1record_id,doi,title,year,venue,authors,abstract,citations,oa_pdf2d9c0cb125b53a1e7f25af5ed5f62b3a2a35f2d02,10.3390/urbansci9120539,"Modeling the Spatial Impact of Short-Term Rentals on House Prices: The Case of Athens, Greece",2025.0,Urban Science,Polixeni Iliopoulou; Vassilios Krassanakis; Kallis Kappelos,"The purpose of this study is to explore the spatial impact of short-term rental activity on house prices in the city of Athens, Greece. It is well established that the increasing number of short-term rentals has a number of consequences on the functions and living standards in several cities around the world. An aspect that is not studied very often is the effect of short-term rentals on house prices and, especially, the spatial distribution of this effect. In this paper, spatial regression models are presented, incorporating several of the commonly employed house characteristics, such as structural and locational characteristics, with the addition of the short-term rentals as an explanatory factor. Geographically Weighted Regression (GWR) models, in particular, produce the geographic distribution of regression coefficients, allowing for the study of the short-term rentals’ influence on house prices at the local level. Furthermore, spatial regression models are compared to global linear models. Although global models indicate that short-term rentals have an overall positive contribution on house prices, Geographically Weighted Regression, through the local regression coefficients, ",5.0,https://www.mdpi.com/2413-8851/9/12/539/pdf?version=17656314993e2af7a0e3d0cf44a6704c4df7427ea1f8f7260aa,10.25008/ijadis.v4i2.1290,Optimization of the Random Forest Method Using Principal Component Analysis to Predict House Prices,2023.0,International Journal of Advances in Data and Information Systems,Emha Ahdan Fahmi Elmuna; Totok Chamidy; Fresy Nugroho,"Investment is an interesting thing, especially property investment. The developer must also be careful in determining the price of the property. It should be noted that every year, both short-term and long-term, property prices increase and rarely go down. In determining the price, it is often also based on the features of the house such as the concept, location, bedrooms, etc. To predict house prices based on their features, the random forest has a good performance for predicting house prices. However, the random forest method has the disadvantage that if you use too many variables, the training process will take longer and feature selection tends to select features that are not informative. One way to reduce features without removing other features is to use Principal Component Analysis. In this research, the method used is Principal Component Analysis (PCA) and Random Forest. From the results of model training, it can be concluded that the use of model evaluation results using PCA has a smaller error rate and more consistent values, with an average of 0.018. While the results of the evaluation without PCA and using only Random Forest have a higher error value with an average of ",5.0,http://ijadis.org/index.php/ijadis/article/download/1290/584ae06a771b74ebe1d41351c839629c376f78c006c,10.1080/10511482.2021.1882533,Are Foreclosure Spillover Effects Universal? Variation Over Space and Time,2020.0,Housing Policy Debate,Yu-Ri Chun; Stephanie Casey Pierce; A. V. Van Leuven,"ABSTRACT Government intervention in the housing market in response to the 2007–2010 mortgage crisis was driven in part by research showing that foreclosures lower neighboring housing values and thus increase neighbors’ risk of foreclosure. Researchers have consistently identified a negative spillover effect of foreclosures on nearby housing values, but the magnitude of the effect varies widely across studies. Although this variation is due, in part, to differences in the geographic region, time period, and empirical strategy of prior research, we argue that the spillover effect on nearby housing prices exhibits hyper-local variation, which may be obscured by models that aggregate spillover effect estimates within existing geographic units. In this article, we employ geographically weighted regression to capture the extent of spatial and temporal variation of foreclosure spillover effects in three Ohio metropolitan statistical areas. We find extensive heterogeneity of foreclosure spillover effect estimates over time and across space, suggesting that such spillovers perhaps should not be thought of as universal phenomena. These findings raise the possibility that policies and program",5.0,https://doi.org/10.2139/ssrn.35758795dbeff8a45649f69192b7d0f6d925e175605cb832,10.1080/24694452.2022.2092443,Estimation and Inference of Special Types of the Coefficients in Geographically and Temporally Weighted Regression Models,2022.0,Annals of the American Association of Geographers,Zhi Zhang; Chang-Lin Mei; Hua-Yi Yu,"Geographically and temporally weighted regression (GTWR) models have been widely used to explore spatiotemporal nonstationarity where all the regression coefficients are assumed to be varying over both space and time. In reality, however, constant, only temporally varying, and only spatially varying coefficients might also be possible depending on the underlying effects of the explanatory variables on the response variable. Therefore, the development of inference and estimation methods for such special types of the coefficients is essential to the deep understanding of spatiotemporal characteristics of the regression relationship. In this article, an average-based approach, relying on a modified estimation of the conventional GTWR models, is proposed to calibrate the GTWR models with the special types of the coefficients, on which a statistical test is formulated to simultaneously infer constant, temporally varying, and spatially varying coefficients. The simulation study shows that the test method is of valid Type I error and satisfactory power and the average-based estimation method yields more accurate estimators for the special types of the coefficients. A real-life example bas",5.0,https://figshare.com/articles/journal_contribution/Estimation_and_Inference_of_Special_Types_of_the_Coefficients_in_Geographically_and_Temporally_Weighted_Regression_Models/20466300663b91dab70d44cf9aab1f8fa177b9bfcc5d94809,10.1038/s42949-025-00291-z,Exploring nonlinear and interactive associations between built environment features and subjective streetscape perceptions,2025.0,npj Urban Sustainability,Sang-Wan Lee; Nahye Cho; Hyejin Park,"This study explores nonlinear and interactive relationships between built environment factors and six subjective streetscape perceptions (i.e., beauty, wealth, safety, liveliness, boredom, and depression) in Jeonju, South Korea. Employing a two-stage methodological framework, we first estimate the perceptual scores using a Convolutional Neural Network trained on Place Pulse data and Street View images, and then utilize a Gradient Boosting Regressor with interpretable machine learning methods to examine the complex relationships. Key findings reveal several patterns. First, although perceptions of beauty and safety are generally positively associated with housing prices, the association with safety becomes marginally negative in neighborhoods with exceptionally high housing values. Furthermore, interaction effects highlight that areas characterized by both a higher proportion of older buildings and elevated housing prices are more likely to be perceived as beautiful. Conversely, neighborhoods with simultaneously low population and employment densities are associated with higher boredom scores. This study contributes to (1) demonstrating the utility of AI-based approaches in the fiel",5.0,https://www.nature.com/articles/s42949-025-00291-z.pdf7eb8db1d91a5b529b1d7ba8aa50228d63084a76b3,10.17089/kgr.2012.19.3.004,Urban Sprawl Index and the Factors Affecting It,2012.0,,Jong-Youl Lee,"The purpose of this study is to find the factors affecting urban sprawl in Korea and China and to determine the similarities and differences between them. This study employed statistical analysis, including correlation and multiple regression analysis in order to do this. The major findings are the following. In the case of Korea, some cities show a very high degree of urban sprawl while some show decreasing urban sprawl. There is a wide range of trends in Korea though urban sprawl is not as serious as expected. The trend is slightly different in China, although urban sprawl is still occurring at a high rate. Korea has more cities becoming more compact. It is not the case in either Korea or China that urban sprawl is caused by the condition of developed roads in contradiction to American cases where urban sprawl has been expedited mainly through the development of highways. This is also related to the close relationship between professionals and urban sprawl (more in the case of Korea than China) as more professionals may want to enjoy both urban and rural life simultaneously by using more roads. Housing price is closely related to urban sprawl in the case of China, but weakly so i",5.0,https://doi.org/10.17089/kgr.2012.19.3.00483257c35310edf02c867d8e91483b790ff8577ed0,10.38177/ajast.2023.7105,Machine Learning Based House Price Prediction Using Modified Extreme Boosting,2023.0,Asian journal of applied science and technology,N. Ragapriya; T. kumar; R. Parthiban; P. Divya; S. Jayalakshm; D. Raman,"In recent years, machine learning has become increasingly important in everyday voice commands and predictions. Instead, it provides a safer auto system and better customer assistance. As a result of all that has been demonstrated, ML is a technology that is becoming more and more popular in a range of industries. To gauge changes in house values, the House Price Index is frequently employed (HPI). Due to the substantial correlation that exists between property prices and other variables, such as location, region, and population, the HPI on its own is not sufficient to accurately forecast a person's house price. Some studies have successfully predicted house prices using conventional machine learning techniques, but they seldom evaluate the efficacy of different models and ignore the more complicated but less well-known models. We proposed Modified Extreme Gradient Boosting as our model in this study due to its adaptive and probabilistic model selection process. Feature engineering, hyperparameter training and optimization, model interpretation, and model selection and evaluation are all steps in the process. Home price indices, which are frequently used to support real estate poli",5.0,https://doi.org/10.38177/ajast.2023.710597ff195b8b2e3fc7dea421e5eda9bfb48182ae9e8,10.3390/jrfm14060244,Information Spillover Effects of Real Estate Markets: Evidence from Ten Metropolitan Cities in China,2021.0,Journal of Risk and Financial Management,Junjie Li; Liyi Zheng; Chunlu Liu; Zhifeng Shen,"With the rapid development of information communication technology and the Internet, information spillover between cities in real estate markets is becoming more frequent. The influence of information spillover in real estate markets is becoming more and more prominent. However, the current research of information spillover between cities is still relatively insufficient. In view of this research gap, this paper builds a research framework on the information conduction effect in the real estate markets of 10 Chinese cities by using Baidu search data, text mining and principal component analysis and analyzes the information interaction and dynamic influence of the real estate markets in each city by using the vector autoregressive model empirically. The results show that the information interaction among the real estate markets in each city has a network pattern and there is a significant two-way information spillover effect in most cities. When the “information distance” becomes closer, the information interaction between the markets of the cities becomes closer and it is easier for cities to influence each other. The results help to explain the information spillover mechanism behi",5.0,https://www.mdpi.com/1911-8074/14/6/244/pdf?version=162389471710598bd3fec6ed44b743198f2608748e5a5669b5b2,10.1038/s42949-025-00312-x,Spatially compounding effects of cumulation and thresholds amplify urban inequality in megacities,2025.0,npj Urban Sustainability,Ming-Xing Chen; Wenming Song; Yuan-Xuan Yang; Hui-Ping Jiang; Liang-Kan Chen,"Urbanization often results in unequal outcomes in social well-being, particularly in rapidly growing megacities within developing countries. This study investigates spatial inequalities in urban public service accessibility across communities in Beijing, with a focus on migrant populations who often face systemic disadvantages. Using fine-scale spatial and census data, we identify a significant negative correlation between facility accessibility and the proportion of migrants. Among the lowest-income communities, those with higher migrant shares experienced accessibility distances 2.09 times greater than others. In high-migrant areas, inequality levels surpassed the city average by 14.57%, reflecting entrenched spatial disparities. Interpretable machine learning models reveal key threshold effects: when housing prices fall below 80,590 yuan/m² and migrant ratios exceed 32%, inequalities rise sharply. Furthermore, more than 18.98% of communities with high migrant population proportions exhibited cumulative inequality, where multiple disadvantage factors overlap and reinforce each other. These findings highlight how spatial, economic, and demographic vulnerabilities intersect, unders",5.0,https://www.nature.com/articles/s42949-025-00312-x_reference.pdf113795efc0d0fb59adea0f9cd681e1962dde9c5adb,10.24818/ejis.2022.05,The Effects of Homeownership on Wealth Distribution,2022.0,European Journal of Interdisciplinary Studies,K. Čermáková; E. Hromada; Eduardo Aguiar Henrique; T. Krulický,"Within the broader context of new dimensions of poverty such as housing poverty, energy poverty, etc., this article describes dependencies between household income, real estate ownership and socio-economic trends. We argue that income is not the principal determinant for home ownership rate, but rather recent lifestyle changes can better explain the homeownership decreasing trend in developed economies. Job mobility, family formation determinants and demographical trends seem to find well-supported basis in literature and data. Using data for the US states we have proved that the decreasing rate of home ownership may be explained by social aspects of changing lifestyle such as increasing share of population moving from rural areas to cities, age of marriage, divorce rate, career-oriented lifestyle, rather than by the frequently cited price-income ratio. We have also observed a short-term correlation between financing availability and homeownership rate, but we conclude that property prices would adjust to lose monetary policy without any long-term effect on homeownership rate. It results that government or monetary policies aimed to cushion the housing unavailability (recently incr",5.0,https://doi.org/10.24818/ejis.2022.05127811ee9a7c63f0b5142320f41305d4d8c4527545,10.1108/ijhma-02-2021-0022,"Dynamic effects and spatial heterogeneity of land supply on housing price: evidence from Nanchang, China",2021.0,International Journal of Housing Markets and Analysis,Zhi-Jiang Wu; Yong-Xiang Wang; Wei Liu,"13Purpose14Economic fundamentals are recognized as determining factors for housing on the city level, but the relationship between housing price and land supply has been disputed. This study aims to examine what kind of impact housing prices have on land supply and whether there is heterogeneity in different regional spaces.151617Design/methodology/approach18This study collects the relevant data of land supply and housing prices in Nanchang from 2010 to 2018, constructs a vector autoregression (VAR) model, including one external factor and four internal factors of land supply to explore the dynamic effects and spatial heterogeneity of land supply on housing prices through regression analysis. Also, the authors use the geographic detector to analyze the spatial heterogeneity of housing prices in Nanchang.192021Findings22This study found that the interaction between land supply and housing price is extremely complex because of the significant differences in the study area; the variables of land supply have both positive and negative effects on housing price, and the actual effect varies with the region; and residential land and GDP are the two major factors leading to the spatial heterogeneity",5.0,23f949a4cf065b922bd089dc601b1c9c022fd09d08,10.1145/3635059.3635065,Explainable Graph Neural Networks on Linked Statistical Data for Predicting Scottish House Prices,2023.0,Panhellenic Conference on Informatics,Petros Brimos; Areti Karamanou; E. Kalampokis; M. Mamalis; Konstantinos A. Tarabanis,"In the complex landscape of the real estate market, accurately estimating house prices is of paramount importance for a wide array of stakeholders. While traditional statistical methods have been employed in the past, machine learning and deep learning models are increasingly demonstrating their efficacy in predicting house prices by capturing intricate patterns and relationships between features of the housing market, such as neighborhood characteristics and geographical proximity to amenities. Recently, Graph Neural Networks have shown great performance in various predictive tasks, effectively capturing complex spatial dependencies in graph-strucured data. Towards this direction, this paper utilizes a Graph Convolutional Network and linked Open Government Data from the official Scottish data portal to predict house prices in Scotland. The results showed that the Graph Neural Network model achieved an accuracy of 0.84 on the test set. The GNN’s decisions are also interpreted using the GNNexplainer model, that identified important graph structures as well as node features that are most influential for the prediction. Comparative Illness Factor, an indicator of health conditions, is",5.0,https://dl.acm.org/doi/pdf/10.1145/3635059.363506524e2ec7703f651dc597c7a3d060bea539c780c6126,10.1007/s11146-025-10030-x,Comparing Housing Valuation Techniques and Stacked Generalization: Exploiting Explainable AI,2025.0,Journal of real estate finance and economics,Elias Bjørgve; A. Oust; A. J. Pollestad; Cato Sandnes; O. Sønstebø,"This study investigates the use of diverse model selections in stacked generalization to improve the predictive accuracy of Automated Valuation Models (AVMs). The research focuses on three commonly used valuation techniques: the Comparable Sales Method (CSM), the linear Least Absolute Deviation (LAD), and the nonlinear XGBoost (XGB). A dataset consisting of 164,619 apartment transactions from Oslo between 2008 and 2022 is utilized for testing, with 25% of the data used for out-of-sample predictions. While the stacked model combining XGB, CSM, and LAD achieves the best performance with a Median Absolute Percentage Error (MdAPE) of 5.17%, the individual XGB model performs nearly as well, achieving an MdAPE of 5.24%. Analysis reveals that stacking provides marginal improvements and primarily relies on XGB predictions. However, the computational cost of stacking raises questions about its practicality. The research highlights the limitations and benefits of different housing valuation techniques and varying data sizes, offering practical insights to enhance the performance of future AI-AVMs. By utilizing explainable AI, this study contributes to a better understanding of how different ",5.0,https://link.springer.com/content/pdf/10.1007/s11146-025-10030-x.pdf25f113e392ccaca6e040f151b80deca6aac4de7dab,10.1515/remav-2020-0020,Italian Auction Market: Features of Discounted Forced Sale Price,2020.0,,P. Amoruso; M. Mariani; M. D'Amato; R. Didonato,"Abstract The purpose of the paper is to investigate the main causes of discount between list price and forced sale value in the Italian real estate auction segment, in favor of more comprehensive determination of the value of mortgage underlying guarantees; this would allow for an improvement of further valorization processes of assets intended to obtain a higher selling price. Starting from the results of existing literature, an ordinary least squares regression analysis was conducted in order to point out the main determinants of the discount between list price and forced sale value. It has been verified, when sampling 225 cases of forced residential property sales in the South of Italy, that the time constraint of the Italian regulatory framework is more relevant with reference to discount rather than the physical characteristics of assets. The influence of the duration of procedures is proof that even if recent government initiatives have led to positive results in terms of reducing the length of procedures, this is not yet satisfactory compared to other European contexts. This provides evidence of the need for further interventions aimed at improving the efficiency of the proc",5.0,https://www.sciendo.com/pdf/10.1515/remav-2020-002026af796977cadee27143bd7a348cdfb93402980b7f,10.1080/15623599.2021.1975021,"Predictive rental values model for low-income earners in slums: the case of Ijora, Nigeria",2021.0,International Journal of Construction Management,C. Iroham; S. Misra; Onyeka Emebo; H. Okagbue,"Abstract It is well known most often that values of properties tend to hike at the effluxion of time. This has necessitated the adoption of predictive models in interpreting outcomes in the property market in the future. Earlier studies have been oblivious of such models' outcomes as it affects any focal group, particularly the vulnerable. This present study focuses on the low-income earners found in the slum. The Ijora community in Lagos was the highlight of this study, particularly Ijora Badia and Ijora Oloye, regarded as slums according to the UNDP report. The entire fifty-two (52) local agents in the Ijora community were surveyed in cross-sectional survey research that entailed the questionnaire's issuance. The nexus of data collection, pre-processing, data analysis, algorithm application, and model evaluation resulted in retrieving rental values within the years 2010 and 2019 on two predominant residential property types of self-contain and one-bedroom flats found within the community. Three selected algorithms, Artificial Neural Network (ANN), Support Vector Machine, and Logistic Regression, were essentially used as classifiers but trained to predict the continuous values. Th",5.0,27267f41b39d429f59ef9f0ca7520e41e1633cf1d0,10.4236/me.2019.101005,An Empirical Analysis of the Impact of Real Estate Tax System on Housing Price in Hong Kong,2019.0,Modern Economy,K. Mo,"This paper selects Hong Kong from 1999 to 2015 as the sample period, sets the variable to the private residential retail price index HP and the real estate related tax revenue HT, selects the multi-log logistic regression model method, empirically analyzes the profits tax, stamp duty, the impact of general rates and property taxes on the retail price index of private housing in Hong Kong. The results show that the real estate tax on the retention link has only a limited impact on the price of private housing. The promotion of property tax is more obvious, and the general difference is less affected. The real estate tax on the transaction link has a significant impact on housing prices. Profits tax has a significant positive effect, and stamp duty also has a driving effect, but the effect is weak. Increasing the tax on the transaction link will stimulate the housing price to rise further. According to the empirical results, this paper believes that the Hong Kong government should further clarify the role of real estate tax, give full play to the government’s functions in the real estate market, simplify the real estate tax system, and improve the property tax package, in order to pl",5.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=89800284051af5dea139fdb0615712287c4d4dfeaf3f99e,10.1080/13467581.2025.2555545,"Invisible lines, visible gaps: administrative boundary effects and housing price differentials",2025.0,Journal of Asian Architecture and Building Engineering,Sangyun Lee; Han-Ming Gu; Kabsung Kim,"ABSTRACT Although institutionally constructed, administrative boundaries can generate significant housing price discontinuities even within otherwise spatially and functionally cohesive urban environments. This study investigates the Surak River City apartment complex, which straddles the border between Seoul Special Metropolitan City and Gyeonggi Province while maintaining nearly identical physical and locational characteristics on both sides. Using unit-level transaction data and multiple regression analysis with year fixed effects, we identify a statistically significant housing price differential of approximately 4.3%, with units located within Seoul’s jurisdiction commanding higher prices. The differential reflects both material advantages, such as access to higher-quality public services, and the symbolic capital associated with being part of the capital city. These findings demonstrate how jurisdictional affiliation is capitalized into housing prices, reinforcing spatial inequality at the micro-neighborhood scale. By isolating the economic impact of administrative delineations, this study contributes case-based empirical insights into how institutional boundaries shape housi",5.0,https://doi.org/10.1080/13467581.2025.25555452959a8a9e473de885fbc6f317fe884d915addc9bb0,10.3390/su14137613,About the Importance of Planning the Location of Recycling Stations in the Urban Context,2022.0,Sustainability,Mats Wilhelmsson,"Recycling is essential to the circular economy and reduces the environmental impact of our consumption. Creating conditions for recycling in new residential areas is relatively easy but finding good recycling opportunities in existing residential areas is more complicated. The recycling of newspapers, plastic and glass must be relatively close to where people live; at the same time, the locations must be relatively discreet and not disturb the residents in the area. The purpose of the article is to analyse the effect of small and local recycling stations (RCSs) on the attractiveness of residential areas. This has been made possible by analysing housing values for almost 200,000 housing units near 250 RCSs in Stockholm, Sweden. Using an identification strategy that relies on postal code fixed effects, we find evidence that the proximity to RCS affects housing prices on average in both owner-occupied single-family houses and cooperative owner-occupied apartments (condominiums). The results indicate that proximity to the RCS is negatively capitalised in housing values (the effect amounts to approximately 1.3 percent of the housing values), which indicates that the city should consider",5.0,https://www.mdpi.com/2071-1050/14/13/7613/pdf?version=165596170630848088b7f141ed2599f30e710a359c2b8c516233,10.1108/ijhma-04-2022-0053,"Flood hazards and housing prices: a spatial regression analysis for Hat Yai, Songkhla, Thailand",2022.0,International Journal of Housing Markets and Analysis,Sukampon Chongwilaikasaem; T. Chalermyanont,"31Purpose32Global warming exacerbates sea level rise and extreme weather events that cause severe flooding, resulting in lost productivity and property damage. To reduce the impact of flooding, residents are avoiding purchasing homes in high-risk areas. There are numerous studies on the relationship between flood hazards and housing prices in developed countries, but few in developing countries. Therefore, this study aims to investigate the relationship between flood hazards and housing prices in Hat Yai, Songkhla, Thailand.333435Design/methodology/approach36This study uses spatial-lag, spatial error and spatial autoregressive lag and error (SARAR) models to analyze the effect of flood risk on property prices. The main analysis examines the degree of flood risk and housing rental prices from our survey of 380 residences. To test the robustness of the results, the authors examine a different data set of the same samples by using the official property valuation from the Ministry of Finance and the flood risk estimated by the Southern Natural Disaster Research Center.373839Findings40The SARAR model was chosen for this study because of the occurrence of spatial dependence in both dependent varia",5.0,41d41387d00b12e4effda835195ceba10624be1925,10.26905/jkdp.v24i1.3565,Clustering and regional growth in the housing market: Evidence from Indonesia,2020.0,,Ariyanto Nugroho; M. Y. Purnama; L. Fauzia,"The housing market is one of the monetary policy instruments in macro-prudential terms. The overall policy adopted through the Loan-to-Value ratio does not accommodate different regional characteristics. Location and area characteristics are important factors that influence the development of housing property prices. This study uses panel data regression analysis, with objects of 15 cities in Indonesia during 2007-2017. The results of the analysis provide a proof that the building rent, population density, construction costs, Gross Regional Domestic Product, unemployment rate, and minimum income, affect housing prices differently in each growth category, while the LTV ratio and inflation do not have a significant effect onto house prices. The regional economic growth is used to form regional clustering based on the speed of house price growth so that monetary policy in the housing sector achieves the target. JEL Classification: E52, O18 , R58 How to Cite: Nugroho, A. A., Purnama, M. Y. I., & Fauzia, L. R. (2020). Clustering and regional growth in the housing market: Evidence from Indonesia. Jurnal Keuangan dan Perbankan, 24 (1), 83-94. DOI : https://doi.org/10.26905/jkdp.v24i1.356",5.0,http://jurnal.unmer.ac.id/index.php/jkdp/article/download/3565/pdf423b47440810374382cb48ec73484a25f61ad3d2a3,10.2139/ssrn.3514560,Determinants of Residential House Rental Prices in Accra Metropolis,2020.0,Social Science Research Network,Raymond Darfo-Oduro,"The study analysed the effect of neighbourhood characteristics and structural characteristics on residential rental prices in the Accra metropolis of Ghana. This is in response to the absence of scientific approach to determining rental prices and as a way to influence policies to deal with the arbitrarily fixing of rental prices. Multiple regression analysis was employed in analysing data sourced primarily from tenants of residential houses. The study specified four regression equations, linear, linear-log, log-linear and log-log. The different regression models were found to have different effect on the relationship between house rental prices and its influencing factors. However, the log-log mode and linear-log model revealed similar results, both models revealed that distance from church or mosque to a residential property were significant and positively related to house rental prices whilst all other variables were insignificant. The linear model and log-linear model also showed some similarities. In both models distance to central business district, distance to market, electricity supply, refuse dump, wooded area and urban effect were significant with the same signs in both m",5.0,43082220f79fc841b35c4f641fd8db60c66a6d0084,10.3390/buildings13102464,Investigation of Real Estate Tax Leakage Loss Rates with ANNs,2023.0,Buildings,Mehmet Yılmaz; Bülent Bostancı,"In Türkiye, many changes have been made in the law within the past fifty years to determine the real estate tax value close to the real market value. However, the changes did not establish a fair valuation system for determining real estate tax. Despite the regulations and records of immovable properties with a geographic information system (GIS)-based inventory in recent years, the problem of leakage loss in real estate tax was still not resolved. Within the scope of this study, a mass appraisal model was created with a dataset of 499 independent sections including trading values from the last year in the district of Kayseri to determine the real estate tax leakage loss rates. Multiple regression analysis (MRA) and artificial neural network (ANN) methods, widely used in mass appraisal, were used in the analysis. Considering the analysis of the test data and the model performances, the ANN model was found to give better results than the MRA model. To conclude this study, the housing values obtained with the mass appraisal methods and the real estate tax values obtained with the existing system were compared, and a 3.7-fold difference was found between them.",5.0,https://www.mdpi.com/2075-5309/13/10/2464/pdf?version=169587531144b15c830746ee34304563681d0edc15ccb94cfcac,10.3390/land12040823,Shantytown Redevelopment and Housing Prices: Empirical Evidence from Chinese Cities,2023.0,Land,Caixia Liu,"China has been undergoing urban regeneration and reconstruction over the past few decades. To improve the housing conditions of the poor and better achieve the goal of new urbanization, the Chinese government launched top-down shantytown redevelopment nationwide in 2008. Little is known about the economic effectiveness of shantytown redevelopment. Based on city-level data from 2014–2018, this study examined the impact of shantytown redevelopment on housing prices by using the fixed effects regression analysis and instrumental variables (IV) methods. The results show that shantytown redevelopment significantly increases the housing prices at the city level, and each 10% increase in the size of redeveloped shantytowns is associated with an average increase of 1.4% in housing prices. There is regional heterogeneity, with shantytown redevelopment in the central and western regions having a greater effect on housing prices. The rising home prices imposed by shantytown redevelopment negatively decrease housing affordability for those households not included in the redevelopment projects; thus, a more inclusive urban redevelopment policy should be considered.",5.0,https://www.mdpi.com/2073-445X/12/4/823/pdf?version=16805998614591eff0fd679efa3ad24377427cfda395ee843bf7,10.1108/pm-02-2022-0013,"Measuring the impact of heavy rail transport infrastructure on house prices in Melbourne, Australia: a case study of Mernda rail extension project",2022.0,Property Management,Maojian Zhang; J. Shukla,"PurposeThe research examines the impact of heavy rail transport infrastructure on residential property prices in Melbourne at different stages of project development using the Mernda Rail Extension Project as a case study.Design/methodology/approachA difference-in-difference approach is used to quantitatively measure the magnitude of change in the house price at different stages of rail transport infrastructure project development.FindingsWhen controlling for a range of structural, neighbourhood, and locational attributes, the authors find that properties within 800 m from the proposed train station are 8.7% higher in value than those outside 800 m (but within 1,600 m). However, during the project's construction, the project's benefits in the form of house price appreciation are not fully realised. “Unrealised benefit” is attributed to the negative externalities of construction works and apprehensions associated with the project's shelving and time delays.Research limitations/implicationsDepending on the availability of data on residential property transactions in the future, a spatial analysis of rail infrastructure's radius of catchment effect is needed.Practical implicationsFind",5.0,462e73314d751c0b233a54667f9f8734283335b9e7,10.1145/3727353.3727388,Research on Relationship Between Aging Population Distribution and Real Estate Market Dynamics based on Neural Networks,2025.0,BDICN,Yiqiu Tang; Shenghan Zhao,"This study aims to analyze the relationship between demographic patterns and real estate market movements. In particular, the study aims to examine the relation with the help of a neural network-based model. Considering that the model in question is using a deep learning approach with, one long short-term memory network enhanced with several optimization techniques, adaptive learning rate methods and data augmentation techniques. Collectively, these strategies provide sufficient means to understand the potential impacts for the dynamics of the housing market, those that run between the population age structure, migration demographics and economic indicators. The model's timeliness is validated through its ability to match market trends tightly, while providing the potential to minimize the impact of an aging population on housing prices from structural angles. The empirical analysis showed that the proposed LSTM model provides more reliable predictions compared to conventional statistical methods. In addition, the model demonstrates excellent generalization for application robustness in different market conditions.",5.0,https://dl.acm.org/doi/pdf/10.1145/3727353.372738847e6f9c9d47be864c5496c9b3e00dea19a3a0c4c08,10.2139/ssrn.4700238,The Impact of COVID-19 on Real Estate Markets in Germany,2025.0,Social Science Research Network,Eyayaw Beze; Patrick Thiel,"Abstract The COVID-19 pandemic has disrupted established urban patterns. The literature on the impact of the pandemic on the US housing market has shown a significant increase in the demand for suburban housing, resulting in a considerable increase in suburban prices compared to those in the city center (termed the “donut effect”). However, the German housing market did not experience such drastic changes. To examine price and rent adjustments during the pandemic, we analyze detailed housing data and find little evidence supporting the donut effect seen in the US. Apartment rents increase in suburban areas, while house prices do not change significantly. Examining the role of amenities, we find no explanation for price and rent differences between the central business district (CBD) and suburbs. The differences between the two markets may be attributed to cultural and structural distinctions. Our analysis, which includes data on population patterns and migration behavior, reveals that residents in Germany exhibit a slower-moving trend. Our findings remain robust across different settings and subsets of cities.",5.0,https://doi.org/10.2139/ssrn.4700238485fbd9fb8a47290b0b6f2258da72a6cc52eefac64,10.1111/1540-6229.12516,Data‐sparse price indexes by spatio‐temporal regularization and PCA: An application to the Australian housing market,2024.0,Real Estate Economics,W. Sijp; Anastasios Panagiotelis; Marc K. Francke,"We present two novel approaches to overcome the limitations of data‐sparse local house price indexes and combine them into a single model pipeline that is simple, computationally efficient, and interpretable. The first contribution is a new spatio‐temporal regularization of least squares dummy variable models, such as the repeat sales regression used here. This regularization encodes prior knowledge of the proximity of houses in space and their sales in time. It handles missing values in a natural way. The second is nonlocal regularization using truncated principal component analysis (PCA) applied to the resulting national collection of local price indexes. The PCA loadings show that there are important underlying socioeconomic factors that can be leveraged in the construction of Australian market indexes. This PCA reveals important socioeconomic factors, showing that many local markets can be described by a few broad aspects of the national market, consisting of a general trend that contrasts regions influenced by the mining industry with Sydney and Melbourne, and another trend that highlights lifestyle.",5.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1540-6229.1251649edc964021ada960a4468e9307b3b58ad128417a6,10.1007/978-981-13-6661-1_13,Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning for Cross-City Property Appraisal Framework,2018.0,Australasian Data Mining Conference,Yi-Han Guo; Shan Lin; Xiao Ma; J. Bal; Chang-Tsun Li,"Most existing real estate appraisal methods focus on building accuracy and reliable models from a given dataset but pay little attention to the extensibility of their trained model. As different cities usually contain a different set of location features (district names, apartment names), most existing mass appraisal methods have to train a new model from scratch for different cities or regions. As a result, these approaches require massive data collection for each city and the total training time for a multi-city property appraisal system will be extremely long. Besides, some small cities may not have enough data for training a robust appraisal model. To overcome these limitations, we develop a novel Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning (HFT+HLF) cross-city property appraisal framework. By transferring partial neural network learning from a source city and fine-tuning on the small amount of location information of a target city, our semi-supervised model can achieve similar or even superior performance compared to a fully supervised Artificial neural network (ANN) method.",5.0,https://arxiv.org/pdf/1812.0548650443e7fe34292b6f8546b99029a7a08965f8ec810,10.1145/3442536.3442552,Effective Tuning of Regression Models using an Evolutionary Approach: A Case Study,2020.0,Artificial Intelligence and Cloud Computing Conference,Séamus Lankford,"Hyperparameters enable machine learning algorithms to be customized for specific datasets. Choosing the right hyperparameters is a challenge often faced by machine learning practitioners. With this research, tuning of hyperparameters for regression models was explored. Models predicting house prices in King County were created using a detailed suite of regression algorithms. Traditional approaches, and evolutionary algorithms, for improving model accuracy were evaluated. A variety of feature selection methods and hyperparameter tuning using grid search, random search and pipeline optimization were also studied as part of the traditional approaches. Furthermore, evolutionary algorithms were applied to model optimization. In this paper, it is shown that an evolutionary approach, implemented with TPOT, achieves the highest accuracy for a regression model based on the King County dataset. Regarding metrics, combining the RMSE and metrics is shown to be an effective means of determining model accuracy. Finally, greedy feature selection performed best when a variety of feature selection methods are compared.",5.0,https://dl.acm.org/doi/pdf/10.1145/3442536.3442552512774f650cb9bf364ca38c5a4475f2b7403dd5a30,10.67231/bacg1854,Semantic Synthesis of Analytical Pipelines: Automated Workflow Composition for Predictive Data Science,2026.0,International Journal of Intelligent Systems and Data Science,Muneeb Uddin Syed,"The proliferation of data science libraries has created a complexity barrier for practitioners seeking to construct effective analytical workflows. This paper introduces a constraint-driven framework for automated pipeline generation that operates at a semantic abstraction layer above technical implementations. By modeling domain-specific ontologies encompassing data manipulation, feature engineering, and machine learning operations, the system synthesizes executable Jupyter notebook workflows through SAT-based reasoning. An alternative Answer Set Programming backend extends synthesis capabilities for knowledge-intensive search problems. Evaluation across three canonical datasets housing price regression, survival classification, and sentiment analysis demonstrates the framework's ability to generate valid workflows while revealing trade-offs between automation flexibility and user control. The approach enables domain experts to rapidly prototype analytical solutions without deep programming expertise, accelerating the data science lifecycle from hypothesis formulation to model evaluation.",5.0,https://journals.femington.com/ijisds/article/download/298/27527b45ca1d04d9dcfcf030ea97277d265fdd32a16f,10.1108/ijhma-04-2021-0048,The impact of external factors on housing prices in Lithuania,2021.0,International Journal of Housing Markets and Analysis,A. Stundžienė; Vaida Pilinkienė; Andrius Grybauskas,"53Purpose54This paper aims to identify the external factors that have the greatest impact on housing prices in Lithuania.555657Design/methodology/approach58The econometric analysis includes stationarity test, Granger causality test, correlation analysis, linear and non-linear regression modes, threshold regression and autoregressive distributed lag models. The analysis is performed based on 137 external factors that can be grouped into macroeconomic, business, financial, real estate market, labour market indicators and expectations.596061Findings62The research reveals that housing price largely depends on macroeconomic indicators such as gross domestic product growth and consumer spending. Cash and deposits of households are the most important indicators from the group of financial indicators. The impact of financial, business and labour market indicators on housing price varies depending on the stage of the economic cycle.636465Practical implications66Real estate market experts and policymakers can monitor the changes in external factors that have been identified as key indicators of housing prices. Based on that, they can prepare for the changes in the real estate market better and take the ne",5.0,672e6c87d5a40e61e68977c9ed3a874f56c005d51e,10.1109/ubmk59864.2023.10286673,"A New Hybrid Approach for Real Estate Price Prediction Using Outlier Detection, Feature Selection, and Clustering Techniques",2023.0,2023 8th International Conference on Computer Science and Engineering (UBMK),F. Abut; H. Arlı; M. Akay; Yildirim Adigüzel,"Real estate price prediction is vital for property valuation, investment analysis, and market trends forecasting. Accurate prediction models offer valuable insights to real estate professionals, investors, and homeowners. However, developing effective models in the real estate domain is challenging due to outliers, feature relevance, and local market dynamics. This study proposes a hybrid approach that combines outlier detection, feature selection, and clustering techniques to enhance real estate price prediction accuracy. The approach integrates Support Vector Regressor (SVR) and LightGBM, leveraging their strengths. The self-created dataset used includes comprehensive real estate attributes like county, building age, floor count, room count, and more. The hybrid approach's performance is evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results demonstrate that the hybrid approach outperforms other traditional experiments with normal prediction, feature selection, and outlier detection, achieving lower MAE, RMSE, and MAPE values, indicating improved accuracy in real estate price prediction. The st",5.0,68ad0e6677a66c39e0f0cac3cdebaaf736c38bd9eb,10.18267/j.polek.1309,Demand and Supply Determinants on the Property Market and Their Importance in Explaining Regional Differences,2021.0,,Libor Votava; L. Komárková; J. Dvořák,"There is growing interest in property prices and price trends at national and regional levels. The paper aims to find links between selected demand and supply determinants based on a panel regression of regional data during 2000-2017 and to contribute to further discussion of the role of these determinants. The main output of the paper is the identification of regional differences in the direction and strength of the influence of individual determinants on the flat price. Regional differences were identified in the mid-year population, the number of inhabitants per completed flat, the number of applicants per job, the amount of household disposable income and the divorce rate. In Prague, the first three determinants were significant. The validity of the research is limited by the fact that some potentially significant data are not statistically monitored and data aggregated at the level of regions do not reflect local conditions. The output of the analysis shows determinants that explain the price variability and indicates unused economic policy instruments.",5.0,http://polek.vse.cz/doi/10.18267/j.polek.1309.pdf6989fed6cc0aa4f9b3666fdea45d1675806b6be48e,10.3233/jifs-179219,Analysis of debt-paying ability of real estate enterprises based on fuzzy mathematics and K-means algorithm,2019.0,Journal of Intelligent & Fuzzy Systems,Qun-Li Qiang,"Currently, there is a certain fluctuation in the real estate industry, so it is particularly important to analyze the solvency of real estate enterprises. In order to find a reliable model suitable for studying the difference in house prices, this study collects the research data through data collection, and uses the K-means clustering method to construct the corresponding model as a basic research in combination with the machine learning research method. At the same time, this paper compares the analysis effects of several common machine learning models and finds the advantages and disadvantages of these methods through mathematical statistics. In addition, combined with practice, this paper constructs a nonlinear generalized additive model, and based on machine learning technology, validates the validity of the model based on data analysis, the collected predictors. In view of the improvement of the solvency of real estate enterprises, diversified operation of real estate enterprises can maintain reasonable cash flow and make up for the defect of poor liquidity of real estate. Furthermore, this paper uses the stability method to find the optimal model. In addition, the generalize",5.0,70fae24d3166d1ad0dd9c5def52f2736637b83a2e7,10.3390/land13050602,Analysis of Factors Influencing Housing Prices in Mountain Cities Based on Multiscale Geographically Weighted Regression—Demonstrated in the Central Urban Area of Chongqing,2024.0,Land,Yi-Duo Chen; Qing-Yuan Yang; Li Geng; W. Yin,"By leveraging a multiscale geographically weighted regression (MGWR) model, this paper delves into the intricate factors that influence housing prices in the prototypical mountainous cityscape of Chongqing’s central urban area. The key findings are as follows: Firstly, the distribution of housing prices in the study region exhibits pronounced spatial heterogeneity, with the core area exhibiting a distinct “high-high” clustering pattern and manifesting characteristics of a multicenter group distribution. Secondly, the MGWR model effectively assigns an individual bandwidth to each feature quantity, allowing for a more nuanced portrayal of the varying influence scales exerted by diverse variables. Lastly, the study reveals that factors such as property cost, greening rate, building age, and proximity to rivers have a notable negative impact on housing prices, whereas, educational facilities exert a marked positive influence. Elevation, floor area ratio, and distance from the Central Business District (CBD) exhibit a more complex influence on housing prices.",5.0,https://www.mdpi.com/2073-445X/13/5/602/pdf71c6976a073f137077559b87e2485fa2d3537e77cf,10.1371/journal.pone.0283339,Liu-type pretest and shrinkage estimation for the conditional autoregressive model,2023.0,PLoS ONE,Marwan Al-Momani,"Spatial regression models have recently received a lot of attention in a variety of fields to address the spatial autocorrelation effect. One important class of spatial models is the Conditional Autoregressive (CA). Theses models have been widely used to analyze spatial data in various areas, as geography, epidemiology, disease surveillance, civilian planning, mapping of poorness signals and others. In this article, we propose the Liu-type pretest, shrinkage and positive shrinkages estimators for the large-scale effect parameter vector of the CA regression model. The set of the proposed estimators are evaluated analytically via their asymptotic bias, quadratic bias, the asymptotic quadratic risks, and numerically via their relative mean squared errors. Our results demonstrate that the proposed estimators are more efficient than Liu-type estimator. To conclude this paper, we apply the proposed estimators to the Boston housing prices data, and applied a bootstrapping technique to evaluate the estimators based on their mean squared prediction error.",5.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0283339&type=printable7253e23665b115c2062857c7ff5a1e01b9a55bd19b,10.4236/me.2018.92016,Study on the Impact of Local Public Expenditure on Housing Price—An Empirical Analysis Based on Provincial Panel Data,2018.0,,Shihao Li,"Based on the discussion about transmission mechanism 73of local public expenditure on housing price under different housing market 74types, the paper performs an empirical study on the influence of various public 75expenditures on housing price by using Chinese panel data of provinces from 762007 to 2015. According to the result, public education expenditure and public 77transportation expenditure have prominent capitalization effect on housing 78price. Among the expansions of various local public expenditures, public 79education expenditure is the most effective type to promote the capitalization 80of commercial housing. In dynamic panel test, the capitalization effect of 81public expenditure maintains significant after the introduction of consumer 82expectation, which is consistent with the regression result of static panel. At 83last, the paper puts forward some relevant policy suggestions for the 84government to optimize the structure of public expenditure, guide consumer 85expectations reasonably and promote the sound development of real estate 86market.",5.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=823848700176b68f2247097a05ac361dde76933bf71f104,10.1038/s41598-025-34099-9,"Evaluating human–machine collaboration through a comparative analysis of experts, machine learning, and hybrid approaches in real estate valuation",2026.0,Scientific Reports,Christopher Kmen; Gerhard Navratil; Markus Kattenbeck; Ioannis Giannopoulos,"Accurate prediction of real estate prices remains a major challenge due to dynamic market conditions and the limitations of traditional valuation methods. Empirical studies that directly compare human experts, machine learning (ML) models, and hybrid approaches are rare. This study examines the predictive accuracy and efficiency of an XGBoost-based ML model, real estate experts, and a hybrid human–machine approach. A model was trained using 21,736 real estate transactions from Vienna (2018–2022). We then conducted an experimental procedure with 13 experts who evaluated newly built apartments sold in 2023 under three conditions: limited information, state-of-the-art expert methods, and collaboration between experts and ML model. The results show that the ML model achieves accuracy comparable to that of experts while significantly reducing the time required for the task. Within the hybrid approach, experts were able to achieve the highest accuracy in comparison to other methods. These results underscore the potential of human-ML collaboration.",5.0,https://www.nature.com/articles/s41598-025-34099-9_reference.pdf88fffad0cfa9a30379da39d021189263152b326bcd,10.53479/42325,Non-linear effects of monetary policy shocks on housing: Evidence from a CESEE country,2026.0,Documento de trabajo,Carlos Cañizares Martínez; Adriana Lojschová; Alicia Aguilar,"This paper estimates the effects of standard monetary policy shocks on housing and other macro variables in Slovakia, a CESEE country. For that purpose, we use a non-linear local projection model which uncovers asymmetries in these effects around three different dimensions: high versus low economic growth, interest rates and inflation. The main findings in this study are as follows. First, we often find no evidence of standard monetary policy eliciting a contractionary response in house prices or housing investment. Second, evidence is weakest during recessions and periods of low interest rates or low inflation. Third, these findings may be linked to the inability of monetary policy to trigger significant contractionary effects on household lending, which in turn may be linked to the effective lower bound on interest rates, the predominance of fixed-rate mortgages in Slovakia or interaction between monetary and macroprudential policy. We also discuss the possible country characteristics that might drive these results and policy implications.",5.0,https://doi.org/10.2139/ssrn.758942589efb32a73a9b46114fc0b85fc46c76dfef95e139f,10.1016/j.landusepol.2021.105898,How do single-family homeowners value residential and commercial density? It depends,2021.0,Land Use Policy,Arthur Acolin; Gregg Colburn; Rebecca J. Walter,"This paper develops estimates of the relationship between local density and single-family home values across five U.S. metropolitan areas using 2017 transactions in Chicago, Los Angeles, Minneapolis, Philadelphia, Seattle. Proposals to build new commercial and residential development projects that would increase local density commonly face opposition from local homeowners. Academic literature links the response from homeowners to concerns that higher density is associated with lower property values but there is limited empirical evidence establishing this relationship at the local level. We find a positive and significant relationship between density and house value in the core area of the five metropolitan regions we analyze. For outlying areas, the estimates are smaller and even negative in several cases. We instrument density based on topographic and soil characteristics and find similar results. These findings point to the need for a more nuanced discussion of the relationship between local density and housing values.",5.0,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC873032290368bc11d84c961fe75e3bc016ca32f61a32d44e5,10.1155/2022/3220986,Forecast of Foreclosure Property Market Trends during the Epidemic Based on GA-BP Neural Network,2022.0,Scientific Programming,Yao Fang,"Due to the epidemic, foreclosure, as a special transaction commodity, has also been affected to a certain extent. This paper uses the sample data of foreclosure auctions combined with the genetic algorithm multi-layer feedforward neural network to apply in the forecast of the foreclosure market trend. First of all, to select the indicators that affect the price fluctuation of foreclosed houses, use factor analysis technology to reduce the dimensionality of the original data to obtain two common factors; then combine the BP neural network and the genetic algorithm to propose a GA-BP neural network The prediction model of the foreclosure housing market takes the influencing factors as the input of the model and the housing price as the output. Finally, the prediction results are compared with the multiple linear regression and the extreme learning machine, which shows that the GA-BP neural network model is in It is a good choice for research, and it provides an effective method for predicting housing prices.",5.0,https://downloads.hindawi.com/journals/sp/2022/3220986.pdf916ad7d98758098c22835eb8fe6b086dcf84bc73ab,10.3390/sym11030390,Incremental Granular Model Improvement Using Particle Swarm Optimization,2019.0,Symmetry,Chan-Uk Yeom; Keun-Chang Kwak,"This paper proposes an incremental granular model (IGM) based on particle swarm optimization (PSO) algorithm. An IGM is a combination of linear regression (LR) and granular model (GM) where the global part calculates the error using LR. However, traditional CFCM clustering presents some problems because the number of clusters generated in each context is the same and a fixed value is used for fuzzification coefficient. In order to solve these problems, we optimize the number of clusters and their fuzzy numbers according to the characteristics of the data, and use natural imitative optimization PSO algorithm. We further evaluate the performance of the proposed method and the existing IGM by comparing the predicted performance using the Boston housing dataset. The Boston housing dataset contains housing price information in Boston, USA, and features 13 input variables and 1 output variable. As a result of the prediction, we can confirm that the proposed PSO-IGM shows better performance than the existing IGM.",5.0,https://www.mdpi.com/2073-8994/11/3/390/pdf?version=15528979509260a316c683571dfaa5c653f3d003e9f1f8b3464d,10.1007/s11146-016-9586-8,The Housing Market Effects of Local Home Purchase Restrictions: Evidence from Beijing,2016.0,Journal of real estate finance and economics,Wei-Zeng Sun; Si-Qi Zheng; D. Geltner; Rui Wang,"Home prices have surged in major Chinese cities, leading to concerns of asset price bubbles and housing affordability. The policy of home purchase restrictions (HPR) has been one of China’s harshest housing market interventions to squeeze out speculative demand and dampen the soaring home prices. Beijing was the first city to implement the HPR. Employing the regression discontinuity design technique, we find that Beijing’s HPR policy triggered a 17–24 % decrease in resale price, a drop in the price-to-rent ratio of about a quarter of its mean value, and a deep (1/2 to 3/4) reduction in the transaction volume of the for-sale market, with no significant change in the rent or the transaction volume of rental units. In submarkets where housing supply was less elastic, the effects of the HPR were larger in price and smaller in quantity, suggesting that wealthy buyers likely benefited more from the HPR. The scope of the analysis does not allow conclusions regarding the persistence or longevity of these effects.",5.0,http://hdl.handle.net/1721.1/11162293784fa9fa20451849fdca62123ffaba6a8e3639aa,10.21834/e-bpj.v2i6.972,Assessment of Heritage Property Values Using Multiple Regression Analysis and Rank Transformation Regression,2017.0,,Junainah Mohamad; S. Ismail; A. Iman; T. Mohd,"The current practice of heritage property valuation is using sale comparison approach. However, there is a limitation in applying this approach for heritage property valuation because of unavailability of market evidence. Therefore, the aim of this paper was to evaluate the capability of Multiple Regression Analysis and Rank Transformation Regression in assessing the values of heritage property. The original dataset consists of 893 commercial properties transacted from 2004 to 2014 in Kota Bharu, Kelantan Malaysia. After filtration process, only 25 units of shophouse heritage property were available and valid to be used. The findings indicate that even we adopted statistical methods of analysis to overcome the issue of unavailability of market evidence this still could not solve the problem. Hence, the findings of this research contribute to the need for an improved method for heritage property valuation by proposing an extended sale comparison using statistical analysis for future investigation.",5.0,https://ebpj.e-iph.co.uk/index.php/EBProceedings/article/download/972/pdf943687b2f139e31d2062f5910972986c9829680d87,10.54097/hbem.v21i.14837,House Price Prediction and Analysis Based on Random Forest and XGBoost Models,2023.0,"Highlights in Business, Economics and Management",Han Li,"Accurate prediction of house price is important in housing market. It’s difficult to forecast housing price because it’s influenced by many factors. There has been many discussions on housing price prediction by kinds of machine learning algorithm. This paper attempt to predict housing price by Random Forest and XGBoost models, and compares the performance between them. In this paper, missing values processing, correlation analysis and standardization of samples are carried on the initial data at first, then two machine learning models are constructed, trained and test on the same dataset. The Kaggle house price dataset (“House Prices-Advanced Regression Techniques”) is used in the paper. The dataset contains 1420 samples with 79 features that represent almost all characteristics of house. The results show that XGBoost algorithm achieve higher R square score of 89% , indicating that XGBoost model can be more efficient and accurate on measurement and prediction of housing sales prices.",5.0,https://drpress.org/ojs/index.php/HBEM/article/download/14837/1438595a55e4f7ffb449f34abf289982eba3f0427812c41,10.3386/w31371,Pricing Neighborhoods,2023.0,Social Science Research Network,Sadegh S. M. Eshaghnia; James J. Heckman; Goya Razavi,"Education in Denmark is freely available. Despite near equal teacher salaries and per-pupil school expenditure across districts, there is substantial spatial heterogeneity in school quality as measured by teacher quality and student test scores. We argue that this is due to sorting of teachers and students across neighborhoods. We develop and apply multiple methods for identifying parental evaluations of measured school quality in the presence of strong neighborhood sorting. There is strong concordance in the estimates across diverse methodologies. We estimate a willingness to pay of about 3% more for a house with average characteristics when test scores are one standard deviation above the mean. Controlling for selection into neighborhoods only slightly reduces our estimates. Given that school quality, as measured by monetary resources, is equalized across all neighborhoods, payments for school quality embodied in housing prices are in fact payments for peer, teacher",5.0,https://doi.org/10.3386/w3137196fb8eee688ea40496609ebe82ad7180db4acffe2c,10.1007/s00168-024-01328-4,The impact of short-term rental activity on house prices: evidence from coastal Virginia,2024.0,The annals of regional science,Navid Safari; Lei Zhang; Timothy Komarek,"Short-term rentals such as Airbnb have grown substantially in recent years. In this study, we investigated the effect of Airbnb rentals on neighboring house prices in Hampton Roads, Virginia. We focus on three different measures of Airbnb activity to obtain more robust results. Our measures include active Airbnb listings, active listings with at least one reservation, and the sum of reservations over three different periods. Consistent with previous literature, our main results show that prior Airbnb rental activity has a positive impact on the surrounding housing sales prices. Furthermore, the localized impact diminishes over geographic space (i.e., distances greater than 500 m). We also examine how Airbnb rental activity impacts house prices across the price distribution of home sales, using quantile regression. We find that the effect is strongest for higher-priced homes, while lower-priced houses are less impacted by Airbnb rentals.",5.0,https://link.springer.com/content/pdf/10.1007/s00168-024-01328-4.pdf976ed816e37b3e4c44c19bd2837a78f0d115631ac0,10.4314/fje.v6i2.4,"Effects of Urban Renewal on Residential Property Values in Two Neigbourhoods of Akure, Ni",2012.0,,Va Bello; A. Nwosu,"The study was carried out to examine Urban Renewal exercise and its effect on residential property values in Oke–Aro and Odopetu neighbourhoods in Akure, A structured questionnaire was administered on selected residents in the two neighbourhoods (Oke–Aro and Odopetu) using the Systematic Random Sampling Technique. Five hundred and sixty eight (568) questionnaires were administered, out of which four hundred and forty eight (448) were retrieved. The retrieved questionnaires were analyzed using Multiple Linear Regression Technique. The result showed an increase in rental value of residential properties due to upgrading of urban infrastructures in the two neighbourhoods. It is recommended that the state government should embark on comprehensive rehabilitation/upgrading of infrastructures instead of concentrating on road network and drainages only in the neighbourhoods. Key words: Urban Renewal, Residential Property and Values",5.0,https://www.ajol.info/index.php/fje/article/download/77538/14806798d82bbbc9bdc2f12116f0522a09b8a13b8492856a,10.1016/j.trpro.2016.05.015,Impacts on Land Use Characteristics from Fixed Link Projects: Four Case Studies from Norway,2015.0,,S. Andersen; Ø. Nilsen; M. D. Gutiérrez; Trude Tørset,"Abstract Infrastructure projects with large increase in accessibility, often experience over- or underestimation in traffic volume prognosis. This has been the case in several fixed link projects in Norway. Fixed link projects replaces ferry services with a bridge or a tunnel, resulting in large time savings across the fjord. This paper investigates four fixed link projects in Norway. The fixed links connects islands to towns with a population of up to 50.000. This study investigates how land use is affected by the opening of a fixed link. Commuting patterns, population, housing prices and number of companies are analysed with the difference in difference method. Results show a growth due to the fixed link for two of the cases, while the other two experience small or no growth caused by the fixed link. The results indicate that land use changes might appear for islands, which get a fixed link connection to non-urban areas.",5.0,https://www.sciencedirect.com/science/article/pii/S2352146516300151/pdf99cde6cddc11fbed3b01fb1049fbf91b70b9b5ed2f,10.6000/1929-7092.2020.09.06,An Empirical Investigation of the Portuguese Housing Prices (2004-18),2020.0,,Jian Luo; Renato Pereira; A. Dias,"This article presents an integrated macro view of the Portuguese housing market with macroeconomic indicators. Firstly, it compares the housing market and several macroeconomic indicators from 2004 to 2018. Then, the dynamic analysis of the housing prices by different regions in Portugal and its typology included. Also, the article is complemented with the regression analysis to identify the relationship between the house prices and macroeconomic indicators. Results show that the current negative interest rates are increasing the demand for houses and the housing prices. The housing stock in Portugal is mostly fixed but may experience limited growth as the rebuild program and new constructions. GDP and the housing prices have a positive correlation. Houses in Algarve and Lisbon are markedly more expensive than in the interior. From the regression analysis, the unemployment rate is the closest correlated variable.",5.0,https://doi.org/10.6000/1929-7092.2020.09.06100c17d9c37a5606857b2bf15a6aae5dae4c55283cf,10.63642/1357-1419.1231,"Property tax assessments, collections, and revenue performance in economic downturns : an examination of large American cities",2021.0,Journal of Property Tax Assessment & Administration,Olha Krupa; Kenneth A. Kriz,"As state and local governments plunged into budget crises caused by the loss of sales, income, and other tax revenues in 2020, this study presents an examination of the Great Recession's effects on property tax collections in large American cities. Herein we explore the interactions between property tax components and key socioeconomic indicators in urban economies. In particular, we report how changes in house prices, household incomes, and employment affect the interactions among assessed values, levies, tions, and tax revenues. The stylized model we develop presents how, through property tax levies, valuations, and collection rates, property tax revenues are impacted. To correct for endogeneity among the variables, we use seemingly unrelated regressions and three-stage least squares estimators. The dataset includes financial indicators for the 147 largest American cities collected from 2003-2012. Results show that during recessions, declining house prices reduce assessed valuations, and reduced tax collection rates further compound this negative fiscal effect. The inelastic response of tax revenues to assessed values suggests that jurisdictions take back the difference by re-set",5.0,1012a87a7466b775e92317afa48d845e8c625a2c408,10.2478/remav-2023-0020,The Effect of Price Anchoring on the Housing Market Based on Studies of Local Markets in Poland,2023.0,Real Estate Management and Valuation,S. Kokot,"Abstract The article attempts to explain market levels of housing prices by supplementing the set of typical objective explanatory variables with variables of behavioral background. The proposed explanatory variables reflect the anchoring effect of prices, understood as the acceptance by market participants of such price levels that are justified not only in terms of socio-economic factors, but also in levels entrenched in their minds. The purpose of the study is to show that the anchoring effect identified through behavioral economics can be generalized and applied to the market behavior of many market participants, and thus explain the weak correspondence between listed housing prices and their objective factors. The study covers 17 local real estate markets in Poland and employs econometric models built under slightly modified procedures of backward stepwise regression.",5.0,https://sciendo.com/pdf/10.2478/remav-2023-00201024d95022d0fb86bcb143948df80b7d36b2ecd5a00,10.1080/09599916.2023.2199764,Trade vs. daily press: the role of news coverage and sentiment in real estate market analysis,2023.0,Journal of Property Research,Franziska Plößl; Nino Martin Paulus; Tobias Just,"ABSTRACT Each week, thousands of newspaper articles on real estate topics are read by market participants. While the market is comparatively intransparent, readers hope to find valuable information. This raises the question of whether this investment of time pays off and whether different types of newspapers are an equivalent source of information. This paper examines the relationship between news-based coverage of real estate topics respectively news-based market sentiment and total returns of the asset classes of residential, office and retail. Using methods of natural language processing, including word embedding, topic modelling and sentiment analysis, three sentiment indicators for each asset class can be derived from 137,000 articles of two trade and two daily newspapers. Our results suggest that trade newspapers outperform daily newspapers in the prediction of future total returns and that the generated sentiment indicators Granger-cause total returns. Moreover, the results indicate that daily newspapers report more negatively on rising returns in the residential market than the trade press. To the best knowledge of the authors, this is the first study to quantify news cover",5.0,103936f098d39da4ba2c40de13448578384b4d75ba2,10.1109/dsaa49011.2020.00070,Urban and socio-economic correlates of property prices in Dublin’s area,2020.0,International Conference on Data Science and Advanced Analytics,D. Dupré,"Understanding the characteristics of the housing market is essential for both sellers and buyers. However, the housing market is influenced by multiple factors. In this paper, the urban and socio-economic structure of an area is used to predict the price of 10387 properties sold in 2018 in the city of Dublin. More precisely, the direct distance from each property to 160 urban features taken from OpenStreetMap is calculated, and an extreme gradient boosting linear regression performed. Using these features, the model explains 45% of the housing price variance. The most important features in this model are the proximity to an embassy and to a grassland. In addition, the results of a population census from 2016 are also used to correlate with the price of properties. From this census, 48 features are used as the input of a gradient boost linear regression model. In all, the socio-economic features are explaining 43% of the housing price variance as well. The density of individuals reporting that they are not providing unpaid personal help for a friend or family member as well as individuals reporting that they have no religion are the most important socio-economic feratures. By taking",5.0,104a8c28e3b3ffddbc8d1bed8440b62cfb09198250d,10.1080/1540496x.2019.1695596,Quantifying the Effect of Real Estate News on Chinese Stock Movements,2020.0,Emerging markets finance & trade,Yan Chen; Zhilong Xie; Wenjie Zhang; R. Xing; Qing Li,"ABSTRACT Both traditional finance and modern behavioral finance consider that the volatility of the stock market comes from the release, dissemination and absorption of information from different views. With the rapid growth of Chinese housing prices and the public’s attention to the issue, listed firms related to Chinese real estate have come to represent one of the most important sectors in Chinese stock markets. In this study, we quantify news articles using natural language processing techniques and investigate the impact of regulatory policies and firm-specific news on real estate stocks in this active market covered by a large amount of information. Our three main findings are as follows: (1) the simple and effective quantitative measure of news emotion can be used to study the media-aware stock movements in the real estate sector of the Chinese stock market; (2) policy news leaks ahead of time, which leads to abnormal fluctuations in the stock market 14 days before the release of information, while the stock market fluctuates only slightly and briefly after the news release; and (3) news pessimism indicates downward pressure on market prices, and optimism tends to increase m",5.0,1054a3d8d0beafeed799e94ecdca205c58da271e8ce,10.1080/14445921.2022.2110369,The dynamics of house price in Vietnam,2021.0,Pacific Rim Property Research Journal,T. Nguyen; Q. N. Tran; Hang Thi Thuy Dinh; Muhammad Najib Razali,"ABSTRACT We have assessed the housing market of Vietnam, with particular apartment prices in 10 urban districts of Hanoi, the capital of Vietnam. Significant determinants of house price include construction cost, income per capita, urban rent expense, and lending rate. Our findings show statistically significant and dynamic determinant effects on the apartment prices of 10 urban districts of Ha Noi. There are signs of volatility clustering in the GARCH effect at nine districts’ apartment prices, all with magnified effects. Amongst the ordinary least square (OLS), robust least squares (RLS) and bootstrap technique, RLS presents more significant fundamentals with higher Rs squared than the OLS and bootstrap on individual districts and overall Hanoi apartment prices. We find signs of price bubbles in the first quarter of 2015. While this quantitative analysis is limited to the north of Vietnam, the findings also provide insights into other significant centres of Vietnam. It also provides a basis for apartment price forecasts to the stakeholders in the housing market of Vietnam, investment decision-making and portfolio management for both household investors and mortgage investors. The",5.0,10665d366608dc12a1acbf050eafe557df106058d1c,10.3846/ijspm.2024.21096,HOUSE PRICE VOLATILITY IN CHINA: A PERVASIVE PATTERN WITH GEOGRAPHIC DISPARITY,2024.0,International Journal of Strategic Property Management,Xiaomeng Liu; Ziliang Yu; Yang Li,"The booming real estate sector has been regarded as the “gray rhino” risk emerging in China over the past decade. Yet, the house price volatility per se has not been thoroughly examined. Filling the gap in the literature, this paper explores the house price volatility and its determinants for 70 large and medium-sized cities in China, using an extensive monthly data set from 2005 to 2019. We find evidence of significant geographical disparities in both the GARCH effects and the best-fitted volatility specification. Significant GARCH effects are found in 57 cities, among which 40% of cities show a persistent volatility pattern. We also find that both the house price volatility pattern and the associated volatility value are affected significantly by education and healthcare amenities.",5.