywords House Price Prediction, Machine Learning, Linear Regression, Real Estate, Data Preprocessing, Feature Extraction, Flask, Web Application.",0.0,,False,JournalArticle,,2025-10-29,010d6fd80cddcfdce46afdd3b6375a1c8ce381ba,multimodelhousepricepredictionusingmachinelearning,m,10.55041/ijsrem53246,multimodelhousepricepredictionusingmachinelearning|2025,1,2195,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 crossref,preços hedônicos imóveis,,10.47456/bjpe.v11i1.47469,,Preços de habitação na Califórnia: uma abordagem para previsões no setor imobiliário Housing prices in California: an approach to forecasting in the real estate sector Precios de vivienda en California: un enfoque para pronósticos en el sector inmobiliario,2025.0,Brazilian Journal of Production Engineering,Christian Gianelli da Silva,"Este artigo apresenta uma análise comparativa de técnicas de aprendizado de máquina aplicadas à previsão de preços no setor imobiliário da Califórnia. Foram investigados os modelos de Regressão Linear Múltipla, Regressão Polinomial, Regressão Robusta (RANSAC) e Floresta Aleatória (Random Forest), sendo cada um avaliado com base em métricas estatísticas como Erro Médio Absoluto (MAE), Raiz do Erro Quadrático Médio (RMSE) e Coeficiente de Determinação (R²). O conjunto de dados utilizado foi obtido do repositório StatLib e contém informações sobre características dos imóveis, localização e perfil socioeconômico da população. Os resultados indicam que, apesar da Floresta Aleatória apresentar melhor desempenho preditivo, há indícios de overfitting, sugerindo que um aumento no número de amostras poderia melhorar a generalização do modelo. Por outro lado, os modelos de Regressão Linear e Regressão Polinomial demonstraram maior estabilidade e capacidade de generalização, ainda que com leve perda de precisão. Este estudo contribui para a compreensão da aplicabilidade dessas técnicas na modelagem de preços imobiliários e discute os impactos do tamanho da amostra na acurácia dos modelos.",0.0,,,journal-article,,,doi_10_47456_bjpe_v11i1_47469,precosdehabitacaonacaliforniaumaabordagemparaprevisoesnosetorimobiliariohousingpricesincaliforniaanapproachtoforecastingintherealestatesectorpreciosdeviviendaencaliforniaunenfoqueparapronosticosenelsectorinmobiliario,silva,10.47456/bjpe.v11i1.47469,precosdehabitacaonacaliforniaumaabordagemparaprevisoesnosetorimobiliariohousingpricesincaliforniaanapproachtoforecastingintherealestatesectorpreciosdeviviendaencaliforniaunenfoqueparapronosticosenelsectorinmobiliario|2025,1,2195,1,preços hedônicos imóveis,0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ebc8506bb3a1757d19f55cc692861f52c150be11,10.1109/icccnt61001.2024.10724138,,"Property Price Estimation Using Machine learning and implementing a website using HTML, CSS, and JavaScript",2024.0,International Conference on Computing Communication and Networking Technologies,Naga Swathi Nidamanuru; K. Rajesh; U. Srinivasarao; Kalpana Koppolu; Kola Balavani; Mogadala Vinod Kumar,"Property price estimation uses various kinds of algorithms in Machine Learning like linear regression, lasso, and decision tree that best predict the house prices with the highest accuracy. In this paper, we use various machine learning algorithms to build a model that predicts the house prices based on the requirements like location, area, number of bedrooms, and no of bathrooms. A Huge set of data on housing prices is trained for various attributes to create an algorithmic model. To better understand the links between the several housing qualities and the selling prices, we will do exploratory data analysis (EDA), which entails visualizing the data and discovering patterns and trends that help to develop the prediction model. Data pre-processing and data cleaning is done before creating the model. We build a linear regression model, and we compare it with the other machine learning algorithms like lasso regression and classification and regression tree algorithms and use the one that yields high accuracy. The tested model is imported through a pickle file and a Flask server is used for developing the website using HTML, CSS, and JavaScript.",0.0,,False,JournalArticle;Conference,Computer Science,2024-06-24,ebc8506bb3a1757d19f55cc692861f52c150be11,propertypriceestimationusingmachinelearningandimplementingawebsiteusinghtmlcssandjavascript,nidamanuru,10.1109/icccnt61001.2024.10724138,propertypriceestimationusingmachinelearningandimplementingawebsiteusinghtmlcssandjavascript|2024,1,2160,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,4,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",286d87e9dd7fb8d8425f935de9bb4d24cabb83e6,10.1109/cisct62494.2024.11134193,,House Price Prediction System Using Bagging Based Ensemble Learning,2024.0,2024 4th International Conference on Innovative Sustainable Computational Technologies (CISCT),V. Gupta; Vidisha; H. Tiwari; Jyoti; Shubhani Aggarwal; A. Dwivedi,"One of the busiest and most vital sectors of any economy is the real estate sector. It is crucial for investors, buyers, sellers, and policymakers to have a precise estimate of home values. This article explains how the creation of numerous advanced machine learning models can be utilized to predict home values. Prediction is made using the most popular models, the most favored datasets, the most instructive features, and the proper evaluation procedures. Size, location, market conditions, and economic conditions are just a few of the physical and nonphysical aspects that make it difficult to accurately predict home prices. Real estate professionals, banks, and government agencies can now analyze trends and create complex models to forecast housing prices more accurately than they could with traditional analytical models thanks to digital advancements, particularly the use of machine learning in the housing sector. Using bagging-based ensemble learning, five machine learning models are hybridized to predict accuracy. In terms of correlation, R-squared, RMSE, and accuracy, our suggested ensemble model is outperforming the current model.",0.0,,False,Conference,,2024-12-27,286d87e9dd7fb8d8425f935de9bb4d24cabb83e6,housepricepredictionsystemusingbaggingbasedensemblelearning,gupta,10.1109/cisct62494.2024.11134193,housepricepredictionsystemusingbaggingbasedensemblelearning|2024,1,2153,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",b140c7964c3ca35e954555871fa688c7bef2442b,10.55041/ijsrem41722,,A Self-Attention-Driven Deep Learning Framework for House Price Prediction Using Multimodal Data,2025.0,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,A. Pujitha,"- House price prediction remains a critical challenge in the real estate domain, requiring the consideration of diverse factors that influence housing prices. Traditional models often fail to capture the intricate relationships between these factors, resulting in limited predictive accuracy. To address this, we propose an end-to-end Joint Self-Attention Mechanism for house price prediction, integrating public facility data (e.g., parks, schools, transit) and satellite imagery to assess environmental contexts. Our approach emphasizes the identification of critical features and their interactions, enabling the discovery of complex spatial and contextual relationships.By combining the Joint Self-Attention Mechanism with Spatial Transformer Networks (STN), we aim to establish a robust and precise framework for understanding housing market dynamics. This innovative methodology introduces a new perspective on integrating spatial and contextual data for more informed and reliable house price predictions. Key Words: House Price Prediction, Joint Self-attention Mechanism, Heterogeneous Data, Google Satellite Map ,Spatial Transformer Network",0.0,,False,JournalArticle,,2025-02-20,b140c7964c3ca35e954555871fa688c7bef2442b,aselfattentiondrivendeeplearningframeworkforhousepricepredictionusingmultimodaldata,pujitha,10.55041/ijsrem41722,aselfattentiondrivendeeplearningframeworkforhousepricepredictionusingmultimodaldata|2025,1,2151,2,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",47a5ab840d2059b1fcd9def4db5b3172baaa9aaa,10.1109/iccrtee64519.2025.11053010,,Feature Driven Property Valuation Using Machine Learning,2025.0,"2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE)",K. S; Keshore T; S. M; Sathya Selvaraj Sinnasamy,"Buying and selling a house is a big deal and the price should always be correct. Traditional methods of valuation have a huge time-factor, human discretion-driven. In this project to fulfil the need of the time more precisely and data-driven ""Property Valuation using ML"" We use state-of-the-art AI methods for better and more accurate prediction of house prices. In terms of the price prediction, we use XGBoost as it is very strong in the gradient boosting algorithm and Generative Adversarial Networks (GAN) to produce extra data in order to validate our models. The dataset goes through a large preprocessing, including feature engineering, normalization and imputation to make sure that we get good inputs for training. Together with our model, generalization error gets smaller and reduces the prediction error by achieving better generalization from real data to synthetic. This approach removes human bias, speeds up the valuation process, and makes real estate pricing more transparent. Whether for homebuyers, sellers, or financial institutions, our system provides a smart and efficient way to determine property values with confidence.",0.0,,False,Conference,,2025-05-28,47a5ab840d2059b1fcd9def4db5b3172baaa9aaa,featuredrivenpropertyvaluationusingmachinelearning,s,10.1109/iccrtee64519.2025.11053010,featuredrivenpropertyvaluationusingmachinelearning|2025,1,2147,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3006871c0722c665abc1eaa6cef565b495ba32a0,10.15864/ajec.2104,,ANALYSIS ON THE STATUS OF RENT OF HOUSING INDUSTRY,2021.0,American Journal of Electronics & Communication,Madhulekha Hazra; Bikram Bhattacharya; Suryasish Sengupta; R. Mandal; Poojarini Mitra; Kaustuv Bhattacharjee; Anirban Das," Abstract - Machine learning has been playing an active role in the past few years in several applications like image detection, spam reorganization, recommending products and in medical fields. