1record_id,doi,title,year,venue,authors,abstract,citations,oa_pdf20b75eaf2598b7c9697584c08a8e7bc9181ab2cb9,10.1145/3357384.3357810,MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data,2019,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",4.0,https://arxiv.org/pdf/2411.1808531b841e343e18283fd0936884afa6cfde8ce6087c,10.15295/bmij.v10i4.2159,Main determinants of house prices: Effects of construction cost and house sales to foreigners,2022,Business & Management Studies: An International Journal,U. Alkan,"Housing prices have increased worldwide with the increase in commodity prices. They have become a primary investment tool most consumers prefer, especially to protect themselves from inflation. One of the main questions is which macroeconomic factors affect the housing price index, especially the increase in the number of houses sold to foreigners and the increase in construction costs. The study will answer whether the increase in housing costs or the increase in sales to foreigners causes an increase in housing prices. The variables in the study consist of macroeconomic variables that were mostly included in previous studies. Data from January 2015 to March 2022 consisted of 87-period data. Time series analysis has been tried to be explained with the help of the ARDL model by performing a boundary test. In the model, all variables were significant, but a long-term relationship was found, not a short-term one. Tests have demonstrated the model's accuracy for deviation from basic assumptions and structural break tests. Taking the logarithms of the variables in the model makes it possible to interpret them flexibly. In this context, contrary to expectations, it was determined that h",4.0,https://www.bmij.org/index.php/1/article/download/2159/173741588daa7c938ce0c1ee6d0196f223c60e135f932,10.3390/buildings11120650,"The Impact of Increased Density on Residential Property Values in Sydney, New South Wales",2021,Buildings,Narvaez Sodhi; S. Shirowzhan; S. Sepasgozar,"This paper investigates the impact of high-density development on low-density residential property values in Sydney, New South Wales (NSW). To do so, it conducts a literature review to ascertain the existing knowledge surrounding the study of property valuation and its economic and societal implications. Limitations within academia were identified and addressed as the objectives of this research. Subsequently, the key objective of this research is to “study the sociological factors dictating the attractiveness of low-density (LD) properties within proximity to high-density (HD) local characteristics.” In addressing this objective, research questions explore the interactions of an area’s local characteristics, its residents’ property types and the perceptions surrounding these interactions. This research studies property value through the lens of market perceptions, as the price of land is a basic indicator of the attractiveness, economic value and amenities accessible to a specific site. Through this seminal understanding, the research methodology was formed in which a questionnaire was completed by Sydney residents, providing data for analysis and discussion. The primary research ",4.0,https://www.mdpi.com/2075-5309/11/12/650/pdf?version=163998480751dd8017f34c0c5d21554850c1caebf8d5f2a6764,10.20900/jsr20210019,Labels’ Standard Deviation in Energy Performance Certificates: Portuguese Housing,2021,Journal of Sustainability Research,N. Cortiços,"Energy Performance Certificates (EPC) went from a solely bureaucratic process to a differentiating factor capable of raising a property’s value. The regulation, as conceived, is thoroughly suitable for new buildings, given their characteristics and available information. Contrary to the aging building stock, which lacks documentation and construction evidence. Surveys demonstrate pressure on qualified assessors to deliver higher EPC labels. The assessor tends to increase the grade to the detriment of a more rigorous approach aligned with the regulatory guidelines, raising the property market value, and benefitting building owners. The research oversees 30 EPCs, strictly following the regulation criteria, and concluded a global (−)8.77% average against the official EPCs, representing (−)21.93% on a 250% scale, which should not change the (letter) grade, except when close to the threshold. The following tendency shows different behavior considering the “C” threshold, when above lowers by (−)7.70% and under that level and including increases by (−)34.37% on the same scale against official EPCs: mainly, upon the uncertainty of isolation presence on opaque façade elements. The study out",4.0,https://doi.org/10.20900/jsr2021001960f6dcae9dc8da736a128b35bd45eb0d06e704941,10.1088/1757-899x/829/1/012009,Exploring the Mechanism of Residential Attractiveness in Compact Urban Areas – a Case Study of Hong Kong,2020,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 pl",4.0,https://iopscience.iop.org/article/10.1088/1757-899X/829/1/012009/pdf7007a7ee2b0695760c90d8044d2acb4be6bc91fa1,10.1111/jfr3.13035,Agent‐based intra‐regional relocation model considering spatial local amenity for urban planning‐based flood risk management: Assessing the impact of urban development on flood exposure,2024,Journal of Flood Risk Management,Tomohiro Tanaka; A. Koga; M. Yokomatsu,"Integrated flood risk management based on urban policies remains challenging compared with infrastructure due to the unclear risk‐reduction effects over time. To consider heterogeneity in social responses to urban planning measures, a previous study developed an agent‐based household relocation model under flood risk (AHR‐FLOOD) by combining flood‐inundation and agent‐based relocation models. However, accurate modelling of relocation incentives remains challenging. This study aimed to improve AHR‐FLOOD by introducing spatial amenities based on transportation convenience and reflecting their impact on the decision‐making processes of agents to analyze the development of different flood risk areas. AHR‐FLOOD considering access to public transportation reproduced the spatial characteristics of the actual population and housing prices. The development of low‐risk areas reduced flood exposure and resulting flood damage. However, this effect was less clear for low‐income individuals, but the development of low‐amenity areas had the potential to induce low‐income population to move to safe areas. Chain migration was observed as a long‐term effect of the spatial amenity policy. This study ",4.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jfr3.130358173fad5adefb807ae60f0e96747804872c442755,10.3390/su10010062,"Exploring the Determinants of Migrant Workers’ Willingness to Buy Houses in Cities: A Case Study in Xi’an, China",2017,,Xiao-Ning Zhang; Mei Qu; Zhe Jin,"Migrant workers’ buying houses in cities can not only help to reduce the number of unsold houses but also improve the efficiency of the use of rural residential lands. A framework is constructed to study how individual resource endowment and the compensation policy of quitting rural residential land act on migrant workers’ willingness to buy houses in cities. The paper adopts the logistic regression model with the data collected from 410 migrant workers in Xi’an. The results can be drawn as follows: firstly, migrant workers’ desire for buying houses in cities has a close relationship with their individual resource endowment; secondly, there is a gap between the existing compensation policy and migrant workers’ actual preference for the compensation policies. Thirdly, the existing compensation policy cannot fully exert its impact. As a result, when migrant workers are allowed to choose their most preferred policies in light of their own conditions, both the policy and resource effect will become more remarked. Thus, the design of compensation policies for quitting rural residential land should take full account of migrant workers’ individual resource endowments in order to provide t",4.0,https://www.mdpi.com/2071-1050/10/1/62/pdf?version=1514461961921933516b79decc88a6510dbe30380fd9042adc0,10.15396/eres2013_34,Veblen Effect in the United States Housing Market: Spatial and Temporal Variation,2013,,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 eff",4.0,https://eres.architexturez.net/system/files/34.pdf100916e726c85e50653fac514f67b0cb29852dc7ce,10.1016/j.insmatheco.2018.11.006,To borrow or insure? Long term care costs and the impact of housing,2019,"Insurance, Mathematics & Economics",Adam W. Shao; Hua Chen; M. Sherris,"We assess the impact of housing, the availability of reverse mortgages and longterm care (LTC) insurance on a retiree’s optimal portfolio choice and consumption decisions using a multi-period life cycle model that takes into consideration longevity risk, health shocks and house price risk. We determine how much an individual should borrow against their home equity and how much to insure health care costs with LTC insurance. We introduce an endogenous grid method, along with a regression based approach, to improve computational efficiency and avoid the curse of dimensionality. Our results confirm that borrowing against home equity provides higher consumption in earlier years and longevity insurance. LTC insurance transfers wealth from healthy states to disabled states, but reduces early consumption because of the payment of insurance premiums. Housing is an illiquid asset that is important in meeting bequest motives, and it reduces the demand for LTC insurance for the wealthy. We show that the highest welfare benefits come from combining a reverse mortgage with LTC insurance because of strong complementary effects between them. This result highlights the benefits of innovative produ",4.0,https://doi.org/10.2139/ssrn.2707350110e4b650a16a57574ef9d5bc321206765f2284c2b,10.1177/09697764251333769,Exploring the network of the short-term rental financial complex,2025,European Urban and Regional Studies,Chiara Iacovone,"The short-term rental market has traditionally been studied at the urban scale, analyzing the socioeconomic consequences of its expansion into cities. By zooming out the scale of investigation, this article proposes to examine the short-term rental complex on a transnational scale exploring how financial systems drive housing exploitation processes through short-term rental markets. Using Airbnb as a lens to observe these dynamics, the goal is to study the composition of its users to better understand the dynamics of speculation and value extraction in the real estate sector. Among Airbnb’s diverse user base, some operate within financial networks, benefiting from increased property values driven by rental demand, while others professionally manage multiple properties. For this reason, studying the panorama of Airbnb users serves as a proxy to understand the new forms of housing financialization and exploitation. The present work aims to fill the existing gap by viewing Airbnb and the short-term rental market as a network, presenting a geography that transcends the local context and emphasizes its transnational nature. The study draws on data analysis of host characteristics, parti",4.0,12e3d40b94789507826f707f95f0b49aebd3b93c16,10.4067/s0718-50732019000200215,Análisis de precios hedónicos de viviendas,2019,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.pdf131cf2d93c22d996d7e36758a680b91549e5233569,10.1080/14445921.2021.1964196,Real estate research trends and most impact real estate journals: a co-citation analysis,2021,Pacific Rim Property Research Journal,T. Nguyen; Cheng-Po Lai; Hoai Phan; M. N. Razali,"ABSTRACT This paper identifies the main trends in real estate research on frequently cited documents on the Web of Science database for 1990–2019, using quantitative methods in document-citation analysis. From the co-citation among the most cited 44 documents, this research presents a two-dimension visual mapping structure of real estate research and its important fields globally. Using statistical analyses including (1) correlation analysis, (2) factor analysis, (3) multi-dimensional scaling, this study identifies nine research trends on reducing significance order: (1) performance and investment features of property, (2) house price – household income, consumption, and investment, (3) house price setting, (4) amenity in property valuation, (5) green factor in the property market, (6) housing discrimination and segregation, (7) urban development, (8) modelling for real estate subsector and (9) urban transformation in cities of China. A further two clusters are formed as housing and investment property with factors surrounding the two. The top five ranking journals from the cited papers are presented. Findings of the study contribute to providing insights on the multidisciplinary s",4.0,141a42693f6b206c4a916bb9d8fee60dae4215f18c,10.1109/bdeim55082.2021.00047,Optimizations of Training Dataset on House Price Estimation,2021,2021 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, t",4.0,1584ab5aec3ac3cd7544da95aba22437a4a836c6bb,10.1007/bf03399265,Introduction to this Volume: Applying Hedonics in the Swiss Housing Markets,2008,,A. Baranzini; J. Ramirez; C. Schaerer; P. Thalmann,"On June 27–30, 2007, we organized at the Geneva School of Business Administration an International Symposium entitled ""Hedonic Methods in Real Estate"". During the first day, called the ""Swiss hedonic day"", Swiss researchers from universities, consulting firms and banks discussed the models they use, the topics analysed, data, econometric issues, and results of the hedonic approach applied to the Swiss housing and property markets. We selected some of the papers that were presented during the Swiss Day, carefully reviewed them and had their authors revise them repeatedly. Those papers are now contained in the present issue of the Swiss Journal of Economics and Statistics. This contribution somehow summarizes the papers presented in this issue.",4.0,https://sjes.springeropen.com/counter/pdf/10.1007/BF03399265160731671c97a0e27d246169336ff25e11c0f81c31,10.1109/eebda53927.2022.9744970,Big Data Analyzing Techniques in Mathematical House Price Prediction Model,2022,"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 res",4.0,170fb883be05d46eeb52221d99d80943c41858384b,10.1080/09669582.2025.2586540,Tourism and housing prices: a meta-analysis,2025,Journal of Sustainable Tourism,S. Tan; Jacob Wood; Xiu Wei Yeap; Chang-Tai Li; Chenyang Zhang; Yu Wang; Qian-Yu Chen; Jie Wu,"Abstract Rising tourism activity has intensified concerns about its effects on housing affordability, especially in destinations experiencing overtourism. This study conducts the first meta-analysis to quantitatively evaluate the relationship between tourism and housing prices. Based on 221 effect size estimates from 28 peer-reviewed studies, we apply meta-regression techniques—including multi-level random-effects models—to assess the average effect and identify sources of heterogeneity and publication bias. We find that tourism positively impacts housing prices at statistically significant levels, with this estimated effect size increasing after controlling for key moderating factors. Differences in country development status, regional location, methodological design, and variable specification significantly shape reported outcomes. These findings point to the need for more standardized empirical frameworks and critical guidance offerings for policymakers aiming to balance tourism development with housing equity. Our results underscore the importance of embedding housing affordability into sustainable tourism governance, in support of inclusive and resilient urban futures.",4.0,181a680b1c4fa31ce364f99713b608fb3fc501af18,10.1162/rest.a.1770,Black Suburbanization: Causes and Consequences of a Transformation of American Cities,2026,Review of Economics and Statistics,Alexander W. Bartik; E. Mast,"19 Since 1970, the share of Black individuals living in suburbs of large cities has risen from 16% to 36%. We first show that Black suburbanization has led to major changes in neighborhoods, accounting for the majority of recent increases in both the average Black individual’s neighborhood quality and income segregation within the Black population. We then use an accounting exercise to show that changes in relative suburban amenities and housing prices explain a large share of Black suburbanization, while regional reallocation, changing educational attainment, and gentrification of Black city neighborhoods play only minor roles.",4.0,https://research.upjohn.org/cgi/viewcontent.cgi?article=1374&context=up_workingpapers20111f13f9a41f309a5321f70a11984c0f7a6d8f74,10.4236/jss.2020.86004,"House Prices, per Capita Income and Real Estate Planning Investment—An Empirical Study Based on Anhui Province",2020,Open Journal of Social Sciences,Yingying Zhang,"In order to explore the 21relationship between housing prices, per capita disposable income and real 22estate planning investment, this article uses the data of average sales price 23of Anhui Province from 2000 to 2018, per capita disposable income and real 24estate planning investment as samples to establish multiple linear regression. The model found a 25significant positive correlation between house prices and income. By using 26EVIEWS9.0 analysis software,three quantitative relationship between those who 27came to the conclusion functional relationship is: In Yt∧ = 5.1649 + 0.0803X1t∧ + 0.4266X2t∧.",4.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=10064328doi_10_1108_14635780010324330,10.1108/14635780010324330,Pricing lease clauses – The prospect of an art becoming science,2000,Journal of Property Investment & Finance,Patrick Rowland,"This paper reviews the literature which models lease covenants using option‐pricing techniques, probabilistic measures of risk and the contractual misalignment of incentives. These quantitative models, in conjunction with conventional discounting mathematics, offer ways to gauge the effects on rent of changes to many lease clauses. With the exception of discounted cash flow analysis to adjust rents for leasing incentives, none appears to be used in practice yet. The program has been designed to bridge the gap between academic developments in this field and current practices in rental valuation. The program works from rental values set on benchmark or standard lease conditions in that market and adjusts for different clauses. The program displays all the stages in calculating the effects of each changed clause and operates entirely from parameters set by the user. Trials of this program are described.",4.0,29doi_10_5209_rev_aguc_2012_v32_n2_39721,10.5209/rev_aguc.2012.v32.n2.39721,La distribución espacial de la población inmigrante en dos ciudades medias: Alcalá de Henares y León y su relación con los precios de la vivienda Spatial Distribution of the Immigrant Population in two Medium-Sized Cities: Alcalá de Henares and León and its Relationship to Housing Prices La distribution spatiale de la population immigrée dans deux villes de taille moyenne: Alcalá de Henares y León et sa relation avec les prix des logements,2012,Anales de Geografía de la Universidad Complutense,María Jesús González González; María Luisa de Lázaro y Torres,"The number of immigrants in Spain is similar to the European countries with the longest tradition of immigration, this event has occurred in a very short time. The present crisis has slowed the increase in the number of immigrants. This fact has not occurred identically in all cities, nor in every neighbourhood within them. We will exemplify this in two very different medium cities, León and Alcalá de Henares. We will observe where and why in those cities there is a particular concentration of immigrants. We use census data of 2001 and 2009. We will also use the second hand housing prices from around 4000 real estate advertisements analysed for the same dates. Therefore we will contribute to scientific debate on foreign immigration in the cities and its relation with housing prices.",4.0,30doi_10_1108_14635780210433490,10.1108/14635780210433490,Dynamics of property value distribution in an Asian metropolis,2002,Journal of Property Investment & Finance,Sun Sheng Han; Shi Ming Yu; Lai Choo Malone‐Lee; Ann Basuki,"This paper seeks to explore the dynamics of the spatial distribution of landed residential property values in Singapore in the 1990s. Topics covered include: spatial patterns that can be discerned in the distribution of landed property values; how property values change over time; and how government intervention influenced this dynamic property value surface. Data are collected from the Singapore Institute of Surveyors and Valuers property transaction database, and are analysed by using the geographic information system, parametric and non‐parametric statistics. Findings of this paper contribute to the understanding of the urban dynamics of an Asian metropolis, especially in terms of its residential property market and internal spatial structure.",4.0,31doi_10_1590_s0101_41612011000400005,10.1590/s0101-41612011000400005,Preços hedônicos no mercado imobiliário comercial de São Paulo: a abordagem da modelagem multinível com classificação cruzada,2011,Estudos Econômicos (São Paulo),Luiz Paulo Lopes Fávero,"Os modelos de preços hedônicos frequentemente não levam em consideração a natureza hierárquica com classificação cruzada existente no mercado de aluguel de escritórios. Este artigo emprega um modelo multinível com classificação cruzada (HCM2), com o intuito de examinar a influência das características dos imóveis, dos distritos e das zonas comerciais sobre os preços de aluguel em São Paulo. Por meio de uma amostra com 277 imóveis provenientes de 15 distritos em classificação cruzada com 10 zonas comerciais, encontra-se representatividade das características das propriedades, dos distritos e das zonas comerciais, em caráter multinível, para a diferenciação das médias dos preços de aluguel dos imóveis no ano de 2008.",4.0,321529e86345168d24217580773a14def0af239db5,10.1109/isemantic55962.2022.9920454,Performance Analysis of Multiple Linear Regression and Random Forest for an Estimate of the Price of a House,2022,2022 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.",4.0,33doi_10_1080_0042098992467,10.1080/0042098992467,Dynamic Equilibrium of the Housing Market,1999,Urban Studies,Raymond Y. C. Tse; James R. Webb,"This paper, derived within a general equilibrium framework, demonstrates that housing price can be explicitly expressed as a combination of an exponential and linear function of housing rental. This model provides an explanation as to why housing appreciation may not match inflation in the long-run steady state. We show that only under a very particular set of conditions, will housing prices grow at a rate greater than the inflation rate. Evidence from the Hong Kong housing market supports the predictions of theory. Our model indicates that the housing market will be in the long-run steady state when the rent-value ratio is equal to the net discount rate.",4.0,34090c18c31a71990a688e0fc2ffbfb42e517bd96e,10.1016/j.trip.2021.100427,The Residential Property Value Premium of the Proximity to Carsharing and Bikesharing Services: Evidence from New York City,2021,,Sang-Wan Lee; Aaron D. Golub,,4.0,https://doi.org/10.1016/j.trip.2021.10042735doi_10_1023_a_1003158231050,10.1023/a:1003158231050,The Auction Price of Apartments in Moscow: Hedonic Estimation in Disequilibrium1,1998,Economics of Planning,Yasushi Toda; Nadezhda Nozdrina; G.S. Maddala,,4.0,36doi_10_3923_sscience_2010_520_524,10.3923/sscience.2010.520.524,Hedonic Analysis of Residential Housing Market in a Third World City: A Preliminary Investigation,2010,The Social Sciences,B.K. Ajide; I.O. Kareem,,4.0,37doi_10_1109_icmse_2009_5317673,10.1109/icmse.2009.5317673,Analysis of residential product's value based on Structural Equation Model and Hedonic Price Theory,2009,2009 International Conference on Management Science and Engineering,Yan Liu; Yong-xiang Wu,,4.0,3815b2bde6393741ff011895178e679ce0fd3fa258,10.1080/713702292,The Use of Content Analysis in Identifying the Determinants of House Prices,1978,,J. Doling,,4.0,39032506256464cb057d099eda9047dfcd294dd6a4,10.1007/s12061-019-09301-x,Analyzing Spatial Heterogeneity of Housing Prices Using Large Datasets,2019,Applied spatial analysis and policy,Yangyi Wu; Y. Wei; Han-Duo Li,,4.0,401c574054da19dd2bcf7afd9efc702178b64ce96f,10.2139/ssrn.2943723,What is the Impact of Broadband Bandwidth Variability on Quality of Life?: Lessons from Sweden,2017,Social Science Research Network,M. Zaber; E. Bohlin; S. Lindmark,"Connectivity opens economic possibilities. Broadband opens the possibility to connect millions via Internet. Economic Impact of broadband has been studied by various policy organizations and scholars. It is widely argued that, broadband technology can directly and indirectly engender economic activities in the region. Many of these arguments are based on various theories of change and innovation. Some scholars have used data at various levels to empirically ascertain the impact. However, the number of data driven research on this area has been small. Many researchers have used some aggregate macroeconomic indicators to ascertain the impact of use of broadband. However, these indicators such as GDP may not always successfully explain various aspects of quality of life of individuals in different societies. Along with that policy makers often debate on the variability of broadband bandwidth. Higher bandwidth should give better experience. Therefore, an important question is to ascertain whether or not variability of Broadband bandwidth is correlated with different aspects of quality of life (QoL). We endeavor to find an answer to this question via econometric estimations using a uniq",3.0,https://doi.org/10.2139/ssrn.294372341652f2a7331fb4caf0e0f56ce5d54e89b7fb17c52,10.1108/ijhma-07-2024-0093,Unveiling the drivers of sustainable housing choices among millennials in emerging markets,2024,International Journal of Housing Markets and Analysis,S. R.; Swamy Perumandla; S. Bhattacharyya,"42Purpose43The purpose of this study is to explore the complex interplay between technology, personal norms and emotional factors in shaping the sustainable housing choices of millennials in emerging economies. It integrates the model of goal-directed behavior, technology acceptance model and norm activation model, incorporating both self-interest and prosocial motivations. Key adaptations involve replacing perceived behavioral control with financial self-efficacy and substituting hedonic motivation for anticipated positive emotions. Moreover, it introduces location as a practical anchor.444546Design/methodology/approach47A quantitative, cross-sectional and descriptive research design was used in this study. Data were gathered from a sample of 610 millennial residential real estate investors across Indian smart cities. A multistage stratified sampling technique was used to ensure a representative sample. For data analysis, partial least squares structural equation modeling was used. The analysis focused on hypothesis testing to examine the relationships between the constructs of interest. Bootstrap t-values and effect sizes were used to assess the significance and magnitude of these rela",3.0,48178c8a396ba18bde056690a78086c1285b7eebdb,10.24136/eq.2021.026,Does the real estate market behavior predict the trust crisis in the financial sector? The case of the ECB and the Euro,2021,Equilibrium,M. Brychko; T. Vasilyeva; Z. Rowland; S. Lyeonov,"Research background: Based on the history of financial crises, real estate market behavior could be thought of as a key benchmark of trust shifts in the financial sector of the economy. Plunging real estate asset prices accompanied by the financial ""bubbles"" explosion could be viewed as the harbinger ? even the cause ? of the public trust crash in the financial sector. 49Purpose of the article: This study intends to assess the extent to which the real estate market behavior determinants, along with financial sector consumers' feelings, are able to predict trust crises in the financial sector, namely to its primary institutions ? European Central Bank and the Euro. 50Methods: In order to estimate the probability of a trust crisis in the financial sector, two logistic regression logit models were developed based on two types of dependent variables as they reflect trust violations in the financial system primary institutions ? net trust in European Central Bank (Model I) and net support for the Euro (Model II). The research was conducted on quarterly panel data of the EU countries from the euro area covering the period from 2000 to 2019. Logit regressions employed for data processing an",3.0,http://economic-research.pl/Journals/index.php/eq/article/download/1924/1816510f780c7a6c03ab9eda46750f6539d4176c21c8a7,10.35784/bud-arch.3635,Socio-economic factors associated with house prices. Evidence based on key macroeconomic aggregates globally,2023,Budownictwo i Architektura,G. Lewandowska; Michał Taracha; Kamil Maciuk,"The aim of this paper is to assess the association between macroeconomic factors and house prices in selected OECD countries. In this paper, authors describe selected socioeconomic factors, adapt a transparent methodology based on the OECD database and derive results shedding a light on the main drivers shaping the trends of the real estate prices. Two main economic aggregates, inflation and gross domestic product (GDP) were analysed for the OECD member and non-member countries for which complete data have been made available for the period of 1990-2020. The OECD provides data for 60 countries in total, out of which 38 are members of the organization. Nevertheless, due to missing observations in certain countries, the analysis was carried out in 19 of them. The aim of the study was to determine how GDP and inflation dynamics are correlated with changes in property prices. Among the analysed countries, Japan and South Africa could be distinguished as outliers in terms of inflation, whereas in the case of GDP, Italy, Japan, Ireland and Norway stood out. Additionally, 12 representative countries were described in detail. These countries comprised four groups of three countries divided",3.0,https://ph.pollub.pl/index.php/bia/article/download/3635/4190520e3ac5b13b1c6cb3be3a17167e2f08d4be41fd86,10.34190/ecsm.10.1.1020,Resale HDB Price Prediction Considering Covid-19 through Sentiment Analysis,2023,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 network",3.0,https://papers.academic-conferences.org/index.php/ecsm/article/download/1020/106553doi_10_5821_ace_v1i2_2342,10.5821/ace.v1i2.2342,Vivienda social latinoamericana: la clonación del paisaje de la exclusión,2006,"ACE: Architecture, City and Environment",Isabel Rodríguez Chumillas,"Las grandes capitales cómo Ciudad de México, Santiago de Chile, o Bogotá amplían desproporcionadamente sus periferias metropolitanas con megaproyectos residenciales de gran superficie y elevado número de viviendas. Se trata de la producción masiva de vivienda barata. El artículo registra e interpreta estos cambios en la producción de vivienda, enfatizando el papel de los promotores inmobiliarios y el protagonismo del urbanismo cerrado que se ha extendido también a los grupos sociales de menores ingresos bajo distintas fórmulas jurídico-urbanísticas entre las que destaca el condominio. Hoy proliferan en las ciudades de Latinoamérica, y en particular en las fronterizas con Estados Unidos, las calles cerradas y las casas sin pasillo, porque la vivienda social deficiente en tamaño, diseño y calidades, se traduce en una morfología cerrada que explicita la generalización de unos modos de habitar que sugieren una mayor especialización y una nueva configuración socioespacial del territorio, de paisajes y arquitecturas de la exclusión . Large capital cities such as Mexico City , Santiago de Chile and Bogota , disproportionately extend their metropolitan peripheries with residential mega-pro",3.0,54217a40f1cfff83bcf4b3c22d38b8813122cb031d,10.54691/bcpbm.v38i.3720,Real Estate Price Prediction Based on Linear Regression and Machine Learning Scenarios,2023,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 p",3.0,https://bcpublication.org/index.php/BM/article/download/3720/3636550edea8d65db6fc7d12a5c145f3890373f04663d2,10.1002/psp.70033,Unraveling the Enigma: Exploring the Impact of Migration Trajectories on Stable Settlement Intention Among Chinese Migrants,2025,"Population, Space and Place",Xue-Ying Mu; Juren Lin; Wen-Tian Shi; Qiang Wang,"The settlement intention of migrants has become a prominent issue for both scholars and policymakers, forming an essential element of China's new urbanization strategy. However, an increasing number of scholars predominantly focus on how individual and contextual factors influence migrants' settlement intentions, often neglecting the multiple and shifting nature of migration, which may also shape their settlement intentions. Using data from 2017 China Migrants Dynamic Survey, this study investigates the relationship between migration trajectories and migrants' settlement intentions, exploring how these trajectories differ in shaping settlement intentions across cohorts. Employing a multinomial logistic regression model, this study reveals that migrants originating from districts and county‐level cities or moving to cities with lower housing prices are more likely to have stable settlement intentions. In addition, those following a “U” migration trajectory are more likely to possess stable settlement intentions, while those with a “UU” migration trajectory exhibit the opposing trend. Moreover, the negative impact of migration trajectory on migrants' stable settlement intentions dimi",3.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/psp.70033561a16527b55042d962cc5a2f9129e939fead6234b,10.58567/jre02010001,The role of economic development in improvements of Cambodian housing conditions,2023,Journal of Regional Economics,Florian Gerth; K. Sikora,"In the past, investing in housing has served as an engine of growth for many economies as it is widely recognized that poor housing conditions can have significant negative impacts on human health, education, and economic opportunities. To assess the housing-related quality of life, indicators such as housing quality, housing environment, and cost burdens can be applied. However, recent studies indicate that materials used for construction are critical. Permanent materials, as opposed to temporary materials, typically offer a range of benefits in terms of durability, low maintenance, improved energy efficiency, increased property value, and better safety. The ultimate aim is to identify the key drivers of housing conditions in Cambodia, with a particular focus on materials and water quality, and the legal status of housing. To find the empirical relationship between economic, socio-economic, and demographic variables on the one hand, and variables measuring housing and living conditions in Cambodia on the other, the analysis employs Ordinary Least Squares and Methods-of-Moments regression modeling. Results indicate that high employment rates and entrepreneurship increase home owner",3.0,https://www.anserpress.org/journal/jre/2/1/6/pdf5709d0b83b84ac013711727e03d57c0d21df105e53,10.30902/jrea.2018.4.2.61,"A Study on the Changes of Investment Demand according to Housing58 Types - Focusing on a Period around the Global Financial Crisis59 -",2018,Journal of Real Estate Analysis,Seong-Ill Woo,"The aim of this study is to investigate determinants of housing tenure choice, paying particular attention to housing specific investment value and market value. This study applied the binary logit model to investigate the determinants of housing tenure choice in terms of household and housing characteristics. From the Housing Survey data from 2006 to 2012, this study found that housing specific investment value varies according to housing types and economic circumstances in the housing market. In Apartment market, in 2006 when housing price increased, the investment value significantly affected home ownership. However, when decreasing housing price caused by global economic crisis, the investment value in the apartment market significantly reduced. When the apartment market was stabilized, the investment value of apartment did not recover. However, single family housing showed indifference to housing price change. The results show that investment demand changes not only by macro-economic factors but also housing type characteristics. This study is significant in that first, it derived from the analysis of housing tenure choice two critical variables, housing specific investment va",3.0,https://doi.org/10.30902/jrea.2018.4.2.61600a4cbd2ff10d4d3721d2dd9404b6de243cd3c8fc,10.3390/urbansci2020047,"The Impact of Compact and Mixed Development on Land Value: A Case Study of Richmond, Virginia",2018,Urban Science,I-Shian Suen,"Sustainable development entails various development patterns commonly associated with compact and mixed development that usually leads to reduced driving, more public transportation utilization, lower energy consumption, better walkability, and improved public health. To examine if the benefits of compact and mixed development are reflected in land value, this study sets forth to measure development compactness and land use mix and assesses the impacts of such patterns on land value. Impacts of accessibility to jobs, retail stores, and public transportation on land value are also investigated and compared to the impacts from development patterns. The study finds that: (1) accessibility has greater impacts than development pattern on land value; (2) compact development and mixed land use influence land value differently depending on the nature of existing land uses and land values; (3) accessibility to jobs and retail stores always contributes to the increase of land value; and (4) accessibility to public transportation helps but it does not influence land value in a consistent fashion. The results suggest that areas with higher residential property values, good proximity to the cit",3.0,https://www.mdpi.com/2413-8851/2/2/47/pdf?version=1528428822610bd50e6cf101b9f91f34ced844dff3dc0e58687c,10.24191/mjoc.v5i2.9466,A STUDY ON THE RELATIONSHIP BETWEEN HOUSE PRICE INDEX AND ITS DETERMINANTS IN MALAYSIA,2020,,S. Saudin; Nur Ashakirin Jehani; Nur Amaelya Mastani; Isnewati Ab Malek,"In Malaysia, House Price is considered high at a certain part of the country causing the lower and middle groups unable to purchase a house. This research examines the long-run relationship and causality effect between House Price Index and determinants of House Price Index. The data was obtained from Valuation and Property Services Department (JPPH), Department of Statistics Malaysia, and Bank Negara. The data was collected over 10 years from 2010 to the first quarter of 2019. Johansen Cointegration Test and Granger Causality Test are applied in determining the long-run relationship and causality effect respectively. The general finding of this study is that the House Price Index shows an upward trend for the past nine years but slightly drop in the first quarter of 2019. This study has found that there is a long-run relationship between the House Price Index and the determinants which are Gross Domestic Product, Interest Rate, Inflation Rate, Population, and Unemployment Rate. Next, all independent variables do not granger cause House Price Index. At the same time, there is only one-way relationship found between House Price Index and Gross Domestic Product, and between House Pri",3.0,https://doi.org/10.24191/mjoc.v5i2.946662118f33445e7159970c0bccb1938a535d4e84bab2,10.4314/ejesm.v8i2.11s,"Effect of Waste Dumpsites on Proximate Residential Property Values in Ibadan, Oyo State, Niger",2016,,A. Nwosu; S. Olofa,"The study examined the effect of waste dumpsite on proximate property values in Apete, Ibadan. Residents of randomly selected 100 residential properties located around a waste dumpsite in the study area were administered structured questionnaires to elicit information for the study. Questionnaires were administered to determine the effect of the proximity of waste dumpsite on the rent paid on their residential apartments. The data collected were analyzed using multiple regressions. The result of the analysis shows that, distance of residential property from waste dumpsite is not significant with a p-value of 0.889. Hence, we conclude that waste dumpsite does not have significant effect on residential property value. However, since the p-value for Xi, X2, and X3 are less than 0.05, it is concluded that income level, building type and demand for building have significant effect on residential property values. This can be attributed to the fact that majority of the occupiers are students and low income earners. Furthermore, most of the building types are tenement, which are of very high demand by the students in the study area. The study recommends that the investors should come and i",3.0,https://doi.org/10.4314/ejesm.v8i2.11s6316ff9ab5caa1f21f40d959d0083736cbf7d703ab,10.1051/itmconf/20258001031,"Comparing Machine Learning Models for House Price Prediction: Linear Regression, Decision Tree, and Random Forest",2025,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 se",3.0,https://www.itm-conferences.org/articles/itmconf/pdf/2025/11/itmconf_acaai2025_01031.pdf64doi_10_3406_bifea_1988_960,10.3406/bifea.1988.960,La vivienda en los pueblos jóvenes de Arequipa y Trujillo: creación de una nueva tipología regional,1988,Bulletin de l’Institut français d’études andines,Marie-Françoise Chanfreau,"Abstract The objective of this article is to make a comparative exploration of the new urban landscape of two traditional peruvian towns: Arequipa y Trujillo. Such common traits as the mediocre organization of urban space within the pueblos jóvenes, and the intensive use of local construction materials will be examined. We will study in depth the different types of housing and construction solutions: the house with patio, constructed of sillar and stone (Arequipa), and the house with corridor of adobe (Trujillo). Starting with analyses of diverse habitat models, use of interior spaces and constructions stages, we will point out different strategies for the creation of new urban typologies. In Arequipa, the consolidation process of the house may be observed, in which a rural spatial organization is being replaced by an urban model. In Trujillo, however, consolidation of the housing style does not necessarily imply a modification of the typology; rather, one sees the substitution of artisanal materials by industrial ones and the progressive occupation of the lot by the construction. We will also examine how the national market of construction materials conditions an apparent regional",3.0,651a641cb29064cb19e857e4c9e85ff21325a76aff,10.54097/m187nw17,Predicting US Airbnb Listing Prices by Machine Learning Models,2024,"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 dec",3.0,https://drpress.org/ojs/index.php/HBEM/article/download/16483/160096621cc3cbb0b23f96fa881196e451ae01adffe53ad,10.2991/aebmr.k.210601.031,The Impact of Foreign Direct Investment on China’s Housing Prices,2021,Proceedings of the 2021 International Conference on Enterprise Management and Economic Development (ICEMED 2021),Pei-Wen Tang,"Nowadays, the real estate industry in China can be referred to as an unique economy leading industry. Due to the rapid development of our country’s macroeconomic environment and urbanization process, this makes the real estate market develop very fast, apart from this, the relationship between the overheated investment in real estate market and the excessive and rapid increased price of real estate has become a hot topic of concern for all sectors of society. This article mainly conducts in-depth research on the impact of foreign direct investment on housing prices in China through theoretical and empirical analyses. Through the multiple regression method and adopted the annual data of 2010-2019, analyzed the influence of foreign real estate investment on our country’s real estate prices. The results show that foreign direct investment is positively related to house prices. Finally, It can be concluded that foreign direct investment will increase our country’s housing prices, and corresponding suggestions are made in three aspects: laws and regulations, regulatory mechanism, and early warning mechanism related to real estate and foreign direct investment according to China's nation",3.0,https://doi.org/10.2991/aebmr.k.210601.031670ffd4d6e1c1775899e35ebc52a4be0d1e14f9ed0,10.22610/imbr.v15i3(i).3528,The Determinants of Household Debt in Malaysia,2023,Information Management and Business Review,Norliza Che Yahya; Bushra Mohd Zaki; Nik Norain Izzati Nik Azid; Nur Fatihah Mohd Ali; Nor Amanie Najihah Hussain,"This study aims to observe the determinants of the factors affecting household debt in Malaysia. This study has listed six independent variables that would affect Malaysia’s household debt: real interest rate, inflation rate, unemployment rate, household consumption expenditure, gross domestic product (GDP), and housing price index (HPI). This research was conducted by using annual data for 30 years (1991-2021); this study also used a quantitative approach to the collection of data using secondary data such as the website of the World Bank, Department of Statistics Malaysia, Eikon Thomson Reuters, and Bank Negara Malaysia. A theoretical model with the hypothesized relationships was tested with the help of the structural equation modelling procedures. Findings showed that the unemployment rate and gross domestic product have a positive and significant relationship with household debt. These determinants are directly affecting household debt in Malaysia, especially in microeconomic factors. This study will contribute to a better understanding that household debt can be influenced by other factors. Further study on the other determinants of household debt may result in varying results",3.0,https://ojs.amhinternational.com/index.php/imbr/article/download/3528/2268681a65cab5a3ea6d45a41f757925ec2df2bc2b0be2,10.1007/s12061-014-9117-z,Estimating the Effects of Light Rail Transit (LRT) System on Residential Property Values Using Geographically Weighted Regression (GWR),2014,Applied spatial analysis and policy,M. Dziauddin; N. Powe; S. Alvanides,"This study assesses the effect of light rail transit system (LRT) on residential property values in Greater Kuala Lumpur, Malaysia, where traffic congestion has been a major issue since the mid 1990s. A relatively new technique namely Geographically Weighted Regression (GWR) is employed to estimate the increased value of land in the form of residential property values as a result of improved accessibility owing to the construction of the LRT systems. Using the Kelana Jaya LRT Line, located in Greater Kuala Lumpur, Malaysia as a case study, this paper reveals that the improvement of accessibility to employment and other amenities provided by the LRT system added premiums on residential property values but with spatial variation over geographical area indicates that the existence of the LRT systems may have a positive effect on residential property values in some areas but negative in others. The use of the GWR in this study is identified as a better approach to investigate the effect of the LRT system on residential property values since it has the capability to produce meaningful results by revealing spatially varying relationship.",3.0,https://doi.org/10.25405/ncl.3423313269186059696fda499ec0a8bc191505c1607f5fb797,10.11113/jt.v73.4321,Housing Bubble in Penang: Prediction and Determinants,2015,,L. Y. Lu; J. Lee; U. Al-mulali; Nurul Afiqah Ahmad; I. Mohammad,"House prices in Malaysian cities increased drastically in the past few years, notably in the state of Penang. The existence of a housing bubble is speculated by major property players. This paper ascertains whether a housing bubble exists in Penang and explores the long-run and short-run determinants of Penang residential prices. Quarterly data (2000Q1 to 2012Q2) of House Price Index is the dependent variable and Gross Domestic Product, Consumer Price Index (CPI), Base Lending Rate (BLR) and Housing Supply as independent variables. Econometric model together with fully modified Ordinary Least Squares regression were used to detect the presence of housing bubble in Penang. The determinants of Penang house prices are based on Granger causality and variance decomposition analysis using the vector autoregressive (VAR) model. The results show no evidence of housing bubble in Penang housing market. CPI has both long-run and short run causality relationship with house prices while CPI and BLR explain a large part of housing price variance. Results show changes in inflation and cost of borrowing will greatly affect Penang house prices.",3.0,https://doi.org/10.11113/jt.v73.43217009777faa14aafa05fd9004b1e1abf68bf20a7c8a,10.47233/jsit.v4i1.1464,Rancang Bangun Sistem Prediksi Nilai Jual Properti Berbasis Web Menggunakan Regresi Linear,2024,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/1464/1269711437031d5059caa3fbb16381437fc279d49f30a7,10.1108/ijhma-08-2020-0104,The external impacts of historic landmarks and buildings on townhouse prices in Vietnam,2021,,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 p",3.0,7202b341c3eeb0bbf85a5763839570bd0d1d9ed55d,10.3390/realestate2030010,Macroeconomic and Demographic Determinants of London Housing Prices: A Pre- and Post-Brexit Analysis,2025,Real Estate,Marianthe Stavridou; Thomas Dimopoulos; Martha Katafygiotou,"This study examines the demographic and macroeconomic factors influencing housing prices in London from Q3 2014 to Q4 2022, focusing on the pre- and post-Brexit referendum periods. Using multiple regression analysis, the research evaluates the impact of interest rates, inflation, construction costs, population changes, and net migration on the housing price index (HPI) across various market segments. The findings suggest that interest rate base rates, consumer price inflation, and construction output price indices were significant predictors of housing price fluctuations. Notably, cash purchases exhibited the strongest explanatory power due to a reduced sensitivity to market changes. Additionally, London’s population was a key determinant, particularly affecting first-time buyers and mortgage-backed purchases. These results contribute to a deeper understanding of the London housing market and offer insights into policy measures addressing housing affordability and investment dynamics.",3.0,https://doi.org/10.3390/realestate20300107362be846525bcdce01e3d5e6a4cae5d81922a8228,10.1007/978-3-030-76059-5_15,Understanding the Economic Value of Walkable Cities,2021,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_78366741bac7fd1813f43b3899e130d58b114f802c5d352,10.1051/shsconf/202215101003,The Determinants of Housing Prices: Evidence from Chinese Cities,2022,SHS Web of Conferences,Wanling Yang,"With the rapid growth of China’s economy, the urbanization process is accelerating, and urban development is getting faster and faster. Housing prices have become a concern for more and more residents. This paper empirically studies the determinants of housing prices, including population, region, land factors, and government fiscal