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/21096/12215107627d1bb4faf61829285a2df1f86a94317de5c3d5,10.17261/pressacademia.2019.1134,"Forecasting of Turkish housing price index: ARIMA, random forest, ARIMA-random forest",2019.0,Pressacademia,Ebru Caglayan Akay; K. Topal; Şaban Kızılarslan; Hoseng Bulbul,"PurposeThe aim of this study is to investigate whether the models developed by using Machine Learning methods are an alternative method for forecasting time series. Methodology-Traditionally, the Autoregressive Integrated Moving Average (ARIMA) model has been one of the most widely used linear models in time series forecasting. In the study, we use Random Forest and Hybrid Random Forest-ARIMA models besides the ARIMA model and compare their forecasting performance for the Turkish Housing Price Index series. Findings-The hybrid model was found to be more successful than other methods in forecasting the housing price index. Conclusion-As a result, hybrid models that combine ARIMA and machine learning method can be used an alternative method in forecasting economic and financial data.",5.0,https://doi.org/10.17261/pressacademia.2019.11341086e3e558a32c796c03ae2dbd78d44227bd1ba325b,10.2139/ssrn.1439161,Neighborhood Dynamics and the Housing Price Effects of Spatially Targeted Economic Development Policy,2009.0,Social Science Research Network,Douglas J. Krupka; Douglas S. Noonan,"Neighborhoods are the result of a complicated interplay between residential choice, housing supply and the influences of the larger metropolitan system on its constituent parts. We model this interplay as a system of reduced-form equations in order to examine the effects of a generous spatially targeted economic development program (the federal Empowerment Zone program) on neighborhood characteristics, especially housing values. This system of equations approach allows us to compute direct effects of the policy intervention as well as the effects mediated through non-price channels such as changes in the housing stock or neighborhood demographics. In the process, we are able to shed light on the rich simultaneity among neighborhood characteristics, including housing prices.",5.0,https://doi.org/10.2139/ssrn.143916110983faf77d42a80958a88efbc48e76ca0b0a7c8e5c,10.1080/10971475.2018.1481007,Structural Changes in the Relationship between Foreign Direct Investments and China’s Housing Price Bubble,2018.0,Chinese Economy,I. Tsai,"Since 2006, China has issued a series of policies to regulate foreign investments (FI) in the attempt to control housing prices. In the present study, the association between foreign direct investments (FDI) and China’s housing price bubble (HPB) was examined to elucidate the rationality of foreign investment controls. Structural changes in the influence of FDI on China’s HPB were observed. FDI and China’s HPB exhibited an increased association between 1985 and 2015, particularly following Q3 of 1997. This may have resulted from the relative stability of the Renminbi during the Asian Financial Crises, which attracted an increased inflow of FDI to China, and consequently aggravated China’s HPB. A threshold regression model was employed to uncover the reasons for the structural changes exhibited in the influence of FDI on China’s HPB. Findings indicated that FDI only influenced China’s HPB during the Renminbi appreciation, and that increased FDI was not correlated to HPB during the Renminbi depreciation or when the value of Renminbi remained unchanged against the U.S. dollar. The empirical results obtained in the present study suggest that the Chinese government should first observe ",5.0,110ca067c4e9099aa00265970a9a761b73f548126c9,10.1111/ajes.12610,The Effect of Housing Prices on Urban Innovation Capability: New Evidence From 246 Chinese Cities,2024.0,The American journal of economics and sociology,Zuanxu Chen; Mingyang Li; M. Zhang,"This study investigates the impact of rising housing prices on urban innovation capabilities from a comprehensive and systemic perspective, using panel data from 246 Chinese cities from 2004 to 2020. The findings challenge traditional views that emphasize only the negative effects of high housing prices on innovation. Instead, the analysis reveals that rising housing prices can enhance urban innovation through specific mechanisms. These include attracting and concentrating talent and generating spatial spillover effects that benefit neighboring cities. The study employs feasible methodological approaches, including the analytic hierarchy process (AHP), to measure urban innovation capability and spatial econometric models to capture spillover effects. The results provide valuable insights for policymakers to promote urban innovation, including stabilizing housing markets, optimizing industrial structures, attracting high‐skilled individuals, and establishing inter‐city innovation coordination mechanisms. This research contributes a novel theoretical framework for understanding the complex relationship between housing prices and urban innovation, enriching the fields of urban economi",5.0,11179333b7379a720ab40da7ac374dad8b581f5d8bf,10.1016/j.jpubeco.2025.105395,Do mandatory disclosures squeeze the lemons? The case of housing markets in India,2025.0,Journal of Public Economics,Vaidehi Tandel; S. Gandhi; Anupam Nanda; Nandini Agnihotri,"We study the effect of a law mandating disclosure of litigation status of housing projects on house prices in India. Information asymmetries between developers and buyers result in overpricing of litigated houses (lemons). We find that the introduction of the disclosure law led to a 4-6% decline in the prices of lemons relative to non-lemons. Our data on unit-level transactions, project details, and buyer characteristics allowed us to separate out the price effects across housing sub-markets and income groups. Our paper demonstrates that a mandatory disclosure law can have important, pro-efficiency effects in a developing country, and suggests that such laws may be efficient in a regime of low-state capacity.",5.0,https://doi.org/10.1016/j.jpubeco.2025.105395112ba3f6856aed8182df819db4c0e5b384401eeee94,10.1145/3514094.3534126,A Bio-Inspired Framework for Machine Bias Interpretation,2022.0,"AAAI/ACM Conference on AI, Ethics, and Society",J. Robertson; Catherine Stinson; T. Hu,"Machine learning algorithms use the past and the present to predict the future. But when given biased historical data, these algorithms can quickly become discriminatory. The area of machine learning fairness has emerged to detect and de-bias these algorithms, but has received widespread criticism for its one-size-fits-all approach, which allows certain cases of bias to slip through the cracks. In this study, we take a deeper look at the mechanisms by which machine learning algorithms develop harmful bias. We introduce a new method to interpret discriminatory systems, an Evolutionary algorithm for Feature Interaction (EFI), which we apply to several commonly used machine learning algorithms in two real-world problem instances: violent crime and median house price prediction. In the results, we discover several complex forms of bias including the encoding of race through other seemingly unrelated attributes. Ultimately we suggest that more informative interpretation tools such as EFI can be used to not only explain machine learning outcomes, but supplement and improve existing machine bias detection approaches to provide a more robust and in-depth ethical evaluation of machine learn",5.0,1135a946d8024a65ebc577eb98b73a33dff8312501a,10.1080/01603477.2022.2136096,"Real estate assets, heterogeneous firms, and debt stability",2022.0,Journal of post Keynesian economics,Jinsong Wang; Luoqiu Tang; Yue-Qiao Li,"Abstract By constructing a “financial accelerator” model that combines real estate assets and heterogenous enterprises, we use China’s housing price data to explore the effect of real estate assets on corporate debt and macroeconomic stability in the context of the Chinese economy. Based on the dynamic economic characteristics of the model, we find that the financial accelerator mechanism of housing prices further amplifies the various effects of housing price fluctuations when transmitted to the macroeconomic system, resulting in the size of corporate debt closely related to the economic boom–bust cycles. However, owing to the institutional factors, heterogenous firms have some differences in debt stability under the influence of asset price fluctuations. There is an obvious crowding-out effect of state-owned enterprise (SOE) credit on private-owned enterprise (POE) credit, and the existence of the implicit guarantee ratio of SOE credit will weaken the external financing constraints on the SOE and promote the expansion of SOE credit. Finally, the effect of fiscal and monetary policies on corporate debt stability is somewhat divergent, institutional factors can similarly weaken pol",5.0,114858db664fb39ee9a01d8a762083ba9f644466f84,10.1080/10511482.2022.2157219,Yes or Not in My Backyard (YIMBY vs. NIMBY)? The Impact of New Social Housing Construction on Single-Family House Prices in Quebec City (Canada),2023.0,Housing Policy Debate,Jean Dubé; François Des Rosiers; N. Devaux,"Abstract The development of new social housing faces important resistance by local population, a phenomenon knows as the “not in my backyard” movement. One argument from residents to oppose such project is the idea that new construction will negatively impact property values. This is what this paper aims to investigate. The analysis is based on a complete recension of the new social housing projects built between 2000 and 2020 and on single-family house transactions that occurred between 2004 and 2020 in Quebec City (Canada). A repeated sales model integrating a difference-in-differences estimator is developed to isolate the net price premium related to the emergence of a new social housing building while accounting for the possible heterogeneity impact related to characteristics of the building, including the number of apartments and the type of clientele hosted as well as the local characteristics, such as the spatial concentration of social housing buildings and distance to the city center. The results show a complex net price premium rent function that leads to mixed conclusions and has important implications for the development of new social housing projects.",5.0,115fe183352fb321feaa73bf58b4d917f7f6c3bfe53,10.1109/isemantic55962.2022.9920395,Residential Property Price Prediction Using Machine Learning: MakanSETU,2022.0,2022 International Seminar on Application for Technology of Information and Communication (iSemantic),Yash Y Panchal; Manan Mer; Abhiroop Ghosh,"MakanSETU is an emerging and advanced solution in the Real Estate industry. Real Estate Industry is at boom in the 21st century and trading Real Estate has become a great opportunity for Real Estate owners as well as others. The projection of Real Estate industry in business acquisitions is expected to reach 11 trillion USD. However, there is no proper solution to deal with inaccurate prices of properties online. The system proposed in this paper uses Native and new age Machine learning algorithms to predict and validate value of residential properties. Supervised learning is used in the system along with multiple Regressors to obtain the best result. Some of the regression algorithms used are Simple Linear regression, Decision tree regression, Random Forest regression (100 n-trees, 200 n-trees, and 500 n-trees), and Extreme Gradient Boost regression algorithm. The development of this system has followed a series of Data Collection, data handling, data processing, EDA, Feature engineering and Feature selection. The system enables investors to get a fair value of a property. The system is considered successful and ready to implement in the real work.",5.0,116bc4ab7219719c85e657610412d9bc495f8640b93,10.2139/ssrn.224093,Labor Supply and Matching Rates for Welfare Recipients: An Analysis Using Neighborhood Characteristics,2000.0,Social Science Research Network,J. C. van Ours; B. van der Klaauw,"This paper investigates how in addition to personal characteristics the neighborhood affects the individual transition rate from welfare to work. We use a unique administrative database on welfare recipients in Rotterdam, the second largest city of The Netherlands. We find that the exit rate to work of young Dutch welfare recipients is influenced by the neighborhood unemployment rate. Other neighborhood characteristics such as the average housing price are not important. From this we conclude that for young Dutch welfare recipients a high local unemployment rate has a negative spillover effect on the transition from welfare to work.",5.0,https://doi.org/10.2139/ssrn.22409311783256fd137f6ef8c2ef6cd8993a867c93eaa3877,10.1109/idciot64235.2025.10915014,Enhancing Real Estate Market Predictions: A Machine Learning Approach to House Valuation,2025.0,2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT),Pradeep Jha; Manju Mathur; Abhay Purohit; Apoorva Joshi; Anantika Johari; Suhani Mathur,"Machine learning (ML) has emerged as a versatile technology, transforming various domains such as speech recognition, medical diagnostics, and real estate analytics. This research addresses the critical challenge of accurately predicting house prices in dynamic and volatile real estate markets. Traditional valuation methods often fail to capture complex relationships between features, leading to suboptimal predictions. To mitigate these limitations, this study proposes a predictive model leveraging multiple ML algorithms, including Linear Regression, Decision Tree Regression, K-Means Clustering, and Random Forest Regression. Among these, Random Forest Regression demonstrated superior accuracy, achieving a root mean square error (RMSE) of 2.913. The findings indicate that integrating advanced ML techniques can streamline the home-buying process by enhancing price prediction accuracy, reducing dependency on intermediaries, and enabling informed decision-making for stakeholders. This study provides a robust framework for applying ML in real estate and highlights opportunities for further advancements in predictive modeling.",5.0,118a2227302eb90e4f6084a34759e9eb77d4e3853ea,10.1016/j.irfa.2018.11.019,Testing the predictive ability of house price bubbles for macroeconomic performance: A meta-analytic approach,2019.0,International Review of Financial Analysis,D. Floro,"Abstract This paper tests for the predictive ability of bubbles in housing markets on several proxies of macroeconomic performance using a panel of eighteen advanced countries. We use robust inference methods to address the bias resulting from the unknown persistence of our house price bubble measure. Evidence of predictability is analyzed by using a meta-analytic p-value combination approach for an overall joint significance, a method that is rarely applied in the panel predictive regression framework. The advantages are that heterogeneous panels are accommodated, and one can make inference on the individual unit for which the null hypothesis of no predictability is rejected. Our findings reveal the following: First, house price bubbles consistently predict an increase in government expenditures, even in the presence of structural change, different testing horizons and sample periods, as well as the inclusion of credit bubbles as as an additional predictor. Second, we find greater evidence that house price bubbles enhance macroeconomic performance in the identified countries for which evidence of predictability exists.",5.0,119d38981179925ad5482beb66234833edc1b9e1463,10.1109/isc2.2018.8656965,Using Geographic Information and Point of Interest to Estimate Missing Second-Hand Housing Price of Residential Area in Urban Space,2018.0,2018 IEEE International Smart Cities Conference (ISC2),Jiabo Tang; Zhi-Cheng Liu; Yu-Ran Wang; Jun-Yan Yang; Qiao Wang,"The real estate market including second-hand real estate market plays an important role in Chinese economy. However, it is not easy to acquire price of the each pieces of residential area in a city, and for instance the data acquired from Internet in our paper can only cover 56% of residential area in Nanjing. To this end, our paper proposed an model to fill the missing price data by using price and locations of second-hand real estates and Point of Interest (POI) information which were acquired from Internet. Our experiment was conducted in Nanjing and Chongqing, and demonstrates that our model is able to perform better than traditional Geographic Information System (GIS) method, such as Kriging interpolation, and general machine learning model, such as K-Nearest Neighbour (KNN). Also, our proposed model can be more interpretable than traditional methods, and able to reveal how the POI information can influence the second-hand real estate price. Our proposed model can help domain experts, e.g. city planners and economists, to better research the second-hand real estate market in the future.",5.0,120046fbfb8a01f244675bca1d6c738ab719e3036fd,10.1109/icaice51518.2020.00035,Analysis on the Weight initialization Problem in Fully-connected Multi-layer Perceptron Neural Network,2020.0,2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE),Wanchen Li,"In this paper, the weight initialization problem in fully connected multi-layer perceptron neural networks (MLPNN) is studied. A structure as 8-10-1 MLP neural network is taken as the experimental object. The training and predicting dataset selected in this paper is California housing price dataset. Eight special weight initialization methods are analyzed and five of them are tested by simply using the basic neural network with structure as 8-10-1 MLP to simulate one common neural network, visualize and compare the weight change process when using random weight initialization and its special weight initialization method. This paper implements the aim that identifying the influence of their special weight initialization method on their weight change process, focusing on what specific problems are solved by these special weight initialization methods to improve the convergence speed and accuracy and verifying the hypothesis that these specific weight initialization method will generate better result with high convergence speed and accuracy than random weight initialization.",5.0,121263bf1c9283573e3d39aa8835463976e8203edda,10.1109/icsca57840.2023.10087486,Evaluating machine learning algorithms for predicting house prices in Saudi Arabia,2023.0,International Conference on Software and Computer Applications,T. Alshammari,"With real estate projects increasingly growing, it has a critical economic role in countries and a significant effect on economic and investment domains. There is limitation of researches have contributed the inclusive evaluation of machine learning algorithms in real estate price prediction in this field. Machine learning algorithms could assist in forecasting real estate prices; thus, this paper presents a comparative assessment of machine learning algorithms to predict housing prices in Saudi Arabia. A real estate transaction datasets is used to evaluate the accuracy of machine learning algorithms. The datasets contains 13 columns and 59,846 instances (records). The results considerably vary in performance across the evaluated algorithms: random forest (RF), decision tree (DT) and linear regression (LR). After extensive examination the three proposed algorithm, Random Forest obtained R2-score 0.65, the decision tree obtained R2- score 0.30 and linear regression obtained R2-score 0.06. Random Forest performed better than the decision tree or linear regression methods.",5.0,122fd67dcefff7f239109dad843c0d5d19749c00f23,10.1590/s0034-76122007000600004,Proposta para construção de um índice local de preços de imóveis a partir dos lançamentos imobiliários de condomínios residenciais,2007.0,,Sérgio Rozenbaum; T. D. D. de Macedo-Soares,"It is very difficult to develop a national housing price index for a country with continental dimensions and the level of cultural and economic diversity such as Brazil. In addition, even at large metropolitan areas, there is a lack of information regarding the actual price of the real estate transactions and the characteristics of those units. The authors propose a model for developing an index of local markets based on information made available during the release of new residential real estate properties.",5.0,https://www.scielo.br/j/rap/a/NPZbPNfnP9GgsVtLFjjcZ3B/?lang=pt&format=pdf123880f771456153ce813c41f36089e1c7a0c3a896f,10.1016/j.tbs.2019.10.005,Home relocation and mobility tool ownership: Econometric investigations in the context of rising fuel prices in the Greater Toronto Area,2020.0,,S. Akbari; Md. Sami Hasnine; Elli M. Papaioannou; A. Bernardino; K. N. Habib,"Abstract Using stated preference data collected in the Greater Toronto Area (GTA), this study presents a combined choice model that considers alternatives consisting of bundles of home relocation and mobility tool choices. The model is estimated for changes in housing and mobility tool ownership choices in response to increasing commuting costs. The major finding is that there is a strong inertia against home relocation, despite significant increases in transportation costs. Using household home location choice data (choice in response to increasing transportation costs), this study also presents a model which predicts whether households will move closer to their workplaces or farther away as a function of several variables, including home prices, household characteristics and commuting distances. The findings reveal that most households relocate farther away from the workplace. Income and housing unaffordability are contemplated as the cause of rigidity in home relocation in the GTA.",5.0,124603941e2008080fb4e19fab4e34222203ac7f68a,10.1111/boer.12284,Average time to sell a property and credit conditions: Evidence from the Italian housing market survey,2021.0,,Tatiana Cesaroni,"This study examines the effect of housing credit conditions on the Time on Market for residential properties in Italy, using a unique dataset from the Bank of Italy survey on the Italian housing market. The analysis was carried out on a sample of roughly 1,000 real estate agents over the period 2011-2015 using panel data techniques. The results support the view that an increase in housing market liquidity, measured by the Loan-to-Value Ratio, reduces the average TOM of a property, thus improving the matching process between buyers and sellers. The results are found to be robust to specifications including the traditional determinants of TOM, used as controls (i.e. price revisions, potential buyers, mandates to sell, the role of brokers and so on). The results also show that a higher price reduction, possibly associated with an initial mispricing by the seller when setting the initial listing price, seems to be associated with a higher TOM for residential properties.",5.0,1256b785cf500c1f34b8adeb9435d8b668659290e81,10.1109/mlise57402.2022.00095,The Comparison of Six Prediction Models in Machine Learning: Based on the House prices Prediction,2022.0,2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE),Yi-Zhi Wang,"There are many different kinds of prediction models that have different performances when faced with different kinds of data. This essay focus on the comparison of the performance of multiple regressions, SVM with RBF kernel function, and Random Forest when predicting the house prices of Boston and using score function, k-fold cross-validation and shuffle cross-validation to evaluate their performance respectively. Finally, parameter adjustment, grid search, and forward selection are introduced to improve their performance. By combining the result given by three evaluating methods, SVM with RBF kernel function is the better model and the Random Forest is the worst one, whose scores are higher than 0.7 and lower than 0.1 respectively. And all of these three functions can slightly improve the performance, especially, the effect of the grid search is the best one, which can improve the score by 0.023 higher than the original score.",5.0,12632699b5d41da22f1f55e5daad6addd938aacac04,10.1109/ictai59109.2023.00105,A reinforcement learning-based weight fusion algorithm for house price prediction,2023.0,IEEE International Conference on Tools with Artificial Intelligence,Yige Zhang; Zong-Wen Fan; Jin Gou,"To address the limitations of existing house price prediction methods that struggle with handling mixed data types, we developed a novel model utilizing a reinforcement learning-based weight fusion algorithm. First, we reconstruct the feature fusion vectors from categorical feature variables. Then, we create individual house price prediction models using a variety of algorithms: Multiple Linear Regression, Regression Decision Tree, Extreme Random Regression Tree, and Multilayer Perceptron. These models' weights are dynamically adjusted using Q-Learning to minimize the Root Mean Square Error (RMSE). Our experiments demonstrate substantial gains in RMSE performance using our weight fusion approach. Specifically, on the small-scale Shenzhen dataset, RMSE improvements vary from 8.65% to 64.61%. On the large-scale Boston dataset, the improvements range between 53.06% and 76.40% in comparison to the standalone prediction algorithms.",5.0,127fbf0103ad800c16f6fcd37aeb05a77329d4d67f8,10.1080/13504851.2023.2208827,Accelerating inflation expectations of households in the euro area: sources and macroeconomic spending consequences,2023.0,Applied Economics Letters,A. Stokman,"ABSTRACT In 2021 and 2022, inflation expectations of households in the euro area rose dramatically. Based on a time-series investigation from 1995 to 2022, I find that inflation expectations are driven by current price developments of three consumption product categories and house prices (which are not included in the consumer price index). About half of the recent rise was fuelled by energy prices, 20% by food prices, over 10% by transportation costs and almost 20% by house prices. On a macro-level, controlling for a standard set of spending determinants, a regression analysis suggests that accelerating inflation expectations in general will slow down growth of area wide real private consumption, but under circumstances of low nominal interest rates helped to cushion the negative shock of the covid pandemic and the energy crisis.",5.0,12876136e7e10008be0a17ef7c006be3f06fb702658,10.23919/cisti52073.2021.9476447,Forecasting real estate prices in Portugal : A data science approach,2021.0,Iberian Conference on Information Systems and Technologies,Sanam Samadani; Carlos J. Costa,"The real estate market is vital from an economic and social perspective. It is why it is essential to understand its behaviour. In this context, our work aims to analyze the prices' evolution and understand what the main components are impacting the price. To understand the evolution and predict prices, we perform a time series analysis, including ARIMA. To understand the most relevant components, we perform a regression analysis. In this analysis, we used several approaches. We have collected our data from INE and PORDATA, the Databases of Contemporary Portugal, to construct the models and forecast house prices in Portugal.",5.0,12964c5dfc8f4ccf843fb8ee8e70e0dccdb148e18b7,10.18402/resci.2017.02.15,重庆市主城区住房价格影响因子的空间异质性@@@Spatial heterogeneity in factors affecting Chongqing housing prices,2017.0,,Ying-Lin Li; Yong Liu; Xiu-Hua Liu; Ying-Lin Li; Yong Liu; Xiu-Hua Liu,,5.0,https://doi.org/10.18402/resci.2017.02.151302e573f3384314fd1e0b0dd7c24f567743146abb7,10.1111/obes.12534,Selection Bias in Housing Price Indexes: The Characteristics Repeat Sales Approach*,2022.0,Oxford Bulletin of Economics and Statistics,Daniel Melser,,5.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/obes.12534131a59bb0465520de22fdcd71e2c23c3bd3cf94c50c,10.12652/ksce.2013.33.2.811,An Analysis on the Determinants of Mountainous and Coastal Area's Housing Value Caused by the Characteristics of the Natural Environment,2013.0,,Yeol Choi; H. Kim; S. Kim,,5.0,https://koreascience.or.kr:80/article/JAKO201312855324707.pdf132cb5aa617a9cd60231ae4339d23962713022a83de,10.2139/ssrn.1614697,Characteristics and Macroeconomic Drivers of House Price Changes in Australia,2005.0,,M. Neukirchen; Helen P. Lange,,5.0,https://doi.org/10.2139/ssrn.1614697133895d62850e3edf58474428e49c81ed24d27bf790,10.1007/s43071-021-00013-4,A spatial model averaging approach to measuring house prices,2021.0,Journal of Spatial Econometrics,Ryan Greenaway-McGrevy; Kade Sorensen,,5.0,1342893d55696c2d2bdd65206093b5ae75859620e50,10.1007/s43071-021-00009-0,Automated valuation model for residential rental markets: evidence from Japan,2021.0,Journal of Spatial Econometrics,W. Cheung; Lewen Guo; Yuichiro Kawaguchi,,5.0,135d6dccddca6043fb77c96ffd2b1b65d5fe5becb71,10.2139/ssrn.4108744,Predicting House Prices using Machine Learning and LightGBM.,2022.0,Social Science Research Network,Linda M. John; Rakshit Shinde; Shoaib M Shaikh; Devanshu Ashar,,5.0,136ef7f7e28c562f9880fe9f1fb339650d782d7fc30,10.19172/kreaa.24.1.5,"Estimation of the Apartment Housing Price Using the Machine Learning Methods: The Case of Gangnam-gu, Seoul",2018.0,,Seongwan Bae; Jung-Suk Yu,,5.0,1372a092a5dbf502c2ee632dac65980d333fdd0cd2f,10.1109/geoinformatics.2011.5980723,Spatial pattern and structural determinants of Shanghai's housing price: A GWR-based approach,2011.0,GEOINFORMATICS,Qi Tang; Wei Xu; Fu-Li Ai,,5.0,138d1c4f03e67657a1f241e7b8fa15bacf2f6c8e9d2,10.1007/s00500-018-3090-4,Affordable levels of house prices using fuzzy linear regression analysis: the case of Shanghai,2018.0,"Soft Computing - A Fusion of Foundations, Methodologies and Applications",Jian Zhou; Hui Zhang; Yujie Gu; Athanasios A. Pantelous,,5.0,139da1b32f0c7ded091ba309f27339297afe10d9387,10.1007/978-3-030-24302-9_46,"Spatial Analysis of the Proximity Effects of Land Use Planning on Housing Prices (Case Study: Tehran, Iran)",2019.0,Communication Systems and Applications,Amin Safdari Molan; Ebrahim Farhadi,,5.0,1408d4b2c6db271f945ac0c89c62b812c147103fcdd,10.25236/ajets.2020.030702,Prediction of California House Price Based on Multiple Linear Regression,2020.0,,Zixuan Wu,,5.0,1419db1823444f8f3accb29078986891e41b947fcee,10.1080/00420987420080391,The Determinants of Relative House Prices: a case of academic astigmatism?,1974.0,,R. Wilkinson,,5.0,142ab04dbf2b6a8f59e7f2adfb94fc8ec678421d406,10.1080/00420987420080401,The Determinants of Relative House Prices: a reply,1974.0,,M. Ball,,5.0,143bc54b81d5033f852f6e98db40ffc8dffad612757,10.1007/s00181-022-02264-y,Housing price diffusions in mainland China: evidence from a spatially penalized graphical VAR model,2022.0,Empirical Economics,Xian-Deng Jiang; Le Chang; Yan-Lin Shi,,5.0,1447bd5e31c4ebb64037ef0ab02e74cbbf6b3d53414,10.1016/j.apgeog.2024.103322,Neighborhood externalities modify the nexus between residential land and housing prices: A joint analysis using regression tree models,2024.0,Applied Geography,Weiye Xiao; Peiyu Tian; Feng Yuan,,5.0,1458b63060d8fd395b1339b1b60075584e8633c4abc,10.1080/02815738508730065,Housing prices: An empirical analysis of the determinants of the price level in the metropolitan area of Helsinki,1985.0,,V. Vihriala; Samuli Skurnik,,5.0,146f3e7f6483031798d68e89d244303c38d63ed5de3,10.1080/12265934.2006.9693583,"Trade-offs between Transportation, House Price and Amenities in Residential Location Choice in Oxfordshire, UK",2006.0,,F. Pagliara; J. Preston; Jae Hong Kim,,5.0,147266a65259d60178ac8bc559bdb9abfea6815d7ae,10.1002/aepp.13196,Valuing impacts of proximity to Saguaro National Park on house prices,2021.0,Applied Economic Perspectives and Policy,Julie M. Mueller; J. Loomis; Leslie A. Richardson; Ryan A. Fitch,,5.0,148f7a50a7657db8aa8e864c6851d5114315d8be3bb,10.19184/geosi.v4i3.9713,"Utilization of GIS Techniques as Decision Support System for Location of Filling Stations in Minna, Niger State, Nigeria",2019.0,,J. Yisa; O. Olubadewo-Joshua; O. S. Okosun,"The study demonstrated that spatial analysis with relevant socio-economic sources and physical parameter from different sources can be evaluated for the filling station sites planning. This has demonstrated the importance of Geographic Information System (GIS) application in predicting and determining of site criteria for filling stations facilities development, most especially in areas where there is land uses competition which requires consumer accessibility, sustainability, environmental safety, environmentally sensitive development solutions, etc. A stratified sampling technique was used to select the sample size and administration of the questionnaire. The data collected was analyzed using descriptive statistics such as frequency distribution, bar chart, pie chart and percentage and maps showing the sampled existing filling stations in the study area. The result shows the distribution of filling stations located across the study area. This study shows that GIS and multi-criteria analysis are essential tools to assist in correct siting to national planners and decision-makers in deciding the most appropriate filling stations location pattern to apply in Minna and its environs. ",4.0,https://jurnal.unej.ac.id/index.php/GEOSI/article/download/9713/7362149e8e10ad658e8dc67021c4b6e33f0ac692470a474,10.48264/vvsiev-20223105,How do metropolitan cities evolve after the 2008/2012 crisis and the Covid-19 pandemic? An analysis from real estate market values,2023.0,Valori e Valutazioni,Ezio Micelli; Eleonora Righetto,"Italian cities have been touched by two major events, the 2008 and 2012 crises and the Covid-19 pandemic in 2020 and 2021. The research aimed to verify whether, and in what way, Italian cities have embarked on a path of transformation, outlining their possible trajectories of change in the intervening decade. The cities considered were the metropolitan cities to which the legislature has assigned the role of territorial reference for areas of a regional nature. The research examined real estate market values for their ability to represent a city’s degree of attractiveness in synthetic form. The other variables used made it possible to detect trends in the determinants of the real estate market: economic growth, demographic development and changes in the territorial capital endowment. Concerning the research objectives, cluster analysis appeared to be the most suitable tool to represent changes by aggregating cities according to common patterns. The survey considered the reactions of the different cities in the two five-year periods related to each exogenous shock and, overall, in the decade under review for a long-term reading of the trends. The conclusions reached by the survey sh",4.0,https://siev.org/wp-content/uploads/2023/02/05_MICELLI-ET-AL_FINAL.pdf1502ceadf55cf5407d588b8d425f77da8b4208cb209,10.3386/w30814,Disparities in Pollution Capitalization Rates: The Role of Direct and Systemic Discrimination,2022.0,Social Science Research Network,J. G. Graff Zivin; Gregor Singer,"We examine how exogenous changes in exposure to air pollution over the past two decades have altered the disparities in home values between Black and White homeowners. We find that air quality capitalization rates are significantly lower for Black homeowners. In fact, they are so much lower that, despite secular reductions in the Black-White pollution exposure gap, disparities in housing values have increased during this period. An exploration of mechanisms suggests that roughly one-quarter of this difference is the result of direct discrimination while the remaining three-quarters can be attributed to systemic discrimination through differential access to complementary amenities. Joshua S. Graff Zivin University of California, San Diego 9500 Gilman Drive, MC 0519 La Jolla, CA 92093-0519 and NBER jgraffzivin@ucsd.edu Gregor Singer London School of Economics Grantham Research Institute Houghton Street London WC2A 2AE United Kingdom g.a.singer@lse.ac.uk While racial segregation in the United States formally ended more than half a century ago, the existence of predominantly Black and White neighborhoods persists, and they continue to differ on a wide range of dimensions. One important",4.0,https://doi.org/10.2139/ssrn.431629815189863c73bfe2209649537196a102772ff2187cb3,10.1088/0026-1394/59/1a/08002,Key Comparison Study - Organic Solvent Calibration Solution - Gravimetric preparation and value assignment of aflatoxin B1 (AfB1) in acetonitrile (ACN),2022.0,Metrologia,R. Josephs; M. Bedu; A. Daireaux; Xiu-Qin Li; Xiao-Min Li; Z. Guo; Xianjiang Li; T. Choteau; G. Martos; S. Westwood; R. Wielgosz; H. Li; M. Simón; C. S. Smersu; M. Villarreal; T. Seal; M. Cirio; E. C. P. Do Rego; R. Leal; L. Carvalho; J. Rodrigues; E. Guimarães; B. Garrido; L. M. Erazo; S. Ramírez; I. Gonzalez; P. Giannikopoulou; C. Alexopoulos; E. Kakoulides; I. Mugenya; D. Prevoo-Franzsen; M. Fernandes-Whaley; S. Marbumrung; J. Nammoonnoy; K. Shearman; C. Boonyakong; H. Klich; R. Torkhani; T. Gokcen; M. Bilsel; S. A. Ozen; J. Cea; O. Martínez,"The CCQM-K154.b comparison was coordinated by the Bureau International des Poids et Mesures (BIPM) and the Chinese National Institute of Metrology (NIM) on behalf of the Organic Analysis Working Group (OAWG) of the Comité Consultatif pour la Quantité de Matière (CCQM) for National Measurement Institutes (NMIs) and Designated Institutes (DIs) which provide measurement services in organic analysis under the 'Comité International des Poids et Mesures' Mutual Recognition Arrangement (CIPM MRA) and/or have participated in the BIPM's Mycotoxin Metrology Capacity Building and Knowledge Transfer (MMCBKT) project as part of its ""Metrology for Safe Food and Feed in Developing Economies"" Capacity Building Programme. Gravimetrically-prepared solutions having an assigned mass fraction of specified organic analytes are routinely used to calibrate measurement processes for the quantification of the same analytes in matrix samples. Appropriate assignments of the property value and associated uncertainty of calibration solutions thus underpin the traceability of routine analysis and are critical for accurate measurements. Evidence of successful participation in relevant international comparisons is",4.0,152b8423fbd198748885548b0369c1cd330303fe334,10.1108/jpif-09-2019-0128,San Francisco: rental restrictions and pre-restriction host listing motivation,2020.0,Journal of Property Investment & Finance,B. Brotman,"PurposeSan Francisco started regulating short-term vacation rentals on rooms/apartments/houses located within city limits in September 2019. The objectives of this conceptual-scenario and regression study are to calculate the present value of the net earnings for a short-term residential rental property located in San Francisco pre-regulation and post-regulation, and consider a financial reason motivating households to list properties as short-term rentals.Design/methodology/approachA present value approach is used to estimate the value of rental space to tourists prior to the passage of San Francisco's short-term rental regulations compared to post-rental rules. Table 2 shows pre- and post-income scenarios. Price increases of +20, +40 and +60 percent over the initial base rate failed to restore host earnings to pre-registration levels. The present value model calculates the net revenue less net cost associated with listing a property. The regression model uses the number of listings as the dependent variable, and housing prices divided by weekly wages as independent variables.FindingsThe short-term rental regulations significantly reduce the profitability associated with short-ter",4.0,153fb800f86d84d1a8e17d268100a16c198a445b53b,10.1108/ijhma-02-2022-0027,An alternative interest-free home financing model,2022.0,International Journal of Housing Markets and Analysis,Levent Sümer,"154Purpose155This study aims to determine the relationship between the banking industry and home financing by conducting a regression analysis between the mortgage loan interest rates and the number of housing sales, and based on the results of the analysis, this paper proposes a new and alternative interest-free home financing model by directing the savings of the people in pension funds into real estate investment funds (housing fund), specifically established to provide a bank loan-free home financing solution. Diminishing Musharakah (partnership) is also integrated into the model from an interest-free and saving economy perspective. The model developed also provides opportunities to increase the size of the real estate investment funds and provide alternative investment tools to pension funds.156157158Design/methodology/approach159While the global financial crisis resulted from the mortgage crisis in the USA in very recent history, the world has been experiencing the evolution of a new health crisis, COVID-19, a pandemic that has been heavily affecting the global economy in the past two years. The housing sector is among one of the major industries that may be affected by this new global c",4.0,1606e95548139ce8d166cb8999c7b25909ae6577a66,10.2139/ssrn.4710928,An Agent-Based Model of the Housing Market Bubble in Metropolitan Washington D.C.,2024.0,Social Science Research Network,Robert Axtell; J. D. Farmer; J. Geanakoplos; P. Howitt; Ernesto Carrella; Benjamin Conlee; Jonathan P. Goldstein; Matthew Hendrey; Philip Kalikman; David Masad; Nathan Palmer; Chun Yang,"Several independent datasets concerning household behavior in the Washington, D.C. metropolitan area are combined to create an agent-based model of the recent housing market bubble and its aftermath. Comprehensive data on housing stock attributes, primarily from local government sources, are used as input to the model, as are administratively-complete data on household characteristics. Data covering all real estate transactions over the period 1997-2009 are used as targets for model output, including inventory levels and average days-on-market in addition to price statistics. Finally, a very large sample of mortgage service data (80-90 percent coverage of metro D.C.) serve as both input to the model (e.g., mortgage types, interest rates obtained) as well as target output (e.g., refinancing rates, foreclosure levels). The model consists of a large number of heterogeneous households who make rent or buy decisions, are matched to homes and commonly seek mortgages with which to purchase homes. These households have homogeneous rules of behavior but heterogeneous realized behavior since decisions depend on local household characteristics (e.g., size, composition, financials). This is a ",4.0,https://doi.org/10.2139/ssrn.4710928161681d71b30d8509b88bda94928936a1ed2ac0b571,10.3389/fenvs.2022.1056042,"Exploration of the regional correlation and network structure characteristics of land prices: A case study of Hebei, China",2023.0,Frontiers in Environmental Science,Can Li; Wei Wei; Yajing Duan; Zhong-Jiang Feng; Xiao-Miao Feng; Chen-Ming Zhu; Pengfei An,"It is important to explore the correlation characteristics of land markets among cities in order to promote coordinated developments. Based on the residential land prices in 168 counties in Hebei Province, this study used spatial econometric models and social network analysis to analyze the regional correlation effect and network structure evolution characteristics of residential land prices. The results indicated that: 1) the regional residential land price level has significant global spatial autocorrelation and local autocorrelation. High-High clusters were concentrated in cities around Beijing and Tianjin and provincial capitals, while Low-Low clusters were mostly distributed in central and southern Hebei. 2) The direct effect and spillover effect of influencing factors of residential land price were significantly different. The residents’ purchasing power, the socioeconomic level, and the land resources had significant impacts on the residential land price of the county itself, while the level of infrastructure and the policy environment had significant impacts on the residential land price of neighboring counties. 3) The degree centrality and betweenness centrality of residen",4.0,https://www.frontiersin.org/articles/10.3389/fenvs.2022.1056042/pdf16229160e56a0a38514091d16e961bd36670315200e,10.1097/phh.0000000000002201,"Balanced Area Deprivation Index (bADI): Enhancing Social Determinants of Health Indices to Strengthen Their Association With Healthcare Clinical Outcomes, Utilization, and Costs.",2025.0,Journal of Public Health Management and Practice,M. Morid; Robert E. Tillman; E. Halperin,"BACKGROUND163With the rapid expansion of value-based care in the US health-care industry, addressing health disparities has become a central focus for stakeholders. Social determinants of health (SDoH) indices serve as critical tools for assessing neighborhood inequities and informing programs aimed at managing patient health and costs. The Area Deprivation Index (ADI), recently endorsed by the US Department of Health & Human Services, is widely used in these efforts. However, the ADI's reliance on housing-related variables, such as median home value, raises concerns about its applicability, particularly in regions with high property values. This reliance may obscure inequalities and poor health outcomes in communities with high living costs, limiting its effectiveness for population health initiatives led by accountable care organizations (ACOs).164165166METHODS167To address these limitations, we developed the balanced ADI (bADI), a novel SDoH index designed to reduce dependency on housing-related variables by standardizing construction factors. We evaluated the bADI's performance through extensive benchmarking using data from millions of Medicare beneficiaries enrolled in fee-for-service a",4.0,https://arxiv.org/pdf/2506.0813116887162c70caab077e75b8f4d4f000fa6d663aa464,10.1108/econ-10-2023-0166,What drives the real estate market? Could behavioral indicators be useful in house pricing models?,2024.0,EconomiA,Evangelos Vasileiou; Elroi Hadad; Georgios Melekos,"PurposeThe objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.Design/methodology/approachIn our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain h",4.0,https://www.emerald.com/econ/article-pdf/25/1/157/9468391/econ-10-2023-0166.pdf16997f9c9da58dc6ca666bffd3cfaabd7b87e6b4bd3,10.15291/geoadria.1465,Pregled razvoja teorija u proučavanju zadovoljstva stanovanjem,2018.0,Geoadria,Silvija Šiljeg; Ivan Marić; B. Cavrić,"Housing satisfaction is one of the indicators of the quality of life. It affects all aspects of life dimensions, from physical, economic, social and psychological well-being. Because of its complexity, housing is considered an interdisciplinary research area. Different professions apply different approaches, concepts, models and measures in exploring housing satisfaction. For housing research to be comprehensive, it is necessary to formulate a theoretical framework first. In a large number of scientific papers, researches and projects, housing theories are often partially explained. For this reason, and based on a review of the latest literature, this paper provides a synthesis and overview of the following theories of housing quality satisfaction: the theory of housing satisfaction on which the model of housing satisfaction is based, starts with subjective perceptions, the theory of housing adjustment as the most frequently cited theory in the study of housing quality satisfaction; the housing mobility theory, which places particular emphasis on the housing environment as an important predictor of housing quality satisfaction; the theory on impression formation which is used in ex",4.0,https://morepress.unizd.hr/journals/geoadria/article/download/1465/21701707006560eed89340fec9e1f7a1b29916bf9073f7b,10.3389/fnut.2024.1374845,Spatial variation of zero fruits/vegetables consumption and associated factors among children aged 6–23 months in Ethiopia: geographical weighted regression analysis,2024.0,Frontiers in Nutrition,Werkneh Melkie Tilahun; M. Simegn; Habtamu Geremew; Z. Gebreegziabher; Lamrot Yohannes Abay; Tigabu Kidie Tesfie,"Background After 6 months, nutrient-dense, varied diets containing fruits and vegetables are crucial to supplement breastfeeding. Like many other low-income countries, Ethiopia has very low FV consumption. Zero vegetable or fruit (ZVF) consumption has been shown to significantly raise the risk of non-communicable diseases and has been ranked among the top 10 risk factors for mortality. And it is associated with poor health, an increased risk of obesity, and a higher risk of non-communicable diseases. Thus, this study’s goal was to investigate the spatial distribution of ZVF consumption and its spatial determinants among children aged 6–23 months in Ethiopia. Methods A cross-sectional study design was employed. A total of 1,489 weighted samples were included from kids’ datasets from the 2019 Ethiopian mini-demographic and health survey. STATA version 16, ArcGIS version 10.8, Kuldorff’s SaTScan version 9.6, and MGWR version 2.0 software were used for analysis. Spatial regression analyses (geographical weighted regression and ordinary least squares analysis) were conducted. Models were compared using AICc and adjusted R2. A p-value of less than 0.05 was used to declare statistically s",4.0,https://doi.org/10.3389/fnut.2024.13748451714ba37b732539249aaf04fa8f84993629204bff93,10.1108/ijhma-05-2018-0033,The effect of ground rent and unexpired lease term on property values in Ghana,2019.0,International Journal of Housing Markets and Analysis,Kwasi Gyau Baffour Awuah; F. Gyamfi-Yeboah,"172Purpose173Although several factors influence property value determination depending on the market, relevant studies in sub-Saharan Africa (SSA) often fail to analyse the impact of factors, such as unexpired term of leasehold interest and ground rent, which are also germane to market transactions and value determination. This study aims to examine the effect of unexpired term of leasehold interests and ground rent on the valuation of residential properties in Ghana.174175176Design/methodology/approach177A questionnaire instrument was used to collect the views of a sample of professional real estate valuers on the relevance of these and other factors that affect value. In addition, the valuers were tasked to value a residential property located in Accra, Ghana. Ordinary least squares and quantile regression models were thereafter used to analyse the data to determine the effect of the subject variables on value.178179180Findings181The study finds a significant relationship between valuers’ views on the relevance of unexpired term of leasehold interest and the value placed on residential properties. Further, the respondents who viewed ground rent as an important factor in estimating values placed sign",4.0,http://usir.salford.ac.uk/id/eprint/47860/1/_system_appendPDF_proof_hi.pdf3.pdf18261e559db48e121efbfbb16d9fba8e6102d41d5e0,10.1108/ijhma-04-2021-0045,"A systematic analysis of the spatial migration patterns and the linkages between market components in Greater Manchester, United Kingdom",2021.0,International Journal of Housing Markets and Analysis,Le-Vinh-Lam Doan; Adipandang Yudono,"183Purpose184This paper aims to bring together research on housing market area, submarket and household migration into a systems approach that helps us gain a better understanding of the structure and dynamics of a housing market and identify housing problems for a large metropolitan area.185186187Design/methodology/approach188The paper uses a geographic information system (GIS)-based method with simple quantitative techniques, including spatial analysis, location analysis, house price clustering and cross-tabulation. The analysis is based on migration data from the 2011 Census, house price data from the Land Registry in 2011 for Greater Manchester at the ward level and the output areas level.189190191Findings192The results show that different submarkets and housing market areas had different patterns of spatial migration and connections with other areas. Through a systematic analysis of migration and house price in combination, it also found a close connection between destination submarkets and the ages of migrants and identified specific problematic patterns for a large metropolitan area.193194195Research limitations/implications196The interactions between the owner-occupied sector and the social and privat",4.0,1973f6ae8f0f5446dac4de123987e334ec532a9ac66,10.3390/ijgi12100410,"Do Locations of Employment and Residence Influence whether People Use Virtual Social Networks? A Case Study of Residents in Wuhan, China",2023.0,ISPRS Int. J. Geo Inf.,Huixia Deng; Qiang Niu; Lei Wu,"High-speed information technology development has made virtual social networking (VSN) a social interaction trend. Studies have been carried out to investigate the spatial clustering characteristics of the locations where there is online social interaction, but they have not yet concentrated on the geographic phenomenon associated with the distribution of occupational and residential locations of citizens who use VSN. According to usage statistics gathered from China Unicom for people living in the Wuhan metropolitan development area, there are geographical characteristics for the sites of employment and residence of virtual social application (VSA) users. Compared with people who live or work in the central city, suburban citizens are more willing to conduct virtual social networking, and those who are most likely to do so are concentrated in the suburbs 20–30 km from the main city. Additionally, we used geographically weighted regressions to evaluate the relationship between the density of physical social facilities and the possibility of the usage of VSAs, revealing the influence of various conventional social conveniences on the propensity to use the VSA. Residents are more inc",4.0,https://doi.org/10.3390/ijgi12100410198c6395b99d45a6cd87eef1d35a9199e893ceece16,10.2139/ssrn.3726037,Commuting in Europe: An Inter-regional Analysis on its Determinants and Spatial Effects,2020.0,Social Science Research Network,Chiara Castelli; Angela Parenti,"Commuting shapes countless everyday-lives around the world, with dynamics varying from city to regional and cross regional level. Taking as reference the free-movement EU-28 area (plus Switzerland and Norway), the analysis considers a total sample of 195 NUTS2 regions over the decade 2007-2017 to depict regional cross-border dynamics, thus including the impacts of the 2008 financial crisis. The tested presence of spatial interactions among regions leads to the adoption of the Spatial Durbin Model in a panel context, thus including fixed effects in order to eliminate any time influence on variables as well as any regional idiosyncrasy (i.e. cultural, institutional etc.). The outcoming analysis highlights the potentiality of temporary contracts in preserving jobs during crisis, as they offer a flexible tool for employment adjustments. Moreover, the regional specialization in the knowledge sector is found to be an important attractor of external workers as well as a relatively effective retaining factor of the domestic labour force. But there are also other factors affecting mobility. For instance, the perceived commuting distance significantly depends on the time needed to reach the ",4.0,https://doi.org/10.2139/ssrn.3726037199fd03d6ee2765f180bee126172511b364582106a0,10.1080/23311975.2024.2407923,"Driving sustainable transportation: an in-depth exploration of influencing factors in Ho Chi Minh City, Vietnam",2024.0,Cogent Business & Management,H. Vu; N. H. Nguyen,"Abstract This study investigates factors influencing sustainable transportation (ST) across social, economic, and environmental dimensions using Structural equation modelling. A survey of transportation enterprises in Ho Chi Minh City, Vietnam, resulted in a final sample of 414. Findings show that historical context, political dynamics, facility quality, and environmental impact reduction strategies positively impact all three ST dimensions. E-commerce developments contribute positively to economic sustainability but not to social or environmental sustainability. Conversely, technological application significantly influences social and environmental dimensions but does not impact economic sustainability. HIGHLIGHTS Environmental impact reduction strategies exert the strongest positive influence across all sustainability dimensions, followed by historical context, political dynamics, and facility quality. Key drivers of environmental strategies include reduced congestion, minimized delays, decreased fuel consumption, and emissions control. Stability in residential area characteristics, road infrastructure, and property value accessibility are key attributes of historical context. Po",4.0,https://doi.org/10.1080/23311975.2024.240792320071dfbb88a56f7cf6aace16f157064eaf06de9430,10.1108/ijhma-01-2023-0007,Do real estate investment companies profit from house price growth? Evidence from Portugal,2023.0,International Journal of Housing Markets and Analysis,A. M. Cunha; A. Borges; M. Ferreira,"201Purpose202This study aims to study the sensitivity of nonlisted real estate investment companies’ accounting earnings to house prices. This study evaluates whether house price changes determined these companies’ return on equity (ROE) or if other factors influenced the industry’s profitability beyond house price growth.203204205Design/methodology/approach206The authors collected a ten-year sample with the aggregate ROE of Portugal’s real estate investment companies, split by regions, and data on house prices and the per capita gross domestic product as a control variable. The authors ran a national-level time series with the canonical cointegrating regression estimator, which is robust to a small sample size; the authors also performed a regression on regional-level panel data with the common correlated effects mean group estimator, thus allowing slope coefficient heterogeneity and controlling for cross-sectional dependence. The authors also ran ordinary least squares regressions as a means of comparison.207208209Findings210This study found that an increase in the house price is not translated into an increase in the aggregate ROE. The results are robust with a reduced survivorship-biased sample, m",4.0,https://repositorio.upt.pt/items/36fcb51e-936e-4faf-beab-110f4f2417ec/request-a-copy?bitstream=aa7b84f2-944d-4206-a5ff-c40bc0576ec521177905b7f338620ef06a6b0e16311752ce5d45d2e,10.1142/s2972426023400032,Spatial Structure of Places in Singapore from a Complexity Perspective of the Public Transport Network,2023.0,International Journal on Smart and Sustainable Cities,H. Huynh,"As cities become increasingly developed, inclusive and equal provision of public services and affordable housing is essential in achieving sustainable growth and development. In Singapore, with much of the population living in public housing and taking public transport, there is a great need to understand their landscape across the country. In this study, using tools from Complexity Science, we elucidated the spatial organization of places in Singapore through public bus transport connectivity, and investigated the association between such pattern to the distribution of the population age groups and property price. From the spatial network of the public transport services, we probed the places’ scale using the continuum percolation approach to uncover their spatial pattern. After that, we employed an information-theoretic measure of complexity to quantify the spatial organization of the system. The results show that there is a critical distance embedded within the public transport system at which the structure of the places in Singapore emerges. In many areas, the connectivity between places map well, and are greatest within the official planning boundary, demonstrating the influen",4.0,212aa826b48c46d7896c2552b27d90272e477450e82,10.3390/math11143160,GERPM: A Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model,2023.0,Mathematics,Guang Hu; Yue Tang,"Accurate prediction of urban residential rents is of great importance for landlords, tenants, and investors. However, existing rents prediction models face challenges in meeting practical demands due to their limited perspectives and inadequate prediction performance. The existing individual prediction models often lack satisfactory accuracy, while ensemble learning models that combine multiple individual models to improve prediction results often overlook the impact of spatial heterogeneity on residential rents. To address these issues, this paper proposes a novel prediction model called GERPM, which stands for Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model. GERPM comprehensively analyzes the influencing factors of residential rents from multiple perspectives and leverages a geographically weighted stacking ensemble learning approach. The model combines multiple machine learning and deep learning models, optimizes parameters to achieve optimal predictions, and incorporates the geographically weighted regression (GWR) model to consider spatial heterogeneity. By combining the strengths of deep learning and machine learning models an",4.0,https://www.mdpi.com/2227-7390/11/14/3160/pdf?version=1689689674213d97fc9d4eeedf8b643ea470a74d54f3f893efb8d,10.3390/ijgi13070257,Sensing the Environmental Inequality of PM2.5 Exposure Using Fine-Scale Measurements of Social Strata and Citizenship Identity,2024.0,ISPRS Int. J. Geo Inf.,Li He; Ling-Feng He; Zezheng Lin; Yao Lu; Chen Chen; Zhongmin Wang; Ping An; Min Liu; Jie Xu; Shurui Gao,"Exposure to PM2.5 pollution poses substantial health risks, with the precise quantification of exposure being fundamental to understanding the environmental inequalities therein. However, the absence of high-resolution spatiotemporal ambient population data, coupled with an insufficiency of attribute data, impedes a comprehension of the environmental inequality of exposure risks at a fine scale. Within the purview of a conceptual framework that interlinks social strata and citizenship identity with environmental inequality, this study examines the environmental inequality of PM2.5 exposure with a focus on the city of Xi’an. Quantitative metrics of the social strata and citizenship identities of the ambient population are derived from housing price data and mobile phone big data. The fine-scale estimation of PM2.5 concentrations is predicated on the kriging interpolation method and refined by leveraging an advanced dataset. Employing geographically weighted regression models, we examine the environmental inequality pattern at a fine spatial scale. The key findings are threefold: (1) the manifestation of environmental inequality in PM2.5 exposure is pronounced among individuals of va",4.0,https://doi.org/10.3390/ijgi13070257214739b323da43444721705d68688bfefe526c69bf6,10.3390/risks13050081,"The Impact of Economic Policies on Housing Prices: Approximations and Predictions in the UK, the US, France, and Switzerland from the 1980s to Today",2025.0,Risks,Nicolas Houlié,"I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to toda",4.0,https://www.mdpi.com/2227-9091/13/5/81/pdf?version=1745414369215f117acd1078949d08d99121e5224b6b6b0c0c34a,10.3390/buildings12101760,"Examining the Effect of Urban Rail Transit on Property Prices from the Perspective of Sustainable Development: Evidence from Xuzhou, China",2022.0,Buildings,Zheng Zhu; Ya-Ting Zhu; R. Liu; Lei Zhang; Jing-Feng Yuan,"Urban rail transit (URT) promotes sustainable urban development by alleviating traffic congestion and environmental degradation. However, many cities have developed URT projects recently, often encumbering local governments with debt. Land value capture (LVC) is an important theory that explores the new financing modes for urban rail transit. Despite prior studies that have found a positive relationship between URT and property prices based on LVC, limited empirical studies explore sustainable paths to better examine the effect of URT on property prices. This study collects 1036 properties in Xuzhou, China. Meanwhile, multiple regression models are established to analyze the impact mechanism of URT on property prices, and to further examine the combination effects of multidimensional neighborhood infrastructure and URT on property prices from the perspective of sustainable development. The results show that the coefficients of URT in all models are negative, indicating that the property prices decrease as the distance from the URT to the property increases, and the positive coefficient of TRANS implies that the transfer station would raise house prices. Combining Park, School (and ",4.0,https://www.mdpi.com/2075-5309/12/10/1760/pdf?version=1666747261216997c0f15f44e3ec2d8f7c299a4aa437bfa59b2ba,10.5539/ass.v12n6p61,"The Analysis of Factors Affecting the Residential Mobility of Afghan Immigrants Residing in Mashhad (Case Study: Municipality Regions 4, 5 and 6)",2016.0,,Y. Zanganeh; A. Hamidian; H. Karimi,"The settlement of the immigrants, especially foreign immigrants in different cities and city areas has a major influence in shaping and changing socio-spatial structure of these areas. Mashhad has been the target of a large number of Afghan refugees in the past decades (160 thousand