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Determining the sale price of the house is very important nowadays as the price of the land and price of the house increases every year. So our future generation needs a simple technique to predict the house price in future. The price of house helps the buyer to know the cost price of the house and also the right time to buy it. The right price of the house helps the customer to elect the house and go for the bidding of that house. There are several factors that affect the price of the house such as the physical condition, location, landmark etc. Our result exhibit that our approach to the issue needs to be successful, and can process predictions that would be comparative with other house rent prediction models. This paper uses linear regression technique to predict the house price. ",0.0,,False,,Business,2021-07-05,3006871c0722c665abc1eaa6cef565b495ba32a0,analysisonthestatusofrentofhousingindustry,hazra,10.15864/ajec.2104,analysisonthestatusofrentofhousingindustry|2021,1,2124,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5f905f2ca6a694a3bb7f93c6905fd62b4d3e2df9,10.1109/amlds63918.2025.11159400,,Machine Learning Engine for Real Estate Price Estimation,2025.0,2025 International Conference on Advanced Machine Learning and Data Science (AMLDS),Abdul-Rahman Mawlood-Yunis; Youssef Roman,"Accurate price estimation is crucial for informed decision-making in the real estate industry. This study explores machine learning (ML) methods for predicting housing prices in Ontario’s Halton Region from 2022 to 2023, using a dataset of over 7,000 detached home transactions. Data preprocessing involved feature engineering, including economic indicators like prime rates. Exploratory data analysis revealed transaction patterns and market shifts linked to interest rate changes.ML techniques, including linear regression, Random Forest, and XGBoost, were employed, with models achieving R-squared values between 0.93 and 0.997. Decision Tree and Random Forest models were the most effective in capturing price variability. Additionally, a Flask-Based price estimation tool was developed, and trained on several regions of the Greater Toronto Area (GTA) allowing users to predict home prices based on specific property features.The study demonstrates the value of ML in enhancing real estate market efficiency by providing reliable price predictions, benefiting stakeholders such as homebuyers, sellers, and investors.",0.0,,False,JournalArticle;Conference,Computer Science,2025-07-19,5f905f2ca6a694a3bb7f93c6905fd62b4d3e2df9,machinelearningengineforrealestatepriceestimation,mawlood-yunis,10.1109/amlds63918.2025.11159400,machinelearningengineforrealestatepriceestimation|2025,1,2121,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",9f6da5b708a04de55ca9657526447d0fe1e9ceab,10.1109/aece67531.2025.11386719,,Real Estate Price Prediction Using Machine Learning: A Comparative Analysis with Stacking Regressor,2025.0,2025 5th International Conference on Advancement in Electronics & Communication Engineering (AECE),Richa Sharma; Sarath Chandra K Venkata Nageshwara; G. Srujana; C. Ramasubramanian; Saurabh Kumar; Ravi Kumar,"This paper is dedicated to the real estate price prediction and its importance and the methods used. It provides a systematic framework to the researchers and practitioners and direct them on factors that influence the housing prices and suggest the appropriate machine learning models. The study commences by first importing the necessary Python libraries, loading a real estate data set and conducting intensive data cleaning to accommodate missing values and outliers. Exploratory data analysis exposes important understanding of determinants of housing prices. Following the division of the dataset, the different machine learning models are trained and assessed by using the metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The Stacking Regressor becomes the best performing model with the lowest RMSE, and this is selected as the final model, which secures the correct pricing predictions of the property in the dynamic real estate environment. In this study, the dataset of Indian residential property of Kaggle is used, which consists of 14,621 rows and $\mathbf{2 3}$ columns.",0.0,,False,Conference,,2025-11-21,9f6da5b708a04de55ca9657526447d0fe1e9ceab,realestatepricepredictionusingmachinelearningacomparativeanalysiswithstackingregressor,sharma,10.1109/aece67531.2025.11386719,realestatepricepredictionusingmachinelearningacomparativeanalysiswithstackingregressor|2025,1,2111,3,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || residential property price determinants",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",82ef25545f39729d8b996e3f09c0cb6f9f887ffa,10.1109/icatc68823.2025.11407866,,House Price Prediction for Sri Lanka Using Machine Learning : A Comparative Study of Algorithms,2025.0,2025 International Conference on Advances in Technology and Computing (ICATC),D. D. Melroy,Machine Learning ML has become increasingly prevalent across various domains revolutionizing tasks such as speech recognition product recommendations and predictive modeling Its applications extend to enhancing customer services and ensuring data driven decision making. In this study we propose the application of ML techniques for predicting housing prices in Sri Lanka aiming to reduce uncertainty in the volatile real estate market The proposed model analyzes a comprehensive dataset 15327 records 172 features 2015 to 2024 sourced from ikman lk ML algorithms including Linear Regression Decision Tree Regression Random Forest Regression and advanced ensemble methods are evaluated Random Forest Regression achieved the lowest RMSE of 41905267 indicating superior accuracy Feature importance analysis highlights key determinants of housing prices This research provides actionable insights for buyers sellers investors and policymakers facilitating informed real estate decisions Ethical considerations related to data privacy are also discussed it this correct,0.0,,False,Conference,,2025-12-16,82ef25545f39729d8b996e3f09c0cb6f9f887ffa,housepricepredictionforsrilankausingmachinelearningacomparativestudyofalgorithms,melroy,10.1109/icatc68823.2025.11407866,housepricepredictionforsrilankausingmachinelearningacomparativestudyofalgorithms|2025,1,2065,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",a8ae82e36827d768d2cb4f8068ffddb9b9e4d9fd,10.54254/2755-2721/46/20241300,,Prediction of second-hand house prices in Ames city based on data mining,2024.0,Applied and Computational Engineering,Chucheng Zhou,"As the secondary housing market continues to play a vital role in the global real estate industry, there is an increasing demand for accurate and efficient prediction of secondary housing prices. In this study, transaction data from the second-hand housing market in Ames, Iowa, USA, available on the Kaggle website, was utilized. A total of 79 influential feature variables, including Lot Area, Overall Quality, and Year Built, were selected to construct seven different models for predicting secondary housing prices: Linear Regression, Lasso Regression, Ridge Regression, LassoCV, RidgeCV, Random Forest, and Neural Network models. To assess the effectiveness of these models, we utilized a range of evaluation metrics including the R2 score, Mean Absolute Error (MAE), and Mean Squared Error (MSE). The results indicated that the RidgeCV model stood out among the others, displaying the most robust predictive performance with a goodness-of-fit of 90.3%. This discovery holds significant implications for improving the accuracy of secondary housing valuation.",0.0,,False,JournalArticle,,2024-03-15,a8ae82e36827d768d2cb4f8068ffddb9b9e4d9fd,predictionofsecondhandhousepricesinamescitybasedondatamining,zhou,10.54254/2755-2721/46/20241300,predictionofsecondhandhousepricesinamescitybasedondatamining|2024,1,2063,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e88843d8526585df3a1fbc287b6a2f0067a5bb5f,10.1109/icih67754.2025.11608689,,Improving Housing Price Predictions with Feature Engineering and Ensemble Models,2025.0,2025 IEEE International Conference on Innovate for Humanitarian: Tech Solutions for Global Challenge (ICIH),Shaik Ishrath Banu; A. Rao; Pattan Imran; Garikapati Sai Venkata Narasimha; Y. Lalitha; Nomula Divya,"This study presents a machine learning-based approach for predicting housing prices in the Indian real estate market. Using Kaggle’s India Housing Prices Dataset with 2.5 lakh records and 23 features, we applied feature engineering techniques such as ordinal encoding, one-hot encoding, and derived variables (e.g., property age, price per square foot). Multiple regression algorithms, including Linear Regression, Decision Tree, Random Forest, and XGBoost, were trained and compared. Among them, XGBoost achieved the best performance with an R2 score of 0.992, a Mean Absolute Error (MAE) of 9.87 lakhs, and a Root Mean Squared Error (RMSE) of 13.98 lakhs, outperforming the other models. The proposed framework was deployed as a web service using Flask, Docker, and Render Cloud, enabling real-time housing price predictions. The results indicate that ensemble