measures. Multiple linear regression models interpret the economic implications of empirical results by collecting unique data sets from multiple sources. The results show that government fiscal revenue, real estate investment, and land value positively correlate to housing prices. This article makes insightful comments on the above analysis, which is significant to the government, real estate developers, and residents. To a large extent, the government can take measures to adjust the housing price and solve the housing problem. Achieve the purpose of sustainable development.",3.0,https://www.shs-conferences.org/articles/shsconf/pdf/2022/21/shsconf_emsd2022_01003.pdf7516750412389e4b10a688a0f59da83c1a43789e0c,10.1080/08882746.2019.1580943,"Exploring the relationship between housing choice vouchers and neighborhood housing dynamics in Louisville, Kentucky",2019,Housing and Society,Wesley L. Meares; John I.Gilderbloom,"ABSTRACT Since the inception of the housing choice voucher (HCV) program, extensive research has focused on large metropolitan areas serving as demonstration sites for federal programs and concerning the impacts of dispersal on the residents and the types of neighborhoods where residents choose to locate. Little attention has been given to medium and small cities. Our analysis focuses on a medium-sized city, Louisville, Kentucky. We focus on the 170 census tracts of Louisville to examine the variations in neighborhood housing dynamics (i.e., median assessed housing values (MAVs), number of housing code violations, number of foreclosures, and crime rates) and the concentration of HCVs. Our findings indicate a significant negative relationship between census tracts with a greater concentration of HCV units and MAVs as well as a positive relationship with foreclosures. We find that HCVs have no impact on housing code violations or crime rates. A split regression analysis, however, shows that the presence of HCVs does not have a negative impact on the top half of Louisville’s neighborhoods with high property values. Finally, we show that HCVs, which are intended to deconcentrate povert",3.0,760fa0d19d9d0981ab0353b0e00a1b2abc5681b42d,10.1080/02692171.2024.2439361,Housing cost overburden and population health: cross-country evidence from Europe before the pandemic,2024,International Review of Applied Economics,A. I. Tavares; Eduardo Costa; P. Albuquerque,"ABSTRACT The recent surge in housing prices across Europe has placed a considerable financial burden on families, with potential effects across multiple dimensions, including health. This study investigates the relationship between housing cost overburden and self-assessed health status and premature mortality across 26 European Union countries between 2011 and 2019, before the pandemic. We estimate a general health production function using a generalised least squares regression with both robust and Driscoll – Kraay standard errors. We conduct a quantile regression via the method of moments to provide additional insights into the relationship between health and housing cost overburden along the distribution of health variables. Controlling for socio-demographic and economic country profiles, our results confirm that housing cost overburden is associated with worse health status. The association with premature mortality appears to be stronger in countries with lower rates of premature mortality or with a lower share of people reporting lower health status, suggesting that housing cost overburden is an important health determinant. In these well-performing countries, even modest imp",3.0,77086fcff65197416ca3c2dcb6a697eb28e4972735,10.1080/08882746.2019.1697090,"Winning in a “lose-lose” environment of economic development: housing, community empowerment, and neighborhood redevelopment in the Columbia Heights neighborhood of Washington, DC",2020,,Kathryn L. Howell,"ABSTRACT Economic development is often billed as the basis and justification for neighborhood revitalization in low-income communities, bringing with it new jobs, amenities, and residents. Best practices in local housing policy suggest that inclusionary zoning can be a remedy to the increasing home prices and rents resulting from the changing demographics. However, inclusionary zoning remedies have been largely insufficient and remain part of a negotiated package of community benefits that do not reflect that collective agency of the existing community. This paper examines how tenant-centered affordable housing and community-based planning can provide a useful counter-narrative to the economic development and community benefits stories of the back-to-the city era urban redevelopment in Washington, DC. While DC has developed progressive laws and resources to enable low-income residents to remain in their communities, the fast-moving market has exposed conflicts between social and economic goals. Using the example of neighborhood revitalization efforts in Washington, DC, I examine the often-conflicting how economic development goals interact with community empowerment and the mechan",3.0,78122a34e7d81c2534c504d2bdffac655420114887,10.14744/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,"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.2024.22316791dbf4d22fa3cfb65dc231a94a65101a027320c3d,10.1007/s10693-023-00409-3,No Reason to Worry About German Mortgages? An Analysis of Macroeconomic and Individual Drivers of Credit Risk,2023,Journal of financial services research,Nataliya Barasinska; Philipp Haenle; Anne Koban; Alexander Schmidt,"This paper analyzes the macroeconomic and borrower-specific credit risk factors of residential real estate mortgages in Germany. Relying on a macroeconomic panel VAR model, we show a significant link between foreclosures, house price dynamics and unemployment. Using microeconomic regressions, we show that defaults are driven mostly by income and liquidity rather than loan-to-value (LTV) ratios. Based on those insights, we calibrate a structural model which predicts a significant increase in mortgage losses in a stress scenario, driven only partially by high-LTV loans. Hence, from a macroprudential perspective our findings support the need for a broad toolkit going beyond LTV-limits.",3.0,https://link.springer.com/content/pdf/10.1007/s10693-023-00409-3.pdf80doi_10_1155_2010_854634,10.1155/2010/854634,Joint Estimation of the Characteristics and Intensity of Poverty in Spain: The Case of Imputed Rent,2010,Economics Research International,José L. Calvo; Cristina Sánchez; Pedro Cortiñas,"In 2007 the Spanish National Institute of Statistics modified the methodological approach to the Survey of Income and Living Conditions and included an estimate of Imputed Rent. It removes one of the main criticisms of Spanish poverty studies since this variable is associated with home ownership, and because more than 80% of Spanish families are homeowners, its exclusion biased the estimates of the size of Spain′s poor population and poverty intensity. We estimate a Heckman model with a selection equation in which the dependent variable is the probability of being poor, and a truncated regression to explain poverty intensity. Our findings have at least two economic policy implications: Spanish social policy against poverty should take into account geographical differences but, at the same time, should consider Imputed Rent. Without this variable efforts should concentrate in Spanish less developed regions and rural areas, but if we include it poverty increases in urban population. The article has also reveals that most retired people cannot be considered poor if we take into account wealth (imputed rent included) instead of current income (pension).",3.0,81206f97e2f1b267e7cbc35faacfacc3691888a6cb,10.1109/rusautocon61949.2024.10694522,Comparative Analysis of Ensemble and Linear Machine Learning Models in the Task of House Price Prediction,2024,2024 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.",3.0,82doi_10_5209_crla_54985,10.5209/crla.54985,La dimensión territorial de la crisis de la vivienda y el despilfarro inmobiliario residencial The territorial dimension of the crisis of housing and the residential real estate waste,2017,Cuadernos de Relaciones Laborales,Julio Vinuesa Angulo; David Porras Alfaro,"The background of this paper is the last real estate bubble and the subsequent and not already finished economic crisis. Considering this context, the main aim of the text is to emphasize the need to abandon a model of urban development mainly based on the real estate business, and foreign to any criteria of rationality, equity and sustainability in the use of the territory and in the construction of the residential space. In this period, housing has been assessed more for its economic value that for the services of accommodation that provides, and its market was the fundamental piece of a model that ignored its role in structuring the territory and its status as an essential element to satisfy a basic human right. The paper proposes a reflection about the roots of the current model, highlighting its most negative consequences such as the waste of spatial resources and the worsening of the access to adequate dwellings. Finally, it is stressed that the assessment of the effects of the actual model, the necessity of new dwellings and the opportunities of using the existing housing stock should be made at the local level.",3.0,83doi_10_1590_1982_3533_2020v29n3art12,10.1590/1982-3533.2020v29n3art12,"Índice de preços hedônicos para apartamentos: uma aplicação a dados fiscais de Belo Horizonte, 1995-2012",2020,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.",3.0,8406ce917b573f3f37c626b91a886f02df73886afa,10.1088/1742-6596/1792/1/012017,An analysis of the “direct effect” and “indirect effect” of urban housing prices on the upgrading of industrial structure——Based on data of 285 cities,2021,,Meng Guo; Zhong-Cheng Du; Shu-Kai Cai,"In order to investigate the extent of the impact of changes in urban housing prices on the transformation and upgrading of industrial structure and the spatial related characteristics under the big data environment, this paper based on the sample data of cities at prefecture level and above in China from 2007 to 2016 establishes a spatial dynamic Durbin model with spatial geographical proximity, spatial geographic distance and spatial economic distance as weight matrix, so as to measure the direct impact of housing price fluctuation on industrial structure upgrading Indirect effect and indirect effect.",3.0,https://iopscience.iop.org/article/10.1088/1742-6596/1792/1/012017/pdf85doi_10_1108_02637470210452944,10.1108/02637470210452944,Application of family life cycle concept in determining potential segment for housing projects,2002,Property Management,João Alberto da Costa Ganzo Fernandez; Roberto de Oliveira; Norberto Hochheim,"This paper highlights the importance and demonstrates the application of a segmentation strategy, based on the concept of the family life cycle, in real estate market analysis. Each stage in the family life cycle has its particular preferences regarding locational attributes. Neighbourhood characteristics can, therefore, reveal which segments should be the target of marketing campaigns. In order to increase the chances of a building successfully meeting the user’s requirements and therefore of achieving quick sales, its spatial conception, facilities and leisure attributes must be orientated to satisfy these different segments. To confirm this hypothesis and to exemplify the method presented, two high rise apartment buildings, in the city of Florianópolis, Brazil, are evaluated. The compatibility of their architectural attributes and the identified needs of respective potential marketing segments (according to the study of the locations) is verified. The results of this analysis, based on how fast the apartments were sold, confirm the effectiveness of the method.",3.0,860ca873ef664d6c9b3e2589f33a5e85d2f7346c6c,10.1109/icbda49040.2020.9101317,Analyses on the Spatial Distribution Characteristics of Urban Rental Housing Supply and Demand Hotspots Based on Social Media Data,2020,2020 5th IEEE International Conference on Big Data Analytics (ICBDA),Li-Fang Zhu; Yue Ting; Liu Yang; Zhou Li,"With the development of big data and the popularity of intelligent positioning systems, new opportunities for urban research and planning are brought about. Unlike traditional data acquisition, Sina Weibo check-in data, a type of social media data, its acquisition method focuses on locationbased network and big data mining. By crawling to get checkin data from Sina Weibo and Wuhan rental housing information, the paper processes and analyzes them statistically. The kernel density analysis is used to analyze the spatial distribution of hotspots, and the results are visualized to circle demand and supply hot spots in Wuhan. Then the price trend surface model is used to show the spatial change of rental housing price in the central city. This article creatively analyzes the characteristics of the rental housing crowd reflected by social media data and the spatial distribution of housing rents in Wuhan. The consistency and inconsistency shown in it can provide reference for the government to monitor the real estate rental market.",3.0,871b6cb0abbe5638e619a4a5c36c577475a8ee7e64,10.1080/07352166.2020.1761257,"Spatial dependence and spatial heterogeneity in the effects of immigration on home values and native flight in Louisville, Kentucky",2020,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.",3.0,880fadb75139f324336e4a8a9203e1a9908c576878,10.17208/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,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.",3.0,8923e3bd0a83117858737aabfe67362f511cf7319d,10.1109/sceecs61402.2024.10481902,Real estate price prognostication through machine learning models,2024,"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",3.0,90doi_10_3406_espos_2003_2067,10.3406/espos.2003.2067,"Immigration et géopolitique urbaine : la question du logement à Oakland, Californie",2003,"Espace, populations, sociétés",Frederick Douzet,"Immigration and Urban Geopolitics: Housing Policy and Minorities in Oakland, CA. The housing issue is an interesting window into the multiple spatial redistributions operating under the intense pressure of Asian and Hispanic immigration in the San Francisco Bay Area. In Oakland, mayor Jerry Brown's housing policy intends to revitalize an economically depressed city by attracting wealthy white populations into the city center thanks to skyrocketing housing prices in the Bay Area. The first signs of economic recovering in Oakland are certainly due to the global context of economic growth but probably also to Jerry Brown's personality if not to the fact he is white. The long term success of his policy is less certain and is likely to have tremendous consequences for the population like pushing the poor out of the city and participating to the already started dilution of Black electoral power.",3.0,9100a3947455c0f184f91537f1928d74d55458fc13,10.1080/13504851.2025.2490723,Simulated stable matching for urban hospitals and residential communities,2025,Applied Economics Letters,Lepeng Huang; Guoqiang Shen; Ke-Xun Zhang,"ABSTRACT This brief research applies the well-known two-sided matching theory to simulate stable matchings between urban hospitals and residential communities considering important factors, such as locations, services, and costs, and aiming to understand spatial interactions and population preferences in healthcare services. The simulation results from the empirical case for Hangzhou, China reveals that most matched hospital-community pairs vary with different population preferences on factor trade-offs in matching. However, some super pairs are consistently matched regardless of trade-offs after at least five rounds of stable matching. Compared with all feasible matchings, these super pairs are relatively concentrated in central urban areas with shorter spatial distances, lower housing prices per unit of living space, and comparable total utilities for corresponding communities.",3.0,92doi_10_2139_ssrn_339482,10.2139/ssrn.339482,"Commuting, Ricardian Rent and House Price Appreciation in Cities with Dispersed Employment and Mixed Land Use",2002,,William C. Wheaton,"For centuries, cities have been modeled as geographically centered markets in which locational scarcity generates Ricardian Land Rent that in turn increases over time as cities grow. This paper first presents some empirical evidence that this is not the case: inflation-adjusted locational rent does not increase over time - despite enormous urban growth. Rather than trying to explain this tendency within a ""monocentric"" framework, this paper develops a model where jobs and commerce can be spatially interspersed with residences, under certain economic conditions. The paper presents new empirical evidence that such job dispersal does characterize at least US cities. The comparative statics of this model are much more consistent with the data - accommodating extensive urban growth with little or no increase in commuting and Ricardian Rent.",3.0,93doi_10_3406_espos_1986_1090,10.3406/espos.1986.1090,Le logement et la différenciation sociale et résidentielle dans la ville pré-industrielle en Europe occidentale (XVIe-XVIIIe siècles),1986,"Espace, populations, sociétés",Dominique Vanneste,"Social differentiation and housing in the European pre- industrial city. In a first part this paper considers a few models of social and residential differentiation of the post-medieval city (16th - 18th century). Sjobergs model shows a centrifugal core-periphery gradation in the spatial differentiation of the pre-industrial city. The model of Lichtenberger, worked out for Vienna, describes a social and residential differentiation on a macro-scale but defends also the integration-idea on a micro scale. The influence of economic cycli, the price of land and houses and typical forms of credit on the pre-industrial housing mar- ket are explored as well as the distribution of social groups and institutions in urban space and the role played by authorities in the social production of this space.",3.0,94doi_10_18389_dearq19_2016_02,10.18389/dearq19.2016.02,"Apropiaciones, delimitaciones, negociaciones en el espacio colectivo. Caracterización multiescalar de la vivienda social en Guayaquil (1940-1970) Appropriations, delimitations, and discussions in communal space: A multiscale characterization of social housing in Guayaquil (1940-1970 Apropriações, delimitações, negociações no espaço coletivo. Caracterização multiescalar da moradia social em Guayaquil (1940-1970)",2016,Dearq,Juan Carlos Bamba Vicente; Alejandro Costa Sepúlveda,"Guayaquil, an old colonial city and port that is host to the largest population in Ecuador, can be used as a relevant case study to analyze modern social housing projects that were constructed in a particular period in which a great number of State-funded housing programs were developed; these framed a city-model that was different to the current disperse urban development model. This research addresses the transformations that have come about in the communal space within these housing programs as a response to dynamics that are part of social segregation and spatial fragmentation. These processes have been brought on by an accelerated loss of communal space to exclusive individualism, which tries to avoid an increasing material and symbolic violence.",3.0,95doi_10_1177_095624789500700201,10.1177/095624789500700201,"Rental and rent-free housing as coping mechanisms in La Paz, Bolivia",1995,Environment and Urbanization,Frans Beijaard,"This paper describes how in La Paz, more than a quarter of all households live rent free in a room, apartment or house with the permission of the owner. This form of tenure is called cedida. The paper also compares how household characteristics vary between owner-occupiers, renters and households in cedida in a sample of consolidated lower-and middle-income neighbourhoods within La Paz. This shows how cedida clearly benefits many younger households, by providing them with an opportunity to live in or near the city centre which would be beyond their means as renters or owners.",3.0,96107390c0e78fe2efd47b358bb195c555aff01c96,10.2139/ssrn.3195004,The Last Mile Matters: Impact of Dockless Bike Sharing on Subway Housing Price Premium,2021,,Junhong Chu; Yi-Ge Duan; Xianling Yang; Li Wang,,3.0,https://doi.org/10.2139/ssrn.319500497139a32c171216feaad674ea1857ab835d598fe3a,10.2457/srs.33.217,"Predicting Housing Prices in Central Ankara, Turkey Based on Spatial Dependence Analysis",2002,,Bahadir Gultekin; E. Yamamura,,3.0,https://www.jstage.jst.go.jp/article/srs1970/33/1/33_1_217/_pdf98doi_10_1111_j_1467_789x_2008_00526_x,10.1111/j.1467-789x.2008.00526.x,Obesity: a failure of homeostasis because of hedonic rewards: response to the letter from Gary Taubes,2008,Obesity Reviews,George A. Bray,,3.0,99doi_10_11132_rea_2002_63,10.11132/rea.2002.63,Modelos de preços hedônicos para a avaliação de imóveis comerciais no Município de São Paulo,2004,Revista de Economia e Administração,Claudio Felisioni de Angelo; Luiz Paulo Lopes Fávero,,3.0,1001d387ccd14a29a13e3ff478949366073f8b821a9,10.17208/jkpa.2021.10.56.5.19,Determinants of Neighborhood Economic Change and Spatial Dependence : An Analysis of Housing Price Change at the Neighborhood Level,2021,Journal of Korea Planning Association,Hee-Jung Jun; Suyoung Jung,,3.0,101doi_10_5821_ace_16_46_9645,10.5821/ace.16.46.9645,"COVID-19 y ciudad: hacia un modelo integrado de vivienda, microbiología, ambiente y urbanismo",2021,"ACE: Architecture, City and Environment",Felipe Encinas; Katia Soto-Liebe; Carlos Andrés Aguirre Núñez; Bernardo González; Waldo Bustamante; Alejandra Schueftan; Juan Ugalde; Carlos Blondel; Ricardo Truffello; Paz Araya; Carmen Freed,"A partir de mayo de 2020, la crisis sanitaria global causada por el virus SARS-CoV-2 traslada su epicentro hacia Latinoamérica, con foco en ciudades que presentan altos índices de pobreza, segregación y hacinamiento. Los avances en microbiología posibilitan comprender en profundidad las relaciones entre ciudad, COVID-19 y otros microorganismos, pero falta establecer un marco conceptual que las articule, especialmente en contextos donde las determinantes sociales son tan relevantes. Este artículo tiene como objetivo el proponer una aproximación integrada de microbiología, vivienda, ambiente y urbanismo, a partir de un modelo de interacciones y un análisis empírico para Santiago de Chile. En base a esto, se pudo analizar cómo el proceso de propagación en la ciudad se ve potenciada por vulnerabilidades de índole socio espacial, sanitaria intradomiciliaria y urbana, más una aproximación desde el concepto de pobreza energética. Al mismo tiempo, se pudo comprobar como las variables asociadas a estas vulnerabilidades permitían explicar la tasa de incidencia de casos confirmados por cada 100 000 habitantes a través de las distintas comunas en el área metropolitana de Santiago de Chile. Den",2.0,102doi_10_5821_ace_9_26_3690,10.5821/ace.9.26.3690,Una visión comparada de la vivienda social en europa. El caso de Barcelona y Londres,2014,"ACE: Architecture, City and Environment",Lenimar Nairt Arends Morales; M. Pilar Garcia-Almirall,"La política de vivienda en Europa está caracterizada por la diversidad, no existe un modelo único de gestión de la vivienda o de concepto de vivienda social. Cada país tiene sus propios criterios para enfrentar el problema de la vivienda, y se encuentran distintas visiones de gestión en relación a la población a atender, el régimen de tenencia, el tamaño de las viviendas, entre otros. Esta diferencia de enfoques está relacionada con los diferentes modelos del Estado de Bienestar (Arbaci, 2007; Balchin, 1996; Kemeny, 1995). El Reino Unido encabeza los países con una gran intervención Estatal (invirtiendo más del 3% del PIB en políticas de vivienda) y cuenta con los sectores de vivienda social en alquiler más extensos (≥ 20%), contrario al caso Español que invierte máximo un 1% del PIB y cuenta con apenas un 2% del stock en vivienda de alquiler social. Este artículo presenta una síntesis de las principales diferencias en el modelo de gestión de la vivienda social en Barcelona y Londres y considera, además, el problema de la vivienda desde un punto de vista social y espacial que no puede ser analizado separadamente de su entorno urbano, ya que como expresa Cortés: “cada vivienda perte",2.0,1030fbe8e32d313fd653f3549cd8019309f3108a3cc,10.26686/wgtn.16575005.v1,Estimating the impact of parking on car ownership and commute mode choices,2021,,Omid Khazaeian,"Researchers in commuting studies predominantly focus on movements. However, every trip starts and terminates in a place. For drivers, commuting is a journey between parking locations. They start their journey from home parking, park near work, and eventually return to home parking. Cars spend most of the day parked, with associated externalities. Drivers waste time and fuel cruising for parking in city centres and waste other drivers’ time and fuel by slowing traffic. More fuel consumption implies more carbon emissions and local air pollution. Providing off-street home parking increases house prices and reduces house affordability. Nonetheless, parking is a small part of the transportation literature and further research is needed to support a comprehensive understanding of parking and its impacts on travel behavior. 104This thesis is centreed around three questions; “How does the quantity of home parking affect car ownership and commute mode?”, “How do home parking type and quantity affect car ownership?”, “How does walking time from parking location to work impact parking type choice?”. Each question is answered in a separate chapter using a discrete choice model and a sample of co",2.0,https://figshare.com/articles/thesis/Estimating_the_impact_of_parking_on_car_ownership_and_commute_mode_choices/1657500510511b814911e9563fb8fcfea33db34c00f4cab2248,10.1353/jda.2019.0025,"Role of Neighborhood Socio-Cultural & Religious Homogeneity in Housing Choice at Dimapur Town, India",2018,Social Science Research Network,U. De; Vitsosie Vupru,"ABSTRACT:Background and statement of the problem: Apart from housing attributes like structural, locational and neighborhood attributes, choice for residential house is also influenced by socio-economic conditions such as income, education, employment as well as community or ethnicity of people and their neighbors. Choice for homogenous community or religion in the neighborhood would be due to convenience of cooperation, adjustment, verbal communication, family security and cultural affinity. Research methodology and data: This paper examines the impact of homogeneity of neighbors' religion and community on the residential housing choice in Dimapur town of Nagaland state, India. Chi-square test and independent sample T-test have been computed to check whether homogeneity of culture and religion significantly affect the demand for housing and its price or not. Cohen's d value has also been estimated to measure their effects. Truncated Regression Techniques have been used to examine the overall impact of religion or community of neighbor on the choice and house price (rent) paid by the individuals. Research findings: Across religious groups on an average Muslims have the lowest avera",2.0,https://doi.org/10.2139/ssrn.3637227106101a2f05887ff84716bb578ffed57490768464b8,10.3390/ijgi14120487,Reducing Extreme Commuting by Built Environmental Factors: Insights from Spatial Heterogeneity and Nonlinear Effect,2025,ISPRS Int. J. Geo Inf.,Feng-Xiao Li; Xiao-Bing Liu; Xue-Dong Yan; Zi-Le Liu; Xue Zhao; Lu Ma,"Nowadays, the number of people enduring extreme commuting is increasing, exacerbating traffic problems and harming individual well-being. To quantify the extreme commuting, we propose an extreme commuting severity (ECS) index that combines the number of extreme commuting trips with their specific distances, where a one-way trip with a commuting distance of at least 25 km is regarded as an extreme commuting trip. In Beijing, the ECS index shows substantial spatial variability, with maximum values exceeding 30,000 for origins and 50,000 for destinations, underscoring the severe commuting burden in specific areas. By integrating the geographically weighted random forest (GWRF) with Shapley additive explanations (SHAP), we model both nonlinear effects and spatial heterogeneity in how the built environment shapes extreme commuting. Compared with benchmark models, the proposed GWRF model achieves the highest predictive performance, yielding the largest R2 and the lowest absolute and relative indicators across both generation and attraction scenarios. Notably, the GWRF improves explanatory power over the global model by a substantial margin, highlighting the importance of incorporating sp",2.0,https://www.mdpi.com/2220-9964/14/12/487/pdf?version=17652696051070772f01a20301434818cca51c003b6b67a2ccc30,10.2991/icied-17.2018.16,The Influence of Housing Attributes on Housing Price in East Surabaya,2018,,Kresno Eka Mukti,"The emerging middle class in Indonesia at the moment have some potentials to be the largest consumers in housing sector which is middle class housing. The phenomenon happens in East Surabaya, even further after the opening access of Middle East Ring Road (MERR). Before buying a house, households make a bid based on their valuation of the housing attributes: physical attributes and neighborhood characteristics. However, developers usually cannot make exact calculation about the influences of those attributes on the housing price; causes the offered housing price is not optimal. The objective of this research is to identify the influence of housing attributes toward middle class housing price in East Surabaya. The respondents are society who bought middle class house in the period of 2011-2015. The data were collected by doing structured interviews with 100 respondents. This research uses multiple regression method in data processing. This research found that seven house attributes simultaneously influence significantly the housing price. The most significant housing attribute in affecting the housing price is the number of bedroom. This research is useful for housing developers, as ",2.0,https://download.atlantis-press.com/article/25892311.pdf1081047e177a56da4d198975f62c8c3f48b0e97fc1a,10.3390/geographies5010011,"Vegetation Structure and Distribution Across Scales in a Large Metropolitan Area: Case Study of Austin MSA, Texas, USA",2025,Geographies,Raihan Jamil; Jason P. Julian; M. Steele,"The spatial distribution of vegetation across metropolitan areas is important for wildlife habitat, air quality, heat mitigation, recreation, and other ecosystem services. This study investigated relationships between vegetation patterns and parcel characteristics at multiple scales of the Austin Metropolitan Statistical Area (MSA), a rapidly growing region in central Texas characterized by diverse biophysical and socioeconomic landscapes. We used LiDAR data to map vegetation types and distributions across a 6000 km2 study area. Principal component analysis (PCA) and regression models were employed to explore tree, shrub, and grass cover across parcels, cities, and the MSA, considering home value, age, size, and distance to the city center. At the MSA scale, tree and shrub cover were higher in the Edwards Plateau than in the Blackland Prairie ecoregion. Tree cover increased with parcel size and home value, especially in suburban areas. Older parcels had more mature trees, though less so in the grass-dominated Blackland Prairie. Shrub cover was higher on larger parcels in the Edwards Plateau, while the Blackland Prairie showed the opposite trend. PCA explained 60% of the variance, h",2.0,https://doi.org/10.3390/geographies5010011109003d1d097ad85ad65e2fec4d0c0e2bdf708ec18a,10.22452/ijps.vol3no1.2,"A Study on Housing Affordability in Kolkata Urban Agglomeration (KUA), Based on Infrastructure Driven Housing Price Distribution and Household Income-Expenditure Pattern",2013,,Banerjee Ankhi; S. Joy,"The paper attempts to assess variations in basic and composite housing affordability across Kolkata Urban Agglomeration (KUA) with respect to housing price distribution, patterns of household income and expenditure, and variations in transportation cost. Composite housing affordability represents total cost of living in a house within a particular location, and is indicative of the impact of off-site infrastructure on housing price as well as household expenditure; whereas basic housing affordability projects the link between house price and household income ignoring locational characteristics. In this paper, housing affordability assessment has been carried out using Price-to-income ratio, Median Multiple method and Residual Income approach, for surveyed households residing in group housing projects for low-to-middle income groups, across KUA. 110The study on housing price distribution together with variations in household income and expenditure of surveyed households reveal significant differences between non-inclusive or basic housing affordability and composite housing affordability across KUA. The housing market within KUA is found to range from mild un-affordability to severe u",2.0,https://ejournal.um.edu.my/index.php/IJPS/article/download/4600/2388111154f55871a6811fb1dd94f5c020e97dabe79c8c3,10.1111/itor.13565,Efficient property value estimation for single-family homes in central Florida,2024,International Transactions in Operational Research,S. Lozano; E. Gutiérrez; Kotryna Klizentyte; A. Susaeta,"This paper proposes the use of a metafrontier efficiency analysis approach to estimate residential property values. The objective is to mitigate inefficiencies in these valuations, thereby enhancing tax revenue and fairness in tax burdens. Property features (such as acreage, living area, number of bedrooms, number of bathrooms, age, and pool presence) along with contextual variables (such as county and median household income) are utilized to estimate just market value. Observations can be grouped based on some of these variables, allowing a metafrontier analysis to be conducted. This analysis not only assesses efficiency scores but also identifies technology gaps for each group. Furthermore, the proposed approach can be employed to estimate the value of any property based on its characteristics. To illustrate the proposed approach, we utilize data on over 30,000 properties in Orange and Seminole Counties in central Florida. Additionally, we present the results of a robust regression approach for comparison purposes. While our approach can serve as a valuable tool across various sectors, its primary aim is to promote best practices in governance regarding land use and real estate v",2.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/itor.13565112022921fccba6721f3a22c5f10e3cbb2feb66119f,10.54097/1qq43384,Dataset Analysis and House Price Prediction,2024,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 h",2.0,https://drpress.org/ojs/index.php/HSET/article/download/16255/15781113137e1f51d64b7e08b91c1a5103065a91cf95d765,10.17762/ijritcc.v10i8.5666,Prediction of Housing Price and Forest Cover Using Mosaics with Uncertain Satellite Imagery,2022,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 character",2.0,https://ijritcc.org/index.php/ijritcc/article/download/5666/5450114073362179d41d905fa9348a3c07d92c70aa561fb,,"Reconciling Australian planning, development and housing outcomes",2017,The Sydney eScholarship Repository (The University of Sydney),Sally Lewis,"Recently Australian governments and the housing industry have argued that planning was, in part, responsible for low rates of housing supply growth during a period of strong population growth and high house prices. In response, this study explored how planning influences the decisions industry makes regarding the housing projects it is responsible for delivering. Thirty nine industry professionals from public, private and community sector organisations, and thirty six public sector planners from local, state and federal agencies, in NSW, ACT and SA participated, while the Sydney Growth Centres provided a contemporary case study. The study found planning was secondary to the priority influences on industry decisions of market, unique organisational characteristics, the site, and financial or economic conditions. Each housing project is a unique combination of these factors, and therefore inconsistent with the strategic planning framework to some extent. The principal planning influence was therefore ‘time’ associated with undertaking strategic planning processes, during which mandatory planning approvals cannot be obtained and new housing cannot be delivered. Contrary to recent plan",2.0,http://hdl.handle.net/2123/168321150e171bc0cf3166211a4ee472f78ace933d94ce7e,10.2478/remav-2023-0025,Why Did Housing Prices Rise to a Record Level in Turkey? An Empirical Analysis,2023,Real Estate Management and Valuation,Reha Z. Yaşar; Erdem Bulut,"Abstract Turkey is one of those countries where housing market has been most severely affected by COVID-19 pandemic. Today the effects of the outbreak have been eliminated significantly. This study retrospectively looks at the days when the world faced a widespread outbreak of a pandemic and investigates the effect of the pandemic and the changes in consumer confidence on housing prices and the volatility of housing prices. Considering the structural breaks in the analysis period (2010:q1-2022:q4 quarterly), we used the Lee and Strazicich structural break unit root test as a research method. The results show that an increase in costs due to a break in the supply chain and containment measures forcing the workforce to stay at home affected the housing supply adversely. It is observable that expansionary economic policies and social assistance programs have a positive effect on housing demand. In this regard, negative supply shock and positive demand pressure are seen to be the determinants of the recent housing price increases in Turkey. However, while there is a positive relationship between consumer confidence and house prices, the effect of house prices on the volatility is stati",2.0,https://reference-global.com/2/v2/download/article/10.2478/remav-2023-0025.pdf11601e2f1bf2915117a013dff8a924c07e2ed16f152,10.1177/23998083221082112,Living apart together: The economic value of ethnic diversity in cities,2018,Environment and Planning B Urban Analytics and City Science,J. Bakens; R. Florax; H. D. de Groot; P. Mulder,"In consumer cities, the presence and location of immigrants impacts house prices through two channels, which both can be valued positively as well as negatively: (i) their presence and contribution to population diversity and (ii) the creation of immigrant-induced consumer amenities like those associated with ethnic restaurants in terms of both their quantity as well as diversity. We hypothesize that these two mechanisms create a trade-off in which city dwellers want to live apart yet consume together. We use unique microdata of house prices and ethnic restaurants in the city of Amsterdam over the 1996–2011 period and a generalized propensity score (GPS) matching technique for multiple continuous treatments to estimate the trade-off between consumers’ love for ethnic goods and their variety on the one hand, and ethnic residential composition on the other hand. We find evidence for the existence of such a trade-off: proximity to ethnically diverse restaurants indeed compensates for a negative correlation between the presence of immigrants and house prices. This trade-off mostly holds for neighborhoods featuring both a diverse population and a high level of amenities in terms of rest",2.0,https://journals.sagepub.com/doi/pdf/10.1177/2399808322108211211705b8d5d73f66d0b656f19524f09b63952c5b5e78,10.22610/jebs.v6i11.545,Profiling some of the dire household debt determinants: a metric multidimensional scaling approach,2014,,N. Moroke,"The purpose of this paper was to use the metric Multidimensional scaling (MDS) to explore the ten dire household debt determinants in the context of South Africa. Macroeconomic data used was collected from the South African reserve bank and Statistics South African websites for the first quarters of 1990 to 2013. SPSS 22 was used to execute the analysis. A Standardized Residuals Sum of Squares (STRESS 1) measure calculated as 0.00077confirmed the best fit of the MDS model and the Tucker’s Coefficient of Congruence implied that 99.9% of variance in the model is accounted for by the two dimensions. This was also a confirmation that the ten selected determinants can better be represented in a two dimensional perpetual map. The findings revealed two profiles of household debts. Gross domestic product and house prices are associated with high levels of household debts. The remainder of the determinants is found to have low effects. MDS demonstrated its effectiveness in classifying household debt determinants according to their contribution. Also revealed is that an MDS is a useful tool to use in quantifying the ubiquitous, but slimy, notion of similarity.",2.0,https://ojs.amhinternational.com/index.php/jebs/article/download/545/545118045824f58ea472e163759e797584e5576a84c3e2,10.24957/hsr.2023.31.2.109,A Study on Young People’s Preference for Public Housing for Sale: Focusing on Perception of Housing and Demographic Characteristics,2023,Korean Association for Housing Policy Studies,Eunbyul Ko; Jeong-Ran Lee,"In order to solve the housing problem of young people, the government announced a plan to supply public housing for sale that combine affordable housing prices and groundbreaking mortgage support. In this study, a survey was conducted to understand the young people's preference for the newly proposed public housing for sale, and the housing perception and demographic characteristics of young people who prefer public housing for sale were analyzed. In addition, through a survey on the policy direction and detailed conditions, it was intended to propose a model of public housing for sale that young people want. The results of analysis are summarized as follows. First, as a result of the survey, 49.8% of the respondents preferred public housing for sale, and such a high preference indicates the need to expand the supply of affordable public housing for sale. Second, since there are differences of opinion on the detailed conditions related to the benefits and restrictions of public housing for sale, it is necessary to set reasonable standards. Third, for young people with relatively lower assets, it is essential to provide cheonsei loans and public rental housing support to secure the ",2.0,11922aee72e39de274ac7e3d768be69281abe204a50,10.54097/hbem.v3i.4704,Analysis and Forecasting of California Housing,2023,"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/4704/45571200d17a85534b3d09fe647317dba6d3777ed7c03b9,10.1088/1742-6596/1948/1/012045,Crime Prediction: An Empirical Study on the Impact of Housing Prices on the Regional Criminal Rate,2021,"Journal of Physics, Conference Series",Yang Lu; Ning Ding; Yadi Wang,"The floating housing prices have been a topic that people have been eagerly paying attention to. The increase in housing prices may lead to the occurrence of group rents, triggering many social security and even criminal problems. This paper establishes a linear regression model and a random effect model, and uses China’s provincial panel data from 2000 to 2013. The results show that housing prices and per capita GDP are the more significant factors that explain the regional criminal crime rate. In the regression analysis of the sub-samples, it is found that the focus of the impact of housing prices and various control variables on criminal behavior is different. Regional economic development, population size, and employment conditions will all have a certain impact on criminal behavior. Therefore, studying the impact of housing prices on criminal behavior is conducive to maintaining regional security and stability. In the future, machine learning and other methods can be combined to construct crime monitoring and early warning methods, which can be applied to grassroots police work.",2.0,https://iopscience.iop.org/article/10.1088/1742-6596/1948/1/012045/pdf121212a7de78f54a54bbaeb57b89da02d02f80ee713,10.18335/region.v9i1.360,Municipal amalgamations and local housing prices,2022,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/360/41312215864a01e27a692b6f52efb3ad56b41b182ece42,10.54097/hbem.v21i.13600,"Research on the Prediction of Boston House Price Based on Linear Regression, Random Rorest, Xgboost and SVM Models",2023,"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/13600/1320912308c80a2a09e00b68a624d91cf47284bd15c142a0,10.54097/nbxk2j14,Application of Composite Kernel Function in Gaussian Process Prediction,2025,Frontiers in Computing and Intelligent Systems,Yulin Zhang,"Gaussian Process is a non-parametric Bayesian method based on kernel functions, widely used in regression tasks and Bayesian optimization. Kernel functions play a crucial role in Gaussian processes as they directly impact the model's ability to fit the data distribution. Different kernel functions can be selected based on the characteristics of the data to improve model performance. In practical applications, combining multiple basic kernel functions can construct more complex kernel functions, enhancing the flexibility and adaptability of Gaussian processes, making them better suited to complex data modeling challenges. This paper uses California housing price data as an example to analyze the impact of different kernel function combinations on model prediction performance. The experimental results validate the importance of kernel selection in Gaussian process models and demonstrate the effectiveness of composite kernels in handling complex data distributions.",2.0,https://drpress.org/ojs/index.php/fcis/article/download/30619/30001124228d721cfddb5895809c603c6a360b601b85385f,10.1177/00207640231194484,The impact of housing-price-related indices on suicide rates in Taiwan,2023,International Journal of Social Psychiatry,Yi-Lung Chen; Dian-Jeng Li; Ying-Yeh Chen; Cheng-Fang Yen,"Background: While certain socioeconomic factors have been studied in relation to suicide, housing-price-related indexes have rarely been investigated. Aims: This article aims to examine the impact of housing-price-related indexes on suicide rates in the general population of Taiwan, a country with high housing costs and suicide rates. Methods: The study utilized three national housing-price-related indexes from 2012 to 2019: (1) housing price index, (2) housing price to income ratio, and (3) housing rental index. Cause of Death Data was employed to calculate suicide rate. A linear regression model with autoregressive errors was used to analyze the association between housing-price-related indexes and suicide rates among different sex and age groups. Results: The findings revealed that higher housing rental index values were associated with increased suicide rates in young and middle-aged adults compared to the elderly population, regardless of sex. However, this association was not observed with the other two housing-price-related indexes (i.e. housing price index and housing price to income ratio). Conclusion: These results offer valuable insights for policymakers, mental health p",2.0,1251ea3eb6493d53d6265e6d3023a34345028e10a53,10.54254/2755-2721/4/20230505,Machine learning models for house price prediction,2023,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/pdf12620c47e6d16d92e45a64084acfa03519ccc6cd322,10.1080/07474938.2025.2486989,Oracally efficient estimation and specification testing of partially linear additive spatial autoregressive models,2025,Econometric Reviews,Shiyuan Chen; Xiaojun Song; Ji-Hai Yu,"Abstract This article proposes a computationally efficient sieve-based generalized method of moments (GMM) estimator for partially linear additive spatial autoregressive models, incorporating both linear and quadratic moment conditions. The proposed GMM estimators remain valid even when all regressors are irrelevant and are shown to be consistent and asymptotically normal. To improve the convergence rate for each nonparametric additive component, we propose a more efficient two-stage estimation procedure and establish its asymptotic validity and oracle property. Additionally, we develop Lagrange multiplier (LM)-type specification tests to assess the significance and functional forms of the nonparametric additive components. These LM tests asymptotically follow a standard normal distribution after appropriate centering and scaling under the null hypotheses. Simulation studies show that the proposed GMM estimators, two-stage estimation, and LM tests perform well in finite samples. Applying our estimation and testing methods to the Boston housing data, we find strong spatial dependence in housing prices and significant interaction effects, as captured by the partially linear additive ",2.0,127doi_10_3917_ecofi_115_0165,10.3917/ecofi.115.0165,La connaissance du patrimoine : un préalable indispensable à la compréhension des enjeux du logement social,2014,Revue d'économie financière,Bérénice Bouculat,"L’activité et la stratégie des organismes de logement social sont étroitement liées à l’âge, la qualité et la localisation de leur parc de logements. Une connaissance fine du patrimoine est donc un préalable indispensable à la compréhension des enjeux du secteur. Une photographie du parc social aujourd’hui met en lumière un âge moyen élevé, conséquence d’une production très dynamique pendant les Trente Glorieuses. Mais cette vision nationale doit être complétée par une analyse à une échelle géographique plus fine. Il apparaît alors que la répartition spatiale et les caractéristiques du patrimoine social ne sont pas uniformes sur le territoire : la construction du parc s’est faite par vagues, qui se sont concentrées sur des régions différentes, suivant les évolutions démographiques et économiques. Le patrimoine d’aujourd’hui est ainsi le reflet des localisations et des caractéristiques du bâti dominantes de chaque période. Les dynamiques de construction de logement social et de réhabilitation du parc social à l'œuvre aujourd’hui et nécessaires pour demain découlent ainsi en partie de l’état du patrimoine dans chaque territoire et de son adéquation aux mutations de la société. Classi",2.0,12802742b133103861a00294c8789ecda40dd1a92cf,10.26509/frbc-ec-202404,Comparing Two House-Price Booms,2024,Federal Reserve Bank of Cleveland. Economic Commentary,L. Loewenstein; Jason R. Meyer,"In this Economic Commentary, we compare characteristics of the 2000-2006 house-price boom that preceded the Great Recession to the house-price boom that began in 2020 during the COVID-19 pandemic. These two episodes of high house-price growth have important differences, including the behavior of rental rates, the dynamics of housing supply and demand, and the state of the mortgage market. The absence of changes in fundamentals during the 2000s is consistent with the literature emphasizing house-price beliefs during this prior episode. In contrast to during the 2000s boom, changes in fundamentals (including rent and demand growth) played a more dominant role in the 2020s house-price