people). The initial settlement of immigrants in marginal areas of the city and residential mobility in the early settlement has obvious consequences on the social and spatial structure of different areas targeted by the immigrants. This study aimed to analyze the factors affecting the residential mobility of Afghan refugees residing in districts 4, 5 and 6 of Mashhad- Iran. The research was a survey type and the required data were gathered by field studies using questionnaires and library. The results of this study suggests that a major portion Afghan immigrant (86%) have been settled at the beginning of their arrival to Mashhad in marginal areas and slums including, Golshahr, Panj-tan, Ghaleh Sakhteman and Tollab. In the initial settlement of immigrants in the mentioned places factors such as proximity to fellow coreligionists and affordable rental housing prices are crucial. In terms of residential m",4.0,https://doi.org/10.5539/ass.v12n6p612174c11d54288b00646d40c52e1d26c8e00ad5c89b9,10.3390/ijgi12050202,Isolated or Colocated? Exploring the Spatio-Temporal Evolution Pattern and Influencing Factors of the Attractiveness of Residential Areas to Restaurants in the Central Urban Area,2023.0,ISPRS Int. J. Geo Inf.,Ruien Tang; Guo-Lin Hou; Rui Du,"Catering and urban elements have a strong spatial association. The spatial clustering and dispersal patterns of catering can effectively influence cities’ economic and socio-spatial reconfiguration. This research first introduced the concept of the ARTR (the attractiveness of residential areas to restaurants) and measured its value as well as its spatial and temporal evolutionary patterns using global and local colocation quotients. The DBSCAN algorithm and spatial hot-spot analysis were used to analyze their spatial evolution patterns. On this basis, a multiscale geographically weighted regression (MGWR) model was used to analyze the scale of and spatial variation in the drivers. The results show that (1) Nanjing’s ARTR is at a low level, with the most significant decline in ARTR occurring from 2005 to 2020 for MRs and HRs, while LRs did not significantly respond to urban regeneration. (2) The spatial layout of the ARTR in Nanjing has gradually evolved from a circular structure to a semi-enclosed structure, and the circular structure has continued to expand outward. At the same time, the ARTR for different levels of catering shows a diverse distribution in the margins. (3) Urban e",4.0,https://www.mdpi.com/2220-9964/12/5/202/pdf?version=16841550622187c847de861d43c54dc733d37fb3a144177024bf7,10.3390/land11122299,Spatial Pattern of the Determinants for the Private Housing Rental Prices in Highly Dense Populated Chinese Cities—Case of Chongqing,2022.0,Land,Gui-Wen Liu; Jiayu Zhao; Hong-Juan Wu; Taozhi Zhuang,"The private housing rental market has rapidly developed and demonstrated its outstanding contribution to improving affordability for the floating population in China. However, the forming pattern of private housing rental prices (PHRP) remains poorly understood in China’s highly dense populated cities. This study aims to comprehensively investigate the determinants of PHRP and depict their spatial pattern, considering the diverse functions of different areas within the city. A theoretical framework of the factors that influence PHRP has been developed based on an extensive literate study. Taking Chongqing city as a case, a Multiscale Geographically Weighted Regression (MGWR) analysis based on data from Lianjia.com and 58.com was conducted to investigate the spatial pattern of those influencing factors. The PHRP in Chongqing were mainly shaped by the factors of traffic condition and the neighborhood environment. The main findings highlighted that the influence of traffic condition on rental prices is more dominating in the industrial and financial zones, and the neighborhood factors represent spatial heterogeneity in the educational and commercial zones. This study provides a compre",4.0,https://www.mdpi.com/2073-445X/11/12/2299/pdf?version=1671607134219c8c71970d0a251d77082d78a8b87dd57432a8e6c,10.63642/1357-1419.1189,"Accounting for locational, temporal, and physical similarity of residential sales in mass appraisal modeling: the development and application of geographically, temporally, and characteristically weighted regression",2017.0,Journal of Property Tax Assessment & Administration,P. Bidanset; M. McCord; J. Lombard; P. Davis; W. Mccluskey,"Geographically weighted regression (GWR) has been recognized in the assessment community as a viable automated valuation model (AVM) to help overcome, at least in part, modeling hurdles associated with location, such as spatial heterogeneity and spatial autocorrelation of error terms. Although previous researchers have adjusted the GWR weights matrix to also weight by time of sale or by structural similarity of properties in AVMs, the research described in this paper is the first that has done so by all three dimensions (i.e., location, structural similarity, and time of sale) simultaneously. Using 24 years of single-family residential sales in Fairfax, Virginia, we created a new locally weighted regression (LWR) AVM called geographically, temporally, and characteristically weighted regression (GTCWR) and compared it with GWR-based models with fewer weighting dimensions. GTCWR was the only model to achieve IAAO-accepted levels of the coefficient of dispersion (COD), price-related differential (PRD), price-related bias (PRB), and median assessment-to-sale price ratio in both the training and testing samples, although it did not fully correct the existence of heteroscedasticity. With",4.0,https://digitalcommons.odu.edu/cgi/viewcontent.cgi?article=1045&context=publicservice_pubs220ec9411fa35f6f0165814eba6893cf3141a2e4cb7,10.1017/dap.2020.4,Household wealth proxies for socio-economic inequality policy studies in China,2020.0,Data & Policy,Jacqueline C. K. Lam; Yang Han; Ruiqiao Bai; Victor O. K. Li; J. Leong; K. Maji,"Abstract In China, one percent of the richest population holds more than one-third of the wealth, while the poorest 25% shares no more than two percent of the total. The country’s rapid economic development has resulted in increasing socio-economic disparities, and a rapidly deteriorating environment. This puts the Chinese citizens, especially the most vulnerable and deprived socio-economic status (SES) groups, at high risks of environmental inequality (EI). In most SES-based EI studies conducted in China, household wealth has often been overlooked, though it potentially serves a good economic indicator to capture the socio-economic effect of environmental change in China. Nevertheless, existing SES databases in China are of low spatial resolution and are insufficient to support fine-grained EI studies at the intra-city level in China. The core research challenge is to develop a representative household wealth proxy in high-spatial resolution for China. This study highlights the research gaps and proposes a new household wealth proxy, which integrates both fine-grained data/features such as daytime satellite imagery and easily accessible wealth indicators such as house prices. We a",4.0,https://www.cambridge.org/core/services/aop-cambridge-core/content/view/48E707C7B9900B173F51FFB545E047E7/S2632324920000048a.pdf/div-class-title-household-wealth-proxies-for-socio-economic-inequality-policy-studies-in-china-div.pdf2218eca511dd96f6b1b4df0b0bc30b342017f13e589,10.1108/ijhma-07-2024-0095,Bricks and sustainability: a look at how environmental variables impact housing markets,2024.0,International Journal of Housing Markets and Analysis,H. Alqaralleh,"222Purpose223This paper aims to assess the interaction between environmental challenges and policy interventions in shaping housing prices. It emphasises the need to understand how policy interventions and environmental conditions can disproportionately affect housing affordability, population growth and building density, especially for vulnerable communities.224225226Design/methodology/approach227The study uses a panel quantile ARDL regression model to thoroughly investigate the asymmetric impact on a sample of 16 UK cities spanning the period 2000–2023.228229230Findings231The study reveals that pollution significantly impacts house prices, with cleaner areas experiencing faster price changes. Cleaner air pollution has a greater impact on property prices in cities with cleaner air. Climate policies and superior environmental technologies also influence consumer purchases. Addressing affordability has little short-term effect on house values, but building activity temporarily affects pricing. Investment in clean technology and climate action legislation may boost house prices and attract environmentally conscious individuals.232233234Practical implications235Based on these findings, policymakers seeking equit",4.0,236f3d938b2f1ae77d72238a3395fb069cc54fb1046,10.17951/b.2016.71.2.223,Planowanie przestrzenne jako czynnik kształtujący atrakcyjność rynkową nieruchomości – skuteczny i niebezpieczny,2017.0,,W. Gorzym-Wilkowski,"A strong correlation occurs between spatial planning and the market attractiveness of properties. Since planning is a determinant of property value, the interests of participants of the property market have an impact on planning decisions. In consequence, local plans primarily specify the functions of areas according to the expectations of property owners so that it is possible to achieve a high land rent. This mechanism generates numerous positive effects for the entire local socio-economic space. Its beneficiaries include not only property owners but also local governments in districts and, to a certain extent, the entire environment. The adaptation of the provisions of local plans can be a trigger of lasting mechanisms ensuring local development. At the same time, however, it often leads to an excessive enlargement of residential areas in relation to the actual demand. This results in the increasingly scattered buildings. Besides the contamination of landscape, it also results in excessive costs of construction and maintenance of technical and road infrastructure. This can lead to a permanent limitation of the local government’s ability to finance indispensable development proje",4.0,https://journals.umcs.pl/b/article/download/3844/4033237d6eef5b64a83bc113346da2238d6aa44dd0507c8,10.3390/app13158921,How Does the Built Environment in Urban Villages Affecting Ride-Sourcing Commuting Trips Interacting with the Spatial Dependence Effect,2023.0,Applied Sciences,Le Yu; Xiaodan Li; Q. Luo,"Urban renewal provides opportunities to improve urban transport structures during the process of improving built environments. It is necessary to clarify the impact of different elements of the built environment on travel behaviors in the context of urban village renewal. This paper presents a microscopic perspective of individual travel behavior by proposing analytical frameworks to investigate travel behavior in terms of DiDi commuting trips. Considering the effect of spatial dependence, a Spatial Durbin Error Model was established, incorporating a spatial lag and spatial error. Traveling information was employed from the ride-sourcing company DiDi during the morning and evening peaks within in the urban village areas and workplaces of Shenzhen, and the variables of a built environment were scaled within travel analysis zones (TAZs). The results show that the impacts of the built environment on ride-sourced commuting were different between job and housing locations, with more influential factors in residential locations (urban villages). On the other hand, working locations had an influential magnitude more than twice that of residential locations. Alongside that, due to the spil",4.0,https://www.mdpi.com/2076-3417/13/15/8921/pdf?version=16910307352388434d1b058bb3c0eb6feae7c03a4184ba9b7f0c0,10.3390/buildings11120631,From Audits to Projects: Evaluating New York State Policy to Encourage Home Retrofit Projects,2021.0,Buildings,Elizabeth L. Hewitt; Janice L. Boucher,"This research explores socio-spatial characteristics of home retrofit projects in New York State and their association with the state’s free home energy audit program. Prior work by the authors found that zip codes with more elder individuals and higher levels of education are more likely to undertake an energy audit; it was also found that higher incomes may be negatively correlated with audit decisions. Less understood is the follow-up decision after an audit to undertake a retrofit. From a policy and climate perspective, the actionable retrofit decision is far more impactful than the informational audit, making it an important area of further research. This work examines this understudied area using a combination of datasets, including census data, American Community Survey (ACS) data, and retrofits data provided by the New York State Energy Research and Development Authority (NYSERDA). Findings indicate that many of the same socioeconomic characteristics that predict audits are influential in retrofit projects as well (age, education, higher home values). A strong statistical relationship was found between audits per capita and subsequent retrofit projects, which is to be expec",4.0,https://www.mdpi.com/2075-5309/11/12/631/pdf?version=16393616772393ac85b564f419ff3023722f763a396d5c461fef1,10.37391/ijeer.100215,Free Hold Price Predictor Using Machine Learning,2022.0,International journal of electrical and electronics research,S. Singh; Shaurya Singh; S. Yadav; Avneesh Kumar,"People who want to buy a new home tend to save more on their budgets and market strategies. The current system includes real estate calculations without the necessary forecasts for future market trends and inflation. The housing market is one of the most competitive in terms of pricing and the same has varied greatly in terms of many factors. Asset pricing is an important factor in decision- making for both buyers and investors in supporting budget allocation, acquisition strategies and deciding on the best plans as a result, it is one of the most important areas in which machine learning ideas can be used to maximize and accurately anticipate prices. As a result, in this paper, we present the different significant factors that we employ to accurately anticipate property values. To reduce residual errors, we can utilize regression models with a range of characteristics. Some engineering aspects are required when employing features in the regression model for improved prediction. To improve model fit, a set of multi-regression elements or a polynomial regression (with a set of varying strengths in the elements) is frequently utilized. In these models, it is expected to be significan",4.0,https://ijeer.forexjournal.co.in/papers-pdf/ijeer-100215.pdf240c378256fcd0cd78c5853a50edb81b324a4bac7e7,10.3390/ijgi9050288,Augmenting Geostatistics with Matrix Factorization: A Case Study for House Price Estimation,2020.0,ISPRS Int. J. Geo Inf.,Aisha Sikder; Andreas Züfle,"Singular value decomposition (SVD) is ubiquitously used in recommendation systems to estimate and predict values based on latent features obtained through matrix factorization. But, oblivious of location information, SVD has limitations in predicting variables that have strong spatial autocorrelation, such as housing prices which strongly depend on spatial properties such as the neighborhood and school districts. In this work, we build an algorithm that integrates the latent feature learning capabilities of truncated SVD with kriging, which is called SVD-Regression Kriging (SVD-RK). In doing so, we address the problem of modeling and predicting spatially autocorrelated data for recommender engines using real estate housing prices by integrating spatial statistics. We also show that SVD-RK outperforms purely latent features based solutions as well as purely spatial approaches like Geographically Weighted Regression (GWR). Our proposed algorithm, SVD-RK, integrates the results of truncated SVD as an independent variable into a regression kriging approach. We show experimentally, that latent house price patterns learned using SVD are able to improve house price predictions of ordinary",4.0,https://www.mdpi.com/2220-9964/9/5/288/pdf?version=158807323524144dc137cbd03895603e79a8ab86470249522a10b,10.1108/ijhma-06-2024-0081,Does urbanization drive up housing prices? Novel evidence from remote sensing and dynamic panel quantile regression,2024.0,International Journal of Housing Markets and Analysis,Ho-Ang Long; Trung-Kien Pham,"Purpose242This study aims to quantify the influence of urbanization on housing prices at the district-based level, while also investigating the heterogeneous impacts across different quantiles of housing prices.243244Design/methodology/approach245The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system generalized method of moments is used to evaluate how urbanization influences district-based housing prices through three spectrometries: Urban Index (UI), Normalized Difference Built-up Index (NDBI) and Built-Up Index (BUI). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo simulation.246247Findings248The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60th percentile of housing price",4.0,https://research.vu.nl/en/publications/49653295-69e6-4d94-a771-a77db26e5e6c249f2911a3c31921494bdd9d4c9235e70e6e029e5b3,10.1088/1757-899x/563/4/042008,"Spatial non-stationarity and heterogeneity of metropolitan housing prices: the case of Guangzhou, China",2019.0,IOP Conference Series: Materials Science and Engineering,Shao-Pei Chen; Ming Fang; Da-Chang Zhuang,"Taking a case study of the city of Guangzhou, this paper applies sophisticated approaches, including spatial autocorrelation analysis models, traditional regression models and Geographically Weighted Regressing (GWR) model to analyse and discuss the spatial non-stationarity and heterogeneity characteristics of housing prices, in term of the data of the average annual housing prices of each sub-district/town from 2009 to 2015. In this study, four types of influence factors, i.e., location condition (distance to central business district (CBD)), traffic access level, commercial service facility and public service facility are selected. As results, the spatial-temporal evolution characteristic of the housing prices in Guangzhou is closely related to the layout and development of the urban areas. Furthermore, among the selected factors, location condition is the most important factors affecting the housing prices, followed by commercial service facility, traffic access level and public service facility. Specifically, based on the GWR model analysis, the spatial patterns of the regression coefficients of location condition and the public service facility illustrate “east strong and west",4.0,https://doi.org/10.1088/1757-899x/563/4/042008250da60fd3f060619b92404da330e15b1f57c070f2b,10.1016/j.trpro.2016.05.369,Modelling the Impacts on Population Caused by Fixed Link Projects,2016.0,,M. D. Gutiérrez; S. Andersen; Ø. Nilsen; Trude Tørset,"Abstract Fixed links are bridges or tunnels that join areas previously separated by geographic barriers without a permanent connection. Fixed links can cause important travel time savings, which might be an incentive for population growth in the affected areas. Traditional population forecasts do not include potential changes caused by infrastructure investments, and this might cause misrepresentation of input to travel forecasts. The purpose of this paper is to address the question of whether the mismatches between the forecasts and real population numbers can be explained by the impacts of a new infrastructure. For this purpose, we observe ten fixed links on the western coast of Norway. Different linear regression models with several independent variables in different time periods are estimated for two situations; after the opening of the fixed links, and after the toll removal on the fixed link. The results show that the differences between forecast and real population after the opening of the fixed link are not related to any of the observed explanatory variables. On the other hand, the mismatches after the toll removal are associated with the travel time and cost reductions be",4.0,https://www.sciencedirect.com/science/article/pii/S2352146516303751/pdf251a8d06871610b2d8f55b487ec525111aa0661d50e,10.1016/j.cjpre.2021.04.013,Examining the determinants of housing prices and the influence of the spatial–temporal interaction effect: The case of China during 2003–2016,2021.0,,Yan-Chao Feng,"Abstract The slowdown of the Chinese economy has been accompanied by a recent rapid rise in housing prices, which has put severe pressure on China's high-quality development. Therefore, understanding the impact of the spatial–temporal interaction effect on housing prices and their potential determinants is critical for formulating housing policies and achieving sustainable urbanization. This study empirically analyzed both of these based on four aspects—the financial market, housing market, housing supply, and housing demand—using 2006–2013 data of 285 prefecture-level (and above) Chinese cities and spatial econometric models. The results indicated that the housing prices of Chinese cities were heavily affected by the interaction effect of space and time, both at the national and regional levels; however, the influence of this interaction effect exhibited a significant spatial differentiation, and only consistently drove up housing prices in Eastern and Western China. Additionally, the regional results based on administrative and economic development levels revealed that wage and medical service levels in first- and second-tier cities had negatively affected the competitiveness and",4.0,https://www.sciencedirect.com/science/article/pii/S2325426221000139/pdf252b0dbed1efe02317b288ed39f5e24fff37ae1d72b,10.17818/emip/2024/1.4,THE IMPACT OF SELECTED MACROECONOMIC VARIABLES ON HOUSE PRICES IN CROATIA,2024.0,Ekonomska Misao i Praksa,Manuel Benazić; Dean Učkar,"Recognizing the macroeconomic determinants that have a statistically significant influence on the formation of house prices is in the interest of the general public due to its informative effect on various interest groups involved in housing construction. Thus, real estate buyers, investors, as well as local government and self-government units should be interested in this information because monitoring of standard macroeconomic variables can provide conclusions about the possible development of residential property prices. Previously conducted research can be classified into four distinctive categories depending on what they take as determining variables: microelements specific to individual micro location, the impact of the COVID-19 pandemic, standard macroeconomic variables, or tourism development. The aim of this study is to analyze the influence of selected macroeconomic variables on residential property prices in Croatia. This study employs Autometrics, i.e. an automatic computer implementation of general-to-specific VAR (vector autoregressive) modelling framework and quarterly data on real GDP, domestic credit, consumer prices, interest rate, tourist arrivals and house price",4.0,https://hrcak.srce.hr/file/459138253fcd2e33a9f46db3a32d3d86aea1f0c45848de155,10.3390/jrfm18090495,Determinants of Financial Stability and Development in South Africa: Insights from a Quantile ARDL Model of the South African Financial Cycle,2025.0,Journal of Risk and Financial Management,Khwazi Magubane,"This study investigates the short-run and long-run dynamics of the financial cycle in South Africa, focusing on its macroeconomic drivers and their asymmetric effects across different phases. It addresses the persistent challenge in emerging market economies of balancing financial development and stability amidst volatile conditions. Using monthly data from 2000 to 2024, the research employs a quantile autoregressive distributed lag (QARDL) model to capture the heterogeneity and persistence of macro-financial linkages across the financial cycle’s distribution. The use of the QARDL model in this study allows for capturing asymmetric and quantile-specific relationships that traditional linear models might overlook. Findings reveal that monetary policy, and the housing sector are key drivers of long-term financial development in South Africa, showing positive effects. Conversely, exchange rate movements, inflation, money supply, and macroprudential policy dampen financial development. Short-term financial booms are associated with GDP growth, credit, share, and housing prices. Money supply and inflation are more closely linked to burst phases. These results underscore the importance o",4.0,https://www.mdpi.com/1911-8074/18/9/495/pdf?version=1756991862254fa4c62c5e4fb2ad053b93a9c3512e8289dfeac68,10.14246/irspsd.1.2_3,"Spatial Pattern of Transportation Carbon Emission based on Behavior Zones: Evidence from Beijing, China",2013.0,,Xiao-Lu Gao; Jue Ji; Fang Wang,"Reducing daily transportation carbon emission is one of the main tasks for accomplishing low carbon city. Most existing studies have evaluated transportation carbon emission from industrial structure or micro-economic viewpoint, such as the efficiency of public transportation, work and home relationship, and modal choice of personal trips, but studies on the general impact of personal behaviors on transportation carbon emission is inadequate. A main reason of this relies on the lack of an appropriate spatial unit for integrating people's greatly diversified behaviors. This study proposes the concept of 'behavior zone' (BZ) based on homogeneous assumption of behaviors, in order to analyze people's traffic behavior and its carbon emission effect by sub-areas. With a survey analysis of the characteristics of people's daily trips in Beijing's sample residential areas, the critical indices of BZ are identified including housing price, development intensity and population density, and access to public transportation. With these indices, Beijing is classified into five BZ types, where the modal choice behaviors of inhabitants are projected. Then the total environmental impact of daily tri",4.0,https://www.jstage.jst.go.jp/article/irspsd/1/2/1_3/_pdf2558c2dce045c7d792e453ac1ec8e2eb8d8415ad4d5,10.1038/s41598-024-51754-9,Analyzing housing supply location choice: a comparative study of the modelling frameworks,2024.0,Scientific Reports,Yu Zhang; Eric J. Miller,"The purpose of this study is to predict the location of new housing supply and compare two different modelling frameworks. Housing supply significantly influences land use simulations in urban microsimulation systems, closely linked with demographic, transportation, and environmental modules. The supply of new dwellings in urban simulation models have evolved from static, exogenous inputs to dynamic, agent-based determinations. This study follows this trend to examine two approaches to modelling the spatial distribution of new housing supply: the first approach models the development choice of each location; the second approach models the location choice of each residential project. Multinomial logit and nested logit models are applied to a Toronto empirical dataset. The results show that although the first approach achieves higher goodness-of-fit and prediction accuracy, the second approach performs better in explaining the locational preference of individual projects. Project characteristics such as structure type and construction cost, as well as location characteristics such as housing price, number of sales, and population density affect the spatial distribution of new housing",4.0,https://www.nature.com/articles/s41598-024-51754-9.pdf2568e6653e6cabe966c754af3436c0e59c5d6e3da33,10.54097/ftyf9665,House Price Prediction Based on Machine Learning Models,2024.0,Highlights in Science Engineering and Technology,Xiaoyan Ouyang,"This study aims to provide accurate house price predictions using machine learning algorithms. These predictions can assist decision-makers in making informed property investments and planning. Multiple linear regression and random forest were employed to achieve this goal. First, the acquired data underwent thorough analysis, including preprocessing and visualization. Subsequently, the study employed multiple linear regression and random forest models for house price prediction and evaluated their performance. The multiple linear regression model yielded promising results with an R² score of 0.73, explaining 73% of the target variable's variance. However, it exhibited prediction errors in specific cases, suggesting potential areas for improvement. In contrast, the Random Forest model achieved a slightly lower R² score of 0.69. Nonetheless, it excelled at capturing complex nonlinear relationships. Additionally, it identified the top five key features influencing house prices: house size, number of bathrooms, number of floors, parking spaces, and air conditioning. This study highlights the potential of machine learning models for house price prediction. Future research can further e",4.0,https://drpress.org/ojs/index.php/HSET/article/download/18525/180632577c4e56abf3a5043eecac8bfbfcf58f149b50241d,10.3390/buildings12070972,Analysis of the Spatiotemporal Heterogeneity of Housing Prices’ Association in China: An Urban Agglomeration Perspective,2022.0,Buildings,Gui-Wen Liu; Ke-Hao Chen; Juan Huang; Xun Deng,"With the rise of urban agglomerations, regional divergence of China’s real estate market has gradually intensified. City-specialized policies have become the main emphasis for promoting the healthy development of the regional real estate market. By adopting a gravity model, social net-work analysis, and impulse response analysis, this paper examines the spatial-temporal heterogeneity of housing prices’ association in the Beijing-Tianjin-Hebei Urban Agglomeration (BTH-UA), the Yangtze River Delta Urban Agglomeration (YRDUA), and the Pearl River Delta Urban Agglomeration (PRDUA), which are the most developed urban agglomerations in China. Meanwhile, the formation mechanism of the housing prices’ association network and spillover effect in urban agglomeration were theoretically analyzed. This paper found that (1) significant aggregation phenomena of housing prices were observed in the urban agglomerations; (2) characteristics of overall and individual networks were dynamically heterogeneous. In the BTHUA and the PRDUA, the associations of housing prices were polarized and sparse, while they were more linked and complex in the YRDUA; (3) polycentric network structure has been demonstra",4.0,https://www.mdpi.com/2075-5309/12/7/972/pdf?version=16572727172581715d53f05ddeec39fbb0b70a6e827297b7ddbd8,10.3390/ijgi13090298,The Impact of Airbnb on Long-Term Rental Markets in San Francisco: A Geospatial Analysis Using Multiscale Geographically Weighted Regression,2024.0,ISPRS Int. J. Geo Inf.,Dongkeun Hur; Seonjin Lee; Hany Kim,"The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap in understanding whether Airbnb financially burdens local renters within different income groups. The authors also differentiated the effect of Airbnb accommodations with different levels of commercialization by categorizing Airbnb listings based on their level of commercialization. Using the multiscale geographically weighted regression technique, this study also considered spatial variations in the relationship between short- and long-term rental markets. The findings indicate that the density of Airbnb only affects the relative rent of renters with a yearly household income between USD 50,000 and USD 75,000. Furthermore, the density of Airbnb listings from more commercialized hosts that own between three and eleven showed a positive relationship with the relative rent cost. This study highlighted the variability in the impact of Airbnb on the local community by income group, listing characteristi",4.0,https://www.mdpi.com/2220-9964/13/9/298/pdf?version=1724384833259f6f9ec167fca2c00e5d0c02ac57205ae87356e0b,10.1080/21650020.2023.2257761,A spatial and statistical approach to evaluate London Opportunity areas policy and relevant factors’ significance,2023.0,"Urban, Planning and Transport Research",Fengyuan Han,"ABSTRACT A mature developed urban area like London has various policy tools to trigger economic development and leverage poverty. The scale of economic inequality in London has called for more effective policy tools to leverage poverty and close the gaps of economic inequality. Opportunity Areas is one of the tools in urban regeneration to alleviate poverty and develop communities. After over a decade of the policy rollout we have little knowledge how effective this type of policy tool is. The project investigates how effective opportunity areas for economic growth and societal development in London. The key economic indicators including unemployment rate and house price were assessed at MSOA/ward levels in London to understand their spatial variation through GIS mapping. Subsequently, socio-economic factors of commercial land use, culture infrastructure and public transport accessibility were discussed and selected to explore whether they have impacts on the difference of economic performance within opportunity areas. The spatial impacts of these factors among wards/MSOA were evaluated by Geographically Weighted Regression. The research discussed these factors’ variations in Lee V",4.0,https://www.tandfonline.com/doi/pdf/10.1080/21650020.2023.2257761?needAccess=true2605d0fe68ce6bf44f20971943a814c3cdcd621f174,10.3390/su15021496,Study on Rationality of Public Fitness Service Facilities in Beijing Based on GIS,2023.0,Sustainability,Xuefeng Tan; Chenggen Guo; Pu Sun,"Public fitness service facilities are one of the most important factors affecting national fitness. Analyzing the distribution characteristics and influencing factors of public fitness service facilities is of great significance to optimizing public fitness services. In this paper, using the population concentration index and consistency test, recent proximity index, coupling coordinated development model, buffer analysis, and correlation analysis, combined with ArcGis 10.7 software research, we found that: the site layout of public fitness service facilities in the main urban area of Beijing is relatively reasonable and has good coupling, and each area becomes a cluster trend; In the main urban area of Beijing, the public fitness service facilities and the transportation line are combined better; Public fitness service facilities in the main urban area of Beijing are well combined with schools (kindergartens, primary schools), with schools as the center; The public fitness service facilities in the main urban area of Beijing are well combined with medical sites, and some marginal areas are not covered; Public fitness service facilities in the main urban area in Beijing show uneven",4.0,https://www.mdpi.com/2071-1050/15/2/1496/pdf?version=16735239612612a240ca456b5c90462faf4acf191fe00a0fcf451,10.18335/region.v4i1.95,The effect of house prices on business start-ups: A review and analysis using Swedish regional data,2017.0,,B. Berggren; Andreas Fili; Mats Wilhelmsson,"Entrepreneurs are at the core of economic development in that they start new businesses or make existing firms grow. To fulfill this important role, entrepreneurs need access to finance. Owing to information asymmetry and the relatively high risk associated with business start-ups, many financiers shy away from engaging in relationships with firms during the early stages of their development. Based on the existing body of knowledge on the financing of entrepreneurship, we know that insider finance is of paramount importance in the early stages of firms’ development. We expand this knowledge base by analyzing the influence of house prices on business start-ups across municipalities in Sweden. In our analysis, we include data from all municipalities in Sweden. Our data on house prices and control variables are collected in period one, and our data on the frequency of start-ups are collected in period two. We find that rising house prices in a municipality lead to a higher frequency of start-ups. In our regression analysis, we find that a 1% increase in house prices leads to a 0.14% increase in start-ups. Our findings are in line with the limited international research that has been p",4.0,http://openjournals.wu.ac.at/ojs/index.php/region/article/download/95/1302629d2b9d34bda19116f6f68b4075f7d1a3dd9463a4,10.1057/s41599-023-02010-4,The impact of housing macroprudential policy on firm innovation: empirical evidence from China,2023.0,Humanities and Social Sciences Communications,Mengtao Chen; Haojie Zhu; Yongming Sun; R. Jin,"The ascent of housing booms and their impact on firm innovation has become a focal point of research, fueled by the remarkable upsurge in housing prices that have been witnessed across global over the past few decades. Despite the attention given to this subject, there has been limited exploration of the spillover effects of housing macroprudential policy (HMP) on firm innovation, which aims to regulate housing booms and ensure financial stability. In this study, we examine the relationship between HMP and firm innovation using panel data from Chinese firms located in 54 cities for the period spanning 2010 to 2019. Our empirical results reveal that tightened HMP promotes firm innovation and is robust to alternative measurements, additional fixed effects, Heckman regression, and IV regression. Moreover, our mechanism analysis demonstrates that HMP promotes firm innovation by reducing leverage and encouraging cash holdings. Further examination of city-level heterogeneity suggests that firms located in areas with lower housing dependency and limited financial development benefit more from HMP’s positive-effect on firm innovation. This paper contributes to the gap in the existing liter",4.0,https://www.nature.com/articles/s41599-023-02010-4.pdf2632adff656b2e13d2e51411c051b4d67797fdf7784,10.7717/peerj.18261,"Spatial patterns and driving factors of plant diversity along the urban–rural gradient in the context of urbanization in Zhengzhou, China",2024.0,PeerJ,Lingling Zhang; Chong Du; Wenhan Li; Yong-Jian Liu; Ge Zhang; Shan-Shan Xie; Yi-Ping Liu; De-Zheng Kong,"Plant diversity is the basis for human survival and development, directly affecting the function and stability of urban ecosystems. Its distribution pattern and causes have been a central issue in ecological and landscape gardening research. Rapid urbanization in Zhengzhou City has led to the fragmentation of urban green spaces and damage to ecosystems, seriously affecting urban biodiversity conservation. Understanding the distribution pattern of plant diversity in the region and its relationship with environmental factors is crucial for maintaining and enhancing urban plant diversity. Plant data from 178 sample plots in the built-up area of Zhengzhou City were collected and combined with environmental factors, and the characteristics of plant diversity, richness patterns, and their main environmental explanations in Zhengzhou City were explored. Results showed that there were 596 plant species belonging to 357 genera and 110 families in the study area. There were five dominant families and four dominant genera. Four distinct spatial patterns of plant diversity were identified along the urban–rural gradient. Urbanization factors such as GDP per capita, house prices, and imperviousn",4.0,264710e10d0208ad10a1e42af3924001a35ae1bf4ae,10.1080/15623599.2020.1811466,Chinese perceptions of corporate social responsibility: an analysis of annual reports in the housing sector (Chinese perceptions of corporate social responsibility),2020.0,International Journal of Construction Management,Bo Xiong; Kun-Hui Ye; M. Skitmore; Weiyan Jiang; Bei-Bei Qin,"Abstract Corporate social responsibility (CSR) has been widely accepted as essential for sustainable development and, with the occurrence of such disasters as food quality scandals and building collapses, its importance has been increasingly recognised in China over the past decade. At the same time, despite property development being frequently linked in the mass media with unaffordable housing prices, bribery, and poor construction quality, many large developers have instituted programmes to improve their CSR presence. However, the perceptions of property development organisations of what CSR means to them are as yet unclear. The aim of this paper, therefore, is to better understand the way CSR is seemingly perceived by property developers in China. This is undertaken by analysing CSR disclosure and prioritising information from CSR corporate reports by developers in the housing sector. Three attributes of CSR disclosure information type, responsibility type, and stakeholder type are used to construct an analytical framework for testing of 44 published company reports by content analysis. The findings indicate a greater emphasis on management approaches and ethical responsibiliti",4.0,https://eprints.qut.edu.au/204186/1/68056504.pdf265e2d4904bf45a2f2320b0c36ddd507e0035c9c6e4,10.3390/buildings15020195,Analysis of the Temporal and Spatial Patterns of Residential Prices in Qingdao and Its Driving Factors,2025.0,Buildings,Yan-Jun Wang; Yin Feng; Kun Han; Zi-Shu Zheng; Peng Dai,"As an important indicator of the level of urban economic development and the quality of the residents’ lives, housing prices are affected by various factors, such as the spatial distribution of the housing market, the housing characteristics of neighborhoods, and the location conditions. This paper summarizes the spatial distribution of housing prices in Qingdao using GIS, analyzing spatial distribution characteristics, and combines these with the Geographically Weighted Regression (GWR) model to explore the influence of various factors, such as community attributes, location, transportation, and peripheral facilities on residential prices. The results show that from 2003 to 2023, residential housing prices in Qingdao exhibited a significant, continuous upward trend, with rapid growth in the early period and more stable growth in the later period; the spatial structure of residential prices evolved from a “single core” in Shinan District to a “double core + fan” structure involving both Shinan and Laoshan Districts, eventually forming a “double core + fan + mosaic” spatial layout; the green environment, congestion, leisure facilities, service management, and other community factors",4.0,https://www.mdpi.com/2075-5309/15/2/195/pdf?version=17365211412667d94023ab29dac89862b2eff39f258c798f5359c,10.3390/app13085180,Utilizing Multilevel Modeling to Measure Neighborhood Dynamics and Their Impact on House Prices,2023.0,Applied Sciences,Bao-Yun Guo; Kenan Li; Cong Fu,"For decades, researchers have been concerned with house price modeling, and recognition has grown for the necessity of considering environmental and contextual variables in the process. This research examines the association between neighborhood characteristics and all individual house sale prices in Lucas County, Ohio, from 2012 to 2016, through a multilevel modeling (MLM) approach. Although there are various ways to define neighborhoods, census tracts and school districts are used in this study. Neighborhood characteristics include a foreclosure score, race heterogeneity, median household income, and built environment variables, such as walkability indexes. School district characteristics include student performance, tuition expenditures per pupil, and percent of expenditures spent on classroom instruction. The advantage of the multilevel model, is that it allows us to derive reliable estimates of place differences, representing a considerable improvement over the single-level model. Significant correlations were identified between house prices and foreclosure score, student performance, and tuition expenditures per pupil. The MLM results indicate that house prices not only lay i",4.0,https://www.mdpi.com/2076-3417/13/8/5180/pdf?version=168206545526780aad25aedad903574e2e770200d1dd5bd898ca9,10.35326/pencerah.v7i2.1126,Real Estate Segmentation: A Model of Real estate Decision Support System,2021.0,Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton,Genesis Sembiring Depari,"Due to human limitations of computational thinking, the quality of rational decision-making is constrained, and as a result, people encounter bounded rationality. A decision support system is widely demanding in tackling this problem, especially in real estate management. This study focuses on 3 main purposes. Firstly, comparing K-means, X-means and K-medoid algorithm performance in clustering sold house characteristics to be further used for pricing houses. Second, characterizing each cluster for developing a suitable marketing strategy by utilizing machine learning technology. Lastly, providing a managerial implication as a decision support system for assisting stakeholders in making a decision. Eventually, K-means and X-means algorithm show very similar performance. X-means can automatically determine the number of clusters while k-means utilize the elbow method to find the optimum number of clusters. Three clusters were identified as cluster 0, cluster 1, and cluster 2. Cluster 0 was occupied by 85.77% of low house prices. There are two practical implications of this study. Firstly, the results of clustering analysis which reflected in a model of decision support system. Secon",4.0,https://doi.org/10.35326/pencerah.v7i2.112626825dcc5b9f26e79e438670bd1800e19b7a8ca97bb,10.3390/buildings13010029,"Spatial Characteristics of the Abandonment Degree of Residential Quarters Based on Data of the Housing Sales Ratio—A Case Study of Kunming, China",2022.0,Buildings,Yang Wang; Xiao-Li Yue; Ying-Mei Wu; Hong'ou Zhang; Sa-Sa Liu,"The abandonment degree of an urban residential building can reflect the popularity of residential areas. This study uses this idea as a basis for proposing the concept of using residential quarters’ abandonment rate to measure the abandonment degree of an area. The spatial pattern of the abandonment rate and its clustering characteristics were obtained by taking 2517 residential quarters in Kunming’s Main Urban District as research object, and using their listing for sale ratio data. Thereafter, curve estimation was used to explore the influencing factors of abandonment rate. The results are as follows. (1) The abandonment rate of the four circles in Kunming’s Main Urban District increases from inside to outside, showing the pattern of the core area–second ring area–third ring area–new urban district, with evident “core–edge” characteristics. (2) The relationship between distance from the city center, housing ages, and abandonment rate can be well fitted using a quadratic function and shows an inverted “U”-shaped “rising–declining” trend. The relationship between housing prices and abandonment rate is fitted by the inverse function, showing an evident “up” trend. This study is a re",4.0,https://www.mdpi.com/2075-5309/13/1/29/pdf?version=1672707457269982e7e552461540c7a26ed5dfdcfa2a05c293cfb,10.14807/ijmp.v9i1.682,A roadmap to determine the important factors of the house value: a case study by using actual price registration data of Taipei housing transactions,2018.0,,Pei-De Wang; Ming-chien Chen,"While many studies have applied data mining techniques to judge housing prices, few have decoded the important attributes or prioritized them simultaneously. This paper aims to utilize five data mining techniques to discover the important attributes for three major types of real estate in Taipei city. The datasets, involving a total of 22,480 transactions, were publicly available from the Taiwan Actual Price Registration from July 2013 to August 2015. The five models are decision trees, random forests, model trees, artificial neural networks and multiple regression. The criteria used to measure the forecasting accuracy are MAPE, R², RMSE, MAE and COR. The model with the best performance for all houses is the Model Tree with a MAPE value of 27.59. As for apartments, the best is Random Forests. Artificial Neural Networks perform best for suites and buildings with elevators. Different housing types need different models. Furthermore, the attributes importance helps us to conclude the really critical attributes, which include the floor area, administrative districts, parking area and land area, and their rankings. This variable ranking and selection procedure proposed by this research ",4.0,http://www.ijmp.jor.br/index.php/ijmp/article/download/682/78727061e47df07258c231c3dd1888dda89193733ce5ee,10.31092/jia.v5i1.1250,IDENTIFIKASI FAKTOR-FAKTOR YANG MEMPENGARUHI NILAI PROPERTI RESIDENSIAL BERDASARKAN PERSEPSI PENILAI PEMERINTAH,2021.0,INFO ARTHA,,"State asset management is conducted in a transparent and accountable manner. One notable requirement that has to be met is the value of the assets. Such value is estimated by a qualified government valuer based on certain valuation approaches. This process normally carries a degree of subjectivity. This research uses statistical procedures to identify property attributes that significantly affect residential property value based on the government valuers’ perception. For this purpose, a set of questionnaires was distributed to government valuers who are part of the Directorate General of State Assets Management (DGSAM). These questionnaires contained property attributes identified from the literatures and validated by several property valuers at the DGSAM and PKN STAN. There were 285 valuers who provided their responses in this research, which was slightly higher than the anticipated number of participants. Further analysis from these responses shows that non-structural property attributes, one that are not directly related to property physical attributes, are indicated to be more significant in affecting residential property value. This research however only involves DGSAM’s value",4.0,https://jurnal.pknstan.ac.id/index.php/JIA/article/download/1250/648271549490db5c5b3276e86f1772d9e5a45101a64483,10.59697/jik.v7i2.135,ANALISIS PREDIKSI HARGA RUMAH DI JABODETABEK MENGGUNAKAN MULTIPLE LINEAR REGRESSION,2023.0,Jurnal Informatika Kaputama (JIK),Inayatul Maula; L. Hasanah; A. Tholib,"The economic and infrastructure development in the Jabodetabek area has increased the rate of population growth in the region continues to increase. This attracted the attention of property agents in the housing price competition in the area, considering that the demand for housing will also continue to increase. There are several kinds of specificities and house prices in the Jabodetabek area, so an appropriate analysis is needed to get the desired house in this study the researcher implements the Multiple Linear method Regression to predict house prices with the desired specifications. This study used a total dataset of 3553 data with 11 variables obtained from the Kaggle.com. website The data will be through the pre-pronce process of data before training the model. Furthermore, at the evaluation stage using a test matrix, namely CC (Coefficient of Correlation), MAE (Met Absolute Error), RMSE (Root Met Square Error) is used to assess the performance of the model. Based on the results of the analysis from the dataset were 965 data, where the data was divided into two parts, namely, 80% as training data and 20% as testing data from the analysis results were obtained at an accuracy ",4.0,https://jurnal.kaputama.ac.id/index.php/JIK/article/download/135/1012722c95d3c480099fcdd9ae90189622b4af16b7e673,10.1108/ijhma-02-2023-0020,How frequent and visible criminal violence affects housing prices: evidence from Mexico City (2007–2011),2023.0,International Journal of Housing Markets and Analysis,Laura H. Atuesta; Monserrat Carrasco,"273Purpose274Between 2006 and 2012, Mexico implemented a “frontal war against organized crime”. This strategy increased criminal violence and triggered negative consequences across the country’s economic, political and social spheres. This study aims to analyse how the magnitude and visibility of criminal violence impact the housing market of Mexico City.275276277Design/methodology/approach278The authors used different violent proxies to measure the effect of the magnitude and visibility of violence in housing prices. The structure of the data set is an unbalanced panel with no conditions of strict exogeneity. To address endogeneity, the authors calculate the first differences to estimate an Arellano–Bond estimator and use the lags of the dependent variable to instrumentalise the endogenous variable.279280281Findings282Results suggest that the magnitude of violence negatively impacts housing prices. Similarly, housing prices are negatively affected the closer the property is to visible violence, measured through narcomessages placed next to the bodies of executed victims. Lastly, housing prices are not always affected when a violent event occurs nearby, specifically, when neighbours or potential buyer",4.0,283b1fd694ae631459acfcd5e3cf363cd3d865e6746,10.56294/dm2024.231,Predicting Housing Sale Prices Using Machine Learning with Various Data Split Ratios,2024.0,Data and Metadata,Awais Azam; Alimul Haque; S. Rai,"Introduction: Recent advancements in technology and data analytics have propelled the rapid growth of artificial intelligence (AI) and machine learning (ML), which are now central to various industries. These technologies have become essential tools in many sectors, especially in predictive modeling for asset pricing.Objective: From stock markets and rental properties to real estate and second-hand goods, AI and ML algorithms are widely applied to estimate values, optimize pricing strategies, and forecast market trends. Method: By analyzing vast amounts of data, these tools enable more accurate predictions and informed decision-making, revolutionizing traditional approaches to pricing and valuation. In this study, the primary goal is to achieve the most accurate price prediction for houses or apartments by experimenting with different data split ratios.Result: RMSE (House Price) 188965.28 is acceptable as best average price for houses. Conclusions: The value of RMSE of this model are relatively low and also the value Squared Correlation is 64% which is above the threshold of 50%, so the predicted price of this model is seems appropriate, so I have presented this model and its predi",4.0,https://dm.ageditor.ar/index.php/dm/article/download/231/889284e53dbe2575706a1fbbf4c7f230e90896c1758820,10.4314/ejesm.v8i2.9,Assessment of the effect of urban infrastructure as a tool for enhancing the values of residential property in Akure,2015.0,,A. Nwosu,"The study is aimed at investigating the effect of urban infrastructure as a tool for enhancing residential property values in Isolo/Araromi/Odokoyi and Irowo/Odige/Odopetu in Akure. The sampling frame of the houses in the study area was 412 and 405 respectively. However, Kothari formula was used to arrive at a sample size of 280 in Isolo/Araromi/Odokoyi and 289 Irowo/Odige/Odopetu for the study. Questionnaires were administered on occupants of residential properties in the study area to elicit information. Out of the 559 questionnaires administered 443 were duly filled and returned for analysis. The information collected was analyzed using descriptive statistics and Multiple Regression. The result reveals that the R 2 statistic (0.819) for Irowo/Odige/Odopetu indicates that the independent variables account for 81.9% of rental value. Also, the R 2 (0.781) for Isolo/Araromi/Odokoyi is 0.781 which implies that the independent variables account for 78.1% of the rental values The study therefore recommends that the state government should put in place the necessary infrastructure in Isolo/Araromi/Odokoyi so as to enhance the residential property values. Also, the state government shoul",4.0,https://doi.org/10.4314/ejesm.v8i2.92859f6d7c05139d687c1decbbdbc52009951e4301a6,10.62051/qjs7y352,A Comparative Study of Regression Models for Housing Price Prediction,2024.0,Transactions on Computer Science and Intelligent Systems Research,Zizheng Li,"The property market is closely related to the regional economy. This study focuses on exploring the problem of house price prediction in the property market. This paper aims to reveal the performance of extreme gradient boosted tree regression, ridge regression, decision tree, and random forest regression models in feature selection and data processing, and assess their effectiveness in house price prediction. Aspects compared include prediction accuracy, data required for fitting, and running time. The study shows that the random forest and decision tree regression models perform best. Although these two models require a certain amount of data, they are easily satisfied in the context of big data. Although the fit of the random forest regression model and the decision tree regression model is similar, the training time required for the random forest regression model is too long. Therefore, it is concluded that the decision tree regression model performs well on general real estate datasets and is a widely applicable regression model in the real estate domain. This study can provide a helpful reference for data analysis and forecasting in the real estate field and provide a basis a",4.0,https://doi.org/10.62051/qjs7y352286c9e5cb60a61365d50c67874ea7180ad72d194bbb,10.3390/geographies5040057,Housing Affordability in the United States: Price-to-Income Ratio by Pareto Distribution,2025.0,Geographies,Francisco Vergara-Perucich,"This study integrates the price-to-income ratio (PIR) with Pareto distribution characteristics to provide a novel approach for evaluating home affordability across U.S. counties. The methodology offers a new lens for the analysis of home affordability by capturing both the extreme values and central tendencies of PIR. The study normalizes the resulting Pareto parameters to a common scale and integrates data from the Zillow Home Value Index and the U.S. Department of Commerce’s SAIPE program to create a single affordability index. The findings point to significant regional differences: coastal and urban regions, such as California and New York, face significant affordability challenges, whereas the Midwest, especially Kansas, has higher affordability. The results highlight the significance of targeted policy interventions and are consistent with the body of research on systemic risk and housing market dynamics. This study also opens new avenues for future research, including the impact of economic factors on affordability and cross-regional comparative studies. The suggested approach encourages more equitable access to housing by providing policymakers with a useful tool to track an",4.0,https://www.mdpi.com/2673-7086/5/4/57/pdf?version=17597408332878353552c7ea711e25a30f12f803edcd0f161187e,10.1108/ijhma-07-2023-0101,"Analyzing the spatial determinants of housing prices in Dammam, Saudi Arabia: an AHP approach",2023.0,International Journal of Housing Markets and Analysis,Umar Lawal Dano,"288Purpose289This study aims to examine the determinants that influence housing prices in Dammam metropolitan area (DMA), Saudi Arabia, by using the analytic hierarchy process (AHP) model. The study considers determinants such as building age (BLD AG), building size (BLD SZ), building condition (BLD CN), access to parking (ACC PK), proximity to transport infrastructure (PRX TRS), proximity to green areas (PRX GA) and proximity to amenities (PRX AM).290291292Design/methodology/approach293The AHP decision model was used to assess the determinants of housing prices in DMA, using a pair-wise comparison matrix to determine the influence of the investigated factors on housing prices.294295296Findings297The study’s results revealed that building size (BLD SZ) was the most critical determinant affecting housing prices in DMA, with a weight of 0.32, trailed by proximity to transport infrastructure (PRX TRS), with a weight of 0.24 as the second most influential housing price determinant in DMA. The third most important determinant was proximity to amenities (PRX AM), with a weight of 0.18.298299300Originality/value301This study addresses a research gap by using the AHP model to assess the spatial determinants of housin",4.0,3022beafc018ab29044159ae2f6ff021753f5486bbe,10.3390/su142113834,The Impact on Bed and Breakfast Prices: Evidence from Airbnb in China,2022.0,Sustainability,Feifei Tian; Feng-Zhi Sun; Beibei Hu; Zhi Dong,"As a new type of accommodation and a new way of life, it is of great significance for the spatial optimization and price management of the tourist accommodation market to explore the spatial differentiation characteristics of and influencing factors on bed and breakfast (B&B) house prices. Taking Shandong B&B merchants on the Airbnb website as the research object, this paper discusses the spatial characteristics of and influencing factors on B&B prices in the Shandong province, combining spatial autocorrelation analysis and interpolation analysis to identify the B&B cluster region. Quantile regression was used to reveal the main influencing factors. The results show that: (1) the spatial agglomeration effect of B&B prices in the Shandong province is obvious, and the high value areas form a new pattern between the provincial economic circle and the Jiaodong Economic Circle; (2) the influence of different factors on B&B house prices is very uneven in space. From the regional point of view, there are “sub-regional effects” on the spatial distribution of the influences of various factors on B&B house prices. The results of the study provide references for reasonable pricing, scientific",4.0,https://www.mdpi.com/2071-1050/14/21/13834/pdf?version=1666700644303ac0641b57d71fb2b2b8250903ac4ba18e290cba6,10.3390/ijerph16203900,The Effect of Dioxin Contamination and Remediation on Property Values,2019.0,International Journal of Environmental Research and Public Health,Adam Zwickle; J. Cox; J. Zhuang; Joseph A. Hamm; B. Upham; M. Chung; S. Cruz; J. Dearing,"Loss of property value is a major concern in communities faced with the toxic byproducts of industrial practices. Even after site remediation, stigma may persist and negatively affect market values of residential properties. To study the effects of contamination and of remediation on property values in Midland, Michigan, where dioxins have been released into the environment through the incineration of contaminated waste and the discharge of contaminated water for many years, records of assessed value were obtained for 229 homes within the same neighborhood for the previous 18 years. A multilevel, longitudinal analysis was conducted to determine if there was a relationship between level of dioxin and assessed value after controlling for housing characteristics. Remediated and un-remediated properties saw increases in value at a similar rate over time. However, a property’s level of dioxin was found to have a small, significant, and negative relationship with assessed value, and this negative effect was present regardless if a home had been remediated or not. These results suggest that while environmental remediation may be effective at removing the contamination, its economic effect",4.0,https://www.mdpi.com/1660-4601/16/20/3900/pdf?version=157111447230480b539044a06f46f7f43b4a0cff1b260317712ee,10.3390/ijgi13090324,"Determinants of Intra-City Residential Migration Patterns of Older Adults: A GIS and Decision Tree Analysis of Yancheng City, China",2024.0,ISPRS Int. J. Geo Inf.,Zhulin Hou; Xiang-Feng Li; Xiao-Ming Li,"This study investigates the spatial patterns of residential migration among older adults in the city center of Yancheng and the influencing factors using data on the home purchases of individuals aged 65 and older from 2016 to 2018, along with peripheral point of interest (POI) data, analyzed with ArcGIS and a decision tree model. The results indicated that persons aged 60–65 accounted for 42.8% of the total sample and primarily chose to migrate in the early stages of retirement. The intra-city migration of older adults exhibits both centripetal and centrifugal patterns, with a greater tendency toward the city center. House prices, floor levels, and commercial facilities significantly impact their choice of migration destinations. Among these, house prices were the most critical determinant, with the majority of older adults migrating to neighborhoods with lower house prices. This study contributes by integrating residential migration and location choice research and constructing an analytical framework based on facility accessibility. The findings provide insights into the key determinants of location choice for intra-city residential migration among older adults and the construct",4.0,https://doi.org/10.3390/ijgi130903243056d197bd11d97d134661de8defd2eded0e9b24f0a,10.5267/j.ijdns.2023.9.008,Leveraging consumer behavior and macroeconomic factors to increase real estate investment potential,2024.0,International Journal of Data and Network Science,R. A. Rahadi; Danang Pangestu Gusti Bagaskara; S. K. Wiryono; Asep Darmansyah; Dziki Firmansyah Hakam; Tuntun Salamatun Zen; Kurnia Fajar Afgani,"The Indonesian real estate market has shown promising development in recent years, attracting investors seeking lucrative opportunities. This research aims to increase the success rate of real estate investments in Indonesia by examining the connection between macroeconomic factors and consumer behavior. This study employs vector autoregression (VAR) models to investigate the influence of macroeconomic data on the Indonesian house price index (HPI). We intend to determine the primary determinants of the HPI by analyzing various macroeconomic indicators, including GDP growth, CPI, crude oil, interest rates, and the unemployment rate. In addition, the research investigates the role of consumer behavior in Indonesian real estate searches. Real estate developers and investors can gain valuable insight into the effect of consumer preferences, motivations, and decision-making processes on demand functions by understanding consumer preferences, incentives, and decision-making processes. A comprehensive analysis of historical data and econometric modeling techniques are employed to accomplish research objectives. The research is conducted using pertinent macroeconomic indicators, real esta",4.0,https://doi.org/10.5267/j.ijdns.2023.9.008306c948572d6427e96f049bcf44723f628bb1c02918,10.58776/jriti.v1i3.64,Penerapan Decision Tree Regression dalam Memprediksi Harga Rumah di Provinsi Jawa Barat,2024.0,Jurnal Riset Informatika dan Teknologi Informasi,Nafis Almajid; Y. Ginting; Alif Izzudin Ramadhan,"A house plays an important role as a basic necessity, not only as a shelter and place of rest. Additionally, the selling price of a house is greatly influenced by environmental factors such as proximity to shopping centers, office buildings, land size, and others. To gain a more accurate understanding and predict the selling price of houses, this research proposes the application of machine learning methods. This study involves the use of three machine learning algorithms: multiple linear regression, decision tree regression, and linear support vector regression to predict house prices in West Java Province. By utilizing historical data and various relevant features, such as the number of rooms, bathrooms, garages, land area, and width, the machine learning algorithms will conduct complex analyses and provide more accurate price estimations. The expected outcome of this research is to provide valuable insights for stakeholders in the property industry in West Java Province. The adoption of machine learning approaches is anticipated to enhance the ability to predict house prices and provide useful information for buyers, sellers, and other relevant parties in making better decisions",4.0,http://dx.doi.org/10.58776/jriti.v1i3.64307af2c84da3468d951fe0c6f94f076c3b075773ac7,10.59934/jaiea.v4i2.733,House Price Prediction Analysis Using a Comparison of Machine Learning Algorithms in the Jabodetabek Area,2025.0,Journal of Artificial Intelligence and Engineering Applications (JAIEA),Indah Ratna Ningsih; Ahmad Faqih; Ade Rizki Rinaldi,"Jabodetabek, as the largest metropolitan area in Indonesia, has complex property price dynamics, making it difficult for developers and buyers to determine house prices. This study aims to analyze and compare the performance of the Multiple Linear Regression and Random Forest Regression algorithms in predicting house prices in the region. The data was obtained through scraping techniques from the rumah123.com website in October 2024, covering 999 data points with variables such as price, location, building area, land area, number of bedrooms, bathrooms, and garages. A comparative approach with cross-validation was applied to evaluate the performance of both algorithms using the metrics MAE, MSE, RMSE, MAPE, and R². The research results show that Random Forest Regression using GridsearchCV has better predictive performance, with an MAE value of Rp.645,764,815, MAPE of 28.12%, and R² of 0.864. The main factors influencing house prices in Jabodetabek include building size, land size, number of bedrooms, bathrooms, garages, and location. This finding emphasizes the superiority of Random Forest Regression in capturing complex data patterns and the significant role of these variables in",4.0,https://ioinformatic.org/index.php/JAIEA/article/download/733/5163088537841eadba8248c280f32a7bd63b8f8ee58531,10.2991/aebmr.k.201128.090,The Effects of Macroeconomic Factors on Housing Prices in China: Empirical Research and Linear Regression Analysis,2020.0,,Jia-Tong Li,"The housing market in China has experienced substantial development over the past years, and the macroeconomy carries considerable weight in driving the rapid increases in housing prices. This paper aims to study the relationship between the macroeconomy and housing prices in China, and analyze three typical macroeconomics factors, consisting with gross domestic product index (GDPI), consumer price index (CPI), and fixed asset investment price index (FAI), along with their effects on housing prices respectively. This paper uses aggregated annual data from 2010 to 2018 in a multivariate linear regression equation and reached a reliable conclusion with quantitative analysis. All three variables are positively related to housing prices, which is represented by commercial residential housing sales price. By model building and regression test, it is seen that the consumer price index is the variable with the most explanatory power for the housing price increase, followed by the gross domestic product index and fixed asset investment price index. The main content of this study also includes a brief review of the history of China’s real estate market under the proper control and policy in",4.0,https://doi.org/10.2991/aebmr.k.201128.09030953078fdbe25acaec2be1541da398e7ccf99b9677,10.5194/isprs-archives-xlii-4-w4-477-2017,"PROVIDING THE FIRE RISK MAP IN FOREST AREA USING A GEOGRAPHICALLY WEIGHTED REGRESSION MODEL WITH GAUSSIN KERNEL AND MODIS IMAGES, A CASE STUDY: GOLESTAN PROVINCE",2017.0,,A. S. pour; P. Pahlavani; B. Bigdeli,"Abstract. According to the industrialization of cities and the apparent increase in pollutants and greenhouse gases, the importance of forests as the natural lungs of the earth is felt more than ever to clean these pollutants. Annually, a large part of the forests is destroyed due to the lack of timely action during the fire. Knowledge about areas with a high-risk of fire and equipping these areas by constructing access routes and allocating the fire-fighting equipment can help to eliminate the destruction of the forest. In this research, the fire risk of region was forecasted and the risk map of that was provided using MODIS images by applying geographically weighted regression model with Gaussian kernel and ordinary least squares over the effective parameters in forest fire including distance from residential areas, distance from the river, distance from the road, height, slope, aspect, soil type, land use, average temperature, wind speed, and rainfall. After the evaluation, it was found that the geographically weighted regression model with Gaussian kernel forecasted 93.4% of the all fire points properly, however the ordinary least squares method could forecast properly only 66%",4.0,https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W4/477/2017/isprs-archives-XLII-4-W4-477-2017.pdf310dc748fa355c757681ca60b09b83ffb8c63e3a687,10.3130/jaabe.16.519,Changing