methods, particularly XGBoost, provide accurate and scalable solutions for property valuation in India, offering valuable insights to real estate agents, investors, banks, and home buyers.",0.0,,False,Conference,,2025-11-01,e88843d8526585df3a1fbc287b6a2f0067a5bb5f,improvinghousingpricepredictionswithfeatureengineeringandensemblemodels,banu,10.1109/icih67754.2025.11608689,improvinghousingpricepredictionswithfeatureengineeringandensemblemodels|2025,1,2051,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 crossref,preços hedônicos imóveis,,10.37885/241218360,,Integração de Machine Learning e Sistemas Automatizados na Avaliação de Imóveis,2024.0,Projetos de Pesquisa em Data Science - Estudos de caso do curso CEDS IBAPE-PR,Luciano Ventura; Filipe Alves Neto Verri,"Este trabalho aplica técnicas de machine learning para aprimorar a avaliação imobiliária, com foco em modelos preditivos mais precisos e automatizados. Através da seleção criteriosa de variáveis e da validação avançada de resultados, modelos como LGBMRegressor e HistGradientBoostingRegressor demonstraram maior precisão na previsão de valores de imóveis, em comparação com modelos tradicionais. O estudo considerou dados de localização, características dos imóveis e variáveis socioeconômicas, além de transformações para reduzir a influência de outliers. Métodos como stepwise, random forest, e PLS-SEM foram empregados para identificar as variáveis mais relevantes, resultando em modelos robustos e generalizáveis. Como contribuição, propõe-se uma metodologia padronizada para coleta e análise de dados no setor imobiliário, aplicável em diferentes regiões e para diversos tipos de imóveis. O trabalho destaca ainda o potencial do web scraping para automatizar a coleta de dados, promovendo mais eficiência e precisão no processo de avaliação.",0.0,,,book-chapter,,,doi_10_37885_241218360,integracaodemachinelearningesistemasautomatizadosnaavaliacaodeimoveis,ventura,10.37885/241218360,integracaodemachinelearningesistemasautomatizadosnaavaliacaodeimoveis|2024,1,2046,1,preços hedônicos imóveis,0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",f28452b78c6d866b474a6f898b4685014c143469,10.1145/3558819.3565221,,House Price Prediction Model Using Bridge Memristors Recurrent Neural Network,2022.0,ICCSIE,Wen-Zhao Shi,"In recent decay, the house price prediction plays important role because of it's the volatile of house price which makes significant impact on property valuation and economic growth. It characterizes are attracted the numerous researchers, businessman and people who buy or sell house towards it. The volatile of house price is occurred based on various factors like location, facility, neighborhood, etc. In this way, researchers are evaluating the factors using machine and deep learning process to analysis the information. Although, regression-based analysis has problem due to its nonlinear and linear information in neural network. Thus, we have proposed a novel Bridge Memristors Recurrent Neural Network to forecast the house price prediction in this paper. In addition, RBP algorithm is used on Bridge Memristors RNN for train the neural network in efficient manner. Besides, our proposed model carried out outstanding performance than existing models to attain the high prediction rate by analyzing the correlation coefficient.",0.0,,False,Book;JournalArticle,Computer Science,2022-09-23,f28452b78c6d866b474a6f898b4685014c143469,housepricepredictionmodelusingbridgememristorsrecurrentneuralnetwork,shi,10.1145/3558819.3565221,housepricepredictionmodelusingbridgememristorsrecurrentneuralnetwork|2022,1,2037,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d0a762907f165883a496e85aac889fa52f2585a5,10.1109/i2ct61223.2024.10543370,,A comparative Analyis of House Price Prediction Algorithms,2024.0,2024 IEEE 9th International Conference for Convergence in Technology (I2CT),Rahul Chauhan; Tushar Sharma; Ruchira Rawat; Rudresh Pillai; K. R. Chythanya,"This paper aims to present the implementation of two machine learning algorithms, Linear Regression and Lasso Regression for the task of predicting the price of the house located in the city of Bengaluru, India. The task of predicting house price accurately is quite difficult as it depends on a lot of factors, and needs of the buyer. For predicting these prices, we can try to create datasets which contains details of the factors that people look for while buying Houses such as size, location, bathroom etc. To analyses the data, we can usecertain algorithms such as linear regression, lasso regression etc. By using algorithms like this, we can reduce the margin of error of our model and try to make it more accurate and usable. Such models can be used by real estate agents,sellers as well as the people who want to buy house to get the best deal for them. In future,researcher can also integrate models like this with the real estate's websites like MagicBricks,99Acres to give better and more accurate recommendations to people.",0.0,,False,Conference,,2024-04-05,d0a762907f165883a496e85aac889fa52f2585a5,acomparativeanalyisofhousepricepredictionalgorithms,chauhan,10.1109/i2ct61223.2024.10543370,acomparativeanalyisofhousepricepredictionalgorithms|2024,1,2037,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",42abdec870c4441dfb16a87db51f7d5e7e19aae8,10.1109/icpics62053.2024.10796220,,House Price Prediction Model Based on Multi-Strategy Improved COOT Optimization Algorithm,2024.0,"2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)",Ziyang Han; Xiang-wei Zhu,"The COOT optimization algorithm holds promise for optimizing machine learning algorithms and feature selection. However, its drawbacks, such as weak overall search capability and sometimes it goes to a local optimum, pose challenges when applied in practice. Aiming at the problems existing in the reference white crown chicken algorithm, a new algorithm named TLCOOT is proposed, which is based on tent Chaos Mapping and Levy Flight Strategy. This strategy changes and improves the strength of the original algorithm to jump out of partial optimal solutions. A variety of functions are used to evaluate the effect of the algorithm. The results show that TLCOOT is superior to the COOT optimization algorithm and several swarm intelligence algorithms in Best, Mean, Std and convergence curve. On this basis, TLCOOT optimized random forest house price prediction model (TLCOOT-RF) was used. The experimental results of the model verify the practicability of the proposed TLCOOT optimization algorithm.",0.0,,False,Conference,,2024-07-26,42abdec870c4441dfb16a87db51f7d5e7e19aae8,housepricepredictionmodelbasedonmultistrategyimprovedcootoptimizationalgorithm,han,10.1109/icpics62053.2024.10796220,housepricepredictionmodelbasedonmultistrategyimprovedcootoptimizationalgorithm|2024,1,2000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",f08cbd1635c5956ec2f82f52f7e78ec7958b2ff8,10.1109/igarss55030.2025.11313991,,"Exploring Non-Linear Relationships Between Green Space Patterns, Accessibility and Housing Prices Using High-Resolution Remote Sensing Imagery",2025.0,IEEE International Geoscience and Remote Sensing Symposium,Zixuan Wang; Xiang Zhang,"The study of the impact of urban green space (UGS) on housing prices is closely related to people's quality of life and the real estate market. This study explores the nonlinear relationships between UGS landscape patterns, accessibility, and housing prices in both the sales and rental markets. By utilizing high-resolution remote sensing images to extract UGS data, eight landscape pattern indices and accessibility in three transportation scenarios were calculated. XGBoost and SHAP methods were applied to analyze the marginal effects of different UGS characteristics on housing prices. The results demonstrate that ecological benefits, aesthetic value, and accessibility of UGS significantly influence housing prices, exhibiting threshold effects. In particular, we revealed the complex non-linear influences of ecological benefits, aesthetic value, and accessibility (cycling, etc.) on housing prices, which provides valuable insights for UGS planning and real estate market strategies.",0.0,,False,,,2025-08-03,f08cbd1635c5956ec2f82f52f7e78ec7958b2ff8,exploringnonlinearrelationshipsbetweengreenspacepatternsaccessibilityandhousingpricesusinghighresolutionremotesensingimagery,wang,10.1109/igarss55030.2025.11313991,exploringnonlinearrelationshipsbetweengreenspacepatternsaccessibilityandhousingpricesusinghighresolutionremotesensingimagery|2025,1,1992,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",6ff06be88701f17286f1bfbee3f1478ba795bb0d,10.1109/icsece65727.2025.11256900,,Second-Hand Housing Prediction Model Prices by Using Hybrid RF-BPNNs,2025.0,"2025 IEEE 3rd International Conference on Sensors, Electronics and Computer Engineering (ICSECE)",Mengzhen Wang; Yujie Ying,"Accurate prediction of second-hand housing prices is crucial for effective decision-making in the real estate market. This study begins by applying Principal Component Analysis (PCA) to select the most relevant 8 features. Initially, a basic Backpropagation (BP) Neural Network was used for price estimation, but it yielded suboptimal R2 scores. To improve performance, we developed an ensemble model that integrates Random Forest (RF) and BP Neural Networks. The RF models generate additional predictive features, which are combined with the original dataset to form an enhanced feature set. Configurable BP neural networks were then trained on this augmented dataset. Further improvements were achieved by training 10 such model and combining their predictions by 4 distinct boosting methods, resulting in a best R2 score of 0.9108. Our results demonstrate the effectiveness