boom.",2.0,http://dx.doi.org/10.26509/frbc-ec-202404129097541d5d8259cc3f136e59466b79c355bbb8adc,10.4236/me.2016.712136,The Effect of Housing Price on Industrial Agglomeration in China,2016,,Z. Li; Xue-Mei Chen,"Based on the panel data of 268 prefecture-level cities in China during 2001-2007, this paper examines the impact of housing price on the agglomeration of manufacturing industries in China. Using instrumental variable method to alleviate the endogeneity problem, after controlling for a series of city characteristic variables, the IV regression results show that: the housing price has a significant negative impact on the manufacturing agglomeration. Further studies show that the negative effect mainly comes from the eastern region after 2003 when China’s land policy has changed greatly. For the central and western regions, the housing price has no significant effect on the agglomeration.",2.0,https://content.scirp.org/pdf/me_2016111815203521.pdf13010f2ce55a94a4420983b1a53810c88f2a191a4c8,10.1109/iccmc65190.2025.11140743,A Comparative Performance Analysis of Linear Regression Models for House Price Prediction,2025,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.",2.0,131115123fc78b07d4317352415f8f8138a2acbb000,10.6339/jds.201901_17(1).0006,The Non-Linear Relationships Of Numeric Factors On Housing Prices By Using Gam,2021,Journal of Data Science,Pei-De Wang; Ming-chien Chen,"Most research on housing price modeling utilize linear regression models. These research mostly describe the actual contribution of factors in a linear way on magnitude, including positive or negative. The goal of this paper is to identify the non-linear patterns for 3 major types of real estates through model building that includes 49 housing factors. The datasets were composed by 33,027 transactions in Taipei City from July 2013 to the end of 2016. The non-linear patterns present in the combination manner of a sequence of uptrends and downtrends that are derived from Generalized Additive Models (GAM).",2.0,https://jds-online.org/journal/JDS/article/132/file/pdf132doi_10_29105_vtga7_1_139,10.29105/vtga7.1-139,Análisis de la valoración hedónica de la vivienda del Área Metropolitana de Monterrey (2021),2021,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.",2.0,133doi_10_1177_1077727x7500300304,10.1177/1077727x7500300304,Apartment Environments and Socialization of Young Children,1975,Home Economics Research Journal,Kathy R. Thornburg,"Interaction between the physical environment, parental attitudes, and socialization settings of young children were investigated. The specific problem related apartment residence on the first versus second floor to spatial location and supervision of children, as well as to maternal attitudes about housing and child rearing. Respondents were 67 mothers who lived in student apartments and who had children aged 30 to 66 months. Each mother kept records of child activity, location, and supervision and also completed a questionnaire that investigated child‐rearing attitudes and satisfaction with housing. Children living in second‐floor apartments were more restricted than those who lived on the first floor, especially during afternoon hours. Although the amount of parental supervision did not vary by floor level, first‐floor children had more contacts with other children and adults. These and other findings suggest that designers and owners of apartments as well as parents living in apartments need to consider possible effects of apartment living on the development of young children.",2.0,134doi_10_1108_14635789410055877,10.1108/14635789410055877,The Legal Implications of Rent‐free Periods at Rent Review,1994,Journal of Property Valuation and Investment,Sandi Murdoch; Neil Crosby,"One of the features of the UK letting market has been the practice of giving new tenants a rent‐free period at the commencement of a lease. Such rent‐free periods seem to fall into one of two types. First, it appears that, even in a very buoyant market, most tenants are able to negotiate a modest rent‐free period for fitting out or, where the lease is a head lease, for arranging sublettings. These rent‐free periods (even where they are a slice of a longer rent‐free period) can be seen to have their own characteristics. They can be viewed as a one‐off concession at the commencement of the lease. Second, in a poor market rent‐free periods are used as incentives, in the sense that they are a direct alternative to an explicit reduction in the passing rent, and often form part of a wider package of inducements. These have an obvious and direct bearing on true rental value. Critically examines the various devices which have been used by those drafting rent review clauses to deal with rent‐free periods at review and comments on how these have been treated by the courts.",2.0,13519c1e5af3fcd518cf12b85ade20e92b6b1d81e15,10.1088/1742-6596/1634/1/012036,Linear Regression from Uncertain Data and its Applications to Housing Price Prediction,2020,Journal of Physics: Conference Series,Shi-wen Gao,"Most of the figures we get in our daily life are imprecise, a person’s height is an approximation. Uncertain data is usually represented by many values around it in a limited region with probability density function. This paper first defines the uncertain data, and then proposes a linear regression algorithm based on the uncertain data. In order to verify the rationality, correctness and effectiveness of the algorithm, many experiments were carried out.",2.0,https://doi.org/10.1088/1742-6596/1634/1/012036136doi_10_35319_lajed_20066244,10.35319/lajed.20066244,Análisis espacial del precio de oferta de la vivienda en el área metropolitana de Cochabamba,2006,Revista Latinoamericana de Desarrollo Económico,Elda Collazos Reyes; Wilma Gamboa Pérez; Pablo Prado Velasco; Vincenzo Verardi,"La idea principal del presente artículo es identificar cuáles son las características que determinan la fijación del precio de oferta en la estructura del mercado inmobiliario (viviendas de propiedad individual), considerando la heterogeneidad espacial. El estudio fue realizado en la zona metropolitana de la ciudad de Cochabamba, donde la oferta de mercancía inmobiliaria responde a múltiples factores, entre ellos la localización, por lo que en el análisis se utiliza un modelo de precios hedónicos, asociado a técnicas de econometría espacial (I de Moran, modelo de autocorrelación espacial y GWR). Los resultados del trabajo son presentados en mapas temáticos generados en el entorno del Sistema de Información Geográfica ArcGis, los mismos que tienen la propiedad de mostrar de acuerdo a la ubicación el nivel de influencia individual de las variables explicativas (características o atributos) sobre el precio.",2.0,1371eb222ded13bcce9a94322fff441317263e0802f,10.1140/epjs/s11734-021-00378-5,Spatial partial causality,2021,The European Physical Journal Special Topics,Marcos Herrera; M. Matilla-García; Manuel Ruiz-Marín,"This paper, on the one hand, proposes a statistical technique to detect potential causal relationships when the researcher has georeferenced data but not time dimension, and, on the other, applies this new methodology to the analysis of potential partial causal determinants of home prices. In particular, we find that the direction of causality for home prices in California goes from the income level to prices.",2.0,https://hdl.handle.net/20.500.14468/24624138doi_10_5209_rev_aguc_2012_v32_n1_39312,10.5209/rev_aguc.2012.v32.n1.39312,"La vivienda unifamiliar, fenómeno característico de la ciudad dispersa. Contrastes sectoriales en la aglomeración urbana de Madrid Single-family homes, a characteristic phenomenon of the scattered city. Sectorial contrasts in the urban agglomeration of Madrid Maison individuelle, phénomène caractéristique de la ville dispersée. Contrastes sectoriaux de l´agglomération urbaine de Madrid",2012,Anales de Geografía de la Universidad Complutense,José Miguel Santos Preciado; Francisco Javier García Lázaro,"Conurbations in western countries have undergone a deep transformation during the last years, evolving from the primitive metropolitan-area model to the current pattern of scattered cities. One of the defining characteristics of this new urban model is the spreading of low population density residential areas over the peripheries of cities, with prevalence of single family-dwellings. This results in an unlimited occupation of developable land such that environmental costs are much higher than hitherto. In Spain, this change has happened in a differentiate manner, more significantly in the great urban centres. This work is an endeavour of quantifying the process of urban transformation over Madrid´s region by using digital cadastral databases and geographic information systems, to assess the contrast between southern and western metropolitan sectors regarding the compactness–dispersion dichotomy.",2.0,13922074274bd7f941d6beb0aefc2de86b0be6d62af,10.1080/17520843.2020.1848896,The foreign capital inflows and the boom in house prices: time-varying evidence from emerging markets,2020,Macroeconomics and Finance in Emerging Market Economies,Boubekeur Baba; Guven Sevil,"ABSTRACT This study applies time-varying parameter methods to investigate the association of foreign capital inflows with the occurrence of house price booms in a sample of emerging markets. The time-varying causality tests show evidence of unidirectional as well as bi-directional causality between gross foreign capital inflows and house prices. Furthermore, the upward evolution of the time-varying impact of foreign capital inflows on house prices appears to be mostly related to the distinct episodes of the housing booms as shown by the results of time-varying parameter regression. However, there are instances where the time-varying impact of foreign capital inflows abruptly shifts upward without causing price booms in the housing market. We also find evidence that some components of foreign capital inflows are negatively affecting house prices during the boom episodes.",2.0,140doi_10_2202_1935_1682_2369,10.2202/1935-1682.2369,The Welfare Implications of Rent Control: A Rent-Seeking Contest Approach,2010,The B.E. Journal of Economic Analysis & Policy,Yang-Ming Chang; Shane D. Sanders,"Abstract This paper uses a contest approach to characterize a probabilistic, non-price contest between potential consumers of rent-controlled apartments. The model extends upon the rent control model of Glaeser and Luttmer (2003) as well as the rent-seeking contributions of Hurley (1998), Dixit (1987), and Hirshleifer (1989) to consider the roles of chance and endogenously-chosen efforts in the allocation of rent-controlled apartments. Nash equilibrium effort levels for each consumer-type imply that the effort-inducing cost of rent control is greater than the misallocative cost. Further, misallocative costs are lower under the effort contest interpretation than under random allocation, as high willingness to pay consumers allocates more effort. The relationship between effort contest and misallocation influences the policys overall social cost.",2.0,141doi_10_1002_bref_67,10.1002/bref.67,Managing risk in apartment development,2002,Briefings in Real Estate Finance,Marc A. Louargand; Brian T. Murdy,"Abstract Multifamily investment is popular with US institutional investors at present despite weak fundamentals in the rental market. There is evidence that apartments provide some protection from recessionary forces and are the first product type to recover from a downturn. Capital flows have increased to the sector, putting downward pressure on capitalisation rates. Development of new stock allows investors to capture the spread between return on cost to build and capitalisation rates on existing properties. Managing the risks inherent in development is a different prospect than underwriting existing properties. This paper examines methods for managing the risks associated with construction, marketing, financing and interest rate risk, among other factors. Copyright © 2002 Henry Stewart Publications",2.0,1420a4864ea200f84ca7460ea3c0bb7aa69a48020cd,10.2139/ssrn.2809367,Mortgage rates and credit risk: Evidence from mortgage pools,2016,European Financial Management,Gaetano Antinolfi; Celso Brunetti; Jay Im,"In the 1990s, securitised subprime loans supported the growth of mortgage lending. We study the evolution of initial mortgage rates as a function of loan and borrower characteristics during 1992–2015. We compare the evolution of initial rates on securitised subprime mortgages with rates of prime privately securitised mortgages, mortgages securitised by government‐sponsored enterprises, and nonsecuritised mortgages. Starting in 2003 the risk premium on subprime loans decreases until it disappears at the onset of the Global Financial Crisis. We find that loading factors on subprime rates are cointegrated with delinquencies and house price movements, providing evidence of the important role of the subprime market.",2.0,14348411b9c587c1cd553d1d623dfb67b221a5a52c5,10.2139/ssrn.837404,"Don't Ask, Don't Tell: The Impact of Real Estate Agents on House Prices Near Environmental Disamenities",2005,,D. Hite; A. Jauregui,,2.0,https://doi.org/10.2139/ssrn.837404144142f8b5b14a282ab80f3540dd2110fa6d20868f9,10.2139/ssrn.2228176,The Spatial Dimension of US House Price Developments,2013,,Katharina Pijnenburg,,2.0,https://doi.org/10.2139/ssrn.2228176145doi_10_1108_03068299810193650,10.1108/03068299810193650,Privatization in Russia: some micro‐evidence based on housing markets,1998,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.",2.0,146doi_10_1016_b978_0_12_374387_9_00018_0,10.1016/b978-0-12-374387-9.00018-0,Characterizing the Homeostatic and Hedonic Markers of the Susceptible Phenotype,2010,Obesity Prevention,John Blundell; Eleanor Bryant; Clare Lawton; Jason Halford; Erik Naslund; Graham Finlayson; Neil King,,2.0,147doi_10_32468_tef_78,10.32468/tef.78,Índice de los precios de la vivienda nueva para Bogotá : metodología de precios hedónicos,2013,,Jéssica Fernanda Castaño-Lavado; Mariana Laverde; Miguel Ángel Morales-Mosquera,,2.0,148doi_10_3917_rce_009_0130,10.3917/rce.009.0130,La méthode des prix hédoniques appliquée au logement,2011,Regards croisés sur l'économie,Jérémy Ducros; Erwann Paul,,2.0,1491522a79b9f036f8b5a70f9dbb6709a506d1f9e63,10.1007/s12061-025-09667-1,Multidimensional Neighborhood Amenity Characteristics and Housing Prices in Urban China,2025,Applied spatial analysis and policy,Zihan Qu; Yang-Yi Wu; Meitong Liu,,2.0,1501bb9f4c3e65f34e16fe91a169f23a72ca86fa773,10.24052/ijbed/v09n01/art-05,Placemaking and Revitalization through Business and Tourism Improvement Districts in Albania,2021,,Ermira Repaj,"As a form of a public-private partnership with local authorities, the business improvement district (BID) is created when most businesses or business property owners agree through balloting to manage a delimited commercial area with prior authorization by the local authority. The district is managed through a non-profit organization that provides additional public services such as security, maintenance, infrastructure improvement, and marketing, to improve decaying commercial and residential areas. BIDs have been praised as engines for urban development, filling the need gap between the public and private sector by providing entrepreneurial local public management and augmented public services for socioeconomic revitalization. The business improvement districts (BIDs) and similar forms of a public-private partnership, as a new mechanism for urban renewal and economic development, have emerged in North America five decades ago and quickly adopted in many cities worldwide. Since 2011, the model has been applied in 8 districts in Albania, contributing to improved business life, infrastructure improvements, and enhanced general public services. This time is considered long enough to of",1.0,https://doi.org/10.24052/ijbed/v09n01/art-05151doi_10_5821_ctv_8665,10.5821/ctv.8665,"Metodología para determinar localizaciones más sostenibles para conjuntos de vivienda social en Valdivia, Chile",2019,"XIII CTV 2019 Proceedings: XIII International Conference on Virtual Cityand Territory: “Challenges and paradigms of the contemporary city”: UPC, Barcelona, October 2-4, 2019",Daniel Alejandro Muñoz Velásquez; Antonio Zumelzu; Horacio Samaniego; Gabriela González,"Dentro de las múltiples decisiones que tienen que ver con el desarrollo de barrios de vivienda social en Chile, la localización de los conjuntos toma especial relevancia debido al potencial de facilitar, o no, la movilidad social, el acceso a mejores oportunidades, y la integración urbana (Sabatini y Brain, 2008). En este sentido la política de vivienda ha sido insuficiente, incluso incorporando incentivos específicos a mejorar la localización, aplica parámetros centralistas que no aportan a mejorar significativamente la condición de nuevos barrios en ciudades de menor tamaño, haciendo urgente la incorporación de un enfoque local.El Subsidio Diferenciado a la Localización (SDL) es parte de la política habitacional que trata de subsanar las falencias en materia habitacional y urbana que se han perpetuado por más de 30 años en Chile. Sin embargo, esta política no ha cumplido todos sus objetivos, ya que sus parámetros de localización permiten la ubicación de viviendas sociales en sectores con acceso deficiente a redes de equipamientos. Si bien se solicita informar la distancia de desplazamiento para aspectos como transporte, salud, educación en las fórmulas para obtener este incentivo",1.0,152doi_10_5821_ctv_7625,10.5821/ctv.7625,"La producción de vivienda y la división social del espacio, en Puerto Vallarta",2010,"6to. Cogreso Internacional Ciudad y Territorio Virtual, Mexicali, 5, 6 y 7 Octubre 2010",Verónica Livier Díaz Núñez; Jorge Javier Acosta Rendón,"En los años 60 y 70 del siglo pasado muchas ciudades latinoamericanas tuvieron una anárquica expansión a través de asentamientos irregulares, formados por la masiva migración rural, los que posteriormente dieron lugar a la formación de colonias populares. En éstas fue común la autoconstrucción precaria de viviendas y un incansable proceso de gestión y lucha para la introducción de los servicios básicos. Si a finales del siglo pasado se veía ya que la modernidad de las ciudades traía aparejado muestras de un agravamiento en la división social del espacio, en la actualidad, cuando ya han transcurrido diez años de este nuevo siglo, estos problemas son notoriamente perversos. Por doquier se producen desarrollos y fraccionamientos de viviendas residenciales con distintos nombres: “cottos”, fraccionamientos defensivos, cerrados, etc. en los cuales los servicios básicos son proporcionados por los mismos usuarios, mientras que al lado de éstos coexisten barrios y colonias populares con viviendas autoconstruidas sin servicios básicos o deficientes. La producción de vivienda es el principal elemento urbano causante del crecimiento y la expansión de las ciudades. Durante este proceso la ciuda",1.0,153069ca9ce54da16257993bdf2854d4ebb24be4ed3,10.1108/ijhma-05-2025-0109,Beyond reach: affordability challenges in state-led housing for lower-middle-income groups in Istanbul,2025,International Journal of Housing Markets and Analysis,Gozde Turkmen Cebecı; Ozge Aysal; Ilkim Markoc,"154 155 This study aims to examine the affordability of state-led housing projects in Istanbul, focusing on whether initiatives implemented by Housing Development Administration (TOKI) and its subsidiary Emlak Konut Real Estate Investment Company (EKGYO) effectively meet the housing needs of lower-middle-income households within an increasingly commodified urban market.156 157 158 159 Utilizing a five-stage methodological framework, the research integrates geospatial mapping of completed housing projects, district level price-to-income ratio modeling, and land cost sensitivity analysis. Unit prices from 98 state-led developments are benchmarked against local market averages. Affordability is assessed using a standardized 120-month payment model and the widely recognized 30% income to housing cost threshold. A multi-scalar affordability index is developed by combining web-scraped sales data with official statistical sources. The study also incorporates multivariate regression and Random Forest modelling to examine the relative influence of district-level land costs, household income, and private market housing prices on project-level pricing.160 161 162 163 The majority of publicly supported housing un",1.0,16402d335cc9cc16c91da5a0f110b3042dabb968b2b,10.36690/2674-5208-2025-2-94-108,DIGITAL NOMADS AS A CONTEMPORARY TREND IN THE GLOBAL LABOR MARKET,2025,"ECONOMICS, FINANCE AND MANAGEMENT REVIEW",G. Myskiv,"The article investigates the phenomenon of digital nomadism, recognizing it as a significant transformative trend within the global labor market. This trend is dynamically evolving under the influence of technological innovations and the ongoing evolution of remote employment. The research aims to comprehensively analyze digital nomadism, systematize knowledge about its essence, characteristics, and typology, and identify the key drivers of its development, as well as the advantages and challenges for both the nomads themselves and their host communities. The methodology relies on a qualitative and quantitative analysis, incorporating a deep theoretical literature review using bibliometric and content analysis. It also includes the analysis of secondary statistical data and analytical reports from international organizations and research firms. Additionally, the study examines government policies regarding digital nomad visa programs and assesses their socioeconomic impact on local communities. The research results define a generalized profile of the digital nomad as a young, highly educated professional, primarily from the IT or consulting sectors, who seeks flexibility and work-l",1.0,https://public.scnchub.com/efmr/index.php/efmr/article/download/330/26516503f9ddbb998e2a8a14e03760f028c83c5df3f6d8,10.3390/su17167412,"Residential Green Infrastructure: Unpacking Motivations and Obstacles to Single-Family-Home Tree Planting in Diverse, Low-Income Urban Neighborhoods",2025,Sustainability,Ivis García,"Urban tree planting on single-family-home lots represents a critical yet underexplored component of municipal greening strategies. This study examines residents’ perceptions of tree planting in Westpointe, a diverse neighborhood in Salt Lake City, Utah, as part of the city’s Reimagine Nature Public Lands Master Plan development effort. Through a mixed-methods approach combining qualitative interviews (n = 24) and a tree signup initiative extended to 86 residents, with 51 participating, this research explores the complex interplay of demographic, economic, social, and infrastructure factors influencing residents’ willingness to plant trees on single-family-home lots. The findings reveal significant variations based on gender, with women expressing more positive environmental and aesthetic motivations, while men focused on practical concerns including maintenance and property damage. Age emerged as another critical factor, with older adults (65+) expressing concerns about long-term maintenance capabilities, while younger families (25–44) demonstrated future-oriented thinking about shade and property values. Property characteristics, particularly yard size, significantly influenced re",1.0,https://www.mdpi.com/2071-1050/17/16/7412/pdf?version=1755314450166083ce3e33086ac7418ef54ab1e96633f719f69d1,10.7716/aem.v15i3.3317,Pricing Differences and Policy Optimization of Converting Existing Commercial Housing to Affordable Housing in Liaoning Province Based on Spatial Econometric Model,2026,Advanced Electromagnetics,S. Wu; H. Song; Y.-L. He,"To address the dual challenges of destocking existing commercial housing and insufficient supply of affordable housing in Liaoning Province, and to accurately reveal the spatial characteristics and driving mechanisms of pricing differences in the conversion of existing commercial housing to affordable housing (hereinafter referred to as ""EC-AH conversion""), this study takes 14 prefecture-level cities in Liaoning Province as research units, selects panel data from 2018 to 2024, and constructs a spatial econometric analysis framework. First, global and local spatial autocorrelation tests are used to verify the spatial dependence of EC-AH conversion pricing; second, Spatial Lag Model (SLM) and Spatial Error Model (SEM) are established to identify the core driving factors of pricing differences, and Geographically Weighted Regression (GWR) model is further adopted to analyze the spatial heterogeneity of the impacts of various factors; finally, differentiated policy optimization paths are proposed based on empirical results. The research shows that: EC-AH conversion pricing in Liaoning Province has significant positive spatial correlation, forming high-value agglomeration areas with She",1.0,https://www.aemjournal.org/index.php/AEM/article/download/3317/1001167168c40738b634753c24fac8e1a57758d85754ccb,10.55057/ijaref.2022.4.3.18,The Effect of Behavioral Changes from Generation Y During Pandemic on Indonesian Housing Prices Growth,2022,International Journal of Advanced Research in Economics and Finance,,"During the COVID-19 Pandemic, the stagnant and even a declining growth of Indonesia residential property prices has become a popular issue and drawn the researcher’s attention about the details behind it, as in terms of buying preferences, changes due COVID-19 and solutions for the problem. The purpose of this study is to comprehend the effect of behavioural changes from Indonesian Generation Y during pandemic on housing price growth. Generation Y referred to in this study are Indonesian citizens born between 1991 to 1998 or early 2000 (Junior Gen Y) and between 1980 to 1990 (Senior Gen Y) who dominate Indonesia's population and also have a massive influence on GDP. COVID-19 forced a change in consumer behaviour which further leads to prioritize the most basic needs. This also brings changes to the effect of buyer preferences on house prices during the pandemic. This study also explains Generation Y's preferences in buying houses in order to understand which preferences will affect demand and house prices because it will also help several parties involved in housing transactions construct strategies to make the property more appealing to buy. The data used in this study is collecte",1.0,https://myjms.mohe.gov.my/index.php/ijaref/article/download/19934/106091681c75686210bce764e40fbd946ab99b42d8d8b859,10.1061/jupddm.upeng-6456,"Determinants of Urban Shrinkage in Small-Sized Cities: A Path Analysis of Demographic Decline in Harsin, Iran",2026,Journal of urban planning and development,Mohammadreza Gholami Gowhareh,"169 170 Urban shrinkage has emerged as a relevant theme in recent urban scholarship, with growing intellectual interest since the start of the twenty-first century. The process, characterized by concomitant population decline and economic downturn, is driven by global and local forces such as economic globalization, deindustrialization, suburbanization, and political–institutional reform. While extensive research in developed countries has explored its causes and consequences, studies in developing contexts, particularly in small cities, remain limited. This study examines urban shrinkage in the small Iranian town of Harsin (Iran) that experienced a loss of roughly 20% of its population over two decades with the aim of identifying dominant drivers through direct and indirect causal relationships and proposing mitigation strategies. In this study, spatial and descriptive analyses were carried out on 15 social, economic, physical, and infrastructural indicators at the neighborhood scale, using GIS-based overlay techniques. To further examine the mechanisms underlying population decline, path analysis with a backward elimination regression procedure was employed to trace both the direct a",1.0,171doi_10_2139_ssrn_389404,10.2139/ssrn.389404,Owner-Occupied Housing as a Hedge Against Rent Risk,2003,,Todd M. Sinai; Nicholas S. Souleles,"Conventional wisdom assumes that homeownership is risky because house prices are volatile. But all households start life short housing services, and homeownership could be a less risky way of obtaining those services than the alternative, renting. While a renter faces year-toyear fluctuations in rent, a homeowner receives a guaranteed flow of housing services at a known price, and so is hedged against rent risk. Although the homeowner is in turn exposed to asset price risk when she sells her house, that risk can be relatively small since it arrives at the end of the stay in the house and so is discounted, or it is deferred even later if the homeowner moves to a correlated housing market. We show in a stylized model with endogenous house prices that rent risk can indeed outweigh asset price risk. The net benefit of homeownership increases in the owner's expected horizon in the home, as the number of rent risks avoided rises and the asset price risk occurs later in time. This effect of horizon on the demand for owning should increase multiplicatively with the magnitude of the volatility of rents. Another implication of our analysis is that the aggregate wealth effect from fluctuation",1.0,172021617384e27e70090afa0ed71ccc25bd4242cf2,10.2139/ssrn.4868703,WHAT DETERMINES HOUSING PRICES IN EGYPT?,2024,Social Science Research Network,Mohamed Rashwan; Sarah El-Khishin,"The Egyptian housing market is a very dynamic market and has many interactions with monetary and financial markets in the economy. In this paper, we investigate the macro-financial fundamentals, institutional as well as specific behavioural and cultural factors that are argued to play a role in housing demand and prices in Egypt. We design a field survey for a representative sample of household homebuyers and sellers as well as real estate developers and brokers. We then run an Ordinal Logistic Regression Model (OLM) based on the results of the field survey and a constructed housing price index. Analysis reveal many important findings, firstly, land construction and licensing costs, government real estate and housing policies are all perceived as main determinants of housing prices in the Egyptian market. On the contrary, macro-financial variables, namely inflation and interest rates were not significant indicating a possibly weak monetary transmission mechanism through the theoretically explained asset-price channel. Results also affirm that housing investment is perceived by Egyptians as the safest form of investment during uncertainty shocks and good hedge against inflation and ",1.0,https://doi.org/10.2139/ssrn.48687031731b9dfc85e40139cac16f18401e0e2150861d4f6e,10.34190/ecrm.23.1.2393,Econometric Advances in the Estimation of Housing Price Determinants,2024,European Conference on Research Methodology for Business and Management Studies,A. M. Cunha,"This paper aims to help improve the estimations produced by researchers who rely on conventional housing market pricing models to determine housing prices. The widespread use of panel data in estimating housing prices is justified by the richness of cross-sectional regional or metropolitan data analysed over several periods. Unfortunately, panel data has slope coefficient heterogeneity and cross-sectional dependence, producing inconsistent and misleading estimates of the coefficients using the Ordinary Least Squares (OLS) estimator. Recent advances in econometrics address these panel data limitations, producing better estimates. We analysed the empirical application of these new estimators on housing market panel data, showing that the Fully Modified Ordinary Least Squares Augmented Mean Group (FMOLS-MG) estimator produces the best estimates of the long-term housing market equilibrium and that the Dynamic Common Correlated Effects Mean Group (DCCE-MG) estimator produces the best estimates of the housing market's short-term dynamics. Adopting a trending methodology like Difference-in-Differences (DID) in housing market research to explain the effects of policy decisions on housing p",1.0,https://papers.academic-conferences.org/index.php/ecrm/article/download/2393/220517417888f2589eb5129df9ff4e8efce7499a0a8b09b,10.1109/iciem54221.2022.9853185,Predicting House Price in India Using Linear Regression Machine Learning Algorithms,2022,2022 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 soci",1.0,175044c9f15cd588686f2877e89ed2c4450c04b5cc2,10.3390/realestate3020004,Spatial Dependence in Urban Housing Prices: Evidence from Zagreb,2026,Real Estate,D. Bečić,"Housing markets display geographical linkages that contravene conventional regression assumptions; yet, Central and Eastern European towns are markedly underrepresented in spatial econometric research. This study provides a systematic spatial econometric analysis of Zagreb’s housing market. It looks at both asking sale and rental prices throughout the city’s 17 administrative districts. There are five model specifications used in the analysis: Ordinary Least Squares (OLS), Spatial Lag of X (SLX), Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). The findings demonstrate significant positive spatial autocorrelation in both markets: Global Moran’s I = 0.29 (p = 0.007) for sales and 0.42 (p < 0.001) for rents. LISA analysis finds important groups of high-priced homes in the center districts and lower-priced homes on the edges. Spatial models significantly surpass OLS: SLX exhibits AIC enhancements of 9.90 (sales) and 20.20 (rentals), but SAR and SEM yield no enhancements, suggesting that local spillover effects from adjacent characteristics prevail over global spatial diffusion or correlated shocks. The higher Moran’s I and AIC gains in ren",1.0,https://www.mdpi.com/2813-8090/3/2/4/pdf?version=17772821511761ccecde3f8f412d0b37520cde11baab729f9cfba,10.1787/880f797c-en,Who pays the price of folly? The business cycle and income and wealth mobility in Spain,2019,OECD Economics Department Working Papers,C. Martínez-Toledano; David Law; David L. Haugh; Muge Adalet McGowan,"Spain has experienced a dramatic business cycle, starting with a large construction based boom followed by a long recession, resulting in a substantial rise in unemployment, and income and wealth inequality. This paper uses longitudinal data from the Survey of Household Finances over the period 2002 to 2014 to examine the distributions of income and wealth in Spain, as well as the mobility of households within those distributions. Results show an increase in the concentration of both income and wealth following the sharp decline in house prices that occurred since 2008, with house price fluctuations affecting more negatively the young than the old. Furthermore, differences in average income and wealth by education, gender and home ownership status were accentuated during the crisis, with lower and middle group incomes falling on average and top group incomes rising. In addition, higher levels of mobility within the middle of the household wealth distribution are observed than at either the top or bottom, although mobility at the extremes of the distribution increased after 2008. Finally, a number of characteristics of households, including age, property ownership, being employed on",1.0,https://www.oecd-ilibrary.org/deliver/880f797c-en.pdf?itemId=%2Fcontent%2Fpaper%2F880f797c-en&mimeType=pdf177051db2dd045449c65ee5cd426acb94623cc8a917,10.47688/rdp2021-10,The Rise in Household Liquidity,2021,RBA Research Discussion Papers,Gianni La Cava; L. Wang,"It is well documented that household wealth has risen significantly in recent decades and that both sides of the household balance sheet – assets and liabilities – have expanded. We document a less well-known phenomenon: household liquid assets (such as cash, deposits and equities) have also risen strongly relative to income over the same period. This is true for Australia and for most advanced economies. We explore the determinants of liquidity across households and over time, using a range of household surveys for Australia. We find that household liquidity is strongly associated with life cycle factors, such as age and housing tenure. The increase in liquidity over recent decades has been broad based across households, though strongest amongst those with mortgage debt. Consistent with this, the share of liquidity-constrained households has declined significantly. The growth in liquidity is closely connected to developments in the housing market. First, higher housing prices have lifted the deposit requirement for potential home buyers, encouraging such households to save more in liquid assets. Second, higher mortgage debt has increased the repayment risks associated with future ",1.0,https://www.rba.gov.au/publications/rdp/2021/pdf/rdp2021-10.pdf178096760f3ae2b35c2a19b745963c17c91ae8db703,10.3390/su18073278,Sustainability Analysis: Research on China’s Real Estate Economy and Business Based on the CFPS Data,2026,Sustainability,Yan Wang; Yan Shi; Xiao-Meng Zhou; Si-Yao Li; Zhongkuan Sun; Xue-Chao Xia; Hai-bin Huang,"Housing prices exert a notable impact on labor force sustainability, a key component of socioeconomic sustainable development. Skyrocketing housing costs tend to postpone young adults’ marriage and childbearing schedules, reduce their fertility intentions, and eventually lead to a shrinking labor force. It is therefore essential to explore the intrinsic links between housing prices, fertility intentions, and labor force sustainability. Based on data on China’s commercial housing prices, fertility rates, and related socioeconomic indicators from 2005 to 2024, this paper analyzes the theoretical mechanisms of how housing prices affect fertility intentions. It examines the trends of housing prices, housing price-to-income ratios, and disposable income growth at the national level, and further discusses the heterogeneous characteristics of these indicators in eastern, central, western, and northeastern China. In addition, this study analyzes the overall trends and regional disparities of fertility rates, conducts regression analyses combined with mortality rates and population growth rates, and implements correlation analyses between housing prices and fertility rates at national and r",1.0,https://www.mdpi.com/2071-1050/18/7/3278/pdf?version=17746108081791a3984f7357b598ddbc1d6b463226c56fefe9a6b,10.1088/1748-9326/ae21f8,Quantitative assessment of building losses in China’s coastal regions due to sea-level rise under extreme climate conditions,2025,Environmental Research Letters,Hao-Lei Zheng; Yang Wang; Yu-Yao Ye; Yingmei Wu; Chao Zhang,"Against the backdrop of global climate change, the superposition of extreme sea level events and long-term sea-level rise poses a severe threat to coastal regions. However, there is currently a lack of large-scale and detailed quantitative assessments for building losses caused by sea-level rise. This study takes 14 coastal provinces (autonomous regions, municipalities, and special administrative regions) in China as the research area. By integrating extreme sea level data, a digital elevation model, and building data, and by utilizing Python scripts and ArcGIS Pro software, this research quantitatively assesses the spatial distribution characteristics of land inundation and building loss under the combined effects of storm surges and tides, aiming to advance the quantitative study of building inundation exposure in coastal areas. The study finds that under sea-level rise resulting from a 100 year return period of extreme climate, the total potentially inundated area in China’s coastal provinces reaches 49 366.22 km2. Among these areas, buildings in 93 cities face varying degrees of inundation exposure. The losses are primarily concentrated in economically developed regions such as",1.0,https://doi.org/10.1088/1748-9326/ae21f8180060ee6f01607a112a298d3118b984f4272b7a082,10.54097/jnq11521,Machine Learning for House Price Prediction,2026,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, achievin",1.0,https://drpress.org/ojs/index.php/ajst/article/download/33366/3265018115fafa249bfb405131b68b8b2bafec4e222337c2,10.30598/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,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 ",1.0,http://dx.doi.org/10.30598/ppcst.knmxxiiv5i1p101-11618216fd9c07a6ae1f1f3fe9add544d2b5c5f33a8e10,10.3390/su11154151,The Synergy of Financial Volatility between China and the United States and the Risk Conduction Paths,2019,Sustainability,Xiaochun Jiang; Wei Sun; Peng Su; Ting Wang,"Based on monthly data of six major financial variables from January 1996 to December 2018, this paper employs a structural vector autoregressive model to synthesize financial conditions indices in China and the United States, investigates fluctuation characteristics and the synergy of financial volatility using a Markov regime switching model, and further analyzes the transmission paths of the financial risk by using threshold regression. The results show that there is an approximately three-year cycle in the financial fluctuations of both China and the United States, and such fluctuations have a distinct asymmetry. Two thresholds were applied (i.e., 0.361 and 0.583), taking the synergy index (SI) as the threshold variable. The impact of the trade factor is significant across all thresholds and is the basis of financial linkages. When the SI is less than 0.361, the exchange rate factor is the main cause of the financial cycle comovement change. As the financial volatility synergy increases, the asset factor and interest rate factor start to become the primary causes. When the level of synergy breaks through 0.583, the capital factor based on stock prices and house price is still th",1.0,https://www.mdpi.com/2071-1050/11/15/4151/pdf?version=1564646860183181b89f8841ad6d7904bbbc418adf681a5fd7034,10.3390/ijfs13010031,A Real Option Approach to the Valuation of the Default Risk of Residential Mortgages,2025,International Journal of Financial Studies,Angela C. De Luna López; Prosper Lamothe-López; Walter L. De Luna Butz; Prosper Lamothe-Fernández,"A significant share of many commercial banks’ portfolios consists of residential mortgage loans provided to individuals and families. This paper examines the default and rational prepayment risk of single-borrower (residential) mortgage loans based on an option pricing model that captures the skewness and kurtosis of the house prices returns’ distribution via the shifted lognormal distribution. Equilibrium option-adjusted credit spreads are obtained from the implementation of the model under plausible values of the relevant parameters. The methodology involves numerical experiments, using a shifted binomial tree model by Haathela and Camara and Chung, to evaluate the effects of the loan-to-value (LTV) ratio, asset volatility, interest rates, and recovery costs on mortgage valuation. Findings indicate prepayment risk significantly influences loan value, as it limits upside potential, while LTV and volatility directly impact default risk. The shifting parameter (θ) in the asset distribution proves essential for accurate risk assessment. Conclusions emphasize the need for mortgage underwriting to consider specific asset characteristics, optimal loan structures, and prevailing risk-fre",1.0,https://doi.org/10.3390/ijfs13010031184229ad63adfebaa8ed206ca11b5bf0ca7a5209692,10.4236/eng.2011.312147,The Bending of Rectangular Deep Beams with Fixed at Both Ends under Uniform Load,2011,,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 poten",1.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=92071850b29ac8adad5af9454dc2202a7a6f9569c27a268,10.1051/itmconf/20257303005,Bayesian Optimization of Lasso and XGBoost Models for Comparative Analysis in Housing Price Prediction,2025,ITM Web of Conferences,Run-Ze Zheng,"Fluctuations in housing prices have a profound impact on the broader economy and people's livelihoods. Accurate housing price predictions contribute to enhanced market transparency and the formulation of evidence-based policies. This paper focuses on optimizing two machine learning models, Lasso Regression and XGBoost, using Bayesian optimization for predicting housing prices. By leveraging economic features such as Average Earnings, Gross Domestic Product (GDP), Mortgage rates, Population, and Unemployment Rate, the models aim to improve prediction accuracy in the housing market. The Lasso model, known for its feature selection capability through L1 regularization, was fine-tuned using Bayesian optimization to minimize mean squared error (MSE). The XGBoost model, designed for handling large-scale, non-linear datasets, was also optimized using the same method. After optimization, the Lasso model achieved an MSE of 240,498,369.05 and an R² score of 0.977, while the XGBoost model showed superior performance with an MSE of 80,273,332.19 and an R² score of 0.9914. SHAP analysis was used to interpret the models, revealing that Average Earnings and GDP were the most influential features ",1.0,https://www.itm-conferences.org/articles/itmconf/pdf/2025/04/itmconf_iwadi2024_03005.pdf186225d6eb968ddfd1d17a7fe26ac30d791e1188f79,10.2139/ssrn.3576073,Fracking and Tracking: The Effects of Oil and Natural Gas Well Locations on the Housing Market.,2020,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",1.0,1870c59335ca1b171d1a7095c8fce95805f7852118d,10.5194/isprs-annals-x-4-2024-91-2024,Exploring the Impact of Mountain Urban Characteristics on Housing Prices: A Study Utilizing Street View Visual Features,2024,"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences",Xuhui Cui; Sha Li; Zhen Li; Zhaohan Dong,"Abstract. Urban mountains shape the spatial and landscape structure of cities. Rapid urbanization and an increase in real estate developments have led to the erosion of unique urban landscape features. However, mountain cities boast distinctive terrain and landscapes, endowing them with a wealth of scenic resources and aesthetic appeal. Residents' growing concern for their living environment's quality and scenic beauty impacts property values during urban development. Mountain landscapes, vital natural elements of mountain cities, are studied less in their effect on housing prices compared to water features and urban greenery, despite their unique appeal to residents. This study integrates various sources of urban data with subjective analysis of human behavior and the multiscale geographically weighted regression (MGWR) to assess the factors affecting housing prices in mountain cities. It examines the spatial distribution and influence of street view elements on property values and uses gradient-weighted class activation mapping (Grad-CAM) to analyze visual perception characteristics of street views. Research findings reveal urban street view elements, especially mountain views, s",1.0,https://isprs-annals.copernicus.org/articles/X-4-2024/91/2024/isprs-annals-X-4-2024-91-2024.pdf18809f324840972eaa8d6071ed19ba14931e663c291,10.54254/2753-8818/39/20240584,Utilizing 31 Chinese province panel data models to investigate the factors influencing house prices,2024,Theoretical and Natural Science,Yuxing Dou,"In the realm of data analysis, this research employs regression models to delve into the complexities of housing market dynamics. The construction sector of real estate development companies, the amount invested in real estate development, and the gross regional product have all been found to be important determinants of home prices. Interestingly, the most significant factor is the investment in real estate development, which has a significant impact on house prices. The analysis reveals a positive correlation between the gross regional product and investment amount in real estate development with housing prices, suggesting that as these economic indicators rise, so too do housing prices. Conversely, the author observes a negative correlation between the construction area of real estate development enterprises and housing prices, indicating that an increase in construction area is associated with a decrease in housing prices. These findings underscore the importance of considering these key factors when analyzing housing market trends, providing valuable insights for policymakers, investors, and researchers alike. By understanding these relationships, stakeholders can make more in",1.0,https://www.ewadirect.com/proceedings/tns/article/view/13320/pdf1890edb5fd62b8b108357e51a05780be9e5c9678a27,10.18069/firatsbed.1285789,Konut Fiyatlarını Etkileyen Faktörler: İstanbul-Ataköy Örneği,2023,Fırat Üniversitesi Sosyal Bilimler Dergisi,E. Şahin,"Different structural and spatial features such as type of housing unit, age of housing unit, size of housing unit, number of rooms, number of bathrooms, construction quality, heating system, parking lot, quality of public services, proximity to centers, distance to health and education facilities, transportation facilities, crime rate, satisfaction with social activities, landscape, environment, pollution and airport noise affect the prices of the housing units. Atakoy is one of the important residential areas of Istanbul with a high quality of life, due to the richness of its green areas and its urban texture, the closeness to centers, high quality of transportation and urban services. The lack of vacant land, the high demand and the high occupancy rate in the region have increased the housing prices considerably, and the importance of the regional housing market. The aim of the study is to determine the characteristics of housing unit that may affect the housing prices in Atakoy. In this context, multiple regression analysis has been made with the data which consist of physical information about 149 housing units for sale in the region and it has been examined which features rela",1.0,https://doi.org/10.18069/firatsbed.12857891901e141bcdf0ccd7a4a7a0532a3da75760829d8101,10.56960/jmap.v4i1.94,"Analisis Tingkat Kerawanan Bencana, Variabel Makroekonomi, dan Tingkat Kemahalan Konstruksi Terhadap Indeks Harga Properti Residensial tahun 2018-2022",2024,Jurnal Manajemen Aset dan Penilai,Riana Nur Cahya Putri; A. Syaharani; Alvina Desti Ningrum; Fadhilah Nur Khazanah,"Residential property is one of the basic human needs to be met. It is therefore important to know what factors affect residential property prices. There's been a lot of research currently on macroeconomic variable impact on the residential property price index, but there is still limited research on the catastrophic and build-level impact on the residential property price index. The study aims to identify both partial and simultaneous effects on disaster levels, macroeconomic variables made up of inflation, PDRB rates, and population density, as well as build omniscience on residential property price indexes. Secondary data sources are then analyzed using regressions of panel data using a common effect. The research site is eight major cities in Indonesia. Analysis shows that disaster and inflation rates have both negative and significant links to residential property price indexes, PDRB velocity variables and population densities have positive and significant links to residential property price indexes and omniscient construction variables are insignificant to the residential property index. Testing f explains that disaster rates, macroeconomic variables, and construction allowanc",1.0,http://dx.doi.org/10.56960/jmap.v4i1.94191106ad76117da837d83434765cc357d73ec2d9f66,10.2478/picbe-2025-0216,House