Korean Housing System and Its Structural Forces,2017.0,,Hyunjeong Lee,"This research aims to examine the historical evolution of the developmental housing system in South Korea, to elicit the characteristics of the system and to explore the structural forces realigning the system. The primary findings are as follows: 1) the Korean housing system strongly supports economic growth and has been formed by a state-controlled, market-driven, pro-homeownership policy, forcing housing to be highly commodified; 2) the state′s export-oriented, industrialization-led economic development prompted urbanization that resulted in a housing shortage, and the persistent demand for housing coupled with limited access to home financing caused housing prices to sharply increase; 3) democratization facilitated state-guided production of massive housing and provision of public rental housing which is residualised; 4) economic crises in the wake of economic globalization revealed institutional drawbacks of the developmental housing system and 5) the housing system has been ostensibly responding to political realignment, economic restructuring and socio-demographic transformation, leading to more variations in housing provision, particularly tenure mode, structure type, size ",4.0,https://doi.org/10.3130/jaabe.16.5193113ec522454895d0cd03e705a78dbe7676fe637199,10.1007/s40615-024-02030-z,"Environmental Levels of Volatile Organic Compounds, Race, and Socioeconomic Markers Correlate with Areas of High Colorectal Cancer Incidence",2024.0,Journal of Racial and Ethnic Health Disparities,Allie Jin; Natalie DuPré; Rochelle Holm; Ted Smith; S. Kavalukas,"Ambient levels of volatile organic compounds (VOCs) released from nearby industrial plants have shown positive associations with increased colorectal cancer (CRC) rates. The objective of this study is to analyze the distribution of CRC in the context of socioeconomic status and its correlation with community environmental data. A retrospective study analyzed CRC patients from 2021 to 2023. The census tracts of the patients’ residential addresses were obtained, and CRC rates were calculated for each census tract. Socioeconomic data was gathered on these communities. Environmental VOC measurements were obtained from the National Scale Air Toxics Assessment. All datapoints were compared to statewide levels. Three census tracts in the county had higher CRC cases comparatively. These areas exhibited higher incidence rates and localized clusters of CRC cases, higher distribution of Black or African Americans, lower household incomes, lower home values, and lower educational attainment. VOC measurements in these census tracts had higher levels compared to county and state averages: specifically, 10.68% higher than county and 48.07% higher than state benzene levels (0.52 µg/m3 clusters vs ",4.0,312bbc333a961f64c0245c510956184745b487f1d31,10.1051/itmconf/20257002018,Research on House Price Prediction based on Machine Learning,2025.0,ITM Web of Conferences,Xiangjun Yang,"Accurately predicting house prices is of vital importance to individual home buyers and investment groups, which not only profoundly affects the formulation of home-buying strategies, but also is closely related to the smooth operation of the economy and the overall development of the society. In recent years, machine learning techniques have shown remarkable potential in house price prediction, as these models can mine the complex nonlinear correlations in large amounts of historical data to produce more detailed and accurate predictions. This study aims to evaluate and compare the performance of various machine learning models on the task of house price prediction. For the house price prediction task, Random Forests generally perform better than Linear Regression and Single Decision Tree because they can better capture complex patterns in the data and reduce the risk of overfitting. Linear regression models are simple and easy to interpret, but may not be accurate enough when dealing with nonlinear relationships and outliers. The advantages of random forests are reflected in higher predictive accuracy, robustness to outliers, and the ability to handle interactions between variabl",4.0,https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02018.pdf313fac0094d8fbb1d7256627687ecb6ff23fb6817f5,10.54254/2754-1169/46/20230317,Analysis and Comparison of House Price Prediction Based on XGboost and LightGBM,2023.0,"Advances in Economics, Management and Political Sciences",Shen Chen; Huihui Jin; Ling Li,"Real estate price prediction is one of the key research topics contemporarily. Based on the rapid development of Big Data, machine learning has gradually become the mainstream tool for housing price prediction. The XGboost and LightGBM models, as new advanced mod-els in recent years, have received widespread attention in the application in housing price prediction. Therefore, this study identifies the house price prediction based on XGboost model and LightGBM model and compares them with other models in order to obtain an analysis of the advantages and disadvantages of these two models in housing price predic-tion. According to the analysis, both models have ad-vantages such as high accuracy, high efficiency, and fast training speed. However, although XGboost has the smallest error pre-diction, it requires more computational time, thereby increasing computational costs. In ad-dition, LightGBM has disadvantages such as high overfitting risk in small sample sizes and increased sensitivity in noisy datasets. Therefore, besides the model studied in this article, feature selection methods such as Filter and Wrapper can also be introduced in subsequent studies to further improve the pred",4.0,https://aemps.ewapublishing.org/media/6ddf24e7e9fc45f3bdf0101499408af2.marked.pdf3144349e9a268f6cdf9deeb588d578c0bd2a09a96e0,10.54097/vcy5n584,A Comparative Study of House Price Prediction Using Linear Regression and Random Forest Models,2024.0,Highlights in Science Engineering and Technology,Yahan Fu,"The accurate prediction of house prices is crucial for stakeholders in the real estate sector, financial institutions, and urban planners. It not only informs investment decisions but also aids in policy formulation and market analysis. This research paper delves into the comparison of two prominent predictive analytics techniques-Linear Regression and Random Forest—to ascertain their effectiveness in forecasting house prices. Using a Kaggle dataset, this study analyzes key house price predictors such as building classification, living area size, construction year, and land area. The analysis shows Random Forest outperforms Linear Regression in accuracy, emphasizing the importance of building classification and living area in price prediction. Detailed visualizations, like feature importance graphs and scatter plots, offer clear insights into model performance. This research contributes significantly to real estate predictive analytics, offering insights to guide investment strategies and policy-making. It also opens avenues for exploring alternative machine learning approaches and socio-economic factors for a more comprehensive understanding of housing market dynamics.",4.0,https://doi.org/10.54097/vcy5n5843153089d0022c5affc99268352dcfeb383907de2c28,10.4314/ejesm.v9i4.2,Assessing the impact of selected macroeconomic variables in the determinants of sustainable residential housing prices in Lagos,2016.0,,E. Olowofeso; A. K. Oyetunji,"This paper examined the effects of some selected macroeconomic variables on residential property price in Lagos. Data were collected from Estate Surveyors and Valuers using the simple random sampling technique and Central Bank of Nigeria (CBN) Statistical Bulletin. The semi-log form of regression equation model was employed to determine the degree of relationship between the identified dependent and independent variables. Findings showed a positive impact of some independent variables in the determination of residential property price. This is evidenced by the interest rate possessing a t -value of 1.020, 1.236 and 1.512 while exchange rate with a t -value of 5.537, 6.845 and 8.822 denoting their influence as they are positively correlated towards the determinants of the prices of block of flats, detached house and duplex respectively. It was also revealed that fluctuation in the macroeconomic variables can lead to a corresponding change in the price of the residential property. It was recommended that financial institutions needs to reduce their lending rates to encourage investors. Keywords: Macroeconomic variables, Housing price, Economic development, Determinants",4.0,https://doi.org/10.4314/ejesm.v9i4.2316d49ad1c1d41b6c3bf498a7409f4d8ffebc27501e,10.54338/27382656-2022.2-012,Automated Real Estate Valuation With Machine Learning: A Case Study on Apartment Sales in Yerevan,2022.0,Journal of Architectural and Engineering Research,Henrik Sergoyan; Grigor Bezirganyan,"Real estate is one of the major sectors of the Armenian economy and has been developing dynamically since Armenia transitions from planned to market economies in early 1990s. More recently, large online platforms have been developed in Armenia to advertise real estate offerings, thus reducing information asymmetry, and increasing liquidity in both sales and rental markets. Simultaneously, granular geospatial data became increasingly affordable via platforms such as OpenStreetMap, Google Maps and Yandex Maps. With granular data concerning a representative portion of the real estate offering available online, it is increasingly tenable to monitor the real estate market in real time and develop analytical tools that can automatically and accurately estimate the value of real estate assets based on their internal and external features. This paper sets out to analyze Armenia real estate market and assess the performance of a special class of machine learning models while predicting the price of a square meter of apartments in Yerevan. Furthermore, it is presented the way to determine the most decisive factors which have an influence on the price of apartments on sale.",4.0,https://jaer.nuaca.am/index.php/jaer/article/download/92/41317714e31ef629d2b65af8bf86830c698b4fe2e33f3,10.1177/1420326x241279567,Organic renewal of property plots in Chinese cities: Sustainable development of built environment via property value enhancement and digital twin construction,2024.0,Indoor and Built Environment,Haining Chen; Jianguo Wang; Tianran Han; Xin Jin,"As the urbanization process in China continues to advance, urban residential property plots have commenced their transition to an organic renewal model. Nevertheless, achieving the sustainable and high-quality organic renewal of urban residential property plots poses a significant challenge for planning. This study postulates that the crux of organic renewal for urban residential property plots lies in the impacts on improvement for sustainable built environment, with the main obstacle being the organization and implementation of intricate renewal activities. A potential solution is to enhance land property value, while establishing a digital twin to optimize the design of renewal strategies and implement renewal activities precisely and efficiently. This study scrutinizes the characteristics of the property value composition of urban residential property plots, investigates basic strategies of organic urban renewal and proposes a theoretical framework to elucidate dynamic property value change, property value enhancement and incremental value allocation during the renewal process. Additionally, a theoretical concept was developed to efficiently promote property value in organic re",4.0,318fe4754dd3b0b5691640f2160331e147b88d77dc9,10.1088/1742-6596/1982/1/012048,Application of support vector regression in prediction model using genetic algorithm optimized,2021.0,"Journal of Physics, Conference Series",Wenke Du; Ruihan Chen; Zhenglong Cong,"No matter in any period, housing is the most basic demand of people’s life, and it is closely related to people’s daily life. Although many domestic and foreign experts have done research and prediction on housing prices, the causes of housing prices are complex, and the results of housing price research in different regions and different periods are very different, and most forecasting models have limitations in the use of them. Therefore, this article is based on the performance of support vector regression depends on the characteristics of key parameter selection, and uses genetic algorithm to optimize the penalty parameters, kernel function parameters and insensitive loss function of the support vector regression model. The optimized parameters are used to establish a support vector regression prediction model, and the housing price is predicted through the prediction model. The simulation results show that the convergence speed and prediction accuracy of the support vector regression prediction model optimized by genetic algorithm have been greatly improved, and the prediction results verify the feasibility and effectiveness of the model..",4.0,https://iopscience.iop.org/article/10.1088/1742-6596/1982/1/012048/pdf3198b5adf7da4a31ffb6c87fc623ba8dd96443bf869,10.22214/ijraset.2022.40466,Prediction of House Price Using Machine Learning,2022.0,International Journal for Research in Applied Science and Engineering Technology,Piyush Chordia; Pratik Konde; Supriya Jadhav; Hrutik Pandhare; Shikha Pachouly,"Abstract: The trend of the sudden drop or constant rising of housing prices has attracted interest from the researcher as well as many other interested people. There have been various research works that use different methods and techniques to address the question of the changing of house prices. This work considers the issue of changing house price as a classification problem and discuss machine learning techniques to predict whether house prices will rise or fall using available data. This work applies various feature selection techniques such as variance influence factor, Information value, principle component analysis, and data transformation techniques such as outlier and missing value treatment as well as different transformation techniques. The performance of the machine learning techniques is measured by the four parameters of accuracy, precision, specificity, and sensitivity. The work considers two discrete values 0 and 1 as respective classes. If the value of the class is 0 then we consider that the price of the house has decreased and if the value of the class is 1 then we consider that the price of the house has increased.",4.0,https://doi.org/10.22214/ijraset.2022.404663207760a72b4efd5417dfd073a3684b829e61ca1661,10.3390/buildings12020222,The Benefit of Rail: Estimating the Impact of Station Accessibility on Residential Property Prices,2022.0,Buildings,Dániel Tordai; András Munkácsy,"This article focuses on the relationship between the prices and the distance from a metro or commuter railway station to residential properties. Relevant efforts have already been dedicated to estimating the effect of station accessibility, but one of the key limitations of previous studies was the heterogeneity of real estate. Here, the data on homogeneous real estate type in Budapest, Hungary, namely panel flats (built with uniform technology between the 1960s and 1990s) are analysed by statistical methods. First, it is demonstrated that this real estate type is indeed highly homogenous. Second, linear regression is used to understand the relationship and its magnitude between flat prices and distance of stations. The results show a statistically significant relationship, i.e., that five additional minutes of travel time to the nearest station providing fast access to the city centre makes real estate prices drop by nearly 1%. In light of current policies promoting rail for passenger transportation, the findings may be applied for value capture policies to increase the viability and feasibility of urban railway projects.",4.0,https://www.mdpi.com/2075-5309/12/2/222/pdf?version=16450084333210248121bc6b6ca19f659e5862f81cfdd6526326b,10.1007/s00180-023-01453-2,Exploring local explanations of nonlinear models using animated linear projections,2022.0,Computational statistics (Zeitschrift),N. Spyrison; D. Cook; Przemysław Biecek,"The increased predictive power of machine learning models comes at the cost of increased complexity and loss of interpretability, particularly in comparison to parametric statistical models. This trade-off has led to the emergence of eXplainable AI (XAI) which provides methods, such as local explanations (LEs) and local variable attributions (LVAs), to shed light on how a model use predictors to arrive at a prediction. These provide a point estimate of the linear variable importance in the vicinity of a single observation. However, LVAs tend not to effectively handle association between predictors. To understand how the interaction between predictors affects the variable importance estimate, we can convert LVAs into linear projections and use the radial tour. This is also useful for learning how a model has made a mistake, or the effect of outliers, or the clustering of observations. The approach is illustrated with examples from categorical (penguin species, chocolate types) and quantitative (soccer/football salaries, house prices) response models. The methods are implemented in the R package cheem, available on CRAN.",4.0,https://link.springer.com/content/pdf/10.1007/s00180-023-01453-2.pdf3223153fc61779211616c33aae02c5e91bb02651d3c,10.1177/2333721416655966,In which neighborhoods are older adult populations expanding?: Sociodemographic and built environment characteristics across neighborhood trajectory classes of older adult populations in four U.S. cities over 30 Years,2016.0,Gerontology and Geriatric Medicine,Pasquale E. Rummo; J. Hirsch; A. Howard; P. Gordon-Larsen,"Objective: We sought to examine characteristics of neighborhoods with changing older adult populations. Method: We used 30 years (1980-2011) of data from four U.S. cities (n = 392 neighborhoods; Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; Oakland, California) and finite mixture modeling to identify trajectory classes: neighborhoods with “stable,” declining, or increasing older adult populations (≥65 years). We then compared mean baseline and change in their characteristics. Results: Neighborhoods with an increasing (vs. “stable”) percentage of older adult populations had lower initial poverty and greater increases in education and income, with lower increases in road connectivity, population density, and housing prices/debt over time. The same was true for neighborhoods with declining older adult populations, with the exception of having higher increases in housing prices/debt. We observed few significant differences in neighborhood amenities or parks across classes. Conclusion: Our results emphasize the need to consider built and social environments when planning communities for older adults.",4.0,https://doi.org/10.1177/2333721416655966323d4cc15f454ce4d3d959a1b5337fb98db5bd3fcb2,10.3390/ijfs8030055,Evaluation of the Reverse Mortgage Option in Korea: A Long Straddle Perspective,2020.0,International Journal of Financial Studies,Kyung-jin Choi; Byungkwon Lim; Jaehwan Park,"This study explored the option value embedded in a reverse mortgage in Korea through an empirical analysis, using the Black–Scholes option-pricing model. The value of a reverse mortgage is affected by the variation in house prices. However, older homeowners using reverse mortgages are able to choose this option due to the unique characteristics of reverse mortgages, such as non-recourse clauses or being able to redeem the loan. This paper found the following results. First, the call option value is 5.8% of the house price at the age of 60, under the assumption of a KRW three hundred million house value, while the put option value is only 2.0%. Contrary to what it is at sixty years of age, only the call option value will remain when the homeowner reaches the age of 80. Second, this article analyzed the sensitivity of the key variables of real-option analytical models, such as the change of the exercise price, the change of the risk-free rate, volatility, and maturity, on the option value of a reverse mortgage. The sensitivity results of the key variables supported economic rationales for the option pricing model.",4.0,https://www.mdpi.com/2227-7072/8/3/55/pdf?version=159961114032437ccbd85d672c1aecc8dee724fb91c9b41490234,10.26905/jkdp.v22i4.2515,Monetary Policy and the Housing Market in Indonesia: Evidence from Selected Regions,2018.0,Jurnal keuangan dan perbankan,Ariyanto Nugroho; M. Y. Purnama; L. Fauzia,"The housing market in Indonesia tends to have different characteristics in several regions. The government has implemented macroprudential policies regarding mortgage loans for housing and apartments through the Loan to Value (LTV) policy in dealing with credit risk in the housing market. The aimed of this study analyze the effect of LTV policy and regional economic indicator on the house prices in Indonesia, compare the impact of LTV policy and indicator on the types of houses and regions. We used secondary data from eight regions which derived from residential property survey and statistics Indonesia at a monthly frequency. In processing estimated data using Generalized Least Square (GLS) Fixed Effect Model (FEM) to ascertain the effect of LTV policy in each region. The result showed most attributes had a significant effect on housing prices. However, LTV had no significant effect on every type of housing (small, medium, and large). The LTV policy is spatial in accordance with housing market condition in each region. JEL Classification : E52, O18, R31 DOI : https://doi.org/10.26905/jkdp.v22i4.2515",4.0,https://jurnal.unmer.ac.id/index.php/jkdp/article/download/2515/pdf3252d1ec28530919e0326785fd5f0571f4784e7d738,10.1080/12265934.2024.2317182,The spatial effects of housing prices on innovation in cities: evidence from the Yangtze river economic belt in China,2024.0,International Journal of Urban Sciences,Ning-Ning Liu; Jin-Shan Liu; Zhanyang Li,"ABSTRACT Economic activities among cities are not mutually independent; rather, they exhibit significant spatial associations. Real estate prices also demonstrate a certain degree of spatial interdependence. Diverging from most of the relevant researches neglecting the existence of spatial spillover effect, our study employs spatial Durbin models for an in-deep investigation into the spatial spillover effects of housing prices on innovation within the Yangtze River Economic Belt. Furthermore, our study explores the talent agglomeration channel. In many studies on housing prices and innovation, the focus has often been on the channel of labour mobility, with limited in-depth exploration of labour characteristics. Our findings reveal the following: (1) An increase in housing prices stimulates innovative activities in local cities but hinders innovation in surrounding cities. (2) In the midstream and downstream regions of the Yangtze River Economic Belt, the escalation of housing prices fosters innovation in cities but suppresses innovation in surrounding cities. Conversely, in the upstream region, the rise in housing prices inhibits innovative activities both locally and in surroundi",4.0,326990dee291c538b7594ca7ab048bc0f71c4529056,10.23977/autml.2023.040103,House Price Forecasting in Ames Based on Bayesian Regularized BP Neural Network,2023.0,Automated Machine Learning,Haiqing Bai; Xiaoyong Chen,": Housing has always been an important issue related to the national economy and people's livelihood. House prices not only affect people's welfare, but also have a significant impact on the national economy and social stability. Therefore, the prediction of house prices is also a necessary means. This paper forecasts the housing price data of Ames City in the United States, and establishes a Bayesian regularized BP neural network model to solve the nonlinear mapping relationship between housing prices and indicators. Through curve fitting analysis of samples, the overall R value is 0.9667, the overall result is better, and the error histogram also conforms to normal distribution. The experimental results show that the BP neural network model based on Bayesian regularization is very effective in dealing with the problem of house price prediction, and can better analyze and predict the trend of house price. This study provides a basis for real estate developers to develop and position products, and also provides model support for buyers to better judge the real price of houses.",4.0,http://www.clausiuspress.com/assets/default/article/2023/03/13/article_1678707835.pdf327eb64c46c0db04d3715515f4a5dbd72a992adde8b,10.54097/hset.v39i.6637,Real Estate Price Prediction based on Supervised Machine Learning Scenarios,2023.0,Highlights in Science Engineering and Technology,Zhihang Liu,"House price prediction is one of the most common supervised learning tasks in the machine learning field, which makes it a perfect criterion for the effectiveness of different learning models. From basic regression models to neural networks, countless methods have been proposed to solve the house price prediction problem. In this paper, the focus is the performance of three regression models, linear, LASSO, and ridge. There will be a selected dataset of sold houses from the open-source website. The data will be explored and visualized for a better understanding and then implement the regression models for further testing. According to the analysis, the LASSO regression model can yield the most accurate prediction with 90.15% accuracy but need a specific λ value. The linear and ridge regression yields similar predictions with close to 90% accuracy. Therefore, the most effective model for the house price prediction problem is the LASSO regression model. Overall, these results shed light on guiding further exploration of the performance of different machine learning models.",4.0,https://drpress.org/ojs/index.php/HSET/article/download/6637/6431328295aa328a0d5ca8e584b712fe4c4c2bfe1ea0cb1,10.15351/2373-8456.1076,Phragmites Removal Increases Property Values in Michigan’s Lower Grand River Watershed,2017.0,,Paul N. Isely; Erik Nordman; S. Howard; R. Bowman,"The presence of Phragmites australis, an invasive wetland plant, negatively affects coastal property values and home prices rise with distance from Phragmites. Home prices increased as distance to Phragmites increased at a rate of $3.90/meter. Removing Phragmites from a property so that the next closest Phragmites was 400 m away results in a property value increase of over $1,500. Removing all Phragmites within 400 m of any property results in a total property value impact of $837,000. This generates about $13,457-$15,121 in additional property taxes each year once the prices and taxes adjust to the plant’s removal. We estimated the cost of Phragmites removal at $687/ha. Removing the approximately 36 ha of Phragmites in the area would cost about $25,041. Future treatments would likely be less than that of the first year. The estimated cost of the first year of Phragmites removal is less than the estimated two years of annual property tax revenue increases. This application notes is available in Journal of Ocean and Coastal Economics: https://cbe.miis.edu/joce/vol4/iss1/5",4.0,https://cbe.miis.edu/cgi/viewcontent.cgi?article=1076&context=joce32939b388fd05d5dd163caea27e1723c2707db685ff,10.1080/14036096.2023.2287133,Interpreting the Changing Meaning of the Peri-Urban Holiday House: The Complex and Paradoxical Nature of Housing Financialisation,2023.0,"Housing, Theory and Society",Deborah Levy; Harvey C Perkins; J. Horan,"ABSTRACT In recent studies of housing financialisation, a “financial paradox” has been identified in which owner-occupied houses are simultaneously places of ontological security and financial investment. This paradox influences the way owner-occupiers interpret their primary and holiday houses, particularly if the latter are located in sought after high amenity locations. In this paper, we argue that, faced with rapidly increasing residential property prices, many long-term owners reluctantly reconceptualise their understanding of their holiday house as both a recreational and financial asset. Our study uses in-depth interviews with owners of holiday houses on Waiheke Island, Auckland, Aotearoa New Zealand to identify how they are interpreting and managing the normalisation of this paradox. The island is historically popular for holiday houses and is currently experiencing substantial property price increases. It is therefore an ideal place to study the complex and paradoxical nature of housing financialisation and thus potential biases in the housing market.",4.0,https://www.tandfonline.com/doi/pdf/10.1080/14036096.2023.2287133?needAccess=true3309954c72790f31af4a213fd51ab8c37694b0c0ce8,10.1109/pdgc64653.2024.10984370,House Price Prediction System Using Ensemble Learning Approach,2024.0,"2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC)",Abhishek Kumar; Paras Jain; Priyank Pandey; Shyamalendu Das; V. Gupta; Dhiraj Kumar Gupta,"The real estate industry is among the most active and critically important industries for any economy. An accurate forecast of the prices of houses is very important to the buyers, sellers, investors and the policy makers. In years gone by, other methods of valuation have been used, but new advancements have been made in machine learning meaning the prediction speeds and the level of accuracy have been boosted. In this article, I would like to know how the development of various sophisticated models of ML can be used to forecast house prices: the most used models, the most favorite datasets and informative features, as well as the appropriate evaluation measures. Real estate is a continually expanding business that is prominently involved in the world economy. To precisely estimate house prices is always a daunting task due to various factors that vary from physical to non-physical factors include size, location, market, and economic conditions. Digital advancements especially, the incorporation of machine learning in the analysis of financial markets, housing and other sectors has enabled real estate specialists, banks and other institutions and government bodies analyze trends an",4.0,331e061c369542705ea1c233cf7f55bc3fb42533927,10.3846/ijspm.2024.22661,Multidimensional house price prediction with SOTA RNNs,2024.0,International Journal of Strategic Property Management,Yasin Kütük,"This paper introduces insights into the Turkish real estate market, which can be generalized globally. It primarily aims to find the best forecasting algorithms for the housing price index and compare their prediction performance over three, six, nine, and twelve months ahead by using recurrent neural networks (RNN) with a comparison of out-of-sample predicting power of econometrical models. For these purposes, we employ three RNN architectures in twenty-four settings, revealing that certain RNN architectures are the best predictors in forecasting the Turkish real housing price index. The RNN architectures outperform traditional econometric models; however, the more months forecasted, the lower the prediction power. The lagged values of the price-to-rent ratio, real rents, and the lagged USDTRY values contribute more than the other predictors in forecasting the real housing price index. The outcomes suggest that stocks, real estate investment trusts, and gold are neither complementary nor competing financial instruments since housing is an illiquid asset.",4.0,https://doi.org/10.3846/ijspm.2024.22661332df571658cee6c502caa5e7f109e8a17702d92322,10.4236/jbcpr.2020.82009,"Inner-City Neighbourhood Changes Predicted from House Prices in Windsor, Ontario, since the Early- or Mid-1980s",2020.0,Journal of Building Construction and Planning Research,A. Phipps,"Changes 333in prices of homes are hypothesized as correlated with the times of their sale 334and resale and the attributes of their dwelling unit and neighbourhood and 335those of neighbouring homes. They may also be correlated with the occurrences 336of events inside the neighbourhoods caused by the activities of individuals and organizations outside the 337neighbourhoods, such as whether the local economy is in a recession or has a 338high unemployment rate. Calibrated hybrid housing price models predict 339precipitous decreases in house prices of approximately 2900 sold and resold 340homes in two inner-city neighbourhoods in 341Windsor, Ontario, during those events since 1981 or 1986. Overall modest 342predicted percentage increases in houses’ prices during more than 30 years 343therefore subsumed periods of inner-city neighbourhood deterioration in dispersed locations of unimproved and disimproved 344homes. Compensatory predictions however are of increasing prices for minorities 345of homes with improvements to several attributes of the dwelling unit and 346neighbourhood.",4.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=101190347c4ea701e02ea7478f62df4251d5bed23aff67a3f,10.1016/j.heliyon.2023.e23891,Testing for stationary of housing prices in China: An examination using efficient unit root tests,2023.0,Heliyon,Na Bian; Yi-Guo Chen,"Changes in housing prices affect all aspects of production and life, and have always been a hot spot of social concern. This paper uses the sequence panel selection method (SPSM) to study the time series properties of housing prices in 100 cities in China from June 2010 to December 2022. It is found that there are large differences in the stationary of housing prices in first/second/third-tier cities. Using the SPSM test method, it is found that housing prices in first-tier cities are all non-stationary series, the samples of second- and third-tier cities can be significantly divided into stable housing prices and non-stable housing prices. After further using the Fourier function to approximate the structural mutation of the data, more second-tier cities show stable housing prices, while less third-tier cities show stable housing prices. These findings provide an important decision-making basis for the government to implement regulatory policies according to local conditions based on the differential characteristics of changes in housing prices.",4.0,https://doi.org/10.1016/j.heliyon.2023.e238913489425d8d442b4e5a1996ec661d32ee9723f8bbdd2,10.1108/ijhma-08-2024-0125,Examining Saudi Arabia’s housing tenure and price trends: a comparative study with global context,2024.0,International Journal of Housing Markets and Analysis,U. Dano,"Purpose349This paper aims to analyze and compare housing tenure and model housing price index (HPI) in Saudi Arabia with selected Organization for Economic Cooperation Development (OECD) countries.350351Design/methodology/approach352The research uses quantitative data from the Saudi 2022 Statistical Census and OECD sources. Analytical methods include polynomial regression modeling for housing price trends and analysis of variance (ANOVA) to explore the relationship between housing variables, alongside descriptive and inferential statistics.353354Findings355The polynomial regression analysis reveals distinct HPI trends across the studied countries, indicating stability and growth. Countries like Australia, France and the US are projected to see substantial HPI increases by 2026, reaching values around 175, signaling strong market recovery and growth. Greece’s trajectory is marked by fluctuations, recovering modestly post-2020, while Saudi Arabia’s market shows stability with a slight increase forecasted to 92.8 by 2026. The ANOVA analysis for Saudi Arabia highlights significant regional differences in housing tenure, with economic conditions and housing types significantly impacting tenure patter",4.0,35688ae35f4c4aac0bd411ec61b6f030d0741310f6e,10.9744/dimensi.44.1.95-100,ANALYSIS OF LANDSCAPE ELEMENTS THAT AFFECT PROPERTY VALUE BASED ON THE PERCEPTION OF HOUSING RESIDENTS IN SURABAYA,2017.0,,Nita Setiawati Wibisono; T. Kwanda; Njo Anastasia,"This research is done to determine landscape elements that affect property value based on housing residents’ perception in Surabaya residential areas. The landscape elements that used in this research are natural elements such as vegetation and soil; and man-made element such as garden statue, road pattern, road width and hierarchy, park and plant, artificial lake, and road equipment. Purposive sampling technique is used to represent respondents in the residential areas that provide landscape elements in East and West Surabaya. Data analysis technique uses validity and reliability test, analysis factor, binary logistic regression, and the average of variable test. The result shows that majority of the residents of East Surabaya and West Surabaya residential areas approved that landscape consist of park and plant, road width and hierarchies, and road pattern affect their residential property values. The residents are also willing to contribute about 7.4% of their house price to improve the appearance of the residential landscape.",4.0,http://dimensi.petra.ac.id/index.php/ars/article/view/20282/1929235757054c72b0ea2098e688f71f5cddd5b2ad3d85d6,10.1088/1742-6596/1693/1/012062,House Price Prediction with An Improved Stack Approach,2020.0,"Journal of Physics, Conference Series",Hang Zhang; Kehan Wang; Mengchu Li; Xinchen He; Ruibo Zhang,"As a typical framework in ensemble learning, Stacking creates a multi-layer learner by integrating the learning results of different individual learners. Despite a general improvement in accuracy, multiple layers in stacking results in high computation cost. In addition, some individual learners do not perform better than others in single-model prediction task. To solve the above drawbacks of traditional stacking, this paper proposes an improved Stacking algorithm based on the concept of “weighted attribute”. For each individual learner, a corresponding weight will be calculated based on its prediction performance on training data. Weights will then be used as evaluation criteria for model performance. Models with larger weights will be selected as attributes for meta-learner. When improved Stacking algorithm is applied on the same dataset, experiment results indicate a slight improvement in prediction accuracy and a relatively large reduction in computation cost. This demonstrates practical application value of the improvement.",4.0,https://iopscience.iop.org/article/10.1088/1742-6596/1693/1/012062/pdf358297ddd71809d045a497710a400ecfce4ee808758,10.18185/erzifbed.1199535,Housing Demand Forecasting with Machine Learning Methods,2022.0,Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi,Şeyma Emeç; Duygu Tekin,"Housing is a place where sustainable urban spaces are produced and where people's physical, cultural, environmental, economic, social and psychological needs are evaluated together with their surroundings, rather than just a building where the need for shelter is met. With the acceleration of urbanization, new needs arise, and the first of these is the need for housing. The housing sector has become one of the most dynamic and continuous sectors associated with the increase in the need for housing. The need for adequate and accessible housing comes to the forefront in our country as well as in the world. Understanding and predicting the key features determining housing prices and value is an important consideration for urban planners and housing policymakers. In this study, machine learning and artificial neural network models were used to predict the housing demand of Konya, and their forecasting performances were compared. As a result, it was concluded that ANN is a better alternative for housing demand forecasting in Konya.",4.0,https://dergipark.org.tr/en/download/article-file/27517003594e0c269e387d67c382679b8061a20cce3e5ea530,10.1080/14631377.2023.2287764,Drivers of household saving in East Central European countries. A push and pull model perspective,2024.0,Post-Communist Economies,V. Endrődi-Kovács; V. Czeczeli; G. Kutasi,"ABSTRACT There is no consensus in the economics theory about the determinants of household saving. The article composes vector error correction (VEC) models to identify the determinants of household saving in the East Central European countries from 2005–2020. Its novelty is that it identifies push and pull factors in accordance with the Keynesian and neoclassical theories whose mixed approach has not been included in the methodology of relevant papers in relation with East Central European countries. The examined countries are indicated to be homogenous from macroeconomic perspective. Results confirm that household saving increases because of decreases in consumer confidence, unemployment, and inflation rates. Increases in the deposit rate and real house price index are related to these pull factors, while decreases in the official share index correlates withincreases in household saving. Results can serve as guidelines for policy makers about incentives which stimulate household saving most effectively in the region.",4.0,https://www.tandfonline.com/doi/pdf/10.1080/14631377.2023.2287764?download=true360915cbe20c2bb18373993eae9f59dccc1673f7c29,10.4028/www.scientific.net/amm.462-463.1081,Optimisation to ANN Inputs in Automated Property Valuation Model with Encog 3 and winGamma,2013.0,,Vo Nguyen; Hao Shi; J. Szajman,"An automated property model for prediction of the sales price of residential properties with optimized inputs was developed. Optimised inputs improve efficiency and speed of an Artificial Neural Network (ANN). Property appraisal ANNs have a great potential not only to save time and money but also help local government authorities to determine the tax revenue. While the criteria for the ANN’s number of hidden layer neurons are well known, there is no theory to support the optimisation to ANN inputs. The proposed optimisation to ANN inputs procedure aims to resolve some of the issues in using ANNs especially in the case of automated property valuation modelling (AVM). A brief review of ANNs and their applications is given, followed by the discussion of the ANN design methodology and optimisation. Details of ANN optimisation using Java based Encog 3 and winGamma are presented in this paper. It is shown that optimisation to ANN inputs can improve the accuracy in residential property evaluation using winGamma and Encog 3.",4.0,https://doi.org/10.4028/www.scientific.net/amm.462-463.10813619df79a673310f527cb045cd5d5f4c9a7257ae9df,10.33893/fer.21.4.29,Impact of Energy Rating on House Prices and Lending Rates,2022.0,Financial and economic review,G. Hajnal; A. Palicz; Sándor Winkler,"The real estate market is a key component of the green transition, and thus it is worth examining the pricing and financing costs of modern residential buildings in Hungary. In our study, we investigate a) whether a significant price premium can be identified for green properties based on new housing projects in Budapest, and b) whether banks finance residential buildings with more advanced energy technologies at lower interest rates. Based on our regression estimation, the green price premium is clearly evident in the Budapest new housing market: on average, homes with an energy rating of BB or better are 5.1 per cent more expensive than homes with an energy rating of CC. Based on our estimate of housing loan interest rates, no significant difference can be identified in the interest rates on loans granted to finance properties with a certificate higher than CC compared to loans granted for properties with CC certificate, i.e. banks do not yet factor energy aspects into the pricing of loans.",4.0,https://doi.org/10.33893/fer.21.4.29362ffeed18d91c1d3980759b6c1880b46535e6fba6f,10.48084/etasr.11266,Enhancing Regression Accuracy and Reliability with Ensemble Learning and Iterative Hyperparameter Optimization,2025.0,"Engineering, Technology & Applied Science Research",Thi Thanh Hoa Tran; Thanh-Ngoc Tran,"Ensemble learning models such as XGBoost, LightGBM, and CatBoost are gaining popularity for regression predictions. As their accuracy significantly depends on hyperparameters, identifying the optimal hyperparameters plays a crucial role. This study proposes a new approach by integrating ensemble learning models with hyperparameter optimization algorithms to assess their effectiveness compared to traditional machine learning models such as RF, SVR, and KNN. This study also conducted multiple repetitions of the hyperparameter optimization process on the Boston Housing Price and Bike Sharing Demand datasets, supported by statistical analyses of the results to enhance the reliability of the findings compared to a single run. The results demonstrate that applying ensemble learning models helps reduce prediction errors, increasing the models' accuracy compared to traditional ML models. Furthermore, comparisons indicate that repeating iterations n times improves the reliability of the results.",4.0,https://etasr.com/index.php/ETASR/article/download/11266/5190363ae24f95bed28e0abda79eae51dc18156df28fc10,10.48550/arxiv.2503.22119,Multimodal Machine Learning for Real Estate Appraisal: A Comprehensive Survey,2025.0,Pacific-Asia Conference on Knowledge Discovery and Data Mining,Chen-Ya Huang; Zhidong Li; Fang Chen; Bin Liang,"Real estate appraisal has undergone a significant transition from manual to automated valuation and is entering a new phase of evolution. Leveraging comprehensive attention to various data sources, a novel approach to automated valuation, multimodal machine learning, has taken shape. This approach integrates multimodal data to deeply explore the diverse factors influencing housing prices. Furthermore, multimodal machine learning significantly outperforms single-modality or fewer-modality approaches in terms of prediction accuracy, with enhanced interpretability. However, systematic and comprehensive survey work on the application in the real estate domain is still lacking. In this survey, we aim to bridge this gap by reviewing the research efforts. We begin by reviewing the background of real estate appraisal and propose two research questions from the perspecve of performance and fusion aimed at improving the accuracy of appraisal results. Subsequently, we explain the concept of multimodal machine learning and provide a comprehensive classification and definition of modalities used in real estate appraisal for the first time. To ensure clarity, we explore works related to data and",4.0,364eda85afed1edd56a447e19b4926cd3ad82665e46,10.1109/iccams60113.2023.10526190,Modeling House Price Prediction Model using XG Boost and Machine Learning Algorithms,2023.0,"2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS)",Ajmeera Kiran; Mudassir Khan; J. Babu; B. S. Santosh Kumar; S. Ahmed,"Every year, the cost of homes rises, necessitating the development of a system that can forecast future home prices. The ability to estimate house prices can be useful to both the developer and the customer in planning the best time to buy a home. The physical state, design, and location of a house are the three variables that affect its cost. This paper discusses using Python modules to estimate housing prices using regression approaches. The proposed method utilized more precise house price calculations and provided more accurate predictions. It also discusses graphical and numerical methods for housing price prediction. This paper describes our machine learning-based house price model and its dataset. Real estate data mining is frequent. Data mining can anticipate house values, essential property features, and more by extracting meaningful information from raw data. Research shows that property price variations worry homeowners and the real estate market. To find the best house price prediction models, a literature review is done. This investigation showed that Artificial Neural Networks, Support Linear Regression, and were the most efficient models. We also found that locationa",4.0,365f72969da23ccdc7884b5ce0c18e7ce575fe66c99,10.1017/s0266466625100212,HOW TO DETECT NETWORK DEPENDENCE IN LATENT FACTOR MODELS? A BIAS-CORRECTED CD TEST,2026.0,Econometric Theory,M. Pesaran; Yimeng Xie,"366 In a recent paper, Juodis and Reese (2022,367 Journal of Business & Economic Statistics368 , 40, 1191–1203) (JR) show that the application of the CD test proposed by Pesaran (2004,369 General diagnostic tests for cross-sectional dependence in panels370 , CWPE 0435, Cambridge) to residuals from panels with latent factors results in over-rejection. They propose a randomized test statistic to correct for over-rejection, and add a screening component to achieve power. This article considers the same problem but from a different perspective and shows that the standard CD test remains valid if the latent factors are weak. A bias-corrected version, CD371 372 373 374 $^{\ast}$375 376 377 , is proposed which is shown to be asymptotically standard normal under the null of error cross-sectional independence which has power against network-type alternatives. This result is shown to hold for pure latent factor models as well as for panel regression models with latent factors. The case where the errors are serially correlated is also considered. Small sample properties of the CD378 379 380 381 $^{\ast}$382 383 384 test are investigated by Monte Carlo experiments and are shown to have satisfactory small sample properties. In an e",4.0,385935ee48b34d47724a1d2319d19624a308fbe10e8,10.1080/17477778.2024.2325428,The spatial structure of housing affordability and the impact of public infrastructure,2024.0,J. Simulation,Omar A. Guerrero; Stephen Law,"ABSTRACT We develop a parsimonious agent-level model of the housing market that reproduces the spatial price structure of London at the level of each property. Prices emerge as agents make utility-driven choices on properties with specific attributes in decentralised market interactions. This specification facilitates matching individual households to specific properties, something not possible in most publicly available large-scale housing datasets. Because location is a feature of properties, we estimate the impact of the new Elizabeth Line that connects the city from east to west. Then, by combining household and property variables, we estimate its impact on affordability. Changes in affordability allow to foresee potential demographic changes across space. Such information is extremely valuable not only to households and developers, but also to planning authorities who wish to anticipate the future demand for services and test the viability of different local tax regimes.",4.0,https://doi.org/10.1080/17477778.2024.2325428386802979f68fa791b2329ce531c56cdb3567bf6aee,10.1553/giscience2014s125,"A Spatial Analysis of House Prices in the Kingdom of Fife, Scotland",2014.0,,Julia Zmölnig; M. Tomintz; S. Fotheringham,"The research presented in this paper focuses on the analysis of spatial variations in house price adjustments due to economic conditions, and to quantify and describe patterns in the variations of house prices in the study area of Fife, Scotland. To show the impacts of national and local economic conditions on house price levels, specific methods of spatial analysis, such as Interpolation and Geographically Weighted Regression, are needed. The data for this analysis comes from the Registers of Scotland. The Registers of Scotland is responsible for compiling and maintaining registers relating to property and other legal documents. Their main aim is to record and safeguard rights (e.g. respond to policies adopted by the Scottish Government), whilst providing open and efficient access to important information. Results show that there are spatial variations in house prices, whereby the house prices in the north are on the rise, whereas in the south the hotspots seem to disappear.",4.0,https://hw.oeaw.ac.at/0xc1aa5576_0x0030d417.pdf38795e406f27b5acca7ea39c6f0edd1bf59e000e167,10.5539/ijef.v10n9p145,The Influence of Home Buyer Sentiment on Chinese Housing Prices—— Based on Media Text Mining,2018.0,International Journal of Economics and Finance,Qing-Hui Gao; Tianxia Zhao,"In recent years, housing prices in major cities in China have generally maintained an upward trend, and housing prices have remained high for a long time. China’s economic growth has its own particularity in the world, and the real estate market as a major support point for the economy, its future development is crucial to the economy. Taking Guangzhou as an example, this paper uses text mining method to extract media influence from media texts, and constructs the buyer confidence index, and uses it as a proxy variable for buyers’ mental expectations. This article finally obtained a total of 115,139 articles on the housing market, forming an article pool for text mining. In the study and analysis of the explanatory effect of the home buyer confidence index on housing prices, this paper uses multiple linear regression to analyze the main factors affecting housing prices, and concludes that buyers’ sentiment is significantly positively correlated with house prices.",4.0,https://www.ccsenet.org/journal/index.php/ijef/article/download/76858/42866388f8557f3b7bdb33ddede4335910d0d4960ca47921,10.1108/ijhma-09-2023-0118,Spatial and temporal diffusion of property prices and sales across British regions,2024.0,International Journal of Housing Markets and Analysis,A. Salov,"389Purpose390The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.391392393Design/methodology/approach394This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.395396397Findings398Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is di",4.0,399cd263c8d718e9e4665c5df77ea0faec847edfbc9,10.5782/2223-2621.2013.16.4.53,Measuring the Impact of Church Externalities on House Prices,2013.0,,G. Babawale,"The notion of “externalities” is a useful concept developed in welfare economics. In real estate parlance, the notion states that external factors could impact on property value positively or negatively (Appraisal Institute, 2008; Do et al., 1994). Because of its physical immobility, real estate tends to be affected by externalities more strongly than most other economic goods, services, or commodities. Ascertaining the effects of externalities on property values provide a very strong test of the nuisance versus amenities effects. If an externality is truly a nuisance, then values of properties within close range will be adversely affected in proportion to the distance from it. If on the other hand an externality is an amenity, then property value will increase the closer a property is located to it. In this regard, location of churches within residential neighborhoods has been a subject of concern and a controversial issue in several jurisdictions.",4.0,https://doi.org/10.5782/2223-2621.2013.16.4.53400924293a66a7f7ec330ded0d18b460162973fd14b,10.5220/0010074313471351,Analysis of Factors Affecting Property Value at Residence with Cluster Concept: Case Study - J City Residence,2018.0,,F. F. Sigit; Khaira Amalia Fachrudin; Hilma Tamiami Fachrudin,": Most of the citizens choose to live in the residence. The need of residence based on trends lead to the development of many residential properties offering residence with cluster type. J City is one of the cluster residences with three types of clusters that have different property values. The purpose of this research is to know and analysis the impact of location, facility, design, neighbourhood and cluster character to property value. The population was taken with a sample of 87 respondents in the residence. The testing of hypothesis uses multiple linear regression test with the level of significance 5%. Statistical application program SPSS and STATCAL are used to analysis data. The results of this study indicate that simultaneously and partially where the location, facility, design, neighbourhood, and cluster characteristics have a positive and significant effect on the value of residence property on J City residence cluster.",4.0,https://doi.org/10.5220/001007431347135140185f9d2fafef0a72ad208c86eeba6cff5d6f1b084,10.1088/1742-6596/1827/1/012186,Investigation on House Price Prediction with Various Gradient Descent Methods,2021.0,Journal of Physics: Conference Series,Yan Sun,"As a branch of artificial intelligence, machine learning has received a lot of attention and has become a popular research field. It connects the huge database obtained from daily life with data prediction, which is of great help in decision making. In this project, we apply linear regression models and use different gradient descent methods, including general, mini-batch, and stochastic gradient descent, to optimize the error. We used convergence rate and accuracy as evaluation criteria, which will be explained via run time of convergence and mean square error respectively. For the regression models, both single-feature and multi-feature hypothesis models are investigated. In consideration of the risks of multicollinearity and overfitting, only simple linear model and its exponential transformation are adopted. The result indicates that mini-batch gradient descent has the biggest comprehensive advantage.",4.0,https://doi.org/10.1088/1742-6596/1827/1/012186402e3bd246280ac6374c560983f43dfab5c25266590,10.3846/ijspm.2018.441,Housing market dynamics under a pegged exchange rate – a study of Hong Kong,2018.0,,Ka Yu; E. Hui,"This study explores the major determinants of prices and rents of properties in the mass housing market and in the luxury housing market of Hong Kong. The findings show that property price (and rental) dynamics are primarily driven by demand factors, rather than by housing supply. While macroeconomic factors and the provision of subsidized homeownership, to varying degrees, influence housing prices and/or rents, it is the result of U.S. monetary policy which has directly (through changes in money supply) and indirectly (through the wealth effect from a bullish stock market fuelled by unconventional monetary policy such as Quantitative Easing (QE) triggered the current affordability issue. Some policy implications with reference to recent U.S. monetary policy developments as well as to the Linked Exchange Rate System between Hong Kong Dollar and U.S. Dollar are then discussed.",4.0,https://journals.vgtu.lt/index.php/IJSPM/article/download/441/307403dd995ae11e39ea4db6410add6bcb07b9bba47f2f,10.1108/ijhma-07-2023-0093,Drivers of Nigeria’s real house prices: long-run analysis and short-run dynamics,2023.0,International Journal of Housing Markets and Analysis,Paul Chinedu Okey,"404Purpose405The purpose of this paper is to assess the long-run and short-run drivers of real house prices in Nigeria from 1991Q1 to 2020Q4.406407408Design/methodology/approach409Vector autoregression and cointegration tests were used to assess the key drivers of Nigeria’s real house prices in the long run and short run.410411412Findings413The empirical findings revealed that household disposable income is the most important determinant of house prices in Nigeria. House prices increased by 1.6% and 60.8% in response to a 1% increase in disposable income in the long run and short run, respectively, while real mortgage credits pushed up house prices by 5% and have no long-run effects, suggesting that most Nigerians depend on their money income rather than credits in securing a home. In addition, prices of oil sector products and real interest rates had negative and significant relationship with house prices, while positive correlations were found for real effective exchange rate and real housing investments regardless of the time horizon. The impact of construction costs and cement prices was also documented.414415416Originality/value417This is likely a pioneering study of its kind to focus on the determinants",4.0,418e64928167fcce5d0aac73603897de5e010ca6dcd,10.21837/pm.v17i9.603,SUPPLY OVERHANG OF AFFORDABLE HOMES: A SURVIVAL ANALYSIS ON HOUSING LOANS APPLICATION,2019.0,PLANNING MALAYSIA JOURNAL,Rosylin Mohd. Yusof; S. Aliyu; Shazida Jan Mohd Khan; Nurul Huda Abdul Majid,"This study aims to investigate the issue of supply overhang of affordable homes and financial exclusion in the Malaysian housing market. It seeks to decipher the issue of unaffordability of homeownership from the financing perspective as offered by the financial institutions in Malaysia. By employing survival analysis via Kaplan-Meier survival estimates for the period covering Q1:2009 to Q4:2014, our findings suggest that higher inflation rate and lower house price volatility may reduce the likelihood for home loans exclusion and thus allow banks to allocate higher loan disbursements. Meanwhile, higher interest rate excludesclients from having access to finance where the compounding effects of interest shows increased hazards of exclusion among the home loans clients. Understanding the determinants of financial exclusion is, therefore, a pertinent issue to be addressed for potential house buyers to be aware of in order to increase the probability of access to financial services. To the financial institutions, identifying the determinants of financial inclusion will assist them to design products that are more marketable. For the policymakers, the findings of the study will shed som",4.0,419434224c6078f7bea7935f5159436b55a9c6916ba,10.22320/07183607.2023.26.48.05,Investification en el mercado de la vivienda: estudio exploratorio en Santiago de Chile,2023.0,Revista Urbano,José Francisco Vergara-Perucich; Carlos Aguirre-Núñez; Carlos Marmolejo-Duarte,"This study uses a quantitative methodological approach with geographically weighted regressions to address the phenomenon of investification in the Greater Santiago rental market. Using the collection and analysis of transaction data and urban indicators, the location patterns of residential investments are explored for specific urban functions at the census tract level. The analysis reveals that proximity to higher education institutions, stability in rental prices, and the presence of older adults are determining factors in the selection of investment properties. These findings evidence the transformation of the urban landscape as rental spaces for capital and highlight the growing socio-spatial segmentation, thus underlining the need for regulations that address the implications of investification in the urban dynamics of Greater Santiago.",4.0,https://doi.org/10.22320/07183607.2023.26.48.05420b6ac12925a426e22a37df67ba8959db6586468d6,10.1109/icac3n56670.2022.10074128,House Price Prediction using regression techniques,2022.0,"2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)",R. Dwivedi; Rishabh Gupta; P. Pal,"Real estate companies keep special care when they purchase or sell a new house. A significant amount of expertise and market awareness is required for making accurate price predictions of houses so as to turn it out into a profitable investment. Also, the setting of prices manually is quite difficult and tedious task and the accuracy of prediction done by the real estate experts is also not good in that case due to the probability of human errors. The primary goal of this project is to provide the best and most profitable house price predictions to real estate investors with the help of a machine learning model so that they can get best returns in their deals as per the market scenario. It can also provide a lot of convenience to buyers who want to purchase a house at best market prices depending upon the desired house features. It consists of a ML-based model which mainly involves the use of three machine learning algorithms namely linear regression, decision tree regression and random forest regression. Three individual models are created on the basis of each of these algorithms and the most suitable model with maximum accuracy is chosen. With the analysis of various features and",4.0,421cdb92e2c78cfeb4d2505dc1e427f50fa693febdc,10.1080/23754931.2022.2100225,"On the Geographic Balance of Risk and Amenity: Changes in Evacuation Zones and Their Ripples in Housing Prices in Pinellas County, FL",2022.0,Papers in applied geography,John D. Morgan; Thomas Douthat; Li-Hsiang Lin,"Abstract This study considers the balance of amenities and coastal hazards on housing prices between 2000 and 2015. We test whether risk communication via hurricane evacuation zone (a proxy for storm-surge risk and coastal flooding) affects sales price while controlling for relevant property characteristics, beach frontage, flood zones, and proximity to the coast. Additionally, we employ a difference-in-difference technique to test whether two highly publicized updates to evacuation zone maps, which changed the status of many homes in the county, led to price penalties for homes. We focus our study on areas of the new evacuation zone maps that reclassified parcels into zones associated with higher coastal surge exposure. This approach allows us to isolate the effects of risk communication about a property independent of changes to its insurance premiums or actual hazard exposure. We find that amenities are associated with significant bonuses and hazard variables correlated with price penalties, and homes in areas reclassified “up” in hazard zone classification have lost value. Our findings shed light on how information changes about hazards may affect coastal real estate markets as",4.0,42280072b7e497b0dfb79d97b1bb6564d1b30f85c31,10.1108/ijhma-05-2024-0073,Exploring the determinants of housing price bubbles in the Turkish economy: a COVID-related framework,2024.0,International Journal of Housing Markets and Analysis,Ö. Doruk,"Purpose423This study aims to explore a novel framework for housing price bubbles in the Turkish economy during the pandemic. It examines the probability of housing bubble formation relative to the pre-pandemic period and identifies possible determinants of housing bubbles in the Turkish economy.424425Design/methodology/approach426In this study, a two-stage novel estimation method is applied. In the first stage, bubble periods are identified through the right-tailed supremum augmented Dickey–Fuller test. In the second stage, the determinants of these bubbles are identified, and the housing bubble determinants during the COVID-19 pandemic are compared to the pre-pandemic period.427428Findings429The findings indicate that there is an asset price bubble in the housing market during the pandemic period. Furthermore, mortgage credit expansion, mortgage credit rates and the depreciation of the Turkish Lira against the USD could increase housing bubble formation. However, housing sector sales to foreign investors do not contribute to housing bubble formation during the pandemic in the Turkish housing market.430431Originality/value432To the best of the author’s knowledge, this is the first study to address the",4.0,4333dc8f771b5c7fa83b9032ce05114aed39787c49b,10.5194/agile-giss-5-41-2024,Artificial intelligence for greater transparency in housing price estimation,2024.0,AGILE Conf.,Christian Mueller-Kett,"Abstract. This paper investigates the use of machine learning (ML) models to predict housing prices. A well-performing housing price model was trained, which can seamlessly be integrated into public sector processes to increase market transparency and is based on modern ML and feature engineering methods. For these models, particular consideration was given to the spatial component. The research uses the Design Science Research approach, with a case study carried out in the city of Duisburg, Germany. The ML models developed showed better performance than traditional models. The models were embedded in official processes using Shapley values to increase interpretability. The study concludes that ML models can contribute to increased market transparency in the real estate sector.434",4.0,https://doi.org/10.5194/agile-giss-5-41-2024435cf4374358fd47d0cdb0b2a53175b118a36f5e5ee,10.1109/bigdata52589.2021.9671549,A text-based mining approach for real estate policy impact monitoring and analysis,2021.0,2021 IEEE International Conference on Big Data (Big Data),Lei Cao; Peng Xu; Wei Shang,"Text mining has been widely used in economic analysis. Besides extracting individual opinion from a single text, it is more important to accurately aggregate group opinions from mass text files. This research proposed a text mining framework for constructing policy impact indicators to monitoring the public opinions on real estate market policies. A real estate domain dictionary has been developed based on the released policy announcements in Chinese real estate government websites. And corresponding public opinion index are constructed to evaluate the impact of real estate policies on the target market. In addition, we have improved the handling of emotional words in the public opinion dictionary. Word2Vec and So-PMI models are used to identify the polarity of trend words, and the score of each sentiment words is calculated based on its co-occurrences with the trends words using a modified label propagation algorithm. Beijing real estate market is taken as the test bed. Empirical study show that the policy public opinion index explains a significant part of Beijing housing price changes. In-sample and out-of-sample forecasts of Beijing housing prices indicate that the policy publi",4.0,43653dddac034d346f321d6ef13aa23563864f24884,10.1080/07352166.2023.2206034,Limitations in the diffusion of collaborative housing in Poland: Expectations and beliefs of young generation as potential users,2023.0,Journal of Urban Affairs,P. Lis; Zuzanna Rataj; Katarzyna Suszyńska,"ABSTRACT There is a strong attachment to individual homeownership in Poland. Young people, just after graduating from college, make housing decisions, most often aimed at purchasing a dwelling and taking out a long-term mortgage. With soaring housing prices in Poland between 2016 and 2021, this is increasingly difficult. Therefore, new integrative, collaborative, and social innovative forms of residence are required, especially when considering the expectations and beliefs of young adults. The authors use Rogers’ five attributes of innovation to examine the constraints on the diffusion of collaborative housing in Poland. A web-based questionnaire survey was conducted among 283 students drawn at the Poznan University of Economics and Finance in Poland. Using the binary logistic regression method, the authors identified a strong relationship between the belief in sharing instead of buying and the respondents’ preference for collaborative housing. Furthermore, lack of application of pro-environmental solutions in collaborative housing, a prolonged process of designing and building such projects, and a strong attachment of young people to individual ownership constitute serious limitat",4.0,437448f5e30fdf474e54429b29b6e466353362aca97,10.1061/40941(247)166,Consumer Concerns and Preference-Tradeoffs in Home Plumbing Systems,2008.0,,"Juneseok Lee; G. Loganathan; D. Bosch; S. Dwyer; Ewa J. Kleczyk, PhD","In the USA, about 90% of drinking water home plumbing systems use copper pipes. Pinhole leaks in copper plumbing pipes have become a concern nationwide. For majority of homeowners, their home is the most valuable asset. The possibility of losing home insurance, falling home value and health concerns have caused considerable anxiety among the affected homeowners. They are seeking a tool that would enable them to decide on whether to continue to repair or replace their plumbing system. They are also looking for advice on what factors are to be considered in a replacement decision. Plastic pipes such as PEX (Cross-linked polyethylene) and CPVC (Chlorinated Polyvinyl Chloride) are used along with the copper. Stainless steel is being considered but does not have a significant market share. In a focus group study, we elicited consumer preferences towards a variety of attributes associated with a plumbing system. We formalized the attribute ranking within the frame work of the Analytic Hierarchy Process (AHP). Multiple attributes including price, corrosion resistance, fire retardance, health effects, longevity, re-sale value of home, and taste and odor are considered. Participants’ overal",4.0,43895554a5fa0d2b12a985ae104681638fec9341a6f,10.2139/ssrn.3726129,House Prices and Rents,2020.0,Social Science Research Network,E. Fama; K. French,"Monthly cross-section regressions show that price-to-rent ratios and lagged growth