of this hybrid approach in achieving high accuracy for second-hand housing price prediction.",0.0,,False,Conference,,2025-08-29,6ff06be88701f17286f1bfbee3f1478ba795bb0d,secondhandhousingpredictionmodelpricesbyusinghybridrfbpnns,wang,10.1109/icsece65727.2025.11256900,secondhandhousingpredictionmodelpricesbyusinghybridrfbpnns|2025,1,1969,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",453ff855ff18dc1d3ac86f4841c7d22aaa7c2dbe,10.34925/eip.2020.122.9.250,,Using artificial neural networks to evaluate residential real estate in two different regions,2020.0,Экономика и предпринимательство,Ю. В. Трифонов; А. Л. Сочков,"Целью работы являлась разработка многослойного персептрона для оценки жилой недвижимости двух регионов, находящихся в ведении одного отделения банка. Использован метод обратного распространения ошибки для обучения сети. Синтезирован многослойный персептрон, позволяющий проводить оценку квартир, расположенных в двух разных регионах со средней относительной погрешностью 4,81%. Сеть предназначена для банковских online сервисов оценки жилой недвижимости в городах Нижний Новгород и Казань. The aim of the work was to develop a multi-layer perceptron for evaluating residential real estate in two regions managed by the same Bank branch. The method of error back propagation is used for network training. A multi-layer perceptron was elaborated that allows evaluating apartments located in two different regions with an MAPE of 4.81%. The network is designed for online banking services for residential real estate valuation in the cities of Nizhny Novgorod and Kazan.",0.0,,False,JournalArticle,,2020-10-03,453ff855ff18dc1d3ac86f4841c7d22aaa7c2dbe,usingartificialneuralnetworkstoevaluateresidentialrealestateintwodifferentregions,трифонов,10.34925/eip.2020.122.9.250,usingartificialneuralnetworkstoevaluateresidentialrealestateintwodifferentregions|2020,1,1968,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",705ade6af361d428f62909a69e5002a84927fdf2,10.1109/compas67506.2025.11381637,,Towards Developing an AI-Enhanced Blockchain-Based Real Estate Management System,2025.0,"2025 IEEE 2nd International Conference on Computing, Applications and Systems (COMPAS)",J. Ferdousi; Israt Jahan Oishi; Solaiman Islam Naiem; Shadman Azad Rivan; Zohirul Islam; Muhammad Nazrul Islam,"This article proposes a platform with Artificial Intelligence (AI) for explainable house price prediction and blockchain-enabled smart contracts for secure and transparent property transactions. The three contributions of this work are: (i) a system architecture that combines explainable AI models with decentralized ownership transfer, (ii) cross-comparison of machine learning and deep learning baselines including XGBoost, LSTM, and Transformer-based models, and (iii) a case study on the apartment data of Dhaka city to demonstrating the viability of the platform. Although the Ethereum’s smart contracts ensure automated and verifiable transfers; the article continues to examine scalability problems (e.g., gas cost) and law enforceability. The test outcomes reveal improved prediction accuracy, improved user trust through interpretability, and improved transaction workflows, making the system a compelling foundation for real estate practice modernization.",0.0,,False,Conference,,2025-10-23,705ade6af361d428f62909a69e5002a84927fdf2,towardsdevelopinganaienhancedblockchainbasedrealestatemanagementsystem,ferdousi,10.1109/compas67506.2025.11381637,towardsdevelopinganaienhancedblockchainbasedrealestatemanagementsystem|2025,1,1966,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b1b194946e7ca389f953d6c6cd4472102bac4ea4,10.1177/297765702022s222,,A Decision Tree Based Model for Predicting the Value of Homes in Boston Using The ‘R’ Tool,2022.0,"Journal of Real Estate, Construction & Management",Sameer Jain; Sachin Jain,": In the construction industry, analytics has modified the underlying trend of data processing and forecasting. It has the ability to derive actionable insights in the industry from massive amounts of data that are produced on a regular basis. In industry, a large amount of structured and unstructured data is generated, and using this data, we could assist a company in making a game-changing decision. Senior management is eager to take advantage of vast amounts of historical real estate data in order to gain insights into different facets of this burgeoning industry. Land size, distance, location, crime rate, age etc. are all variables to consider. We'll use the Boston Housing dataset, which was compiled by the US Census Service and includes data on housing in Boston, Massachusetts. The dataset contains 506 cases and 13 attributes. The aim of this paper is to develop a decision tree-based model for predicting home values in Boston.",0.0,,False,JournalArticle,,2022-12-01,b1b194946e7ca389f953d6c6cd4472102bac4ea4,adecisiontreebasedmodelforpredictingthevalueofhomesinbostonusingthertool,jain,10.1177/297765702022s222,adecisiontreebasedmodelforpredictingthevalueofhomesinbostonusingthertool|2022,1,1945,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6898c97490acfe692d4c6c80ee696d78727feb4f,10.1109/ictacs59847.2023.10389820,,An Ensemble Regression Model to Predict a Rent of House,2023.0,2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS),Rahul Bhandari; Sanjay Singla; Daulat Sihag,"In today's era, assessing property values has become essential for multiple sectors, including real estate, taxation, the economy, and the stock market. The price of a house is closely intertwined with factors such as population and geographical location and necessitates the incorporation of additional data for accurate individual price predictions. In a contemporary setting, property prices are heavily influenced by a variety of factors within the economy and society. The current study focuses on the application of ensemble algorithms for predicting real estate and housing prices. These algorithms encompass bagging regression, histogram boosting, stacking, and ridge regression. To evaluate the prediction accuracy, the research examines the root square and root mean square error scores of the training models. The most noteworthy achievement in this study is an accuracy of 83.74%, achieved through the use of bagging regression.",0.0,,False,Conference,,2023-11-01,6898c97490acfe692d4c6c80ee696d78727feb4f,anensembleregressionmodeltopredictarentofhouse,bhandari,10.1109/ictacs59847.2023.10389820,anensembleregressionmodeltopredictarentofhouse|2023,1,1940,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,4,2,1,2,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",2a15263c822b4d72ccd70cb7615d75ab6130676f,10.64751/ajmimc.2026.v5.n2(2).357,,REALESTATE HOUSEPRICE PREDICTION,2026.0,American Journal of Management and IOT Medical Computing,Prateek Kumar Kar; Debaprasad Mahala; Mohapatra Girashree Sahu,"The rapid growth of AI-driven real estate analytics and increasing complexity of property valuation have created significant demand for intelligent house price prediction systems. Traditional valuation methods rely heavily on manual analysis and market expertise, which are time-consuming and inconsistent. This paper presents the design and implementation of an AI-Based Realestate Houseprice Prediction System that leverages Machine Learning, Data Analysis, Regression Models, and predictive analytics to estimate property prices accurately. The system analyses features such as location, area, number of bedrooms, amenities, market trends, and property images to generate accurate predictions. A web-based dashboard allows users to input property details and instantly receive estimated house prices. Experimental results demonstrate improved prediction accuracy and efficient performance for real-world real estate applications.",0.0,,False,JournalArticle,,2026-06-06,2a15263c822b4d72ccd70cb7615d75ab6130676f,realestatehousepriceprediction,kar,10.64751/ajmimc.2026.v5.n2(2).357,realestatehousepriceprediction|2026,1,1932,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",4518721f959c7b0123164103f41e6415067df0da,10.1109/aisc56616.2023.10085127,,A Strategic Alternative to Predicting Property Investment Value Through Random Forest and Gradial Enhancing,2023.0,Artificial Intelligence and Symbolic Computation,Anubhav Sony; Aarju,"The good investment instruments include the following items gold, stocks, and real estate. Investment in real estate in particularly has increased significantly. Developments in home prices are important for both sellers and buyers because they provide insight into the health of the economy. Property expenses are influenced by a variety of variables, including location, bedroom count, and floor layout. The cost of housing is also influenced by a property's proximity to roads, major highways, schools, retail centers, and local job possibilities. Normal home prediction becomes challenging, hence other methods for predicting house prices have been created. The least transparent sector in our society is real estate. Daily fluctuations in housing prices occur, and they are sometimes overstated instead of based on appraisal. Our group's primary objective is to predict housing values using actual information.",0.0,,False,Conference,,2023-01-27,4518721f959c7b0123164103f41e6415067df0da,astrategicalternativetopredictingpropertyinvestmentvaluethroughrandomforestandgradialenhancing,sony,10.1109/aisc56616.2023.10085127,astrategicalternativetopredictingpropertyinvestmentvaluethroughrandomforestandgradialenhancing|2023,1,1915,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,5,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ad15fcd0be4aa0b26e2d1fe53d80246935f7f4f8,10.1109/adsssc67751.2026.11582632,,House Price Prediction Using Support