Price Determinants in the European Union: The Role of Macroprudential Policy,2025,Proceedings of the International Conference on Business Excellence,Mihai Aliman; R. Popa,"Abstract The study examines key factors influencing residential real estate markets across EU countries using fixed effects panel estimation on EU countries from 2011Q1 to 2024Q3. Housing markets vulnerabilities were one of the main factors behind the Great Financial Crisis, urging policy makers to design dedicated macroprudential instruments in order to decrease RRE risks. Current literature highlights GDP growth, unemployment, interest rates, and construction output as main potential drivers of house price growth. The research methodology involves establishing the determinants of house prices and investigating the direct and indirect effects of macroprudential policies on housing markets, particularly through the credit channel. Key findings confirm the results in the literature regarding house price drivers and reveal that capital buffers have a minor impact on house prices and mortgage credit, while borrower-based instruments significantly affect credit markets but not directly housing markets. The study contributes to the field by providing empirical evidence on the nuanced effects of macroprudential policies, offering insights for policymakers aiming to stabilize housing mark",1.0,https://reference-global.com/2/v2/download/article/10.2478/picbe-2025-0216.pdf192doi_10_5821_siiu_9136,10.5821/siiu.9136,"Asentamientos informales, inundaciones y vulnerabilidad físico-espacial de la vivienda y el entorno inmediato: Caso de estudio Arroyo La Esmeralda en el Distrito de Barranquilla, Colombia",2018,"X Seminario Internacional de Investigación en Urbanismo, Barcelona-Córdoba, Junio 2018",Myrna Inés Arévalo Carmona,"En este artículo se presenta un enfoque metodológico para identificar variables de análisis de la vulnerabilidad, desde la dimensión físico espacial,y permitir una mejor comprensión del estado en el sector Suroccidental del Distrito de Barranquilla,como un aporte a la metodología incorporada para el Plan de Ordenamiento Territorial. Muchas de las características propias del lugar que configuran condiciones físicas y aportan a la vulnerabilidad social no fueron incluidas en esta,por lo tanto,se conoce menos acerca de su comportamiento en un sector no homogéneo de origen informal. A partir de la operacionalización del concepto de vulnerabilidad se identifican variables y características de la vivienda y el entorno inmediato que inducen a una descripción desde la micro escala, vinculadas con las prácticas de habitar y la forma de apropiación inherentes del lugar. This article presents a methodological approach to identify variables of vulnerability analysis, from the spatial-physical dimension, and to allow a better understanding of the state in the Southwestern sector of the Barranquilla District, as a contribution to the incorporated methodology for the POT (ZonningPlan). Many of th",1.0,193191e707f1da108b5b31f8a013f58cac6d9c3e616,10.54254/2755-2721/2024.18220,Predicting House Prices with a Linear Regression Model,2024,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/pdf194188a7b9b83a1a7f0b7d67ed0523d8f1644fccb98,10.31235/osf.io/wf6ns,Vaccination and partisanship during Covid-19 in England,2021,,Margaryta Klymak; Tim Vlandas,"An emerging literature documents the presence of large partisan differences in the views and behaviours of different voters towards the Covid-19 pandemic. How does this affect vaccination rates? We address this question in the case of England using a cross-sectional regression analysis of constituency level vaccination data in October 2021 matched with the latest General Election results in England. Our results show that partisanship is crucial to account for differences across English constituencies. We find a positive and robust association between the share of Conservative voters and vaccination rates in different constituencies. This effect holds when controlling for differences in house prices and wages, population composition, the health and deprivation of the constituency and past austerity, and when rerunning the analysis on vaccination rates for different age groups. Our results contrast with studies of beliefs about Covid-19 and compliance with national lockdowns in England, where Conservative voter appear more sceptic, but also with the US where Republican States and individuals tend to vaccinate less.",1.0,https://osf.io/wf6ns/download1950fe006456dad338d64f25d3ec924d56f4b073706,10.14400/jdc.2016.14.5.177,An Empirical Study on the Influencing factors of Household Debt,2016,,S. Lee,"Abstract The purpose of this study is to analyze the affecting factors and debt characteristics with household debt. The method of analysis was used SPSS 22.0. The main results were as follows. First, psychological debt burden group were found to be lower academic background, more household members, job in agriculture, forestry and fisheries and women among demographic characteristics. This suggests the need for the governments who are interested in the risk from household debt. Second, psychological debt burden group showed higher level of debt and average monthly debt repayments. This study implies that it is necessary to do financial education and consulting. Third, psychological debt burden group seemed that the households with more the average monthly interest payments and repayments on income, the more total liabilities compared to total assets. As the ages get higher, it was gradually increased. It suggests that the development of psychological counseling program for debtor with household debt. Other factors(property prices or the financial environments) will be dealt with in the subsequent studies.",1.0,http://koreascience.or.kr:80/article/JAKO201617338764416.pdf1960115f6449a20e7566b6fe3e5ef34fb2dd7358d09,10.3390/land10111238,"How Does the Effect Fade over Distance? An Inquiry into the Decay Pattern of Distance Effect on Property Values in the Case of Taipei, Taiwan",2021,Land,Lin-Han-Chiang Hsieh,"It is generally accepted that the perception of homeowners towards certain potential risks or amenities fades as distance from the risk or amenity increases. This study aims to illustrate the distance–decay pattern with an appropriate mathematical function. Distance–decay functions and parameters that yield the minimum residual sum of squares (RSS) for a given regression model are considered to be the optimal approximation for the pattern of decay. The effect of flood risk and mass rapid transit (MRT) accessibility on residential housing prices in Taipei, Taiwan, are used as examples to test the optimization process. The results indicate that the type of distance function affects both the significance and the magnitude of the regression coefficients. In the case of Taipei, concave functions provide better fits for both the flood risk and MRT accessibility. RSS reduction is up to 10% compared to the blank. Surprisingly, the impact range for the flood risk is found to be larger than that for MRT accessibility, which suggested that the impact range of perception for uncertain risks is larger than expected.",1.0,https://www.mdpi.com/2073-445X/10/11/1238/pdf?version=163671481119718527c1a8b716c3d8d438f75d1a326a79f3e8c55,10.65748/fiqf-2012-0003,HOUSING MARKETS IN SELECTED EUROPEAN COUNTRIES AND THE USA,2024,Financial Internet Quarterly,Magdalena Gostkowska-Drzewicka,"The aim of this article is to present the changes taking place in housing markets in selected European countries and in the USA. The basic research period covers the years 1998-2009. However, wherever availability of data allowed so, we covered with our research also the years 1963-2010. The development of domestic housing markets is determined both by specific features of local markets and by macroeconomic factors influencing the economic situation. The availability of capital to finance investment is of primary importance for the changes taking place in housing markets. This factor can be expressed by the volume of mortgage debt related to GDP and inflation and the level of interest rates on mortgage loans. The evaluation of the changes occurring in domestic housing markets was based on the identification of such structural characteristics as: the volume of the housing stock, the number of ready-to-use flats, the number of issued building permits, the indices of housing prices, and the number of transactions concerning purchase of flats or houses.",1.0,https://journals.wsiz.edu.pl/fiq/article/download/814/7701981ef780452306d3363b15761e5de34008a9ea4839,10.53106/160792642022092305022,A Study on House Price Prediction Based on Stacking-Sorted-Weighted-Ensemble Model,2022,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,"199 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.200 201",1.0,https://jit.ndhu.edu.tw/article/download/2774/27972020225308182c70763f3fd97aa79ec2fc36391fd24,10.1109/sist58284.2023.10223463,A Case Study of Machine Learning Comparisons for Predicting Apartment Prices in Astana,2023,2023 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",1.0,203073b3012cd513014b5ebb052940c2263aa953e04,10.54097/2v133k11,The Relationship between Income and House Prices on People's Consumption,2024,"Highlights in Business, Economics and Management",Shu Yu,"This study aims to explore the factors that influence consumer spending, particularly focusing on the relationship between income levels and housing price fluctuations. Using Univariate regression analysis, the research draws on data samples from three major cities in China, covering the period from 2007 to 2024. According to the assumptions, three variables were selected to symbolize income levels (rate of increase in disposable income), house price volatility (second-hand house sales price index), and consumption expenditure (rate of growth in sales of all consumer retail goods). The study examines the connection between these factors by calculating R^2 and beta values. The findings indicate a clear and consistent connection between disposable income and consumption expenditure, whereas the influence of fluctuations in house prices on consumption expenditure does not reach statistical significance. Generally, the amount of money people earn has a greater influence on their spending habits compared to changes in housing prices.",1.0,https://drpress.org/ojs/index.php/HBEM/article/download/27470/2700920410f59485f7108f7bbae9b8b2d614ff6cf596247e,10.1109/conmedia67140.2025.11290600,"Predicting Property Prices Using MLR, Gradient Boosting, and Random Forest: A Case Study in South Tangerang, Indonesia",2025,2025 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 ",1.0,20518d8a842e83ac984b7fdc4280f227e679ce37ae0,10.2478/sh-2021-0019,The Role of the Bank in Meeting the Housing Needs of an Average Household – Evaluation of the Situation in 2018,2021,,Marta Martyniak,"Abstract The aim of the article is to determine the profile of an average Polish household and the possibility of satisfying average housing needs thereof as well as to indicate the role played by the bank in their implementation. The characteristics of an average household, the value of income and expenses thereof, the prices of residential properties and the data of the credit market were determined. statistical studies, The research material was obtained from reports and including data from the Statistics Poland, Eurostat, the database of residential property prices of the National Bank of Poland and the average offer prices of the rental market – for 2018, which reflects the current economic situation. The research was of a quantitative nature (statistical measures, housing availability index, credit simulations). The results indicate that as of 2018 an average household cannot afford to buy or rent a flat. The bank’s role in satisfying housing needs is not only important but rather necessary.",1.0,https://www.sciendo.com/pdf/10.2478/sh-2021-00192061908d2ee0b0995f38129f51d7446a3626531b8d6,10.67913/vner/2026.1.1.1,Mind the Anticipation Gap: A Difference-in-Differences Analysis of the Elizabeth Line’s Impact on London Housing Prices,2026,Vietnamese Economic Review,T. Nguyen,"This paper estimates the causal impact of London’s Elizabeth Line on residential property prices using a Difference-in-Differences framework applied to 1,602,487 transactions from the UK Land Registry over 2000–2024. Treated properties within five distance bands of stations are compared to a baseline beyond 10 km, separately for the construction window (2007–2015) and the post-opening window (2022–2024). During construction, prices within 1 km of stations rose by 4.9% relative to control, with substantially larger gains of 7.7–8.1% in the 2–10 km bands, indicating broad anticipatory capitalisation and spillover effects. Following the May 2022 opening, station-adjacent prices fell by 2.0%, consistent with overshooting during the construction phase, while outer-band prices remained unchanged. Effects are concentrated in zones previously under-served by Transport for London. The findings have implications for transport appraisal, value-capture timing, and equity-oriented infrastructure planning.",1.0,https://vner.org/index.php/vner/article/download/3/32070768907acee7e6cebe1190682c1580b989857ab8,10.21799/frbp.wp.2022.41,The Push of Big City Prices and the Pull of Small Town Amenities,2022,Working paper (Federal Reserve Bank of Philadelphia),H. Artigue; Jeffrey C. Brinkman; Svyatoslav Karnasevych,"As house prices continue to rise in large, supply-constrained cities, what are the implications for other places that have room to grow? Recent literature suggests that amenities that improve quality of life are becoming increasingly important in location decisions. In this paper, we explore how location amenities have differentially driven population and price dynamics in small towns versus big cities, with a focus on the role of housing supply. We provide theory and evidence that demand for high-amenity locations has increased in recent decades. High-amenity counties in large metropolitan areas have experienced relatively higher price increases, while high-amenity counties in small metros and rural areas have absorbed increased demand through population growth. This divergence in population dynamics between big cities and small towns was driven by domestic migration, with high-amenity small towns and rural areas experiencing significant domestic in-migration",1.0,https://doi.org/10.21799/frbp.wp.2022.4120814acb1ea3d8335492dc3ce1ebf386e9449efce19,10.1080/02673037.2026.2631435,The impact of commercial land housing on residential land housing in China: a perspective of housing submarkets,2026,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 ho",1.0,209059420b9faab486c7eff57197c01329304af34b3,10.48175/ijarsct-29500,Smart House Price Estimator Using Artificial Intelligence,2025,"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-29500210doi_10_3917_reru_022_0239,10.3917/reru.022.0239,Différenciation spatiale des prix du logement à Monaco : une reformulation du concept de distance au centre,2002,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 rappo",1.0,2111090db8fe3e5470de3fd3a674b2e617ebef1bb26,10.1155/2022/1078057,Modeling and Simulation of Real Estate Valuation Based on Genetic Neural Network Model,2022,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.pdf2121f96707052fcbac6fa65014e6e6a2b8308e831c4,10.71016/hnjss/fdnsfz64,"Determinants of Housing Prices in District Mardan KPK, Pakistan",2022,Human Nature Journal of Social Sciences,Muhammad Asif; Sareer Ahmad; Majid Ali; Sameed Hussain,"Residence is basic need of life. Sale and purchase of house is common practice among the society. To investigate the factors that affects house pricing, researchers condicted this study. This study is based on influencing factors or determinants of housing prices in district Mardan. The study has collected primary data through a questionnaire, using a stratified random sampling technique. The data was collected from 250 house buyers; the buyers were selected on the bases of listing done through property dealers. The respondents were selected on the base of house buyers, who bought a house in the last 5 to 10 years. The study has used a multiple regression model. The study found that house size, number of floors, and electricity connection in a house increase house prices. Bad smell and distance to urban areas negatively affect house prices in district Mardan.",1.0,https://hnpublisher.com/ojs/index.php/HNJSS/article/download/277/2602130c30cbad9086dfc4f0e219e80a80bfe73b1dfd2f,10.55041/isjem03420,Enhancing House Price Prediction Using Hybrid Model Approach,2025,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, invest",1.0,21422f968258ee167f2f3ff2c0153397f109db9780d,10.1109/iscit63075.2024.10793572,Predicting Marriage Rate Using Machine Learning and Panel Data Analysis: A Case Study of China,2024,International Symposium on Communications and Information Technologies,Deyu Zhang; Worasak Rueangsirarak; Surapong Uttama,"After China's accession to the WTO and 20 years of rapid development, the marriage rate has shown a downward trend. This paper aims at fitting machine learning models with the marriage-related data, understanding which attributes affect the marriage rate and predicting the marriage rate. The data collection scope includes seven independent variables related to marriage rate such as GDP, house prices, birth rate, education level etc. over 31 regions in China during 2003–2022. Then the study applied three regression models - Pooled OLS, Random Effects, and Fixed Effects - in predicting China's crude marriage rate. The Random Effects model outperformed both the Pooled OLS and Fixed Effects models, as evidenced by its highest R2 value (0.2910). However, based on Hausman Test, p-value of 6.458e-16.the Fixed Effects model is preferrable. All models suggested that the average year of education has the most positive effect to the marriage rate while the house price greatly negates the marriage rate. Results show the Random Effects model, with an R2 of 0.2910, as the best fit. Key predictors include GDP, house prices, and gross dependency ratio (negative effects), and sex ratio and educatio",1.0,2150fa30bd48be19459c88ec70eee9999df12131004,10.1080/17421772.2023.2271519,On the geography of inequality: labour sorting in general equilibrium,2023,Spatial Economic Analysis,Santiago Truffa; A. Montecinos,"ABSTRACT We study how cities’ amenities and limited housing supply contribute to aggregate wage inequality and affect housing prices through the sorting of heterogeneous skilled workers. We develop a general equilibrium model where workers differ along a continuum of skills and compete for limited housing. Our analysis suggests that spatial sorting accounts for 7.5% of the aggregate wage dispersion, increases average housing prices by 20–40% in constrained cities, and makes the economy 1.9% more productive. In addition, we evaluate a place-based policy that aims to expand the supply of houses in 1% of constrained cities and find that it improves aggregate productivity between 0.2% and 0.4%. However, the place-based policy has the unintended consequence of aggravating aggregate wage inequality by the same magnitude.",1.0,https://figshare.com/articles/journal_contribution/On_the_geography_of_inequality_labour_sorting_in_general_equilibrium/24541478/1/files/43105883.pdf2160bb03709c510b536f0af6feb5ee893626d47af9e,10.24251/hicss.2020.130,Improving Prediction Models for Mass Assessment: A Data Stream Approach,2020,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.pdf2171b74d33204ff11e1e5ed0c2bbdaef13ca70a5fe9,10.1117/12.2658679,City’s house pricing prediction based on machine learning algorithms,2022,"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",1.0,218021a611843ac3260fbdb6f1bd3abedb3da1c47f3,10.1080/02664760600746699,Applications of a General Stable Law Regression Model,2006,,Ian G. McHale; P. Laycock,"Abstract In this paper we present a method for performing regression with stable disturbances. The method of maximum likelihood is used to estimate both distribution and regression parameters. Our approach utilises a numerical integration procedure to calculate the stable density, followed by sequential quadratic programming optimisation procedures to obtain estimates and standard errors. A theoretical justification for the use of stable law regression is given followed by two real world practical examples of the method. First, we fit the stable law multiple regression model to housing price data and examine how the results differ from normal linear regression. Second, we calculate the beta coefficients for 26 companies from the Financial Times Ordinary Shares Index.",1.0,http://usir.salford.ac.uk/id/eprint/270/219doi_10_3406_receo_1988_1345,10.3406/receo.1988.1345,Le secteur du logement en France et en Pologne,1988,Revue d’études comparatives Est-Ouest,Maria Lissowska; Michel Mouillart,"The housing sector in France and Poland The comparison this article is trying to present is built upon a direct parallel between the housing sectors in France and in Poland. The differences and similarities between the situations encountered in both countries are underlined : the crisis exists in France and in Poland, but this is a different crisis by its deepness and its nature, according to the particularities of the economic system in each of the two countries. The comparative analysis of the housing situation (age structure, amenity factors and equipment...) and of the evolution of building for the last decade gave an opportunity to show the role of the State in the housing sector. The perspectives of the evolution of the sector, both in France and in Poland, display the importance of the financial conditions for the development of the sector. Even if the financial instruments are different in each of the two countries, their evolution will undoubtedly determine the level of the construction in the forthcoming years. The article is completed by an annex displaying the differences of the financing, in France and in Poland, based on an example of a young household buying its own ",1.0,22021456c3623d022ebc9461dbda8eba284322bbb02,10.24957/hsr.2025.33.1.5,Analysis of the Factors Influencing the Gift of Apartment During the Surging of Housing Prices: Focusing on the Characteristics of Real Estate Regulations,2025,Korean Association for Housing Policy Studies,Seong-hyun Park; J. Suh,"Since 2020, the Korean real estate market has risen unusually steeply, mainly in apartment prices. With the continuous rise in apartment prices, gifts increased especially in situations where taxation and regulations on multiple homeowners were in effect. Research on apartment gifts has been mainly conducted in the past but considered only the perspective of tax law and accounting. Hence, this study conducted multiple regression analysis and empirically compared and analyzed how the characteristics of the local housing market affect the number of apartment gifts from 2020 to 2023, the period of soaring housing prices. The findings demonstrated that the most common factors influencing apartment transactions were as follows: increase in sales price, the ratio of the elderly population, the amount of tax determined by the comprehensive real estate tax (for housing), the metropolitan area (dummy variable), the regulated area (dummy variable), and the number of households exceeding one house. Based on these results, this study is expected to provide fundamental data for real estate policy establishment, such as the government's real estate market regulation policy and local housing poli",1.0,221aa5d23ccd2255ed5c269f8202bea0b6b68f687f4,10.4028/www.scientific.net/amm.744-746.1665,Application of Geographic Information System on Urban Residential Real Estate Mass Appraisal,2015,,Yuan Tian; Jian-Ping Yang,"Real estate mass appraisal is the key preliminary work of imposing real estate tax in urban area.Various methods have been used for real estate mass appraisal,among which geographic information system(GIS) is a powerful method to improve the efficiency due to it’s capacity of spacial analysis and management.This article tries to explore the combination of GIS and Hedonic model,and uses GIS spacial analysis function to quantitative analysis influencing factors of real estate.At last we have a real estate mass appraisal practice on Xi’an,the result shows it is feasible and efficient to apply GIS in urban residential real estate mass appraisal.",1.0,https://doi.org/10.4028/www.scientific.net/amm.744-746.1665222095237a9bb9d5b7ec7b0835c9762968643434a53,10.1109/iceeict61591.2024.10718610,Optimizing House Price Prediction: Comparative Analysis of Machine Learning Techniques,2024,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.",1.0,22304212ec06b4bf4e7affd140b80489edf27c94800,10.1109/seeda-cecnsm68644.2025.11329623,Evaluating Machine Learning Approaches for Residential Property Price Estimation,2025,"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.",1.0,224doi_10_22456_2176_5456_10881,10.22456/2176-5456.10881,MERCADO IMOBILIÁRIO E A IMPORTÂNCIA DAS CARACTERÍSTICAS LOCAIS: UMA ANÁLISE QUANTÍLICO-ESPACIAL DE PREÇOS HEDÔNICOS EM BELO HORIZONTE,2009,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.",1.0,22500880b62ef689980c93afe7995a3e5d3843a07ab,10.1353/jef.2017.a678329,The Relationship Between Magnet Status and Neighborhood Home Values in Chicago,2017,Journal of Education Finance,C. Evans; J. Malin,"abstract:226 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.227",1.0,2281dd91dd8834240b4270c77b0b0afb292dd1d678a,10.1111/1540-6229.12479,Homebuyers’ geographic proximity as a predictor of future housing price growth,2024,Real Estate Economics,Hayoung Kim,"Considering the prevalent information asymmetry in housing markets, this study demonstrates the predictive power of homebuyers’ geographic proximity on housing prices. At the ZIP‐code level, a 10‐percentage‐point increase in the fraction of local buyers corresponds to a 1.1‐percentage‐point higher housing price growth over the subsequent 2 years. At the individual level, out‐of‐town buyers experience a 0.64‐percentage‐point lower annual return compared to local buyers within a county. These results not only highlight the significant information advantages enjoyed by geographically proximate buyers, but also imply that informationally privileged buyers’ revealed preferences for specific locations could provide informationally disadvantaged buyers with hints about which areas are likely to experience higher housing price growth in the near future.",1.0,229203fdb2f69ca1a9e3fdd93e9b3fab4adb2cdd4cb,10.1109/ised59382.2023.10444604,Real Estate Price Predictor – A Literature Review,2023,International Symposium on Embedded Computing and System Design,Abhinav Verma; S. Deepu; Parveen Bajaj,"The expectation of property prices stands as a vital pursuit within the real estate geography, impacting opinions of buyers, investors, and assiduity professionals likewise. This review paper navigates the multifaceted realm of property price prediction by illuminating its significance and checking the different methodologies and statistical ways employed. The conflation of this converse extends a palpable frame for casing inventors and experimenters, enabling them to navigate the maze of attributes bolstering house prices and offering perceptive recommendations for apt machine learning models. As a capstone, this review underscores the indispensability of nuanced position understanding and advanced prediction methodologies, therefore fortifying the precision of property price predictions within the ever-evolving real estate terrain.",1.0,230045f79b424a72b6b446701d38060888d0f8f16ea,10.1111/jors.70083,Can Land Use Regulation Lower the Price of Housing in Cities?,2026,Journal of Regional Science,A. Yezer,"231 Previous theoretical analysis using classical urban models suggests that planning or topographic restrictions on structure density raise the price of housing in cities. This paper demonstrates that these past results are an artifact of special assumptions about the spatial distribution of planning or topographic restrictions. When alternative and more realistic regulations are examined using either classical or neoclassical models, the average housing price falls compared to laissez faire in the absence of amenity benefits. The standard empirical finding that housing prices rise with regulation implies that planning raises urban amenity. This illustrates the importance of the specific spatial distribution of land use and topographic restrictions and of amenity effects in research on the implications of urban development policies.",1.0,232doi_10_1111_1540_6229_00370,10.1111/1540-6229.00370,An Empirical Analysis of Tenant Selection Under Federal Rent Supplement Programs: A First Step,1986,Real Estate Economics,John D. Jackson; Charlotte A. Jones; Philip W. Balsmeir,"The purpose of this study is to consider the question of whether characteristics of applicants for federally subsidized housing can be used in predicting whether one is accepted or rejected by management as a tenant. Using probit analysis, a statistical model was developed for use in assisting management in objectively screening applicants. Factors found significant in determining the probability of acceptance or rejection were credit ratings, character references, length of time employed, and student status. The percent of rent subsidized by government and sex of household head had little effect on applicant acceptance. Although a meaningful model for estimating the probability of eviction could not be developed, low eviction rates resulting from the tenant selection model suggest that it may serve as a reasonable proxy.",1.0,233doi_10_1177_0096144210384247,10.1177/0096144210384247,The Cosmos of the Paris Apartment: Working-Class Family Life in the Nineteenth Century,2010,Journal of Urban History,Eliza Ferguson,"Drawing on Bachelard’s notion of “cosmicity” this article investigates the living conditions of Parisian working-class families in the second half of the nineteenth century. Nineteenth-century social critics claimed that the lack of privacy in urban apartments made decent family life impossible. However, evidence from judicial dossiers concerning attentat à la pudeur (intimate assault against children) illuminates the lived experience of children and their families in Paris apartments. Rather than a sharp divide between public and private, children experienced their apartment homes as the core of a social and spatial world under the surveillance of parents, neighbors, and other children.",1.0,2340525620d5bfc12c6d7f3f610e2c159110d0cfc03,10.1177/0739456x221082500,Leveraging Main Street as a Real Estate Amenity: Downtown Revitalization and Residential Property Values,2022,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.",1.0,235doi_10_5209_hics_62320,10.5209/hics.62320,Una casa para todos. Uso propagandístico de la vivienda en NO-DO durante la dictadura de Franco (1939-1975),2020,Historia y Comunicación Social,Antonio Raúl Fernández Rincón; Pedro Hellín; Eneus Trindade,"This article analyzes the use of housing by the Dictatorship of Franco (1939-1975) through the Documentary Newsletter (NO-DO). A total of seventy-five documents whose main theme is housing were analyzed. Categorization and content analysis were the methods used. It is analyzed if the house functioned as a propaganda element for the political and social legitimation of the Franco regime. From this point, we suggest a reflection on the current characteristics of the Spanish real estate sector and its possible relationship with the imaginary and francoist policies on housing.",1.0,236161eaf2dc5367c21e77726487c608a8699796250,10.15793/kspr.2007.54..007,The Effect of Spatial Autocorrelation in Analyzing the Relationship between the Characteristics of Walkable Neighborhoods and Multi-Family Residential Property Values,2007,,동욱 손,,1.0,https://doi.org/10.15793/kspr.2007.54..007237doi_10_1016_0094_1190_79_90010_x,10.1016/0094-1190(79)90010-x,Hedonic price and depreciation indexes for residential housing again,1979,Journal of Urban Economics,Peter Chinloy,,1.0,238doi_10_1109_icee_2010_729,10.1109/icee.2010.729,An Empirical Study of Residential Hedonic Prices in Nanjing,2010,2010 International Conference on E-Business and E-Government,Zhu Jian-jun; Li Qi-ming,,1.0,23917757549437bae45b2067f130c12e89fcb629494,10.24957/hsr.2017.25.2.175,The Comparison of Local Housing Price Determinants by Housing Type,2017,,Jae-Ik Kim,,1.0,24018921a9e183f4d7b7210f68b64dbcfe65924e692,10.1007/978-981-16-0115-6_136,Gaussian Process Regression for Forecasting of Boston Housing Prices,2021,Lecture Notes in Electrical Engineering,Yan-Ran Xu; Haoyang Zhang; Haoxuan Li,,1.0,241https://openalex.org/W1608429913,10.2139/ssrn.1334089,Hedonic Price Indexes for the Housing Market in Italian Cities,2008,SSRN Electronic Journal,Alessandra Michelangeli; Alberto Zanardi,,1.0,2421111ba145576bff27131188f83a6eb5c64c13146,10.15760/honors.782,The Green Gentrification of N. Williams Avenue,2019,,Marcco T Higham,"The Albina District in North Portland, Oregon has experienced a tumultuous cycle of disinvestment, decline, and now reinvestment that has caused a shift in its racial and socioeconomic composition. As new sources of investment and capital have been introduced into this historically black neighborhood, a new housing redevelopment trend has emerged that reflects consideration towards sustainable design and ""eco-conscious"" lifestyles. This style of redevelopment in N. Williams Avenue caters to those with higher incomes that desire an ""urban"" and ""sustainable"" lifestyle through their housing choices. In this paper I argue that this new housing trend is a gentrifying phenomenon that establishes a pretext of environmentalism and sustainability to sell market rate housing that is largely unaffordable to the neighborhood's original residents and contributes to an ongoing pattern of displacement. Sharon Zukin's sensitizing concepts of gentrification and the marketplace and Karen Gibson's ""Bleeding Albina"" form the theoretical foundation of my inquiry on this type of housing redevelopment. Discussion on how low income communities face the paradox of desiring to ameliorate their local environ",0.0,https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1881&context=honorstheses243doi_10_5821_ctv_8763,10.5821/ctv.8763,"La Verticalidad en la vivienda turística: el proceso de transformación de Puerto Vallarta, Jalisco, México",2019,"XIII CTV 2019 Proceedings: XIII International Conference on Virtual Cityand Territory: “Challenges and paradigms of the contemporary city”: UPC, Barcelona, October 2-4, 2019",David Israel Bustos Bernal; Jorge Ignacio Chavoya Gama,"Debido a la relevancia turística de ciudad de Puerto Vallarta, Jalisco, México, el crecimiento urbano, ha propiciado un flujo migratorio intenso que surge dando alojamiento como un proceso direccionado a la verticalidad en la vivienda turística, mirando atributos arquitectónicos en la planeación urbana de la ciudad. En estos nuevos esquemas la vivienda turística juega un papel determinante en la reconfiguración territorial de la ciudad, toda vez que detona la tendencia a la verticalización promoviendo a su vez nuevos atributos arquitectónicos con la modificación del paisaje urbano, todo esto inserto hasta el momento en un modelo extractivo. Es por ello que en esta investigación me he planteado las siguientes preguntas: ¿Qué factores urbanísticos y arquitectónicos determinan la opción de la verticalidad en la vivienda turística en la ciudad de Puerto Vallarta, Jalisco? y ¿Qué procesos urbanos propicia la vivienda turística y como responde la tendencia a la verticalización en la ciudad de Puerto Vallarta, Jalisco?, con el objetivo de identificar y demostrar los factores arquitectónicos y urbanísticos que han propiciado la transformación urbana y que han influido en la tendencia de la",0.0,2442123fe91a55f9305fcbabffe5b0a489323f418a4,10.24912/jmstkik.v6i2.8710,FAKTOR – FAKTOR YANG MEMPENGARUHI KEPUTUSAN PEMBELIAN RUMAH TAPAK SEDERHANA DI PARUNG PANJANG,2022,"Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan",Charlescian Anggi; Nurahma Tresani; Liong Ju Tjung,"The high demand for housing caused by the increasing number of urban residents and the limited land to be used as residential areas is one of the reasons residential property prices in cities have soared. The need for housing is increasing but there is no provision in the city center, so that demand is being met outside Jakarta, such as in the areas around Bogor, Depok, Tangerang and Bekasi. The Parung Panjang area, Bogor is one area that is being eyed by many private developers in the development of affordable, simple-sided houses. Affordable land prices coupled with factor support from public transportation services in the form of mass electric rail train (KRL) transportation have made the development of simple housing on a site in Parung Panjang successfully absorbed by the market. The high interest from the public to own simple landed houses in the Parung Panjang area has made residential property developers flock to offer their respective property products to the general public. In their journey, these residential property developers certainly do not experience smooth sailing due to the rivalry factor from other property developers, therefore every property developer is requir",0.0,https://journal.untar.ac.id/index.php/jmistki/article/download/8710/13869245doi_10_5821_ace_v2i6_2432,10.5821/ace.v2i6.2432,La tipología en la vivienda como precedente sustentable,2008,"ACE: Architecture, City and Environment",Selenne Galeana Cruz,"El presente artículo intenta realizar una aportación para el análisis tipológico de la vivienda, en particular a la vivienda social. Esta intención se enmarca en el análisis de las características de la vivienda adecuada, que se inscribe como uno de los Objetivos del Milenio propuesto por la ONU, cuya evaluación y monitoreo ha sido responsabilidad del Programa UNHABITAT. Para su tratamiento se ha elegido un enfoque Pospositivista (los principales exponentes son Wilhelm Dilthey y Willian James), es decir experimental, cuantitativo, causal y comparativo de una realidad conocida en términos de probabilidad. Para abordarlo se propone un Instrumento de Análisis Gráfico Espacial-IAGE, partiendo para su construcción de la transformación espacial como categoría de análisis estrechamente vinculada con el referente sustentable físico-espacial a través del concepto de tipología. Por lo tanto este instrumento se estructura en tres dimensiones: formal, funcional y sociopolítica, las cuales estarán vinculadas a variables e indicadores desarrollados a partir de la composición de preceptos conceptuales y herramientas gráficas. Se deduce que dicha herramienta de limita a aspectos cuantitativos, sin",0.0,246223c13ae5f81b7730f7fd8e3e6fc333d865e7aab,10.2139/ssrn.3816289,Agglomeration and State Personal Income Taxes: Time to Apportion (With Critical Commentary on New Hampshire’s Complaint Against Massachusetts),2021,Social Science Research Network,Darien Shanske,"The Supreme Court is currently considering granting certiorari in New Hampshire v. Massachusetts. At issue is the State of New Hampshire’s (and its amici’s) claim that Massachusetts’s insistence on applying its income tax to residents of New Hampshire, who once commuted to work in Massachusetts but now work for the same businesses remotely in New Hampshire because of the COVID-19 pandemic, violates the Due Process Clause and dormant Commerce Clause. The claim is simple and seductive: if these New Hampshire residents barely leave their own homes, much less their state, how can it accord with due process for Massachusetts to tax them? 247 248In this short colloquium essay, I make the following points about the merits of this case. (I think the case should be dismissed on procedural grounds, but believe the merits are important and will eventually be reached on a similar fact pattern.) 249 2501. The plaintiffs are essentially arguing for physical presence as a predicate for individual income taxation. To the extent that the argument is that physical presence is currently required by the Due Process Clause or the dormant Commerce Clause, I argue that contention is wrong. 251 2522. I also argue tha",0.0,https://doi.org/10.2139/ssrn.381628925370344258ee4d364be24228830e16949563bd107b,10.18869/modares.ecopersia.4.1.1251,"Application of Nested Logitech Model for Ecosystem Services Valuation (Case study: Gavkhony Wetland, Isfahan Province, Iran)",2016,,Seyed Parviz Jalili Kamju; R. Khoshakhlagh; Yashar Shirinkhah; S. Samadi; G. Kiani,"Wetlands as a situ for the growth of native plants, as a habitat for certain species of fish and aquatic birds, and because of their potential economic, cultural and recreational services, are valuable heritage so their protection and conservation is very essential. Mostly due to the absence of wetlands services’ valuation, lack of special regulations, and lack of guarantee for these properties, resources and services of wetlands are not utilized appropriately, and destructed and evacuated in a free and unrestricted fashion, leading to inefficiency in use. The purpose of this study is the economic valuation of Gavkhony wetland ecosystem attributes, estimation of implicit price for attributes, impact assessment of socioeconomic variables such as age, marriage, indigenous, family size and education on willingness to pay (WTP), and analyzing welfare and compensation variation due to variation of hypothetical policy. The approach being used is choice experiment that is a subset of choice modeling procedure and stated preference method. Data were collected from six different choice experiments provided in the questionnaires, which were filled out by 500 randomly selected households in I",0.0,https://doi.org/10.18869/modares.ecopersia.4.1.1251254doi_10_20868_ade_2018_3783,10.20868/ade.2018.3783,Evaluación de la sostenibilidad en la rehabilitación energética de vivienda social en países mediterráneos = Evaluation of sustainability in the energy rehabilitation of social housing in Mediterranean countries,2018,Anales de Edificación,B Gimeno; J. Aranda; D. Zambrana; A. Conserva; P. López; F. Albiac,"Resumen En España, donde existen más de 18 millones de hogares según el último censo del Instituto Nacional de Estadística en 2011, alrededor del 8% de la población reside en viviendas de alquiler social. Del parque de viviendas español, más de la mitad de los edificios se construyeron antes de 1980 y alrededor del 35% entre 1981 y 2006, año en que fue implantado el Código Técnico de la Edificación. Asimismo, más del 80% de los certificados energéticos de edificios existentes registrados hasta julio de 2015, obtiene una calificación E o inferior en términos de emisiones de CO2. Para mejorar estos resultados, la Unión Europea tiene como objetivo alcanzar una tasa de rehabilitación de edificios privados del 2,5% anual, mejorando la eficiencia energética y ampliando la vida útil del parque edificatorio. Sin embargo, los CEEE únicamente representan parte de la etapa de uso, dejando atrás otras, como la de producción, cuyo impacto puede representar un cuarto de las emisiones de CO2 del edificio a lo largo de su ciclo de vida. Para desarrollar una rehabilitación óptima, se propone evaluar la sostenibilidad de los proyectos de rehabilitación incluyendo las etapas de producción, construcci",0.0,2550be84537630a2f778f79d2e1255a41d2775353e9,10.19184/jrsl.v8i2.52079,Distribusi Spasial Harga Lahan Berbasis Jarak Ke Pusat Kota (Studi Kasus di Yogyakarta),2024,Jurnal Rekayasa Sipil dan Lingkungan,Kusuma Aji Radiansyah; Dhany Aristawati; Ilma Aurarisa; Namira Paradhiba; Zha Zha Noor Zavitri,"Abstract 256The development of Yogyakarta city as a hub for economic, cultural, and tourism activities has led to changes in land use patterns and property values. Nol Kilometer Point in Malioboro symbolizes the city center's pivotal role in determining the spatial distribution of land prices, consistent with the theories of Von Thunen, which depict the relationship between distance and property value. This study aims to investigate the relationship between distance to the Nol Kilometer point as the urban center in Yogyakarta and land prices. Primary data was collected through field surveys using online interviews with property owners, supplemented by GPS applications to determine the geographical coordinates of land locations. Spatial analysis was conducted using ArcGIS 10.8 software to map land price contours and generate isochrone maps of land distances to the city center. Linear regression models were used to examine the relationship between distance from the city center and land prices based on land use categories. The results of the analysis show consistent patterns with theoretical expectations, where land values tend to decrease with increasing distance from the Nol Kilometer",0.0,https://doi.org/10.19184/jrsl.v8i2.52079257doi_10_20868_cn_2020_4477,10.20868/cn.2020.4477,Il problema dell’alloggio : la contribución de Adalberto Libera a la vivienda moderna en Italia durante la década de 1940 = Il problema dell’alloggio : Adalberto Libera’s contribution to modern housing in Italy during the 1940s,2020,Cuaderno de Notas,David Escudero,"ResumenEs relativamente poco conocida la componente teórica del trabajo que realizó Adalberto Libera como Director de la oficina técnica del programa estatal de vivienda INA-Casa, un cargo que desempeñó desde la aprobación del plan en 1949 hasta 1951. Este programa, el más ambicioso en Italia tras la guerra, terminaría construyendo más de 350.000 hogares durante sus 14 años de funcionamiento siguiendo, en buena medida, las directrices iniciales marcadas por Libera en un momento clave para la asimilación de los tipos de vivienda moderna en Italia. Aún menos conocido es el profuso trabajo sobre vivienda que Libera realizó durante la Segunda Guerra Mundial en su retiro en Trento, y que resultó esencial en su designación para ocupar tal cargo en el programa INA-Casa. Libera proyectó tipos y agrupaciones de vivienda según condicionantes no sólo espaciales sino también socio-económicos de los destinatarios. En ese sentido, el trabajo no fue solamente gráfico: volcó algunas reflexiones sobre la condición del habitar y el habitante. Este artículo profundiza en estos estudios sobre vivienda con el fin de revelar cómo influyeron en el carácter de la arquitectura promovida por el programa I",0.0,2580131bd0eac4be3a77c2999e35aba028f79de492c,10.1108/jes-03-2026-0291,Job commuting and home moving: an extended Pissarides-style spatial model,2026,Journal of Economic Studies,Gaetano Lisi,"259 260 This paper studies the effects of commuting and moving costs on job creation and job mobility within an extended Pissarides-style spatial search and matching model. The analysis connects three strands of literature: commuting and job search, commuting and home moving and the housing liquidity issue. The paper also examines how housing market frictions, commuting distance and relocation costs shape labor market outcomes and workers' spatial allocation across regions.261 262 263 264 The paper develops a theoretical search and matching model à la Pissarides with two regional labor markets and a housing market characterized by search frictions. Workers can commute or relocate when receiving outside job offers. The housing market equilibrium is modeled through the interaction between house prices and time-on-the-market. The framework is further extended to account for heterogeneous workers, heterogeneous housing quality and the effects of transport policies and housing externalities.265 266 267 268 The analysis shows that commuting costs increase reservation wages, reduce job creation and raise unemployment. Moreover, commuting costs distort the spatial allocation of workers across regions. Relocat",0.0,2690fe4f692b87dcb29c7e3f3d88b5653b772487926,10.2139/ssrn.3433765,Homelessness and Housing Market Condition in the United States,2019,Social Science Research Network,Praopan Pratoomchat,": The study estimated the relationship between the homelessness rates, the housing market factors, and the socio-economic factors in the country level, regional level, and the state level in the United States from 2007 to 2016. The results show that the housing price index, personal expenditure on housing utilities, rental vacancy rate, poverty rate, number of job loss and income inequality are significant determinants of the homelessness in the country level. For the regional level, the West had the highest homelessness rate intercept while the South West is the region with the lowest intercept. Housing price, the expenditure on housing utilities and poverty rate are the factors determining the homelessness rate in the regional level. When the study adds the fixed effects of fifty states to the model, the result shows that rental vacancy rate, number of job loss and the income inequality are three factors that can explain the change in homeless population number in the state level. To slow down the growing homelessness in the country level, the economic policy should be focusing at poverty reduction, healing people who suffered from job loss, and changing the tax policy to increas",0.0,http://rais.education/wp-content/uploads/2019/07/02-PP.pdf27003ce431f5186393a8baffca24805ff462dd689db,10.35473/jamastika.v5i1.4703,Prediksi Harga Rumah Menggunakan Algoritma Regresi Linier,2026,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",0.0,271doi_10_19044_esj_2022_v18n1p86,10.19044/esj.2022.v18n1p86,Análisis de la adquisición de vivienda a través de créditos hipotecarios con instrumentos de prorrateo de edad,2022,"European Scientific Journal, ESJ",L. Enrique Aranda-Guerrero; J. Manuel Lozano-de Poo,"Identificar áreas en la zona metropolitana de San Luis Potosí (ZMSLP) para la compra de vivienda usada susceptible a un crédito hipotecario, genera que los personas no tengan que adquirir alguna propiedad en zonas retiradas de su espacio de movimiento. El objetivo de este trabajo es detectar las áreas donde se han recuperado distintos tipos de edificaciones para uso habitacional por medio de un análisis de las valuaciones realizadas en los últimos meses, determinando áreas de oportunidad para la adquisición de viviendas susceptibles a reincorporarse al mercado inmobiliario. Este diagnóstico refleja la oportunidad de evaluar la posibilidad de comprar algún patrimonio mediante un producto bancario o crédito gubernamental. Estos proyectos reciben una intervención física que ayuda a disminuir su vida útil normal, mejorando sus condiciones de habitabilidad. Para esta investigación se revisaron 119 propiedades en un período de 6 meses, de las cuales solamente 35 presentaron la opción de aplicación de la tabla de prorrateo de edad con distintas características físicas y de ubicación. Esta tabla es un instrumento que se utiliza en los procesos de valuación profesional para mejorar los porc",0.0,2721bb0dbe96c9863d2f870e60a2e7d663dc8ecc865,10.15372/gipr20250615,ФАКТОРЫ ТЕРРИТОРИАЛЬНОЙ ДИФФЕРЕНЦИАЦИИ РЫНКА ЖИЛЬЯ В ИРКУТСКОЙ ГОРОДСКОЙ АГЛОМЕРАЦИИ,2025,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",0.0,27308a2a8a0c0b3b22794b186c3595ad2a3147dae5f,10.9734/sajsse/2020/v8i230206,Affordable Housing in the Wake of Global Pandemics: A Reality or a Mirage the Kenyan Perspective?,2020,,E. Kiganda; P. Shavulimo,"Aim: This was an investigative study on affordable housing in the wake of global pandemics: A reality or a mirage the Kenyan perspective? 