rates of house prices for eleven U.S. metro areas predict variation in the cross-section of rent growth rates. Details of the interplay between house prices and rents pose open questions. What generates the 1998-2012 boom and bust in house prices relative to rents and the subsequent 2012-2020 surge in prices and rents? Why are similar wide swings in house prices relative to rents absent for at least 45 years prior to 1998? General patterns in house prices and rents are shared by many metropolitan areas, but there are large differences of degree, and some areas pass entirely on the boom and bust in 1998-2012 house prices. What explains this diversity?",4.0,https://doi.org/10.2139/ssrn.3726129439ed3176c03ec02efa860cb5f2eadee69bec891c2b,10.1108/ohi-04-2019-b0006,Determinants of Residential Location Choice in Istanbul: A Longitudinal Study,2019.0,Open House International,Tayfun Salihoğlu; H. Türkoğlu,"Residential mobility is a dynamic urban process resulting from the ever changing internal and external factors of households' residential preferences. Factors effecting these preferences are related to households'perception of spatial issues such as the characteristics of a house, accessibility to urban functions and transportation, land values and house prices, neighborhood characteristics, residential satisfaction, and attachment. The effects of these issues vary through the life cycle as well as social, economic, and employment status of a household. As a result of the legal arrangements and state incentives that have helped to develop the construction industry, current housing patterns have shifted in Istanbul since the 2000s'. This study aims to investigate the changing trends in the factors that affected the residential location preferences of Istanbulites from 2006 to 2014. Data was collected through a stratified sampling method from two different surveys in 2006 and 2014. The paper explains the change in the destination choice of residents in the city at the district level and furthermore, changes in the households' intention to move to one of the three different housing pa",4.0,440547c78c869f3a6bb62207757ed7f55663c849350,10.13060/23362839.2024.11.1.560,The Impact of Ukrainian War Refugees on Rental Prices in Europe: A Panel Data Analysis,2024.0,Critical Housing Analysis,Adam Czerniak,"This study examines the impact of Ukrainian war refugees on actual rental prices in 27 European countries. Using panel data regression analysis for the period 2017Q1-2023Q1, the study found that inflation, house price growth, and interest rates were the primary drivers of rental price growth after February 2022. The study also showed that an inflow of Ukrainian refugees equal to 1% of the host country’s population translated into an increase in rental price growth of 0.2-0.3%, but this effect was not statistically significant at the 5% probability threshold. Auxiliary estimations revealed the statistical significance of Ukrainian migration when adjusting for the stringency of rent controls and size of the rental market.",4.0,https://doi.org/10.13060/23362839.2024.11.1.5604419782bfdeafb924bbcc5c4e59a87902e47ed8ad6d,10.2991/msie-15.2015.64,Panel Data of China's Real Estate Prices and Real Estate Vacancy — Based on Data Analysis of 35 Large and Medium Size Cities,2015.0,,Luyun Zhang; Meng Yuan; Hanwei Li,"According to the National Bureau of Statistics data show that China's real estate market, there are high vacant space and high housing prices coexistence phenomena. I believe that the reasons for the high vacancy space resulting mainly from the real estate market supply and demand characteristics, quasi-rational expectation of the market and the failure of the market. In addition, the market non-effective supply and demand is a key factor that led to high housing prices and high vacancy space coexistence. On this basis, the use of 2004 ~ 2014 of 35 large and medium size cities real estate prices and real estate vacant space analysis, the results indicate the presence of a significant correlation between the two.",4.0,https://www.atlantis-press.com/article/25842772.pdf442d59220f5045d357b05dc9302abcf7bbf97dbbd5b,10.3934/qfe.2018.4.904,Out-of-sample forecasting of housing bubble tipping points,2018.0,,Diego Ardila; D. Sanadgol; D. Sornette,"This paper analyzes the information content of statistical tests for bubble detection in the context of international real estate markets. We derive binary indicators from the causal application of five statistical tests to log house prices, and via logit regressions we assess the indicators’ out-of-sample performance in the forecasting of tipping points of housing bubbles and systemic financial crises. In our assessment, three of the indicators - two based on the identification of super-exponential trends and one based on the scaled ratio of the sum of squared forecast errors - exhibit significant out-of-sample results. Combining the indicators via simple threshold-rules yields the most robust and best results.",4.0,http://www.aimspress.com/article/doi/10.3934/QFE.2018.4.9044434a6c4412ae803b09046980401041b08141e48e51,10.2139/ssrn.3461820,'No Drugs in My Back Yard:' the Ambivalent Reception of Cannabis Retailers,2019.0,Social Science Research Network,L. M. Bruijn; R. Ribas,"Can individuals' aversion to drug markets curb the benefits of decriminalization? We investigate the effect of two policies on housing demand in the Netherlands: the distance-to-school criterion, which closed some cannabis shops in a few cities; and the zero-tolerance policy, which banned shops within municipal jurisdictions. While a small increase in the distance to retailers raised house prices by 1-5%, a substantial increase reduced them by 1-6%. Both policies reduced property crime, but the zero-tolerance was also related to fewer jobs. Our findings reveal that cities benefit from having cannabis shops, but households' aversion to related nuisances depreciates surrounding areas.",4.0,https://pure.rug.nl/ws/files/219695415/1_s2.0_S0167268122001603_main.pdf444d00e8356223d4a1c9b93b5f3bd286adb9a807e1b,10.1080/00343404.2024.2325613,Herding and reverse herding in US housing markets: new evidence from a metropolitan-level analysis,2024.0,Regional studies,M. Pollock; Masaki Mori; Yi Wu,"ABSTRACT This study is the first to examine herding and reverse herding in US metropolitan housing markets based on Zillow ZIP-level house price indices. Reverse herding is found to be more prevalent than herding, which differs markedly from equity markets and outcomes derived from less granular house price indices. The results suggest that the interaction between price appreciation and overconfidence may drive reverse herding. Also, herding and reverse herding show strong dependency on market conditions. Wide spatial and temporal variation in herding and reverse herding suggests the importance of local characteristics as determinants of the rationality of market responses.",4.0,https://www.tandfonline.com/doi/pdf/10.1080/00343404.2024.2325613?download=true445c3b38d5a05145cea9c21b0ec762415d96341430c,10.1109/icemis56295.2022.9914345,Applying C atboost Regression Model for Prediction of House Prices,2022.0,2022 International Conference on Engineering & MIS (ICEMIS),Rafea. M. Almejrb; Omar M. Sallabi; Abdelhafid Ali Mohamed,"Every year, the cost of houses rises, demanding the development of a method to forecast home prices in the future. This paper aims to apply the Catboost regression model by changing the iteration and learning rate. A feature and house value dataset for King County, Washington, is used. During the pre-processing of the data, extreme values are winsorized, and features with a high degree of correlation are removed. There are twelve possible models, including Catboost, RandomForestRegressor, and KNeighborsRegressor. Several metrics are used to assess them, such as Mean Squared Error (MSE), R-squared score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) (MSE). The model with a low RMSE of 0.013166 performs well compared to other research, especially in the test set where R2 is 0.915256. In this study, Catboost is the model that performs the best overall and can be used to estimate home prices. The most significant factors affecting property prices are location, living area, and house condition. It is confirmed that the conclusions in this research are consistent with real-world experience after comparing and contrasting with other works.",4.0,446c761750a9fa77dab0a7a706faaf68325f3907fb5,10.1109/iicaiet51634.2021.9573830,Using Machine Learning to Forecast Residential Property Prices in Overcoming the Property Overhang Issue,2021.0,International Conference on Artificial Intelligence in Engineering and Technology,Wan-Yee Lim; N. A. A. Bakar; Noor Hafizah Hassan; N. M. M. Zainuddin; R. Yusoff; N. Rahim,"Overhang property issue has sustained over the past ten years in Malaysia. Major overhang property issue was contributed from the unsold residential property. Though the government announced to build a data system and provide the housing data to prevent a mismatch of supply-demand in the property market, there are still not many relevant studies or research on predicting residential property prices. Hence, it is essential to understand the factors that influence the price of residential properties. The study aims to predict the price of a residential property by using a machine learning algorithm. Three algorithms were selected, namely Decision Tree, Linear Regression, and Random Forest, tested against the training and testing datasets obtained from the Malaysian Valuation and Property Services Department. Results show that the Random Forest model produced high accuracy with lower r_squared (R2), RMSE, and MAE values. Significantly, the study has contributed a new insight into essential property features that primarily influence the property price, which will be useful for property developers and buyers who wish to invest in the property market.",4.0,4477e43914d96cf593ea92c851c78640c4208f1f3e5,10.3233/jifs-210917,Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model,2021.0,Journal of Intelligent & Fuzzy Systems,Chao‐nan Liu; Jie Lu; Weijin Fu; Zhuoyi Zhou,"How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.",4.0,448d31b3a6427797447404aa11ae63a0bc01252b12f,10.35784/bud-arch.815,Property price dependence from noise level on example of local real estate market,2020.0,,E. Debinska; Joanna Pałubska,"The research issues discussed concern the impact of the environmental factor on the level of real estate transaction prices. In the face of the dynamically developing road infrastructure but also the increase in the volume of car traffic, it is necessary to take into account the effects of these changes in the area of property appraisal. In the group of price-generating attributes should be taken into account the noise level, because in many cases it may prove to be the most important factor from the perspective of real estate buyers. The research was carried out on the example of housing properties located at the main communication artery of Krakow.",4.0,https://ph.pollub.pl/index.php/bia/article/download/815/1225449a27d4329b03c114711979e68c74dad7eb3ddd84d,10.1109/gcrait55928.2022.00029,Analysis and Forecast of Influencing Factors on House Prices Based on Machine Learning,2022.0,"2022 Global Conference on Robotics, Artificial Intelligence and Information Technology (GCRAIT)",Chen-Yue Lv; Yingxin Liu; Li-Dong Wang,"Taking Shenzhen as an example, based on the relevant quantitative data affecting commercial house prices in Shenzhen from 2001 to 2020, the main factors affecting commercial house prices in Shenzhen were screened out by using Lasso regression, and grey prediction model was constructed by using the data passing the grade ratio test to predict the data of each variable in the next 5 years. Afterwards, a Support Vector Regression model was constructed to predict the trend of commercial house prices of Shenzhen based on the values of each variable from 2001 to 2025. The results of the study show that the main influencing factors of Shenzhen commercial house prices are the sales area of residential commercial housing, the purchase and sale prices of second-hand houses, etc. The growth of commercial house prices in Shenzhen from 2021 to 2025 is greater and faster compared to previous years. The analysis of the influencing factors and trends of Shenzhen's commercial house prices can provide reasonable policy suggestions for the development of the real estate industry and construction of long-term regulation of real estate in the future.",4.0,4503f744b730d1c0192ec0efac670316e0ba3df594b,10.1080/09644008.2020.1764541,Similar Origins – Divergent Paths: The Politics of German and Dutch Housing Markets,2020.0,German Politics,A. Cooper; Paulette Kurzer,"This article is a comparative study of Dutch and German housing markets. Until the early 1990s, the two housing markets shared many commonalities, both in terms of their specific characteristics and the larger political, economic, and institutional contexts in which they were embedded. Since the 1990s, in contrast, the two housing markets have diverged significantly; with home ownership, mortgage debt and housing prices much higher in the Netherlands than Germany. We explain this divergence, first, by pointing out the more extensive turn to neoliberalism in housing policy in the Netherlands than in Germany. Second, we look at the differences in the treatment of private renting versus not-for-profit social housing by governments in each country. Third, we draw attention to mortgage liberalisation after 1993 in the Netherlands, which did not happen in Germany, and the treatment of fiscal subsidies for home ownership. This paper contributes to the debate on varieties of residential capitalism and stresses that housing patterns diverge even among countries with the same welfare state typologies and similar rental markets.",4.0,451c9de61ed0a6396e560164c7e6fa0463c73ca3810,10.1111/cwe.12515,Does City Shape Affect China's Economic Development?,2024.0,China & World Economy,Wei Zou; Fei Yang,"This paper constructs a general equilibrium spatial urban model and measures city geometric compactness using the patch‐shape index based on evidence from satellite imagery and basic vector maps of China. It adopts the ordinary least squares and instrumental variable approaches to examine the effect of city shape on the urban development of 279 Chinese cities at or above the prefecture level. The empirical results show that there was a significant negative correlation between city shape and economic outcomes. Specifically, every 1 percentage point increase in the patch‐shape index led to a decrease in city‐scale GDP by 0.009 percent, housing prices by 0.044 percent, and wages by 0.024 percent. More compact urban layouts attracted an inflow of households and firms, stimulated city economic growth, and were associated with increased housing prices and wage rates. The paper considers the cities' initial conditions, trends in population changes (expanding, shrinking, and stagnant cities), and geographic factors, and finds that the results are robust. An array of policy implications can be drawn from the research.",4.0,4527e39fcaa743e8b6d3da8c7ef98fe778b3ca16e02,10.1080/13504851.2019.1677844,The Spanish housing market: is it fundamentally broken?,2019.0,Applied Economics Letters,J. C. Cuestas; M. Kukk,"ABSTRACT This paper aims to investigate the relationship between housing prices and their main fundamental determinants using the example of Spain and considering the possibility of structural breaks in the relationship. We find that the cointegrating coefficient estimates are quite unstable over 2001Q1-2017Q4 and need to be estimated for different subperiods. Specifically we find that the main long-run fundamentals explain the behaviour of equilibrium house prices well during the boom-bust period. However, only corporate profit, or capital income, seems to explain the evolution after the recovery from the recession.",4.0,http://hdl.handle.net/10234/18496545352124f73e34425247b554e78e84d9caa3c2abc0d,10.1109/ijcnn48605.2020.9207685,Predicting Gentrification in Mexico City using Neural Networks,2020.0,IEEE International Joint Conference on Neural Network,Leon F Palafox; Pedro Ortiz-Monasterio,"Gentrification is a process that affects millions of people every year. In this process, high-income residents replace low-income residents in neighborhoods near city centers and business centers. The gentrification process drives low-income people to find unfamiliar places to live, which in and of itself brings multiple social problems about housing, transportation, and schooling.Many studies in social geography have looked at the onset of gentrification in a neighborhood. Yet, studying gentrification in a single neighborhood is a slow process that requires expertise about the different elements that can cause it, like housing prices, businesses in the neighborhood, and other social elements. Each of these elements is idiosyncratic to each country and area.In this work, we mix the predictive power of Neural Networks with an Interpretability Method called LIME, which helps understand which factors are driving the classification given the data and a trained classifier. With this, we expect to have an overall model that gains a deeper understanding of which effects drive gentrification in different settings.",4.0,454dde22a8458111f4d97568d09ce7efc13ea6e96aa,10.18757/ejtir.2020.20.2.3979,Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw,2020.0,European Journal of Transport and Infrastructure Research,Marcin Torzewski,"This paper identifies the relation between metro proximity and the prices of residential properties in Warsaw. The analysis is based on 2006-2013 AMRON-SARFiN data. The results show that metro has a positive impact on property prices if it is located at most 1.5 km from the property. The greatest increase in price of 8-13 per cent is observed when metro is located 400-800 meters from the property. The results indicate that when the metro transportation is poor, it causes a large increase in property prices, which is not comparable to the impact of other transports, such as bus or tram.",4.0,https://journals.open.tudelft.nl/ejtir/article/download/3979/4655455d32ea091b22de2b7a4f847844f79b4168d9bda0a,10.1111/1540-6229.12533,"Capitalization of state tax rates in housing values at state borders, 2000–2017",2025.0,Real Estate Economics,Yulong Chen; Liyuan Ma; P. Orazem,"We derive a model that demonstrates the interrelationship between housing prices, tax rates, government services, and naturally occurring amenities between adjacent markets. The model is tested against county‐level data at state borders using US Census housing price data from 2000 to 2017. The data show that property taxes will lower housing prices at state borders by an average of $197 per mill rate or $3326 in 2017 prices. The capitalization rate of property taxes at state borders is 14.4%. The largest tax‐induced gap in average housing prices is at the New York–Connecticut border.",4.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1540-6229.1253345692c4c49804f820fc1278bbde78d7f15ce6b08673,10.1111/j.1435-5597.1986.tb00981.x,EXPECTATIONS OF HOUSE-PRICE CHANGES,2005.0,,R. Muth,"Expectations of the future are of great importance for many economic theories. While expected values of economic variables are typically unobservable, expectations of house-price changes can be inferred from data on the space-rental value of dwellings and its other determinants. This paper first discusses the behavior of expected changes in house prices so estimated. The formation of these expectations is then examined. The hypothesis of adaptive expectations is found to explain the data quite well. The rational expectations hypothesis, however, does very poorly.",4.0,https://doi.org/10.1111/j.1435-5597.1986.tb00981.x45761e8346502445117387a4b042ee39f48cc513933,10.1145/3512850.3512860,A hybrid model for predicting Mobile Price Range using machine learning techniques,2022.0,International Conferences on Computing and Data Engineering,Ahsanul Hoque Sakib; A. K. Shakir; Shanjoy Sutradhar; M. Saleh; Washim Akram; Khalid Been Md. Badruzzaman Biplop,"Machine learning based classification techniques helps to solve the problem related to decision making. In many areas of price prediction are used like housing price prediction, stock price prediction different classification algorithm used. Some of them are used artificial neural network. In this study, different classification techniques used for prediction the mobile price range. The first one is Decision Tree second one is Random forest machine learning algorithm. The accuracy got by first two techniques respectively 83% and 84%. So, for improving the accuracy of Decision Tree, the parameter has been pruned and later Random Forest has been used. It gives 90% accuracy for this dataset. Here, also a hybrid ensemble method is implemented using combination of five different heterogeneous weak learners because it has a record of manifesting performance in machine learning techniques and was not used before on this dataset. And also, performance evaluation is performed for Decision Tree, Random forest and also for hybrid model like Precision, Recall.",4.0,458bc742e135a391a0b1a116483f4afe71e55b67192,10.1109/icosec58147.2023.10275956,Investigating the Effectiveness of Machine Learning Techniques for Real Estate Price Prediction,2023.0,2023 4th International Conference on Smart Electronics and Communication (ICOSEC),Baljap Singh; Jaspreet Singh; Anubhav Kumar; Shikha Gupta,"In the real-estate industry, the capacity to extract significant information from raw data makes it extremely valuable for predicting home values, housing attributes, and much more. The cost of housing varies from day to day and is frequently increased arbitrarily rather than using calculations. The housing market and homeowners are routinely impacted by changes in property values. A literature study is done to evaluate the most important factors and reliable models for predicting housing values. The results of this analysis confirmed the use of Support Vector Regression, Linear Regression, and Artificial Neural Networks as the most effective models when compared to others. Furthermore, our data imply that geographical and real estate agents are important determinants in forecasting property values. This research will be very helpful in determining the most important variables for estimating housing costs and the best machine learning model to employ when doing this type of research, especially for academics and housing developers.",4.0,4595ed921cd8504e49b6ac9338c3916dd69e04587a8,10.1002/jcop.22589,Using geographic information systems to assess community-level vulnerability to housing insecurity in rural areas.,2021.0,Journal of Community Psychology,Kristen D Gleason; Matthew P. Dube; Elizabeth Bernier; Jennifer Martin,"Research examining homelessness in rural areas has been sparse. The current study aims to expand conceptions of rural homelessness by mapping community-level risk factors related to housing insecurity. Geographic information systems (GIS) techniques were used to map the distribution of select community-level risk indicators in the State of Maine. Three methodological choices related to this process are demonstrated: (1) selection and distribution of housing insecurity risk indicators; (2) use of location quotients; and (3) use of spatial lags. After examining and mapping selected risk factors against the location of homeless service supports, four areas in Maine were identified as communities of concern for housing insecurity. Better understanding the extent and location of areas of high need that are resource poor can help service and funding agencies to plan for the more efficient and effective distribution of homeless prevention and mitigation services. Implications for research in rural areas are discussed.",4.0,460641dfca1c027153bda3546bb7e3dae4f79037650,10.1109/ietacs68750.2025.11385743,An Optimized Feature Based House Price Prediction Model Using Machine Learning,2025.0,2025 International Conference on Innovations and Emerging Technologies In AI & Communication Systems (IETACS),Deepak Rawat; Ankita Aggarwal; Saurabh; Geetanjali Sharma,"Household accord is directly impacted with price which further has constraints on choices. This paper focuses on the house price prediction considering all the attributes which impact the property price. Machine learning along with approaches plays crucial role in predictions and leads to the decision to hold the house or not. Traditional algorithms predict the house price and have the variations in performance and show the scope of improvement. Identification of prominent features and optimization improves the accuracy upto 90.8% in the training phase and the accuracy to 98.8% in the testing phase. Predictions of house price helps the buyers to decide the locations, area, faces and other important attributes about the property before buying. Optimization techniques are employed to build a robust structure of the model. Robustness of the model implies how well the model performance in the best and worst situation. The real estate analysis is really very important aspect for the house price prediction.",4.0,461bd496e6b12ae4d851731821ee3fdf3218880e6ef,10.1080/10511482.2020.1821747,"Effects of Proximity to Multifamily Housing on Property Values in Little Rock, Arkansas, 2000–2016",2020.0,Housing Policy Debate,Michael J. Craw,"ABSTRACT A common form of not-in-my-backyard (NIMBY) activism is resistance to multifamily housing. Although NIMBY activism often targets both market-rate and subsidized multifamily development, studies of the effects of multifamily housing primarily focus on subsidized rental apartments. This study addresses this gap by analyzing the effects of condominiums and market-rate apartments as well as subsidized rental housing. Taking Little Rock, Arkansas, as a case study, this research uses a difference-in-differences approach to measure the effects of five types of multifamily housing on nearby single-family home sales prices: condominiums, market-rate rental apartments, subsidized rental apartments, senior and special needs apartments, and other multifamily housing (such as dormitories). The results suggest that most forms of multifamily housing have either no effect or a positive effect on sales prices for single-family homes within 2,000 feet of a new multifamily housing development.",4.0,46208a37fab927c92aaeb4625d201f0395a866ebbb8,10.1109/icsp54964.2022.9778548,Prediction of Birth rate in China under three-child Policy based on neural network,2022.0,International Conference on the Software Process,Jingui Wu,"China’s birth rate has not increased since the introduction of the two-child policy on January 1, 2016, and has even been on a downward trend since 2018. The aging trend has already appeared in the age structure of Our country. In order to further improve the young population ratio, the three-child policy was put forward on May 31, 2021. Birth rates are influenced by a variety of factors, such as housing prices and the cost of education, and these factors are cross-correlated, making it relatively difficult to study comprehensively. But the neural network model overcomes this shortcoming better, it is mainly the law of data itself to train its own law model to predict. Therefore, this paper collects the birth rate data of China from 1980 to 2020, and uses neural network to predict it. In this paper, the experimental results show that the birth rate will not increase much in the next 2-3 years, but will increase a few points in the next few years.",4.0,4636545a91ed3d84413425e2c4bd9de5224f96850d5,10.1109/icite50838.2020.9231460,Prediction Of Housing Price Along The Urban Rail Transit Line Based On GA-BP Model And Accessibility,2020.0,International Conferences on Intelligent Transportation Engineering,Ruo-Qi Li; Jun Hu,"In order to study the relationship between the accessibility along the urban rail transit line and the change in housing price, GA-BP model, which combining the genetic algorithm(GA) and back propagation neural network(BPNN), was used to predict the housing price along the urban rail transit line. The reliability of the model was verified by taking the price of residential areas along Nanjing Metro Line 10 as an example, and the performance between GA-BP model and the BP model was compared. It was found that the average absolute error rate of GA-BP model is 6.91%, which is 6.45% lower than that of BP model, and the mean square error of prediction results is significantly reduced. It can be seen that the price prediction algorithm based on GA-BP model for those residential areas along the urban rail transit line is reliable and can fully tap the potential relationship between the accessibility of urban transport network and the housing price.",4.0,464bb74dac121e0a8c5bfd867cdf074b59bdb68d8e5,10.1109/apcit62007.2024.10673571,Predicting Real-Time House Prices: A Machine Learning Approach Using XGBoost Algorithm,2024.0,2024 Asia Pacific Conference on Innovation in Technology (APCIT),L. M. I. Leo Joseph; Edunuri Harini Reddy; M. Srinidhi; Pamu Venkata Saketh; D. Mohan; Ch.Rajendra Prasad,"The housing market involves consideration of numerous factors by prospective buyers that may change over time. As a result, the market for real estate prediction will constantly fluctuate. Machine Learning is used to analyze and predict real estate property prices. The XGBoost algorithm is used to achieve a high accuracy in forecasting the market values. The Primary Objective is to assist users in finding a suitable price according to their preferred geographical factors. The model provides accurate predictions for future real estate prices by analyzing the previous trend and market. This study sought to predict prices in Bengaluru City using the XGBoost Algorithm. The results of this study showed that the XGBoost Model achieved a prediction accuracy of 91.77%. Such an accurate valuation tool could benefit homeowners and prospective buyers when pricing properties, without requiring the help of a real estate agent.",4.0,46559490816f646a381603e0e788bfd532c39f9a5c0,10.1109/irtm54583.2022.9791508,Object Detection of Furniture and Home Goods Using Advanced Computer Vision,2022.0,2022 Interdisciplinary Research in Technology and Management (IRTM),Narayana Darapaneni; S. M; Mukul Paroha; A. Paduri; Rohit George Mathew; Namith Maroli; R. Sawant,"Object Detection Technology has been a subject to much research and development due to increasing use of images and videos as data sources and their huge number of applications. Traditional models for object detection had limitations in training and did not use transfer learning for their benefit. With the evolution of deep learning and Neural networks, newer and powerful tools have made way to achieve Object Detection in real-time, with the added advantage of transfer learning and detection of multiple instances of different classes of interest in the given image context. The proposed system is an Object Detection model based on the Single Shot Detector (SSD) algorithm trained with MobileNetV2 feature extraction that can be utilized and integrated in e-commerce, hospitality industry, security and surveillance, real estate, self-driving cars and floor inventory management.",4.0,46611ba67c8cc609b3303f3ddfbebe9be62851529d0,10.1109/icsd60021.2024.10751593,A Comprehensive review on House Prediction System,2024.0,IEEE International Conference on Solid Dielectrics,Parveen Bajaj; Lalit Kumar; Kamlesh Lohani; Sudhanshu Mehta,"Predicting the prices of the houses accurately is a very difficult task in the real estate industry. In the last few years, due to the readiness of huge datasets and progress in the machine learning techniques, researchers have attempted to the develop models that can accurately predict house prices. In this paper, we want to build a house price prediction system that utilizes a dataset of residential housing prices to train and evaluate the predictive model. There is a lot of research which is being done in this field from the past few years such as the number of bedrooms, square footage, bathroom, location, and other factors. Overall, the research in this field contributes to the development of more accurate and efficient house price prediction models which builds the substructure for the future researches in this field.",4.0,4673f5e6149b0bc4abf9c5794b4f0cf7a70d50cf235,10.1108/ijhma-04-2023-0047,The long-run impact of remittances on house prices in Kenya,2023.0,International Journal of Housing Markets and Analysis,F. Chege; Hassan F. Gholipour; Sharon Yam,"468Purpose469Given the coincidental and sustained rise in house prices and foreign capital flows in Kenya, this study aims to understand whether a long-run relationship exists between real diaspora remittances and real house prices.470471472Design/methodology/approach473This study uses data from 2004-Q1 to 2020-Q4 and applies an autoregressive distributed lag model for estimation.474475476Findings477The results indicate that a positive and significant relationship exists between real remittances and real house prices in Kenya in the long run.478479480Originality/value481To the best of the authors’ knowledge, there is no study exploring the relationship between real remittance inflows and house prices in Kenya, after controlling for other key macroeconomic determinants of house prices. This study addresses this research gap.482",4.0,483d6d6f0c7a11cf7d092f98283bec0027bc5cacfbf,10.1093/rfs/hhaf008,Effects of Credit Expansions on Stock Market Booms and Busts,2025.0,The Review of financial studies,Christopher Hansman; Harrison G. Hong; Wen-Xi Jiang; Yu-Jane Liu; Juanjuan Meng,"484 There is causal evidence that mortgage credit expansions increase house prices. Does an expansion of margin lending increase stock prices? Because unconstrained arbitrageurs are more important for pricing stocks than homes, the impact is not obvious. Tests are limited because sizable shocks to margin lending are rare. We examine a major Chinese margin-lending expansion between 2010 and 2015. Institutional holding, regression discontinuity, and event study evidence—exploiting the rollout of margin lending across stocks—shows that arbitrageurs anticipated and bought in advance of a significant causal effect of credit. We develop a model to rationalize our findings. Our estimates suggest that margin debt contributes to stock market fluctuations. (JEL G01, G11, G12, G18)",4.0,48560b98c6ec83d8ecfed4d0426b98a179793bdfb3e,10.1109/icoris60118.2023.10352211,Machine Learning-Based Classification of House Prices: A Comparative Study,2023.0,2023 5th International Conference on Cybernetics and Intelligent System (ICORIS),B. Budiman; Nur Alamsyah; T. P. Yoga; R. Alamsyah; Chairul Habibi,"This research investigates the problem of house price classification using various machine learning methods, including Random Forest, Gradient Boosting Machines (GBM), XGBoost, Logistic Regression, and K-Nearest Neighbors (KNN). The best performing method for house price classification is Random Forest, achieving an R2 value of 85.32% and an AUC of 0.93 according to the ROC evaluation. The results demonstrate Random Forest’s effectiveness in handling non-linear and complex datasets, while o ther methods such as GBM, XGBoost, Logistic Regression, and KNN also show competitive performance. This study guides house price classification modeling and supports accurate decision-making in the house market context.",4.0,486fdba4fbe71256760c773da72235a1d052f1bd319,10.1177/08912424221112074,The Value of Rural and Urban Public Infrastructure,2022.0,Economic development quarterly,David Albouy; Heejin Kim,"The authors estimate the value of public infrastructure using a panel of rural and urban counties in the United States from 1970 to 2012. Regression estimates imply public infrastructure increases employment more in urban counties, while improving property values more in rural ones; positive effects on income are similar. Spatial equilibrium modeling suggests public capital has similar quality-of-life and productivity benefits in urban and rural areas but does more to reduce costs of providing housing in urban ones. While public investments in rural and urban counties appear to pass conventional cost–benefit tests, dollar-per-dollar they are more valuable in urban counties.",4.0,487e4dbc46325b3c6498198f0dba5f49ca932002202,10.1109/icsp54964.2022.9778372,Boston house price prediction: machine learning,2022.0,International Conference on the Software Process,Songyi Bai,"The Boston housing problem has been studied by many data scientists for over 50 years. The problem is proven to be extremely profitable, and it is considered as one of the most classical machine learning problems. Using machine learning techniques, computer scientists have already reduced the error of their estimation to around 4%. To solve this problem, computer scientists have developed a number of machine learning algorithms in recent years. Some are simple, while others are more complex; some offer a rough estimate of house value, while others offer more precise estimates In this essay we will see how they approach the problem using machine learning regression methods.",4.0,48857c2b21be5f2bf241f4fdbd74557ad6ad47ea525,10.1016/j.physa.2020.124561,Pricing various types of mortgage insurances with disposal and discount costs under a mean-reverting Lévy housing price process,2020.0,,Xiaoye Gong; Ying Li; Yang-Che Wu; Wan-Shiou Yang,"Abstract This paper studies various types of mortgage insurance contract which show different series of mortgage cash flow. The valuations of such contracts are expressed by analytical pricing formulas with the disposal cost and price discount incurred in repossessing and selling collaterals. The dynamics of housing price is assumed to follow a mean-reverting Levy process. Sensitivity analysis shows that initial housing price and coverage rate, mean-reverting rate, and characteristics of individual housing price all play important roles in valuing MI contracts. Two actual costs increase the premium of MI contracts.",4.0,489882ebab87d193b9d70daabc4167fe747b61ea37d,10.2139/ssrn.3435126,Il mercato degli affitti nelle città italiane: un'analisi basata sugli annunci online (The Rental Market in Italian Cities),2019.0,Social Science Research Network,Michele Loberto,"We analyze the most recent trends in the residential rental market within the Italian provincial capitals based on a new database of rental listings. First, we assess the consistency of online listing data with the main official statistical sources. Then, we examine the characteristics of the listed dwellings, the main market dynamics and the determinants of rental prices. Finally, we show that the physical characteristics of a dwelling affect the probability of it being listed in the rental market or in the property sales market, examining the consequences for the estimation of the rate of return on housing.",4.0,4907e3392e9ba82e4cc236b5aa03be32b6f1f9e21a9,10.11108/kagis.2012.15.1.043,"A Comparative Study on the Goodness of Fit in Spatial Econometric Models Using Housing Transaction Prices of Busan, Korea",2012.0,,Kyoun-Sup Chung; Sung-Woo Kim; Yang-won Lee,,4.0,https://doi.org/10.11108/kagis.2012.15.1.043491454137efb653c063c5c59be3c8dc19a8156d1ed5,10.1007/s10018-019-00242-w,"The cost of floods in developing countries’ megacities: a hedonic price analysis of the Jakarta housing market, Indonesia",2019.0,Environmental Economics and Policy Studies,José Armando Cobián Álvarez; Budy P. Resosudarmo,,4.0,https://acde.crawford.anu.edu.au/sites/default/files/publication/crawford01_cap_anu_edu_au/2018-04/acde_2018-05.pdf492967ecac198a1c85c393e915e0e5bacf428c08a8e,10.1016/j.jhe.2025.102044,Using machine learning to estimate the heterogeneous impact of Airbnb on house prices: Evidence from Corsica,2025.0,Journal of Housing Economics,Daniel Brunstein; G. Casamatta; Sauveur Giannoni,,4.0,https://doi.org/10.1016/j.jhe.2025.10204449333bc977b4d9440a4a6bac249394d7ee9e8f460ca,10.1111/rsp3.12455,"The Spatial Variation of Moderating Effects of Density and Natural Amenities on Housing Prices in Wuhan, China",2021.0,Regional Science Policy & Practice,Hao-Cheng Huang; Jian-Yi Li,,4.0,https://doi.org/10.1111/rsp3.1245549443a2b10abcf6c2779b386ab058830b95d073f6e6,10.1080/08965803.2020.1810524,The Effects of Government-Announced Soil Liquefaction Potential on Housing Prices in Reported Areas: A Two-Stage Spatial Quantile Regression Analysis,2020.0,,Chih-Min Liang; Chun-Chang Lee; Jia-Wei Lee; Zheng Yu,"This study examines the effects of a government announcement of soil liquefaction potential on housing prices in the reported areas, and explores the rate at which these prices changed after the an...",4.0,495b3902e82ba9f679ee0c8dd3886e15951dfd7a920,10.22300/1949-8276.11.1.2,The Impact of Shuttered Golf Courses on Property Values,2019.0,The Journal of Sustainable Real Estate,Stephanie R. Yates; Lary B. Cowart,"We measure the impact of a golf course as a residential amenity on surrounding home values in several communities in Shelby County, Alabama. We compare the values of homes in golf course communitie...",4.0,4964ca0658dc87c5204d73556ad63ed7da7e9fbf476,10.22004/ag.econ.151291,The Effect of School Quality on House Prices: A Meta-Regression Analysis,2013.0,,Anita P. Yadavalli; R. Florax,,4.0,497878656516d92bfdfae8f5fc2f249bfe52ff2eaa7,10.1016/j.jnc.2025.126916,Measuring the premium effect of marine ecological restoration on housing prices: A quasi-experimental design based on real estate transaction data,2025.0,Journal for Nature Conservation,Yufei Zhou; Jing-Mei Li; Shuqin Li; Qi Chen,,4.0,498be00e04665be8385b80f457fa2196d5c436fbbb8,10.1007/978-3-030-96308-8_90,A Machine Learning Framework for House Price Estimation,2021.0,International Conference on Intelligent Systems Design and Applications,Adebayosoye Awonaike; S. Ghorashi; Rawad Hammaad,,4.0,499bc0d3b9b1319300b1c1c5d89ea22e63a1415e646,10.1007/s11769-025-1565-7,How Does Urban Public Transit Accessibility Affect Housing Prices? A Comprehensive Analysis with Geographical Detector Combined and Geographically Weighted Regression,2025.0,Chinese Geographical Science,Jingjing Tang; Hui-Ran Han; Cheng-Feng Yang; Lingyi Xu; Hui Geng; Lei Li,,4.0,500cb18deb200f1bb47549ae34dea1616e9a0e4c82a,10.1353/sgo.0.0058,"Neighborhood Characteristics and Housing Values within the San Juan, MSA, Puerto Rico",2009.0,,José R. Díaz-Garayúa,,4.0,501280bd1c740314fbd052db13357879392ed94bb7f,10.1007/s12061-021-09403-5,Do Urbanization and Energy-environment Policies Affect Housing Values? Evidence From Spatial Textual Analysis,2021.0,Applied spatial analysis and policy,Yi-Ming He; Meng Chen,,4.0,502a1fb23eca0dbd0c5d233440bf64edfc4d698e665,10.4156/jcit.vol6.issue11.37,Housing Price Forecasting Based on Least Squares Support Vector Regression with Particle Swarm Optimization,2011.0,,Rong-Peng Liu; Hong-Yang Ren,,4.0,503a826bfed66a1684bfdc2c9368d186f467204ac8d,10.2139/ssrn.2866115,Determinants of Residential Property Value in New Zealand: A Neural Network Approach,2016.0,,M. Abraham,,4.0,504984bef4ae73c6050a8ddc967c5a4d2f814493c0d,10.5762/kais.2021.22.4.228,Prediction of Housing Price Index Using Artificial Neural Network,2021.0,,Jiyoung Lee; Jaepil Ryu,,4.0,505b7758b64fa5f9fc264578ebdf47da981a061d027,10.1016/j.cities.2025.106263,Revisiting urban environmental amenities: A review of housing price impacts and policy perspectives for equitable amenity provision,2025.0,Cities,K. Hassett; Ioannis Tikoudis; Grace Alexander; Apolline Saliou; Lea Stapper; Walid Oueslati,,4.0,506aec9179686f613a5ed9bd3b9350a9a681f9f09e3,10.4018/978-1-7998-7685-4.ch005,An Improved Model for House Price/Land Price Prediction using Deep Learning,2022.0,,B. Mallikarjuna; M. S. Ram; Supriya Addanke; Munish Sabharwal,,4.0,5075e45b812313203dad3a6b4abbb5f61c96a864856,10.1016/j.neucom.2025.131103,Deep-DFVAR: Dynamic factor vector autoregression with deep learning for regional house price index forecasting,2025.0,Neurocomputing,Jisu Yeo; Artyom Stitsyuk; Jaesik Choi,,4.0,50857079ef47cdb248582dcf982885c35a17b6ba140,10.1016/j.eswa.2015.03.005,"Corrigendum to ""Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data"" [Expert Systems with Applications 42 (2015) 2928-2934]",2015.0,Expert systems with applications,Byeonghwa Park; J. Bae,,4.0,509f2591674a9143059bd66819f7171ca7385a7553e,10.1109/bcgin.2012.55,Spatial Econometric Analysis of Housing Price of Chinese Provinces,2012.0,2012 Second International Conference on Business Computing and Global Informatization,Zhao-Hui Hu,,4.0,510adf7d808dcf743b7c5f42b9115bc4dd74faf398a,10.1080/10835547.2018.12092140,Housing Depreciation Revisited: Hedonic Price Modeling Versus Assessor Estimates,2018.0,,S. Shultz,,4.0,5111ff50b8b5aed82e659e42dc8d2f25fd0e316742e,10.21273/hortsci.50.3.408,Willingness-to-pay Comparisons for Flats of Groundcover Plants in Plantable Containers: Consumers versus Commercial Buyers in Kentucky,2015.0,,D. Ingram; T. Woods; Wu-Yang Hu; S. Nambuthiri,"Increasing demand for groundcover plants and increasing consumer preference for more sustainable products encourage nursery crop producers and landscape management companies to assess efficiency and sustainable practices. Ajuga reptans ‘Bronze Beauty’ and Sedum kamtschaticum ‘Variegatum’ were grown in standard plastic containers or plantable containers (Ellepot and SoilWrap) and 12or 18-count flats. These production alternatives were presented in personal surveys of commercial industry personnel and consumers to determine their willingness to pay for these attributes. A conjoint analysis revealed an affinity for both groups to purchase flats of groundcovers and preferred sedum over ajuga. Commercial buyers from larger companies were more likely to purchase plantable containers than those from smaller firms. Generally, flats of Ellepots were preferred over flats of SoilWraps and 18-count over 12-count flats by commercial buyers. Price had a negative impact on consumer willingness to pay. Consumers revealed no specific preference for the plantable containers, although preference for plastic containers declined with age and presence of children at home. Groundcover plants are in deman",3.0,https://journals.ashs.org/downloadpdf/journals/hortsci/50/3/article-p408.pdf512e70517f3e24fefde5665284ea0011bfd980f71e4,10.24052/ijbed/v06is03/c-05,Housing finance market and economic growth of West Africa region: a study of Nigeria and Ghana,2019.0,International Journal of Business & Economic Development,T. N. Ofor; A. S.; M. Ifurueze,"The aim of this study is to investigate the effect of housing finance on economic growth of West African States while the specific objectives were to: analyse the contributions to housing finance by mortgage banks on the economic growth of Nigeria, examine the impact of aggregate housing finance on economic growth of Ghana among others. Secondary data were used, covering the period of study 1985-2014. The expost facto research design was adopted in this study. The data gathered were analysed using Ordinary Least Square Regression Method. The findings revealed that the housing finance markets in Nigeria and Ghana are under developed. The policy implication is that if urgent steps is not taken to address the housing needs of the region, more people are going to live in slumps. We recommend that the government of WA States should create an enabling environment to attract both local and foreign investors to invest in the housing sector. Corresponding author: Alagba Ochuko S Email address for corresponding author: emurovwo@gmail.com First submission received: 6th November 2018 Revised submission received: 19th December 2018 Accepted: 24th December 2018 Introduction Housing finance marke",3.0,https://ijbed.org/cdn/article_file/2019-01-09-22-24-32-PM.pdf51330752201454f60d5c7348b8cb062523b31874f57,10.2991/essaeme-16.2016.128,The Inheritance and Development of Traditional Culture in Interior Design and Three Dimensional Structure,2016.0,,Wei-Wei Li,"In the modern context, interior design has inevitably become a part of social culture. How to deal with the relationship between interior design and traditional culture, Confucianism, Taoism, Buddhism and other main ideas in traditional culture and its application in interior design are worthy of our thinking. This paper discusses how to link the traditional culture with the modern art design, in order to form an interior design style with the characteristics of traditional culture. The Relationship between Interior Design and Traditional Culture Interior design is an organic part of social culture, from the point of view of the horizontal dimension, no matter what kind of style design, have a specific mental and psychological structure of culture is in certain cultural context start and finish, and reflects the different value and aesthetic concepts, reflect the prevailing cultural outlook. From the longitudinal perspective, the design of the interior space of any era is closely related to the productivity and culture at that time. Design form comes from culture, people in accordance with the rich experience (history and tradition) of the processing of all the existing impression.",3.0,https://www.atlantis-press.com/article/25860297.pdf51476d600b0a18627b3524e440ebdae628f9a290c21,10.2991/icemct-16.2016.238,Discussion On the Inheritance and Development of Traditional Culture in Interior Design,2016.0,,Xiao-Lin Zhu,"In the modern context, interior design has inevitably become a part of social culture. How to deal with the relationship between interior design and traditional culture, Confucianism, Taoism, Buddhism and other main ideas in traditional culture and its application in interior design are worthy of our thinking. This paper discusses how to link the traditional culture with the modern art design, in order to form an interior design style with the characteristics of traditional culture. The Relationship Between Interior Design and Traditional Culture Interior design is an organic part of social culture, from the point of view of the horizontal dimension, no matter what kind of style design, have a specific mental and psychological structure of culture is in certain cultural context start and finish, and reflects the different value and aesthetic concepts, reflect the prevailing cultural outlook. From the longitudinal perspective, the design of the interior space of any era is closely related to the productivity and culture at that time. Design form comes from culture, people in accordance with the rich experience (history and tradition) of the processing of all the existing impression.",3.0,https://www.atlantis-press.com/article/25853776.pdf5156c6a0da0994e267cf022ca66bbe205346e751c3d,10.4236/jgis.2011.31003,"Adjusting Second Moment Bias in Eigenspace Using Bayesian Empirical Estimators, Dirichlet Tessellations and Worldview I Data for Predicting Culex quinquefasciatus Habitats in Trinidad",2011.0,Journal of Geographic Information System,B. Jacob; D. Chadee; R. Novak,"Temporally weighted regression models with a spatial autoregressive component may estimate nonlinearities in spatiotemporal-sampled data of Culex quinquefasciatus, a major vector of West Nile Virus (WNV) which can help implement control strategies by determining optimal predictors associated to prolific habitats. The design of this kind of mixed model can specifically incorporate spatial autocorrelation whilst including the influence of other aspatial predictor variables. Currently, the lack of an estimation theory that allows for het- eroscedasticity and corresponding joint hypothesis testing in the presence of spatial dependence in georefer- enced Cx. quinquefasciatus habitat data is a serious shortcoming in WNV research. In this paper we used spatially lagged and simultaneous autoregressive models based on multiple predictor variables of immature Cx. quinquefasciatus and Worldview 1 (WV-1) data to help implant a remote habitat-based surveillance sys- tem in Trinidad. Initially, we used Geomatica Ortho Engine® v. 10.2 for extracting a Digital Elevation Model (DEM) from the WV-1 raw imagery. Results of the DEM analyses indicated a statistically significant inverse linear relations",3.0,http://www.scirp.org/journal/PaperDownload.aspx?paperID=3702516680bfd71abd5437b0d0e7ffaf8bef2da2994126e,10.1108/ijhma-09-2024-0137,Spatial analysis of dependency culture resulting from exchange rate fluctuations on herding behavior in Iran’s housing market,2024.0,International Journal of Housing Markets and Analysis,Vahid Nikpey Pesyan; Yousef Mohammadzadeh; A. Rezazadeh; Habib Ansari Samani,"Purpose517The study aims to examine the impact of cultural dependency stemming from exchange rate fluctuations (specifically the US dollar) on herding behavior in the housing market across 31 provinces of Iran from Q2 2011 to Q1 2022, using a spatial econometrics approach. After confirming the presence of spatial effects, the Dynamic Spatial Durbin Panel Model with Generalized Common Effects (SDM-DPD(GCE)) was selected from various spatial models for these provinces.518519Design/methodology/approach520The study examines the impact of cultural dependency stemming from exchange rate fluctuations (specifically the US dollar) on herding behavior in the housing market across 31 provinces of Iran from Q2 2011 to Q1 2022, using a spatial econometrics approach. After confirming the presence of spatial effects, the Dynamic Spatial Durbin Panel Model with Generalized Common Effects (SDM-DPD(GCE)) was selected from various spatial models for these provinces.521522Findings523The model estimation results indicate that fluctuations in the free market exchange rate of the dollar significantly and positively impact the housing market in both target and neighboring regions, fostering herding behavior characteriz",3.0,524255aabfd88543b20d32a67377bc7e5e074b5b396,10.3390/su172210208,"Investigating Spatial Variation Characteristics and Influencing Factors of Urban Green View Index Based on Street View Imagery—A Case Study of Luoyang, China",2025.0,Sustainability,Jun-Hui Hu; Yang Du; Yueshan Ma; Dan-Feng Liu; Lu-Yao Chen,"As a key indicator for measuring urban green visibility, the Green View Index (GVI) reflects actual visible greenery from a human perspective, playing a vital role in assessing urban greening levels and optimizing green space layouts. Existing studies predominantly rely on single-source remote sensing image analysis or traditional statistical regression methods such as Ordinary Least Squares and Geographically Weighted Regression. These approaches struggle to capture spatial variations in human-perceived greenery at the street level and fail to identify the non-stationary effects of different drivers within localized areas. This study focuses on the Luolong District in the central urban area of Luoyang City, China. Utilizing Baidu Street View imagery and semantic segmentation technology, an automated GVI extraction model was developed to reveal its spatial differentiation characteristics. Spearman correlation analysis and Multiscale Geographically Weighted Regression were employed to identify the dominant drivers of GVI across four dimensions: landscape pattern, vegetation cover, built environment, and accessibility. Field surveys were conducted to validate the findings. The Multis",3.0,https://www.mdpi.com/2071-1050/17/22/10208/pdf?version=1763131450525ac92ff33b11f2fe3afd88d1c376e531ed8cae6c4,10.1186/s12942-026-00453-x,Unraveling nonlinear impacts of seasonal climate and built environments on exercise walking in high-density cities via a modified machine learning approach,2026.0,International Journal of Health Geographics,Bo Lu; Tian-Xiang Long; Bo Li; Yu Chen; Ling Zhu,"Background Physical inactivity is a major health risk worldwide, while walking is one of the most accessible forms of exercise that improves public health and supports sustainable urban mobility. Yet the combined and nonlinear effects of the built environments and seasonal climate on exercise walking in high-density cities remain insufficiently explored. This study aims to uncover these relationships and provide insights for health-oriented and climate-adaptive urban planning. Methods Crowdsourced walking trajectory data were analyzed for three representative high-density Chinese cities, Beijing, Wuhan, and Guangzhou, covering both summer and winter. A comprehensive variable system was established, incorporating built environments, seasonal climate, and socioeconomic factors. A geographically weighted extreme gradient boosting model was developed with Bayesian optimization and cross-validation to improve robustness. Interpretability was achieved through Shapley Additive Explanations, partial dependence plots, and clustering analysis to identify global and local drivers of walking activity. Results The geographically weighted extreme gradient boosting model outperformed traditional ",3.0,https://doi.org/10.1186/s12942-026-00453-x526f0b7c27d33c91fb380455b209282a52956d4a5e5,10.24247/ijmperdjun2020206,"Infill Development and its Attendant Consequences on Neighbourhood Property Value, Evidence from Gwarinpa, Abuja, Nigeria",2020.0,International Journal of Mechanical and Production Engineering Research and Development,N. Peter; Tjprc,"This paper examines the impact of infill developments, which involve developing vacant or under-used parcels within existing urban areas that are largely developed, on local housing prices. Infill developments are encouraged because it helps in curbing urban sprawl and because the newer and more expensive buildings, which are constructed in place of old buildings or vacant sites, increase the city's tax base. However, infill developments may impose externality effects on nearby properties and thereby affect their values. It is not uncommon for existing residents to resist new developments within their neighbourhood for reasons such as visual pollution, increased traffic noise, disruption to local traffic patterns, or loss of neighbourhood's character. Nevertheless, a new development tends to have a positive net effect, since the new building, by virtue that it is cleaner and more attractively designed, adds to the overall appeal of the neighbourhood. This, in turn, attracts more desirable inhabitants, such as higher income occupants or homeowners, to live in the area. The study made use of questionnaires for data collection. A total of 106 questionnaires were distributed, 50 to est",3.0,https://doi.org/10.24247/ijmperdjun2020206527649cf04e665cc566eaad0c923e7b76550526deb1,10.3390/buildings15101740,Deciphering Socio-Spatial Integration Governance of Community Regeneration: A Multi-Dimensional Evaluation Using GBDT and MGWR to Address Non-Linear Dynamics and Spatial Heterogeneity in Life Satisfaction and Spatial Quality,2025.0,Buildings,Hong Ni; Jia-Na Liu; Hao-Ran Li; Jin-Liu Chen; Peng-Cheng Li; Nan Li,"Urban regeneration is pivotal to sustainable development, requiring innovative strategies that align social dynamics with spatial configurations. Traditional paradigms increasingly fail to tackle systemic challenges—neighborhood alienation, social fragmentation, and resource inequality—due to their inability to integrate human-centered spatial governance. This study addresses these shortcomings with a novel multidimensional framework that merges social perception (life satisfaction) analytics with spatial quality (GIS-based) assessment. At its core, we utilize geospatial and machine learning models, deploying an ensemble of Gradient Boosted Decision Trees (GBDT), Random Forest (RF), and multiscale geographically weighted regression (MGWR) to decode nonlinear socio-spatial interactions within Suzhou’s community environmental matrix. Our findings reveal critical intersections where residential density thresholds interact with commercial accessibility patterns and transport network configurations. Notably, we highlight the scale-dependent influence of educational proximity and healthcare distribution on community satisfaction, challenging conventional planning doctrines that rely on s",3.0,https://www.mdpi.com/2075-5309/15/10/1740/pdf?version=1747753904528362f7a72c5356bc2ceec9eca24ad9b45de1130c4,10.24136/oc.2019.031,Compensation due to land use restrictions: the case of limited use area in the vicinity of Polish airports,2019.0,,I. Foryś; Michał Głuszak; Jan Konowalczuk,"Research background: The economic benefits that arise with the development of airport infrastructure are accompanied by negative externalities. Legal, technical and institutional instruments are used to mitigate or limit these effects. It involves state intervention in the use of real estate located in the vicinity of the airport, and the cost of such an intervention. On the other hand, as a result of state interventions, real estate market mechanisms are distorted. The balance on the market, prices and as a result the number of transactions is changing. 529Purpose of the article: The study evaluates adaptive efficiency, which is known as the ability of the real estate market system to adapt to the purpose of public intervention. The effectiveness of state intervention is measured as the difference between market transaction costs and costs after intervention. The former means the full coverage of all individual claims of property owners at market prices. However, after the intervention, these are costs of compensation and litigation (judicial, expert opinions, provisions for payment of damages), as well as the risk of the airport's insolvency. The state intervention system is also a",3.0,http://economic-research.pl/Journals/index.php/oc/article/download/1736/1627530cfbd2470f206d7a00bcc1fd7ecc21de62685d09c,10.32515/2664-262x.2025.11(42).1.14-26,A Data-Driven Approach for Balancing Overfitting and Underfitting in Decision Tree Models,2025.0,Central Ukrainian Scientific Bulletin. Technical Sciences,Mykola Zlobin; V. Bazylevych,"This article aims to develop a data-driven framework for balancing overfitting and underfitting in decision tree models. Overfitting occurs when a model captures noise, reducing generalization, while underfitting leads to poor predictive accuracy. The study systematically tunes the max_leaf_nodes parameter and evaluates model performance using Mean Absolute Error (MAE). The objective is finding the most optimal balance that ensures model accuracy while preventing excessive complexity.531A Decision Tree Regressor has been trained on the Ames Housing dataset, which includes 79 explanatory variables related to home prices. The dataset has been splitted into training and validation sets. The model has been evaluated by iterating over different max_leaf_nodes values, ranging from 2 to 5000, and computing the MAE for each configuration. The results show that increasing max_leaf_nodes initially improves accuracy, but beyond 400 nodes, MAE stabilizes around 242,906, indicating that further complexity does not improve performance. The paper highlights that models with too few leaf nodes underfit the data, while models with too many leaf nodes overfit, capturing spurious patterns. To mitigate ",3.0,https://dspace.kntu.kr.ua/bitstreams/ec118000-d857-4eb5-b92a-28c76ddc42ff/download532ab58fc6fc25f52c43fc3fb2b071e0577c0d83993,10.14505/tpref.v15.2(30).24,"Evaluating the Impact of Borrower Characteristics, Loan Specific Parameters, and Property Conditions on Mortgage Default Risk",2024.0,Theoretical and Practical Research in Economic Fields,Ali Mahmoud Antar,"This study empirically examines the impact of borrower, loan, and mortgage parameters on default risk in residential mortgage loans. Using 6743 individual housing loan accounts data from Housing Finance Institutions in Lebanon, we develop a comprehensive model using the multivariable binary logistic regression, best subset logistic regression, and stepwise regression analysis procedures to investigate the impact of 21 predictors and 29 sub-predictor parameters on log odds of default risk. In addition, the study conducted a model diagnosis using the Hosmer - Lemeshow Goodness of fit test, Likelihood Ratio Test, Model accuracy- Classification Table, Statistically Significant Test- ROC curve, and Pregibon Delta Beta Statistics. The study aims to assist financial institutions in managing and assessing the default risk more effectively and develop effective strategies to mitigate this risk. The empirical results suggest that the estimated probability of defaulting on a housing loan is approximately 3.8% when all predicted variables are set at their lowest value. In addition, marital status and the existence of dependence have a positive impact on default risk. The higher the number of d",3.0,https://doi.org/10.14505/tpref.v15.2(30).2453392c377f11ad9db2d80dc0c1c339abb792d7aa66b,10.1155/2021/1085549,Analysis of Urban Residents' Sports Consumption Demand Based on Intercepted Regression Model,2021.0,Journal of mathematics,Qing Liu,"At this stage, broadening the consumer market, upgrading the consumption system and gradually establishing a consumption-led development concept are key factors in promoting high-quality economic development. At the same time, China's macro economy is also experiencing another test. The rapid development of China's real estate market in recent years has attracted a large number of investors, and real estate prices have produced irrational and substantial increases. Behind the boom of the real estate market is a social system crisis driven by profiteering and the growing seriousness of real estate financial bubble. So exploring the mechanism of the influence of real estate prices on the upgrading of residents' consumption is important for the current stage of China. Therefore, it is important to investigate the mechanism of real estate price impact on consumer upgrading for the coordinated development of real estate industry and national economy. In this paper, we analyze and examine the theory on the consumption improvement by the literature survey method. We also summarize the present research on the correlation and the influence mechanism of the real estate price and the consumpt",3.0,https://doi.org/10.1155/2021/108554953431a14d5fc93d4e4c8464c7b2ee089fefba7c2fd8,10.32983/2222-0712-2022-4-133-139,Marketing Strategy of the Residential Real Estate Construction Entities in the Consumer Market,2022.0,Problemi Ekonomiki,I. Biletskyi,"The purpose of the study is to determine the marketing characteristics of the activities of entities in the field of residential real estate construction and to formulate proposals for the development of an adequate instrumentarium for the implementation of the marketing complex within the terms of the current challenges of the external environment. The analysis of the available scientific literature made it possible to obtain a conclusion about the fragmentary nature of research in the specified sector of economic relations and to attribute the aspects of the systemic implementation of the marketing complex in the activities of construction companies to the current issues. The fundamental differences of construction products from mass or series production of goods are revealed as a starting point of the research. The search for modification options for the use of classic marketing tools to influence the behavioral patterns of potential consumers in the residential real estate market in the B2C segment was carried out using the methodology of the 4P conception or marketing-mix. Decomposing spheres of influence into such elements as Product, Price, Place, Promotion made it possible ",3.0,https://doi.org/10.32983/2222-0712-2022-4-133-139535fed453a2f7b0c4c78299fe5d87b945e4b78953ad,10.1038/s41598-026-46528-4,Spatial heterogeneity and drivers of social vulnerability in chinese floodplains: a PCA-MGWR approach,2026.0,Scientific Reports,Linzhe Yang; Ying Zhang; Quanyi Zheng; Meng-Xiao Jin,"The spatial heterogeneity of social vulnerability in China’s floodplains remains underexplored, particularly regarding how rapid urbanization and unique demographic shifts-specifically the “floating population” and an aging society-reshape human-water relationships. This study quantifies flood exposure across 361 Chinese cities by integrating the 2020 Seventh National Population Census with high-resolution (250 m) floodplain maps via a pixel-based spatial aggregation approach. This methodology captures both absolute population scales and relative structural dependencies within hazard zones, effectively eliminating estimation bias from administrative-level averages. We propose a coupled Principal Component Analysis and Multiscale Geographically Weighted Regression (PCA-MGWR) framework to mitigate multicollinearity among socioeconomic variables while capturing spatially varying scale effects. The analysis identifies four distinct vulnerability dimensions: floating population (PC-FP), livelihood instability (PC-LI), low education (PC-EL), and lack of action capabilities (PC-AC). The PCA-MGWR model (R2 = 0.713) significantly outperforms traditional OLS and GWR models in explanatory pow",3.0,https://www.nature.com/articles/s41598-026-46528-4_reference.pdf5368f63ca9442e1ac52edc160fb6021bc2a188a4971,10.3390/su16031180,Assessing Tornado Impacts in the State of Kentucky with a Focus on Demographics and Roadways Using a GIS-Based Approach,2024.0,Sustainability,Mehmet Burak Kaya; Onur Alisan; A. Karaer; E. Ozguven,"Although the literature provides valuable insight into tornado vulnerability and resilience, there are still research gaps in assessing tornadoes’ impact on communities and transportation infrastructure, especially in the wake of the rapidly changing frequency and strength of tornadoes due to climate change. In this study, we first investigated the relationship between tornado exposure and demographic-, socioeconomic-, and transportation-related factors in our study area, the state of Kentucky. Tornado exposures for each U.S. census block group (CBG) were calculated by utilizing spatial analysis methods such as kernel density estimation and zonal statistics. Tornadoes between 1950 and 2022 were utilized to calculate tornado density values as a surrogate variable for tornado exposure. Since tornado density varies over space, a multiscale geographically weighted regression model was employed to consider spatial heterogeneity over the study region rather than using global regression such as ordinary least squares (OLS). The findings indicated that tornado density varied over the study area. The southwest portion of Kentucky and Jefferson County, which has low residential density, show",3.0,https://doi.org/10.3390/su16031180537e8d9638a384d58a9a72084f892a61230b2a7e022,10.3390/iocbd2023-15359,An Investigation of the Parameters Affecting Affordable Housing Location Choice,2023.0,The 1st International Online Conference on Buildings,N. Afshan; R. Sinha,": The provision of affordable housing has become a major concern for policymakers and urban planners, especially in developing nations of the world. One of the key challenges in this domain is the identification of suitable locations for the development of affordable housing. Appropriate locations of affordable housing will improve the housing satisfaction of residents by increasing the social and economic benefits for low-income residents, providing better accessibility to opportunities and services, and reducing the poverty concentration. A bidirectional relationship exists among affordable adequate housing and health, and has been recognised as an essential sustainable development component. Therefore, this research aims to identify the factors that govern the spatial location choice of affordable housing. The determinants were derived via an exploratory study of relevant literature studies encompassing different geographical contexts, socio-economic backgrounds, and technological and economic advancement levels. A total of 61 parameters were identified, each of which lies in one of the following six categories: neighbourhood characteristics, urban characteristics, social factors, ",3.0,https://www.mdpi.com/2673-4591/53/1/33/pdf?version=1700800500538d427d3752d0020389c462d7a79ada2d18579c1f8,10.3390/realestate2010002,The Impact of Non-Market Attributes on the Property Value,2025.0,Real Estate,Julia Buszta; Iwona Kik; Kamil Maciuk,"In the realm of real estate, each property owns a unique set of characteristics that distinguish it from others. While each property has its own distinctive features, the appraisal process prioritises only those qualities that meaningfully affect the value in the given market context. However, in the dynamically evolving market situation, expectations of real estate buyers can also transform. This study aims to explore how the surrounding environment and micro-location aspects affect the property value, which can deliver valuable outcomes for real estate market participants and researchers. For that purpose, the authors selected nine factors, called non-market attributes, that may affect the estimated value: air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings. Moreover, apart from non-market attributes, the authors selected six market attributes usually used for the determination of residential real estate values according to the Polish regulations in this field. The detailed analysis of factors influencing the property value has been conducted based on ",3.0,https://doi.org/10.3390/realestate20100025393d8a3fc9f49f93f4c2fa3e3d3ff926b01df4289c,10.1093/geroni/igac022,Ownership Change and Care Quality: Lessons From Minnesota’s Experience With Value-Based Purchasing,2022.0,Innovation in aging,Zachary Hass; K. Abrahamson; G. Arling,"Abstract Background and Objectives Minnesota’s implementation of a new nursing home value-based reimbursement (VBR) system in 2016 presented an opportunity to compare the response of nursing homes (NHs) to financial incentives to improve their quality and efficiency. The state substantially increased reimbursement for care-related costs and tied this rate increase to a composite quality score. Coinciding with rate increases of the new VBR system was an increase in ownership changes, with new owners being primarily for-profit entities from outside of Minnesota, including several private equity firms. Our objective was to examine NHs that underwent a change in ownership to determine their cost and quality response to the change. Research Design and Methods Our sample consists of 342 Minnesota NHs that submitted Medicaid cost reports each year from 2013 to 2019. A time differential two-way fixed-effects difference-in-difference model is used to assess changes in quality metrics by comparing measures in years prior to and years following the sale for NHs that changed ownership versus NHs with consistent ownership. Nursing home characteristics, revenue, and spending patterns are examine",3.0,https://academic.oup.com/innovateage/article-pdf/6/4/igac022/44061789/igac022.pdf5407059eb726206d6598f1ef88ae9a43a35a98c6937,10.1371/journal.pone.0292657,The relationship of single-family detached house prices with neighborhood walkability and disaster risk,2023.0,PLoS ONE,Hongjik Kim; H. Baba; C. Shimizu; Kimihiro Hino,"People’s preferences regarding their neighborhood environment can vary depending on their socioeconomic status and the cities where they live. This study aims to discern the relationship between neighborhood environment factors and single-family detached house sales by sale price and by central and noncentral cities. We analyzed sale prices in the Tokyo Metropolitan Area from 2015 to 2020. The neighborhood environment was assessed using flood/sediment risk and neighborhood walkability measured by net residential density, intersection density, and facility density (walking opportunity). Flood and sediment risk is a major concern that restricts the available land and is included as a negative aspect of the neighborhood environment, taking the topographic features into consideration. A comparison of the results showed that the preference for neighborhood walkability varies by socioeconomic status as well as by target cities. For most facility types, the number of walking opportunities within walking distance from houses was found to be positively related to the sale price of single-family detached houses in all quantiles. The relationship of house price with population and intersectio",3.