Vector Machine in Kerala: A Web-Based Approach,2026.0,2026 International Conference on AI-Driven Solutions for Sustainable Smart Cities: Challenges and Opportunities (ADSSSC),K. M. Aliaskar; K. N. Haneefa; N. Anitha; R. Afmitha; M. Anjana; S. Sivambika,"This paper explains how to create a web-based application to predict housing costs in Kerala. The tool will provide users with the ability to assess homes’ fair market values based on certain characteristics (geographical location, size, number of bedrooms, distance from City Centre, etc.). The tool is designed to increase the transparency of the real estate market in Kerala as well as give users accurate data to make better decisions when buying or selling a home. Housing prices will be predicted using Support Vector Machines (SVMs). The predicted housing pricing application will be developed in Python and Django using several open source Machine Learning (ML) Libraries and will have an interactive user interface that allows for real-time housing price predictions. The experimental and simulation results indicate that the predicted housing pricing system provides accurate and efficient predictions.",0.0,,False,Conference,,2026-04-09,ad15fcd0be4aa0b26e2d1fe53d80246935f7f4f8,housepricepredictionusingsupportvectormachineinkeralaawebbasedapproach,aliaskar,10.1109/adsssc67751.2026.11582632,housepricepredictionusingsupportvectormachineinkeralaawebbasedapproach|2026,1,1912,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,4,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d168a54a79dca9e1800dd13e818f5afc4c2eb261,10.1145/3557738.3557856,,DESIGN AN AGILE OF MACHINE LEARNING TO PREDICTIVE HOUSE PRICING AND TARGETING SEGMENTED MARKET,2022.0,ICONETSI,J. Wijaya; Heru Purnomo Ipung; M. Soetomo,"Because of too high expectations or having a wrongly segmented target market, the developer hasn't received a good response from the target market. Developers need a new marketing tool that is based on data. The use of machine learning systems as marketing tools to help solve the problems in house price prediction is an important topic in the real estate industry. The design of machine learning will use CRISP-DM as a framework and to analyze using linear regression and random forest as the best possible accuracy. Besides that, to find a potential market, we will use K-Means as a clustering method. The modeling and experiments to design a machine learning engine that can predict a range of selling prices using linear regression can give maximum accuracy and analyze the target market. The research focusing on different attributes will bring different dominant attributes to the table too.",0.0,,False,JournalArticle;Book,Computer Science,2022-09-21,d168a54a79dca9e1800dd13e818f5afc4c2eb261,designanagileofmachinelearningtopredictivehousepricingandtargetingsegmentedmarket,wijaya,10.1145/3557738.3557856,designanagileofmachinelearningtopredictivehousepricingandtargetingsegmentedmarket|2022,1,1898,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",955e47397d4f0bca58f3f244868aeb61ed3c0325,10.1177/14727978251358642,,A method for predicting house prices in Beijing based on deep learning algorithm,2025.0,Journal of Computational Methods in Sciences and Engineering,Yun-Xing Wang; Xiao-Hua Chen; Yang Liu; Zhicheng Wen,"The trend of market house prices is influenced by various factors, and house price prediction algorithm remains a very classic and challenging nonlinear problem in data analysis. Analyzing various factors that may affect market house prices can help to provide a more accurate assessment of future trends in house prices. Multiple nonlinear regression is suitable for analyzing data affected by many factors. It is more efficient and practical to predict the house price by multiple independent characteristics than only one independent variable. This article is based on a deep learning algorithm and uses the PaddlePaddle experimental platform to learn about the past period of house sale prices and other related data in Beijing. Multiple nonlinear regression methods are used to analyze the data and predict the future house price trend in this region.",0.0,,False,JournalArticle,,2025-07-04,955e47397d4f0bca58f3f244868aeb61ed3c0325,amethodforpredictinghousepricesinbeijingbasedondeeplearningalgorithm,wang,10.1177/14727978251358642,amethodforpredictinghousepricesinbeijingbasedondeeplearningalgorithm|2025,1,1856,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",e414d68bdc4a00c57adbae8e59a8b31fab1fb72e,10.1109/mlise57402.2022.00027,,Channel exchange network model for house price prediction,2022.0,2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE),Yuantao Li; Rui Zhang; Jie Wang; Jia-Hao Shi,"As an important indicator of economic development, house price prediction is a hot issue in the field of business and research in urban computing. However, due to a variety of influencing factors and sparse transaction records, how to construct a housing price prediction framework is still a challenging problem. However, the existing machine learning and deep learning methods lack consideration of multi-source factors. Therefore, this paper uses the method based on channel exchange network to solve the problem of urban house price prediction. This paper also considers the space-time factors, regional factors and housing price expectations. In this paper, a neural network framework based on channel switching network is designed to capture these factors, and the characteristics of different model training are fused through channel switching",0.0,,False,Conference,,2022-08-01,e414d68bdc4a00c57adbae8e59a8b31fab1fb72e,channelexchangenetworkmodelforhousepriceprediction,li,10.1109/mlise57402.2022.00027,channelexchangenetworkmodelforhousepriceprediction|2022,1,1850,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",d43cb8b58778b03db2f4c5579de73e4a8dbf4ebc,10.1109/icdiss68238.2025.11320610,,A Comparative Study of Predictive Analytics Techniques in Big Data Analysis,2025.0,2025 International Conference on Digital Innovations for Sustainable Solutions (ICDISS),Udita Shukla; Anjali Sharma; Sonal Pathak,"A key part of getting valuable insights from complex datasets in today's world of ever-increasing data is predictive analytics. This study uses the prediction of housing prices as a case study to compare six contemporary and conventional machine learning techniques: Random Forest, Support Vector Regression, Gradient Boosting, Decision Tree, Linear Regression, and Extreme Gradient Boosting (XG Boost). When compared to traditional statistical techniques, the results demonstrate the higher accuracy and consistency of ensemble-based techniques, especially XG Boost. In addition to performance assessment, the study discusses how predictive analytics is changing in 2025 and how cutting-edge techniques like federated learning, transformer-based models, and Auto ML with Neural Architecture Search are changing forecasting skills.",0.0,,False,Conference,,2025-11-14,d43cb8b58778b03db2f4c5579de73e4a8dbf4ebc,acomparativestudyofpredictiveanalyticstechniquesinbigdataanalysis,shukla,10.1109/icdiss68238.2025.11320610,acomparativestudyofpredictiveanalyticstechniquesinbigdataanalysis|2025,1,1831,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b3cd25f18ea4f3318e0559e02e9258be23c8d3fa,10.1109/icspe62629.2024.10924379,,House Price Prediction Using ML,2024.0,2024 International Conference on Sustainable Power & Energy (ICSPE),Anurag Kumar; Kalyani Singh; A. Raj; Aryan Shakya,"With the use of several Python modules, this project gives us a general idea of how to forecast home prices using several machine learning models. This suggested model offers the most accurate prediction and is thought to be the most accurate model for determining home prices. This gives a quick overview that is necessary to forecast the cost of a home. Using the datasets we will utilize in our proposed model, this study aims to explain what and how the house pricing model functions with the aid of machine learning techniques. Finding the right moment to purchase a property and figuring out The purchase price of a house in a certain area is helped by a price forecast.The objective of this machine learning task on house price prediction is to develop a machine learning model based on data in order to forecast house.",0.0,,False,Conference;Review,,2024-11-28,b3cd25f18ea4f3318e0559e02e9258be23c8d3fa,housepricepredictionusingml,kumar,10.1109/icspe62629.2024.10924379,housepricepredictionusingml|2024,1,1826,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",d73949b9e8302e61295272aeea270858b7664d2a,10.1117/12.2655351,,Research on prediction model of second-hand house price,2022.0,Conference on Intelligent and Human-Computer Interaction Technology,Yin Haoran,"With the rapid development of China's economy, the real estate industry has become an important topic of livelihood issues in China in recent years. This paper mainly takes the second-hand house price in Beijing as the data source, analyzes the status quo of the second-hand house price in each district, and studies the housing price prediction problem according to the properties of the second-hand house in the region, floor, year, layout and so on. In this paper, the housing price is predicted by linear regression model and decision tree regression model respectively, and the prediction results are scored by R2_Scroe scoring method, and the results are 0.66 and 0.79 respectively. Experimental results show that the prediction effect of decision tree regression model is better than that of linear regression