22 % of Kenyans stay in towns and the inhabitants in these cities continue to grow at the rate of 4.2 % annually. This growth rate has outstripped the supply of housing units built. For instance, Nairobi needs a minimum of 120,000 new houses per annually to satisfy the demand but a paltry 35,000 units are constructed annually. The excess demand is likely to continue pushing the housing prices beyond the reach of many Kenyans. Studies conducted in Kenya on housing prices focused on non-macroeconomic determinants and more importantly none of the studies globally envisaged how global pandemics can influence housing prices. Therefore, the influence of global pandemics like Corona Virus Disease (COVID19) and macroeconomic factors on housing prices in Kenya remains unknown. Study Design: Correlational research design. Original Research Article Kiganda and Shavulimo; SAJSSE, 8(2): 14-23, 2020; Article no.SAJSSE.60816 15 Methodology: The study employed unrestricted Vector Autoregressive analysis involving quarterly time series from quarter 1 of 2014 to q",0.0,https://doi.org/10.9734/sajsse/2020/v8i23020627419df264cd86957e8b1d3d02e2a84274c27d62d3c,10.37634/efp.2024.5.5,Economic and mathematical model of residential property prices in Dnipro city,2024,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.27524 classifiers were created to determine quantitative signs of qualitative characteristics of housing, in which class numbers start from 1.276Based on these data, k-means clustering was carried out, as a result, 6 different classes were formed.277An economic-mat",0.0,http://dx.doi.org/10.37634/efp.2024.5.5278doi_10_5194_egusphere_egu21_13833,10.5194/egusphere-egu21-13833,Hydro-meteorological hazard analysis for new settlements framed in the Colombian peace process,2021,,Edier Vicente Aristizábal Giraldo; ,"&lt;p&gt;Colombia is a country with a recent history of an armed conflict from 1960. In 2012, a negotiation process started between the Colombian National Government and the largest left-wing guerrilla group (FARC - Fuerzas Armadas Revolucionarias de Colombia in Spanish). Finally, in 2016 a final peace agreement was signed, where several compromises were taken by both sides. For starting, one of the most important element of the agreement was to define concentration areas into the Colombia territory, where the entire FARC members should be located transitory, and posteriorly those areas must change to permanent settlements following the current regulation related to land use planning in Colombia. This study shows the hazard, vulnerability and risk assessment for 5 concentration areas, which were prioritized, of 24 total areas established. The multi-hazard assessment was analysed from a regional (10-m resolution) and detailed (0.5-m resolution) approach.&lt;/p&gt;&lt;p&gt;For the regional approach, landslide susceptibility was assessed through analytic hierarchy process and weight of evidence methodologies compared to logistic regression and landslide hazard ",0.0,2791de0404c3a1dbbbd77fa539968743a7afaa9ea15,10.70382/ajbegr.v10i4.048,Effects of Environmental Features and Qualities on Rental Values of Residential Properties in Kaduna Metropolis,2025,Journal of Built Environment and Geological Research,Mamman Matthew; M. S. Mohammad; Bala Ishiyaku; Nneoma Violet Iroaganachi,"Concerns have been expressed over the years regarding the negative impact of environmental externalities on property values. Environmental quality has potentially significant effects on environments and their populations, thus it is important to understand its drivers and how to measure them. The aim of this study is to develop a model for measuring the effects of environmental quality on rental values of residential properties in Kaduna Metropolis with a view to establish the most sustainable attributes that significantly contribute to change in rental values in order to guide in making the best real estate investment decisions. The study adopted quantitative research approaches and survey strategy because of the nature of the research questions with a sample size of 320. Structured questionnaires were distributed to the respondents. The data was analysed using mean ranking, frequency distribution tables and multiple regression analysis. The result revealed that, the average (Mean) rent paid in different types of rented residential properties in Ungwan Rimi, Kaduna metropolis, Nigeria, stands for flat ₦436,093, bungalow ₦547,043 and tenement ₦116,083. Meanwhile, the study found th",0.0,https://africanscholarpub.com/ajbegr/article/download/1058/101728009c0c7c9b4ed3c1d91e9b84f471597179eb8c7a8,10.3390/jrfm19040261,House Price Determinants: Evidence from Bulgaria as a New Eurozone Member State,2026,Journal of Risk and Financial Management,Andrey Zahariev; G. Zaharieva; L. Shaulska; M. Oryekhov,"This study examines the relationship between house prices and the factors driving their growth during the transition from a long-standing currency board regime to Eurozone membership. The main objective is to identify and quantify the key factors explaining the variation in house price growth in Bulgaria under conditions of prolonged currency convergence. The study applies a set of econometric techniques, including stationarity tests (ADF and KPSS), diagnostic checks for normality, serial correlation and heteroscedasticity, and robustness checks. The study is based on 40 quarterly observations covering the period 2015Q4–2025Q3 and 48 selected predictors of the General house price index. The final ARIMAX(0,2,1) model is estimated using second-differenced data. The model includes a first-order moving average component and three exogenous regressors: the owner-occupiers’ housing expenditures, the actual rentals for housing in Bulgaria and the homeowners’ utility expenses. The model explains 87% of the variation in house price acceleration, with a comparatively low mean squared error. The diagnostic analysis confirms model adequacy. The three exogenous regressors are statistically sign",0.0,https://www.mdpi.com/1911-8074/19/4/261/pdf?version=17752071072811518f980a813e5ab1c1a4f9e577034017e9005f5,10.36818/2071-4653-2026-2-4,Institutional and spatial dimensions of housing affordability in regional centers of Ukraine,2026,Socio-Economic Problems of the Modern Period of Ukraine,Natalya M. Lysyak; O. Karyy,"The article examines the institutional and spatial determinants of housing affordability in regional capital cities of Ukraine. It argues that housing affordability cannot be adequately explained by traditional market factors alone and requires consideration of the spatial and institutional nature of land markets. The study is grounded in the theory of land rent and introduces the concept of “accessibility rent” as a mechanism through which spatial advantages and regulatory constraints are capitalized into housing prices. Methodologically, the research employs the Median Multiple indicator, which measures the ratio of median housing prices to median household income, allowing for a more accurate assessment of affordability. The empirical analysis is based on panel data for Ukrainian regional centers over the period 2015–2025. Correlation analysis to applied to identify the influence of price, income, spatial, and institutional factors. The findings indicate that housing affordability in the analyzed cities ranges between 5 and 11, corresponding to “severely” and “critically unaffordable” categories. The strongest relationship is observed between affordability and housing prices, wh",0.0,2821c61b9c3d899e9189c440530688c2d26de672f95,10.31891/2307-5732-2022-313-5-265-273,APPLICATION OF MACHINE LEARNING MODELS FOR PREDICTING PRICES ON THE REAL ESTATE MARKET,2022,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 th",0.0,http://journals.khnu.km.ua/vestnik/wp-content/uploads/2022/12/vknu-ts-2022-n5313-265-273.pdf2831f6eb6115a8640c03c447d89b257ffb2531489c0,10.1136/injuryprev-2020-savir.8,"38 School shootings, neighborhood characteristics, and google trends",2020,Oral Presentations,B. Kalesan,"Statement of Purpose The neighborhood socio-economic factors associated with school-shootings and the differences in the public interest are unknown. We characterized the relationship between median home price, poverty rate, black population, homicide rate and population size with the incidence of school shootings at the zip code level, and additionally intent of the shooter and number of victim fatalities with google trends of school shootings. Methods We performed a cross-sectional study between 01/01/2013–12/31/2018 using school-shooting data from Everytown database (K-12 and colleges/Universities) and K12 (only K-12). Median house price, poverty rate, black population, and population size was obtained from American Community Survey, homicide rate from Center for Disease Control and Prevention, and intent of shooting and number of fatal victims were from the school shooting databases. Google trends for each day for ‘school’ and ‘shooting’ was obtained for six years and standardized using year weights created as total proportion of total hits in months per total hits during six years. Results There were 405 and 295 school shooting incidents in Everytown and K12 datasets respectiv",0.0,https://injuryprevention.bmj.com/content/injuryprev/26/Suppl_1/A3.3.full.pdf28417f05d0b267a6b20ec060aa8f7b13608df8dbf4b,10.55041/ijsrem32966,Analyzing the Relationship between Real Estate Investments and Portfolio Diversification,2024,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,Kunika Saral,"Real estate has long been considered an attractive investment option for individuals and institutions seeking to build wealth and diversify their portfolios. Unlike traditional investment vehicles such as stocks and bonds, real estate offers unique characteristics that can potentially enhance returns and mitigate risk. This analysis aims to explore the role of real estate investments in portfolio diversification and assess their potential impact on overall portfolio performance. Portfolio diversification is a fundamental principle in investment management, as it helps to spread risk across different asset classes and mitigate the impact of market fluctuations on a portfolio's overall value. By including assets with low or negative correlations, investors can reduce the volatility of their portfolios and potentially achieve higher risk-adjusted returns. Real estate investments, including direct property ownership, real estate investment trusts (REITs), and other real estate-related securities, have traditionally exhibited low correlations with other asset classes, such as equities and bonds. This low correlation can be attributed to the unique characteristics of real estate, includi",0.0,https://ijsrem.com/download/analyzing-the-relationship-between-real-estate-investments-and-portfolio-diversification/?wpdmdl=32723&refresh=6944cbe8a5eaf17661163282851f9ec93ea62e6db567f4e34f08026cd3dfa37e13,10.25125/ijoer-aug-2026-1,House Price Prediction in Nanjing Based on Machine Learning and SHAP Interpretability,2026,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 exhibi",0.0,https://ijoer.com/assets/articles_menuscripts/file/IJOER-AUG-2026-1.pdf28611577fd6c21bb7978a5a227de1042ddc5425040e,10.22214/ijraset.2025.67094,Effects of Educational Buildings in Residential Areas on the Surroundings,2025,International Journal for Research in Applied Science and Engineering Technology,P. G,"The presence of educational buildings in residential areas significantly impacts the surrounding environment,287impacting social, infrastructural, and environmental aspects. This research explores the effects of educational institutions on288residential neighbourhoods by examining both the benefits and challenges associated with their integration. Educational289buildings contribute positively to urban communities by bringing in a sense of security, enhancing property values, and290encouraging economic activity through local businesses that cater to students and faculty [1], [9]. Additionally, they provide291convenience for teachers, students, and staff in terms of proximity and accessibility to their houses, reducing travel time and292promoting community engagement [3].293However, several challenges arise due to the presence of educational buildings amongst residential areas. Increased traffic294congestion, noise pollution, and parking shortages during peak hours and school events often lead to conflicts between295institutions and local residents [10]. Furthermore, the lack of basic amenities such as bookstores, print shops, and cafes near296educational buildings adds inconvenience for students and st",0.0,https://doi.org/10.22214/ijraset.2025.67094297211fd7653280c12c2de6776972fbfe9e01089c03,10.70382/tijbees.v13i4.096,ARCHITECTURAL AESTHETICS AND INDUSTRIAL DESIGN AS DETERMINANTS OF RESIDENTIAL PROPERTY VALUE IN NIGERIA,2026,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 prop",0.0,https://taapublications.com/tijbees/article/download/1264/1234298136d561ad282f055652ea2ed643727746b36375d,10.37075/ea.2025.1.11,Impact of Housing Prices on the Standard of Living of the Population in the Provinces of the Republic of Bulgaria,2025,Economic Alternatives,,"The rapidly rising housing prices over the past few years in Bulgaria have raised concerns about the situation with the standard of living of large groups of the country’s population. Income differentiation and the purchase of housing by foreign citizens or by Bulgarians living abroad creates the illusion that housing provision levels are high and that there is enough housing space for all those in need. Basically, however, a large part of the homes are uninhabitable because they tend to be located in settlements with a declining population or were purchased for the purpose of investment and/or as a measure of protection against inflation. This study establishes the existence of a high negative correlation coefficient between housing prices and the population’s standard of living, when measured on the basis of nine objective indicators, united in the form of complex criteria. Two statistical methods were experimented to get to a general estimate of the standard of living based on the selected indicators, and eventually the better one was used. Regression analysis was used to assess the direction, shape and strength of the relationship between the price levels and the standard of li",0.0,https://doi.org/10.37075/ea.2025.1.112991ff8a7d4cbd47c5dffe0f6663406fdafa34dc301,10.53920/es-2026-3-9,БАГАТОКРИТЕРІАЛЬНЕ ОЦІНЮВАННЯ УПРАВЛІНСЬКИХ РІШЕНЬ ЗА ЧАСОВИМИ ТА РЕСУРСНИМИ ПАРАМЕТРАМИ,2026,Economic Synergy,Ю. А. Чуприна; Дмитро Бойчук,"Ukrainian development companies operate under expensive capital and limited housing demand; therefore, decisions on organising an investment and construction project determine not only its duration but also its economic result, while construction acceleration requires additional expenditure and financing. The purpose of the study is to develop and test a model for multi-criteria evaluation of a development company’s management decisions by time and resource parameters. The methodology combines monthly cash-flow modelling, probabilistic assessment of compliance with the directive deadline, the analytic hierarchy process for criteria weighting, ranking by similarity to the ideal solution, Pareto dominance and sensitivity analyses; input data rely on normative construction duration, indirect housing construction cost, Kyiv primary market statistics and lending rates of the National Bank of Ukraine. The results for five organisational variants of a 16-storey residential building project show that maximum acceleration is inexpedient: compared with the rational variant, it reduces net present value by 7.78 million hryvnias and increases capital requirements by 17.7 million hryvnias. The ",0.0,https://es.istu.edu.ua/index.php/EconomicSynergy/article/download/425/3213001e364efd781e0c4d681fc4413377d8876f25cf34,10.1145/3773966.3779390,Adapting to Evolving Data: Test-Time Expert Aggregation for Imbalanced Tabular Regression,2026,Web Search and Data Mining,Yung-Chien Wang; Kuang-Da Wang; Wei-Yao Wang; Wen-Chih Peng,"Many critical web applications, from e-commerce price prediction to user engagement forecasting, rely on regression models trained on tabular data. These models often face a dual challenge: the inherent imbalance in continuous target values and, more critically, the unpredictable distribution shifts that occur when the model is deployed online. While data imbalance in classification is well-studied, its intersection with regression tasks in dynamic, real-world settings is underexplored. Existing methods for imbalanced regression often assume that the test data distribution is known and stable, an assumption that rarely holds true for live web systems and can lead to significant performance degradation. To address this gap, we propose a novel framework featuring two key innovations: (i) a Region-Aware Mixture of Experts that leverages a Gaussian Mixture Model to identify distinct data sub-populations. This allows us to synthesize targeted training data and train specialized experts, each tailored to a specific data region. (ii) a Test-Time Self-Supervised Expert Aggregation mechanism. This is the core of our adaptation strategy, dynamically adjusting the weights of each expert based",0.0,https://doi.org/10.1145/3773966.37793903011f1bb58c9730f5443413b665e8af6f2a03aafb76,10.1108/ijhma-07-2025-0152,Assessing the influence of cemetery proximity to rental values of residential properties in Calabar urban,2025,International Journal of Housing Markets and Analysis,Roseline Okoro; Ekerette Thomas,"302 303 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.304 305 306 307 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 sign",0.0,3081793ab881b5c9b30ef5844197595ca24e88d3035,10.33093/jiwe.2026.5.2.7,Machine Learning-based Prediction of House Sale Prices in Hulu Langat,2026,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 inter",0.0,https://journals.mmupress.com/index.php/jiwe/article/download/1991/13633091f81643d43ef2b727ac87089db9c575273f86dac,10.3389/fpls.2026.1926163,Urban land-use filtering favors herbaceous spontaneous flora and reshapes alien-native composition in a semi-humid inland city,2026,Frontiers in Plant Science,Yi-Chao Wang; Qian Wang; Xin-Yi Zhang; Xin-Na Li; M. M. Nizamani; Qing-Hui Sun,"Urban spontaneous plants record how vegetation responds to land use, disturbance, management, and propagule pressure, but their composition and assembly remain insufficiently studied in semi-humid inland cities. We surveyed 151 urban functional units (UFUs) in Jinan, China, combining field vegetation inventories, high-resolution land-cover interpretation, socioeconomic and management variables, and count-data robustness models to test how urban habitat filters structure spontaneous plant richness and composition. Across all UFUs, 379 vascular plant species were recorded, including 140 spontaneous species. The spontaneous flora was almost entirely herbaceous (138 species, 98.6%), with only two woody species occurring as seedlings and no mature spontaneous trees recorded. Alien species accounted for 74 spontaneous species (52.9%), slightly exceeding native species. Spontaneous richness differed among UFUs, with residential districts supporting the highest mean richness and transportation areas supporting the lowest. Exploratory linear regression identified positive associations of richness with residential land use, fertilization frequency, housing price, and UFU area. After accounti",0.0,https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1926163/pdf3100b725aadd68cbec974b1e40c54733f058bd2bc02,10.33137/utjph.v2i2.36812,A spatio-temporal modelling of Covid-19 infections in Toronto’s neighbourhoods,2021,University of Toronto Journal of Public Health,Hang-Fu Sze,"Introduction & Objective: Besides age and sex as established risk factors of COVID-19 infection, social factor is found to be a determinant, with people of lower socioeconomic status suffer disproportionately from the disease. The city of Toronto has one of the highest COVID-19 infection rates in Canada. This analysis aims to explore the socioeconomic correlates associated with COVID-19 infection and the temporal trends among different age groups in Toronto using geospatial modeling. 311Methods: A Bayesian spatio-temporal analysis was conducted using public COVID-19 cases data for Toronto. The case data were modeled using the Besag-York-Mollie (BYM) model, implemented in R-INLA. The model adjusted for age, sex, neighbourhood-level socioeconomic factors, crime rates, and population density. Random effects were included to account for neighbourhood-level variation and for spatial autocorrelation. Temporal trends of COVID-19 cases were modelled using second-order random walks to allow non-parametric estimations. 312Results: The model estimates showed that men are at higher risk of COVID-19 infection. Among neighbourhood factors, higher home prices, education level, and population density ",0.0,https://doi.org/10.33137/utjph.v2i2.368123130da98562261bd1b27b7edb9e5b8bf6e905797dd7,10.70382/ajbegr.v12i4.059,"Impact and Enforcement Efficacy of Environmental Regulations on Property Values: A Study of Selected South East States, Nigeria",2026,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, th",0.0,https://africanscholarpub.com/ajbegr/article/download/1259/1241314194c32966936219ef7d227a03d9506db0f78f073,10.18775/ijied.1849-7551-7020.2015.95.2003,"Sentiment Analysis, Social Media and Urban Economics: The Case of Singaporean HDB and Covid-19",2023,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-",0.0,https://researchleap.com/wp-content/uploads/2024/02/03_Sentiment-Analysis-Social-Media-and-Urban-Economics.pdf3150e56704ae78a676a6bd7c0d5c0c0128ad3d29937,10.59490/abe.2018.3.3569,Architecture and the Built Environment: Introduction,2018,Architecture and the Built Environment,A. Teye,"The rate of home-ownership across Europe and in many countries has increased significantly in recent decades. This is partly because most governments have promoted home-ownership as part of an asset-based welfare system with the notion that home-ownership will generate wealth for households through the accumulation of housing equity. Changes in house prices play an important role in the generation of the housing equity and the wealth inherent in home-ownership. In general, house prices change in cycles of upward and downward trends. Each of these cycles may be driven by different sets of fundamental determinants and by the prevailing conditions in the wider economy. Over the long term, home-owners usually accumulate significant housing equity, yielding welfare benefits. However, even periods of brief house price decline can erode the value of housing equity accrued over several years. Following the 2007-08 Global Financial Crisis (GFC), for example, the severe decline in house prices caused many recent home-owners to run into negative equity. Figures from Statistics Netherlands show that following the GFC, in the Netherlands alone the total wealth in residential properties declined",0.0,https://journals.open.tudelft.nl/abe/article/download/3569/37003160025fd8e34851c8e0a1489aef5319153aa24cb61,10.32479/ijefi.24244,Determinants of Non-Performing Loans in Cambodia,2026,International Journal of Economics and Financial Issues,Sheng-Chen Ke; Sounman Hong,"This study investigates the determinants of non-performing loans (NPLs) in Cambodia’s banking sector, with particular focus on the impact of lending rates within a highly dollarized financial system. Cambodia’s banking industry has experienced rapid credit expansion over the past decade; however, the post-pandemic period has been accompanied by a significant rise in NPLs, raising concerns about financial stability and credit risk management. Using annual data from 2020 to 2025, this study employs an Ordinary Least Squares (OLS) regression model to examine the effects of lending rates, house prices, GDP growth, and loan restructuring policies on NPL dynamics. The empirical results indicate that lending rates have a positive and statistically significant impact on NPLs, suggesting that borrowing costs are the primary driver of credit risk in Cambodia. GDP growth is negatively associated with NPLs, implying that favorable macroeconomic conditions enhance borrowers’ repayment capacity and reduce default risk. House prices exhibit a negative but statistically insignificant relationship with NPLs, indicating that collateral value plays a relatively limited role in explaining credit risk.",0.0,https://econjournals.com/index.php/ijefi/article/download/24244/1027931715b9a79f237f3983b2edab912209f4a3a9964e3f,10.1136/bmjopen-2026-116230,Spatial population study on the association among socio-economic indicators and oral health in preschool children in Buenos Aires,2026,BMJ Open,A. Squassi; Eliana Belén González; Fiorella Ventura; Rocio Lazzati; Glenda Rossi; Pablo Salgado; A. Cappai; M. Dettori; G. Campus,"Objectives Dental caries is the most prevalent chronic condition among Argentine children, with distribution and severity strongly shaped by social and territorial inequalities. This study evaluated caries treatment needs and their spatial and socio-economic associations among preschool children in Buenos Aires. Design, setting, participants An ecological population study was conducted among 54 337 6-year-old children attending public schools in Buenos Aires. Caries severity was measured using the Caries Treatment Needs Index (CTNI) by calibrated examiners. Primary and secondary outcome measures Socio-economic indicators included individual health coverage, neighbourhood housing prices, distance to the nearest primary health centre, population density and the proportion of households with unsatisfied basic needs. Analyses comprised descriptive statistics, multivariate regression, ORs and spatial autoregressive models. Results Overall, 67.9% of children had treatment needs (CTNI >2) and 17.5% had high needs (CTNI >10), with significant heterogeneity across municipalities (p<0.01). Public health coverage showed a strong gradient with increasing caries severity (OR: 1.77–3.68; p<0.01)",0.0,https://doi.org/10.1136/bmjopen-2026-116230318218228fbccbd2c1f178ab4b57770aefd836ffea3,10.2139/ssrn.6405858,Smart Prediction of House Cost Price Using AI and Machine Learning,2026,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 9",0.0,https://doi.org/10.2139/ssrn.64058583195fef177c286eb2bf6a1a19dd0c901ebd5cda33f7,10.26565/2311-2379-2020-99-12,FORECASTING PRICES IN THE RENTAL HOUSING MARKET WITH MACHINE LEARNING METHODS,2020,Bulletin of V. N. Karazin Kharkiv National University Economic Series,,"The study of pricing factors in the market of the short-term rental has been done. Airbnb was chosen as the object of the study; it is a platform for accommodation, search, and rental around the world. At the beginning of 2021, the company offers 7 million homes from more than 220 countries. The Data Science methods play a significant role in the company's success. One of the key algorithms of the company is the pricing algorithm. Using the ""Price Recommendations"" feature, the homeowner can analyze which dates are most likely to be booked at the current price and which are not, it helps form a favorable offer. The system calculates the recommended cost of housing based on hundreds of parameters, some of which are easy to recognize, but there are less obvious factors that can also affect demand. The paper proposes an algorithm for identifying implicit pricing factors in the short-term rental market using machine learning methods, which includes: 1) data mining and data preparation; 2) building and analysis of linear regression models; 3) building and analysis of nonlinear regression models. The study was based on ads from the Airbnb site in Washington and New York using scripts deve",0.0,https://doi.org/10.26565/2311-2379-2020-99-123201faff2c2b8602f68af6b51ca55a52f9babf84aa6,10.2991/assehr.k.211209.132,Effect of Rural Social Pension Insurance on Total Retail Sales of Consumer Goods,2022,Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021),Xiao-Yan Huang; Zhi-Yi Ren; Yi-Lin Gao,"Based on data of 31 provinces of China in 2019 collected from the National Bureau of Statistics, this paper explores the impact of the number of urban employees' endowment insurance on the total retail sales of social consumer goods in different provinces in China. Taking the population, per capita disposable income, per capita life expectancy and regional per capita GDP of each province as the variables, this paper establishes regression models to do the empirical research. The results show that: (1) The increase of the number of urban employees' old-age insurance has a significant positive impact on residents' consumption. The wider the coverage of old-age insurance, the more it can enhance the consumption confidence of the residents. (2) Due to the economic development, the level of urbanization has been greatly improved, leading to an increased birth rate and total population, and thus consumption has been driven. Therefore, the increase of the total population has a significant positive effect on the increase of the total retail sales of social consumer goods. (3) The increase of per capita disposable income has insignificant impact on the increase of total retail sales of soc",0.0,https://www.atlantis-press.com/article/125965926.pdf3210e16ddb99c7f2a5b95ab6c2bc4993147dc13e677,10.48175/ijarsct-15953,Housing Price Prediction using Machine Learning Technique,2024,"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 orde",0.0,https://doi.org/10.48175/ijarsct-159533221746a8d17e2935c2e8601af098516ecfe941ddc3,10.1093/acrefore/9780190625979.013.650,School Choice and Accountability,2021,Oxford Research Encyclopedia of Economics and Finance,E. Greaves; S. Burgess,"School choice and accountability are both mechanisms initially designed to improve standards of education in publicly provided schools, although they have been introduced worldwide with alternative motivations such as to promote equality of access to “good” schools. Economists were active in the initial design of school choice and accountability systems, and continue to advise and provide evidence to school authorities to improve the functioning of the “quasi-market.”323 School choice, defined broadly, is any system in which parents’ preferences over schools are an input to their child’s allocation to school. Milton Friedman initially hypothesized that school choice would increase the diversity of education providers and improve schools’ productivity through competition. As in the healthcare sector and other public services, “quasi-markets” can respond to choice and competition by improving standards to attract consumers. Theoretical and empirical work have interrogated this prediction and provided conditions for this prediction to hold. Another reason is to promote equality of access to “good” schools and therefore improve social mobility. Rather than school places being rationed th",0.0,3240adc44457443e82a936511a258c194ee26bf4518,10.1515/jem-2022-0017,Identifying Common and Idiosyncratic Explosive Behaviors in the Large Dimensional Factor Model with an Application to U.S. State-Level House Prices,2023,Journal of Econometric Methods,Tetsushi Horie; Yohei Yamamoto,"Abstract This study applies the date-stamping methodologies for explosive behaviors proposed in the seminal work of Phillips, P. C. B., and J. Yu. (2011. “Dating the Timeline of Financial Bubbles during the Subprime Crisis.” Quantitative Economics 2 (3): 455–91), Phillips, P. C. B., S. Shi, and J. Yu. (2015a. “Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500.” International Economic Review 56 (4): 1043–78), and Phillips, P. C. B., S. Shi, and J. Yu. (2015b. “Testing for Multiple Bubbles: Limit Theory of Real Time Detectors.” International Economic Review 56 (4): 1079–134) to a large dimensional factor model. To this end, we compare two methods of identifying common and idiosyncratic components: the Panel Analysis of Nonstationarity in Idiosyncratic and Common Components (PANIC) method by Bai, J., and S. Ng. (2004. “A Panic Attack on Unit Roots and Cointegration.” Econometrica 72 (4): 1127–77) and the Cross-Sectional regression (CS) method by Yamamoto, Y., and T. Horie. (2022. “A Cross-Sectional Method for Right-Tailed PANIC Tests under a Moderately Local to Unity Framework.” Econometric Theory (forthcoming)). We show that, when the explosi",0.0,https://doi.org/10.1515/jem-2022-0017325197fa6ad93e5d180f0576979a40e1bc4b05eea69,10.3390/su17198665,Sustainable Housing Market Responses to Landslide Hazards: A Three-Stage Hierarchical Linear Analysis of Urban Scale and Temporal Dynamics,2025,Sustainability,Seungil Yum; J. Kim; Ho Gul Kim,"This study hypothesizes that the impacts of landslides on housing prices are not uniform but instead vary depending on their spatial proximity to hazard zones, as well as on neighborhood, urban, and temporal characteristics of each city. To test this hypothesis, we analyze APT price responses to landslides across three South Korean cities with distinct urban characteristics: Seoul (capital city), Busan (metropolitan city), and Gunsan (medium-sized local city). Using 120 three-stage hierarchical linear regression (HLR) models, the analysis incorporates housing characteristics, neighborhood attributes, and urban–temporal factors to capture multilevel variations in price dynamics. The results reveal distinct spatial and temporal patterns. At the national level, immediate post-event changes are not uniformly negative: within 250 m of landslide zones, prices increase by 0.8%, while 500-m and 750-m groups rise by 0.5% and 1.9%, respectively, and only the 1000-m group declines by 0.9%. However, in the following year, the 250-m and 500-m groups experience notable declines before showing partial recovery in the second year. City-specific trajectories further underscore regional heterogeneit",0.0,https://doi.org/10.3390/su171986653260479a7f9dce822da6952d90dd415cf07722fcc05,10.1080/21606544.2026.2625954,The effect of green space views and home elevation on assessed home values: evidence from a homogenous village,2026,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 a",0.0,327205ca6dea68f15d631101dfb62b6b56d74b86be8,10.18001/trs.7.6.57,Strategies of Residential Area Regeneration and Urban Development in China from the Perspective of Mismatch between Population Mobility and Land Supply: Take Shenzhen as Example,2021,Tobacco Regulatory Science,Ling Zhang; Yang Zhao; Ying-Lian Deng; Li-Ying Zhang; Yan-Liang Yu,"Objectives: After the global financial crisis in 2008, the housing price in cities of China turned from comprehensive prosperity to regional differentiation, and the phenomenon of coexistence of “high housing price” in some cities and “high inventory”328 in some other cities appeared. The problem of mismatch of land resources and population mobility in China has generated superimposed effect on the high housing price and high inventory in different areas, and the outbreak of COVID-19 in the world has also brought new challenges to the urban329 development modes; thus, how to develop mega-cities to guarantee the residents’ welfare has become an important academic issue and practical issue. In this paper, with Shenzhen, the most representative city in China, as an example, typical characteristics of real estate market in cities330 are analyzed, and the methods such as field investigation, questionnaire survey and behavior observation are adopted to analyze the conflicts between the spatial environment of the old residential areas and the demands of residents from the perspective of man-land mismatch. On the basis of331 expounding the corresponding relationship between the residents’ actual d",0.0,https://doi.org/10.18001/trs.7.6.57332227a0c9a9887c8a998a5ac0a6f554f967e2c3347,10.21831/jiety.v3i1.1262,Information System Prediction of Room for Rent Price in Yogyakarta Region Based on Website Using Linear Regression Algorithm,2025,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 us",0.0,https://journal.uny.ac.id/publications/jiety/article/download/1262/3903331b966ba824b734e331501c2e9a87fb8606deb07c,10.38124/ijisrt/25may475,Residential Property Price Forecasting with a Machine Learning Approach,2025,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 that334impact the dynamic and complicated housing market. For investors, buyers, sellers, and legislators to make wise choices,335accurate home price forecasting is essential. Conventional assessment techniques mostly rely on manual value, which is336frequently biased and inconsistent. By revealing hidden patterns in vast and intricate datasets, machine learning has337become a potent tool for modeling and predicting real estate prices in recent years. Using a variety of property-related338characteristics and historical sales data, this study suggests a strong machine learning framework for predicting home339values. Numerous factors are included in the model, including location, square footage, number of bedrooms and baths,340property age, ease of access to amenities, and neighborhood data. To find the best model, a number of techniques are341investigated and contrasted, such as Linear Regression, Decision Trees, Random Forests, and Gradient Boosting.342Performance optimization involves several crucial phases, including feature selection, data preprocessing, and343hyperparameter adjustment. Model ",0.0,https://www.ijisrt.com/assets/upload/files/IJISRT25MAY475.pdf34405c343801567ee898109cc58fa52c661d2bbc9b3,10.59139/ws.2026.06.2,The effect of anchoring transaction prices to offer prices in the housing market,2026,Wiadomości Statystyczne. The Polish Statistician,S. Kokot; Mariusz Doszyń,"Repeated attempts in scientific research to explain housing prices formation using socio-economic factors have not yielded satisfactory results. Traditional approach to explaining market phenomena has been increasingly criticised, while theories derived from behavioural economics have been gaining popularity. In particular, there have been attempts to explain certain market phenomena with the help of factors originating in the human psyche.345The aim of the study presented in the article is to show that housing price levels can be subject to the anchoring effect. This phenomenon consists in price levels in the property market being shaped not only by socio-economic factors, but also as a result of accepting certain price levels by market participants, who become accustomed to them.346In this study, we examine the influence of objective socio-economic factors and an anchor variable (here: the average offer prices of apartments) on the average prices of apartments in 17 cities in Poland. We employ the Hellwig and the backward stepwise regression methods. The study covers the period from 2010 to 2022. The data were drawn from the residential property price database of the National Bank of",0.0,http://ws.stat.gov.pl/WS/2026/6/gus_ws_2026_06_sebastian_kokot_mariusz_doszyn_the_effect_of_anchoring_transaction_prices_to_offer.pdf3470fcd659d90b60d586d8606131bf954c141dd4a24,10.4108/eai.4-11-2020.2304567,The Determinants of Home Mortgage Default Probability: The Effect of Loan and Borrower’s Characteristics,2021,"Proceedings of the First International Conference on Economics, Business and Social Humanities, ICONEBS 2020, November 4-5, 2020, Madiun, Indonesia",Y. Rachmansyah; A. Ra; H. Harijono; R. Prabowo,". Housing is one of the individuals' basic needs besides food and clothing, and house demand will increase continuously to respond to the increasing population. Consequently, the gaps between housing needs and availability (backlog) in Indonesia remains high. However, home mortgage default probability is still a crucial issue for banks. Investigating the factors that explain home mortgage default probability is important for banks to mitigate the credit default risk. Among these numerous factors, loan and borrowers' characteristics likely play significant roles in determining home mortgage probability. Hence, this study seeks to analyze the effects of credit and borrowers' characteristics on home mortgage default probability. One hundred and thirty-three mortgage customers of the Semarang Branch Office of Bank X are selected as the research sample through the purposive sampling method. Because the dependent variable has discrete values representing different loan qualities (from liquid to doubtful), the data is then analyzed using the ordered probit regression analysis. The results show that loan to value (LTV) is the credit characteristic that affects home mortgage default probabi",0.0,348doi_10_17271_23188472149120266199,10.17271/23188472149120266199,Uma revisão das tecnologias habilitadoras aplicadas à precificação de imóveis A Review of Enabling Technologies Applied to Real Estate Pricing Una Revisión de las Tecnologías Habilitadoras Aplicadas a la Valoración de Inmuebles,2026,Revista Nacional de Gerenciamento de Cidades,Larissa Kelly da Silva França Barbosa; Emilia Rahnemay Kohlman Rabbani; Eliane Maria Gorga Lago; Felipe Mendes da Cruz,"Objective – To understand how enabling technologies have been applied to real estate pricing through a systematic literature review, mapping methods, applications, and trends in real estate appraisal engineering. Methodology – A Systematic Literature Review (SLR) was conducted following the PRISMA protocol. Searches were carried out in relevant databases such as Science Direct, Scopus, and Web of Science, using a search string with Boolean operators. A total of 55 articles published between 2014 and 2024 were analyzed. Originality/Relevance – This study systematizes scientific articles published over the last ten years on the use of emerging technologies in real estate appraisal, highlighting the contrast between traditional methods and new approaches based on artificial intelligence. Results – Most studies still rely on traditional valuation methods, but there is a growing use of enabling technologies such as machine learning and geoprocessing. China stands out as the country with the largest number of publications. Regarding applications, real estate investment studies predominate (49%), followed by urban planning. The application of artificial intelligence improves accuracy and ",0.0,3490d4ac511415bfe435b351caf7aeb5245033ece68,10.22214/ijraset.2023.55990,Predicting the Rental Values of Houses in Bangalore City Using the Linear Regression Approach,2023,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. ",0.0,http://dx.doi.org/10.22214/ijraset.2023.5599035005353ca9e1cea72db72b65dd6cf9e241ab0a67ce,10.1007/s10644-026-10041-5,Explosive housing price dynamics in Türkiye: a city and district-level analysis before and during the COVID-19 pandemic,2026,Economic Change and Restructuring,Lokman Gunduz; I. Genc; Mustafa Çakır,"This study investigates house price dynamics in Türkiye using monthly data for 55 cities and 87 districts over the period 2010–2022. Employing a three-stage empirical framework, we first detect exuberant episodes using Generalized Sup Augmented Dickey-Fuller (GSADF) and backward Sup ADF (BSADF) right-tailed unit root tests at both the city and district levels. We then analyze co-explosivity among the five largest cities (Istanbul, Ankara, Izmir, Bursa, and Antalya) using logistic regression models. Finally, we explore the drivers of bubble formation across 55 cities using panel logistic regression models. The results indicate that most cities and districts experienced synchronized explosive house price increases across three periods: 2013–2015, 2017–2018, and 2020–2022. The 2021–2022 period saw the most widespread bubbles, particularly in the western and Marmara regions than in the eastern part of the country, indicating a clear regional asymmetry in housing market dynamics. The evidence suggests that housing price exuberance was more synchronized in the largest urban markets. The panel logistic regression analysis revealed that new house sales were associated with bubble formation",0.0,https://doi.org/10.1007/s10644-026-10041-5351005792f10c12856ae80946b34ea6d475f6597cd1,10.3390/su18031586,The Built Environment Exacerbates Disparities in the Accessibility of Public Charging Stations: An Analysis Based on MGWR,2026,Sustainability,Shu-Xiao Ma; Yan Xu; Ziyu Wang; Lele He; S. Hou; Yang-Yang Liang,"The disparities in accessibility of public charging stations (PCSs) have emerged as a critical bottleneck constraining urban social equity and sustainable development. This study focuses on PCS accessibility within the central urban area of Tianjin. Employing an enhanced Gaussian-based two-step floating catchment area method, spatial autocorrelation analysis, and multi-scale geographically weighted regression, the research investigates spatial configuration, spatial dependence, and influencing factors from three analytical dimensions. The study elaborates on the disparities in PCS accessibility among different income groups and examines the determinants contributing to these variations. Research findings indicate that: (1) PCS accessibility1 in the central urban area exhibits a core-periphery spatial structure, with notable heterogeneity in the spatial distribution patterns of PCS accessibility across different income groups. (2) The MGWR model demonstrates superior performance compared to conventional models, with an increase in R2 explanatory power of at least 23.7% and a reduction in AICc values ranging from 3.94% to 47.45%. (3) Housing prices are positively correlated with PCS ",0.0,https://www.mdpi.com/2071-1050/18/3/1586/pdf352152b8d30472903f6ac97ad08bd85e9d991b43a79,10.62051/w7ph1k70,A DID Empirical Analysis of The Impact of Urban Purchase Restrictions on Residents' Consumption Structure,2025,"Transactions on Economics, Business and Management Research",Yixuan Li; Miao Wang,"To assess the dynamic impact of the housing purchase restriction policy on the consumption structure of residents and its mechanism of action, this study constructs a theoretical analysis framework that includes the mediating effect of housing prices. Based on the urban panel data of Beijing from 2010 to 2023, we conducted an empirical test using the difference-in-differences method. The research results show that the impact of purchase restrictions on the consumption structure of residents has significant dynamic characteristics. Benchmark regression indicates that after controlling for a series of urban economic characteristics, the direct effect of the policy is not significant, but its indirect effect generated by suppressing housing prices is intricately intertwined with wealth effect, crowding-out effect and mortgage effect. Both the parallel trend test and a series of robustness tests support the reliability of the above findings. Further mechanism analysis confirmed that housing prices are the key intermediary channel through which purchase restrictions affect the consumption structure. This study reveals the intrinsic path by which macro housing regulation policies influen",0.0,https://tebmr.com/index.php/ojs/article/download/33/313531fe3d995ec0f3d2e0682124e48792005be125338,10.54254/3029-0880/2025.30128,The application of machine learning in house price prediction,2025,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 lib",0.0,https://www.ewadirect.com/journal/aorpm/article/view/30128/pdf354doi_10_5821_siiu_9171,10.5821/siiu.9171,Forma urbana y vivienda colectiva: una aproximación a la evaluación de densidad,2018,"X Seminario Internacional de Investigación en Urbanismo, Barcelona-Córdoba, Junio 2018",M. Cecilia Marengo,"El problema de investigación se fundamenta en la necesidad de reexaminar las formas de los tejidos residenciales, a la luz de las transformaciones derivadas de los procesos de crecimiento en las ciudades en las últimas décadas. El concepto de densidad, se presenta como una herramienta útil para proponer acciones de densificación, compactación o intensificación. Sin embargo, no es un concepto neutro, sino que fundamentalmente es relacional. Densidad y forma urbana se abordan como un binomio para analizar lo construido y reflexionar sobre las condiciones de alojamiento que permitan construir entornos urbanos vitales, con calidad residencial. La ponencia analiza conjuntos de vivienda colectiva con formas de ocupación más compactas y densas, incorporando un modelo de simulación para comparar diferentes alternativas de organización espacial disímiles en forma urbana y densidad. Atendiendo a la condición relacional del concepto, vinculamos Densidad con Calidad residencial, y aportamos conclusiones sobre criterios de densificación residencial. The research problem is based on the need to reexamine the forms of the residential tissue, derived from the transformations on growth patterns tha",0.0,35517b90b91e02b1dad8ca3e953d60c87c50a2eebfd,10.55493/5009.v14i3.6192,Determinants of household debt in Thailand: A cointegration and VECM analysis,2026,Asian Journal of Economic Modelling,Saranya Raksong; Byung-Nam Yu,"Household debt has become a major macroeconomic concern in Thailand since its persistently high level and potential risks to economic and financial stability. This study investigates the long-run and short-run macroeconomic and financial determinants of household debt in Thailand, utilizing quarterly data from 1998Q1 to 2025Q3. The Johansen cointegration approach and Vector Error Correction Model (VECM) are employed to estimate the long-run relationship and short-run dynamics of household debt ratio and its determinants, respectively. The results reveal that gross domestic product (GDP), the domestic interest rate, and non-performing loans (NPLs) are negatively associated with the household debt ratio in the long run, whereas the housing price index, unemployment rate, and household consumption have positive effects. In the short run, GDP positively affects household debt, implying that stronger economic activity may initially encourage borrowing, while sustained income growth may reduce indebtedness over time. Household consumption, domestic interest rate and non-performing loans significantly influence short-run household debt dynamics. The error correction term indicates that ap",0.0,https://archive.aessweb.com/index.php/5009/article/download/6192/89843562af491946ef4b5ee6bcb8c4181e083da450ee3fb,10.68128/jocrebe.2021.wb04ezv4,"CONTRIBUTING EFFECT OF LAND TITLING TO LAND VALUE: LEARNING FROM AKURE, NIGERIA",2021,Journal of Contemporary Research in the Built Environment,V. Ige; R. A. Oladapo; Amos Adewusi,"Purpose: The purpose of this paper is to explain the contributing effect of land titling on land 357value with a special focus on Akure, Nigeria. Land titling has an abstract economic value that 358untitled physical assets do not have. The inability to measure this economic value as it affects 359land value in the context of the urban land market often poses a difficult task as there were no 360readily available data. The consequence may mean difficulty in determining the precise land 361value under competitive conditions in the urban land market.Design/methodology/approach: Questionnaires are distributed to 2026 household heads who 362are landowners of selected private residential layouts/estates in the study area. Data gathered 363are analysed using 2 stages hierarchical form of semi-log hedonic price analysis to determine 364the contribution of land titling to land value. Findings: The finding shows that when land titling is considered alone with land value, there 365is a statistically significant contribution of about 27% to land value and 126% when 366considered alongside the other independent variables.Originality/value: The outcome of the paper will join the body of existing studies to 367empiric",0.0,https://jocrebe-uu.com/article/contributing-effect-of-land-titling-to-land-value-learning-from-akure-nigeria.pdf3681245ccee04e0e0e30d1384387bd68cfb3c36b200,10.1108/ijhma-04-2026-0119,Structural interactions between administrative and market prices: evidence from asymmetric adjustment in Korea’s dual assessment system,2026,International Journal of Housing Markets and Analysis,Junggwan Heo,"369 370 This study examines how administrative and market price indicators interact in the Korean housing market, focusing on officially assessed housing prices (OHP), officially assessed land prices (OLP) and market transaction prices (MSP). This study aims to identify whether these indicators exhibit distinct adjustment patterns under policy intervention.371 372 373 374 This study uses a long-run panel data set of apartment complexes from 2006 to 2025. Fixed-effects panel regression models with interaction terms are used to analyze how policy changes affect relationships among price indicators and their adjustment dynamics.375 376 377 378 The results show that OHP and OLP exhibit distinct adjustment mechanisms and diverge under policy intervention. OHP displays asymmetric adjustment, responding more strongly in upturns than downturns. The divergence between OHP, OLP, and MSP is not easily explained by macroeconomic factors alone, suggesting a role for institutional valuation mechanisms.379 380 381 382 This study is based on a limited sample and does not fully identify causal mechanisms. Future research should incorporate broader data sets and explore transmission channels linking administrative prices and m",0.0,38303ff23b0df4c61d5c4b23e16503555430d9f8237,10.15678/krem.18747,Monetary Determinants of House Prices in the European Countries,2026,Krakow Review of Economics and Management/Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie,Viktor Shevchuk; Roman Kopych,"Objective: The aim of this article is to investigate monetary policy effects on the house prices for the panel of 12 Central and Eastern European (CEE-12) countries, 12 small European countries (EU-12) and 5 largest European countries plus the UK (EU-6).384Research Design & Methods: A five-variable Panel Vector Autoregressive (PVAR) model is estimated for quarterly data from 2010Q1 to 2023Q2.385Findings: An increase in the short-term interest rate has a depressing effect on the house prices in the European countries, along with higher inflation and exchange rate depreciation (to less extent). An increase of output over the business cycle brings about a temporary decrease in house prices in both CEE-12 and the EU-12 countries. As intuitively expected, a housing boom is expansionary and inflationary in all European countries, though with a different time profile. There is strong evidence of the interest rate response to house prices in the CEE-12 countries and the EU-12 countries (at longer horizons).386Implications / Recommendations: This paper supports the proposals for using monetary tightening in response to the boom in the housing market. It is especially the case for the CEE countrie",0.0,https://krem.uek.krakow.pl/index.php/krem/article/download/18747/26973870cd8fe3e501ff7cd8823d7460093959c04768b5d,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,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 ",0.