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292657&type=printable54134f822f9ee5baf70913147b3efa58abc566e2a3e,10.1142/s3082841425500078,Predicting Residential Real Estate Values in China: An Empirical Analysis of Quzhou Utilizing a Novel Hybrid Computational Framework Integrating Gaussian Process Regression with Bayesian Hyperparameter Optimization,2025.0,Economics Open,Bing-Zi Jin; Xiao-Jie Xu,"Over the past decade, China’s real estate sector has experienced a transformative evolution marked by distinct cyclical phases, transitioning from a period of sustained expansion to an abrupt economic slowdown initiated in late 2021. These systemic realignments, driven by dynamic regulatory adjustments and heightened macroeconomic instability, have presented substantial operational challenges for stakeholders — including investors, policymakers and urban development strategists — engaged in forecasting housing price dynamics across metropolitan regions. Addressing gaps in traditional econometric approaches, this study advances a novel analytical framework leveraging Gaussian process regression (GPR) enhanced by multi-dimensional kernel architectures and context-sensitive basis functions. The model interrogates temporal price variations in residential assets within Quzhou, Zhejiang Province, drawing on a longitudinal transactional dataset encompassing nine years of observations (April 2015–July 2024). Bayesian inferential methods for hyperparameter optimization, augmented by stratified cross-validation protocols, achieved significant performance gains during exogenous validation int",3.0,https://doi.org/10.1142/s30828414255000785423e91bc77f865a9937b585adfcb0baff4cb7e7870,10.3390/su15129648,Spatial Characteristics and Influencing Factors of Commuting in Central Urban Areas Using Mobile Phone Data: A Case Study of Nanning,2023.0,Sustainability,Jin-Feng Wang; Guowei Luo; Yan-Jiao Huang; Min Liu; Yinheng Wei,"Urban commuting characteristics have important implications for both the spatial planning and governance of cities. However, the traditional methods of surveying the characteristics of commuting are very time- and labour-intensive, with the results susceptible to subjective influences. In this work, taking the central city of Nanning as the research object, the commuting space of the population was constructed on the grid-block-subdistrict scale, and the distribution characteristics of the commuting space were systematically analysed. In addition, the influencing factors of the commuting volume were explored by combining multi-source and spatiotemporal data with a geodetector. From our analysis, it was demonstrated that the population density in the central city of Nanning showed a spatial distribution pattern of “decaying distance from the city centre”, with a weak agglomeration effect of large-scale commuters at the grid scale and a larger east-west than north-south commuter scale. At the block scale, large-scale commuters were more concentrated, and the commuting distances were shorter in areas with large commuter populations. At the subdistrict scale, the internal commuting pop",3.0,https://www.mdpi.com/2071-1050/15/12/9648/pdf?version=1686898765543455d057806e4d158bdf609d6725a184bfeae32c0,10.29244/jp2wd.2022.6.2.146-164,Pola Sebaran Permukiman Kumuh di Kabupaten Tangerang dan Keragaman Spasial Faktor yang Mempengaruhinya,2022.0,Journal of Regional and Rural Development Planning,Muchamad Subhans Adiputra; Ernan Rustiadi; A. E. Pravitasari,"Rapid development and high population density are among the factors that cause slum settlements Tangerang Regency. Factors that cause slum settlements in Tangerang Regency have not been identified specifically and the handling of slum settlements are still seen to be uniform between regions. The purpose of this research is to identify the pattern of distribution of slum settlements in Tangerang Regency and the spatial diversity of factors that influence it. Methods used to identify the distribution pattern of slums are Moran's Index, Moran's Scatterplot, and Local Indicator Spatial Autocorrelation. Meanwhile, to understand the spatial diversity of the factors that affect slum settlements, multiple regression analysis was performed using forward stepwise regression, followed by Geographically Weighted Regression (GWR) analysis. Results shows that the pattern of spatial distribution of slum settlements in 2016 and 2018 was concentrated in the northern and southern areas of Tangerang Regency. The distribution pattern of slums shows a centered movement towards the district border with the surrounding areas, namely DKI Jakarta, Tangerang City, and South Tangerang City. The relationship ",3.0,https://journal.ipb.ac.id/index.php/p2wd/article/download/36439/23602544d20a423948de6c8650f85bc0a123956446ce5458,10.11113/intrest.v14n1.140,"Exploring Spatial Pattern of Residential Property Value around Cultural Heritage Sites in Ile-Ife, Nigeria",2020.0,International journal of real estate studies,O. B. Adebara; T. Adebara; A. Badiora; D. Ojo,"Cities in both developed and developing economies are characterised by various cultural heritage sites. Such sites can impact the value of residential properties around them either positively or negatively. In view of this, this study explores the spatial pattern of residential property values around cultural heritage sites in Ile-Ife, Nigeria. One (1) cultural heritage site each was purposively selected in the core and sub-urban areas of Ile-Ife. Furthermore, systematic sampling technique was used to select one of every twenty (20) buildings within different radii (1-300, 301-600 and 601-900 meters) of the selected cultural heritage sites. A total of two hundred and twenty-three (223) buildings were selected. Hence, 223 questionnaires were administered on the residents of the buildings. Interview was also conducted on ten (10) real estate practitioners in the study area to optimally provide answers rental values of residential properties around the cultural heritage sites. The extracted data were analysed using multiple regression and rent differential technique. Findings showed that as distance increases from the cultural heritage sites, the average rental value of residential pr",3.0,https://intrest.utm.my/index.php/intrest/article/download/140/87545e9a72b5824572acfddb674d2ad81f7cae26f02d4,10.18196/jerss.v7i2.19225,The Effect of Campus Existence on Boarding House Rental Prices: A Case Study of Universitas Muhammadiyah Yogyakarta,2023.0,Journal of Economics Research and Social Sciences,Farhan Ibnu Al Hanif; Romi Bhakti Hartarto; Ibnu Hajar,"The establishment of the Universitas Muhammadiyah Yogyakarta (UMY) campus has acted as a catalyst for economic expansion in the local neighborhoods, particularly evident in the real estate sector, characterized by the proliferation of boarding houses. This study endeavors to comprehensively examine the impact of the presence of Universitas Muhammadiyah Yogyakarta on the rental rates of boarding houses. The research's primary objective is to make a valuable contribution to the domain of property economics, utilizing secondary data derived from online platforms specializing in boarding house listings, notably Mamikos. A dataset encompassing 200 entries was gathered from the Mamikos website, encompassing variables such as boarding house rental fees, the proximity of the boarding houses to UMY, room dimensions, available facilities, and the categorization of boarding houses based on gender. The analysis of the dataset employs a multiple regression model, applying the Ordinary Least Square (OLS) technique. The findings derived from this study underscore several significant observations. Notably, the distance between the boarding house and the UMY campus displays a significant and, negat",3.0,https://journal.umy.ac.id/index.php/jerss/article/download/19225/85545466a163679928a99f55b0869429104610317479364,10.1057/s41599-024-03702-1,Spatial heterogeneity analysis of biased land resource supply policies on housing prices and innovation efficiency,2024.0,Humanities and Social Sciences Communications,Jinsi Liu; Hui Xiang; Shengjiao Zhu; Shixiang Chen,"To rationally allocate land resources, the government has formulated biased land resource supply policies. However, this policy will promote the fluctuation of regional housing prices, thereby affecting regional innovation efficiency. To elucidate the intrinsic logical relationship among these factors, this study investigates the influence of biased land resource supply policies on housing prices and innovation across 31 Chinese provinces over a 16-year period. It places particular emphasis on probing into the spatial disparities in this impact. The innovation of this paper lies in the development of a theoretical analysis model, which creatively utilizes the regional land supply status as an instrumental variable to address the endogeneity problem. At the same time, a double robustness test was conducted by substituting the explanatory variables and employing the fixed effects model. The results of the TSLS regression indicate: (1) The land resource supply policy, which prioritizes the central and western regions, has resulted in an increase in housing prices across the majority of regions in China, with the eastern region experiencing a more rapid rise compared to the central and",3.0,https://www.nature.com/articles/s41599-024-03702-1.pdf5476ab6e79e278b367dfb5e77048f5ca949fe64386e,10.1080/00497878.2019.1695612,Fifty Shades of Social Distance,2020.0,,B. Coventry; P. Case,"For nearly a century, social scientists have explored various dimensions and applications of social distance. Bogardus invited a broad use of the concept with his statement that “the measurement of social distances is to be viewed simply as a means for securing adequate interpretations of the varying degrees and grades of understanding and feeling that exist in social situations,” (“Measuring Social Distance” 299). A familiar application of social distance may be the NIMBY (not in my backyard) phenomenon regarding land use from hazardous waste facilities to group homes. In these situations, residents may concede the need for such facilities, but they do not want them close to their homes (Dear 288). Research has found that attitudes about such social situations are mediated by the perceived effect on one’s personal stake in the situation (Cook 293; Schwartz & Rabinovitz 105). The idea of social distance argues that the more closely related to the situation you perceive yourself greatly influences your tolerance. For example, a group home is a good idea as long as it does not interfere with your property values or perceived safety. Research has also found that neighbors’ actual expe",3.0,54880c7a3882a0d8e30de9d0f2d3d4f1558e6df1c50,10.14246/irspsd.2.3_46,The Transitional Spatial Pattern of Housing Prices in Beijing: Factors and Implications,2014.0,,Fang Wang; Xiao-Lu Gao,": The shift from the free-allocation housing welfare system to a market system was one of the most important parts of the economic system reform implemented in China from 1998 onwards. In recent years, however, soaring housing prices have become a serious public concern. In this study, we explored the spatial characteristics of housing prices, using spatial data on the average transaction price of Beijing’s housing blocks from 2005 and 2012, in order to reveal the factors influencing changes in housing price and address the way in which such changes in turn can affect land use, transportation, the living environment, and quality of life. The analysis revealed that the spatial autocorrelation effects associated with housing prices (and their increase) became significantly stronger between 2005 and 2012, especially in the central part of Beijing. Autocorrelation was, however, identified for both years. This finding explains the emergence of sub-area markets in recent years. It also provides evidence which suggests that developers or real estate agencies may have boosted housing prices purposely, which can lead to a biased market environment. Targeted policies are required in order to",3.0,https://www.jstage.jst.go.jp/article/irspsd/2/3/2_46/_pdf5497b7a99d8a78d38f64a31ef211941debd94841c6d,10.2139/ssrn.6746247,Data Centers and Local Economies in the Age of AI: A Shift--Share Approach,2026.0,Social Science Research Network,F. Álvarez; David Argente; Joyce Chow; Diana Van Patten,"Data centers are the physical infrastructure behind cloud computing, artificial intelligence, and enterprise software. The rapid diffusion of artificial intelligence (AI) is intensifying demand for compute, accelerating investment in data centers, and raising concerns about the local economic and environmental footprint of these facilities. Their expansion creates a local policy tradeoff. A data center can bring capital investment, construction activity, and specialized employment, but it can also increase demand for electricity, land, and grid capacity. This paper studies these effects at the U.S. county level. We assemble a facility-level panel of global data centers with precise coordinates, scale metrics, and annualized revenue. We map facilities to U.S. counties and combine them with County Business Patterns, county-level IRS income, county-level house prices, and electricity prices. To address endogenous siting, we instrument for data center growth using two shift-share instruments, which leverage pre-existing proximity to InterTubes long-haul fiber nodes and the 1980 county share of U.S. urban college population as shares, and both Chinese and rest-of-the-world data center r",3.0,https://doi.org/10.2139/ssrn.6746247550429391b30a0b68e14e06ac2685608db99abd0080,10.31979/mti.2022.2147,"“TELE-commuting” During the COVID-19 Pandemic and Beyond: Unveiling State-wide Patterns and Trends of Telecommuting in Relation to Transportation, Employment, Land Use, and Emissions in Calif",2022.0,,Tian-Jun Lu; J. Ke; Fynnwin Prager; J. Martinez,"Telecommuting, the practice of working remotely at home, increased significantly (25% to 35%) early in the COVID-19 pandemic. This shift represented a major societal change that reshaped the family, work, and social lives of many Californians. These changes also raise important questions about what factors influenced telecommuting before, during, and after COVID-19, and to what extent changes in telecommuting have influenced transportation patterns across commute modes, employment, land use, and environment. The research team conducted state-level telecommuting surveys using a crowd-sourced platform (i.e., Amazon Mechanical Turk) to obtain valid samples across California (n=1,985) and conducted state-level interviews among stakeholders (n=28) across ten major industries in California. The study leveraged secondary datasets and developed regression and time-series models. Our surveys found that, compared to pre-pandemic levels, more people had a dedicated workspace at home and had received adequate training and support for telecommuting, became more flexible to choose their own schedules, and had improved their working performance—but felt isolated and found it difficult to separate",3.0,https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1415&context=mti_publications551b4e534cbd63a5092e355fe6518499bd43bedb879,10.1080/23322039.2025.2509613,"Do policy uncertainty and volatility index affect dynamic connectedness among house price, financial and commodity market indices? Evidence from TVP-VAR and quantile regression analysis",2025.0,Cogent Economics & Finance,Nilotpal Sarma; Prabina Rajib,"Abstract This study initially analyses the connectedness among five indexes representing five asset classes: equities, bonds, commodities, currencies, and housing prices. Subsequently, it analyses the influence of policy uncertainty and equity market risk on the total connectedness of assets and their impact on the significance of the home price index. A time-varying parameter vector autoregressions (TVP-VAR) model has been employed to explore the connectedness of the assets. Secondly, quantile regression is employed to gauge the impact of uncertainty and market risk in this study. The findings show that the S&P500 is the market’s most influential index. The home price index has proven sensitive to shocks from government bonds, commodities, and stock indexes across the period analyzed. Finally, findings have indicated that policy uncertainty positively influences the overall connectedness among the variables at all the quantiles. Moreover, at higher quantiles, an increase in policy uncertainty and equity-market risk decreases the net connectedness of the house price index. These findings hold significant implications for investors and policymakers in terms of diversification of por",3.0,https://www.tandfonline.com/doi/pdf/10.1080/23322039.2025.2509613?needAccess=true55237d391705eb314d1e8e652112ff8ff24daf98add,10.1007/s11069-025-07923-8,Equity-focused flood risk assessment in Maryland using a hybrid explainable machine learning framework with FEMA and USGS data,2026.0,Natural Hazards,C. Ibebuchi,"Flooding poses a major global threat, causing loss of life and property damage. Accurate flood risk mapping is critical for effective mitigation. Traditional flood zoning approaches often rely on static analyses, failing to capture the dynamic nature of floods primarily driven by changing weather patterns and landscapes. This study develops a hybrid approach integrating a flood inventory from Federal Emergency Management Agency (FEMA) flood zones and U.S. Geological Survey streamflow discharge data with an explainable AI (XAI) model to predict flood susceptibility at the census block group (CBG) scale in Maryland. Trained on a curated flood inventory and environmental variables, the XAI achieves an area under the ROC curve (AUC) and Brier Score of 0.84 and 0.03, respectively. SHAP analysis identifies elevation, distance to water bodies, topographic wetness index, slope, storm frequency, and northern tidewater soils as key contributors to model predictions. Our model shows spatial agreement with FEMA maps (Mantel correlation = 0.72) while capturing further localized hotspots, especially in highland areas. Flood-prone zones (> 70% predicted flood susceptibility) are concentrated alon",3.0,https://link.springer.com/content/pdf/10.1007/s11069-025-07923-8.pdf553e1abe44115db060783baeae45cd3ee6fdd6d75c9,10.32508/stdjelm.v7i1.1184,Housing inequality in relation to housing tenure: Evidence from Ho Chi Minh City,2023.0,Economics,T. Huynh,"Unfair distribution of social resources, including valuable assets such as housing, leads to inequality and threatens the sustainable development of countries. This phenomenon is a hot issue, attracting the attention of many researchers worldwide. Ho Chi Minh City is considered one of the economic-financial centers of Vietnam, with a high average annual GDP growth rate, rapid urbanization, and the flow of migrant workers to live and work has increased rapidly in recent years. Although many achievements are based on outstanding developments, Ho Chi Minh city has faced many difficulties in housing policy due to the limited urban land fund, colossal population, and housing prices many times higher than the residents’ income. It is shown that there are difficulties in access to affordable housing in Ho Chi Minh City, especially for the population's low- and middle-income segments. This creates inequality and considerable pressure on housing development policy in urban areas. The article uses OLS regression to analyze issues related to housing inequality in Ho Chi Minh City in the relaion to living space and homeownership, then gives some implications for housing policy toward improving",3.0,https://stdjelm.scienceandtechnology.com.vn/index.php/stdjelm/article/download/1184/1716554f11a9ca546f4bbcbe57d036eaa82fe430bb0f784,10.12928/jampe.v2i2.7983,Analysis of House Price Determination in 13 G20 Countries: Random Effect Model,2023.0,JAMPE (Journal of Asset Management and Public Economy),Ignatius Abasimi; Anisa Rupaningtyas Aulia; Uswatun Khasanah,"A house or a place to live is a primary need for all beings, but the lack of affordable housing to buy or rent can trigger a global housing crisis. After the collapse of the housing market in the US or the subprime crisis of 2008-2009 there was a revival of focus on the housing market. Empirical research has been conducted to look at the variables that affect house prices. However, it is still rare for researchers to examine the influence of macroeconomic variables in countries that have a strong role in the world economy. Therefore this study aims to determine the effect of the Consumer Price Index, Construction GDP, Unemployment Rate, Population Density, Exchange Rate and Power Purchasing Parity on the House Price Index as an indicator that reflects house prices. So, the contribution of this research is to provide novelty in the use of the house price index to determine the determinants that influence it. This research is a quantitative research using secondary data. The regression model used in this study is the Random Effects Model. The result of this study is the Consumer Price Index and Purchasing Power Parity have a significant positive impact on house prices. Therefore, the",3.0,http://journal2.uad.ac.id/index.php/JAMPE/article/download/7983/37495556f640ca457e6fcedb96092f98aaf114510d4af4a,10.3390/su14127116,An Analysis of the Price Determinants of Multiplex Houses through Spatial Regression Analysis,2022.0,Sustainability,Jae-Joong Kim; M. Cho; Myeong-Hun Lee,"This study established a model for price determinants with the combination of the GIS technique and spatial regression model based on the parcel prices of multiplex houses in an effort to integrate and utilize spatial data and choose a suitable model. This study established a spatial weights matrix to apply interrelation with adjacent areas and performed row standardization to specify the effect of adjacent areas. Moran’s I was used for measuring the spatial autocorrelation of the parcel prices of multiplex houses. Through this, the parcel price of multiplex houses was analyzed to have a strong spatial autocorrelation and be related to the jeonse price of an apartment. A lot of multiplex houses are supplied to Seoul. In previous studies, multiplex houses studies were analyzed through simple regression analysis excluding spatial effects, however, in this study, a suitable model was derived through spatial regression analysis. Moreover, the jeonse price of an apartment, which is a representative housing type, was firstly analyzed as a variable added and found whether the jeonse price of apartments would have an effect on the other housing types in the neighborhood. This study also fo",3.0,https://www.mdpi.com/2071-1050/14/12/7116/pdf?version=1654840814556639ac23c5f13dafd945cdd40a5f60f7978c08db0,10.48550/arxiv.2205.01180,Using Machine Learning to Evaluate Real Estate Prices Using Location Big Data,2022.0,Systems and Information Engineering Design Symposium,W. Coleman; Ben Johann; N. Pasternak; Jaya Vellayan; N. Foutz; Heman Shakeri,"With everyone trying to enter the real estate market nowadays, knowing the proper valuations for residential and commercial properties has become crucial. Past researchers have been known to utilize static real estate data (e.g, number of beds, baths, square footage) or even a combination of real estate and demographic information to predict property prices. In this investigation, we attempted to improve upon past research. So we decided to explore a unique approach - we wanted to determine if mobile location data could be used to improve the predictive power of popular regression and tree-based models. To prepare our data for our models, we processed the mobility data by attaching it to individual properties from the real estate data that aggregated users within 500 meters of the property for each day of the week. We removed people that lived within 500 meters of each property, so each property's aggregated mobility data only contained non-resident census features. On top of these dynamic census features, we also included static census features, including the number of people in the area, the average proportion of people commuting, and the number of residents in the area. Finally,",3.0,http://arxiv.org/pdf/2205.011805574ecf623f40ce01fde8d4ac0ee88291986f7a72fa,10.17645/up.v6i4.4407,Economic Assessment of South African Urban Green Spaces Using the Proximity Principle: Municipal Valuation vs. Market Value,2021.0,Urban Planning,L. Lategan; J. Cilliers; Zinea Huston; N. Blaauw; S. Cilliers,"Urban green spaces (UGSs) deliver ecosystem services and potential economic benefits like increases in proximate residential property prices. The proximity principle (PP) premises that property prices increase as distance to UGS decreases. The PP has generally been confirmed by studies using municipal valuations and market values internationally. Conversely, South African studies have mostly employed municipal valuations and results have rejected the PP. There is an accepted interrelationship, but also often discrepancies, between municipal valuations and market values, presenting scope for this article to explore whether negative results are confirmed when market values replace municipal valuations in PP studies in the South African context. Accordingly, a statistical analysis of market values is completed in the Potchefstroom case study, where five test sites are replicated from studies that employed municipal valuations for longitudinal comparison. Results verify generally higher market values than municipal valuations and confirm the PP in two, but reject the PP in three, of five test sites. Previous studies employing municipal valuations in the case study confirmed the PP in o",3.0,https://www.cogitatiopress.com/urbanplanning/article/download/4407/4407558ee85018489345a5ce51a7f190baa574d01ba2339,10.31234/osf.io/7wt89,Local Housing Market Dynamics Predict Rapid Shifts in Cultural Openness: A Nine-Year Study across 199 Cities,2021.0,,F. Götz; T. Ebert; S. Gosling; M. Obschonka; Jason Rentfrow,"Accumulating evidence suggests that culture changes in response to shifting socioecological conditions; economic development is a particularly potent driver of such change. Previous re-search has shown that economic development can induce slow but steady cultural changes within large cultural entities (e.g., countries). Here we propose that economically driven culture change can occur rapidly, particularly in smaller cultural entities (e.g., cites). Drawing on work in cultural dynamics, urban economics, and geographical psychology, we hypothesize that changes in local housing prices – reflecting changing availability of local amenities – can induce rapid shifts in local cultures of Openness. We propose two mechanisms that might underlie such cultural shifts: selective migration (i.e., people selectively moving to cities that offer certain amenities) and social acculturation (i.e., people adapting to changing amenities in their city). Based on trait Openness scores of 1,946,752 U.S. residents, we track annual changes in local Openness across 199 cities for nine years (2006-2014). We link these data to annual infor-mation on local housing markets, an established proxy for local ameni",3.0,https://psyarxiv.com/7wt89/download559c4b30194cb34aab8ab61fc3e8fdd8d4bc493d21b,10.17576/jem-2019-5303-9,Constructing an Enhanced House Price Index Model in Malaysia: Empirical Evidence,2019.0,Jurnal Ekonomi Malaysia,Nik Mohd Sukrri; Norazlina Abd Wahab; R. Yusof,"The objective of this study is to construct an enhanced house price index model in Malaysia. Having reviewed the current model of the Malaysian House Price Index (MHPI), it is currently found that this index is constructed based on the demand-driven variables. Previous studies explained that both macroeconomic factors (income levels, interest rates, and labor market) and supply factors are included in the construction of the house price index. This study begins by examining the determinants of the existing house price index in Malaysia. This study employs the Autoregressive Distributed Lag Model (ARDL) to discover the short and long-run dynamics between the variables. The study considers the quarterly data from the first quarter of 2008 to the fourth quarter of 2017. The findings reveal that construction cost (CC) and housing loan (HLN) are significant in determining HPI while Overnight Policy Rate (OPR) and land supply (LS) are insignificant with HPI. Then, the housing loan was found to be the most significant variable in determining HPI in Malaysia. Hence, we propose a new enhanced house price index that incorporates new demand and supply variables, by using the Laspeyres approac",3.0,https://www.ukm.my/jem/wp-content/uploads/2021/05/jeko_533-9.pdf56044dffb44ce79b18806a58817b24964455f2a444f,10.3390/systems13110985,Research on the Analysis of Influential Factors of Short-Period Passenger Flow of Urban Rail Transit Based on Spatio-Temporal Heterogeneity,2025.0,Syst.,Minlei Qian; Lin Cheng; Jia-Nan Sun,"Urban Rail Transit (URT), as an important part of the modern urban transportation system, undertakes a large number of daily commuter passenger flow transportation needs. In this context, the in-depth analysis of influential factors of URT passenger flow has become an important issue in transportation management and optimization. This paper selects 13 POI (Point of Interest) types and the surrounding demographic data as the independent variables, and constructs a multi-scale spatio-temporal geographically weighted regression (MGTWR) model with the daily morning peak inbound traffic of the URT station as the dependent variable. The results of the study show that the positive effect of the business and residential variables on the URT morning peak inbound passenger flow is the most significant, reflecting the fact that the increase in these variables promotes the morning peak inbound passenger flow; relatively speaking, the scenic spot variables have a negative effect on the URT morning peak inbound passenger flow, indicating that the increase in these variables inhibits the morning peak inbound passenger flow. In addition, the corporate variables have a negative effect on the mornin",3.0,https://www.mdpi.com/2079-8954/13/11/985/pdf?version=1762248813561be2ce7b4748665f3ccf58ffef171bc63aa55e95b,10.1177/09697764261416384,Intra-urban mobility in Spain and its effects on dispersion patterns of the Spanish-born population and immigrants: The cases of Madrid and Barcelona,2026.0,European Urban and Regional Studies,Jordi Bayona‐i‐Carrasco; Joaquín Recaño,"The territorial distribution patterns of the foreign-born population in large cities are primarily determined by intra-urban mobility, which redistributes its population among the city’s neighbourhoods, significantly influencing their social and demographic composition. Using data from Madrid and Barcelona, this study analyses the intra-urban mobility patterns of immigrant groups in cities that have experienced significant growth in the foreign-born population over the last two decades, reaching 28.0% and 32.9% of the resident population, respectively. The period considered is 2007–2021, divided into four phases according to economic cycles. Six groups of countries are identified, and flows between origin and destination neighbourhoods are analysed using specific migration rates. The main objectives are (1) to characterise the evolution of intra-urban residential changes by origin; (2) to study the impact of different economic periods on these flows; (3) to determine the impact of internal mobility on the territorial distribution patterns of different origins and (4) to compare Madrid and Barcelona. The study applies spatial econometric techniques, including Global Moran’s I, Local",3.0,https://ddd.uab.cat/pub/artpub/2026/326881/Intra_Urban_mobility_Spain.pdf562cb4df3d5beb4805b39256af24029758f2ff6ec51,10.26565/2311-2379-2020-99-12,ПРОГНОЗУВАННЯ ЦІН НА РИНКУ ОРЕНДИ ЖИТЛА З ВИКОРИСТАННЯМ МЕТОДІВ МАШИННОГО НАВЧАННЯ,2020.0,,Vladyslav Bobrov; Andriy Kim,"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",3.0,https://doi.org/10.26565/2311-2379-2020-99-12563d77bc62b697f261c48f40b2b651d9eced25b2462,10.1371/journal.pone.0312143,Socioecological drivers of mutualistic and antagonistic plant-insect interactions and interaction outcomes in suburban landscapes,2024.0,PLoS ONE,G. Fitch; L. Adler; Rebecca E. Irwin; Paige S. Warren,"Cities are complex socioecological systems, yet most urban ecology research does not include the influence of social processes on ecological outcomes. Much of the research that does address social processes focuses primarily on their effects on biotic community composition, with less attention paid to how social processes affect species interactions. Linking social processes to ecological outcomes is complicated by high spatial heterogeneity in cities and the potential for scale mismatch between social and ecological processes, and the indicators used to assess those processes. Here, we assessed how social and ecological processes jointly influence the frequency and outcomes of species interactions among the native perennial vine Gelsemium sempervirens and its insect pollinators, nectar robbers, and florivores across 28 residential subdivisions in the Research Triangle region, NC, USA. We integrated data on socioeconomic attributes (mean property value, mean property size, subdivision age), vegetation attributes (forest cover and richness and density of managed and unmanaged floral resources), species interactions (conspecific and heterospecific pollen deposition, nectar robbing, f",3.0,56457310c7970ce614afd18989dae1638a3c2bbc80b,10.1080/10826084.2023.2223258,"Health Outcomes by Neighborhood (HON): Effects of neighborhood, social instability, and health factors on 12-month trajectories of substance-use disorder symptoms",2023.0,Substance Use & Misuse,Landhing M. Moran; L. Panlilio; Sara K. Hertzel; J. Bertz; M. Tyburski; John R Etter; D. Epstein; K. Preston; Karran A. Phillips,"Abstract Background Previous studies have shown that environment and health can influence drug use trajectories and the effects of substance use disorder (SUD) treatments. We hypothesized that trajectories of drug use-related problems, based on changes in DSM-5 symptoms, would vary by type(s) of drugs used, health factors, and neighborhood characteristics. Methods We assessed mental and physical health, stress, social instability, neighborhood characteristics (disorderliness and home value), and DSM-5 symptom counts at two study visits, 12 months apart, in a community sample (baseline N = 735) in Baltimore, MD. Three prominent categories of drug-use trajectory were identified with K-means cluster analysis of symptom counts: Persistent (4 or more symptoms at both visits or at Visit 2), Improved (decrease from 4 or more symptoms at Visit 1 to 3 or fewer symptoms at Visit 2), and Low-Stable (3 or fewer symptoms at both visits). Baseline health and neighborhood measures were tested as predictors of trajectory in mediation and moderation models. Results Among people with current opioid- and/or stimulant-use, odds of an Improved trajectory were (1) decreased with neighborhood disorder an",3.0,https://pmc.ncbi.nlm.nih.gov/articles/PMC10534204/pdf/nihms-1917921.pdf565b0b23dede5d56e0fea3c22b927ffa0362e94b9b0,10.3390/su18031209,"Aligning Supply and Demand: The Evolution of Community Public Sports Facilities in Shanghai, China",2026.0,Sustainability,Lyu Hui; Peng Ye,"Community public sport facilities are core carriers of the national fitness public service system, with their supply–demand alignment directly linked to megacity governance efficiency and residents’ well-being. To address structural issues, such as “human–land imbalance” in facility layout, this study uses the 2010–2024 panel data from Shanghai’s 16 districts, applies supply–demand equilibrium theory, and integrates quantitative methods to analyze spatio-temporal supply–demand coupling and identify key influencing factors. The study yields four key findings: (1) The spatial distribution of facilities and population demonstrates a differentiated evolutionary trajectory marked by “central dispersion and suburban stability”. (2) Supply–demand alignment has continuously improved, as evidenced by the increase in coordinated administrative districts from six to thirteen. Nonetheless, the distance between sports facilities and population centers widened, suggesting that spatial adaptation remains incomplete. (3) Urban population growth exerts a significant positive impact on facility supply. Elasticity coefficients are generally high in suburban areas, while negative elasticity is detecte",3.0,https://www.mdpi.com/2071-1050/18/3/1209/pdf5669ef91b412795537c0b8fff3c465b6b51ff5a7345,10.12928/jampe.v4i1.11927,Determinants of Residential Property Price in Selected Asian Countries: A Sys-GMM Approach,2025.0,JAMPE (Journal of Asset Management and Public Economy),Ignatius Abasimi; A. Nabila; Muhammad Ramdhan; Muhammad Syariful Anam,"This study analyzes the determinants of residential property prices in 13 selected Asian countries. Key variables include the residential property price index, GDP per capita reflecting economic conditions, real interest rates impacting borrowing costs, inflation affecting purchasing power), population growth influencing housing demand, energy consumption reflecting demand pressures, and the Gini Index measuring income inequality. The research employs the System Generalized Method of Moments (Sys-GMM) for dynamic analysis of these endogenous variables. The results indicate that property prices are significantly influenced by prior price levels. Furthermore, GDP per capita, inflation, and population growth positively affect property prices, underscoring the roles of economic growth and demographic factors in shaping the property market. Higher GDP growth enhances household purchasing power, while population growth increases housing demand. Conversely, energy consumption negatively impacts property prices, reflecting cost pressures from rising energy demand. Interestingly, the real interest rate and Gini index do not significantly influence residential property prices, suggesting the",3.0,http://journal2.uad.ac.id/index.php/JAMPE/article/download/11927/507456747a2698a58847daca516caf3f2e452173412bdfa,10.17059/ekon.reg.2023-3-23,Determinants of Regional Disparities in Housing Prices: A Spatial Analysis of German Regions,2023.0,Economy of Regions,E. Semerikova; A. Blokhina; A. Nastansky,"Germany is characterised by heterogeneous regional development in various economic spheres, including housing markets. Differences in housing prices persist during decades, causing undesirable inequality, affecting migration and employment patterns. The purpose of this work is to identify regional factors which affect regional housing prices in Germany. The peculiarity of the study is the consideration of the spatial location of regions for analysing the influence of the characteristics of neighbouring regions. Based on data from 397 German regions for 2004-2019, spatial econometric panel data models are built, which consider both selling and rental prices. The following factors that affect demand in the housing market are used as determinants of housing prices: the unemployment rate, the balance of pendulum migration at the place of work and living, the share of employment, wages, the number of employees, gross regional product. The analysis findings revealed that factors that raise income for the population trigger an upsurge in demand and prices for housing. Conversely, opposite effects result in a decline in prices due to a decrease in demand. Moreover, it was verified that nei",3.0,https://www.economyofregions.org/ojs/index.php/er/article/download/691/243/4110568ad8b8e5158e5851705de2d7d6b6883b23f62c45f,10.11648/j.ajere.20200504.11,Effect of Neighborhood Security on Housing Price in Lagos,2020.0,American Journal of Environmental and Resource Economics,Olalekan Dimeji Bamiteko; O. Adebiyi,"Effect of neighborhood security on housing price in Lagos State Nigeria. The nonstop increase in urban population has created a gap between unceasing demand and limited supply of housing units in Nigeria particularly Lagos State. Housing being an heterogenous commodity has a lot of independent variables attached to it such as structural, location and neighborhood attributes (including crime rate). The population growth equally presented unemployment and insecurity in most neighborhood going by high rate of crime in Lagos State. The inadequate provision of security for life and properties by successive governments over the years along with population growth have caused unimaginably increase in crime rates in the city. The need for safety and prevention of crimes resulted into neighborhood security. The study confirms that the rate of crime in a particular neighborhood have a direct effect on housing price. This study investigates the effect of neighborhood security on housing price in Lagos State using Amuwo-Odofin Local government Area. The study employed primary data and surveyed 140 residents of the LGA through multistage sampling technique. Ordinary Least Squares (OLS) was used ",3.0,http://article.sciencepublishinggroup.com/pdf/10.11648.j.ajere.20200504.11.pdf56948c9e0d80ce1bbac353796f9d318e7a7b1ca0998,10.1016/j.procs.2012.09.068,Housing Starts Forecast of Retail Sales through the 2007-2009 Recession,2012.0,Complex Adaptive Systems,Anthony Joseph; Maurice Larrain,"Abstract The expansion following the 2001 recession was in part stimulated by a boom in housing market investments. Many economists were concerned that a severe drop in residential investments would cause a recession throughout the economy because of residential investment's relationship to the gross domestic product and financial markets, and that a decline in housing prices would negatively impact consumer spending. A severe decline in the housing market and the illiquidity in the financial market were both evident by the third quarter of 2007. Some models based on historical data were showing the certainty of a recession in 2007. The best predictors of recessions are respectively interest rate spread, unemployment claims, and building permits. Building permits/new private housing units are a leading economic indicator whereas retail sales are a component of manufacturing and trade sales, which is a coincident economic indicator because it is highly correlated to GDP. This study attempts to use housing starts and past values of retail sales to forecast out-of-sample retail sales values through the period of January 1998 to August 2010, which is inclusive of the recent 2007-2009 r",3.0,https://doi.org/10.1016/j.procs.2012.09.068570fd68bfdee500310dd80ad27c10ce8bd3d3d2efda,10.2478/remav-2019-0027,The Assessment of a Building’s insurable Value using Multivariate Statistics: The Case of the Czech Republic,2019.0,Real Estate Management and Valuation,Martin Cupal; M. Sedlačík; J. Michálek,"Abstract When concluding a property insurance agreement, adjustment of the insured amount poses a certain risk. From the policyholder’s point of view, the risk measure translates into the chosen target amount, which should correspond to the insurable value. The aim of the research is to determine a statistical model for prediction of the insurable value with using current models in the Czech Republic. The model for insurable value prediction proposed in this paper accepts the risk of decision making under uncertainty suitably. The model’s foundation is a synthesis of four core models discussing the addressed issue. The methodology is based on a classification tree created by the CART method, and multivariate linear regression. After the classification tree is created, the input variables which contributed to the classification are used in the regression model. The database consists of 125 family houses which went through a detailed examination (they were documented, measured, and their technical state and legal status were determined), and described in experts’ reports. The obtained results showed a high degree of statistical association of selected predictors with the estimated in",3.0,https://www.sciendo.com/pdf/10.2478/remav-2019-0027571f6a91c486f2609d7f8c58cd888dd22b295f2f6e2,10.1007/s11135-025-02164-0,A spatio-temporal analysis of house prices in Brunei Darussalam,2025.0,Quality & Quantity: International Journal of Methodology,Haziq Jamil,"This paper presents the first spatio-temporal analysis of property prices in Brunei Darussalam, a small, resource-rich economy with distinct housing market characteristics. Despite global interest in quantitative housing market analyses, Brunei’s market remains underexplored, with prior studies predominantly qualitative. Addressing this gap, N=3763\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N=3763$$\end{document} residential transactions from 2015 to 2023 were analysed using Conditional Autoregressive (CAR) priors to model spatial dependencies and temporal trends in house prices accounting for other price determinants. Emerging Hotspot Analysis was then employed to classify price clustering across time. Key findings revealed significant spatial autocorrelation (ρ=0.43\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho =",3.0,https://doi.org/10.1007/s11135-025-02164-0572e45f3985047d4948234b10b7605f48dde8f3a445,10.1016/j.jth.2021.101063,Impacts of light rail transit development on neighborhood health: A scoping review through a social determinants of health lens,2021.0,,Alissa Klingbaum; Adwoa Afful; Vinusha Gunaseelan; Thrmiga Sathiyamoorthy,"Abstract Introduction Light rail transit (LRT) has become a popular intervention for addressing the social and economic complexities associated with urban growth. LRT development can exert influence on neighborhood characteristics, such as property values, employment opportunities, and service access. Many of these changes can impact the health of nearby residents by influencing their exposure to the social determinants of health (SDOH). This study maps the literature on LRT development through an SDOH lens and comments on gaps and implications for neighborhood health and transit planning. Methods The Arksey and O'Malley scoping review methodology was used to examine existing literature on neighborhood-level impacts of LRT development related to the SDOH. Peer-reviewed articles were included if they focused on an LRT project in an urban center in Canada or the United States, reported on a neighborhood-level impact related to the SDOH, and were published in English between 2004 and 2019. Standardized information was extracted from each included article and thematic analysis was applied to generate impact themes. Results A search of three databases yielded 767 non-duplicate records a",3.0,57383bebae8bfef0920ed1fcbc4b819bd0688a042b6,10.11113/ijbes.v2.n2.66,Conceptual Framework for Gentrification Analysis of Iskandar Malaysia,2015.0,,R. Khalil; F. Johar; S. Sabri,"Gentrification is generally defined as the transformation of a working class living in the central city into middle-upper class society. It has both positive and negative consequences. Gentrification caused loses of affordable home, however, it is also beneficial because it rejuvenates the tax base as well stimulates mixed income. Question arises whether the characteristics of gentrification in developing countries will appear to be the same or varies to those in developed countries. Because of this research growth, a review of the body of literature related to the mutation of gentrification, i.e. type of gentrification and its characteristics is believed necessary. This will serve as a basis for a conceptual framework to analyze what is happening in Iskandar Malaysia (IM). As globalized urbanization area, IM offers a particularly interesting case as there are already signs of gentrification due to its rapid urbanization. In the residential market, house price in IM shows a rapid and continuous increment. Many foreigners are attracted to the new residential area in IM being promoted as exclusive while promising a quality lifestyle. The locals meanwhile face difficulties in owning a",3.0,https://ijbes.utm.my/index.php/ijbes/article/download/66/31574af72de54c2b7cd896d230b1f20f64560e7b493d1,10.1353/jda.2019.0021,Will Population Ageing Cause a House Price Meltdown in Australia?,2018.0,Journal of Developing Areas,Wasanthi Thenuwara; Mahinda G. Siriwardana; N. Hoang,"ABSTRACT:The life cycle hypothesis suggests that when the Baby Boomers retire, many are likely to sell their assets to finance their retirement, exerting downward pressure on house prices. This paper examines whether demographic transitions, particularly the increasing proportion of the population in the old age cohort due to the retirement of Baby Boomers, will precipitate a dramatic decline in real house prices in Australia. The study uses a structural vector autoregressive framework. This is an important improvement over the reduced-form regression strategies usually employed in the literature. Both the demographic and non-demographic variables used in the empirical analysis are treated as endogenous and reverse causality between the variables is taken into account. The population ageing dynamics are modelled using impulse response functions and, thus, an insight into the potential magnitude of demographic shocks, particularly retirement shocks, is obtained. A much longer time series from 1950 to 2014 is used in the empirical analysis. The findings reject the predictions that population ageing, or more specifically, changes in age structure particularly due to retiring Baby Boom",3.0,5752f199781ce8be94da1b208ebff17a7a06510cf4f,10.1016/j.sbspro.2014.07.324,The Decline in Housing Supply after the Emergence of Lapindo Mudflow Disaster in the Peri Urban Areas of Surabaya,2014.0,,I. D. M. F. Septanaya; P. G. Ariastita,"Abstract The space in peri urban areas always becomes a popular choice for private developers nowadays to build housing in a large scale. The growing number of housing in the peri urban with this large scale commonly termed as invasive formative process. It happens in almost all major cities in the world, as in major cities’ peri urban in America, Mexico City, Beijing and even in the peri urban of Jakarta and Bandung. However, a different phenomenon has occurred in the peri urban of Surabaya, particularly in some districts that scattered in Sidoarjo Regency. In 2006-2010, the provision of housing has drastically decreased. The comparison between the target and the development realization of the new housing were less than 50%. The number of developers who were not active even has risen to 132 developers at the end of 2010. This causing an imbalance situation between supply and demand chains because the number of housing needs will reach at least 107.277 units by 2015. A hypothesis that appears as the cause of the problem is caused by the presence of Lapindo mudflow disaster. However, the results of this research have shown that the factors led to the decline in housing provision iss",3.0,https://doi.org/10.1016/j.sbspro.2014.07.324576a4c77c070c690ba826d983901aa4569a60e73785,10.1049/itr2.70165,Revisiting Accessibility to Amenities: Equity Implications From Comparing Cumulative Opportunity Measure and Two‐Step Floating Catchment Area Method,2026.0,IET Intelligent Transport Systems,Yue Chen; Shun-Ping Jia; Steve O'Hern; Qi Xu,"577 Evaluating the accessibility of amenities is fundamental to achieving equitable urban planning of cities. The cumulative opportunity measure (CO) and the two‐step floating catchment area method (2SFCA) are widely used in previous studies. Although there is ongoing debate and discussion about the choice of method for measuring accessibility, the comparison between these two methods has not been thoroughly examined and differences in the spatial distribution of accessibility and the resulting equity from them have tended to be ignored. Here, we contrasted the similarities and differences in spatial accessibility in 12 different scenarios by using CO and 2SFCA, respectively. The scenarios considered the two models in two transportation modes, public transport (PT) and private car (PC), and six key urban services, company, education, healthcare, shopping, restaurant, and scenery, are thoroughly explored. Equity, between different housing price areas, was also evaluated by using the Gini coefficient and Palma ratio. The findings show that the spatial distributions of accessibility from CO are more related to the whole city structure, while the results from 2SFCA can better reflect the",3.0,https://doi.org/10.1049/itr2.7016557830c05bf4c7b034fd12b54347d2050eb229fecf44,10.36982/jiig.v14i2.3189,Penerapan Algoritma K-Means Untuk Clustering Harga Rumah Di Bandung,2023.0,Jurnal Ilmiah Informatika Global,Briyan Gifari Aji; Dwi Chandra Aditya Sondawa; Muhammad Rifky Gifari; Sena Wijayanto,"The need for shelter is one of the fundamental aspects of daily life for humans. A house serves not only as a place to seek protection and rest but also as a venue for socializing with family. One of the factors influencing the decision in choosing a house is its price. House prices vary in each region, depending on factors such as location and other attributes. In major cities like Bandung, house prices differ based on their categories. However, many people still find it challenging to determine the value and discern whether a house is classified as affordable or expensive. Hence, there is a need for a clustering process of house prices in Bandung to aid in comprehending and categorizing house prices based on attributes such as the house price, total building area, and total land area. To understand and analyze the patterns of house prices in Bandung, this study utilizes the K-Means method to cluster the house price data into several groups based on their similarity in attributes. Additionally, the research aims to determine the optimal number of clusters through the cluster validation process using the silhouette index. The findings show that when using n_cluster=2, a silhouette ",3.0,https://ejournal.uigm.ac.id/index.php/IG/article/download/3189/1935579a5ba4a4155d6a2a2c1b46be3864301760ca1a90e,10.1016/s2468-2667(25)00279-8,Commercialisation and care sufficiency: the privatisation of children's homes in England.,2025.0,Lancet Public Health,B. Goodair; François Schoenberger; A. Bach-Mortensen,"Although the commercialisation of care services is intended to produce markets that respond to care need, it is still unclear whether profit incentives align with population need. In this Health Policy, we examine the provision of the children's residential care market in England and whether it responds effectively to geographical need. We analysed comprehensive data on all children's homes in England between 2014 and 2023 and categorised providers as Local Authority, third sector, investment owned, individual owned, or corporate owned. We operationalised area need through net loss measures: the difference between the number of children placed outside a Local Authority versus those placed within it. Using Bayesian hierarchical models, we assessed the relationships between area characteristics, children's home locations, and ownership structures. In 2023, 852 (29·8%) of 2861 children's homes in England were owned by investment firms, doubling the number they ran in 2014 (414 of 1350 homes). All for-profit homes disproportionately located in areas of low need and investment-owned homes located more in areas with low house prices. Compared with Local Authority homes, investment-owned ",3.0,https://doi.org/10.1016/s2468-2667(25)00279-8580d8442d8e8df30b62544eab224ca0fde684836add,10.1145/3715335.3735485,Machine Learning Fairness in House Price Prediction: A Case Study of America’s Expanding Metropolises,2025.0,The Compass,Abdalwahab Almajed; Maryam Tabar; Peyman Najafirad,"As a basic human need, housing plays a key role in enhancing health, well-being, and educational outcome in society, and the housing market is a major factor for promoting quality of life and ensuring social equity. To improve the housing conditions, there has been extensive research on building Machine Learning (ML)-driven house price prediction solutions to accurately forecast the future conditions, and help inform actions and policies in the field. In spite of their success in developing high-accuracy models, there is a gap in our understanding of the extent to which various ML-driven house price prediction approaches show ethnic and/or racial bias, which in turn is essential for the responsible use of ML, and ensuring that the ML-driven solutions do not exacerbate inequity. To fill this gap, this paper develops several ML models from a combination of structural and neighborhood-level attributes, and conducts comprehensive assessments on the fairness of ML models under various definitions of privileged groups. As a result, it finds that the ML-driven house price prediction models show various levels of bias towards protected attributes (i.e., race and ethnicity in this study). T",3.0,https://arxiv.org/pdf/2505.0159158120109b26c096ce0b42f7f541f33162060cea0356,10.5539/ijef.v13n1p12,Analysis of China’s Pure Electric Vehicle Sales Based on Spatial Econometric Models,2020.0,,Hua-Ying Gu; Chao-qun Han,"This paper empirically investigates the spatial dependence and serial correlation structures among different China’s brands of pure electric vehicle (EV) sales using spatial econometric models. Based on the newly proposed economic distance spatial weight matrix, the empirical results show that EV endurance mileage, power battery capacity, charging time, government subsidy, retail price, and each brand market share have important impacts on EV sales. The per capita disposable income of urban households, gasoline price, loan rate and the number of charging pile are statistically significant determinants of EV sales. In particular, the improvements of the number of charging pile and the rise of gasoline price can increase EV sales, while the rise of loan rate or tight monetary policy may increase the consumers’ cost of purchasing EVs and then decrease EV sales. Another interesting finding is that though the per capita disposable income of urban households increases the EV sales decreases. A plausible explanation would seem to be that the impact of the per capita disposable income of urban households on the EV sales is offset by the decline in government subsidies or the incomplete inf",3.0,https://ccsenet.org/journal/index.php/ijef/article/download/0/0/44356/467625823de3f341df4cad23ece2e697a77501bcd37cb672,10.3390/smartcities7040069,Enhancing Property Valuation in Post-War Recovery: Integrating War-Related Attributes into Real Estate Valuation Practices,2024.0,Smart Cities,Mounir Azzam; Valerie Graw; Eva Meidler; A. Rienow,"In post-war environments, property valuation encounters obstacles stemming from widespread destruction, population displacement, and complex legal frameworks. This study addresses post-war property valuation by integrating war-related considerations into the ISO 19152 Land Administration Domain Model, resulting in a valuation information model for Syria’s post-war landscape, serving as a reference for property valuation in conflict-affected areas. Additionally, property valuation is enhanced through visualization modeling, aiding the comprehension of war-related attributes amidst and following conflict. We utilize data from a field survey of 243 Condominium Units in the Harasta district, Rural Damascus Governorate. These data were collected through quantitative interviews with real estate companies and residents to uncover facts about property prices and war-related conditions. Our quantitative data are analyzed using inferential statistics of mean housing prices to assess the impact of war-related variables on property values during both wartime and post-war periods. The analysis reveals significant fluctuations in prices during wartime, with severely damaged properties experienci",3.0,https://doi.org/10.3390/smartcities7040069583329ccdcc93c50715627c4ebc7eea631c6518e0ef,10.53383/100340,Vertical Housing Price Gradient and Ground Floor Premium in Taipei City,2022.0,International Real Estate Review,Yu-hui Chen; Chien-Wen Peng; Chunghsien Yang,"This study aims to examine the influence of the floor level on housing prices based on the vertical dimensions of the building. A two stage least squares regression (2SLS) and two stage quantile regression (2SQR) are utilized to analyze the urban regions in Taipei City by using data obtained from the online actual price registry of real estate transactions from August 2012 to April 2020. After considering the spatial autocorrelation, the following conclusions are reached. First, in addition to the ground floor, the floor level premium is 0.55% for residential buildings with an elevator, and the premium increases when the housing price quantile increases. However, for walk-up apartments, there is a floor level discount of -4.21%, and the price discount increases when the quantile for the housing price increases. Secondly, when compared to the other floors, the ground floor premium is 9.61% for residential buildings with an elevator, which is a little higher than walk-up apartments for which the ground floor premium is 8.54%. The results reveal that the influence of the type of residential building and the total number of floors on the ground floor premium is not as high as expected.",3.0,https://doi.org/10.53383/10034058457ebd7b14cfc0e37a26b8915733174442cce757a,10.3390/su17219373,Marketization and Household Consumption Upgrading: Evidence from China,2025.0,Sustainability,Meiqi Zhao; Meng-Xia Zhang,"This paper examines how marketization influences the spatial effects of household consumption upgrading in China, by analyzing provincial panel data from China between 2010 and 2022. The study employs a two-way fixed-effects Spatial Durbin Model to capture both the direct effects of marketization within a region and the spillover effects transmitted to neighboring regions. This model incorporates spatial dependence in both dependent and independent variables, providing a comprehensive assessment of spatial interactions. The results reveal that marketization and consumption upgrading both have the spatial pattern characteristics of significant spatial difference and agglomeration features. Marketization considerably encourages the upgrading of local people’s consumption and has positive spillover effects on the consumption upgrading levels of nearby regions. Mechanism analysis shows that market competition and enterprise innovation play key roles in this process. Heterogeneity analysis shows in eastern regions, areas with high industrial upgrading levels, high financial agglomeration levels, and high house prices, and the promotion effect of marketization on household consumption up",3.0,https://doi.org/10.3390/su172193735856b6d9eaf09bcc0046338f28f9628f1b09e164ba3,10.1186/s12913-020-05833-y,The relationship of nursing home price and quality of life,2020.0,BMC Health Services Research,Sarah Raes; S. Vandepitte; D. De Smedt; H. Wynendaele; Yannai DeJonghe; J. Trybou,"Knowledge about the relationship between the residents’ Quality of Life (QOL) and the nursing home price is currently lacking. Therefore, this study investigates the relationship between 11 dimensions of QOL and nursing homes price in Flemish nursing homes. The data used in this cross-sectional study were collected by the Flemish government from years 2014 to 2017 and originates from 659 Flemish nursing homes. From 2014 to 2016, data on the QOL of 21,756 residents was assessed with the InterRAI instrument. This instrument contains 11 QOL dimensions. Multiple linear regression analyses were conducted to examine the research question. The multiple linear regressions indicated that a 10 euro increase in the daily nursing home price is associated with a significant decrease (P < 0.001) of 0.1 in 5 dimensions of QOL (access to services, comfort and environment, food and meals, respect, and safety and security). Hence, our results indicate that the association between price and QOL is very small. When conducting a subgroup analysis based on ownership type, the earlier found results remained only statistically significant for private nursing homes. Our findings show that nursing home pri",3.0,https://bmchealthservres.biomedcentral.com/track/pdf/10.1186/s12913-020-05833-y586e15f986dcdf43dfef817c3a9fbec519bdce4ce55,10.21512/bbr.v14i1.8225,The Influence of Macroeconomic Factors on Residential Property Prices in Malaysia Before and During COVID-19,2022.0,Binus Business Review,Puji Isyanto; Siti Hafsah Zulkarnain; Abdol Samad Nawi,"The macroeconomic environment directly and significantly impacts a country’s economy. For example, residential property prices in Malaysia have steadily increased in recent years. It has become a significant issue during the COVID-19 pandemic since a home is one of the essential items for each living human being. The research identified a few macroeconomic factors, such as Gross Domestic Product (GDP), private consumption, government expenditure, and household saving which influenced Malaysian residential property prices. The research applied a regression model to analyse the data to encounter the uncertainty problem related to Beaver univariance analysis. All data were collected by the Department of Statistics Malaysia, the National Property Information Centre (NAPIC), The World Bank Data, and other data streams. Then, the research was tested using the time series method to predict and forecast the model. Based on the findings, the research shows that the distribution data for all variables are not normal. Overall, the variables are significant using the time series regression with the level of significance at 1%, 5%, and 10%. All variables tend to have a positive impact on reside",3.0,https://journal.binus.ac.id/index.php/BBR/article/download/8225/4684587b5db3b0406a10459ffc13d01ce60ae766927928b,10.1007/s10901-024-10153-2,Scalable multimodal assessment of the micro-neighborhood using orthogonal visual inputs,2024.0,Journal of Housing and the Built Environment,Miroslav Despotovic; Wolfgang A. Brunauer,"The features of the micro-location and in particular the micro-neighborhood that residents perceive on a daily basis have a considerable influence on the quality of living and also on housing prices. For automated valuation models (AVMs), the use of micro-neighborhood information would be beneficial, as incorporating additional spatial effects into the price estimate could potentially reduce the empirical error. However, measuring related features is difficult, as they must first be defined and then collected, which is extremely challenging at such a small spatial level. In this study, we investigate the extent to which the quality of micro-neighborhoods can be assessed holistically using multiple data modalities. We design a scalable approach using alternative data (images and text), with the potential to expand coverage to other urban regions. To achieve this, we propose a multimodal deep learning architecture that integrates both textual and visual inputs and fuses this information. In addition, we introduce a training strategy that enables a targeted fusion of orthogonal visual representations of the residential area within the model architecture. In our experiments, we test an",3.0,https://link.springer.com/content/pdf/10.1007/s10901-024-10153-2.pdf588cb13d7882e9f9411b86b2542821773fbb66399b5,10.21837/pm.v23i39.1897,URBAN HOUSING AFFORDABILITY: ANALYZING THE IMPACT OF AFFORDABLE HOUSING SCHEMES ON HOUSING PRICES,2025.0,PLANNING MALAYSIA,Ahmad Fawwaz Saleh; Rohayu Ab. Majid; N. A. Ab Rahim; Khairul Afzan Lazi; Nuratiqah Aisyah Awang,"Housing affordability remains a critical issue in urban areas, where rising demand and limited supply drive property prices upward. Governments have introduced affordable housing schemes to address these challenges, but their effectiveness hinges on alignment with local market conditions and demand-side factors such as financial capability, government regulation on affordable housing, and location preferences. Despite their importance, few studies have examined the specific attributes of affordable housing schemes and their impact on housing prices. This study bridges this gap by investigating the relationship between affordable housing prices and housing demand, employing a mixed-methods approach and using correlation and regression analysis, an initial framework of 15 attributes was evaluated, categorized