model.",0.0,,False,Conference,Engineering,2022-10-07,d73949b9e8302e61295272aeea270858b7664d2a,researchonpredictionmodelofsecondhandhouseprice,haoran,10.1117/12.2655351,researchonpredictionmodelofsecondhandhouseprice|2022,1,1823,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",6fd6c05d44ed0f229522a17ae8a0ea990680defc,10.51244/ijrsi.2025.120800153,,"Predictive Modelling and Statistical Analysis of Housing Prices in Lagos State, Nigeria",2025.0,International journal of research and scientific innovation,Odukoya E. A.; Oyelakin O. P.; Lawal O. J.,"This study delves into housing market analysis and price prediction, leveraging statistical modeling and machine learning techniques to uncover patterns and forecast property prices. The housing market is influenced by diverse factors such as location, property features, economic indicators, and market trends, necessitating a comprehensive analytical approach. Using a dataset comprising historical housing prices and relevant attributes, the study employs exploratory data analysis to identify key determinants of property values. The findings highlight significant predictors of housing prices and demonstrate the potential of predictive analytics in guiding buyers, sellers, and policymakers. This research offers valuable insights into market dynamics and contributes to data-driven decision-making in real estate",0.0,,False,JournalArticle,,,6fd6c05d44ed0f229522a17ae8a0ea990680defc,predictivemodellingandstatisticalanalysisofhousingpricesinlagosstatenigeria,a.,10.51244/ijrsi.2025.120800153,predictivemodellingandstatisticalanalysisofhousingpricesinlagosstatenigeria|2025,1,1819,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",b9ca1245f97f4e5fa9f69ed8caa4ab616400af96,10.1109/icce-taiwan58799.2023.10226936,,Urban Second-Hand Housing Price Evaluation System Based on Bayesian Probabilistic Model,2023.0,2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan),Zhaojun Tang; Ping Zhang; Xinjing Qin; Bin Cheng; T. Liu,"Combining data mining technology into housing price evaluation problem has increased great attention in recently years because it improves the prediction accuracy. To facilitate the application, this paper builds an urban secondhand housing price evaluation system based on our Bayesian probabilistic model under location submarket division. Using urban data such as house location, surrounding environment and point of interest (POI) information, a prediction model is constructed based on the second-hand house transaction data crawled from the network. It helps users get the price as well as location, POI information and characteristic attributes of the target house, and query suitable houses meeting some given requirements. The system provides visual display of query results and evolves by using query results.",0.0,,False,JournalArticle;Conference,Computer Science,2023-07-17,b9ca1245f97f4e5fa9f69ed8caa4ab616400af96,urbansecondhandhousingpriceevaluationsystembasedonbayesianprobabilisticmodel,tang,10.1109/icce-taiwan58799.2023.10226936,urbansecondhandhousingpriceevaluationsystembasedonbayesianprobabilisticmodel|2023,1,1819,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,3,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",95c832bc28fe9ebe94055ece7fdc2cff0b39b445,10.1109/ickecs65700.2025.11035241,,Predicting House Prices Using Various Machine Learning Algorithms,2025.0,2025 International Conference on Knowledge Engineering and Communication Systems (ICKECS),Rithvik Manohar; R. Janarthanan; S. Pavithra,"The research uses the Ames dataset from Kaggle for predicting house prices. It applies six different machine learning models. These are XGBoost Regressor, Support Vector Regressor, Decision Tree Regressor, Random Forest Regressor, Bagging Regressor, and Gradient Boosting Regressor. All the above models have been used with different features of property, including lot size, building type, and year built. Hyperparameter tuning optimized model performance; a comparative analysis shows better accuracy and robustness, especially with complex feature interaction, with methods being Random Forest and Gradient Boosting. The results in this paper emphasize the effectiveness of those models in the field of predictive real estate analytics and suggest valuable insights for future applications and research.",0.0,,False,Conference,,2025-04-28,95c832bc28fe9ebe94055ece7fdc2cff0b39b445,predictinghousepricesusingvariousmachinelearningalgorithms,manohar,10.1109/ickecs65700.2025.11035241,predictinghousepricesusingvariousmachinelearningalgorithms|2025,1,1806,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",5450419c6a46f3356f91add995e949a9dacd25e0,10.1109/icteasd57136.2023.10585192,,Supervised Machine Learning Approach to Housing Market,2023.0,2023 International Conference for Technological Engineering and its Applications in Sustainable Development (ICTEASD),Izat Hakimi Mohd Ismayatim; Hanis Najiah Zakaria; I. Ramadhani; N. Abdullah; Fakhitah Ridzuan; W. A. Mustafa; Mohammed Al-Farouni,"Understanding the multifaceted factors influencing housing prices is crucial for facilitating informed decision-making among diverse stakeholders, including homebuyers, sellers, investors, and policymakers. Therefore, the primary objective of this research is to develop a predictive model for housing prices utilizing regression analysis. By leveraging machine learning techniques, such as regression and classification analysis, this study aims to uncover the intricate relationships between various factors and housing prices, ultimately providing valuable insights to stakeholders involved in the real estate market. Through rigorous data analysis and model development, this research seeks to enhance understanding and contribute to more accurate predictions in the realm of housing price dynamics.",0.0,,False,Conference,,2023-11-14,5450419c6a46f3356f91add995e949a9dacd25e0,supervisedmachinelearningapproachtohousingmarket,ismayatim,10.1109/icteasd57136.2023.10585192,supervisedmachinelearningapproachtohousingmarket|2023,1,1803,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",74ff267d8452744f196dde43f090e7c74cbf4168,10.51765/tayod.1421771,,Comparison of expert opinion and artificial neural network models in housing price prediction,2024.0,Türkiye Arazi Yönetimi Dergisi,S. Yalpır; Erol Yalpir,"Bu çalışmada Ankara ili, Yenimahalle ilçesi, Batıkent Bölgesi sınırları içerisinde bulunan konutların öznitelik verileri ve coğrafi konumlarının taşınmaz değerine olan etkileri incelenmiştir. Veri seti düzenlenirken Coğrafi Bilgi Sistemlerinden faydalanılmıştır. Çalışma kapsamında nominal değerleme ve Yapay sinir ağları (YSA) modellemede kullanılmıştır. Bölgedeki taşınmazların elde edilebilecek en yüksek hassasiyet ve en yüksek doğrulukta taşınmaz değer haritaları oluşturularak değer tespitleri yapılmıştır. Modellemede YSA ile daha başarılı piyasa sonuçları tahmin edilmiştir. Nominal değerlemede ise uzman görüşü ile oluşan matematiksel modelin piyasa değerini tahmininin göz ardı edilemeyecek başarı elde ettiğini ve gelecekte modelin güncellemelerle sürdürülebilir olduğu gözlemlenmiştir",0.0,,False,JournalArticle,,2024-02-19,74ff267d8452744f196dde43f090e7c74cbf4168,comparisonofexpertopinionandartificialneuralnetworkmodelsinhousingpriceprediction,yalpır,10.51765/tayod.1421771,comparisonofexpertopinionandartificialneuralnetworkmodelsinhousingpriceprediction|2024,1,1796,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9b1ab5f72bd8f0381fb36ae4fa7f6e6abaf3fe06,10.1109/iciss66954.2025.11389339,,Smart City Ai for Homebuyers: Personalized Recommendations Derived From Historical Property Records,2025.0,International Conferences on Information Science and System,Davy Ronald Hermanus; S. Supangkat; Fadhil Hidayat,"This work proposes a personalized real estate recommendation system to enhance smart city public services by combining human-centered design ideas with machine learning concepts. Trained on past housing data from Bandung, Indonesia, the Random Forest Regressor in the system forecasts house prices. Via an interactive Streamlit interface, users assign preference weights for elements including architecture, location, and security. After that, the model ranks properties depending on a compatibility score obtained from preference matching and SHAP-based explainability and forecasts house prices. In line with ethical, open, and citizen-centric Smart City design, evaluation measures reveal great accuracy ($R^{2}=0.94$, MAPE $=22.01 \%$), and the system is fully deployed for public usage.",0.0,,False,Conference,,2025-09-03,9b1ab5f72bd8f0381fb36ae4fa7f6e6abaf3fe06,smartcityaiforhomebuyerspersonalizedrecommendationsderivedfromhistoricalpropertyrecords,hermanus,10.1109/iciss66954.2025.11389339,smartcityaiforhomebuyerspersonalizedrecommendationsderivedfromhistoricalpropertyrecords|2025,1,1791,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,3,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",87f08bce76f5913e565072d94837959199e06c94,10.1109/icuis67429.2025.11380639,,AI-Driven House Price Prediction for Bangalore Region,2025.0,2025 5th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS),Sushmitha Megharaj Doddamani; Vaishnavi Savalagi; R. N,"Accurate prediction of house prices plays a crucial role in the real estate industry and aids stakeholders in making informed decisions. The process involves analysing a wide range of features such as location, size, number of rooms, and nearby amenities. By applying machine learning techniques, particularly Linear Regression, Random Forest, and XGBoost algorithms, the prediction process becomes more efficient and reliable. Among these models, XGBoost demonstrated the highest accuracy in estimating housing prices. The analysis includes data preprocessing, feature selection, and performance evaluation using metrics like RMSE and R2 score. The findings highlight the effectiveness of