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0356472&type=printable38818de90d2328459c1e80a6d6103c24f939ea9b771,10.33476/jamer.v3i2.210,Key Factors Shaping Millennials' Decisions to Choose Sharia Mortgage Financing,2025,"Journal of Accounting, Management, and Economics Research (JAMER)",Faila Sufa; Nursanita Nursanita,"This study examines the factors influencing millennials' decisions to take Sharia mortgages. It employs causal hypothesis testing with a quantitative approach, using multiple linear regression analysis via SPSS Statistics 25. The population includes DKI Jakarta residents aged 20-40 years with income or employment. A sample size of 100 respondents was determined using the Slovin formula based on the Nine Targeted Samples Method. Primary data were collected through online questionnaires distributed via Google Forms. The study findings reveal that house prices positively and significantly affect millennials' decisions to opt for Sharia mortgages. Conversely, house location has a significant negative effect on their decisions. Additionally, promotion and religiosity both have positive and significant influences on the decision to take Sharia mortgages. The results underscore the importance of promoting the benefits and advantages of Sharia mortgages, particularly highlighting their differences from conventional mortgages. The implications of these findings suggest that stakeholders should provide clear, accessible, and educational information to help millennials understand the unique f",0.0,https://academicjournal.yarsi.ac.id/ojs3/index.php/jamer/article/download/210/15638905ee370964c54632a49c9bef170c0b594c36d704,10.19139/soic-2310-5070-1764,"Geoadditive Semiparametric Regression For Modeling Property Price In Surabaya, Indonesia Using Marketplace Data",2024,"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 infl",0.0,http://www.iapress.org/index.php/soic/article/download/1764/109839021c336f4a650a4a612747694c4605d293d7f040a,10.48550/arxiv.2305.08887,Covariate-distance Weighted Regression (CWR): A Case Study for Estimation of House Prices,2023,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 ",0.0,http://arxiv.org/pdf/2305.088873911750440a68ea041225869eb9f508aae074940442,10.54254/2754-1169/2025.bl29312,The Connection Between Smart-City Planning and the Real-Estate Market: A Case Study of Shanghai and Comparator Cities in Mainland China,2025,"Advances in Economics, Management and Political Sciences",H. Cui,"Chinas largest cities have shifted from broadly rising housing prices to uneven, node-driven dynamics shaped by smart-city programs and special-region policies. This paper develops a nodecorridorring lens that integrates classical land-rent theory with contemporary digital-governance and infrastructure interventions. Using Shanghai as the focal casesupplemented by observations from Shenzhen (Qianhai) and Zhuhai (Hengqin)we examine how bundled upgrades in digital infrastructure, transport connectivity, administrative streamlining, and talent pipelines create productivity and policy rents that capitalise into land values. We argue that these interventions flatten the inner concentric gradient while generating secondary and tertiary price peaks at policy-favoured nodes and along high-capacity corridors, producing a multi-peak surface superimposed on an average distance decay. Macro-prudential housing controls moderate citywide amplitudes but do not overturn the spatial ordering created by node productivity, corridor accessibility, and governance quality. The analysis implies that managing stability and spatial equity requires coordinating land release, transport sequencing, and amenit",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/29312/pdf39208b8b4d7899720b5dab505d66d30958e6ed40885,10.3390/economies13120347,Predicting the Business Cycle in South Africa: Insights from a Real-Financial Activity Gap,2025,Economies,Khwazi Magubane; Phindile Mdluli-Maziya; Boingotlo Wesi,"Traditional approaches to predicting business cycles are limited by their omission of financial variables, which, in turn, leads to failures to signal financial-sector crises and to misestimate the duration and intensity of economic events. This study addresses this challenge by constructing a real-financial activity gap for South Africa and utilising it to predict the occurrence of economic recoveries. The study examines the period from 1970Q1 to 2023Q4, using real GDP, domestic credit, house prices, and share prices. The dynamic factor model and the Hodrick–Prescott filter are employed to construct the real-financial activity gap. The recursive ADF unit root test is used to assess the presence, frequency, and duration of economic recoveries. To validate the results, a Markov switching dynamic regression model is applied. The results reveal that the new gap tends to produce economic recovery predictions that are less frequent but longer in duration. In contrast, predictions based on real GDP lead to more frequent but shorter recoveries. The new gap suggests that financial variables contribute to stabilising growth over extended periods, whereas real GDP reflects quicker but more v",0.0,https://www.mdpi.com/2227-7099/13/12/347/pdf39305cb5ad0e42aadfc526307c46a981e6607ddc612,10.54097/37f82n25,The Balance Between Economic Development and Environmental Protection: Sustainable Tourism in Juneau City,2025,Mathematical Modeling and Algorithm Application,Bin-Han Yang; Qitong Gao; Zi-Teng Wang; Xuqi Cao; Nan Hu,"To effectively balance economic gains and ecological conservation in tourism-intensive cities, this study examines Juneau, Alaska—a quintessential tourism-dependent city—and constructs a multi-objective optimization framework for sustainable tourism based on a stochastic movement model. By modeling the stochastic fluctuations of beer and housing prices and incorporating symbolic functions to quantify tourist behavior choices and local residents' response strategies, the framework establishes a constrained optimization model to systematically optimize overall social, economic, and environmental benefits. Key parameters were estimated using linear regression methods, combined with Monte Carlo simulation techniques to conduct stochastic simulations and evaluations of profit and sustainability indicators under various scenarios. Results indicate that setting hotel accommodation prices at $160 per night and limiting annual visitor numbers to approximately 15,800 can maximize regional profits while significantly reducing environmental pressure, thereby alleviating resource competition between local communities and external tourists. The modeling mechanism proposed in this study not only ",0.0,https://drpress.org/ojs/index.php/mmaa/article/download/32666/319703941dc1dc84f4e6e0dc8daca160ca2a1ed20cc2631c,10.54254/2754-1169/64/20231536,Research on Influencing Factors of the Real Estate Market in Beijing,2023,"Advances in Economics, Management and Political Sciences",Yuan-Zheng Liu,"As a large-scale real estate industry, the real estate industry plays an important role in regulating the world economy. As a pillar industry in my country’s development, it plays an important role in promoting my country's GDP growth. Consumption and investment demand for real estate also have a significant impact on overall consumer demand. Many scholars at home and abroad have conducted relevant research on real estate demand factors. This article uses annual data from the China Bureau of Statistics and Beijing, selects Beijing’s GDP, per capita disposable income of all residents, etc. as explanatory variables, and uses the commercial housing sales area sarea as the explained variable. A multiple regression model was established to analyze the influencing factors of Beijing's real estate market demand. The results show that policy, income, population and housing price factors have a significant impact on demand. Among them, GDP, population change, and policy dummy variable factors have a positive impact, while housing prices and per capita income have a negative impact. Based on the conclusion, this article puts forward suggestions for narrowing the income gap, further curbing r",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/8755/pdf395doi_10_5821_siiu_6203,10.5821/siiu.6203,Vivienda e integración de grupos sociales vulnerables en la ciudad: el caso de la Operación Bolsas de Deterioro Urbano en Madrid,2015,"VII Seminario Internacional de Investigación en Urbanismo, Barcelona-Montevideo, junio 2015",Angela Bedoya Ruiz,"En el contexto urbano europeo, se ha abordado comúnmente la integración de grupos sociales vulnerables, económica o socialmente, a través de políticas de vivienda y programas de realojo. Sin embargo, estas medidas y su eficacia actualmente son debatidas, en base a la limitada incidencia que la transformación espacial de ámbitos reducidos ha mostrado tener sobre otros factores de la exclusión social. Este estudio busca, a partir del estudio de la Operación Bolsas de Deterioro Urbano en Madrid, vincular procesos de planificación y gestión del suelo a aquellos referentes a la integración de grupos vulnerables en la ciudad. Se concluye que cambios efectuados en las disposiciones de ordenamiento y gestión de algunas áreas de la operación, así como la falta de seguimiento de los procesos de desarrollo urbano posteriores a las intervenciones efectuadas, y de los efectos del realojo sobre la población objeto, desvían a ésta de su postura ideológica inicial. In the context of European urban development, housing policies and relocation programmes are commonly used to foster urban integration of vulnerable social groups. However, these measures and their associated effects are still up for de",0.0,39611135c13b52c3fe849cfd9899af8e92968906ec5,10.1007/s41885-026-00200-3,Assessing the Impact of Wildfires on the Swedish Housing Market: a Case Study of the 2014 Västmanland Wildfire,2026,Economics of Disasters and Climate Change,Tommaso Piseddu; David Stenvall,"The literature on wildfires and residential property prices is limited and primarily focuses on events in North America. There is a lack of studies examining this relationship in Europe. With the largest forest area in the entire EU, understanding this impact is particularly relevant in a Swedish context. In this paper, we investigate how the largest wildfire in Sweden’s recent history, the 2014 wildfire in Västmanland County, affected nearby housing prices and time-on-market. Using a difference-in-differences method, we find a significant negative effect on housing prices. Our most conservative estimate indicates an approximate 2.7% reduction in final selling prices for post-fire sales located within 20 km of the wildfire area. However, the negative effect is even larger when defining the treated area as a 5 km (-10.1%) or 10 km (-8.9%) distance to the fire, and these results are confirmed by a repeated sales model that includes only single- and multi-family houses. In a heterogeneity assessment with respect to the housing types, we find that our effects are mostly driven by the impact on single-family houses rather than apartments. For the former group, we find large negative and",0.0,https://link.springer.com/content/pdf/10.1007/s41885-026-00200-3.pdf39703b367ed870cdb6abc429f6673e48dcb31bb0a51,10.33423/q2mpyb41,Mapping the Environmental and Socioeconomic Footprint of Data Centers in the United States,2026,Journal of Strategic Innovation and Sustainability,Linda Loubert,"The rapid expansion of artificial intelligence and cloud computing has accelerated the construction of data centers across the United States, raising important questions about the spatial factors influencing their location. This study examines tract-level patterns of data center siting using Geographic Information Systems (GIS) and regression analysis. Data center locations were spatially joined to U.S. census tracts and enriched with demographic, housing, and economic characteristics. Descriptive comparisons indicate that tracts hosting data centers have higher median household incomes, lower poverty rates, lower population densities, and higher home values than non-host tracts. However, regression models incorporating metropolitan (CBSA) fixed effects reveal that these socioeconomic differences largely reflect variation across metropolitan areas rather than within them. Population density remains the only consistent predictor of data center presence, with facilities systematically located in lower-density environments within metropolitan regions. By integrating GIS-based spatial analysis with metropolitan fixed-effects regression, this study distinguishes regional from within-met",0.0,https://articlegateway.com/journals/index.php/JSIS/article/download/8376/7908398098dd5995f350c19f68ca944f8e805f2cd7084a8,10.1093/restud/rdag079,The Long Shadow of Housing Discrimination: Evidence from Racial Covenants,2026,The Review of Economic Studies,A. Sood; Kevin Ehrman-Solberg,"399 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 pri",0.0,https://doi.org/10.1093/restud/rdag07940005152376830f9e262641274db9680f0405ed2316,10.54066/jrime-itb.v2i1.1144,Pengaruh Religiusitas Dan Pengetahuan Konsumen Terhadap Keputusan Pembelian Properti Pada PT.Griya Permata Kencana Jambi,2023,JURNAL RISET MANAJEMEN DAN EKONOMI (JRIME),Lady Ragia Alfa; A. Tarmizi; Agustina Mutia; Victor Diwantara,"The demand for houses is increasing from year to year in line with the growth in population in Indonesia. Meanwhile, the availability of houses is not proportional to the large number of requests. This is one of the factors causing the very high increase in house prices. In the city of Jambi, there are already several houses that use the conventional system, namely using interest in their operational transactions, such as KPR Bank BRI, Bank Mandiri, Bank BNI and others. Meanwhile, housing developments that use the sharia system have increased. The middle-urban community who are critical and want to implement an Islamic lifestyle one hundred percent are starting to switch, they are no longer using conventional and sharia mortgages that are sold in national banks, but instead are utilizing sharia-based mortgage facilities in sharia housing, which are not products of banking financial institutions, but from developers. This study aims to determine the effect of religiosity and consumer knowledge on purchasing decisions of sharia property at PT. Griya Permata Kencana Jambi. The subjects of this research are consumers of PT. Griya Permata Kencana Jambi. The population in this study amou",0.0,https://jurnal.itbsemarang.ac.id/index.php/JRIME/article/download/1144/10884011accde62e91a15256a43138ff3fc622d567ba6ff,10.54097/t59jyd53,Analysis on the Influencing Factors of Housing Prices in Shanxi Province Based on multiple Linear Regression,2026,Journal of Innovation and Development,Jiamei Cheng,"As a pillar industry of the national economy, real estate price fluctuations affect residents' living quality, macroeconomic stability and urban development. Housing prices in Shanxi Province have also fluctuated in recent years, affecting people's living standards. However, there is no clear formula to reveal the essential reasons for changes in housing prices. To explore the law of housing price changes in Shanxi Province, this paper uses SPSS to build a multiple linear regression model, and analyzes the impact mechanism of various factors on housing prices from the perspective of supply and demand based on relevant data of Shanxi Province from 2013 to 2024. With regional GDP, per capita disposable income of urban residents, residential sales area, population, residential completion investment and residential completed area as independent variables, and the average sales price of commercial housing as the dependent variable, the study shows that residential commodity investment, regional GDP and residential commercial housing completion area have a great impact on housing prices in Shanxi Province, and the real estate industry is highly correlated with economic development and re",0.0,https://drpress.org/ojs/index.php/jid/article/download/32812/321164021b1ce25ba43987a53746ab26ca5e28722e019c82,10.11648/j.sjbm.20261403.15,A Comparative Study of Machine Learning-Based Predictive Models for House Price Prediction,2026,Science Journal of Business and Management,Praveen Singh; Rachna Jain; Shikha Sharma,"403 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 (R404 2405 ) 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 R406 2407 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 re",0.0,http://article.sciencepg.com/pdf/j.sjbm.20261403.15408235965f0dd928bdd4752851ad45ce71d20991456,10.5220/0013689900004670,Machine Learning-Based Approaches to Forecasting Housing Prices in the Canadian Market,2025,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. ",0.0,https://doi.org/10.5220/0013689900004670409doi_10_5821_ace_vi14_2504,10.5821/ace.vi14.2504,Imaginarios habitacionales de la vivienda de lujo frente a la globalización. El caso de la ciudad de Guadalajara,2010,"ACE: Architecture, City and Environment",Verónica Livier Díaz Núñez; César Abraham Hernández Rodríguez,"Algunos efectos de la globalización han sido los de homogenizar, en este sentido las políticas económicas, sociales y culturales, para la producción de la vivienda, manifiestan un código que parece ser eficaz al ofrecer desarrollos habitacionales aparentemente adecuados a la demanda actual de consumo. A través de la fenomenología este trabajo analiza las arquitecturas de “apariencia” cuyo propósito es convencer y seducir; incidiendo en la conformación del imaginario arquitectónico-social. Nuestras lecturas finales reflejan que en muchas ocasiones no corresponde lo ofertado por la publicidad con el producto terminado. The homogenization has been one impact derived from globalization, in this respect, the economic, social and cultural policies for the production of the housing, express a code that seems to be effective to offer residential developments apparently suitable to the current demand of consumption. Using phenomenology this work analyzes the architectures of ""appearance"" which intention is to convince and to seduce; affecting in the conformation of the architectural - social imaginary. Our final analysis reflects that in many occasions the characteristics advertised don't c",0.0,4100faf41b3a48225a17edc93450012d54be60cbc48,10.37676/jmcs.v5i3.11579,Prediksi Harga Kos di Wilayah Jimbaran Menggunakan Algoritma Decision Tree Regression Berbasis Web,2026,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 Decisio",0.0,https://jurnal.unived.ac.id/index.php/jmcs/article/download/11579/85444111b624d489572288a1419b9f25ed2007255d7c70f,10.3390/jrfm19020156,A Panel Study on the Determinants of Profitability of Bulgarian Commercial Banks,2026,Journal of Risk and Financial Management,P. Peshev,"This study examines the determinants of profitability for 21 Bulgarian commercial banks over the period from the first quarter of 2007 to the first quarter of 2025, using financial statement data. Bank profitability is measured by return on assets (ROA) and return on equity (ROE) and modeled within a panel autoregressive distributed lag (PMG-ARDL) framework. The empirical specification combines bank-specific and macroeconomic variables, allowing for the identification of both long-run equilibrium relationships and short-run bank-level dynamics. The long-term results indicate that the net interest margin (NIM), net fee and commission margin (NFM), government bond yields, the growth of the gross domestic product (GDP), and the loan-to-deposit ratio (LDR) positively affect profitability. On the other hand, higher unemployment, rising housing prices, increased loan loss impairments, and the ratio of cash holdings to total assets reduce profitability. The findings provide policy-relevant insights for bank management, regulators, and macroprudential authorities regarding efficiency, income diversification, and credit risk management. The findings facilitate a more comprehensive assessmen",0.0,https://www.mdpi.com/1911-8074/19/2/156/pdf4120a5b61602e8aa59d1825d725cae816ac356c581e,10.65644/eiie.079.02.0199,Determinants of Non-Performing Loans in Namibia,2026,Economia Internazionale/International Economics,Valdemar J. Undji; J. Sheefeni,"This paper uses time-series data from 1996Q1-2021Q4 to examine the determinants of non-performing loans (NPL) in Namibia’s banking industry and test for causality between NPL and its determinants. To accomplish this, the Autoregressive Distributive Lag (ARDL) and the Vector Autoregressive (VAR) pairwise Granger causality modelling approaches are employed. The findings reveal that in Namibia, NPL is influenced by a host of factors, including its own past values, output gap, unemployment rate, housing prices, return on assets, return on equity, lending behaviour, loan-to-deposit ratio, loan growth, narrow money supply, broad money supply, net foreign assets, repo rate, interest spread, deposit rates, private sector credit extension, oil prices, COVID-19 pandemic crisis, stock market prices, regulatory quality, government effectiveness, and the rule of law. The Granger causality test results indicate strong unidirectional causality running from past values of NPL, unemployment, housing prices, capital adequacy ratio, loan growth, and oil prices to NPL. Additionally, a bidirectional causal relationship exists between the repo rate, lending rate, and NPL. The policy implications emanati",0.0,https://doi.org/10.65644/eiie.079.02.01994131f553d129b386787afecee714206046bb530cec2,10.54254/2753-8818/2026.dl34252,Analysis of Second-Hand Housing Price Fluctuations in Beijing and Changsha Under the Impact of the Epidemic Based on an Interrupted Time Series Regression Model,2026,Theoretical and Natural Science,Shiduo Gong,"The arrival of the epidemic is hitting different industries, leading to an economic downturn. As one of the pillars of the national economy, the real estate industry holds indispensable significance for the stability of the national economy. There are many sectors within the real estate industry, but the secondary housing market constitutes an important part of the real estate industry and best represents it. This study aims to explore the fluctuations of the second-hand housing market in Beijing and Changsha, investigate the impact of the pandemic on the second-hand housing market from 2018 to 2022, while also considering the effects of M2, and include a dummy variable in the regression analysis. Using an interrupted time series regression model can effectively isolate mutation conditions, observe the instantaneous impact of an epidemic, and also monitor long-term fluctuations. It can also serve as research findings and provide purchase advice for homebuyers. At the same time, provide recommendations for government policy implementation, and consider using different policies for different cities' capital situations to quickly and effectively address current issues. Internationally",0.0,https://tns.ewapub.com/article/view/34252.pdf4141f7c276c0e09e75740719a9d1d7d1f4118ce73b6,10.56632/bct.2025.4103,Machine Learning Impact on Modern Business Intelligence,2025,"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 tech",0.0,https://bctjournal.com/article_461_9b60b778a55f888f512cd0ebaba7c5d4.pdf415098f019656ba0591a949c228ea100966eaaf17e9,10.55041/isjem05364,Machine Learning Based Smart House Price Prediction System,2026,International Scientific Journal of Engineering and Management,V. Mageswari; S. Sanjai,"Abstract416417Accurate 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",0.0,418f057e065aee340ad6d6f9c16dc8a24191f08d8e1,10.5430/rwe.v13n1p1,(Un-)affordability of Homes From a Resident’s Point of View in Two Mid-Sized Canadian Cities 30 Years Apart,2022,Research in World Economy,A. Phipps,"A new theoretical criterion of housing affordability is defined as a mismatch between where a resident likes to live if preferences are unconstrained, and where they can afford to live if preferences are budget constrained. This study theorizes and quantifies the compensatory amounts of money to be spent to reduce these mismatches by acquiring unconstrained most preferred attributes’ levels of homes. Compensatory amounts are quantified with the predicted implicit prices of almost 3,000 sold single-detached(-like) homes in each of two mid-sized Canadian cities. The analysis predicts approximately one-half of up to 74 respondents in each city in 1987 and 2020 will experience a residential mismatch if they choose their budget-constrained most preferred home. Unaffordable compensatory expenditures are especially predicted for non-managerial or professional workers if they want to afford their unconstrained most preferred attributes’ levels of house type and size, house age and exterior finish, basement condition and home renovations, and lot size and garage. Moreover, average predicted compensatory expenditures exceed loans or grants in four past and current public policies in Canada f",0.0,https://www.sciedu.ca/journal/index.php/rwe/article/download/22146/1371641910a151a07fb3e4efc3728c4f962f1f9bde8a8c89,10.15388/tibe.2026.25.2.1,Macroeconomic determinants of housing affordability in the European Union: panel evidence from the house price-to-income ratio,2026,Transformations In Business & Economics,Vytautas Snieška; Daiva Burkšaitienė,"Housing affordability has become an increasingly important socio-economic issue across the European Union as housing prices have grown faster than household incomes in many countries. This study investigates the macroeconomic determinants of housing affordability using the house price-to-income ratio as the main indicator. The empirical analysis is based on panel data for EU member states covering the period 2000–2024 and combines macroeconomic indicators from Eurostat, including GDP per capita, purchasing power parity indicators and GDP price level indices. Panel econometric models with country and time fixed effects are used to estimate the relationship between macroeconomic variables and housing affordability dynamics. In addition, cluster analysis is applied to identify groups of countries with similar housing affordability trajectories. The results indicate that macroeconomic factors play a significant role in shaping housing affordability across EU countries. Higher price levels are associated with higher house price-to-income ratios, while income growth partially mitigates affordability pressures. The cluster analysis reveals substantial heterogeneity between European housin",0.0,https://www.zurnalai.vu.lt/TIBE/article/download/46206/42705420074a39b4080cddd7fcc32113952e2a6dbabe5686,10.3390/su16229716,"A Mechanistic Study of the Coexistence of High House Prices, Low Income, and High Homeownership Rates in China",2024,Sustainability,Hui Zeng; Hong-Yi Fan; T. Phan; Xiao-Feng Yu; Yi Pan,"An important feature of China’s housing market is the coexistence of a high house-price- to-income ratio and high homeownership rates. The purpose of our study is to reveal the root causes of this paradox from a new perspective and theoretical foundation. Based on questionnaire data from Hangzhou and logistic regression models, our research finds that the most important factors driving middle and lower-income groups to buy homes are its unique household registration and school district housing system, underdeveloped housing rental market and inadequate regulatory system, and the wealth appreciation effect caused by the continued rise in housing prices. Furthermore, intergenerational wealth transfers, private lending, and China’s generous home mortgage policies have made homeownership possible for this groups. However, the high house-price-to-income ratio leads to heavy financial pressure on the middle- and low-income groups and is not conducive to sustainable and healthy economic development. To this end, we suggest that the government accelerate the equalization of public services, improve the regulatory system governing the rental housing market, and control the unreasonable rise",0.0,https://www.mdpi.com/2071-1050/16/22/9716/pdf?version=17309897474211e6daa6c15bd9d379c8d281f9900b569fb3aa7b2,10.70382/caijeres.v13i4.087,"EFFECT OF DESIGNED AND BUILT INDUSTRIAL ESTATE ON PROPERTY VALUES IN ILUPEJU, LAGOS",2026,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 indust",0.0,https://cambridgeresearchpub.com/ijeres/article/download/1385/12574220b9eefecd90b818651be4c57fe476bab87958359,10.54254/2754-1169/2024.ld19187,Determining Factors on Housing Price in Beijing Using Regression Analysis,2025,"Advances in Economics, Management and Political Sciences",Yoshiki Hayashi,"This paper investigates the correlation between the characteristics of the houses and their prices using Multiple Linear Regressions. Using datasets from Lianjia, this study reveals factors including total housing area, proximity to subways, number of drawing rooms, and community average price, with robust standard errors confirming the reliability of these results. In the initial Ordinary Least Square (OLS) Regression, multicollinearity was not detected by the VIF test, while heteroskedasticity was detected by the White test so robust standard error is used. To address potential endogeneity issue, Two-Stage Least Squares (2SLS) regression with appropriate instrumental variables is further applied. Notably, the results show a negative relationship between housing size and its price, indicating a demand for smaller, centrally located properties, driven by high housing prices and preferences of younger buyer. As a persuit for accuracy, variables bathroom and followers are used in combination as IVs for the potentially endogenous variable square. The conclusion that socio-economic factors, urban development, and access to subway station play significant roles in determining housing pr",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/19187/pdf423119aef63684b069dcd4b2fc48e8a101a97167a81,10.54254/2753-8818/38/20240562,Research on factors influencing housing prices in Beijing,2024,Theoretical and Natural Science,You-Peng Huang,"This paper delves into the issue of soaring housing prices in Chinas real estate sector, with a particular focus on Beijing. Employing a multiple linear regression model, it conducts an empirical analysis to identify the factors influencing housing prices. Furthermore, it synthesizes scholarly perspectives to offer a comprehensive discussion on these influencing factors. The research highlights Beijings real estate development, residential investment, and residential sales area as pivotal determinants of housing prices. Building upon these findings, the study advocates for the implementation of effective measures such as land policy regulation and restrictions on real estate transactions to guide the real estate market development prudently and maintain housing prices within a rational range. Results show that real estate development residential investment has a significant positive impact on housing prices, while residential sales area has a significant negative impact. Ultimately, the research endeavors to furnish a scientific foundation for policy formulation and assess its impact, thereby fostering the healthy growth of the real estate market, ensuring stable economic progress,",0.0,https://www.ewadirect.com/proceedings/tns/article/view/13303/pdf42410cddcdee54d6771cbd44d51b773c7d1b6316f82,10.2478/picbe-2025-0218,Business and Financial Cycles in Romania: An Empirical Investigation,2025,Proceedings of the International Conference on Business Excellence,Cristina-Elena Bejenaru,"Abstract In this paper we study the relationship between business and financial cycles in Romania, highlighting their distinct characteristics and interactions. The global financial crisis highlighted the role of financial factors in shaping macroeconomic dynamics, especially in developing countries which are more vulnerable to foreign credit reversals. While the business cycle is well-defined, financial cycles are less “standardized” because of their longer duration and greater amplitude. Using quarterly data from 2005 to 2024, this study applies turning-point analysis, band-pass filtering, and finance-neutral output-gap estimation to highlight the key traits of Romania’s business and financial cycles. The results show these cycles rarely align, though GDP and real house prices move together more closely than credit measures. Real credit and property prices prove to shape the output gap significantly: booms in credit and housing push standard methods to overstate potential output, while financial contractions deepen downturns beyond what trend filters suggest. These insights underscore the value of weaving financial-cycle signals into macroeconomic analysis and help illuminate the",0.0,https://reference-global.com/2/v2/download/article/10.2478/picbe-2025-0218.pdf42508e7f9cc60eeaa4b30b9ab12cd95926a848afd98,10.31002/rep.v8i1.858,"THE DISTRIBUTION OF HOME MORTGAGES BY COMMERCIAL BANKS, ISLAMIC BANKS, AND RURAL BANKS DURING THE COVID-19 PANDEMIC",2023,Jurnal REP (Riset Ekonomi Pembangunan),Annisa Rahima,"This research aimed to examine the distribution of home mortgages by various banks, i.e., commercial banks, Islamic banks, and rural banks during the 2-year Covid-19 pandemic in Indonesia. This study used panel data as the analysis method and the variables consisted of Residential Property Price Index (RPPI) and interest rates. The regression results showed that RPPI had a positive but not significant effect on the distribution of home mortgages during the pandemic. From the regression results, it is found that IHPR has an insignificant effect on KPR distribution during the pandemic. That is, when property prices increase, it will not affect the distribution of mortgages provided by commercial banks, Islamic banks and rural banks. Loan interest rates also have no significant effect on mortgage distribution during the pandemic. This implies that when the loan interest rate increases, it does not affect the distribution of mortgages provided. However, the increase in mortgage distribution during the Covid-19 pandemic is due to government efforts through subsidized mortgage policies such as Loan to Value (easing credit limits), VAT exemption incentives and expanding these policies not",0.0,https://journal.untidar.ac.id/index.php/rep/article/download/858/2434261d04232f4117b89ca1e0a3ca08d717848b324620,10.2139/ssrn.3780359,Marginal Propensities to Consume Before and After the Great Recession,2021,Social Science Research Network,Yunho Cho; J. Morley; Aarti Singh,"Using a quasi maximum likelihood approach for a semi-structural model, we obtain precise estimates of consumption responses to idiosyncratic income shocks for households grouped by various balance sheet characteristics. Homeowners stratified by higher and lower liquid wealth exhibit the most heterogeneity in marginal propensities to consume out of transitory income shocks. Time-varying estimates before and after the Great Recession support the importance of homeownership status and balance sheet liquidity, with economically and statistically significant increases in transitory consumption responses for homeowners, especially those with lower liquid wealth, associated with the collapse in house prices. We find permanent consumption responses to transitory income shocks are small and stable across time for different households, while consumption insurance against permanent income shocks is higher for homeowners than renters, but is also stable across time. These findings are consistent with theories of consumption that include housing as an illiquid asset. *This paper is a significant revision of an older paper ""Household Balance Sheets and Consumption Responses to Income Shocks"" num",0.0,http://econ-wpseries.com/2019/201911-04.pdf427053e8000e160f5450be436cceb8676e0e13952c7,10.21834/ajqol.v4i15.183,Housing Qualities Characteristics towards Relationship on House Prices,2018,Asian Journal of Quality of Life,A. Nawawi; Edie Ezwan Mohd Safian; A. Saleh,"The research was conducted in order to obtain measurement for characteristics quality of houses in Malaysia. The aim of this research was to produce the results needed for the relationship between house qualities and house prices. The methodology used in this research was primarily by AHP and Regression Analysis conducted between the characteristics and house prices. Data collection gathered through a sampling of landed terrace houses in the Klang district was incorporated for this. The results from the research showed several significant findings that can improve quality of life for targeted households 428Keywords: House Quality; Regression; GIS; AHP 429eISSN 2398-4279 © 2019. 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. https://doi.org/",0.0,https://ajqol.e-iph.co.uk/index.php/ajqol/article/download/183/262430058ccc45c8d1e1961b2030f5f659cf953834610e,10.22313/reik.2023.21.1.51,A Study on the Effect of Changes in the National Housing Affordability Index (HAI) on the Local Housing Sales Market,2023,Residential Environment Institute Of Korea,Jeong-Ah Ku; Sung Gil Kim; Y. Lee,"This study analyzed the effect of changes in the national housing purchasing power index on the local housing sales market, which is recognized as a heterogeneous market with the metropolitan area. The analysis method used the Vector Error Correction Model in consideration of the time-series observation characteristics of the main variables of this study. The summary and implications of this study are as follows. 431First, if housing prices in the metropolitan area soar and housing investors' investment capacity in the metropolitan area decreases, it is estimated that they are interested in the local housing market with relatively high price benefits. In particular, the effect of the change in housing purchasing power is expected to occur mainly in the housing market in local hub cities. Second, it can be seen that the local housing market is most affected by income increase through economic growth in the long run. Third, the total population of Jeollabuk-do is expected to gradually flow out due to the concentration of the economy in the metropolitan area, and the trend of aging is expected to increase rapidly. However, Jeonju, a hub city, is expected to show a significant difference",0.0,432183debbb3f2d76550a76f0be9c2c609dc551ed3a,10.54097/xbqqjf85,A Stable and Interpretable NNLS Ensemble for House Price Prediction on the Ames Dataset,2025,"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 commerci",0.0,https://hbemdata.org/index.php/ojs/article/download/96/824330469a9c27d678bb9e9f28c31ce6d6def7987adf0,10.2208/proge.11.99,GIS-Based CO2 Emission Assessment Caused by Suburbanized Activities Case Study in MUKO River Basin Region for Automobile Trips,2003,,EI-Lithv Ichaled; T. Fujita; T. Morioka; Jun-Ichiro Tokuoka,"This paper attempts to estimate CO2 emission generated by suburbanized urban activities growth. Taking MUKO River Basin Region, Hyogo, Japan as a case study for the emission caused by automobile trips to shopping areas in the region, site significant effects of economic growth and suburban activities growth were found. The analysis was carried out utilizing 1km grid GIS data, lkm statistics data for land use, population allocation, commercial activities, and other related data in this region focusing on the commercial areas where the CO2 emission by automobile trips is most serious. Comparative analysis for changes assessment between 1975s and 1995s two steps are done as follows; First step is to establish “sales regression model” to explain the correlation of the sales changes with several explanatory variables including population, retail floor space, trip distance and time length from residential area surrounding commercial areas. Second step is to calculate the CO2 emission amount which is regressed by parameters of the analysis and other statistical data. The results show significant impact of automobile trips generation to shopping areas oriented CO2 emission on the property ",0.0,https://www.jstage.jst.go.jp/article/proge1998/11/0/11_0_99/_pdf4341d36c57677f1559e8d4f4ddfcf39766d00050f6c,10.1051/itmconf/20269102016,Machine Learning Approaches to House Price Determinants,2026,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/2026910201643517cbf4e0dcb1e0cb4c967ac829450dc4c81410a9,10.61173/th4d9q35,Research on the Factors Affecting House Price using Multiple Linear Regression Model,2024,"Science and Technology of Engineering, Chemistry and Environmental Protection",Ju Ye,"This article aims to identify the factors that affect the change in house prices. In this paper, the Multiple Linear Regression Model is used to analyze the factors with 1000 random samples from the USA which was collected from the 2nd of May in 2014 to the 10th of July in 2014. Based on the datasets, 13 variables (bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, sqft-above, sqft_basement, yr_built, yr_renovated, street, city) are selected as independent variables, and used in scatter diagram to show the correlation with the dependent variable price. Turns out that 3 variables have not shown a significant correlation with the price (yr_built, yr_renovate, city). To find which of the factors have the most significant impact on the change in house prices, VIF value is used to compare the collinearity of those 10 variables. Finally turns out that only 5 variables (bedrooms, view, condition, waterfront, sqft_lot) show the most significant effect on the price of housing. Overall, the main factors affecting the price of housing in the USA were the number of bedrooms and views next to the house, as well as the conditions needed for buying the house.",0.0,https://doi.org/10.61173/th4d9q35436183fa1e35cbc466d64de3e45e8919e6277cc8337,10.1108/ijhma-01-2023-0016,A dynamic analysis of the influence of foreign real estate investments on residential land prices in Mauritius,2023,International Journal of Housing Markets and Analysis,N. Gopy-Ramdhany; B. Seetanah,"437Purpose438Mauritius’s residential real estate sector has undergone an increase in foreign investment over the past decades. This study aims to establish if the increasing level of foreign real estate investments (FREI) has increased land demand and land prices. The study also aims to depict whether the relation between FREI and land prices prevails at an aggregate and/ or a regional level.439440441Design/methodology/approach442Data from 26 regions, classified as urban, rural and coastal is collected on an annual basis over the period 2000 to 2019, and a dynamic panel regression framework, namely, an autoregressive distributed lag model, is used to take into account the dynamic nature of land price modeling.443444445Findings446The findings show that, at the aggregate level, in the long-term, FREI does not have a significant influence on land prices, while in the short term, a positive significant relationship is noted between the two variables. A regional breakdown of the data into urban, rural and coastal was done. In the long term, only in coastal regions, a positive significant link was observed, whereas in urban and rural regions FREI did not influence land prices. In the short term, the positiv",0.0,4477052313fc3b70ba3d92d6169f9f05b066f688b7c,10.5220/0005907403780383,Households and sustainable green technologies: A review,2016,International Conference on Smart Grids and Green IT Systems,Simona Bigema; C. Bollino; S. Micheli; P. Polinori,"There is widespread consensus in the climate research community that households show different attitudes toward the broad spectrum of technologies and policy instruments implemented to reduce CO2 emissions. The aim of this paper is to investigate the monetary aspect of socio-economic acceptability of four sustainable green technologies development: green electricity, energy savings in residential buildings, smart meters and alternative fuel vehicles. We obtained information on willingness to pay, and/or willingness to accept, for such technologies from a sample of 35 papers taken from the literature. We homogenize this information computing an implicit price of a Kg of CO2 avoided, named PCO2. A qualitative analysis is carried out to explain the households' attitude to avoid CO2 in monetary terms. Results show that on average PCO2 is positive. There are, however, some negative attitudes only in the case of alternative fuel vehicles. In conclusion, empirical results show that households have a favorable attitude toward sustainable green technologies, but further research is desirable to design new policies to make the future of the sustainable society more plausible.",0.0,https://doi.org/10.5220/00059074037803834481568d94fbdf9523844ee96dc7dc4f537a8977608,10.5296/ifb.v10i1.20893,The Analysis of Residential Property Price Bubble and Sharia Bank Financing Using MIDAS Regression Model,2023,International Finance and Banking,Aula Ahmad Hafidh Saiful Fikri; Mustofa Mustofa; Maimun Sholeh; Sri Indah Nikensari,"In economic modeling there are constraints in terms of time frequency differences in the data used as input for estimating a model. One real example is when modeling the relationship between the Islamic banking financing (FIN) and the Housing Price Index (HPI), where the frequency of FIN data is monthly and HPI is quarterly. In this study, it has been tried to develop a model which can be used in the forecasting of Residential Property Price Bubble by using MIDAS (mixed data sampling regression) method which allows the series to be used in the same regression equation from different frequency.Considering the AIC and SIC criteria, it was found that the best performing model out of four alternatives was the weighted equation according to the U-Midas method. The research result shows that the MIDAS specification for modeling the relationship between Islamic bank credit and the property price index is MIDAS with the U-Midas function at lag 4. This model is able to explain variations in property price bubbles of 98.8916%, meaning that the model can be used well for forecasting. Performance the resulting forecast is very good, it is shown that the MAPE value is 0. 