under financial approvals, government regulations, and location preferences. The results reveal that nine attributes significantly influence housing prices within affordable housing schemes. Key factors include credit history, down payment, loan margin, approval location, qualification requirements, and procedural complexities, all of which exhibit strong correlations with housi",3.0,https://www.planningmalaysia.org/index.php/pmj/article/download/1897/15745895d5729cfff3731136718a6ffa9ca843872923afa,10.1097/fch.0000000000000372,"Interpersonal discrimination, neighborhood inequities, and children’s body mass index: A descriptive, cross-sectional analysis",2023.0,Family & Community Health,Alicia S. Kunin-Batson; Christopher P. Carr; A. Tate; A. Trofholz; Michael F. Troy; Rachel Hardeman; J. Berge,"Psychosocial stressors have been implicated in childhood obesity, but the role of racism-related stressors is less clear. This study explored associations between neighborhood inequities, discrimination/harassment, and child body mass index (BMI). Parents of children aged 5-9 years from diverse racial/ethnic backgrounds (n = 1307), completed surveys of their child's exposure to discrimination/harassment. Census tract data derived from addresses were used to construct an index of concentration at the extremes, a measure of neighborhood social polarization. Child's height and weight were obtained from medical records. Multiple regression and hierarchical models examined child's BMI and racism at the individual and census tract levels. Children residing in the most Black-homogenous census tracts had 8.2 percentage units higher BMI percentile (95% confidence interval, 1.5-14.9) compared with white-homogenous tracts (P = .03). Household income and home values were lower, poverty rates higher, and single parent households more common among Black-homogeneous census tracts. Almost 30% of children experienced discrimination/harassment in the past year, which was associated with a 5.28-unit ",3.0,https://doi.org/10.1097/fch.00000000000003725908e134744ba0d8048607b643385db45fbc8b5e0f7,10.1016/j.regsciurbeco.2018.05.005,Mortgage recourse provisions and housing prices,2018.0,Regional Science and Urban Economics,Robert R. Reed; A. LaRue; Ejindu S. Ume,"Abstract In light of the large swings in housing prices in the United States in recent years, there has been considerable interest in trying to understand the various factors which led to the boom and bust of the housing market. In this paper, we explore the impact of the legal environment from provisions for mortgage default across U.S. states. To do so, we develop a rigorous framework with microeconomic foundations for financial intermediaries. To be specific, we introduce a housing market and a residential mortgage market in the Diamond and Dybvig framework which emphasizes the role of depository institutions to help depositors manage idiosyncratic liquidity risk. Notably, we think of non-recourse provisions as a legal arrangement to protect risk-averse homeowners from the loss of housing value. While housing demand should be higher in markets where mortgage borrowers have full insurance, lenders also adjust the amount of mortgage credit provided to protect their risk-averse depositors. Thus, a priori, there is not an obvious connection between mortgage recourse provisions and housing prices. To draw further insights into the issue, we proceed to look at empirical evidence on ho",3.0,5916b40a1968152e059fe1fce10fdb04a3e92c00041,10.3390/su11164336,"Misrecognition in a Sustainability Capital: Race, Representation, and Transportation Survey Response Rates in the Portland Metropolitan Area",2019.0,Sustainability,Raoul S. Liévanos; Amy Lubitow; J. McGee,"US household transportation surveys typically have limited coverage of and responses from people of color (POC), which may lead to inaccurate estimation of POC transportation access and behavior. We recast this technocratic understanding of representativeness as a problem of “racial misrecognition” in which racial group difference is obscured yet foundational for distributive transportation inequities and unsustainability. We linked 2008–2012 population and housing data to an apparent stratified random sample of 6107 household responses to the 2011 Oregon Household Activity Survey (OHAS) in a “sustainability capital”: the Portland, Oregon metropolitan area. We detailed how the 2011 OHAS consistently overrepresented White households and underrepresented Latinx/Nonwhite households in aggregate and at the tract-level. We conducted tract-level spatial pattern and bivariate correlation analyses of our key variables of interest. As expected, our subsequent tract-level spatial error regression analysis demonstrated that the percent of Latinx/Nonwhite householders had a significant negative association with 2011 OHAS household response rates, net of other statistical controls. Further anal",3.0,https://www.mdpi.com/2071-1050/11/16/4336/pdf?version=1565509715592d6b5702c59ba607062630d0ae155d78c12fad3fd,10.54097/hbem.v5i.5039,Model For Predicting London House Prices,2023.0,"Highlights in Business, Economics and Management",Ming-Zhe Yin,"With the growth of population, housing prices have become one of the important indicators to reflect the economic performance. Forecasting prices around the world will be part of social and economic development. Through the analysis of various factors affecting the market housing price, it is helpful to make a more accurate assessment of the future housing price trend. In a broad sense, accurate housing price forecast is helpful to the country's macro-control of the market housing price trend, and the prediction of future housing price is part of the strategic planning of enterprises. For consumers, the housing price forecast has a positive effect on the rational planning of individual economy. Since house prices are related to many factors, and there is a linear relationship between house prices and some factors that affect house prices, there are many linear graphs to study this problem. The research direction of this paper is to obtain data from Kaggle and conduct exploratory analysis on these data sets. The main method used in this article is linear regression, and no other forecasting methods will be added. What this work needs to do is use the right machine learning methods t",3.0,https://drpress.org/ojs/index.php/HBEM/article/download/5039/4882593de57c81acbdeb7fd07e4da7bd82e0d35695886fd,10.63642/1357-1419.1169,Improving assessment equity in mass appraisal models,2014.0,Journal of Property Tax Assessment & Administration,Carmela Quintos,"53 Assessment equity is a primary concern for municipalities that levy ad valorem taxes. Property taxes are a popular source of revenue for funding local services, requiring that assessment levels be fair for properties across price levels (vertical equity) and across properties with similar characteristics (horizontal equity). The standard measures for horizontal and vertical equity published by the International Association of Assessing Officers (IAAO) are the coefficient of dispersion (COD) and the price-related differential (PRD), respectively. Modeling heterogeneity is essential to achieving a COD and a PRD that conform to IAAO standards. Failure to model the variation in price by correctly specifying the regression equation with relevant property characteristics can be reflected in residuals that are heteroskedastic. Heteroskedasticity, which means that the variance of the residuals (or pricing error) is not constant across certain property characteristics, is by definition a failure of horizontal equity and indicative of a high COD. Similarly, the PRD is known to be affected by unexplained heterogeneity (i.e., heteroskedastic residuals). Regression methods, which average eff",3.0,5947b8a1be07acee545ffdae60e25c15033014d38a3,10.29303/e-jep.v6i10,DETERMINAN PERMINTAAN PERUMAHAN DI KOTA MATARAM,2024.0,Elastisitas - Jurnal Ekonomi Pembangunan,Emi Salmah; Iwan Harsono; Baiq Ismiwati; B. Wijimulawiani; Tuti Handayani,"A place to live or a house is a necessary necessity in living human daily life. Mataram City is the capital of West Nusa Tenggara (NTB) Province and is one of the centers of economic growth, with a high population growth rate compared to 10 other districts/cities in NTB. This population growth causes the demand for housing/own houses or property rights to be very high, but on the other hand, land is getting narrower and more expensive, resulting in house prices becoming expensive and this has a big impact on people who are less fortunate in terms of their income. To meet the demand for housing from the community, we encourage developers to build housing with various types/models that are tailored to the income and desires of the community. The aim of the research is to analyze the influence of population, per capita income and inflation on housing demand in Mataram City. This research uses secondary data obtained from BPS Mataram City, BPS NTB Province, NTB Housing Statistics and other data sources relevant to this research. The data were analyzed using a multiple linear regression model with panel data and using the Eviews 12 calculation tool. The results of the study showed that ",3.0,https://elastisitas.unram.ac.id/index.php/elastisitas/article/download/164/1175959db426526e9a045557e911f5c03a75d40d7c677f,10.1371/journal.pone.0309019,How did COVID-19 case distribution associate with the urban built environment? A community-level exploration in Shanghai focusing on non-linear relationship,2024.0,PLoS ONE,Jingyi Gao; Yifu Ge; O. Murao; Yitong Dong; Guo-Fang Zhai,"Several associations between the built environment and COVID-19 case distribution have been identified in previous studies. However, few studies have explored the non-linear associations between the built environment and COVID-19 at the community level. This study employed the March 2022 Shanghai COVID-19 pandemic as a case study to examine the association between built-environment characteristics and the incidence of COVID-19. A non-linear modeling approach, namely the boosted regression tree model, was used to investigate this relationship. A multi-scale study was conducted at the community level based on buffers of 5-minute, 10-minute, and 15-minute walking distances. The main findings are as follows: (1) Relationships between built environment variables and COVID-19 case distribution vary across scales of analysis at the neighborhood level. (2) Significant non-linear associations exist between built-environment characteristics and COVID-19 case distribution at different scales. Population, housing price, normalized difference vegetation index, Shannon’s diversity index, number of bus stops, floor–area ratio, and distance from the city center played important roles at different ",3.0,https://doi.org/10.1371/journal.pone.0309019596c09350912604b5fa961b57cd636b918705f746a4,10.3982/qe2625,A unified empirical framework to study neighborhood segregation,2025.0,,G. Caetano; Vikram Maheshri,"We incorporate the endogenous feedback loop at the core of the seminal Schelling (1969) model of segregation into a dynamic model of neighborhood choice and use it to study the forces that shaped racial and income segregation in the San Francisco Bay area from 1990 to 2004. Such an analysis requires causal identification of households' responses to the socioeconomic composition of their neighbors. We achieve this with novel instrumental variables that can be rationalized with a dynamic choice model with frictions. These IVs have potentially broad application: studying sorting along any observable demographic dimension, estimating the effects of neighborhood composition on outcomes such as house prices, or identifying other network externalities. We find that discriminatory (taste‐based and statistical) sorting by race and by income is widespread and complex: almost all households respond positively to similar neighbors and negatively to different neighbors, although at varying degrees of intensity. In spite of these discriminatory responses, frictions—moving costs and uncertainty—mitigate their impact on segregation. This implies that sorting on the basis of other neighborhood amen",3.0,https://doi.org/10.3982/qe26255977b30a30ef5f1da83b7d3103216b86b18f447d183,10.3390/su16167233,Community Group Purchasing of Next-Day Delivery: Bridging the Last Mile Delivery for Urban Residents during COVID-19,2024.0,Sustainability,Ying-Ying Wang; Feng Xu; Zhe Lin; Jian-Ying Guo; Gang Li,"The rapid development of new retail and the impact of COVID-19 have catalyzed the blowout growth of community group purchasing. The emergence of community group purchasing collection and delivery points (CGPCDPs) has become a new way to solve the “last mile” problem of new retail delivery. Based on the point of interest (POI) data of CGPCDPs of Nansha District, Guangzhou City, this study advances our understanding by identifying unique operational models, service targets, and spatial distribution patterns of CGPCDPs, which differ significantly from traditional pick-up points (PPs). The conclusions are as follows: (1) Most CGPCDPs depend on wholesale and retail shops, and their main service targets are urban and rural communities, followed by industrial areas. (2) The distribution of CGPCDPs has apparent spatial differentiation. At the macro scale, it shows the characteristics of “central agglomeration and peripheral dispersion”. It is distributed along the “northwest-southeast” direction and presents a “dual-core multi-center” pattern. At the meso–micro scale, different built environments in developed areas of cities, villages in the city (ChengZhongCun), and rural areas show disti",3.0,https://www.mdpi.com/2071-1050/16/16/7233/pdf?version=17244072145983aa30685de2f57df81e909bf3c0e16720351793c,10.4236/jwarp.2009.14029,Modelling the Financial Value of the Maroochy River to Property Values: An Application of Neural Networks,2009.0,,A. Higgins; L. Pearson; L. Laredo,"The Maroochy River, which is located on east coast of Australia, provides a variety of uses and values to the community. Changes in structure, function and management of the river will influence the value that the community derives from it. Therefore, critical to the river’s continued management is the development of policy relevant tools based on the community’s value of the river. This paper focuses on estimating the fi-nancial value the local residents derive from living close to the river through investigation of changes in residential property values due to attributes of the Maroochy River. It is a complex analysis since there are several confounding geographical and property variables. Given a large and complete dataset of 28,000 properties for the Maroochy region, Artificial Neural Networks (ANN) was applied to estimate the economic value of the properties. This ANN was then able to simulate scenarios for property values with respect to changes in environmental features. It showed the Maroochy River contributed AU$900,000,000 to the unim-proved capital value of the whole region, a value that could not be estimated previously, and much higher than anticipated. Calculating pot",3.0,https://content.scirp.org/pdf/jwarp20090400002_60799627.pdf599fdcfe87837d0a8bd9109f95937450351010db95e,10.1080/14697688.2025.2468269,Harnessing uncertainty: a new approach to real estate investment decision support,2025.0,Quantitative finance (Print),A. J. Pollestad; A. Oust,"This paper investigates the efficiency of AI-uncertainty quantification as a decision support tool to mitigate adverse selection issues for home buyers and real estate investors. The study is grounded in the premise that buyers can use uncertainty estimates from AI models to identify properties with uncertain price predictions, thus enabling them to filter out risky investment prospects and place bids closer to the fundamental value. We employ an automated valuation model trained on a dataset of over 50,000 historical apartment transactions in Oslo, Norway, spanning 2016–2022. Through a financial performance simulation, we evaluate three bidding strategies: random selection, human model evaluation, and AI-uncertainty quantification. Our findings reveal that AI-uncertainty quantification outperforms human evaluation in identifying uncertain investment opportunities, yielding higher potential profit margins and purchase ratios. The results are robust across different sample sizes and models. Explainable AI analysis using SHAP values further reveals the AI model’s ability to identify spatial uncertainty patterns, enhancing its effectiveness in mitigating adverse selection. Our study c",3.0,https://doi.org/10.1080/14697688.2025.2468269600b5fdbb46fb9826180526e3bbc98d2c164d13da70,10.2478/geosc-2022-0011,The Neighborhood Impact of Industrial Blight: A Path Analysis,2022.0,GeoScape,R. Banai; E. Momeni,"Abstract Historically, industry shaped the space-economy of the American city, a major source of employment opportunity for residents that selected housing nearby or within a convenient or affordable commuting distance. However, the contemporary American city is structurally characterized by abandoned, blighted, vacant industrial properties due to urban expansion, deindustrialization and the suburbanization of both jobs and population. The urban studies literature rarely documents the neighborhood impact of industrial blight akin to studies of residential blight. We determine the proximity-effect of industrial blight on the neighborhood thought of not as an isolated and closed entity, but as a connected and open entity within the city and the region. Unlike studies confined to the property value impact, we determine Pearson correlations of industrial blight and vacancy expansively with the socio-economic and physical characteristics of neighborhoods. We use path analysis to determine direct, indirect, and total neighborhood impact of industrial blight and vacancy. The census block group and parcel-level geographic information system (GIS) provide our principal sources of data. The ",3.0,https://sciendo.com/pdf/10.2478/geosc-2022-0011601f9e69a438090ea46fae94562ac0cbb7001383565,10.3390/buildings13041043,Spatial–Temporal Differentiation of Housing Burden of Urban Floating Population and Migration in China,2023.0,Buildings,Jie-Kai Wang; Yanhua Luo; Wei-Xuan Song,"Housing costs, including rent, have become a significant economic burden for millions of floating population families in urban China, affecting their living standards and influencing migration decision-making. Using data from the China Migrants Dynamic Survey (CMDS) in 2012 and 2017, this study analyzes the spatial patterns of rent, family income, and the rent-to-income ratio among different regions, cities, and groups. Results show that rent and the rent-to-income ratio have an inverse correlation with the regional economy, with lower ratios observed in eastern coastal areas compared to the central and western regions, especially the northeast. High-level cities record higher incomes, higher rents, and higher rent-to-income ratios, and groups with higher educational levels and occupational characteristics exhibit higher affordability. Rent plays a role in the flow and changes of the floating population, and the housing burden has become a key constraint for long-term residence or migration. Developed provinces and municipalities in the eastern region and high-level cities remain major destinations for migrants, but rising house prices impede permanent settlement through commercial",3.0,https://www.mdpi.com/2075-5309/13/4/1043/pdf?version=1681629459602c9107a13ffcc98f31476360b4c7041edf06078b7,10.46544/ams.v30i2.04,Visegrad Region Dependency on Mining Industries: Impact of Raw Material Prices on Housing Prices and Investments,2025.0,Acta Montanistica Slovaca,Laima Okunevičiūtė Neverauskienė,"This study examines the impact of the mining industry and raw material prices on housing prices and real estate investment in the Visegrad region (Poland, the Czech Republic, Slovakia, and Hungary) from 2014 to 2023. Given the historical and economic links of this region with the development of the mining sector, the study aims to assess how fluctuations in raw material prices (e.g., coal, copper, steel) affect housing price dynamics and real estate investment decisions. The analysis uses Eurostat and World Bank data on key raw material prices, house price indices, and real estate investment flows. Panel regression analysis was used to identify the relationship between raw material price changes and housing prices, and the ARIMA model was used to forecast the impact of raw material prices on housing market trends. The study results show that the mining sector remains important for the real estate market of the Visegrad countries, and raw material price fluctuations have a significant impact on both residential real estate prices and investment flows to the housing sector in this region. In countries where mining remains the primary industry, property prices tend to be more cyclical",3.0,https://doi.org/10.46544/ams.v30i2.04603caea36fa629317e8ed72aafcc5b5841cb60bd1d3,10.1080/19397038.2022.2059590,The influence and analysis of urban spatial elements on national economic development,2022.0,International Journal of Sustainable Engineering,Yulin Zhao; Jun-Ke Li; Kai Liu; Jiang Wang,"ABSTRACT Cities play an essential role in promoting the development of the whole national economy. Many scholars have proposed different methods to study this problem, but all have ignored the critical influence of network attention on the economy. Therefore, this study uses big Network data to profoundly analyse the relationship between urban spatial development and economic growth in eastern, central, and western China, further study this issue, and provide theoretical support for urban economic development. This study first divides China into three regions: eastern, central, and western, then selects the indicators of the national economy and urban development from 2014 to 2019, and finally constructs a panel regression model to explore the relationship between urban space elements and national economic development. The results show that China’s national economic development is closely related to spatial factors such as urban transportation, environment, science and technology, energy consumption, and housing price. These findings will help to make full use of the natural advantages of each region and guide the rational planning of the urban economy. This study provides referenc",3.0,https://www.tandfonline.com/doi/pdf/10.1080/19397038.2022.2059590?needAccess=true6049489dd84ddb61791b8aa63b93440e586879c3ae7,10.24191/myse.v9i1.17293,A CONCEPTUAL FRAMEWORK FOR MINIMISING MIDDLE-INCOME HOUSING MISMATCH,2022.0,Malaysian Journal of Sustainable Environment,N. Mustapa; Suhaila Ali,"The housing demand of the middle-income group cannot be ignored as they are the majority of the population in Malaysia which is about 40% out of the population. However, there is a mismatch between the supply and middle-income housing demand. The developer has always been eager to develop high-end products instead of affordable housing. This circumstance leads to the insufficient supply of middle-income housing. The mismatch is not only in terms of housing price, but also does not meet their housing preferences towards other housing attributes. Accordingly, this paper attempts to develop a conceptual framework for minimising middle-income housing mismatch. Thus, this paper reviewed the existing literature on the housing mismatch occurrences and the strategies to overcome this situation through government intervention. Almost all evidences from the studies indicate government, developer behaviour and buyer preferences have contributed to the middle-income housing mismatch occurrences. The findings were compiled through the creation of a conceptual model. Government intervention, developer behaviour and buyer preferences are included in the model as aspects that cause the housing mi",3.0,https://myjms.mohe.gov.my/index.php/myse/article/download/17293/90416058f5d78c1cf63dd98f1b2d9cd708bc545b347ff8d,10.1057/s41599-026-06910-z,A multi-scale perspective on the accessibility of public electric vehicle charging stations and community equity disparities,2026.0,Humanities and Social Sciences Communications,Congcong Wang; Yang Gu; J. Shen,"The accessibility of public electric vehicle charging stations (EVCS) is a critical factor in the widespread adoption of electric vehicles (EVs). Optimizing their spatial distribution to achieve equitable service coverage has become a vital task for promoting sustainable urban development. Taking the central urban area of Chengdu as a case study, this research evaluates the accessibility of public EVCS for residents within 1 km and 3 km service radii using an opportunity-based accessibility assessment method, incorporating inter-community competition effects and internal demand. This study employs spatial autocorrelation analysis to investigate the spatial clustering patterns of public EVCS accessibility and its relationship with community characteristics from a multi-scale perspective. The spatial equity of EVCS distribution was assessed, thereby providing a scientific foundation for optimizing infrastructure layouts. Key findings include: (1) Both EVCS and communities exhibit distinct central clustering in spatial distribution; (2) Compared to the inner-core areas, the outer-core areas face more severe imbalances in EVCS resource allocation; (3) The spatial autocorrelation betwee",3.0,https://www.nature.com/articles/s41599-026-06910-z.pdf606512e17b033dd039935f8ea577c1ce66a87cb5d8b,10.1155/2022/2068353,A Study on the Prediction of House Price Index in First‐Tier Cities in China Based on Heterogeneous Integrated Learning Model,2022.0,Journal of mathematics,Yaqi Mao; Yonghui Duan; Yibin Guo; Xiang Wang; Shen Gao,"To address the difficulty of low prediction accuracy, insufficient model stability, and certain lag associated with a single machine learning model in the prediction of house price, this paper proposes a multimodel fusion house price prediction model based on stacking integrated learning. Firstly, web search data affecting house prices were collected by web crawler technology, and Spearman correlation analysis was performed on the attribute set to reduce its complexity and establish a prediction index system for four first‐tier cities in China. Secondly, with the goal of improving accuracy, diversity, and generalization ability, the types of base learners as well as metalearners are determined, and the parameters of the base learners are optimized using the grey wolf optimization algorithm to produce the GWO‐stacking model, and the experimental results from four datasets demonstrate that the model has high prediction accuracy. Finally, to solve the issue of unintelligible black boxes in machine learning models, we have used the state‐of‐the‐art interpretation method SHAP combined with the LightGBM algorithm to interpret the model, and the result can be used as a basis for real esta",3.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/2022/2068353607cf0bb4bd18f7c0af6ffc114c3d73b1286b013b23,10.2139/ssrn.4196051,Regulation and Frontier Housing Supply,2022.0,Social Science Research Network,Danny Ben-Moshe; David Genesove,"Regulation is a major driver of housing supply, yet often difficult to observe directly. We show that frontier cost, the non-land cost of producing housing absent regulation, is identified from prices and quantities alone, without instruments even when quantity is endogenous in a mean regression. Identification requires regulation to enter as a nonnegative wedge with zero in its conditional support. The difference between price and frontier cost yields the regulatory tax. We apply the approach to new multi-floor, multi-family residential construction in Israel. Accounting for random housing quality, we estimate economies of scale at low heights (minimum efficient scale about five floors), nearly constant marginal cost at middle heights, and an elasticity of substitution between land and non-land inputs of about 0.15 to 0.2 at the greatest heights. The estimated mean regulatory tax is 47% of housing prices, with substantial variation across locations, and is positively correlated with centrality, density, and prices. We also construct a lower bound allowing quality to differ systematically over location and time, assuming weak complementarity between quality and demand. In 2017, whe",3.0,https://doi.org/10.2139/ssrn.4196051608ac5750645549b70b1a0f4104b8cbc139ee9b9db3,10.59543/comdem.v1i.10235,A Novel Perspective On The Analysis Of Residential Property Prices Near Transportation Investments: Wide-Range Vs Narrow-Range Factors,2024.0,Computer and Decision Making: An International Journal,Onur Şahin; Ilgin Gokasar,"A new perspective is available for the relationship between transportation systems and property prices, “evaluation by considering bandwidths”. The term “bandwidth” indicates the number of neighbor data points having an influence on the target point. The main motivation of this research is eliminating the restricted effects of a spatial analysis. When a single bandwidth value is defined for all models, indicate that an equal number of neighbor data in GWR influences all factors of the model for target points. When the multiscale geographically weighted regression (MGWR) is used, a unique bandwidth value can be defined for each factor. This study, by using MGWR, provides researchers the ability of distinguishing the effects of factors and classifying them as either wide-range or narrow-range. A total of 3487 geographical data were collected for a study area covering Esenyurt and Beylikduzu Counties in Istanbul, Turkey. As a result, when considering different bandwidths, factors are indeed recognized as either narrow-range or wide-range. The output of this study proved that the researchers are strongly recommended considering the bandwidth effects while performing a spatial regressio",3.0,https://comdem.org/index.php/comdem/article/download/10235/75836096844d55ddfac217fce786648d60b3a52ad3526cc,10.22495/rgcv15i1p3,Traditional or advanced machine learning approaches: Which one is better for housing price prediction and uncertainty risk reduction?,2025.0,Risk Governance and Control: Financial Markets & Institutions,Long Phi Tran; Hoang Duc Le; Ta Thu Phuong; Dung Chi Duy Nguyen,"Predicting housing prices is particularly of interest to many scholars and policymakers. However, housing prices are highly volatile and difficult to predict. This study used both traditional and advanced machine learning (ML) approaches to address the issue of housing price prediction. This study involves and compares the predictive power between advanced ML models, including random forest, gradient boosting, k-nearest neighbors (KNN), bagged classification and regression trees (CART), and traditional ML models based on linear regression and its modifications. Notably, in this study, we employed both performance metrics, including the mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and k-fold cross-validation (CV) procedure in order to investigate the predictive performance of each model. Empirically, based on a dataset comprising 78,704 real estate sales in Hanoi, Vietnam, we find that advanced ML approaches outperform traditional approaches. Specifically, advanced ML models enhance the accuracy of house price prediction and the decision-making process related to housing buying and selling activities. Our findings also reveal that amon",3.0,https://doi.org/10.22495/rgcv15i1p36108bff7327e8542a1f416c0b7b74bd792b21404ba4,10.36937/ben.2023.4768,The Importance of Digitization in Estimating Housing Fair Value with the Artificial Neural Networks Method: The Case of Yenimahalle/Ankara/Turkey,2023.0,Brilliant Engineering,Orhan Doğan,"With the rapid development in the construction sector in recent years, housing sales, which is one of the economic investments, have accelerated, making an objective valuation difficult and making it impossible to predict the real price due to the fact that there are too many parameters in the valuation stage and there is no definite formula. In addition, in real estate appraisal processes, the use of artificial neural networks (ANN), which is one of the artificial intelligence methods, has made it attractive to perform and adapt machine learning using examples, to provide information about unprecedented examples, to work fast and easy to identify, to provides solutions to complex problems, to work with little information. In this study, ANN models were created by rearranging the quantification values in an existing study by creating ANN models with 14 parameters that are effective in determining the fair value of a total of 220 houses for sale advertised on an e-commerce site in different neighborhoods of Yenimahalle district of Ankara/Turkey, and it was observed that with the use of rearranged quantification values, the ANN architecture selected with mean square error (MSE) 0.000",3.0,https://doi.org/10.36937/ben.2023.4768611f8de45d9b16decf990a27e0582db42735bff416f,10.3846/ijspm.2025.24225,Enhancing real estate mass appraisal in Type II metropolitan cities: A GIS-MGWR approach,2025.0,International Journal of Strategic Property Management,Yu Zhao; Jixiang Zhang; Xuejia Shen; Miao Yu; Wen-Bo Zheng,"At present, China’s real estate appraisal sector confronts a number of challenges, including low appraisal efficiency, significant human influence, lack of objectivity, and absence of unified standards. Particularly, conducting a scientific, precise, and efficient mass appraisal of existing housing is vital for fostering the industry‘s healthy development. This study adopts Zhangjiakou City in Hebei Province as a case study, integrating Geographic Information Systems (GIS) and utilizing the Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), Semi-parametric Geographically Weighted Regression (SGWR), and Multiscale Geographically Weighted Regression (MGWR) models to assess the prices of existing housing. The research delves into the specific challenges in mass appraisal of real estate within Type II metropolitan cities. The study reveals spatial heterogeneity in the prices of existing housing in Zhangjiakou City and shows that the MGWR model excels in mass appraisal of housing prices in Type II metropolitan cities. This research offers strategic guidance for real estate market investment and transactions in Zhangjiakou City and provides valuable references for ot",3.0,https://doi.org/10.3846/ijspm.2025.242256122aa3edfef405157ebe0aa47e1488928be39f77f6,10.3390/app142310799,Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen,2024.0,Applied Sciences,Wenjing Wang; Hai-Yan Wang; Jun Liu; Chengfa Liu; Shi-Peng Wang; Yong Zhang,"This paper aims to analyze the influence mechanism of built environment factors on passenger flow by predicting the passenger flow of Shenzhen rail transit in the morning peak hour. Based on the classification of built environment factors into socio-economic variables, built environment variables, and station characteristics variables, eight lines and one hundred sixty-six stations in Shenzhen Railway Transportation are taken as research objects. Based on the automatic fare collection (AFC) system data and the POI data of AMAP, the multiple regression model (OLS) and the geographically weighted regression (GWR) model based on the least squares method are established, respectively. The results show that the average house price is significantly negatively correlated with passenger flow. The GWR model considering the house price factor has a high prediction accuracy, revealing the spatial characteristics of the built-up environment in the administrative districts of Shenzhen, which has shifted from the industrial structure in the east to the commercial and residential structure in the west. This paper provides a theoretical basis for the synergistic planning of house price regulation ",3.0,https://doi.org/10.3390/app142310799613e47c72c1d4892127c75a5ac6f316ce10266ad34d,10.51201/jusst/21/05280,House Price Prediction using Machine learning and Image Processing,2021.0,Journal of University of Shanghai for Science and Technology,J. Chougale; A. Shinde; Ninad Deshmukh; Dhananjay Sawant; Vaishali Latke,"We demonstrate that these urban features can be recorded by street views and satellite image data and enhance the estimate of house prices. In order to estimate house prices in London, UK, we recommend a pipeline that uses a deep neural network model to automatically extract visual features from images. In calculating the house price model, we use typical housing characteristics, such as age, size, and accessibility, as well as visual features from Google Street View images and Bing aerial pictures. We see promising outcomes where learning to describe a neighborhood’s urban efficiency facilitates the estimation of house prices, even when generalizing to previously unseen London boroughs. We discuss the use of non-linear vs. linear approaches to combine these signals with traditional house pricing models and explain how the interpretability of linear models helps one to specifically derive the visual desirability of neighborhoods as proxy variables that are both of importance in their own right and can be used as inputs to other econometric methods. This is particularly useful as it can be extended elsewhere after the network has been trained with the training data, enabling us to p",3.0,https://doi.org/10.51201/jusst/21/0528061412bbfe4cc29e430ab2d93d37abc4053ed4965a00,10.1007/s44327-024-00032-4,The nonlinear impact of urban blue-green space landscape pattern on residential housing price in a new first-tier city of China,2024.0,Discover Cities,Yi-Yang Wu; Xiaodan Liu; Mingze Li; Henghui Xi; Muwu Xu; Weixin Ou; Conghong Huang,"Urban green and blue spaces (UGBS) play an increasingly vital role in enhancing urban environmental conditions and have been proven to increase residential property values. However, the nonlinear relationships between landscape patterns of UGBS and residential property values were less considered. To fill the gap, we employed nonlinear random forest (RF) models and partial dependence plots (PDPs) to analyze the impact of the spatial pattern of UGBS on housing prices in a new first-tier city (i.e., Nanjing) in China. The results showed that (1) The RF model explained 71.9% of the variation in housing prices. (2) Landscape patterns of UGBS significantly contributed to housing price variation, with the sum of relative importance scores of all the landscape pattern predictors amounting to 60.56%. (3) The PDPs results revealed that the percentage and spatial distribution of green and blue spaces positively influence housing prices. (4) Combining green and blue spaces boosted property values, with an average increase of approximately 0.41% and 0.53% in the percentage of landscape and mean patch area compared to green spaces alone. The findings of this study can inform urban planning poli",3.0,https://doi.org/10.1007/s44327-024-00032-4615064ff96c42ad49a4ee0754cc084e0b760473a622,10.1371/journal.pone.0322821,Forecasting second-hand house prices in China using the GA-PSO-BP neural network model,2025.0,PLoS ONE,Ji-Ning Wang; Hua-Bin Ji; Lei Wang,"While the traditional genetic algorithms are capable of forecasting house prices, they often suffer from premature convergence, which adversely affects the reliability of the forecasts. To address this issue, the research employs a genetic-particle swarm optimization (GA-PSO) algorithm and develops a GA-PSO-BP neural network model through the integration of the BP neural network. Building upon this foundation, the study considers several pivotal factors affecting housing prices and employs a dataset comprising 1,824 transactions of second-hand homes from 2023 to 2024, gathered from Lianjia.com, to forecast housing prices in China. This work shows that the GA-PSO-BP neural network model demonstrates exceptional forecasting performance when dealing with complex and high-dimensional data, significantly minimizing forecasting errors. The test set achieved an RMSE of 0.786 and a MAPE of 8.9%. Its effectiveness in forecasting prices of second-hand houses notably surpasses that of a BP neural network model optimized by a single algorithm. This research provides more accurate forecasts of second-hand house prices in rapidly growing urban areas such as Guangzhou, thus providing essential in",3.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0322821&type=printable616fcd08efaef6312ba34c7d0777e9ee20a7bc0df02,10.5089/9798400218095.001,Housing Boom and Headline Inflation: Insights from Machine Learning,2022.0,IMF Working Papers,Yun-Hui Zhao; Yang Liu; Dichao Yang,"IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. ABSTRACT: Inflation has been rising during the pandemic against supply chain disruptions and a multi-year boom in global owner-occupied house prices. We present some stylized facts pointing to house prices as a leading indicator of headline inflation in the U.S. and eight other major economies with fast-rising house prices. We then apply machine learning methods to forecast inflation in two housing components (rent and owner-occupied housing cost) of the headline inflation and draw tentative inferences about inflationary impact. Our results suggest that for most of these countries, the housing components could have a relatively large and sustained contribution to headline inflation, as inflation is just starting to reflect the higher house prices. Methodologically, for the vast majority of countries we analyze, machine-learning models outperform the VAR model, suggesting some potential value for",3.0,https://doi.org/10.5089/9798400218095.0016174b24880040758f0d8498d44a9815cadd8748cd61,10.21837/pm.v19i17.1004,A PROPOSAL FOR AFFORDABLE WAQF HOUSING PROJECTS IN MALAYSIA: PUBLIC PERCEPTION OF HOUSE CHARACTERISTICS,2021.0,PLANNING MALAYSIA,Nor Azizan Che Embi; Salina Kassim; R. Ramlee; Wan Rohaida Wan Husain,"Housing affordability is important to ensure houses are affordable to everyone across all income categories, whether they are in the low-income, middle-income (M40), or high-income group. Building housing projects on waqf land will help increase the supply of affordable houses, especially targeted at the M40 group, while also addressing the shortage of affordable housing for the M40 cohort. This study analyses public perceptions of house characteristics and relate these factors to affordable housing prices. The independent variables are location, infrastructure, facilities, size, design and quality. By applying a quantitative research design, the study aims to understand the relationship between various demanded housing characteristics vis-a-vis the price of the house. A sample of 261 usable responses was analysed using the Structural Equation Modelling (SEM). The results show that house size is not statistically significant in influencing the housing price, while location, infrastructure and design of the house are positively significant factors. These findings are expected to provide important inputs to the relevant authorities on factors that are critical in influencing the pric",3.0,https://www.planningmalaysia.org/index.php/pmj/article/download/1004/700618bc77caed50e74dab71c4275da1c5f92783d9fa72,10.1108/ijhma-07-2022-0102,News coverage vs sentiment: evaluating German residential real estate markets,2022.0,International Journal of Housing Markets and Analysis,Franziska Ploessl; Tobias Just,"619Purpose620To investigate whether additional information of the permanent news flow, especially reporting intensity, can help to increase transparency in housing markets, this study aims to examine the relationship between news coverage or news sentiment and residential real estate prices in Germany at a regional level.621622623Design/methodology/approach624Using methods in the field of natural language processing, in particular word embeddings and dictionary-based sentiment analyses, the authors derive five different sentiment measures from almost 320,000 news articles of two professional German real estate news providers. These sentiment indicators are used as covariates in a first difference fixed effects regression to investigate the relationship between news coverage or news sentiment and residential real estate prices.625626627Findings628The empirical results suggest that the ascertained news-based indicators have a significant positive relationship with residential real estate prices. It appears that the combination of news coverage and news sentiment proves to be a reliable indicator. Furthermore, the extracted sentiment measures lead residential real estate prices up to two quarters. Finall",3.0,62940013dcf66e67693970973566144b2b8c645e281,10.11591/ijeecs.v35.i2.pp1110-1121,"Machine learning for real estate valuation: Astana, Kazakhstan case",2024.0,Indonesian Journal of Electrical Engineering and Computer Science,A. Barlybayev; Arman Sankibayev; R. Niyazova; Gulnara Akimbekova,"Purpose of this research is to investigate the accuracy of machine learning models in forecasting and evaluating house prices, and to understand the key factors that impact pricing. The study involved analyzing data scraped from real estate ads in the “sale of secondary housing” category on the website krisha.kz. The paper emphasizes the importance of understanding the factors that affect house prices, such as quality, location, size, and building materials. It was concluded that these factors have a strong correlation with house price prediction. The information available on krisha.kz was found to be a useful resource for finding good apartments. The data collected by the scraper was analyzed by models: Linear regression (LR), interactions linear regression (ILR), robust linear regression (RLR), fine tree regression (FTR), medium tree regression (MTR), coarse tree regression (CTR), linear support vector machine (LSVM), quadratic SVM (QSVM), medium gaussian SVM (MGSVM), rational quadratic gaussian process regression (RQGPR), boosted trees (BoosT), bagged trees (BagT), neural network based on the bayesian regularization algorithm (BR-BPNN). BR-BPNN showed better results than other m",3.0,https://ijeecs.iaescore.com/index.php/IJEECS/article/download/36338/18498630e20cd68bb7ed86c70d9107a624ed0fa631d8d77a,10.30591/jpit.v10i2.8200,Prediksi Harga Rumah di Bandung 2024 Menggunakan Ensemble Learning: Analisis Komparatif dan Interpretabilitas,2025.0,Jurnal Informatika: Jurnal Pengembangan IT,Muh Naufal Hibatulloh; Gilang Danu Prakoso; Adiestiana Dwi Putri Yunus; Tommy Dwi Putra,"House price prediction plays a crucial role in investment decision-making and financial planning, particularly in developing cities like Bandung with its complex property market dynamics. This study aims to evaluate and compare the performance of various ensemble learning techniques in predicting house prices in Bandung for the year 2024, with a specific focus on model interpretability analysis. The data was collected through web scraping from www.rumah123.com in March 2024, covering attributes such as location, number of rooms, land area, and building area. The evaluated ensemble techniques include Random Forest, Gradient Boosting Machines, Xtreme Gradient Boosting, Linear Regression, and Stacking Ensemble. Model performance was assessed using MAE, RMSE, and R-squared metrics, while interpretability analysis was conducted using SHAP values. The Model Stacking Ensemble shows the most optimal results with R² 0.9076, RMSE 0.311, and MAE 0.216 in experiments involving location features. Features such as land size, building size, and location have proven to have the greatest impact in predicting prices based on SHAP analysis. This model has been successfully integrated into a Flask web",3.0,https://doi.org/10.30591/jpit.v10i2.8200631386a68a3afceb29aec93e7835015c0dc3634bd11,10.1007/s40747-024-01516-x,An adaptive trimming approach to Bayesian additive regression trees,2024.0,Complex & Intelligent Systems,Taoyun Cao; Jin-Ran Wu; You-Gan Wang,"A machine learning technique merging Bayesian method called Bayesian Additive Regression Trees (BART) provides a nonparametric Bayesian approach that further needs improved forecasting accuracy in the presence of outliers, especially when dealing with potential nonlinear relationships and complex interactions among the response and explanatory variables, which poses a major challenge in forecasting. This study proposes an adaptive trimmed regression method using BART, dubbed BART(Atr) to improve forecasting accuracy by identifying suspected outliers effectively and removing these outliers in the analysis. Through extensive simulations across various scenarios, the effectiveness of BART(Atr) is evaluated against three alternative methods: default BART, robust linear modeling with Huber’s loss function, and data-driven robust regression with Huber’s loss function. The simulation results consistently show BART(Atr) outperforming the other three methods. To demonstrate its practical application, BART(Atr) is applied to the well-known Boston Housing Price dataset, a standard regression analysis example. Furthermore, random attack templates are introduced on the dataset to assess BART(At",3.0,https://doi.org/10.1007/s40747-024-01516-x632b75840eff2ba1deb1330c439ffe790a94b64215e,10.1007/s43762-021-00027-6,Understanding the urban life pattern of young people from delivery data,2021.0,Computational Urban Science,Yining Qiu; Jiale Ding; Mengxiao Wang; Linshu Hu; Feng Zhang,"Young people are the backbone of urban development and an important pillar of social stability. The growth of young floating population in China has given rise to urban land expansion. Understanding the urban life pattern of urban life for young people benefits rational and effective land expansion. In this article, we introduce food delivery data into the process of exploring behavioral patterns of urban youth in Hangzhou, Zhejiang Province, China. The dynamic time warping (DTW) distance-based k-medoids method is applied to explore the main activity areas and activity patterns of the urban youth population. The results indicate that many young people from Hangzhou work in Internet companies, and most of work hotspot areas are observed in high-tech parks. The existence of overtime work is proved. Combined with the housing price data in Hangzhou, it is found that young people consider both housing prices and education environment when choosing where to live. The analysis combined with road network data reflects the planning characteristics of the city, also looks into differences between the actual city functions and the planning map. The proposed methods can provide useful guidance",3.0,https://link.springer.com/content/pdf/10.1007/s43762-021-00027-6.pdf63399263269f37367d876d05cc14bef78f47e159749,10.54691/bcpbm.v36i.3378,The real estate price prediction of US prediction based on multi-factorial linear regression models,2023.0,BCP Business & Management,Xiaoyu Xu,"In every period, housing price prediction has always been a fascinating topic. Fluctuations in housing price are not only relevant to each individual resident but also to the politics and economy of the country. This essence of this research project is the usage of some real influencing factors to predict housing prices. In the Ames Housing dataset from Kaggle.com, five real factors that have a relatively strong correlation with housing prices are the overall material and finish quality, the above ground living area, the size of garage in car capacity, the garage area, and the total basement area. Based on these five real factors, two multiple linear regression models are constructed for predicting residential prices in Ames, Lowa, US. According to the analysis, when two independent variables are closely related, removing one of them does not necessarily reduce the fit of the model significantly, even if both independent variables are closely related to housing price. Therefore, choosing more appropriate variables is very important to increase the fit of the model. These results shed light on guiding further exploration of using more significant variables to find more accurate mode",3.0,https://bcpublication.org/index.php/BM/article/download/3378/3317634bcfa4502082fba6d28b7f0cc91c7b7d05582a4dd,10.1002/for.2878,Uncertainty and Predictability of Real Housing Returns in the United Kingdom: A Regional Analysis,2022.0,Journal of Forecasting,Afees A. Salisu; Rangan Gupta; A. E. Ogbonna; M. Wohar,"The predictability of uncertainty for real housing returns in the United Kingdom is examined using regional data covering twelve (12) regions namely East Midlands, East of England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands, Yorkshire and the Humber. We utilize both housing policy uncertainty (HPU) and economic policy uncertainty (EPU) data while we render analyses for three data samples – full sample and two sub-samples covering the periods before and after the emergence of global financial crisis (GFC). Relying on a predictive model that accounts for the salient characteristics of the data, we find a negative relationship between HPU and real housing returns, on the average, regardless of the region analysed. Also, the model that accounts for HPU outperforms the benchmark model that ignores it while controlling for relevant covariates further improves the forecast performance. Additional analyses involving the EPU measure depict lower predictive contents for house price movements relative to the HPU measure and therefore using sector-specific uncertainty measure is crucial for more precise forecasts of real housing ret",3.0,https://repository.up.ac.za/bitstream/2263/90815/1/Salisu_Uncertainty_2022.pdf6357d0ecd251da2b3ebc1ac32ae6ea379bd0ed48ab8,10.5901/ajis.2013.v2n8p627,Evidence of a Housing Bubble in Beijing,2013.0,,Qiang Li; S. Chand,"The price of residential housing in Beijing has increased at an annual nominal rate of 20 percent for the past decade. This rapid growth has happened in a period of rapid urbanization where rural people have moved to cities and towns and there is a rapid and sustained growth in per capita income. This paper reports on the findings from a purpose-designed survey administered to a total of 420 households in 16 districts in Beijing on the determinants of house prices. Our analysis reveals that housing prices in Beijing have deviated above the norms in terms of expectation and affordability. Economic fundamentals in the form of income growth and low interest rates on home-loans are able to explain just 12 percent of the inflation in house prices. The remainder is due to speculation which is another reason for the bubble in house prices. Policies, in the form of interest rate hikes, reduced loans to purchase second and third homes, and limits on the purchase of additional homes have been introduced to slow the growth of the bubble. Respondents to the survey doubt the effectiveness of these policies in slowing the growth of price bubble, both in the short and the long term. DOI: 10.5901/",3.0,https://www.richtmann.org/journal/index.php/ajis/article/download/784/8156362d64dfb71ca6996f4f84f583f0f0d5fc64aa3227,10.1080/14445921.2017.1372037,Chinese investors investment strategies in the Australian residential property market,2017.0,,P. Wong; D. Higgins; R. Wakefield,"Abstract Active participation by foreign investors in Australian residential property market is common subsequent to the Global Financial Crisis 2008. Many residential properties in the Australian capital cities were purchased by overseas buyers. An estimated 18% of new dwellings in Sydney and 14% in Melbourne were purchased by foreigners in 2014. Simultaneously, house prices rose by 6.0% in 2014 and 5.0% in 2015 in the Australian capital cities. The focus of this research is to uncover the emerging determinants for the Australian residential property market. Cross border semi-structured interviews were conducted in Shanghai (China), the first such undertaken in Australia. Some unique investment strategies of the private investors from China emphasised on non-capitalist factors, such as early education and residential tourism, were identified along with some insights on the Chinese government policies that have incentivised the cross border investments from China. It is believed that understanding these investment strategies will assist policy makers to effectively manage the overheated Australian residential property market without compromising the steady flow of Foreign Real Esta",3.0,https://figshare.com/articles/journal_contribution/Chinese_investors_investment_strategies_in_the_Australian_residential_property_market/27420039637a5479e6447253f914c85e0d7a2e986699903eecd,10.59324/ejtas.2023.1(5).22,Prediction of House Prices in Lagos-Nigeria Using Machine Learning Models,2023.0,European Journal of Theoretical and Applied Sciences,M. P. Nwankwo; Ndukaku Macdonald Onyeizu; Emmanuel Chibuogu Asogwa; Chukwuogo Okwuchukwu Ejike; Okechukwu J. Obulezi,"This paper considers the relationship between the price of houses and the features namely the number of bedrooms, parking space, and different house types. In this study, a machine learning approach was used to develop prediction models that predicted house prices in Lagos. Different machine learning techniques were used, train-test split to split the data into training sets for training and building the model and test data to test the accuracy of the model, performance metric mean absolute error to set the baseline for the model, Variance Inflation Factor (VIF) to help remove multicollinearity between features and Streamlit interactive dashboards to communicate with the model. Correlation and regression methods were used to examine the relationship and build the model. It is observed that there is a strong positive correlation between the number of bedrooms and the number of toilets, likewise the number of bedrooms and the number of bathrooms. It also shows that there is a moderate positive correlation between the number of bedrooms and price. The model shows that the number of bedrooms, parking spaces, and house types play an important role in determining the price of houses.",3.0,https://ejtas.com/index.php/journal/article/download/281/23463871418138cbee1cb6ec35d6718a5b2ceadcbeab13,10.66674/bfh8vd86,Macroeconomic Drivers of Singapore Private Residential Prices: A Markov-Switching Approach,2017.0,Capital Markets Review,G. Rangel; J. Ng,"This paper attempts to address the relationship between various macroeconomic variables affecting Singapore private residential prices using a Markov-Switching approach rather than a single state linear regression framework. We adopt the 3-regime approach used by Nneji et al. (2013). The dataset encompasses a period from 1978Q1 to 2012Q1. Based on the extant literature, various macroeconomic variables that affect house prices were chosen. They are inflation rates, exchange rate changes, real interest rate changes, population growth, changes in public housing supply, and growth in real disposable income. The results indicate that in the steady and boom state, inflation rates, population growth, disposable income growth, and public housing supply changes are significant in explaining growth in private residential prices. Several abnormal results are also documented namely the non-significance of interest rate changes. Using a Markov-switching approach provides added information in identifying significant variables in each state allowing government policymakers to be more specific in using proper policy measures when addressing private residential price growth.",3.0,https://mfa-cmr.com/cmr/article/download/47/4463975064714a8fe4a250571ac85ccef2b4701d48fae,10.1088/1755-1315/479/1/012017,"Structural, Locational and Environmental Attributes effects the Residential Property Value in Flood Risk Area",2020.0,IOP Conference Series: Earth and Environment,Siti Hafsah Zulkarnain; M. A. Yuzir; M. N. Razali; Z. Tarmidi,"The arts and science in determining the residential property value make property value has evolved due to the changing in external factors such as economy, environmental and social. This research is aim to determine the residential property value by taking into account the economic attributes that could affect the value of residential property in flood risk areas. The economic attributes consist of structural, locational and environmental attributes involved in residential property valuation in relations with flooding. This paper will discover the significance and effect of each economic attributes in determining residential property value in flood risk area. An extensive review of previous studies in economic valuation of property for different floods disaster studies. It is considered to be the main restrictive factor resulting in lack of empirical studies in this field. Practitioners and researchers will find this study useful in developing an improved understanding of the economic valuation of flooding. The finding reveals that the economic attributes response to floods for a residential property value with positive, negative and none expected effects.",3.0,https://iopscience.iop.org/article/10.1088/1755-1315/479/1/012017/pdf640a5d91e7a3abc9f270d62fa765d16060c3c7aa690,10.1177/0308518x251333709,Exploiting the life course: Life course segmentation in the Brussels co-living sector,2025.0,Environment and Planning,Charlotte Casier; N. Revington,"Co-living’s strong growth in recent years has attracted increasing attention from the media, the real estate industry and public authorities. We argue that co-living is not radically new in terms of its design and services, which are similar to other ‘beds sectors’, but rather in terms of its target audience, young professionals. To understand this discrepancy between its innovative image and commonplace characteristics, we develop the concept of life course segmentation – a form of class monopoly rent – which highlights the mechanisms underlying the expansion of ‘beds sectors’ and ‘total-life landlordism’. Based on an in-depth qualitative study of the Brussels co-living sector, we demonstrate how the realization of class monopoly rent in co-living relies on the segmentation of the housing market according to the life course, through the promotion of distinct lifestyles associated with specific housing types. Co-living companies take advantage of the constraints facing young professionals in the housing market to discursively position co-living as an exclusive product uniquely suited to addressing these constraints. Recent developments in co-living, now targeting older adults, furt",3.0,6419e584952822304a1a6401260dee9a7bd9e88d9cb,10.3390/realestate1030012,"Property Valuation in Latvia and Brazil: A Multifaceted Approach Integrating Algorithm, Geographic Information System, Fuzzy Logic, and Civil Engineering Insights",2024.0,Real Estate,Vladimir Surgelas; V. Puķīte; Irina Arhipova,"This study aimed to predict residential apartment prices in Latvia and Brazil using algorithms from machine learning, fuzzy logic, and civil engineering principles, with a focus on overcoming multicollinearity challenges. To explore the market dynamics, we conducted four initial experiments in the central regions of Riga and Jelgava (Latvia), as well as São Paulo and Niterói (Brazil). Data were collected from real estate advertisements, supplemented by civil engineering inspections, and analyzed following international valuation standards. The research integrated human decision-making behavior with machine learning and the Apriori algorithm. Our methodology followed five key stages: data collection, data preparation for association rule mining, the generation of association rules, fuzzy logic analysis, and the interpretation of model accuracy. The proposed method achieved a mean absolute percentage error (MAPE) that ranged from 5% to 7%, indicating strong alignment with market trends. These findings offer valuable insights for decision making in urban development, particularly in optimizing renovation priorities and promoting sustainable growth.",3.0,https://doi.org/10.3390/realestate1030012642f3fd237a1982fab49b6272fa063b64276a0c4bb3,10.18178/ijscer.8.1.59-62,Tenants’ Motivations to Lease Green Office Buildings: an Exploratory Study of Sydney Central Business District,2019.0,International Journal of Structural and Civil Engineering Research,Sumin Kim; B. Lim; Jinu Kim,"Environmentally friendly and energy efficient green buildings have gained their popularity across many countries including Australia. Despite their increasing popularity and rental premium, it is not known why tenants are more inclined to lease green over non-green office buildings in Australia. In addressing this gap, this study aims to explore tenants’ motivations to leasing green office buildings. To this, this study first analysed over 2,000 tenants occupying over 100 green buildings in Sydney CBD. This allowed this study to identify which tenants are more likely lease green buildings than the others. The results show that tenants from; (i) the Finance and Insurance services sector, (ii) the Professional, Scientific and Technical services sector, and (iii) Real estate services sector account for over 50% of the entire tenant sample. Then, a judgemental purposive sampling method was adopted, and nine interviews were conducted with the key leasing decision-makers from the top three sectors. The interview findings reveal that the traditional aspects of location and costs are still the key motivational factors for green building tenants. However, many interviewees acknowledge that ",3.0,643b922ab81201881b47779769df9e76de174e13df7,10.36253/aestim-15344,"Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil",2025.0,Aestimum,Antônio Augusto Ferreira de Oliveira; Fabián Reyes-Bueno; M. A. Stumpf González; Everton Nunes da Silva,"Mass appraisal has significant applications, such as urban planning, real estate appraisal, and property tax. Due to the challenges of analyzing massive models, they are often developed using semi-automatic assessment methods and machine learning techniques. This article explores different appraisal model methods that utilize statistics and machine learning. It also looks at incorporating spatial information to see if the chosen method can effectively capture the typical spatial dependency of the real estate market. This can help reduce the spatial autocorrelation observed in the residuals. The study compared nine machine learning methods with traditional statistical approaches using a dataset of over 43,000 apartments in Fortaleza, Brazil. The results of the machine learning algorithms were similar. The XGBoost minimized spatial autocorrelation. The easiest interpretations were with MRA, M5P, and MARS techniques. Although, these techniques had the greatest residual spatial autocorrelations. There is a trade-off between the methods, depending on whether the aim is to improve accuracy or provide a clear explanation for property taxation.",3.0,https://oaj.fupress.net/index.php/ceset/article/download/15344/13315644593563416cab0fbe52aa35319de51a18005aee71,10.1142/s2345748121500251,"The Impact of Subway Network Expansion on Housing Rents: An Empirical Study in Beijing, China",2022.0,Chinese Journal of Urban and Environmental Studies,Xin Ma; Tao Huang,"Most previous studies have focused on the impact of subways on housing prices instead of rents, while the latter could better measure residential values. Based on a dataset collected from a real estate agency in Beijing, which contains more than 900,000 housing rental transaction records from 2011 to 2020, this paper empirically evaluates the causal effect of subway network expansion on housing rents. It employs a series of progressive difference-in-difference (DID) approaches, to estimate the impact and determine the impact scope. The findings demonstrate that a reduction of the distance to subway stations by 1 km increases the rents by 2.32%; the impact scope is about 1.5 km and the average rent appreciation within the range is 5%. The addition of a line for non-transfer stations raises the rents by 10% for houses 1.5–2 km away from the stations, extending the impact scope. Houses with large areas in upscale and old neighborhoods near the city center are affected less by subways. It also confirms the siphon effect in the rental market, i.e.: rents of houses far away from the new stations fall after the opening of the stations.",3.0,https://doi.org/10.1142/s234574812150025164554ac58cfe359a71f171d608bdf8a44e4f46643d0,10.1016/j.econedurev.2018.12.001,"Which New Yorkers vote with their wallets? The impact of teacher quality data on household sorting, and residential and school demographics",2019.0,Economics of Education Review,E. R. Rivera Rodas,"This paper examines the relationship between the February 2012 New York City teacher quality data release, the racial and ethnic composition of elementary and middle public schools and their neighborhoods, and housing prices. A unique dataset that links teacher quality to school characteristics, housing prices and characteristics, and Census data is used to estimate a difference-in-difference model to estimate the impact of the teacher quality release on housing prices. These results are then used to estimate the impact of the data release on residential and school demographics. The results in this study provide the first evidence of the effects of teacher quality measures on the housing market in New York City and the demographic shifts in residential and school mobility patterns as a result. The data suggests that the housing market responds significantly to the new information that was provided by the release of the teacher quality information, even when taking into consideration the school grades and other variables that may influence teacher quality measures. The magnitude of how much housing prices increase is greatly impacted by certain neighborhood demographics and the resu",3.0,646c7f4aa6f5a705ff35af99018add7f46e18793106,10.30902/jrea.2021.7.1.29,"Early Warning System of Housing Market Using Machine647 Learning",2021.0,Journal of Real Estate Analysis,Daehyeon Park; Jeonghwan Kim; Doojin Ryu,"This study proposes an early warning system for risks of the housing market based on machine learning models. We adopt a signal approach to detect the housing market risk and establish the early warning system using classification methods. Considering the moment when the housing market falls into recession as a warning signal, we set the signal as the price which is more than the sum of the average and standard deviation of upcoming prices. The detected signals are consistent with empirical observations in the Korean housing market. We select the best performing function among classification models for machine learning which predicts a warning signal. As a result of an intercomparison of models including the logistic regression, the support vector machine, the random forest and the artificial neural network with the use of inputs such as housing price indices, macroeconomic variables and other housing market variables, we find that the random forest demonstrates the highest prediction performance. Our early warning system yields policy implications in terms of relevant detection of price fluctuations in the housing market.",3.0,http://www.ejrea.org/download/download_pdf?pid=jrea-7-1-296483ac2c19cb46ce6ecc8eafc786c6c629b50cf44b8,10.2139/ssrn.3711891,Regional Heterogeneity and U.S. Presidential Elections,2020.0,Social Science Research Network,Rashad Ahmed; M. Pesaran,"This paper develops a recursive model of voter turnout and voting outcomes at U.S. county level to investigate the socioeconomic determinants of recent U.S. presidential elections. It is shown that the relationship between many socioeconomic variables and voting outcomes is not uniform across U.S. regions. By allowing for regional heterogeneity and using high-dimensional variable selection algorithms, we can explain and correctly predict the unexpected 2016 Republican victory. Key factors explaining voting outcomes include incumbency effects, voter turnout, local economic performance, unemployment, poverty, educational attainment, house price changes, urban-rural scores, and international competitiveness. Our results corroborate evidence of ‘short-memory’ among voters: economic fluctuations realized a few months prior to the election are indeed powerful predictors of voting outcomes as compared