ensemble learning methods in delivering precise outcomes for property value estimation.",0.0,,False,Conference,,2025-11-26,87f08bce76f5913e565072d94837959199e06c94,aidrivenhousepricepredictionforbangaloreregion,doddamani,10.1109/icuis67429.2025.11380639,aidrivenhousepricepredictionforbangaloreregion|2025,1,1777,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,automated valuation model residential,7c133e01957a598c90d11b36fa9d8771350dc9a4,10.1002/isaf.70019,,Lost in the Modeling Stage: A Comparative Analysis of Machine Learning Models for Real Estate Data,2025.0,Intell. Syst. Account. Finance Manag.,Ian Lenaers; L. De Moor,"Machine learning dominates automated property valuation, yet comprehensive comparisons of predictive models remain scarce. This study compares 28 rent prediction models using 79,735 Belgian residential rental properties from 2022. Predictive performance is evaluated with traditional and alternative metrics for train data, test data, and across deciles. The results confirm that tree‐based ensemble models outperform others, with stacking and averaging yielding superior results at a higher computational cost. Furthermore, middle‐range rents show better predictive accuracy than extremes. Traditional and alternative metrics provide consistent findings. These insights aid real estate stakeholders seeking to enhance their expert systems for real estate price modeling.",0.0,,False,JournalArticle,Computer Science,2025-10-12,7c133e01957a598c90d11b36fa9d8771350dc9a4,lostinthemodelingstageacomparativeanalysisofmachinelearningmodelsforrealestatedata,lenaers,10.1002/isaf.70019,lostinthemodelingstageacomparativeanalysisofmachinelearningmodelsforrealestatedata|2025,1,1771,2,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"") || automated valuation model residential",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",5be82af55989931f991fbf6f78fbea93a05bd3de,10.35453/nedjr-ascn-2022-0001r3,,ANALYSING HOUSE PRICE PREDICTIONS ACCORDING TO LIVING STANDARDS BASED ON MACHINE LEARNING METHODS,2022.0,NED University Journal of Research,Khalid Mahboob; Nida Khalil; Saniah Rehan,"There is a lack of reliable economical methods for forecasting house prices for those who wish to buy a house according to their living standards. This paper presents details of predictive analytics for house pricing in three different towns of Karachi, Pakistan according to different living standards based on machine learning (ML) methods. The purpose of this study is to determine which data set features contribute greatly to the accuracy of the predictions when experimenting with selected predictive techniques. The house price value has been analysed using five different ML methods. A model selection has been made by comparing the accuracy of the techniques based on some performance metrics and the best technique was used to predict the house price value.",0.0,,False,,,2022-09-05,5be82af55989931f991fbf6f78fbea93a05bd3de,analysinghousepricepredictionsaccordingtolivingstandardsbasedonmachinelearningmethods,mahboob,10.35453/nedjr-ascn-2022-0001r3,analysinghousepricepredictionsaccordingtolivingstandardsbasedonmachinelearningmethods|2022,1,1767,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",4e0f5f33fa3bd6c26232a544bfdd68d4c89bd7d6,10.1145/3677779.3677831,,Housing price prediction based on multiple regression algorithms,2024.0,"Proceedings of the International Conference on Modeling, Natural Language Processing and Machine Learning",Jiangang Wang; Xiao-Yan Wang; Zixuan Cao,"Obtain relevant property data through the network and preprocess the data, including viewing the correlation between features, removing outliers, performing normal distribution processing, filling in missing values, etc. Multiple regression algorithms such as elastic regression, lasso regression, ridge regression, and gradient boosting regression were used for modeling, and a stacked model was established on this basis. By comparing the root mean square error of the single model and the stacked models established by different models, the results show that the training effect of the single model is not as good as that of the ensemble model, so the stacked model is selected to predict the house price, which achieves the expected effect.",0.0,,False,Book;Conference,,2024-05-17,4e0f5f33fa3bd6c26232a544bfdd68d4c89bd7d6,housingpricepredictionbasedonmultipleregressionalgorithms,wang,10.1145/3677779.3677831,housingpricepredictionbasedonmultipleregressionalgorithms|2024,1,1744,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,3,2,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",3b61ae6e608effd006774ff7d20b0f0a9290e8c8,10.1109/i-coste63786.2024.11024733,,Spatial Characteristics of CA: A Narrative into San Francisco,2024.0,2024 International Conference on Sustainable Technology and Engineering (i-COSTE),F. H.,"This study delves into the spatial characteristics of housing prices within California, with a specific focus on San Francisco. We explore various models to predict housing prices based on different feature sets, including coordinate and noncoordinate attributes. Through extensive analysis, we find that incorporating geographic coordinates significantly enhances the predictive accuracy of housing prices. Utilizing a neural network model, we achieve nearly 100% accuracy in predicting the, the quartile of median housing prices, underscoring the complexity and influence of location-specific features. Finally, we utilized iSLFN algorithm for developing a precise price prediction model and describing communities characteristics.",0.0,,False,Conference,,2024-12-18,3b61ae6e608effd006774ff7d20b0f0a9290e8c8,spatialcharacteristicsofcaanarrativeintosanfrancisco,h.,10.1109/i-coste63786.2024.11024733,spatialcharacteristicsofcaanarrativeintosanfrancisco|2024,1,1733,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,4,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0ed50c8ec2872ac705642c937da09a50a8525350,10.1109/bdicn55575.2022.00114,,A housing price prediction method based on neural network,2022.0,"2022 International Conference on Big Data, Information and Computer Network (BDICN)",Yu-Chen Ni,"This paper proposes a housing price prediction model based on the neural network for the problem of rising housing prices. First, the data are read from Kaggle, then the data are collected for preprocessing, and 10 relevant features are obtained and the data are normalized for each feature. Then build the model, train and configure the model. After that, the loss function and optimization algorithm are set using the stochastic gradient descent method. After the model is trained on the data set, a housing price prediction model is obtained. The experimental results show that the prediction model can accurately predict housing prices, and can make a more scientific reference for the trend of housing prices.",0.0,,False,Conference,,2022-01-01,0ed50c8ec2872ac705642c937da09a50a8525350,ahousingpricepredictionmethodbasedonneuralnetwork,ni,10.1109/bdicn55575.2022.00114,ahousingpricepredictionmethodbasedonneuralnetwork|2022,1,1714,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ae012ea924873256cfc3d6615d34e7ec5109f642,10.1109/csce60160.2023.00297,,Improved Machine Learning Approach for Shanghai City's Secondhand Housing Price Prediction,2023.0,"2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE)",Guang Hu; Yue Tang; R. Yi,"This paper presents an improved machine learning approach for prediction of second-hand housing prices in Shanghai. It firstly builds the random forest model and the XGboost model with Shanghai second-hand housing transaction data, and then analyses the challenges in the improvement of the models' prediction accuracy and generalization performance. In the light of that, it introduces the Lasso model for variable selection, and deletes three variables with insignificant regression coefficients, and then rebuilds the random forest and XGboost prediction models. The experimental results show that the prediction accuracy and the generalization performance of the models is well improved.",0.0,,False,,,2023-07-24,ae012ea924873256cfc3d6615d34e7ec5109f642,improvedmachinelearningapproachforshanghaicityssecondhandhousingpriceprediction,hu,10.1109/csce60160.2023.00297,improvedmachinelearningapproachforshanghaicityssecondhandhousingpriceprediction|2023,1,1691,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,3,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0c467b5ecc54cee056ccd030afa0be1712ab94e4,10.1109/ispds54097.2021.00016,,Boston Housing Price Forecast Based on HMM,2021.0,"2021 International Conference on Information Science, Parallel and Distributed Systems (ISPDS)",Ruhua Lu; Shuangwei Wang; Yalan Li,"Hidden Markov Model (HMM) is a stochastic process of hidden Markov chain, which plays an important role in pattern recognition. It has been successfully applied to many industries of classification or evaluation or prediction. House price prediction is a multi linear regression problem, which is suitable for the optimal path solution problem in HMM. Therefore, this paper tries to analyse the basic principles of housing price prediction and HMM, and introduces HMM into the field of house price prediction for the first time. In theory, a set of HMM based housing price prediction framework is constructed, which provides a guiding theoretical reference for relevant researchers.",0.0,,False,Conference,,2021-08-01,0c467b5ecc54cee056ccd030afa0be1712ab94e4,bostonhousingpriceforecastbasedonhmm,lu,10.1109/ispds54097.2021.00016,bostonhousingpriceforecastbasedonhmm|2021,1,1682,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,2,2,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",ea04faa3629d6ec5277a775909d75463bd04b9e8,10.1109/icaiccit68829.2025.11433976,,Smart