2141%.",0.0,https://www.macrothink.org/journal/index.php/ifb/article/download/20893/16614449doi_10_5102_pic_n2_2016_5523,10.5102/pic.n2.2016.5523,IMPACTO DO RUÍDO AERONAÚTICO NO PREÇO DE IMÓVEIS RESIDENCIAIS: ESTUDO DE CASO DO AEROPORTO INTERNACIONAL DE BRASÍLIA,2018,Programa de Iniciação Científica - PIC/UniCEUB - Relatórios de Pesquisa,Renata Da Silva Boitrago; Izabela Rodrigues Pereira Lopes; Edson Benício de Carvalho Júnior,"O ruído aeronáutico é um dos principais problemas relacionados à atividade aeroportuária com efeitos adversos sobre a saúde e qualidade do sono em populações residentes próximos a aeródromos, uma outra externalidade devido ao ruído que atinge a comunidade é a depreciação no valor dos imóveis situados na região de influência da operação aeroportuária. No Brasil, poucas pesquisas são desenvolvidas com foco na avaliação dos efeitos do ruído aeronáutico no valor de imóveis. Desse modo, o presente trabalho possui por objetivo principal avaliar o efeito do ruído aeronáutico sobre o preço de imóveis residenciais em áreas no entorno do Aeroporto Internacional de Brasília. Essa avaliação foi determinada por meio do Método de Preços Hedônicos (HPM). Com uso do HPM foi determinado o Índice de Depreciação por Sensibilidade ao Ruído (NSDI). Esse índice indica as mudanças nos preços dos imóveis, devido à exposição ao ruído aeronáutico, para a região objeto de análise. Para a realização desse estudo, admitiu-se a existência de atributos relacionados ao preço do imóvel, características do imóvel, da cidade e ambientais. O banco de dados é composto por um total de 400 imóveis. A análise realizada f",0.0,4501b514deb00a487036131d7274d7603aaf2414f99,10.2478/mosr-2022-0004,Assessment of Factors Determining the Level of Private Credit in European Union Countries,2022,Management of Organizations: Systematic Research,Lina Garšvienė; Kristina Balčiūnaitė; Kristina Matuzevičiūtė; Dovilė Ruplienė,"Abstract This paper aims to evaluate the factors determining countries’ private credit level as well as to identify the differences of their effect during the periods when the levels of country private credit exceeded 100 percent of GDP or were below. The research methodology relies on two modifications of the multiple regression model with log differences of variables. Research results showed a negative impact of economic growth and a positive impact of consumer prices and housing prices on the level of private credit. It has also been found that in the first period when the level of private credit to GDP exceeds the 100 per cent threshold households tend to borrow more than in other periods. In the second model distinguishing between periods when the level of country’s private credit was below 100 per cent of GDP and when this level was reached or exceeded the research showed that the effects of economic growth do not differ between periods of high and low indebtedness, but the difference becomes apparent when assessing the impact of household income and expenditure, thus confirming the impact of the marginal financial depth.",0.0,https://sciendo.com/pdf/10.2478/mosr-2022-000445111f8430693370192d44916781b50a271d4f9f3e3,10.1109/aece62803.2024.10911214,House Price Prediction Using Machine Learning,2024,2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE),S. Rathore; M. Khan; Saurabh Kumar; Arvind Hans; P. Gangwar; Chinmay Jain,"This study presents a machine learning algorithm aimed at predicting home values in the housing market. By utilizing a comprehensive dataset that includes details such as location, size, number of bedrooms, and bathrooms for each house, the model goes through several preprocessing steps. These steps include handling missing values, encoding categorical variables, and managing outliers. Feature selection techniques are applied to identify the most significant attributes. To ensure robust model evaluation, the dataset is divided into training and testing subsets. A comparative analysis of various machine learning algorithms is conducted, including Linear Regression, Ridge Regression, Lasso Regression, ElasticNet, SVR, Decision Tree, Gradient Boosting Regressor, Extra Trees Regressor, K Neighbors Regressor, Dense Deep Learning, Regularization, Deep Learning, Random Forest Regressor, XGBoost Regressor, and Polynomial Regression (Degree=2). Evaluation metrics such as mean squared error, mean absolute error, and R-squared are used to assess model performance. Ensemble techniques, particularly Random Forests and Gradient Boosting, consistently demonstrate strong predictive capabilities, o",0.0,45223d3cce411ca2b2e1081631866285f47f0d62ca3,10.25077/jmua.15.2.163-178.2026,Locally Weighted KNN-Based Fuzzy Regression for Property Valuation under Market Uncertainty,2026,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/1653/105145332e004872a5c15f367ee1c336d38c16ea64ba68e,10.3311/ppar.14671,Market Value of Residential Property Using the Value Methodology: A Combined Approach,2020,Periodica Polytechnica Architecture,I. Hajnal,"Real Estate Valuation and Value Methodology are two consultancy businesses that are similar in name. Both fields have a several decade-long long history, are subject to strict regulations and standards and have their practitioners integrated into prestigious professional organizations. That said, it is very difficult to identify any professional relationship between the two consultancy businesses, given the little overlap between the approaches used. To date, property consultants are yet to use the versatile toolkit of VM.This article presents a model that combines the hedonic analysis of Real Estate Valuation with the function analysis methods elaborated within VM. Previously resting on a strictly empirical basis, the hedonic model can thus incorporate a value driver vector constructed against end-user criteria, resulting in a more accurate methodology for the establishment of value. The approach described in this article can also be used for a more detailed analysis of other types of property. This article is is an extended version of a conference paper presented at Creative Construction Conference 2019 (Hajnal, 2019).",0.0,https://doi.org/10.3311/ppar.14671454176c9a0e78397604c0301e9ae62940e8b0cd7930,10.56409/kreis.2026.9.2.349,An Analysis of the Distributional Characteristics and Risk-Return Structures of Regional Apartment Sales Price Movements across Different Phases,2026,KOREA REAL ESTATE INDUSTRY SOCIETY,Dong-Wan Han; Jin-Hyeong Seo,"This study empirically analyzes the structure and volatility characteristics of the housing market across major cities in South Korea using monthly apartment transaction price indices from January 2015 to February 2025. Unlike previous studies that mainly focused on inter-market linkages and shock transmission using dynamic econometric models such as VAR or GARCH, this study emphasizes the distributional characteristics of city-level housing price growth rates. Basic statistics—including mean, standard deviation, coefficient of variation, skewness, and kurtosis—were calculated, and market phases were divided into an upturn (2017-2021) and a downturn (2022-2023) to compare risk-return structures by phase. The results suggest that changes in growth rates may provide earlier signals of market phase transitions than price levels., reflecting market levels and regional polarization, while the growth rate serves as a leading indicator, signaling potential market phase transitions. During the downturn, volatility increased, the distribution exhibited slight left skewness, and kurtosis rose, indicating a higher probability of extreme price fluctuations. Regional analysis between the Seoul ",0.0,455093fbec598ea7e9c0fd12a48abae5c9a1345b8b4,10.1117/12.3123692,Analysis of regional housing price fairness and public resource mismatch in Hangzhou based on K-Means++ and OLS regression,2026,"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 ove",0.0,456doi_10_2139_ssrn_280796,10.2139/ssrn.280796,Institutions and Market Reform in Emerging Economies: A Rent Seeking Perspective,2001,,Ram Mudambi; Pietro Navarra; Chris W. Paul,"The role of institutions as determinants of rent seeking success is well established. In this paper, we focus on institutions that have received little attention in the literature, namely electoral institutions. We examine three measures of electoral institutional structure that are hypothesized to be instrumental in determining the level of rent seeking. These are the type of electoral system, pluralistic or proportional; method of selection of the chief executive, presidential or parliamentary; and the number of electoral districts. An index of economic freedom is used as the metric for rent seeking opportunities created by governments. Theoretical implications of variation in these electoral institutions are developed and empirically tested employing data from 29 countries classified as having emerging market economies. Countries with emerging economies are expected to exhibit more institutional flexibility that more developed countries whose property rights are well established and defended. The empirical results are controlled for differences in a number of demographic and historical factors. Plurality electoral systems are more resistant to the political demands of rent seeki",0.0,457doi_10_2139_ssrn_4058487,10.2139/ssrn.4058487,Impacto del COVID-19 sobre la demanda y oferta de vivienda nueva No VIS en Colombia (Impact of COVID-19 on the Demand and Supply of New No VIS Housing in Colombia),2022,,Daniela Álvarez Zuluaga; Catalina Betancur Carvajal; John Garcia Rendon,"<b>Spanish Abstract:</b> En este trabajo examinamos el impacto del COVID-19 sobre la demanda y oferta de vivienda nueva No VIS en Colombia. Usando un modelo de ecuaciones aparentemente no relacionadas, encontramos que el COVID-19, medido a través del número de contagios y la tasa de interés en UVR presentan una relación inversa con la oferta de vivienda nueva No Vis, disminuyendo las cantidades ofertadas durante los meses más críticos de la pandemia. Además, el índice de precios de la vivienda nueva cumple con la ley de la oferta, lo cual puede validarse con lo que se ha observado en 2021 sobre el crecimiento en el lanzamiento de nuevos proyectos inmobiliarios, al mismo tiempo que los precios también han sido mayores, justificado, en parte, por el incremento en los costos a raíz de la pandemia.<br><br><b>English Abstract:</b> In this paper we examine the impact of COVID-19 on the demand and supply of new No VIS housing in Colombia. Using a model of Seemingly Unrelated Regression, we find that COVID-19, measured through the number of infections, and the interest rate in UVR present an inverse relationship with the No Vis new housing supply, decrea",0.0,4581af0a7f1a257386ba8413be30903af24995be467,10.54097/gfxf8r63,Significance Level of Factors Effecting House Price,2024,"Highlights in Business, Economics and Management",Jinhe Liu,"This paper is about analyzing dataset recorded between 1975 and 2021 about the house prices in United State and uses multiple linear regression to plot linear equation to help to predict the future house prices. The data includes some essential factors, population for example, that influences house prices index both directly and indirectly and did a further analysis to find the correlation between these factors and house prices index, and the correlation between each influencing factors. These factors all have difference significance level comparing to house price index, making it worth to find out even deeper the most influencing factors that affect house price index so the most important factors can be deduced. In the result, real disposable income is being concluded as the most important factors effecting the house price index among all other factors. In addition, the author also uses determinant coefficient to further confirm the accuracy of the multiple linear regression, making sure linear regression is a valid method to analyze the significance of all influencing factors.",0.0,https://drpress.org/ojs/index.php/HBEM/article/download/27430/269694591af6d482f136f3670644241e9cf4d95410ccb37c,10.1109/icscss64956.2025.11501097,House Price Prediction Algorithms and Model based on Machine Learning,2025,2025 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 prope",0.0,460doi_10_29327_2290393_1_1_7,10.29327/2290393.1.1-7,Regressão polinomial e redes neurais artificiais na avaliação de imóveis: um estudo de caso,2022,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 al",0.0,4610cb7ddecd4a9daddf8fe86dd7018047ae1181b74,10.54097/k82s4204,Comparison and Analysis of Boston Housing Price Predictions Using Different Machine Learning Methods,2026,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/33374/326584621f0be637068f0974b2c62f28f9a662f03f0f2423,10.54254/2753-8818/51/2024ch0199,Research on the Influencing Factors of House Prices based on Multiple Linear Regression Model,2024,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/pdf4630ca92c6cacb3cd39a24f1572e4d3e1b819687205,10.37339/e-komtek.v9i1.2314,Literature Study: Prediction of the Type of Company where Students Work Using Naïve Bayes and Neural Network Algorithms,2025,Jurnal E-Komtek,A. Saputra; Luqmān; Herman Bedi Agtriadi,"Research was conducted to evaluate the effectiveness of various machine learning algorithms, such as Naive Bayes, Support Vector Machine, Random Forest, and Artificial Neural Network (ANN), in predicting and classifying data. Naive Bayes proved to be efficient and accurate in structured data classification, such as predicting alumni's waiting time to get a job (94%) and vocational school students' job readiness (96.95%). On the other hand, neural network methods such as ANN and GRNN are superior in handling non-linear regression problems, such as house price prediction or college students' study period, although there is still room to improve accuracy. Random Forest is more suitable for complex data, while Naive Bayes is more effective for simple data. This research emphasizes the importance of selecting relevant variables, such as gender, major, and GPA, to improve model performance. Therefore, the selection of machine learning methods should be tailored to the type of data and the purpose of the analysis, as each algorithm has its own advantages and disadvantages.",0.0,https://jurnal.politeknik-kebumen.ac.id/E-KOMTEK/article/download/2314/1217464doi_10_29105_vtga7_1_123,10.29105/vtga7.1-123,Influencia de las motivaciones hedónicas en la intención de compra de una vivienda en el Área Metropolitana de Monterrey,2021,Vinculatégica,Karen Viridiana Segovia Martínez,"En el presente artículo se expone una reseña histórica y teórica para comprender los conceptos básicos sociales en el área metropolitana de Monterrey y su desarrollo social, económico e ideológico, así como la relación que tiene con el resto del mundo. El objetivo de esta investigación será comprobar si realmente es necesario incluir un estudio social profundo para el diseño de las viviendas prototipo en el Área Metropolitana de Monterrey (AMM) en resaltar las características de las personas que habitan en la región de estudio y saber que buscan al momento de comprar una vivienda, utilizamos métodos cuantitativos en la investigación para ello se realizó un breve cuestionario de 18 preguntas a 100 personas que han realizado alguna remodelación y han requerido servicios de diseño y construcción. Analizamos en esta investigación las características hedónicas en cuanto a los espacios de la casa habitación, nivel de ingreso en conjunto de la vivienda, descubrir los espacios de mayor interés de los individuos y ver que tan conformes están con los espacios sugeridos en las viviendas ofrecidas al público en serie en la actualidad. Los principales hallazgos son que los individuos que tienen",0.0,4650505be8a01043aa35d7fb41cfbdc22d5f9db666f,10.36948/ijfmr.2026.v08i02.73872,A Comprehensive Review of Web Scraping and Machine Learning Techniques for City-Wise Rent and Living Cost Estimation,2026,International Journal For Multidisciplinary Research,Ashwini Dhase; Priyanka Bhore; Vaishnavi Gawade; Snehal Gavhane; Pranali Gopane,"The rapid growth in urban populations, and the movement of individuals for education or job opportunities have put pressure on creating reliable methods for estimating rental costs. Current platforms that focus on real estate primarily use rental listings as a means of providing access to finding a place to live, however they do not generally include comprehensive living cost estimates, fairness analysis of prices, or personalised recommendations. This paper will address 36 research papers from 2001 to 2025, examining predictive rental price algorithms and web scraping techniques, big data analytics and ensemble machine learning466models, deep learning methods, and spatial analysis methods, as well as using XAI in the housing domain. According to current research, ensemble-based learning methods—like Random Forest and boosting frameworks—perform better than conventional regression models in predicting rental and home prices. It was also shown that using geographic features, socio-economic indicators,467multi-modal inputs, and explainability based on SHAP contribute to enhanced accuracy and transparency of the rental price prediction models. However, a unified model for web scraping in ",0.0,4682234e4ae7426772354989aa89f67382566b4c869,10.26689/pbes.v8i4.11922,An Empirical Study on the Impact of Bank Credit on Real Estate Price Fluctuations in China—A Case Study of 35 Large and Medium-sized Cities,2025,Proceedings of Business and Economic Studies,Xuenian Zhao; Qun Zhang,"Fluctuations in real estate prices are closely linked to the macro-economy, exerting a profound influence on social investment and consumption levels. As a key source of funding for the real estate market, bank credit significantly affects housing price changes in major Chinese cities. This paper explores the transmission mechanisms and pathways of bank credit on real estate prices through theoretical analysis and empirical research. It constructs a panel regression model to empirically analyze the relationship between bank credit scale and housing prices in 35 large and medium-sized Chinese cities from 2012 to 2022, assess the impact of credit on housing price fluctuations, and compare differences between first-tier and second-tier cities. Based on these findings, the paper proposes suggestions for regulating housing prices by controlling credit scale, aiming to deepen the understanding of the relationship between bank credit and housing prices and support the stable development of China’s macro-economy and real estate market.",0.0,https://doi.org/10.26689/pbes.v8i4.11922469091f04772d7eebaf0403167d2ee33dc05440a931,10.54097/0crx5r02,Using The Regression Method to Discover the Relationship Between Real Estate and Stock Market,2024,Highlights in Science Engineering and Technology,Zixuan Li," Recently, with the economic fluctuation, the relationship between house price and the stock market index has been paying much attention. However, this relationship has not been fully clarified yet through historical research. In this paper, several regression models including linear regression model and non-linear regression model are constructed to test for both single and dual relationship between the house price and market index. The assumption and method used in building the model will also be discussed. From testing, there is a positive linear effect on house price from the market index (approximately 80% of house price variation has been explained by the market index). On the opposite side, house price affects 93% of the market index in a positive non-linear pattern. Besides that, GDP can explain around 90% of house price variation in a positive linear relationship. This paper aims to help investors to have a better understanding of the relationship between these two and to help them build a better investment portfolio.",0.0,https://drpress.org/ojs/index.php/HSET/article/download/18419/17955470doi_10_5821_siiu_6974,10.5821/siiu.6974,"La vivienda de interés social, detonador dedesigualdad en México: el caso de los Héroes Tecámac, Estado de México",2019,"XI Seminario Internacional de Investigación en Urbanismo, Barcelona-Santiago de Chile, Junio 2019",Guadalupe García Balderas,"En este trabajo se busca demostrar que la metodología oficial para la medición de la pobreza en México, oculta la presencia de ésta en los conjuntos habitacionales de interés social, asimismo, se considera que dadas las características de los conjuntos habitacionales que fueron construidos posteriormente a los cambios a la política de vivienda realizados en 1988,es decir, en el contexto del neoliberalismo, representa un detonador de desigualdad para sus habitantes, quienes enfrentan mayores desventajas respecto al resto de la población que se localiza en zonas con mayor infraestructura o mejor situadas. Por lo anterior, se propone un concepto y una metodología alternativa para la medición de la pobreza y la desigualdad que tenga aplicación en los conjuntos habitacionales de interés social. This paper seeks to demonstrate that the official methodology for the measurement of poverty in Mexico, hides its presence in the housing complexes of social interest, also, it is considered that given the characteristics of the housing complexes that were subsequently built to the Changes to housing policy made in 1988, that is, in the context of neoliberalism, represents a trigger for inequalit",0.0,4710ac8fab28e8518c602e5bf5b3b7341a97fbf1053,10.1111/jors.70046,"Hear Ye, Bear Ye: Housing Values, Noise Levels, and Noise Inequality",2026,Journal of Regional Science,Jeffrey P. Cohen; Cletus C. Coughlin; Felix Friedt,"472 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.5058147473026fd0ca53ecb0f64931e41ecac683b270fa9c68,10.54740/ros.2022.005,Influence of Railroad Infrastructure on Residential Property Prices on the Example of Kórnik Municipality,2022,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.pdf474105ce0ab8618e42e638662d5e1a0610dda43b82c,10.1016/j.dib.2026.112884,A comprehensive dataset for uncovering housing price drivers in emerging cities: Nanning as a case study,2026,Data in Brief,Wenbo Lin; Kaichuang Wu; Xiao-Lu Zhang; Xiaoling Gu; Ming-xia Jiang; Jun Wang; Feng Han,"This dataset supports the research article titled ""Exploring housing price dynamics in sustainable cities through a cooperated big data driven machine learning method: case study on a typical city in China"", published in City and Environment Interactions. The data were collected from multiple sources, including web-scraped real estate listings, air quality monitoring stations, public amenities using the Gaode Map API, and population data from the LandScan global dataset. The dataset includes variables describing property characteristics, accessibility, environmental quality, and land use patterns. Random Forest modeling and SHAP values were used to interpret the contribution of each feature to housing price volatility. This dataset is valuable for urban economists, planners, and data scientists studying housing market dynamics, land use policy, or spatial machine learning. It enables replication, benchmarking, and comparative studies in similar urban contexts across developing cities.",0.0,https://doi.org/10.1016/j.dib.2026.1128844751ce6b4a62036a2a4717dc7e5687e085dfd34c4d5,10.54254/2753-8818/52/2024ch0125,Factors influencing housing prices: A case study of Shanghai,2024,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.pdf4761ad007ac2e8575311162da739431607dfdaeeb1b,10.54254/2753-8818/2025.23756,The Research on Factors Influencing Housing Prices-Take Chicago as an Example,2025,Theoretical and Natural Science,Zhaoyi Chen; Siqian Ji; Zi-Yu Li,"This paper purposes to research what factor is influenced the house price in Chicago. The Multiple Linear Regression will be used as the main method in the article. The 158 samples are chosen to analyze the factors. According to this data, there are 8 variables will be related with housing price. There are the number of bedrooms, spaces, rooms, lots, taxes, bathrooms, garages, conditions respectively. Also, this paper considers the area, stories, guestroom, basement, hotwaterheating, air-conditioning, parking, prefarea, furnishing status, and use Pearson correlation to show the strength of them. To prove the accuracy of the set of data, the model will use the value of F to check. The fact is verifying that area, bedrooms, bathroom have significant relation with the house price in Chicago and the other factors are not have direct relationship with price. Overall, the house price in Chicago can be influenced by the condition of the environment around the house.",0.0,https://www.ewadirect.com/proceedings/tns/article/view/23756/pdf477062e42160a6df6f6521975d1fc6b2b76706f90a2,10.53479/44185,The impact of air pollution on the housing market: The case of Madrid,2026,Documento de trabajo,Marina Gómez-García,"This paper estimates the causal impact of air pollution on housing prices in Madrid, a large European city with relatively high and persistent nitrogen dioxide (NO2) concentrations and a home ownership rate of around 70%. Using location-specific data on pollution levels and the universe of housing transactions, my estimation controls for house and neighborhood characteristics, year and neighborhood fixed effects, a bad weather index and a set of time-varying neighborhood characteristics. To address endogeneity concerns, I make use of quasi-experimental variation in nitrogen dioxide levels resulting from temperature inversions. The results suggest that a 10% increase in air pollution reduces housing prices by 0.65% (around €1,327 for the average house). This effect is robust to alternative measures of air pollution, different bad weather indices and the inclusion of seasonality dummies, and is highly non-linear, being larger for higher levels of pollution.",0.0,https://repositorio.bde.es/bitstream/123456789/44185/1/dt2624e.pdf478doi_10_61636_bpg_v1i113_3620,10.61636/bpg.v1i113.3620,Os movimentos espaciais e de preços de moradias em Belo Horizonte entre 2009 e 2022 The The spatial and housing price movements in Belo Horizonte/MG between 2009 and 2022,2025,Boletim Paulista de Geografia,Ícaro Neri Pereira de Souza; Ricardo Alexandrino Garcia,"Este trabalho tem como objetivo analisar os movimentos recentes dos preços de vendas de apartamentos em Belo Horizonte e investigar possíveis processos de gentrificação e suas características. Para isso, a pesquisa utilizou uma Análise Exploratória de Dados Espaciais (AEDE) e o método estatístico I de Moran Local, aplicados a dados de vendas de apartamentos na cidade, obtidos a partir dos registros de pagamento do ITBI. Os resultados revelaram uma forte segregação socioespacial na cidade, com pouca movimentação de moradias nos segmentos de mercado voltados para as classes mais abastadas, concentradas principalmente na região Centro-Sul e na Pampulha. Entretanto, observou-se um significativo espraiamento de segmentos voltados para a classe média, com ocupação dos últimos grandes vazios urbanos municipais. Destaca-se o caso do bairro Diamante, na regional Barreiro, que apresentou uma ocupação inédita por moradias direcionadas a esse segmento em um vazio urbano cercado majoritariamente por bairros de baixa renda e algumas favelas. Assim, os resultados da pesquisa apontam para movimentos semelhantes aos processos de reestruturação urbana e formação de “rent gaps” descritos por Neil Smi",0.0,4790848e26ae9a314de2560b0cf706d70619babaeba,10.55041/ijsrem35291,House Price Prediction Website,2024,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=665d136bd1d361717375851480190bb3f0d7a3df1e1422f134cf45c6e1a7266229,10.2991/aebmr.k.220307.365,The Influence of Real Estate Tax on House Price in Shanghai,2022,Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022),Xin-Tong Feng; Minxi Jia; Chenghao Yang,"The rapid growth of housing prices prevents the Chinese government from introducing policies related to real estate. This paper takes Shanghai's second-hand real estate market as the research object, takes GDP and income as the main indicators, analyzes and establishes an econometric multiple linear regression model to study the impact of the real estate tax. The research results of this paper show that the per capital disposable income of urban residents in Shanghai is highly correlated with the average price of commercial housing, and the real estate tax has relatively little impact on the housing with demand, but a relatively large impact on the improvement of housing and real estate speculators. After implementing the property tax, it will have a small impact on people below the tax-free zone and who only have one house, and to some extent, it will limit the demand of middle-class buyers who have enjoyment needs.",0.0,https://www.atlantis-press.com/article/125971687.pdf4811026bde64745c9e05ca122b1c9cd272f9e0c9f72,10.2991/febm-18.2018.86,The Impact of Real Estate Policies on the Housing Price in Major Cities in China,2018,,Wenjun Lyu; C. Bu,"This paper used the panel data from August 2015 to March 2018 to analyze the effect of the policies such as purchase restriction policy, loan restriction policy, sales restriction policy, price restriction policy and rent and purchase system in the past two years on the housing price in major cities of China by establishing regression models. We found purchase restriction policy and price restriction policy can effectively restrain the growth of the housing price in the short term, but in the long run, they will lead to the retaliatory growth on price. Sales restriction policy has a significant impact on the housing price in the long run and it can lower the price. Rent and purchase system makes a limited effect on the price. All the policies should be combined together and be reasonable so as to stabilize the real estate market. Keywords—The housing price; Restrcition policy; Real estate market; Regression models",0.0,https://download.atlantis-press.com/article/55907685.pdf4821f1e429f9e627f1d6560eabcd2210b3b2c67dbf7,10.1109/icad69378.2026.11608473,Predicting St. Louis Housing Prices with Machine Learning on Market and Assessor Data,2026,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_v148323e8fd5af8166238daa1ee622fa1b05a4520a9d1,10.54691/bcpbm.v34i.3136,Research on the Prediction and Influencing Factors of House Price Based on Regression Analysis Model,2022,BCP Business & Management,Jiaxin Zhang,"To analyze what factors may have an impact on house price, this research used the house sales information from king county in U.S from May 2014 to May 2015 including house prices and 16 other factors to see whether they are correlated with house price or not. To minimize the error the research pre-processes the initial data and runs an endogeneity analysis among the independent variables. The research used single regression models along with multiple regression models to analyze the data. The research found out that the square feet of the living and the surrounding environment as well as the grade of the house are highly correlated with the house price. The research then suggests that the government should pay attention to these factors when building a house and the real estate company should adjust their marketing strategy based on this model in order to promote the overall value of the house.",0.0,https://doi.org/10.54691/bcpbm.v34i.31364840f3d3f74bc1f4f9e077018124e26b1eaa383e526,10.1109/icsit65336.2025.11293872,Comparative Analysis of Regression Models on Datasets with Varying Feature Complexity,2025,"2025 International Conference on Sustainability, Innovation & Technology (ICSIT)",Aniket G. Patel; S. Shanmugam,"This study looks at how different regression models perform and respond to variations in the number of features in the dataset. This study focuses on how models behave, diverge, or fail under different feature complexities rather than just comparing accuracy. This study included various regression models such as Linear Regression, Decision Tree, K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM), Gradient Boosting, and a simple Neural Network. The House Price Prediction dataset and the Auto MPG dataset were used in this study. The House Price Prediction dataset was chosen because of its extensive feature set. Whereas the Auto MPG dataset has very few features. The results show that simpler models usually perform better with fewer features. However, powerful models such as Support Vector Machines (SVMs) and neural networks can perform poorly on highdimensional data if improperly tuned. On the other hand, treebased ensemble models like Random Forest and Gradient Boosting showed stable and reliable results in both cases. To evaluate the model's performance four metrics: R-squared, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMS",0.0,48515b0f8893b2d601ec0f55c78ae001d69859145ae,10.62051/ijgem.v4n1.28,Study on the Factors Affecting Housing Prices in Chengdu,2024,International Journal of Global Economics and Management,Z. Tao,"Based on the background of the sluggish real estate market in recent years, this paper organizes the relevant data on the average selling price of commercial housing in Chengdu from 2002 to 2022 and studies the factors affecting housing prices. It also collects data such as per capita GDP, housing completion area, and real estate development investment in Chengdu from 2002 to 2022, and constructs the STIRPAT model for multiple linear regression. After a series of model tests and empirical analysis, it is concluded that both per capita GDP and real estate development investment have a positive effect on Chengdu housing prices, while the housing completion area is negatively correlated with Chengdu housing prices. Based on the research results, this paper summarizes the conclusions and puts forward suggestions for the Chengdu Municipal Government and real estate developers.",0.0,http://dx.doi.org/10.62051/ijgem.v4n1.284862351fb834dbda9ab369528d65c095a7b0d50a26c,10.30672/njsr.163282,Does Access to Housing Wealth Relax Liquidity Constraints for Entrepreneurs? Evidence from Sweden,2026,Nordic Journal of Surveying and Real Estate Research,Å. Hansson,"Liquidity constraints are often seen as a barrier to entrepreneurship, with many relying on personal or informal capital to start a business. This paper examines whether increased access to equity ─ via rising value of owner-occupied housing ─ affects the likelihood of business entry. Using detailed Swedish tax return data and exploiting the 2008 property tax reform as a natural experiment, we analyze how changes in house prices influence entrepreneurial activity. While higher housing prices correlate with business ownership and entry, this relationship disappears when isolating the causal effect of the tax reform. These findings contrast earlier findings and suggest that the value of owner-occupied housing proxy for individual characteristics rather than easing liquidity constraints, indicating that such constraints are not a major barrier to entrepreneurship in Sweden.",0.0,https://doi.org/10.30672/njsr.16328248712ab711760bfab76641e0bf59660c05e5add7ec7,10.2298/pan230420009a,The role of housing-dominated attributes in housing booms: Evidence from China,2023,Panoeconomicus,P. Arestis; Mianshan Lai,"This paper investigates the dominant attribute of housing in China for the488 period 2000 to 2017 with national monthly data. By employing VAR methodology489 and Chow test, the empirical results suggest that there is a statistically490 significant structural breakpoint in February 2009, and that the housing491 boom before 2009 was driven by self-occupation consumption demand while the492 rise in house prices since 2009 is a monetary phenomenon. These are493 consistent with the institutional backgrounds, when the first period494 encountered the end of housing as a welfare and rapid urbanization and the495 second period experienced the expansionary monetary policy introduced at the496 end of 2008. This implies that housing is gradually evolving from a497 necessity to a financial item, and policymakers should adopt policies that498 address the dominant attributes of housing at different periods.",0.0,http://www.doiserbia.nb.rs/ft.aspx?id=1452-595X2300009A4990cbd0dc9e83099456998376b2af563dfd440d27c,10.1145/3583133.3590643,Evolution Enhancing Property Price Prediction by Generating Artificial Transaction Data,2023,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 dat",0.0,500doi_10_7200_esicm_142_0432_2e,10.7200/esicm.142.0432.2e,Heterogeneidad en precios explicada por la participación en actividades recreativas Explaining price heterogeneity through leisure participation,2012,ESIC Market,Juan Luis Nicolau Gonzálbez,"The purpose of this article is to analyze the effect of distinct participation frequencies of leisure activities at and away from home on the consumer price sensitivity. The idea is that the individual price sensitivity is influenced, at the moment of choosing a destination, by the motivation that drives people to search for destinations that allow them to do specific leisure activities; these motivations condition the influence of the residual culture (through which people exhibit at destination a similar behavior to their home environment) and tourist culture (by which at the destination they change the patterns of activities they get involved in at their place of origin). For this purpose, we measure and identify individual price sensitivities individual by individual. The empirical application is carried out on a sample of 2,127 individuals, and the operative formalization used to estimate individual sensitivities to price follows a Random-Coefficient Logit Model; and to detect the way these sensitivities relate to the different participation frequencies in leisure activities, a regression procedure is employed. The results show that the residual and tourist cultures have an ",0.0,50118b92b0e188c00f5f9717ce170369158aafe285b,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,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 beenre",0.0,502178e9dc9fcfb91b018c4cd5ff1e2862baeb4434a,10.46609/ijsser.2025.v10i10.032,"Total Fertility Rate relations in South Korea, evaluating the correlation of factors with the TFR of South Korea: Approach with Regression Analysis",2025,International journal of social science and economic research,Jinwoo Lim,"South Korea’s low fertility rate poses significant economic challenges, as it reduces workforce numbers, lowers productivity, and increases dependency. This study examines the economic factors and policy interventions that may impact total fertility rates. Using linear regression models, the analysis tests the hypothesis that tax credits, parental leave duration, and health expenditure have a positive correlation with fertility, while childcare costs, housing price index, youth unemployment, and contraception rate have a negative correlation. Results show that fertility rates are most strongly associated with health expenditure priorities and contraceptive availability, while the association of family benefits such as childcare support and parental leave policies remains limited once long-term demographic patterns are taken into account.",0.0,https://doi.org/10.46609/ijsser.2025.v10i10.0325031362a388aaa49c104fc63f2f104e177950830ad9,10.1109/icdabi63787.2024.10800514,"Comparing Linear Regression, Random Forest and Support Vector Machine for Rental House Price Forecasting in Bahrain",2024,2024 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 contra",0.0,504doi_10_5377_akademos_v2i25_4449,10.5377/akademos.v2i25.4449,La vivienda popular en la ciudad de San Salvador: bases para su comprensión historiográfica The popular housing in the city of San Salvador: bases for its historiographical understanding,2017,AKADEMOS,Juan Carlos Chicas Molina,"The present article aims to draw an analytic historical line, about the urban development of San Salvador, El Salvador. This approximation it is set, as basis to the comprehension of popular housing development in two periods of time, ex ante and ex post of president Osorio´s regime that started on 1950. Methodologically we used as text, the San Salvador´s evolution of its urban grid, in different development phases in the light of the historical conditions, in an open dialog between causes and conditions. The time line set, respond firstly, to an effort to situate the urban development of San Salvador, in the big frame of the urban history of Latin-America in this period; and secondly, because the military regimens started an urban development phase, that breaks down the traditional morphology of the city from the spatial form, that became in the actual fragmented city. The institutionalization and technification of housing production started in 1950 in San Salvador, is a period of time that has to be studied in deep, to expose and debate, the contemporary socio-spatial fragmentation of the city; historical statement from which, we have to begin to propose new architectural and ur",0.0,505155cdb782ffa7fba53354dbcbe02741bf0ca615e,10.1109/isaect68904.2025.11318703,Innovative Predictive Framework for King County Housing Prices: A Comparative Study of Machine Learning and Ensemble Approaches,2025,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 importa",0.0,5061fec682f2edbf03139a222440c2d1ccdcaf330bc,10.1145/3729605.3729703,Analyzing the Influencing Factors of Real Estate Prices Based on Multiple Models,2025,Proceedings of the 2025 International Conference on Big Data and Informatization Education,Jia-Xin Ou; Jiaoxia Zhang,"This article analyzes the data of gross domestic product, consumer price index, per capita disposable income, total real estate development investment, annual population at the end of each year, national sales area of commercial housing, and national sales of commercial housing from 2008 to 2022. Multiple linear regression, stepwise regression, Lasso regression, and random forest models are established to compare the national average housing sales with other factors. The regression model is analyzed using R language, and the best model is selected by comparing and adjusting the values and significant variables of R2, VIF, RMSE, MSE. On the basis of this model, analyze the relationship between housing prices and each factor, and provide corresponding countermeasures and suggestions.",0.0,https://dl.acm.org/doi/pdf/10.1145/3729605.3729703507136360c272ec89e97a97f38e58993b9b521d7353,10.33050/sensi.v10i2.3480,Analysis of House Price Using K-Means and Naïve Bayes Methods,2024,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.v10i2.3480508doi_10_35429_jad_2019_10_3_1_10,10.35429/jad.2019.10.3.1.10,"Determinación de horarios de aberturas en una vivienda de construcción masiva509para su corrección térmica interior",2019,Revista de Arquitectura y Diseño,Arlees Ysrael Diaz-Salazar; Jorge Armando Ojeda-Sanchez; Gonzalo Bojórquez-Morales,"Currently the choice of massive construction models is the alternative with greater recurrence to acquire a house in warm humid climate in Mexico, however, much of this is not adapted to its climatic environment and causes thermal discomfort in the user because of the internal environment generated as response of the characteristics of its architectural envelope. To correct in a short time the effect of thermal discomfort, the user usually employs mechanized climatizations solutions such as fans or air conditioners. It was proposed the use of schedule for opening hours in the architectural envelope in a warm humid climate, mainly in windows, as a strategy to correct the internal thermal environment, considering its advantages as the immediate application and easy implementation, in contrast to some architectural or physical modification of the envelope. Dry bulb temperature and relative humidity measurements were made in a massive construction model to determine its thermal performance in the hottest month of the year. Subsequently, simulations were made with EnergyPlus to determine suitable opening times to correct the thermal environment by prolonging the hours of interior comfor",0.0,510087e2c4888cf8abeb34c745891220c0cbe793278,10.56726/irjmets43945,Forestalling House Prices Using Linear Regression in Machine Learning,2023,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/irjmets439455111193c6e60fafd317276072a4905b639d19d1d8ec,10.4108/eai.22-5-2026.2365118,Boston Housing Price Prediction and Factor Analysis: A Comparative Study of Linear Regression and Random Forest,2026,"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.22-5-2026.2365118512doi_10_14393_rcg249668395,10.14393/rcg249668395,POTENCIALIDADES E LIMITAÇÕES DOS DADOS DE WEB SCRAPING PARA O MAPEAMENTO DOS PREÇOS DOS IMÓVEIS URBANOS POTENTIALS AND LIMITATIONS OF WEB SCRAPING DATA FOR MAPPING URBAN PROPERTY PRICES,2023,Caminhos de Geografia,Thaís Góes de Souza; Vivian de Oliveira Fernandes; Julio César Pedrassoli; Fernanda Doracy Rocha Fonseca,"While the availability of online data has increased, the processing and evaluation of this data give rise to discussions regarding the scientific instruments that enable the measurement of critical data characteristics while minimizing inherent inconsistencies related to the subject. This article analyzed the representativeness of data obtained through web scrapping from advertisements on two nationally recognized websites. It applied a data refinement technique to property sales data and examined its proportion concerning the municipal residential real estate registry. The method proposed the utilization of web-available data retrieved through the technique of online data scraping. The approach focused on average prices during reference periods, as well as on the assessment of the potentialities and limitations of big data, along with the spatial concentration mapping of real estate market prices categorized by type and spatialized by neighborhood. The research concluded that the Olx website dataset exhibited lower completeness and volume than Imovelweb (Iw) yet demonstrated greater diversity regarding spatial coverage of properties, including mapping the distribution of average p",0.0,5131033609cdf4a20236dcd6f82be15ba3cdbf04f0b,10.32473/edis-fe1062-2019,Valuing Florida Water Resources: Prices of Waterfront Properties,2019,EDIS,Tatiana Borisova; Xiang Bi; T. Wade; Kurt Oehlbeck; Kelly A. Grogan,"This 9-page fact sheet written by Tatiana Borisova, Xiang Bi, Tara Wade, and Kurt Oehlbeck and published by the UF/IFAS Food and Resource Economics Department explores the relationship between water quality and sale prices of waterfront properties, that is, the amenity value provided by water resources to waterfront communities. Being near to water to water generally increases the value of a residential property. However, poor water quality may decrease waterfront property prices. In other words, investments in restoring water quality can translate into increases in property value and tax collection. https://edis.ifas.ufl.edu/fe1062 514This paper is a part of a series “Economic Value of Florida Water Resources”.",0.0,https://journals.flvc.org/edis/article/download/106381/117213515aacdb1cbd2441f177a5ec5f17548dab334963f09,10.37625/abr.26.1.83-94,Urban Crime and Its Net Implicit Price to North Carolina Households,2023,American Business Review,Bishwa S. Koirala; Rik Chakraborti; G. Pradhan,"This study investigates the impact of urban crime rates—property and violent—on wages and rents and estimates the net implicit monetary value of crime rates for living in metropolitan areas, using the American Community Survey 2019 data for cities in North Carolina. A seemingly unrelated regression estimation finds that the crime rates are capitalized into both wages and rents, and suggests crime rates affect wages positively and rents negatively. This investigation estimates a negative value of $51.80 per month—the average net marginal implicit price—for living in cities with high city crime rates. This negative value suggests that households are being compensated for living in cities with high crime rates.",0.0,https://digitalcommons.newhaven.edu/cgi/viewcontent.cgi?article=2018&context=americanbusinessreview516doi_10_1016_1057_0810_95_90021_7,10.1016/1057-0810(95)90021-7,Estimating the Consumption and Investment Demands for Housing and Their Effect on Housing Tenure Status,1995,Financial Services Review,Yannis M. Ioannides; Stuart S. Rosenthal,"
 
 
 Theoretical work suggests that families live in owner-occupied housing if their invest- ment demand for housing exceeds their consumption demand for housing. Using household data from the 1983 Survey of Consumer Finances, an analysis tests this theory by estimating an ordered probit model of whether families rent without owning property, rent while owning property other than their home, own their home without owning other properties, or own their home in addition to other properties. For owner-occupiers who own additional property, both the investment and consumption demands are directly observed, permitting the separate identification of these functions. The results suggest that investment demand is more sensitive to wealth and income than is consumption demand, but that consumption demand is more sensitive to demographic variables and proximity to urban suburbs. In addition, test results indicate that the principal residence of most owner-occupiers is determined by their consumption demand for housing, not their investment demand. Review of Economics & Staristics, February 1994, 76(l): 127-141. (Reprinted with permission of ABV/Inform, Copyright UMI.)�",0.0,517d9fb17f32e98ce2eca7315eec0dd890bec9aba6c,10.1177/00302228241297726,The Impact of Unnatural Death Stigmatization on Neighboring Property Prices: Propensity Score Matching and Difference-In-Differences Analysis,2024,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 ",0.0,5180098af4c9518bb222ceda78a5135e74c1ee29e9f,10.21632/saki.7.1.17-31,Pengaruh Harga Properti Residensial terhadap Proporsi Kredit Pemilikan Rumah pada Portofolio Kredit di Indonesia,2024,Studi Akuntansi dan Keuangan Indonesia,Matthias Hardy Putera; M. Kamal; Timothy Tedjasukmana; Yane Chandera; Dewi Saraswati,"This research aims to analyze the effect of the Residential Property Price Index (RPPI) on the composition of Home Ownership Credit (KPR) in the banking credit portfolio in Indonesia. The study was conducted using secondary data from the period of 2010 to 2022 from various sources such as Bank Indonesia and the Central Bureau of Statistics. Using the time-series regression, the research indicated that RPPI insignificant effected the composition of KPR in the banking credit portfolio in Indonesia. However, inflation rate and interest rates have a significant impact on the proportion of KPR. For credit regulator, this study can add perspectives on the impact of RPPI changes on credit portfolio composition.",0.0,http://dx.doi.org/10.21632/saki.7.1.17-315190a18829dfe20685d1048d5a37fe8ee184fff8faa,10.32628/ijsrst229242,House Pricing Prediction using ML Algorithm - A Comparative Analysis,2022,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/ijsrst229242520010d6fd80cddcfdce46afdd3b6375a1c8ce381ba,10.55041/ijsrem53246,Multimodel House Price Prediction Using Machine Learning,2025,INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,S. M; M. Sengaliappan,"Abstract521522The 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.523524Keywords525526House Price Prediction, Machine Learning, Linear Regression, Real Estate, Data Preprocessing, Feature Extraction, Flask, Web Application.",0.0,527doi_10_47456_bjpe_v11i1_47469,10.47456/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,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.",0.0,528doi_10_2139_ssrn_3417248,10.2139/ssrn.3417248,Determinantes de los precios relativos de la vivienda: Bogotá versus Medellín (Determinants of the Relative Prices of Housing: Bogotá Versus Medellín Cities),2019,,John Garcia; Carlos Esteban Posada; Hermilson Velasquez,"<b>Spanish Abstract:</b> Este documento presenta un modelo de oferta y demanda para explicar por qué el precio de la vivienda es mayor en Bogotá que en Medellín. Por medio de una estimación por máxima verosimilitud (Vector de Corrección de Error - VEC), los resultados muestran un efecto permanente de la relación de tasas de ocupación para explicar los precios relativos entre estas dos ciudades. En el ajuste de corto plazo, no solo los rezagos de la relación de precios de la vivienda y de la relación de la tasa de ocupación son estadísticamente significativos sino que además variables como la relación del ingreso promedio de los hogares y la relación de la cartera hipotecaria rezagada un periodo presentan significancia estadística.<br><br><b>English Abstract:</b> This paper uses a supply and demand model to explain why the price of housing is higher in Bogotá than in Medellín. The results related to a VEC model show that there is a permanent effect of the employment rate explaining the relative prices between these two cities. In the short-term, variables such as the households income and the mortgage portfolio have statistical significance.",0.0,529doi_10_15446_bitacora_v36n2_122718,10.15446/bitacora.v36n2.122718,"Estado y mercado en la asignación de precios del suelo: la periferia sur de la Región Metropolitana de Buenos Aires, Argentina State and market in land pricing: the southern periphery of the Buenos Aires Metropolitan Region, Argentina L'État et le marché dans la fixation des prix fonciers: la périphérie sud de la région métropolitaine de Buenos Aires, en Argentine O Estado e o mercado na determinação dos preços dos terrenos: a periferia sul da Região Metropolitana de Buenos Aires, Argentina",2026,Bitácora Urbano Territorial,Luis Enrique Baer; Juan Pablo Venturini,"The analysis of land markets has gained ground on government agendas and research programs in recent years, with land prices being an essential indicator for its study, as they reflect the social valuation of properties according to location attributes. This work aims to analyze the differential valuation of land based on three territorial variables: its use, the socio-residential context, and the coverage of basic services, in the Buenos Aires Metropolitan Region. Using a quantitative methodological approach, a data collection on selling prices was developed in municipalities on the southern metropolitan periphery: Almirante Brown, Esteban Echeverría, Ezeiza, Presidente Perón, and San Vicente. The results reveal a segmentation of land markets associated with marked territorial fragmentation, high land prices in consolidated areas, and the boom of gated communities for upper-middle-class sectors as a form of expansion. To illustrate possible lines of intervention, a housing policy initiative carried out between 2019 and 2023 is shared, which highlights the possibility of producing serviced and well-located land at an affordable price, and the need to regulate land markets.",0.0,530doi_10_1115_ht_fed2004_56379,10.1115/ht-fed2004-56379,Simulation of Contaminant Dispersal in an Apartment Building,2004,"Volume 2, Parts A and B",Sankalp Soni; Bakhtier Farouk; Charles N. Haas,"Bio-terrorism events (like the 2001 anthrax attacks) accentuate the importance of countering these incidents. In order to develop reliable countermeasures for these events, it is essential to understand the associated transport processes. The transport processes involved pose challenges as they occur over wide ranges of spatial and temporal scales. CONTAMW, a multi zone indoor air quality and ventilation analysis program is used to predict the contaminant dispersal in an apartment building. Detailed simulation results and analysis of controlled release of propylene within a generic apartment building is presented. A zonal analysis is carried out for the entire apartment building (using CONTAMW) to obtain time histories of propylene concentration in different zones. The simulations provide the dispersion, transport and contaminant concentration within each zone of the apartment. This study also considers the effect of flow obstructions and ventilation rates on contaminant dispersal. The results are validated with the experimental results reported in Cybyk et al. (1999). We have also simulated propylene transport in the apartment with FDS, a large eddy simulation model.",0.0,531007d3ee369739f598abc0bc02bcf14b0fdd445b6,10.2991/assehr.k.210407.152,A Study on Influencing Factors of Commercial Housing Price Based on Eviews in Shaanxi Province,2021,Proceedings of the 2021 6th International Conference on Social Sciences and Economic Development (ICSSED 2021),Wei-tong Wang,"The problem of housing prices has always been a hot spot in the whole society, which is closely related to the development needs of individuals, families and the whole society. In recent years, Shaanxi as a western region rapid development, housing prices continue to rise and become a hot topic of special concern to the public. Taking Shaanxi as an example, this paper selects the statistical data of commercial housing from 2008 to 2018 to establish econometric model, and uses EViews software to establish stepwise regression and White heteroscedasticity test to study the influencing factors of housing price rise in Shaanxi. And put forward the relevant suggestions.",0.0,https://www.atlantis-press.com/article/125955065.pdf532doi_10_33110_rnee_v15i1_307,10.33110/rnee.v15i1.307,"Precios hedónicos y valoración económica de áreas verdes en la ciudad de La Paz, Bolivia",2022,Revista Nicolaita de Estudios Económicos,Rafael Vidaurre Cladera,"Varios servicios y bienes ambientales (v.g. áreas verdes) no se comercializan en mercados formales, con consecuencias importantes en politicas públicas y la toma de decisiones privadas. Este estudio permite valorar las áreas verdes de la ciudad de La Paz, Bolivia, en base a una evaluación empírica del impacto de áreas verdes en el precio de las viviendas, período 2015. Se usa un método indirecto de valoración ambiental, denominado ""Modelo de Precios Hedónicos"", que se basa en determinar el efecto -en los precios de la vivienda- de variables como ""unidades de espacios verdes"", ""estructura habitacional"", ""ubicación"", entre otros, lo que finalmente permite dar valor económico a las áreas verdes de la ciudad. Los resultados revelan un incremento del 100% en la variable determinante ""unidades de vegetación de área verde"", eleva el precio por metro cuadrado de la vivienda, en 7,04. También un incremento del 100% en la característica ""distancia entre área verde y vivienda"", ocasionaría una disminución del precio unitario de la vivienda, en 6.80 %. La Disposición a pagar anual, por la conservación de áreas verdes, por m2 de vivienda, fue de US $. 134.02",0.0,533doi_10_4025_econrev_v31i1_64247,10.4025/econrev.v31i1.64247,ANÁLISE DE PREÇOS HEDÔNICOS NO SETOR IMOBILIÁRIO DE RESIDÊNCIAS NA CIDADE DE LONDRINA – PR,2023,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.",0.0,534153cae7c568a3579870e866b179b463815a819c6,10.4028/www.scientific.net/amr.838-841.3115,Research about the Disequilibrium Degree of Supplying and Demanding Market for Newly-Built Commodity-Housing from 1998 to 2011 in Xian,2013,,Jian-Ping Yang; Ming-Meng Fan,"Basing on the disequilibrium theory, taking use of SPSS software, taking advantaging of multiple linear regression methodology, this article analyzed the state of supply and demand of newly built commodity-housing market from1998 to 2011 in Xian, constructed the equation about land supply, housing price, load rate and housing-supply, and constructed the equation about per capita disposable income, and housing-demand. Basing on the supply-equation and demand-equation, this article obtained the state of supply and demand of new-built commodity- housing in Xian, at last, calculated the non-equilibrium degree and drew a conclusion.",0.0,https://doi.org/10.4028/www.scientific.net/amr.838-841.3115535doi_10_12795_habitatysociedad_2021_i14_16,10.12795/habitatysociedad.2021.i14.16,Una aproximación al estudio de las políticas públicas de vivienda rural en Tucumán.,2021,Hábitat y Sociedad,Ana Garay; Claudia Fernanda Gómez López,"In Argentina, the housing policy model conceived and applied during the 1950s, 1960s and later, deepened by the National Housing Fund (FONAVI) in 1972, was based on the idea that mass production would favor the sustained development of the construction industry, making it possible to overcome the deficit and benefiting the economy as a whole. This model was implemented in the same way in rural areas, and public housing policies have not tended to address rural areas with their own identity, nor have they taken into account the ways of life and habitation of this population. In this sense, this paper sets out to carry out a historical review of the public rural housing policies carried out in Tucumán from 1860 to 2018, analyzing the logics and processes that they promote. The results show that, since the creation of the Provincial Institute of Housing and Urban Development (IPVDU) in 1969, the production of state housing continues to focus on works with urban characteristics, without differentiating towards the rural interior of the province. This excludes the lifestyles and ways of life of the target populations.",0.0,536doi_10_46421_sibragec_v11i00_52,10.46421/sibragec.v11i00.52,Metodo para el diseño de estrategias de posicionamiento para el desarrollo de suelo y vivienda,2021,SIMPÓSIO BRASILEIRO DE GESTÃO E ECONOMIA DA CONSTRUÇÃO XI SIMPÓSIO BRASILEIRO DE GESTÃO E ECONOMIA DA CONSTRUÇÃO,Salvador Garcia; Miguel Davis; Eva Campos,"In the context of rapid urban expansion, as well as an increased growth and competition in the land development and construction industry; the earned value granted by differentiating attributes in projects is decisive for the brand positioning of a company and its competitive advantage. This study presents a method of designing positioning strategies for the land and housing developer. The objective is to outline initiatives that guarantee competitive advantage and that are recognized and appreciated by the clients, through innovative strategies and processes that increase the value of the attributes of the end product and that result in a sustained growth for the land development practices. The method included the review of the universe of attributes that housing and land developers consider important, their classification into attributes of ""corporate identity"" and ""technical quality"" to weight them through the valuation given by experts, competitors and clients; in order to determinate the degree of implementation based on its assessment by the experts, estimated cost and feasibility of implementation.",0.0,5371634612a819bdd7270d89be5b4510548e7989768,10.21107/rekayasa.v15i3.21818,PEMETAAN HARGA RUMAH DENGAN MENGGUNAKAN MODEL STATISTIK : GEOGRAPHICALLY WEIGHTED REGRESSION,2023,Rekayasa,Kukuh Winarso; Achmad Dafid,"the house close to the business center is one of the things that causes house prices to be expensive. Besides that, residential centers based on population density, on the one hand, cause house prices to rise, on the other hand, they cause house prices to fall. This research is based on mapping house prices which are influenced by business centers and residential centers in the city of Surabaya. This house price mapping uses the Geographically Weighted Regression (GWR) method. is a technique that takes the framework from a simple regression model to a weighted regression model.",0.0,https://journal.trunojoyo.ac.id/rekayasa/article/download/21818/85555380286d41a042ed46e4207bdd6b1b1842764a01afb,10.1109/ieem50564.2021.9672872,Analysis of Housing Prices of Urban with Port and City Integration Taking Kaohsiung Example,2021,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.",0.0,539005dd55a041ee1425ddb651b049b46184f710717,10.20944/preprints202102.0149.v1,Is There a Slope Discount?,2021,,Yi Huang; G. Hewings,"This paper focuses on the physical attributes of land that intrinsically limit land use and possibly affect land values. In particular, we investigate if the slope of a land does decrease its price and investigate the role of land slope in forming more reliable constant-quality land price indices and aggregate house price indices. We find that, while land slopes do decrease the land price per unit, they have a small effect on the quality-adjusted land price indices in selected neighborhoods in Auckland, New Zealand, where sloped terrain is common.",0.0,https://www.preprints.org/manuscript/202102.0149/v1/download540doi_10_37885_241218360,10.37885/241218360,Integração de Machine Learning e Sistemas Automatizados na Avaliação de Imóveis,2024,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.",0.0,541doi_10_46421_entac_v21i1_8962,10.46421/entac.v21i1.8962,Avaliação do valor locativo diário de teatro pelo método comparativo direto de dados de mercado: uma proposta de adaptação para imóveis de destinação específica,2026,ENCONTRO NACIONAL DE TECNOLOGIA DO AMBIENTE CONSTRUÍDO XXI ENCONTRO NACIONAL DE TECNOLOGIA DO AMBIENTE CONSTRUÍDO,Arthur Flecha Correa; Felipe Luiz Cacefo Alfino; José Victor Machado Nascimento; Raul Suzuki Pinto Rabêlo; Sidney Rosa Junior,"This paper presents a proposal to adapt the Direct Market Data Comparison Method (MCDDM) specified by ABNT NBR 14653 for assessing the daily rental value of theaters, properties classified by the literature as special-purpose buildings. The study was motivated by the limitations of the traditional model, which yields inconsistent values when applied to short-term rentals. The methodology adopted the unit value per available seat as the dependent variable, modeled through multiple linear regression on a sample selected of fourteen theaters, considering audience capacity, administrative typology and the Ross-Heidecke depreciation factor. The model achieved satisfactory fit (adjusted R² of 0.9416) and produced an estimate consistent with values practiced in the market, with a confidence interval of 30.57%. The conclusion is that adopting the value per seat as the dependent variable enables the application of MCDDM to theaters, providing a robust alternative for cases in which the traditional literature signals restrictions.",0.0,54202b67b54079cc96791bd1773120f983f3b72a223,10.4028/www.scientific.net/amm.405-408.3340,Application of ARMA Regression Model of Housing Prices Influence Factors of Xi'an,2013,,Zhi Hui; Yue-Fang Wang,"By selecting the relevant factors affect the real estate price, with the qualitative analysis method to analyze the housing prices changes of Xi'an, and then establish ARMA regression model of the housing price index, found that the factors exist long-run co-integration. In order to better reflect the actual, the government policy as a dummy variable is introduced into the model to make regression results more significantly, showing that government policies play an important role in the control of the impact on real estate prices.",0.0,https://doi.org/10.4028/www.scientific.net/amm.405-408.3340543doi_10_13043_dys_60_6,10.13043/dys.60.6,¿Comprar o arrendar una vivienda? Una aplicación para el caso de la ciudad de La Paz Housing tenure choice: An application to the city of La Paz,2007,Desarrollo y Sociedad,Mauricio Tejada; Rafael Vidaurre,"In the city of La Paz, the housing market is experiencing a favorable situation characterized by an increasing volume of available resources in the financial system, low real interest rates, a currency that is continuously being appreciated and an increase in the stock of different types of housing. In this context, the question that arises concerns the determinants of purchasing a home. Among those determinants, it is possible that some socioeconomic characteristics of the families determine whether the families have access to the credit market, which leads to the fact that even though it could be optimum to purchase a home the family I obligated to rent. To model the decision of purchasing under the rationing of credit availability we follow the methodology of Henderson and Ioannides (1986; 1983). We found that the characteristics such as income, age, education level, and marital status of the head of household play a role in determining the possibility of having access to a credit market for the financing of a home.",0.0,544doi_10_17271_23178604123720245413,10.17271/23178604123720245413,A dicotomia entre a revitalização urbana e o abandono de imóveis The dichotomy between urban revitalization and property abandonment,2024,Periódico Técnico e Científico Cidades Verdes,Mariana Pereira Moura; Daniel Henrique da Silva Torres; Ricardo Victor Rodrigues Barbosa,"This paper aims to discuss the problem of abandoned buildings from the perspective of sustainability. The theoretical article investigates the current situation of these buildings in the city, presenting the environmental, social, economic and cultural aspects. The analysis of the characteristics of the unsustainable urban environment helps to understand how the reuse of empty buildings and lots could promote urban sustainability. At the same time, urban revitalization processes expose a dichotomy, as they cause other problems relating to sustainable practice. The methodology used was based on the evaluation of three urban revitalization projects in former abandoned areas: Lx Factory, Wynwood and Porto Maravilha. The aim of the case studies is to explore the different contexts and scales of the projects, as well as to find congruent aspects through the application of the concept of sustainability and its relationship to both the problems seen with abandonment and the interventions that seek to undermine its effects.",0.0,5456165d69c156c016e6ad4f63089696afe6259f736,10.24957/hsr.2023.31.3.93,A New Approach to Establishing an Apartment Price Index in Seoul based on Blinder-Oaxaca Decomposition,2023,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.",0.0,546doi_10_12795_habitatysociedad_2021_i14_11,10.12795/habitatysociedad.2021.i14.11,"Vivienda social: necesidades comunes, caminos comunes y dificultades comunes. La experiencia uruguaya.",2021,Hábitat y Sociedad,Benjamín Nahoum,"This paper attempts to describe a particularly successful model of social production of habitat, that of Uruguayan housing cooperatives, which has already been developed for more than half a century, linking it with the conclusions of studies on the management of common goods by the people own selves by the Elinor Ostrom. Uruguayan housing cooperative´s characteristics and central aspects are analysed. Main singularities of the system are self-management, direct involvement of future users throughout their work or savings, and collective ownership of the houses, granting the right to use and enjoy to households. Subsequently, it is made a brief presentation of Ostrom’s work on commons and the Uruguayan cooperative model is taken up considering these concepts. This paper concludes that this social housing model would have great potential if had the support of the governments, currently oriented to free market, throughout development of an adequate legal framework, public funding, and access to land.",0.0,5471bfab1a9a82e81cd37e0674cbc808811bb9503d0,10.1002/mma.70910,Robust Optimization of High‐Dimensional Partially Linear Models Using Huber Loss and Elastic Net,2026,Mathematical methods in the applied sciences,Jiaxin Fu; Ai-Fen Feng; Ming-Hui Li,"This paper proposes a new Huber‐Elastic‐Net regression model for partial linear models to address the complex and diverse characteristics of high‐dimensional data. It combines the piecewise flexibility of Huber loss function and the mixed penalty term of elastic network to improve the anti‐interference ability of outliers and alleviate multicollinearity among high‐dimensional variables. Furthermore, we designed the Alternating Direction Method of Multipliers(ADMM) to solve this optimization problem and proved the convergence of this algorithm under certain conditions. Then, we conducted numerical simulations on two types of high‐dimensional data, namely multicollinearity and outliers, to demonstrate the mitigating effect and anti‐interference ability of the model, and compared and analyzed it with Lasso and Ridge regression methods. Finally, taking Boston housing price data as an example and comparing it with other models, we validated the effectiveness and practicality of the proposed model.",0.0,548doi_10_1121_1_421928,10.1121/1.421928,Boxes and sound quality in an Italian opera house,1998,The Journal of the Acoustical Society of America,Alessandro Cocchi; Massimo Garai; Carla Tavernelli,"The sound field in the boxes of an 18th century Italian opera house was studied by means of binaural measurements and computer simulations. The differences in listening between the boxes and the stall is best described by the values of ITDG, clarity, and spatial criteria resulting from the measurements. The periodic variation of the ITDG along the boxes depends also on the reflections coming from the ceiling. The best boxes for clarity and spatial impression are found in very different positions. Computer simulation supports these conclusions and helps in understanding the two different mechanisms by which the boxes influence the sound field in the hall: the equivalent sound absorption of the boxes and the periodic structure of the box-covered walls. A validated computer model allows one to forecast how the sound field in the historical theater could be if some interior lining were added to the boxes, trying to restore the velvet lining which was supposed to exist in ancient times.",0.0,549doi_10_2139_ssrn_287632,10.2139/ssrn.287632,Rent Control and Tenancy Duration,2001,,Jakob Roland Munch; Michael Svarer,"This paper investigates how rent control affects mobility on the Danish private rental housing market. Based on a unique and extensive data set a measure of the degree of rent regulation of each housing unit is calculated, and this is coupled with socio-economic characteristics and spells of tenancy duration for each household. To accommodate the special features of such a data set we apply a proportional hazard duration model, that encompasses both the presence of left truncated tenancy durations, right censored observations and allows for a very flexible specification of the time dependency as captured by the baseline hazard function. We find that tenancy mobility is severely reduced by the presence of rent control. Tenancy duration for a typical household in the private rental sector is found to be more than six years longer if the apartment belongs to the 10 per cent most regulated units than if it belongs to the 10 per cent least regulated units.",0.0,550doi_10_47328_revcch_v2i20_11013,10.47328/revcch.v2i20.11013,UMA ANÁLISE DO PREÇO DE ALUGUÉIS DE IMÓVEIS EM UMA CIDADE UNIVERSITÁRIA,2020,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.",0.0,55101ecea3b287f213a325ef1a505f325c6e5d128a1,10.1002/9781119261506.ch3,Let the Buyer Beware,2019,Race for Profit,Keeanga-Yamahtta Taylor,"One major problem with the HUD’s response to the urban housing crisis was the quality of the homes made available to working class and poor African Americans. While affordable housing was a government goal, it relied on private businesses that operated in the interest of profit. Additionally, the business of home appraisal was based on the assumption that property value decreased with proximity to African Americans. This racist ideology greatly limited the housing options of working class and poor African Americans. Homes with major issues were deemed inhabitable and sold. Unsuspecting buyers often did not have the disposable income to keep up with home repairs and mortgages. Mortgage lenders made a habit of profiting off houses that went into foreclosure quickly. The HUD was unable to effectively address the predatory practices of the private sector because of low staffing, over-extension, and anti-black racism within the organization.",0.0,552doi_10_15174_au_2018_1770,10.15174/au.2018.1770,Accesibilidad de la vivienda: un tema de bienestar social. El caso de la zona metropolitana del Valle de México,2018,Acta Universitaria,José Alberto Lara-Pulido; G. Estrada-Díaz; A. Guevara-Sanginés; M. Uribe,"Este trabajo examina la relación entre la accesibilidad de una vivienda con su entorno y su precio. A partir de un modelo de precios hedónicos se analiza el valor que le asignan las personas a tener accesibilidad a elementos de su entorno como equipamientos y redes de transporte. Los resultados indican que las personas valoran más habitar en lugares en los que puedan acceder a otros lugares que están a una distancia mayor a 12 000 m, pero a la vez valoran su privacidad, lo cual hace que prefieran viviendas más apartadas (en un radio menor a 500 m) de redes viales transitadas. Este resultado es relevante para la planeación urbana, porque sugiere que una política de desarrollo urbano podrá generar mayores beneficios para la población en tanto se enfoque a proveer mayor accesibilidad a mayores niveles de integración; a niveles menores es mejor que los propios agentes definan la configuración del entorno.",0.0,553doi_10_5377_entorno_v0i45_7119,10.5377/entorno.v0i45.7119,Techos ecológicos en vivienda multifamiliar como opción de preservación del medio ambiente Ecological roofs in multifamily housing as an option for environmental preservation,2010,Entorno,Ana Cristina Vidal Vidales,"The present investigation tries to define the necessary criteria to implement ecological roofs and variants of them in existing multifamily housing buildings, specifically in the metropolitan area of San Salvador. Starting from the benefits that the garden roofs have, this solution is seen from the point of view of contributing to the balance of the climatological activities, such as generating a surface that absorbs the excess of rainwater to reduce the potential of floods and also as barriers of the different pollutants of the air, typical of the city. Based on the physical and climatological characteristics and its history, regarding natural phenomena in San Salvador and how these have affected the city and how it responds to them, three different solutions are proposed, with the intention of reducing the harmful effects of the constant growth of the city and the attitudes taken towards it",0.0,55402b46ab8d787bc5d33edc5a9c8a769d0eb3b3249,10.1109/pci60110.2023.10325931,Determination of the Location Valuation Index based on Public Transportation in Big Cities,2023,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.",0.0,55508db6acce1f3cedd237d614f98304412102f3431,10.37407/kres.2024.42.4.107,Study on structural factors of housing price fluctuations : Focusing on Time Series and Breakpoint Analysis,2024,Korea Real Estate Society,Y. Chang,"The results of the autoregressive time series analysis (ARIMAX) indicate that housing supply-demand balance was a significant variable in the Seoul metropolitan area, while autoregressive terms and jeonse prices were identified as key factors in non-metropolitan regions. 556In the breakpoint regression analysis (BREAKLS), during the downturn period, sale prices and jeonse prices were significant in both metropolitan and non-metropolitan areas. During the upturn period, supply-demand balance exhibited a strong influence in the metropolitan area, while supply-demand balance and money supply were significant in non-metropolitan areas. Notably, in the highly volatile metropolitan area, supply-demand balance was confirmed as a critical determinant of housing prices during the upturn period.",0.0,557doi_10_36113_rec_v5i2_2659,10.36113/rec.v5i2.2659,INFLUÊNCIA DAS AMENIDADES NOS PREÇOS DE IMÓVEIS,2020,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.",0.0,55810bcfa0101f43980801fe9105a3b4f596fb5a6b0,10.1080/00036846.2025.2499744,Stable expectations and housing prices: quasi-experimental evidence from the promulgation of China’s Civil Code,2025,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.",0.0,559doi_10_5377_entorno_v0i51_6960,10.5377/entorno.v0i51.6960,Diseño de un modelo de vivienda bioclimática y sostenible. Fase II Design of a bioclimatic and sustainable housing model. Phase II,2012,Entorno,Ana Cristina Vidal Vidales; Guillermo Francisco Vásquez,"This research reviews the house model that was proposed in the first phase of the investigation, using an experiment which collected temperature and relative humidity data in scale models built with three different lightweight materials. That way it was possible to determine which material better suits the different climatic conditions existent in El Salvador according to the individual characteristics of each site. The research also includes a deeper study of the elements that should be taken into account in the design of a house that satisfies its inhabitant’s basic needs and the application of bioclimatic design which is complemented by the proposal of conceptual models that contain technological alternatives and their applicability.",0.0,560doi_10_3895_rbpd_v11n2_13827,10.3895/rbpd.v11n2.13827,Uma aplicação do método de preços hedônico no mercado imobiliário em duas zonas urbanas do oeste paranaense,2022,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.",0.0,561doi_10_14393_ree_v40nesp_maioa2025_78370,10.14393/ree-v40nesp.maioa2025-78370,Condicionantes Macroeconômicos dos Preços Imobiliários no Brasil e em suas Capitais Macroeconomic Determinants of Real Estate Prices in Brazil and its Capital Cities,2025,Revista Economia Ensaios,Vinicius Sorensen Berro; Benito Odelmo Salomão Neto,"O artigo analisa a dinâmica dos preços dos imóveis residenciais no Brasil e em cinco capitais entre dezembro de 2013 e dezembro de 2023, utilizando o Método dos Momentos Generalizados (GMM). Os resultados sugerem que a atividade econômica não influencia os preços, indicando movimentação desprendida da economia. Os termos de troca têm impacto significativo, especialmente em Goiânia, devido à nova fronteira agrícola. O crédito direcionado é relevante no Nordeste, refletindo características socioeconômicas locais. Já o custo unitário básico de produção afeta cidades turísticas, onde a construção civil compete com o turismo pela força de trabalho tendenciando a dinâmica de preços.",0.0,5620c467b5ecc54cee056ccd030afa0be1712ab94e4,10.1109/ispds54097.2021.00016,Boston Housing Price Forecast Based on HMM,2021,"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.",0.0,5631c8b80fbf8d32e874e59719281deb7698608e68c,10.1080/03610926.2018.1473614,Penalized proportion estimation for non parametric mixture of regressions,2020,,Qing-Hua Ji; Zheng Ji,"Abstract In this article, we propose a penalized local log-likelihood method to locally select the number of components in non parametric finite mixture of regression models via proportion shrinkage method. Mean functions and variance functions are estimated simultaneously. We show that the number of components can be estimated consistently, and further establish asymptotic normality of functional estimates. We use a modified EM algorithm to estimate the unknown functions. Simulations are conducted to demonstrate the performance of the proposed method. We illustrate our method via an empirical analysis of the housing price index data of United States.",0.0,5641a955d645bc31cae466d9144be438e323b3268ae,10.24191/myse.v7i2.10263,"AN ASSESSMENT OF HERITAGE PROPERTY VALUES USING MULTIPLE REGRESSION ANALYSIS: GEORGE TOWN, PENANG ISLAND MALAYSIA",2020,,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.",0.0,565doi_10_32997_2463_0470_vol_14_num_14_2006_411,10.32997/2463-0470-vol.14-num.14-2006-411,Valoración hedónica del canon de alquiler de la vivienda en Cartagena de Indias,2006,Panorama Económico,Jessica Patricia Mendez-Florez; Antonio José Orozco-Gallo,"Este documento fue diseñado como parte de un trabajo de grado para optar al título de Economistas por la universidad de Cartagena, cuyo objetivo fundamental, fue explicar los precios de la vivienda en alquiler en Cartagena de Indias para los sectores 01-02, en función de sus características con la metodología de los Precios Hedónicos. De igual forma en el trabajo se implementaron metodologías como la transformación Box-Cox y el Método de Máxima Verosimilitud, con el fin de hacer más confiable y rigurosa la investigación.Palabras Clave: Precios Hedónicos, Transformación Box-Cox, Canon de Alquiler, Máxima Verosimilitud.",0.0,566doi_10_6008_cbpc2237_9290_2019_002_0004,10.6008/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,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.",0.0,5671770a9a7ab1f23b5565f55ac869ccc944ef090d7,10.25236/ajbm.2022.040315,Analysis of Commodity Housing Price in Shanghai Based on Multiple Linear Regression Model,2022,Academic Journal of Business & Management,,,0.0,https://doi.org/10.25236/ajbm.2022.04031556812742f4b0b75661cf63849f61f5871916ab07ae3,10.2139/ssrn.2612233,A Statistical Valuation of Residential Home Attributes and Predictive Model: A Framework for Predicting Home Values Based on Home Characteristics and Changes in Macroeconomic Variables in a Defined Target Market,2014,,A. Guy,,0.0,https://doi.org/10.2139/ssrn.2612233569030a44a203c606d1a3fe6b6d72a0431b01cfce8d,10.1007/s11146-011-9359-3,Spatial and Temporal Dependence in House Price Prediction,2012,Journal of real estate finance and economics,Xiaolong Liu,,0.0,https://papers.ssrn.com/sol3/papers.cfm?abstract_id=231383457018995513ed7f5a7aeaa378e245225afecae42d18,10.15396/eres2023_275,"The Impact of Proximity to Cell Phone Towers on House Prices in Randpark Ridge, Johannesburg",2023,29th Annual European Real Estate Society Conference,Monwabisi Mdleleni; Kolawole Ijasan,,0.0,https://eres.architexturez.net/system/files/P_20230402105823_2446.pdf5710821556734421ab476d190beb1e5eccd501696db,10.15793/kspr.2008.57..003,The Effect of the Peripheral Land Use Characteristics on a Determination of Housing Price: Focus on Multi-Family Housing in Ilsan New Town,2008,,종형 임; 진홍 유; 주현 이,,0.0,https://doi.org/10.15793/kspr.2008.57..0035720691053d013aa720746dda99198b82d5b9dd385b,10.5392/jkca.2023.23.11.590,A Study of the Impact of Asset and Debt Characteristics of Household on the Outlook into the Housing Price,2023,The Journal of the Korea Contents Association,Seungjoon Oh; Kang-Yong Lee; Jun-Ho Jeong,,0.0,https://doi.org/10.5392/jkca.2023.23.11.590573200e2d41943c693a8d5ed31607a33aa7a83fbd37,10.56557/ajpam/2025/v7i1235,"Comparative Analysis of Linear and Non-Linear Regression Models in Predicting House Prices in Delta State, Nigeria",2025,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/v7i12355740dd53505bda7abae28dccfea8899b09c8a6aafed,10.2139/ssrn.989049,Retail Proximity and Residential Values or Do Nearby Stores Really Run Down Property Values,2006,,J. Matthews,,0.0,https://issuelab.org/resources/4941/4941.pdf575cb7f14661805a7e83f0caf54a5354b084037e904,10.15396/eres2016_107,Energy Capitalization in the Dutch Rental Market,2016,,Dorothee Hillrichs; E. Aydın; D. Brounen,,0.0,57682ae71644210560096f2f0cf039cad6650423f44,10.1109/iceice.2012.1220,Housing Project Hedonic Price Model of Qingdao Based on Principal Components Analysis,2012,,Lixin Zhang; Xin Li; Jie Wang,,0.0,577doi_10_5345_jkic_2004_4_3_117,10.5345/jkic.2004.4.3.117,A Feasibility Study Method for Apartment Remodeling by Hedonic Model,2004,Journal of the Korean Institute of Building Construction,In-Geun Yu; Cheon-Hak Kim; Yeo-Wan Yun; Geuk-Yeong Yang,,0.0,578doi_10_3130_jaabe_9_71,10.3130/jaabe.9.71,An Hedonic Valuation of Landmark Factors on Super High-Rise Residential Building Prices in Seoul (Republic of Korea),2010,Journal of Asian Architecture and Building Engineering,Woncheol Choy; Sanghyo Lee; Yoonsun Lee; Jaejun Kim,,0.0,579doi_10_4197_eco_2_1_3,10.4197/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,Journal of King Abdulaziz University-Economics and Administration,ABDULAZIZ DIYAB,,0.0,580b53531340f1f4ae81cd50baf5340de1c92eee9ea,10.15396/eres2015_289,Estimation Of Hedonic House Pricing Model Through Non-Parametric Methods: Istanbul Real Estate Market,2015,,Sinem Guler Kangalli Uyar; N. Yayla; Bulent Guloglu,,0.0,5818cbed6a212d9ea5ab59dc504b3367cf76dbeeaf1,10.1007/s43071-021-00013-4,A spatial model averaging approach to measuring house prices,2021,Journal of Spatial Econometrics,Ryan Greenaway-McGrevy; Kade Sorensen,,0.0,582e82c2ab651388045005bcb5235ffd2e84d85bb63,10.15396/eres2012_354,The impact of earthquake risk on the spatial pattern of house prices: A multi-level analysis,2012,,Berna Keskin; Craig Watkins,,0.0,583d9c72b955f4aff856407f035e77977c713586448,10.15396/eres2010_399,DO PEER EFFECTS SHAPE RESIDENTIAL VALUES? RECONCILING THE SALES COMPARISON APPROACH WITH HEDONIC PRICE MODELLING,2010,,F. D. Rosiers; Jean Dubé; M. Thériault,,0.0,584doi_10_2139_ssrn_2776232,10.2139/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,SSRN Electronic Journal,John J. Garcia; Juan Carlos Upegui; Ermilson Velasquez,,0.0,585doi_10_18361_2176_8366_rara_v8n2p128_143,10.18361/2176-8366/rara.v8n2p128-143,Impactos dos Atributos na Precificação de Imóveis Residenciais de Mossoró/RN a Luz do Modelo Hedônico,2016,Revista de Administração e Negócios da Amazônia,F.C. Nobre; A.J. Sousa; E.V. Prado; L.G.C. Nazareth; D.B. Polezi; M.I. Montebello,,0.0,586362e4bb8a07e6bdf2e4aea7c58cfa515c5a53561,10.15396/eres2012_083,The incidence of characteristics in housing prices and offer prices,2012,,E. Fregonara; P. Semeraro,,0.0,5876d0ef173a8d3d26b2a35fb7ef9a9adc927ce1bfd,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,,Dong-Jin Yu; Jianlei Xie; Wei Yang; Wei Wu,,0.0,58811cb2667a75c059912f664fcf01a876a95d1b96b,10.1007/s11146-014-9456-1,Determinants of Housing Prices in Hong Kong: A Box-Cox Quantile Regression Approach,2014,Journal of real estate finance and economics,Hyung-Gun Kim; K. Hung; Sung Y. Park,,0.0,58905d14796055558f1cc168b355c4a4ee754c154e4,10.22423/kreus.2018.11.1.43,A Study on the Determinants of Country Housing Prices,2018,Konkuk Research Institute of Real Estate and Urban Studies,S. Nam; Man-Hyeon Baek,,0.0,59020c6586dba15c14df63fd63115c3810a7d001d22,10.1016/j.cities.2026.107142,Effects of housing price determinants in major international financial centers,2026,Cities,Lin Deng; Xueqing Zhang,,0.0,5910bdae4f8ae6e8cf3b8ff1f877d308b7887ffd2cd,10.23843/as.17.3.1,Nonlinear Effects of Physical Characteristics of Apartment on Housing Prices: Focusing on Multivariate Adaptive Regression Spline,2018,Appraisal Studies,Jihun Oh; Jeongseob Kim,,0.0,59201d75ce442406b0088b34ed2d1d9ad30a55003a6,10.37103/kapp.20.4.1,A Study on the Characteristics of Causal Flow and Volatility between Owner Housing Prices and Rental Housing Prices in Seoul Apartment Market,2018,The Journal of Korean Public Policy,Chongho Kim,,0.0,593doi_10_1145_3342240,10.1145/3342240,Take a Look Around: Using Street View and Satellite Images to Estimate House Prices,2018,arXiv,Stephen Law; Brooks Paige; Chris 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 learnin",,https://arxiv.org/pdf/1807.07155v2594doi_10_1177_0308518x19869678,10.1177/0308518x19869678,Online Rental Housing Market Representation and the Digital Reproduction of Urban Inequality,2019,arXiv,Geoff Boeing,"As the rental housing market moves online, the Internet offers divergent possible futures: either the promise of more-equal access to information for previously marginalized homeseekers, or a reproduction of longstanding information inequalities. Biases in online listings' representativeness could impact different communities' access to housing search information, reinforcing traditional information segregation patterns through a digital divide. They could also circumscribe housing practitioners' and researchers' ability to draw broad market insights from listings to understand rental supply and affordability. This study examines millions of Craigslist rental listings across the US and finds that they spatially concentrate and over-represent whiter, wealthier, and better-educated communities. Other significant demographic differences exist in age, language, college enrollment, rent, poverty rate, and household size. Most cities' online housing markets are digitally segregated by race and class, and we discuss various implications for residential mobility, community legibility, gentrification, housing voucher utilization, and automated monitoring and analytics in the smart cities pa",,https://arxiv.org/pdf/1907.06118v2595doi_10_1140_epjds_s13688_024_00488_9,10.1140/epjds/s13688-024-00488-9,Shift in house price estimates during COVID-19 reveals effect of crisis on collective speculation,2022,arXiv,Alexander M. Petersen,"We exploit a city-level panel comprised of individual house price estimates to estimate the impact of COVID-19 on both small and big real-estate markets in California USA. Descriptive analysis of spot house price estimates, including contemporaneous price uncertainty and 30-day price change for individual properties listed on the online real-estate platform Zillow.com, together facilitate quantifying both the excess valuation and valuation confidence attributable to this global socio-economic shock. Our quasi-experimental pre-/post-COVID-19 design spans several years around 2020 and leverages contemporaneous price estimates of rental properties - i.e., real estate entering the habitation market, just not for purchase (off-market) and hence free of speculation - as an appropriate counterfactual to properties listed for sale, which are subject to on-market speculation. Combining unit-level matching and multivariate difference-in-difference regression approaches, we obtain consistent estimates regarding the sign and magnitude of excess price growth observed after the pandemic onset. Specifically, our results indicate that properties listed for sale appreciated an additional 1% per mon",,https://arxiv.org/pdf/2209.03936v2596doi_10_1109_tmm_2020_2966890,10.1109/tmm.2020.2966890,What Image Features Boost Housing Market Predictions?,2021,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",,https://arxiv.org/pdf/2107.07148v1597doi_10_1214_12_aoas613,10.1214/12-aoas613,Modeling US housing prices by spatial dynamic structural equation models,2013,arXiv,Pasquale Valentini; Luigi Ippoliti; Lara Fontanella,"This article proposes a spatial dynamic structural equation model for the analysis of housing prices at the State level in the USA. The study contributes to the existing literature by extending the use of dynamic factor models to the econometric analysis of multivariate lattice data. One of the main advantages of our model formulation is that by modeling the spatial variation via spatially structured factor loadings, we entertain the possibility of identifying similarity ""regions"" that share common time series components. The factor loadings are modeled as conditionally independent multivariate Gaussian Markov Random Fields, while the common components are modeled by latent dynamic factors. The general model is proposed in a state-space formulation where both stationary and nonstationary autoregressive distributed-lag processes for the latent factors are considered. For the latent factors which exhibit a common trend, and hence are cointegrated, an error correction specification of the (vector) autoregressive distributed-lag process is proposed. Full probabilistic inference for the model parameters is facilitated by adapting standard Markov chain Monte Carlo (MCMC) algorithms for d",,https://arxiv.org/pdf/1303.2915v2598doi_10_1186_s42854_021_00024_2,10.1186/s42854-021-00024-2,Tilted Platforms: Rental Housing Technology and the Rise of Urban Big Data Oligopolies,2021,arXiv,Geoff Boeing; Max Besbris; David Wachsmuth; Jake Wegmann,"This article interprets emerging scholarship on rental housing platforms -- particularly the most well-known and used short- and long-term rental housing platforms - and considers how the technological processes connecting both short-term and long-term rentals to the platform economy are transforming cities. It discusses potential policy approaches to more equitably distribute benefits and mitigate harms. We argue that information technology is not value-neutral. While rental housing platforms may empower data analysts and certain market participants, the same cannot be said for all users or society at large. First, user-generated online data frequently reproduce the systematic biases found in traditional sources of housing information. Evidence is growing that the information broadcasting potential of rental housing platforms may increase rather than mitigate sociospatial inequality. Second, technology platforms curate and shape information according to their creators' own financial and political interests. The question of which data -- and people -- are hidden or marginalized on these platforms is just as important as the question of which data are available. Finally, important d",,https://arxiv.org/pdf/2108.08229v1599doi_10_1109_icdm_2016_0134,10.1109/icdm.2016.0134,House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis,2018,arXiv,Bang Liu; Borislav 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 evaluati",,https://arxiv.org/pdf/1803.00919v1600doi_10_1016_j_cities_2026_107220,10.1016/j.cities.2026.107220,Social Integration and Housing Behaviours of Immigrants: Evidence from Singapore's Public Housing Market,2026,arXiv,Yi Fan; Ho Pin Teo; Yong Tu; Wayne Xinwei Wan,"This study investigates the impact of social integration on immigrants' housing behaviours from a temporal perspective, using Singapore's differential public housing policies on immigrants as a quasi-natural experiment. With the support of a local town council, we conducted a survey on social integration among 1,128 immigrant and local households living in public housing estates. In the public open rental housing market - primarily accommodating yet-to-integrate immigrants - we find immigrant renters live up to 3.04% farther from their workplace and pay lower rents up to 0.67% per additional year of residency. Such impacts are more substantial among minority ethnic groups. The results remain robust when using alternative subjective or objective measures of social integration. However, in the public resale housing market - primarily accommodating native and well-integrated naturalised citizens - we find that naturalised citizens face no price premiums relative to native homebuyers, implying no further effect of integration on housing prices after well-integration. This study extends the literature of spatial assimilation focusing on ethnic residential segregations and is generalizab",,https://arxiv.org/pdf/2607.17633v1601doi_10_1145_3705328_3748136,10.1145/3705328.3748136,Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search,2025,arXiv,Ivo Silva; Pedro Nogueira; Guilherme Bonaldo,"QuintoAndar Group is Latin America's largest housing platform, revolutionizing property rentals and sales. Headquartered in Brazil, it simplifies the housing process by eliminating paperwork and enhancing accessibility for tenants, buyers, and landlords. With thousands of houses available for each city, users struggle to find the ideal home. In this context, location plays a pivotal role, as it significantly influences property value, access to amenities, and life quality. A great location can make even a modest home highly desirable. Therefore, incorporating location into recommendations is essential for their effectiveness. We propose a geo-aware embedding framework to address sparsity and spatial nuances in housing recommendations on digital rental platforms. Our approach integrates an hierarchical H3 grid at multiple levels into a two-tower neural architecture. We compare our method with a traditional matrix factorization baseline and a single-resolution variant using interaction data from our platform. Embedding specific evaluation reveals richer and more balanced embedding representations, while offline ranking simulations demonstrate a substantial uplift in recommendation qu",,https://arxiv.org/pdf/2510.01196v1602doi_10_1007_s00138_018_0922_2,10.1007/s00138-018-0922-2,Vision-based Real Estate Price Estimation,2017,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/1707.05489v3603doi_10_20944_preprints202412_1927_v1,10.20944/preprints202412.1927.v1,Predicting House Rental Prices in Ghana Using Machine Learning,2025,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/2501.06241v1604doi_10_5121_csit_2022_121311,10.5121/csit.2022.121311,Comparison of Forecasting Methods of House Electricity Consumption for Honda Smart Home,2022,arXiv,Farshad Ahmadi Asl; Mehmet Bodur,"The electricity consumption of buildings composes a major part of the city's energy consumption. Electricity consumption forecasting enables the development of home energy management systems resulting in the future design of more sustainable houses and a decrease in total energy consumption. Energy performance in buildings is influenced by many factors like ambient temperature, humidity, and a variety of electrical devices. Therefore, multivariate prediction methods are preferred rather than univariate. The Honda Smart Home US data set was selected to compare three methods for minimizing forecasting errors, MAE and RMSE: Artificial Neural Networks, Support Vector Regression, and Fuzzy Rule-Based Systems for Regression by constructing many models for each method on a multivariate data set in different time terms. The comparison shows that SVR is a superior method over the alternatives.",,https://arxiv.org/pdf/2208.07217v1605doi_10_1007_978_3_030_84060_0_11,10.1007/978-3-030-84060-0_11,Airbnb Price Prediction Using Machine Learning and Sentiment Analysis,2019,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/1907.12665v1606doi_10_1111_1468_2257_00066,10.1111/1468-2257.00066,Measuring Amenity Benefits from Farmland: Hedonic Pricing vs. Contingent Valuation,1997,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.",27.0,607c6b33e6b839caa1157a283c6f9a1e3350edd0558,10.1111/1540-6229.12320,Modeling unobserved heterogeneity in hedonic price models,2020,Real Estate Economics,Marc K. Francke; A. van de Minne,"This paper studies unobserved heterogeneity in hedonic price models, arising from missing property and locational characteristics. Specifically, commercial real estate is very heterogeneous, and data on detailed property characteristics are often lacking. We show that adding mutually independent property random effects to a hedonic price model results in more precise out-of-sample price predictions, both for commercial multifamily housing in Los Angeles and owner-occupied single-family housing in Heemstede, the Netherlands. The standard hedonic price model does not take advantage of the fact that some properties sell more than once. We subsequently show that adding spatial random effects leads to an additional increase in prediction accuracy. The increase is highest for properties without prior sales.",22.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1540-6229.123206084ab70a44ce3dc804e1c016b2dc2e73367eecaca7,10.31219/osf.io/4rt7p,The (dis)use of the golf landscape in Galicia through the most significant courses,2020,,Sergio Carballo-Pérez,"In the decade of 1960, the first golf courses were built in Galicia. On the one hand, the Golf Club of La Coruña, whose shape is not compact and creates several loops of routes that provide a broad perimeter, a condition that is highly desired by developers. The designers are conditioned by the quantity and disposition of the houses, giving priority to the real estate performance and the views towards the course. They are inserted in the interstitial spaces derived from the morphological configuration of the course, and although a large part has not been built, it is possible to read the trace of the intentions on the landscape. Seven years later, La Toja Golf Club was born, a model of tourist golf where the design of the course prevails, the number of houses is much smaller and its degree of visual contamination towards the golf course is better treated. In the decade of the nineties, the tendency of the interventions that use the golf courses as a transforming element of the landscape in a real estate incentive is reflected in Galicia with the Ría de Vigo Golf Club, where soil movements have been important, terracing the terrain to generate several levels from which to glimpse th",0.0,https://doi.org/10.31219/osf.io/4rt7p609