House Price Prediction: Evaluating Crime Rates and Employment Trends Using Machine Learning Algorithms,2025.0,"2025 3rd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)",Rakeshreddy; Payamvineesha; Kambleavinash; Kumar Reddy; M. Jagdish,"This study proposes a Flask-based machine learning system to improve house price prediction by incorporating crime rates and employment trends — two critical socioeconomic factors often neglected in existing models. The key contribution of this work lies in integrating these external indicators into a Random Forest-based prediction pipeline, significantly enhancing accuracy over traditional attribute-based models. By deploying the system as an interactive web tool, the study also bridges the gap between predictive analytics and real-world usability, enabling dynamic, real-time price estimations for diverse stakeholders.",0.0,,False,Conference,,2025-10-31,ea04faa3629d6ec5277a775909d75463bd04b9e8,smarthousepricepredictionevaluatingcrimeratesandemploymenttrendsusingmachinelearningalgorithms,rakeshreddy,10.1109/icaiccit68829.2025.11433976,smarthousepricepredictionevaluatingcrimeratesandemploymenttrendsusingmachinelearningalgorithms|2025,1,1627,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",b4cb621d411693e41acd452211dd18e4dcf00310,10.1109/iccsc62048.2024.10830341,,Real Estate Price Prediction Model Analysis Using Machine Learning,2024.0,IEEE International Conference on Circuits and Systems for Communications,Kapil K. Rajput; N. Bahadure; Amit Kumar Mishra; Manoj Diwakar; Sumita Lamba,"The research illustrates use of ML algos in the field of housing price prediction. The models have been analyzed on real datasets downloaded from Kaggle created by Amitabha Chakraborty. We know that the source on the research of ML prediction in India are scarce, but we encounter conditions which pose need of efficient methodologies to solve this problem. This research gives a glance about the use of ML techniques on the dataset. After careful analysis of data processed and running tests in multiple ways, the results indicate the best suited algorithms for this work.",0.0,,False,Conference,,2024-10-24,b4cb621d411693e41acd452211dd18e4dcf00310,realestatepricepredictionmodelanalysisusingmachinelearning,rajput,10.1109/iccsc62048.2024.10830341,realestatepricepredictionmodelanalysisusingmachinelearning|2024,1,1573,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",9d56372e8d98e104cc9870182b688c13914ac685,10.1109/icbds58040.2023.10346417,,Comparative Analysis of Regression Techniques for Accurate House Price Prediction Using Machine Learning: A Statistical Analysis Method,2023.0,2023 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS),Tanu Patel; Jyoti Sahu; M. K,"Accurate house price prediction is crucial in real estate. This research aims to build a reliable house price prediction model using regression techniques. We evaluate model performance through 3-fold cross-validation and employ the mean R2 score as the evaluation metric, measuring predictive capacity. The top-performing model undergoes hyperparameter tuning. Our findings shed light on the effectiveness of regression techniques in house price prediction, identifying the most accurate model.",0.0,,False,Conference,,2023-10-06,9d56372e8d98e104cc9870182b688c13914ac685,comparativeanalysisofregressiontechniquesforaccuratehousepricepredictionusingmachinelearningastatisticalanalysismethod,patel,10.1109/icbds58040.2023.10346417,comparativeanalysisofregressiontechniquesforaccuratehousepricepredictionusingmachinelearningastatisticalanalysismethod|2023,1,1495,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,False,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"")",c653c6384ea88a44bed6ee65ba75bf0384d146e7,10.25436/e2vc71,,Examining geographical generalisation of machine learning models in urban analytics through street frontage classification and house price regression,2021.0,,S. Law; P. Jeszenszky; K. Yano,,0.0,,False,,Computer Science,2021-09-01,c653c6384ea88a44bed6ee65ba75bf0384d146e7,examininggeographicalgeneralisationofmachinelearningmodelsinurbananalyticsthroughstreetfrontageclassificationandhousepriceregression,law,10.25436/e2vc71,examininggeographicalgeneralisationofmachinelearningmodelsinurbananalyticsthroughstreetfrontageclassificationandhousepriceregression|2021,1,1000,3,"(""computer vision"" | ""street view"" | ""satellite imagery"" | ""images"") + (""house price"" | ""housing price"" | ""property value"" | ""real estate"") || (""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",c44e35d2a3cc668e4f694b81448d6d26ab564897,10.61463/ijset.vol.13.issue2.434,,House Price Prediction Using Machine Learning Teachniques,2025.0,International Journal of Scientific Engineering and Technology,P. Ajay,,0.0,,False,JournalArticle,,2025-04-15,c44e35d2a3cc668e4f694b81448d6d26ab564897,housepricepredictionusingmachinelearningteachniques,ajay,10.61463/ijset.vol.13.issue2.434,housepricepredictionusingmachinelearningteachniques|2025,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",beade792d1a98d9b4d97a7dd3c2dcb463ec2509d,10.1007/978-981-33-6969-6_60,,Assessment of Real House Price Using Machine Learning,2021.0,,S. Shukla; Samir Kumar Pandey; U. Bharadwaj; A. Yadav,,0.0,,False,,Computer Science,,beade792d1a98d9b4d97a7dd3c2dcb463ec2509d,assessmentofrealhousepriceusingmachinelearning,shukla,10.1007/978-981-33-6969-6_60,assessmentofrealhousepriceusingmachinelearning|2021,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",57874d59666a6ca0c32d32dd0110005216e04344,10.1063/5.0319431,,Enhancing the prediction of house price using XGBoost regression with random forest to improve accuracy,2026.0,AIP Conference Proceedings,Dr. N. Uday Kiran; Gomathi,,0.0,,False,Conference,,,57874d59666a6ca0c32d32dd0110005216e04344,enhancingthepredictionofhousepriceusingxgboostregressionwithrandomforesttoimproveaccuracy,kiran,10.1063/5.0319431,enhancingthepredictionofhousepriceusingxgboostregressionwithrandomforesttoimproveaccuracy|2026,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,2,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",1e029313cdd3243ab89cea94636164ac43d4b257,10.56726/irjmets99694,,VISION-DRIVEN HOUSE PRICE ESTIMATION USING CONVOLUTIONAL NEURAL NETWORKS,2026.0,International Research Journal of Modernization in Engineering Technology & Science,,,0.0,,False,JournalArticle,,2026-06-02,1e029313cdd3243ab89cea94636164ac43d4b257,visiondrivenhousepriceestimationusingconvolutionalneuralnetworks,,10.56726/irjmets99694,visiondrivenhousepriceestimationusingconvolutionalneuralnetworks|2026,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",7550d7ba89b985b2b39c6b27b73c62c6e26ec32f,10.1063/5.0277314,,Evaluating the impact of features on house prices with lasso regression compare to polynomial regression,2025.0,AIP Conference Proceedings,Poondla Dharmendra; S. Saravanan,,0.0,,False,Conference,,,7550d7ba89b985b2b39c6b27b73c62c6e26ec32f,evaluatingtheimpactoffeaturesonhousepriceswithlassoregressioncomparetopolynomialregression,dharmendra,10.1063/5.0277314,evaluatingtheimpactoffeaturesonhousepriceswithlassoregressioncomparetopolynomialregression|2025,1,1000,1,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance)",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",7b071895cc806dc50e0a2ac12361e1edccb4d30e,10.5281/zenodo.3956571,,Determining House Price Using Regression,2020.0,,A. Chakraborty,,0.0,,False,,Business,2020-08-01,7b071895cc806dc50e0a2ac12361e1edccb4d30e,determininghousepriceusingregression,chakraborty,10.5281/zenodo.3956571,determininghousepriceusingregression|2020,1,1000,2,"(""house prices"" | ""housing prices"" | ""home prices"" | ""property prices"" | ""property values"" | ""home values"") + (regression | determinants | characteristics | attributes | amenities | proximity | distance) || (""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",ac58a9aa7c116a06b40a1d3f58b2af3467d7db59,10.1049/icp.2025.4761,,Machine learning-based forecasting of residential property prices in Mumbai,2026.0,IET Conference Proceedings,Rishi V. Badhe; Sujata R. Kolhe; Digvijay R. Borse; Mitesh M. Diwase,,0.0,,False,JournalArticle;Conference,,2026-02-01,ac58a9aa7c116a06b40a1d3f58b2af3467d7db59,machinelearningbasedforecastingofresidentialpropertypricesinmumbai,badhe,10.1049/icp.2025.4761,machinelearningbasedforecastingofresidentialpropertypricesinmumbai|2026,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,1,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",97c34145109223f193aa5a24a8bc139c90d2a128,10.1007/978-981-16-1335-7_41,,House Price and Renovation Prediction Analysis Using Different Machine Learning Algorithms,2021.0,Sixth International Conference on Intelligent Computing and Applications,M. Karthik; M. Krishna; A. Posonia,,0.0,,False,Conference,Computer Science,,97c34145109223f193aa5a24a8bc139c90d2a128,housepriceandrenovationpredictionanalysisusingdifferentmachinelearningalgorithms,karthik,10.1007/978-981-16-1335-7_41,housepriceandrenovationpredictionanalysisusingdifferentmachinelearningalgorithms|2021,1,1000,1,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",0,2,1,1,0,4,True,candidate,True,include,ELIGIBLE_ML_PREDICTIVE_ONLY,1 semanticscholar_bulk,"(""machine learning"" | ""deep learning"" | ""neural network"" | ""random forest"" | ""gradient boosting"" | XGBoost) + (""house price"" | ""housing price"" | ""property valuation"" | ""real estate valuation"" | ""property price"")",679f855f7434eb89a11299e1dc7164f104e998c3,10.1063/5.0265756,,Efficient house price prediction using deep belief network compared over random forest with improved accuracy,2025.0,AIP Conference Proceedings,K. S. M. Reddy; V. 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