to their longerterm analogues. The paper then presents real time forecasts for the 2020 U.S. Presidential Election based on data available at the end of July 2020 which are then updated based on data available as of mid-October.",3.0,http://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe2092.pdf649c989ad31c3ab6153621e449484ae7aefaaf516ba,10.1109/seb4sdg60871.2024.10629723,Housing Price Prediction Using Machine Learning Techniques,2024.0,"2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)",I. Obagbuwa; Samantha Danster,"This research employs advanced machine learning algorithms to predict housing prices, specifically Linear Regression, Random Forest, Gradient Boost Machine, Multilayer Perceptron (MLPs), and Long Short-Term Memory (LSTM). The study aims to address the oversight of crucial house factors such as front area, location, size, and others, to provide effective housing pricing predictions. The methodology involves structured processes, including data collection, preprocessing, feature extraction, dataset splitting, and the implementation of machine learning models. Using a housing prices dataset from Kaggle, the study explores attributes like sale price, lot area, street type, and more. The dataset, comprising four files (Data description, sample submission, test, and train), consists of 163 columns, each record encompassing 80 attributes that describe specific houses. The study emphasizes the significance of data preprocessing involves handling missing values, outlier removal, and log transformation for improved results. The models' performance is evaluated using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) on the test dataset. The results indi",3.0,6506a72a8855b21bb02d955590e8dfd6e3b4516078f,10.1109/access.2023.3281895,Model Selection Based on Residual Dependence Measure,2023.0,IEEE Access,Lingjuan Lyu; Yi-Ming Ding; Yuan Wan,"Choosing the most suitable model from a set of models is crucial. Based on the assumption that the noise and independent variables in the model are independent, the degree of fit of the model was determined by studying the correlation between the residuals of the fitting equation and independent variables. The dependence measure can measure the dependence relationship between random variables. Common dependence measures include the Canonical Correlation Coefficient (CCA) and Distance Correlation (DC). In this study, the feasibility of the standard correlation coefficient and distance correlation in a two-dimensional case is proven by numerical simulation experiments, and the robustness of different noises, noise intensity and model types are also demonstrated. On the Boston housing price dataset, ridge regression, lasso regression, Bayesian regression and other methods were used to obtain different fitting equations. By comparing the CCA and DC of the residuals and independent variables, the estimation equation of the ridge regression was found to be the best, which proved the feasibility of the method.",3.0,https://ieeexplore.ieee.org/ielx7/6287639/10005208/10141611.pdf65151af0488b198367636ddd2f2c4c67e0c10500ed9,10.1108/ijhma-11-2023-0160,Comparative analysis of different approaches to the Ukrainian residential property market evolution modelling and its forecast for the years 2019–2024,2024.0,International Journal of Housing Markets and Analysis,V. Yakubovsky; Kateryna Zhuk,"652Purpose653This study aims to provide a comprehensive analysis of various approaches to the residential property market evolution modelling and to examine the macroeconomic fundamentals that have shaped this market development in Ukraine in recent years.654655656Design/methodology/approach657The study uses a comprehensive data set encompassing relevant macroeconomic indicators and historical apartment prices. Multifactor linear regression (MLR) and ridge regression (RR) models are constructed to identify the impact of multiple predictors on apartment prices. Additionally, the ARIMAX model integrates time series analysis and external factors to enhance modelling and forecasting accuracy.658659660Findings661The investigation reveals that MLR and RR yield accurate predictions by considering a range of influential variables. The hybrid ARIMAX model further enhances predictive performance by fusing external indicators with time series analysis. These findings underscore the effectiveness of a multidimensional approach in capturing the complexity of housing price dynamics.662663664Originality/value665This research contributes to the real estate modelling and forecasting literature by providing an analysis of mult",3.0,666c2838459cfde8bfcf9b8d7f7950d69c969d97ce1,10.1145/3653724.3653753,A Multivariate Regression Analysis of Factors Influencing California Housing Prices,2023.0,ICMML,Jia-Jun Yu,"This study investigates the factors influencing home price volatility in California, with a focus on home age, size, bedrooms, population, family structure, and location. Analyzing diverse California housing data using multiple linear regression and multicollinearity analysis, key findings emerge: Firstly, home age significantly impacts prices, with newer homes typically commanding higher prices due to buyer preferences for newer properties and lower maintenance costs. Secondly, geography plays a vital role, with proximity to the bay and ocean correlating with higher house prices, reflecting regional price disparities. Additionally, factors like the total number of rooms, bedrooms, and family structure also influence home prices, reflecting the property's size and layout. These insights benefit homebuyers, investors, and market participants seeking to understand trends and risks in California's real estate market. Future research should expand datasets and consider more factors, particularly delving into the effects of different geographical locations and home age on house prices.",3.0,https://doi.org/10.1145/3653724.365375366772152657f15e6b82243d90293a2e357dbd817f82,10.47264/idea.lassij/4.1.8,Are the Millennials Getting Less Married?,2020.0,,Khalid Khan; Seema Zubair; S. Köseoğlu,"The study investigates the causal link between the Millennials (ML) Population (18-37 year age) and the Marriage Rate (MR) (married population/total population) for the countries of France, Germany, Italy, Netherlands, Spain, and the United Kingdom (UK) by using the bootstrap causality test. The findings suggest that ML population has a significant negative impact on MR in Italy and the Netherlands, while MR has a significant negative impact on ML population in Spain. Besides, the System Generalized Method of Moment Regression (SGMM) is conducted to release the effects of the Divorce Rate (DR), Education Attainment (EA), Globalization (GB), Social Protection (SP), Secularization (SEC), House Prices (HP), Financial Crisis (FC), and Working Population of women (WP) variables on MR and ML population. Likewise, the outcomes display that these are the leading factors of explaining ML population. Our results support the two-period model of Peters (1986), which states that MR is the combination of the economic, social, and religious elements and has important policy implications.",3.0,https://ideapublishers.org/index.php/lassij/article/download/75/73668fe63dca1260ccb4db00ba7abaef1628b2f2ed67b,10.1145/3745133.3745175,Prediction of House Price in Boston Based on the Fusion of Crested Porcupine Optimizer Algorithm and BP Neural Network,2025.0,Proceedings of the 2025 International Conference on Digital Economy and Information Systems,Qiao Hong; Hao-Qin Li,"Aiming at the problems that the traditional backpropagation (BP) neural network is prone to fall into local optimum and has a slow convergence speed in house price prediction, this paper proposes a BP neural network fusion model (LCPO-BP) based on the improved crested porcupine optimizer algorithm. By introducing the elite reverse learning strategy and the adaptive weight mechanism, the global search ability is enhanced, and the initial weights and biases of the network are optimized, thereby improving the performance of the model. The experimental results based on the Boston house price dataset show that compared with the traditional BP, the LCPO-BP model reduces the mean absolute error (MAE) and root mean square error (RMSE) by 58.81% and 59.63% respectively, and the coefficient of determination (R²) increases to 0.93269. The effectiveness of the improved algorithm in avoiding local optimum, accelerating convergence and improving prediction accuracy was verified, providing a new reference for intelligent optimization methods for complex nonlinear prediction problems.",3.0,https://dl.acm.org/doi/pdf/10.1145/3745133.37451756697e4196dcb8047c15ebac7cad28c72aff4ef735fc,10.2139/ssrn.3398897,Regional-Specific Determinants of Non-Performing Loans,2019.0,Social Science Research Network,Sergey Avetisyan,"This paper investigates the determinants of non-performing loans in the Armenian banking sector during the period 2004-2016. The study is motivated by the hypothesis that both macroeconomic and regional-specific variables have an effect on loan quality. The study uses macroeconomic and regional specific factors to identify the determinants of NPLs of Armenian banks. To our best knowledge this study is the first to analyse the detriments of both national and foreign currency NPLs on regional level. I analyze NPLs in national currency and foreign currency, the results suggest NPLs in national currency are more driven by macroeconomic factors than NPLs in foreign currency. The analysis includes fundamental determinants of the NPL, such as GDP, wages, interest rate, housing price, return on assets, loan loss. The results suggest that the NPL is sensitive to interest rate in national currency and unemployment in almost all samples. I find statistically significant effects of wage increases on NPLs in OLS and FE models. The impact of the financial crisis is incorporated into the model by using a financial crisis dummy, the crisis dummy variable takes the value one for years 2008, 2014, w",3.0,670387931cda44f6a153341aafdd3ec67794f0de328,10.1109/icisc62624.2024.00067,Machine Learning based Real Estate Price Prediction,2024.0,International Conference on Information Security and Cryptology,A. Mukherjee; Rtwik Nambiar; Deepjyoti Choudhury,"The dynamic nature of real estate markets gives rise to huge challenges for buyers and sellers in accurately predicting property prices. This paper presents a detailed study on real estate price predictions, focusing on using machine learning techniques to forecast property prices effectively. Using a dataset from Bengaluru, as a case study, it explored the intricacies of Real Estate markets and developed a predictive model to estimate the property prices. Our studies consist of data preprocessing, featuring engineering, model selection, and evaluations, giving insights into the factors that affect property prices and the performances of various regression algorithms. After performing multiple experiments, we showcase the efficiency of our approaches in predicting property prices. Existing techniques that have tried to predict house prices have often struggled to deal with nonlinear relationships present among the factors. Recent techniques such as Linear regression, Ridge and Lasso Regression and Support Vector Machines(SVM) have come up short of being perfect when it comes to dealing with computational intensity and these techniques require extensive hyperparameter tuning. Howeve",3.0,671f1aa8e80dd9a9d5f2137ad346f5617fb4cdc01e6,10.1016/j.procs.2016.07.071,A Study on Repeat Sales House Price Index Based on Penalized Quantile Regression,2016.0,,Kyupil Yeon,"Abstract House price index (HPI) based on real transaction prices is commonly estimated by the repeat sales model which utilizes sales pairs traded over twice during the period of interest. Since the conventional repeat sales model is based on the ordinary multiple linear regression, it is affected considerably by outliers. In addition, it produces quite unstable HPI in case of insufficient sales pairs. To tackle these problems, a quantile regression based repeat sales model with an appropriate penalty function is suggested. The induced HPI from the proposed model shows much stability compared to the conventional methods. Moreover, the HPIs derived from the penalized quantile regressions for various quantile parameters provide us with valuable information which cannot be obtained from the existing repeat sales models based on ordinary multiple linear regression. As a result, the proposed method for constructing HPIs can be considered as an alternative to the conventional repeat sales price indices in that it provides very stable and informative HPIs.",3.0,https://www.sciencedirect.com/science/article/pii/S1877050916312546/pdf672445a41995cafbd9392ed22cb883ebf29b6c6b86b,10.30997/ijar.v4i1.283,GEOGRAPHICALLY WEIGHTED REGRESSION WITH DIFFERENT KERNELS: APPLICATION TO MODEL POVERTY,2023.0,Indonesian Journal of Applied Research (IJAR),N. Sunusi; Aan Subarkah,"Poverty is still a significant problem in Indonesian development. The poverty alleviation programs implemented have yet to pay attention to spatial aspects, so the policies are often not on target. This study aims to reveal the spatially varying relationships between the poverty level and its factors at the regional scale and compare three fixed kernels as a weighting matrix for GWR. The method used is geographically weighted regression (GWR) with poverty data for 2021. The study results show spatial autocorrelation and is grouped in 29 regencies/cities. The expenditure per Capita, life expectancy, percentage of houses and households with proper drinking water, open unemployment rate, labor force participation rate, and GDP at constant prices show different effects in each region. The results strengthen the argument that spatial aspects cannot be ignored in regional development, especially poverty alleviation. Therefore, area-based poverty alleviation can be used as a basis for determining/determining policies so that they can be more targeted.",3.0,https://iojs.unida.ac.id/index.php/IJAR/article/download/283/1486732811c6965de93da0257edb1d6ca512bb72a4434d,10.24957/hsr.2024.32.3.155,A Study on the Factors Affecting the Default of Returning the Jeonsei Deposit : For Busan Metropolitan City,2024.0,Korean Association for Housing Policy Studies,Woogue Choi; Jae-Pyeong Park; C. Choi,"Recently, the number of cases of jeonse deposit repayment defaults, where tenants cannot receive their jeonse deposits when their lease contracts expire, has increased rapidly. Based on the data on jeonse deposit repayment guarantees of the Housing and Urban Guarantee Corporation in Busan Metropolitan City, this study analyzed the factors affecting jeonse deposit repayment defaults using binary logistic regression. The results of the analysis showed that the factors affecting jeonse deposit repayment defaults were housing type, real jeonse ratio, and jeonse supply and demand index. In particular, from the result that the real jeonse ratio has a positive effect on jeonse deposit repayment defaults, it was confirmed that the self-capital theory is also applicable to the jeonse market. In addition, the impact of jeonse deposit repayment defaults varies depending on the type of housing and the liquidity of submarkets. The real jeonse ratio is a positive factor for apartments and efficiency apartment, but a negative factor for detached houses, and interest rates are a positive factor for apartments and a negative factor for efficiency apartment. Housing prices are not significant factor",3.0,674c53e5cef9b7f9754f0e7bd9056b50d7e8c12944d,10.17485/ijst/2016/v9i17/82861,State of Art Report on Variables Affecting Housing Value,2016.0,,S. Sandbhor; N. Chaphalkar,Background/Objectives: Physical characteristics of real property are considered as the deciding factors of housing value. Value calculated on the basis of only property characteristics may not give accurate figure. This can be due to ignorance in considering variables which indirectly affect the value. Methods/Statistical Analysis: Real property prices vary due to impact of not only property characteristics but also macroeconomic variables. Impact of macroeconomic variables is indirect and is difficult to assess in terms of quantum of variation in value of property. It is required to study available literature to summarize the probable attributes of real property value. Findings: Variables have been identified and presented in two categories namely intrinsic variables and extrinsic variables. Applications/Improvements: Wide range of variables from gross domestic product of nation to type of fixtures in the house affect property value. Appraisers should know which variables to be given consideration to get fairly accurate result.,3.0,https://doi.org/10.17485/ijst/2016/v9i17/828616755f2571676195f5af48c3363aca86620764c5ea94,10.3390/su14116433,"A Data Mining Study on House Price in Central Regions of Taiwan Using Education Categorical Data, Environmental Indicators, and House Features Data",2022.0,Sustainability,Min-Feng Lee; Guey-Shya Chen; Shaojun Lin; Wei-jie Wang,"This study takes the city of Taichung, Taiwan, as the research area, combines the survey results about the demand for residential houses for the next year, and uses relevant parameters and data of real price registration as the prediction results. In this study, eight types of school district features (such as teachers and students of secondary and elementary schools) and five types of air pollution features are selected and processed with a data mining method to discover the total transactions of real estate properties in various districts of Taichung. The results of K-means clustering and decision tree classification reveal that the four districts of the old Taichung City, namely, Beitun District, North District, Xitun District, and Nantun District, have houses meeting the conditions of egg yolk districts; houses in the old Taichung County have attributes of egg white districts. The results of decision tree classification show that the total price is the most important attribute influencing egg yolk and egg white districts.",3.0,https://www.mdpi.com/2071-1050/14/11/6433/pdf?version=16535691806768e382679d0eeb62d236d8390d911eedf3d052cbd,10.1080/15567249.2024.2418126,Key influences on residential photovoltaic solar panel adoption in the United States,2024.0,Energy Sources Part B Economics Planning and Policy,Diren Kocakuşak; Michael R. Greenberg; Clinton J. Andrews,"ABSTRACT The prevalence of low-density residential development to host solar PV, the ubiquity of solar irradiation, and incentivizing policies have created substantial opportunities for homeowners in the United States. Federal, state, and municipal governments implement regulatory policies and financial incentives to promote photovoltaic solar systems in residential buildings with limited resources. Thus, understanding the main factors and their spatial variations is vital for expanding the accessibility of renewable energy benefits to specific socio-economic groups and submarkets. Our research includes linear regression, principal component regression, and spatial error models to provide empirical evidence for the relationship between the adoption rates and socio-economic, geographical, and technical factors while identifying characteristics of adopter groups. The results suggest that the relative advantage factors – electricity prices and solar irradiation – play the most significant role across all regions and market segmentations. Statewide policy indicators are the second most significant factor, followed by socio-economic variables on employment status, remote working, car ow",3.0,677d7a3927620ebe5d6fd80dd8ffdc9b49395fc8b9f,10.1016/j.cities.2026.107063,"Disentangling spatial and structural drivers of housing prices through Bayesian networks: A case study of Madrid, Barcelona, and Valencia",2025.0,Cities,Alvaro Garcia Murga; Manuele Leonelli,"Understanding how housing prices respond to spatial accessibility, structural attributes, and typological distinctions is central to contemporary urban research and policy. In cities marked by affordability stress and market segmentation, models that offer both predictive capability and interpretive clarity are increasingly needed. This study applies discrete Bayesian networks to model residential price formation across Madrid, Barcelona, and Valencia using over 180,000 geo-referenced housing listings. The resulting probabilistic structures reveal distinct city-specific logics. Madrid exhibits amenity-driven stratification, Barcelona emphasizes typology and classification, while Valencia is shaped by spatial and structural fundamentals. By enabling joint inference, scenario simulation, and sensitivity analysis within a transparent framework, the approach advances housing analytics toward models that are not only accurate but actionable, interpretable, and aligned with the demands of equitable urban governance.",3.0,https://arxiv.org/pdf/2506.09539678f5c1e72c83a73497a0bd606c08b2fd73a6bbf596,10.47836/ac.16.2.paper06,"TOURISM GENTRIFICATION: IMPACTS ON LIVABILITY IN CIHIDEUNG VILLAGE, WEST BANDUNG, INDONESIA",2023.0,Design,R. Margono; Y. Yulia; W. D. Pratiwi,"The tourism presence affects the livability of the tourist destination area. It boosts development while simultaneously creating issues, namely rising housing prices, inequality, and displacement,which resembles the sign of gentrification. This study analyses how tourism and gentrification affect livability. This study chooses Cihideung Agritourism Village as a case study. It employs two parts of analysis. The first analysis is to pinpoint the tourism gentrification characteristics within the study area through indicators. The second analysis assesses the livability through a set of livability679indicators. From the result of both analyses, it is discovered that Cihideung Agritourism Village is still in the early stage of gentrification, which is indicated by the transformation of land use and the influx of wealthier populations to the area. Based on livability assessment, gentrification is advantageous for the economic and spatial developments of the area, but less for environmental and social aspects.",3.0,https://doi.org/10.47836/ac.16.2.paper0668072dabbf1c736ec8ec54bbdc63fc1bece8be83d4a,10.1080/12265934.2024.2346149,"Still prefer, but not that much: the premium of subway access on house prices during the COVID-19 pandemic period",2024.0,International Journal of Urban Sciences,Dongwoo Hyun,"ABSTRACT Rail access has traditionally been considered a factor that enhances the amenity value of urban areas, resulting in a price premium for properties located near these stations. However, the Covid-19 pandemic has exerted a profound influence on people's mobility patterns and travel behavior within urban areas. One of the most conspicuous changes is a substantial decrease in subway ridership. This study investigates whether the decline in public transit ridership has led to shifts in housing location preferences driven by proximity to rail access during the Covid-19 pandemic. This study focuses on assessing the impact of the pandemic on the premium associated with rail access in relation to house prices. Using approximately 180,000 apartment transaction records from Seoul, South Korea, the empirical findings, based on a difference-in-differences approach, reveal an immediate and significant depreciation effect resulting from this exogenous shock during the 25-month pandemic period. The premium decreases significantly within a 1.2-kilometer radius from a subway station, and this reduction exhibits a distance-decay pattern within the designated area. Highlights Traditional amen",3.0,681bda551de4b69a79dcecd600556612e5d61769cc0,10.1109/icocs.2019.8930776,"Urban, agricultural and touristic land use patterns: combining spatial econometrics and ABM/LUCC",2019.0,World Conference on Complex Systems,E. Innocenti; C. Detotto; Corinne Idda; Dominique Prunetti,"This work deals with computer simulation of complex spatiotemporal systems based on Agent-Based Models of Land Use Cover Change (ABM/LUCC). We propose an ABM/LUCC for modelling the complex system of tourist areas facing an intense residential development. In this paper, we present a model suited to the geographical context of the Corsica, which is sufficiently flexible to be adapted to others similar contexts to simulate and evaluate territorial planning policies. The model describes a collection of tourist areas that have faced -and still face- an intense residential development leading to a huge pressure on land prices as well as to land-use conflicts (local residential market, tourist rental investment, agricultural production). The objective is to realize simulations of new management practices, able to combine economic development and land competition in a strongly constrained environment. By means of computer models, we intend to represent the behavior of heterogeneous economic agents in both time and space. Existing spatial territory management models are mostly analytically intractable. Agent-based modeling formalisms offer practicable solving techniques. In particular, the",3.0,68261ba8cd2b2cf66b68d0c4ee4b1ce30074b2e187e,10.59324/ejaset.2025.3(3).29,Using Artificial Intelligence-Based Machine Learning Regression Models for Predictions of Home Prices,2025.0,"European Journal of Applied Science, Engineering and Technology",Mukund Sai Vikram Tyagadurgam; Venkataswamy Naidu Gangineni; S. Pabbineedi; Ajay Babu Kakani; Sri Krishna Kireeti Nandiraju; Sandeep Kumar Chundru,"Accurate forecast of home prices is crucial in making informed decisions in real estate, financial plans and investment practices. This work exploits the Volusia County Property Sales Dataset to create a strong machine learning model for predicting home prices. By undertaking extensive data preprocessing, feature engineering, and transformation of the outcome and having an XGBoost regression model as a result, we trained and measured its performance Metrics include R², MSE, and MAE. The XGBoost model outperformed baseline models such as Random Forest (R² = 0.87) and Artificial Neural Networks (ANN) with R² = 0.97, MSE - 8.17, and MAE = 4.03, R² = 0.84. These results show the effectiveness and accuracy of XGBoost when identifying complex, nonlinear relations in the housing data. The proposed pipeline is scalable and can be adapted to fit into live property valuation systems, lending valuable insights to stakeholders in urban planning, real estate investments, and public policy formulations.",3.0,https://ejaset.com/index.php/journal/article/download/293/215683621072befd6daa6b245a9f93df0680ba050de3dc,10.1080/19407963.2025.2466192,Explanatory factors for the distribution of short-term rentals in coastal destinations: the impact of second homes in Alicante,2025.0,"Journal of Policy Research in Tourism, Leisure and Events",Patricia Valenzuela-Martin; Sofía Mendoza de Miguel; R. Villar-Navascués; Daniel Gálvez-Pérez; Begoña Guirao Abad; L. Bañón,"ABSTRACT The popularization of the use of STRs as an alternative to traditional accommodation has significantly changed the accommodation sector. This is not a new phenomenon; however their exponential growth accounts for a disruptive scope in many cities. In this context, the article aims to identify the factors that drive the location of STRs in the city of Alicante, a traditional sun and beach destination, to assess whether there is a relationship with second homes and whether the influence of this factor varies spatially, which could indicate the existence of different patterns of use and occupation of these dwellings by their owners. A Geographically Weighted Regression model is used to identify its determining factors. To complement the main methodology, a clustering analysis was carried out considering the number of blocked days per month of each STRs with the purpose of spatially characterizing the typology of the STRs offer in Alicante. The results of the research show, on the one hand, that there are four factors that explain the location of the STRs in Alicante: the percentage of non-main housing, the percentage of foreign population, the percentage of main housing in re",3.0,684e401ca1d8b10aa7172f001ae3ff2902435051147,10.1080/1351847x.2021.1941173,The influence of a mortgage interest deduction on house prices: evidence across tax systems in Europe,2021.0,European Journal of Finance,Wouter Vangeel; Laurens Defau; L. De Moor,"In a large majority of European countries, a tax relief on mortgage interest payments is granted in order to enhance homeownership. Although there is a common belief stating that a mortgage interest deduction (MID) is capitalized into house prices, empirical evidence remains scarce. Therefore, our paper is about the influence of this fiscal relief on house prices. The scope of our analysis includes fourteen European countries, for which an unbalanced panel dataset over the period 1990–2015 is constructed. All our regression results support the hypothesis that a MID has had a significant price-increasing effect in the selected countries over the observed period. However, this result does not hold for a MID in the countries where a dual income tax (DIT) is applied, suggesting a significant difference between tax systems. These results are relevant for governments because there has been much debate about whether and to what extent countries should limit its MID over the last decades.",3.0,https://biblio.vub.ac.be/vubirfiles/94775698/paper_MID_final_acc_Lieven_DE_MOOR.pdf685f6fd138d77496bedd9ea96a5c5ef586a67aea9f3,10.36898/001c.22311,House Price Prediction With Statistical Analysis in Support Vector Machine Learning for Regression Estimation,2021.0,Curiosity: Interdisciplinary Journal of Research and Innovation,Cesar A. Vasquez; V. Chellamuthu,"Support vector learning machines are supervised models that analyze data for classification analysis. However, with modifications these models can be extended for regression estimation. In this paper, we use Dean De Cock's Iowa house price dataset, composed of quantitative and qualitative variables, to build a predictive house pricing model. We employ a systematic approach to preparing the data and processing the features using statistical analysis. To reduce model over-fitting, we use stratified k-fold cross-validation. We also use various metrics for measuring model performance including r-squared (*R*^2^) and mean absolute error (MAE). Moreover, we explore various kernels for hyper-dimensional feature mapping to account for data that is not linearly separable. We compare the kernel performance and find that there is a trade-off between model complexity and performance. Furthermore, there also exists a trade-off between maximum accuracy achievable and general model performance.",3.0,https://curiosity.scholasticahq.com/article/22311.pdf686f6dc3049603a372f1008008a5729d92968782f6a,10.1080/09599916.2024.2403998,On the uncertainty of real estate price predictions,2024.0,Journal of Property Research,João A. Bastos; J. Paquette,"ABSTRACT Uncertainty quantification associated with real estate appraisal has largely been overlooked in the literature. In this paper, we address this gap by analysing the uncertainty in automated property valuations using conformal prediction, a distribution-free procedure for constructing prediction intervals with valid coverage in finite samples. Through an empirical study of property prices in the San Francisco Bay Area, we find that prediction intervals obtained using conformal quantile regression have exact coverage. In contrast, prediction intervals obtained from non-conformal quantile regressions severely undercover the data. Furthermore, we show that the intervals adapt to various characteristics of the dwellings, which is crucial given the heterogeneous nature of real estate data. Indeed, we observe that larger and older properties, those in both low and high-income neighbourhoods, as well as those on the market for less than one year are more challenging to evaluate.",3.0,https://repositorio.ulisboa.pt/bitstreams/649995ec-b0df-4d4d-ae9b-2c65b7e2f9c5/download687aab434f6813b5928621565d90dfc7c68dcafeb02,10.15446/dyna.v87n212.80202,Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes,2020.0,,Jorge Iván Pérez Rave; Favián González Echavarría; J. C. Correa-Morales,"The objective of this work is to develop a machine learning model for online pricing of apartments in a Colombian context. This article addresses three aspects: i) it compares the predictive capacity of linear regression, regression trees, random forest and bagging; ii) it studies the effect of a group of text attributes on the predictive capability of the models; and iii) it identifies the more stable-important attributes and interprets them from an inferential perspective to better understand the object of study. The sample consists of 15,177 observations of real estate. The methods of assembly (random forest and bagging) show predictive superiority with respect to others. The attributes derived from the text had a significant relationship with the property price (on a log scale). However, their contribution to the predictive capacity was almost nil, since four different attributes achieved highly accurate predictions and remained stable when the sample change.",3.0,https://doi.org/10.15446/dyna.v87n212.802026887484bb278f29c86f0aa0b049a4979a956fa43736,10.1080/21681376.2024.2314396,Flattening which curve? Property-price gradients in New South Wales during the COVID-19 pandemic,2024.0,"Regional Studies, Regional Science",Nicholas Sheard,"ABSTRACT This paper tests how the patterns of residential property values in the Australian state New South Wales changed during the COVID-19 pandemic. Between 2019 and 2021, property values became relatively high in medium-sized towns and cities, suggesting those locations became more attractive. In its capital and largest city Sydney, the premium on proximity to the central business district (CBD), where employment is heavily concentrated, decreased after 2019 and that decrease persisted beyond the end of pandemic measures. The premium in Sydney on proximity to the beach has increased, but on a trajectory consistent with an existing trend. The decreased premium on proximity to the CBD since 2019 appears to have initially been related to the value of access via public transport, but then also to the value of access by car. The findings suggest shifts in regional development and urban form that could have implications for how cities are planned and developed.",3.0,https://doi.org/10.1080/21681376.2024.2314396689bec3ab8232b583167f74d09c4507ce183d0eb997,10.3233/jifs-210369,Housing affordability evaluation based on the third-generation prospect theory and the improved VIKOR method,2021.0,Journal of Intelligent & Fuzzy Systems,Guan-Cen Wu; Chen Li; Xing Niu,"Housing affordability is an important issue and can be measured by an increasing number of indicators. Different urban settings may lead to different housing affordability criteria. This study incorporates the third-generation prospect theory and improved VIKOR method to construct a novel, comprehensive evaluation model for assessing housing affordability. The housing price to income and rent to income ratios were chosen as evaluation indicators, and the yearly median value of each indicator was taken as a dynamic reference point. The housing affordability indicators’ realistic prospect value matrix for large- and medium-sized Chinese cities were obtained for the study period’s duration. The comprehensive housing affordability prospect values were ranked using the improved VIKOR with entropy weight method. The novel proposed approach’s rationality and effectiveness were examined by comparing the original and prospect values, performing sensitivity analysis on the prospect value parameters, contrasting the ordinary and improved VIKOR methods, and comparing the proposed approach with the TOPSIS method. The results demonstrate that the proposed method can consider the decision maker’s",3.0,690b1608fc75ce83165dfc44fc63b06bdb8a470326d,10.2991/assehr.k.201214.522,A Geographic Feature Integrated Multivariate Linear Regression Method for House Price Prediction,2020.0,Proceedings of the 2020 3rd International Conference on Humanities Education and Social Sciences (ICHESS 2020),Yu-Hang Mao; Rui Yao,"Housing price prediction is of great significance in financial real estate investment and urban construction planning. Multiple linear regression models are commonly used for housing price prediction. However, traditional methods are mostly focused on the characteristics of the houses themselves, without or little considering the features of the surroundings. The features of the surroundings are also important for house price prediction. Motivated by these, we propose a geographic feature integrated multivariate linear regression method for house price prediction. Especially, the Zip Code is chosen as the additional geographic feature for its convenience to obtain. Then the integrated features are used to learn the multivariate linear regression model. We conduct an extensive experiment on the real-world case of the King County area and compare our method linear regressions. The results verified the effectiveness and superiority of our model.",3.0,https://doi.org/10.2991/assehr.k.201214.5226919aafea506a119105f24195a60fe6b33bfc29ad65,10.3390/cmsf2025011024,Drift and Diffusion in Geospatial Econometrics: Implications for Panel Data and Time Series,2025.0,ITISE 2025,James Ming Chen,". Abstract Economic data is highly dependent on its arrangement within space and time. Perhaps the most obvious and important definition of space is geospatial configuration on the Earth’s surface. Consideration of geospatial effects produces a dramatic improvement in the prediction of median housing prices across 20,640 districts in California. Unconditional regression with engineered variables, two-stage least squares regression (2SLS), and iterative local regression approach r 2 ≈ 0.8536, the goodness of fit attained in the original California study. Geospatial methods can be generalized to panel data analysis and time-series forecasting. Distance-sensitive analysis reveals the value of treating time-variant data as potentially discrete and discontinuous. This insight highlights the value of methodologies that sus-pend the assumption that data varies in a continuous or even linear fashion across space and time.",3.0,https://www.mdpi.com/2813-0324/11/1/24/pdf?version=1757054189692cb26d6490b2572154b54d4788e5ce513c5f4a26d,10.1109/icac3n60023.2023.10541614,Machine learning-Based Prediction of Home Prices,2023.0,"2023 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)",Dimpal Kumar; Aman Rawat; S. Jha; D. Yadav,"In the past few years, machine learning has played an effective part in product orders and recommendations in the medical industry and every day. In return, we offer great customer support and safer automotive systems. As a result of all that has been illustrated, ML may be a innovation that’s getting to be increasingly prevalent over a wide range of businesses. The genuine domain advertise is one of the foremost competitive and price-focused in presence nowadays. People are endeavouring to buy a modern domestic by considering their budgets and showcase procedure. However, the essential blemish within the existing framework is that it decides the cost of a house without making the fundamental expectations around future advertise patterns, which leads to an increment in cost. Our project’s essential objective is to precisely expect the cost of a house without causing any misfortunes. A few components must be taken into consideration in arrange to accurately anticipate domestic costs and to supply customers with compelling house estimating that’s in line with their inclinations and budget. So, we are creating a show to assess lodging costs. using machine learning advances such as K-M",3.0,693a04aba2bf5ce8e0ccf7a64c0da0b17ac73ef25f2,10.1007/s10901-023-10090-6,The COVID-19 pandemic and house price convergence in Poland,2023.0,Journal of Housing and the Built Environment,Mateusz Tomal,"The effect of the COVID-19 pandemic on price convergence in the housing market is largely unknown. This paper aims to assess the impact of the pandemic on the convergence of sales and rental housing prices. The residential markets in Polish voivodeship capitals were chosen as a case study. The convergence evaluation was based on a local linear version of the log t regression test, which allowed the estimation of a time-varying convergence speed parameter. The causal effect of the pandemic on convergence was examined using a Bayesian structural time series model. The study results revealed that the pandemic led to divergence in the primary sales housing market, growth convergence in the secondary sales housing market, and a weakening growth convergence in the rental housing market. Finally, this paper provides the R function logt that enables running the local linear and dummy variable log t regression tests.",3.0,https://link.springer.com/content/pdf/10.1007/s10901-023-10090-6.pdf694bc217239ec6037ff78cc3e96c444ae0f70c2d985,10.1080/09654313.2023.2195886,Housing dynamics around Romania’s developing cities: the high demand for specific planning instruments in functional urban areas,2023.0,European Planning Studies,M. Drăghia; R. Cocheci; Bogdan Olariu,"ABSTRACT Metropolitan planning has been a dominant trend in spatial development for decades. In this context, Functional Urban Areas (FUAs) have emerged as a concept to illustrate the economic interdependencies between an urban core and its hinterland, being typically determined by commuting patterns or accessibility criteria. In Romania, FUAs were defined for the county capitals (40 cities) as areas where urban development projects could be financed from regional development funds. This paper analyzes the territorial dynamics in one of Romania's fastest-growing functional urban areas, Cluj-Napoca, using a comparative analysis of satellite images and selected indicators. The results demonstrate the spatial impact of economic development in the past 20 years, revealing expanding boundaries of artificial surface and housing units in FUAs. Unfortunately, the current fragmented territorial configuration is unable to manage the new functional relationships generated by real estate dynamics. Therefore, we support the need to introduce a normative territorial plan at the intercommunity level to correlate the spatial development vision between the main city and its fast-growing suburban ar",3.0,69571edf2dd4447425f1fea4775136c017af64db7a0,10.3390/educsci12100727,Engaging Students in Learning the Relations of Geographical Elements through GIS-Enabled Property Price Visualization,2022.0,Education sciences,Chuan-Bing Wang; Dai-Hu Yang; Hui-Min Xu,"Understanding the relations of urban elements is important in secondary school geography. Based on the price data of 22,684 listed apartments, this article aims to enable students to understand how geographical elements are related to the distribution of apartment prices through spatial analysis using ArcGIS 10.8. The spatial analysis showed that apartment prices are related to their proximity to government offices, schools, hospitals and main traffic roads. The content analysis of field notes, interviews and discussions suggests that students favor geographic information system (GIS)-based teaching and feel geospatial technology is useful to visualize the relationship of apartment prices to other geographical elements. Moreover, GIS-based teaching not only improves students’ skill of generating datasets and raster maps, but also provides more room for students’ enquiries compared to conventional teaching.",3.0,https://www.mdpi.com/2227-7102/12/10/727/pdf?version=1666341194696d20a209dc2336611a8fcf14923e3b0ba70ec1233,10.1080/17477891.2022.2109569,"Trends in recovery aid concentration following Hurricane Florence in North Carolina: exploring the role of physical damage, community vulnerability, and Hurricane Matthew",2022.0,Environmental Hazards: Human and Policy Dimensions,Julia Cardwell; C. Konrad,"ABSTRACT The intensity of extreme events like hurricanes is predicted to increase. As such, the role of federal disaster aid programmes in assisting community recovery will also grow, and potential inequities in these programmes could compound in an increasing disaster landscape. This study analyzes recovery efforts after Hurricane Florence (2018) to identify trends in areas that were targeted for recovery aid. Using a series of Ordinary Least Square (OLS) and spatial lag models, divergences in aid are investigated after controlling for physical damage and the study suggests that these divergences can be partially predicted by social and community factors, including characteristics that are typically associated with increased social vulnerability (such as high renter population, older housing stock, and high population of non-white residents). In addition, because North Carolina experienced two major hurricanes in the period of just two years (Hurricane Matthew in 2016), this study also analyzes the role of successive extremes in the outcomes of aid concentration and finds that communities successful in achieving aid after Hurricane Matthew were similarly successful after Hurricane",3.0,6976c18977f9a44604959d35fd25428f858b76fdb07,10.3390/jrfm16030167,The Effect of Proximity to Universities on House Prices after the COVID Outbreak,2023.0,Journal of Risk and Financial Management,Bing-Bing Wang,"COVID-19 has made virtual interactions an integral part of learning modes. This made it possible for college students to live further away from school than before, which might change the house price neighboring universities. This article studies the effect of proximity to school on house prices after the COVID-19 outbreak using a non-parametric difference-in-differences approach with property-level transaction data surrounding 128 universities in the U.S. The results show that house prices within 0.5 miles of universities experienced a maximum decrease of approximately 7% after three months of the outbreak. The effects vary for universities that implemented different teaching modes of in-person, hybrid, and online. Since house prices are important indicators for local economic conditions, the results help local homeowners, investors, and governments in their decision-making processes.",3.0,https://www.mdpi.com/1911-8074/16/3/167/pdf?version=1677750729698fb1e85a16f197c70dd622edca3d1d6262aaca531,10.1109/inocon60754.2024.10511732,Predicting House Price Model : A Comprehensive Analysis with Random Forest and Decision Tree Method,2024.0,2024 3rd International Conference for Innovation in Technology (INOCON),Mansi Sharma; Deepak Sharma; Rushikesh Burle; Pawan Patil; Isha Joge; Chetan G. Puri,"This paper explores the field of predicting property prices through a thorough comparison of several regression techniques. Linear regression, decision tree regression, random forest regression, K-neighbors regression, gradient boosting regression, and XGBoost regression are the regression models that are being examined. Finding the best model to reliably estimate house prices based on a set of features is the aim.The dataset used in this study includes location, bedroom count, square footage, and other relevant parameters that are known to affect property values. Each regression model is trained and tested on this dataset using a methodical evaluation procedure, and its performance is evaluated using important metrics including R-squared, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and overall correctness. predicting house prices mainly use harlfoxem (Owner) repository of housing dataset will contain the homes sold between May 2014 and May 2015 with 21613 entries and 21 attribute were used to evaluate the performance of machine learning prediction model. For practitioners and scholars working in real estate, housing market analysis, and related sectors, this study o",3.0,69952f8b4229596c17fd4a4bb7c7acf0bbdfaf25daf,10.1108/jrf-09-2024-0270,House prices drivers in Greece and Portugal: a cointegration analysis,2025.0,The Journal of Risk Finance,Petros Gkiosis; X. Chapsa,"PurposeThe purpose of the article is to contribute to the existing growth literature by exploring the complex dynamics between house prices and macroeconomic factors such as gross domestic product (GDP) per capita, interest rates, employment and inflation in two countries sharing common geopolitical and socioeconomic characteristics, namely Greece and Portugal.Design/methodology/approachThe autoregressive distributed lag (ARDL) cointegration methodology is applied, as proposed by Pesaran and Shin (1995) and Pesaran et al. (2001) in the presence of structural breaks. The bounds-testing ARDL model is used due to its numerous advantages, while the unit root test with breaks was performed in order to capture events capable of causing regime changes.FindingsThe analysis reveals evidence of a strong association between housing prices and inflation in both the long run and the short run. With respect to the other housing price determinants, evidence is mixed according to the country selected.Originality/valueThis work is inspired by the determinant role of housing prices in the economic system, household wealth and the standard of living in the last decades. Using various theoretical and ",3.0,7001394e6041b939e855033f7dfa0b687125fb7a489,10.3390/a18020093,Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks,2025.0,Algorithms,Iosif Viktoratos; A. Tsadiras,"Accurately estimating house values is a critical challenge for real-estate stakeholders, including homeowners, buyers, sellers, agents, and policymakers. This study introduces a novel approach to this problem using Kolmogorov–Arnold networks (KANs), a type of neural network based on the Kolmogorov–Arnold theorem. The proposed KAN model was tested on two datasets and demonstrated superior performance compared to existing state-of-the-art methods for predicting house prices. By delivering more precise price forecasts, the model supports improved decision-making for real-estate stakeholders. Additionally, the results highlight the broader potential of KANs for addressing complex prediction tasks in data science. This study aims to provide an innovative and effective solution for accurate house price estimation, offering significant benefits for the real-estate industry and beyond.",3.0,https://www.mdpi.com/1999-4893/18/2/93/pdf701661ef978923d01c80099eb2406cd8e881f3aefa1,10.1109/icpcsn58827.2023.00101,House Price Rate Change Prediction Using Machine Learning,2023.0,2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN),R. Reddy; E. Padmalatha; S. D. Devi,"The real-estate market is the central tenet amongst the most focused businesses regarding pricing and it is keep fluctuating. The rate change is normal for most of the business in the real world scenario, most of the businesses will be linearly incremented not in a drastic manner, but in the real-estate sector the price change is very drastic either increment or decrement. Based on the different global and local geopolitical factors it is varying. These factors will highly influence on the price of the property. The term valuation of a property is considered as the analytical process of evaluating the net worth of the property. However, there are a wide range of factors which affect the valuation process. In prediction of the prices there are so many approaches are there based on machine learning and analytics. Majorly the regression based approaches are giving appropriate results. But in certain circumstances it is going to deviate and sudden price spike may not be reflected there. The main objective of this study is to find such rate changes along with the correlation of the global or national circumstances. The prediction or forecast the future house prices that are generated by",3.0,7028609f463089804ced70e93d779db5c4f3baaace8,10.1108/ijhma-07-2021-0082,Examining the spatial and non-spatial linkages between suburban housing markets,2021.0,International Journal of Housing Markets and Analysis,Morteza Moallemi; Daniel Melser; A. de Silva; Xiaoyan Chen,"703Purpose704The purpose of this paper is on developing and implementing a model which provides a fuller and more comprehensive reflection of the interaction of house prices at the suburb level.705706707Design/methodology/approach708The authors examine how changes in housing prices evolve across space within the suburban context. In doing so, the authors developed a model which allows for suburbs to be connected both because of their geographic proximity but also by non-spatial factors, such as similarities in socioeconomic or demographic characteristics. This approach is applied to modelling home price dynamics in Melbourne, Australia, from 2007 to 2018.709710711Findings712The authors found that including both spatial and non-spatial linkages between suburbs provides a better representation of the data. It also provides new insights into the way spatial shocks are transmitted around the city and how suburban housing markets are clustered.713714715Originality/value716The authors have generalized the widely used SAR model and advocated building a spatial weights matrix that allows for both geographic and socioeconomic linkages between suburbs within the HOSAR framework. As the authors outlined, such a model ca",3.0,717fb9a8b0be366371a3d53577485ed161b9a2dde84,10.2139/ssrn.3980310,Fan Charts 2.0: Flexible Forecast Distributions with Expert Judgement,2024.0,Social Science Research Network,A. Sokol,"I propose a new model, conditional quantile regression (CQR), that generates density forecasts consistent with a specific view of the future evolution of some variables. This addresses a shortcoming of existing quantile regression-based models, for example the at-risk framework popularised by Adrian et al. (2019), when used in settings, such as most forecasting processes within central banks and similar institutions, that require forecasts to be conditional on a set of technical assumptions. Through an application to house price inflation in the euro area, I show that CQR provides a viable alternative to existing approaches to conditional density forecasting, notably Bayesian VARs, with considerable advantages in terms of flexibility and additional insights that do not come at the cost of forecasting performance. JEL Classification: C22, C53, E37, R31.",3.0,https://doi.org/10.2139/ssrn.3980310718b891b00de6a2f77a6f3ae15c049591e2631205b5,10.1109/sds.2019.00010,Predicting Housing Market Trends Using Twitter Data,2019.0,Swiss Conference on Data Science,Marlon Velthorst; Çiçek Güven,"In this study, we try to predict the Dutch housing market trends using text mining and machine learning as an application of data science methods in finance. Our main goal is to predict the short term upward or downward trend of the average house price in the Dutch market by using text data collected from Twitter. Twitter is widely used as well and has been proven to be a helpful source of data. However, Twitter, text mining (tokenization, bag-of-words, n-grams, weighted term frequencies) and machine learning (classification algorithms) have not been combined yet in order to predict the housing market trends in short term. In this study, tweets including predefined search words are collected relying on domain knowledge, and the corresponding text is grouped by month as documents. Then words and word sequences are transformed into numerical values. These values served as attributes to predict whether the housing market moves up or down, i.e. we approached this as a binomial classification problem relating text data of a month with (up or down) trends for the following month. Our main results reveal there is a correlation between the (weighted) frequency of words and short term housi",3.0,719a6fabb111ec3ad1ef9d3b80000bfc2c0ef6f0d72,10.48550/arxiv.2405.17505,Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models,2024.0,arXiv.org,Ting-Ting Chen; Shijing Si,"Housing has emerged as a crucial concern among young individuals residing in major cities, including Shanghai. Given the unprecedented surge in property prices in this metropolis, young people have increasingly resorted to the rental market to address their housing needs. This study utilizes five traditional machine learning methods: multiple linear regression (MLR), ridge regression (RR), lasso regression (LR), decision tree (DT), and random forest (RF), along with a Large Language Model (LLM) approach using ChatGPT, for predicting the rental prices of lane houses in Shanghai. It applies these methods to examine a public data sample of about 2,609 lane house rental transactions in 2021 in Shanghai, and then compares the results of these methods. In terms of predictive power, RF has achieved the best performance among the traditional methods. However, the LLM approach, particularly in the 10-shot scenario, shows promising results that surpass traditional methods in terms of R-Squared value. The three performance metrics: mean squared error (MSE), mean absolute error (MAE), and R-Squared, are used to evaluate the models. Our conclusion is that while traditional machine learning mode",3.0,720b5fd8a575c9603df68f30dd35dc0f60e6ca76035,10.22616/rrd.25.2019.022,Analysis of different approaches to real estate appraisal,2019.0,,Vladimir Surgelas; I. Arhipova; V. Puķīte,"The traditional valuation of real estate in the field of civil engineering did not include the uncertainty of human behaviour, which cannot be explained by the traditional approach. There are different valuation methods for real estate appraisal, which are basically classified into three groups as a classic, statistical and advanced. In this article, we estimated the different housing price models using the sample of 37 residential apartments in Riga, Latvia, October 2018. In order to evaluate if there is a possible association between the variables involved in relation to the property price, the analytical data were analysed by correlation analysis, analysis of variance (ANOVA), regression analysis, covariance analysis (ANCOVA), principal component analysis (PCA) and cluster analysis. The models estimation results show that using ANCOVA models for the prices forecasting the model fitting to data is less than 58%. The preliminary results of this study suggest that the estimated properties can be distributed in 4 groups, depending on number of rooms, area and age. In addition, the decision tree was created based on algorithms (J48) and a preliminary definition of the best rules was ",3.0,721504bd6092a7aefba2fe4323a56e0a9f5efa63538,10.1109/ic457434.2024.10486255,Real Estate Cost Prediction Through Web Application Using Random Forest Machine Learning Algorithm and Python Flask,2024.0,Journées Francophones d'Ingénierie des Connaissances,Mohit Kumar; Priyash Kumar,"Machine learning has become progressively sophisticated and sturdy due to the development of new algorithms. A compelling application is observed in general is the prediction of the house price. The model which can be created using ML can help the customers to get an accurate price of the house. These Price Prediction models make use of attributes of the house under consideration namely location, count of bedrooms, tax rates, Number of years the house has existed, and commercial complexes in a nearby location. Price prediction forecasting for houses is a critical characteristic of real estate. The objective of this project is to uncover Crucial patterns and trends from historical property records. Historical property data from Bengaluru is subjected to machine learning scrutiny to extract valuable insights. The diverse factors influencing house prices have stimulated the curiosity of researchers across various domains. The research utilizes numerous regression models to investigate the determinants of house price changes. This research leverages various feature selection techniques, and feature scaling techniques to augment the efficacy of the prognostic model. The effectiveness of",3.0,72269262694227082982fec65e40dd3ccf061258569,10.1080/08882746.2021.1902192,"Weak housing demand in a shrinking city: the potential effects of the water crisis on Flint, Michigan’s housing market and homeowners’ perceived mobility",2021.0,Housing and Society,Victoria Morckel; Bernadette Hanlon,"ABSTRACT This study explores perceived and real effects of a human-made environmental crisis (the Flint water crisis) on residential mobility and housing demand in the weak market city of Flint, Michigan, USA. We surveyed Flint residents in summer 2016 and found that a large proportion of homeowners believed the water crisis negatively affected their property values; more than half wanted to leave the city but felt constrained by the inability to sell their homes; and one-fifth considered abandoning their homes to leave the city. We used regression models to examine predictors of these responses, and spatial statistics to examine whether responses clustered in the city. Then, to see if the perceptions of homeowners reflected actual changes in the housing market caused by the crisis, we used descriptive statistics, maps, and repeated measures ANOVA models to examine changes in home sales prices and mortgages originations in Flint from 2012–2017, before and after the start of the crisis. The results indicate the water crisis had no discernable effect on the city’s housing market, likely because the market was incredibly weak prior to the crisis. The severe lack of housing demand irre",3.0,7237f09ad07ffee2836f4e627207ea40d55e1a8daf2,10.1111/1540-6229.12276,Getting on and Moving Up the Property Ladder: Real Hedging in the U.S. Housing Market Before and After the Crisis,2019.0,Real Estate Economics,D. Damianov; Diego Escobari,"Real hedging is the practice of getting onto the property ladder in order to trade up to a larger home in the future. We define the value of the real hedge of home ownership as the difference between the risk premiums of renting and owning and explore how this value depends on local housing price dynamics and household characteristics. Controlling for the potential endogeneity of housing bubble bursts across different U.S metropolitan areas, we find a significantly higher correlation in the appreciation rates across the Standard & Poor's Case-Shiller tiered house price indices in the period after the housing crisis. We conclude that real hedging has become more attractive in the period after the crisis, particularly in markets exhibiting momentum and high volatility in returns.",3.0,https://durham-repository.worktribe.com/output/1302996724a094e3d2ea94420ed4f781fa527ea93c05ec994a,10.1109/iitcee57236.2023.10091037,Performance Estimation of Real Estate Business Price Using the Regression Analysis of Machine Learning,2023.0,"2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)",S. Geetha; Pretty Diana; P. S,"Domain ontology construction is an important task in Numerous researches have been done for demonstrating the fundamental non-straight connection between estimating characteristics and cost of property to conjecture the lodging deals costs. Lately, further developed non-direct demonstrating methods like Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have arisen as viable strategies to foresee the house costs. This current undertaking's motivation is to foster a system for anticipating land property costs using an assortment of regression techniques. The objective of this task is to give an approach to foreseeing property costs for land using different regression procedures. The entirety of the strategies is run on a formerly made dataset. To think about regression draws near, the Root Mean Square Error is utilized as a presentation pointer. The motivation behind this task is to utilize a scope of regression procedures to foster state property valuing. We utilize different regression methods in this pathway, and our outcomes are not the sole assurance of one strategy rather it is the weighted mean of different strategies to give the most exact outcomes. The outco",3.0,725887eb02c5fd4368c15c2828dd7350b7ba57f1930,10.1007/s10614-021-10149-y,House Prices as a Result of Trading Activities: A Patient Trader Model,2021.0,Computational Economics,Ralf Korn; Bilgi Yilmaz,"We present a new modeling approach for house price movements as a consequence of the trading behavior of market agents. In our modeling approach, all agents are assumed to assign a personal threshold value to a (standardized) house and update the threshold value permanently by a continuous-time filtering procedure based on observing the quoted house prices and the resulting price movements. The traders then trade according to a threshold price strategy (try to sell if the personal threshold value is lower, try to buy if the personal threshold value is higher than the actually quoted house price). Our modeling approach and its resulting characteristics are illustrated via numerical examples that highlight certain realistic constellations between various traders.",3.0,https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85110407282&origin=inward726ec8d743f4c96dcd6a77ef4047870ca192bf3b1a5,10.1109/icses63445.2024.10763162,Enhancing House Price Forecasting with State-Action-Reward-State-Action Algorithm and Deep Q-Learning,2024.0,International Conference on Signals and Electronic Systems,Ramya Polaki; Keshavagari Smithin Reddy; V. Sulochana; Gundala Pallavi; P. R,"The research aims to optimise housing pricing strategies using reinforcement learning algorithms, specifically SARSA and Deep Q-Network (DQN). The study employs the Kaggle House Prices dataset and focuses on variables such as year built, neighbourhood, and overall quality to enhance real estate price decisions. The SARSA algorithm was designed to dynamically change asking prices by providing incentives based on the difference between asking and actual selling prices, as well as a penalty for time on the market. This model fared well, with an average sale duration of 34.52 days and a total reward of around $1,640,667.27. The DQN algorithm used deep learning to find subtle patterns in the data in order to make better decisions. The DQN model outperformed SARSA, with an average total payout of $1,780,345.82 and a shorter average sale time of 31.75 days. The findings demonstrate how RL, specifically DQN, can be utilised to improve pricing strategies and provide valuable information about the real estate market. This study demonstrates the utility of sophisticated reinforcement learning approaches in complicated decision-making and identifies areas for further research into larger datas",3.0,72795042546de583386a6f04db7930f39b8dce10252,10.1080/03796205.2022.2143883,Determinants of house price dynamics and household indebtedness in Namibia,2022.0,Studies in Economics and Econometrics,Charles Kakuru; T. Kaulihowa,"Abstract The paper aims to investigate the determinants of supply and demand-side factors of house price dynamics and their impact on household indebtedness in Namibia. An autoregressive distributed lag (ARDL) model was used. The findings support the hypothesis’ that supply-side and demand-side factors had a significant effect on house price as well as household indebtedness. It was found that money supply, working population, construction cost index, mortgage loans, previous quarter house prices, and current GDP were found to be the key determinants of both supply and demand-side factors of house-price dynamics in Namibia. Additionally, an increase in mortgage loans would increase household indebtedness in the short run and was found to be the key determinant of household indebtedness in Namibia. This indicates that an expansionary monetary policy could address the twin problem of rising house prices and household indebtedness. Policy implications derived from the study indicate that although monetary policy can be used to address the issues of escalating house prices and indebtedness, this policy intervention may be limited because Namibia does not have an autonomy regarding its ",3.0,728b619f06e13c034855e6bcf19dbc09a6ba3495a76,10.22320/07183607.2021.24.43.04,Determinantes urbanos del precio de la vivienda en Chile: una exploración estadística,2021.0,Revista Urbano,J. Vergara-Perucich,"Starting from the implementation of the National Urban Development Council in Chile, an Urban Development Indicators and Standards System has been put in motion. One of the main urban problems faced by Chilean cities is the increase in housing prices. From this, the study looks to find how urban attributes influence housing prices. For this purpose, 2047 housing projects were surveyed to review which urban development standards indicators best explain their prices. An analysis of main components and multiple linear regressions are applied, together with the variables. The results show a high price dependence on the financial autonomy of the communes, and indicate that residential segregation is incidental to the formation of housing price value.",3.0,https://doi.org/10.22320/07183607.2021.24.43.047298ecd7faabae6824cbbb7b22b8fcfdc0a4f8c839a,10.1016/j.qref.2018.05.013,The knotty interplay between credit and housing,2018.0,Quarterly Review of Economics and Finance,Mihnea Constantinescu; Povilas Lastauskas,"Abstract We employ the recent Jorda, Schularick, and Taylor (2016) and Knoll, Schularick, and Steger (2017) datasets to investigate the long-run relationship between house prices and credit volume, allowing for interest rate, real exchange rate and real gross domestic product (GDP). We refine the analysis using data at the quarterly-level to define relevant co-integrating relationships across a number of European economies. Housing, GDP and credit cross-sectional averages are included in the analysis to detect the effects of common factors. Empirical results indicate the presence of cross-country heterogeneities and an uneven feedback mechanism between credit and housing – the full loop is established only for several countries in the dataset. Grouping countries for panel-like econometric exercises may lead to spurious regression results, poor inference and misleading policy implications. Short-run dynamics, compared to the long-run may often lead to contradicting policy advice if the order of integration of the house price series is not properly accounted for. Furthermore, the presence of spatial patterns of house prices and credit highlight the need to consider global housing and",3.0,7303942ac9e71996ec57700985bf8d438c12d92e8d2,10.1080/01639625.2024.2378111,"A Video Data Analysis of Pet Theft Incidents: An Examination of Offense Form, Situational Dynamics, & Offender Characteristics",2024.0,Deviant Behavior,Benjamin F. Stickle; Brenda Vose; J. M. Miller,"ABSTRACT Pet theft is a type of property crime, but pet-owner relationships include emotional dimensions and life experiences that can compound their loss far beyond property value. Their bondedness with humans includes companionship as well as healthy lifestyle and mental wellness benefits, which are abruptly halted at junctures of pet loss. Though pets are stolen for various motives (e.g. ransom, reward, resale, or breeding), this crime form has received little research attention from academic criminology and criminal justice. Toward building a line of inquiry, this paper empirically explores the phenomena of “dognapping” as dogs are the most frequently stolen pet and the only pet for which there is a database large enough for meaningful exploration. Content analysis of online posts and web-based video data (Twitter/X1 posts, YouTube videos, and social media websites dedicated to missing and stolen pets) enabled a delineation of dog theft forms, offense dynamics, and offender characteristics. Findings regarding residential and commercial dog theft center discussion on situational crime prevention implications and additional research opportunities to further evidence and explore t",3.0,7319413f247a17fb20683f72b9ee38acc9bf5e4c328,10.1080/13676261.2019.1695765,"Home ownership, housing differentiation and experiences of living: evidence from young, promising middle-class Beijingers",2019.0,,Hong-Ping Lian,"ABSTRACT Young people and their housing conditions are closely linked, since housing determines whether an individual can lead a smooth transition from dependence to independence. Nevertheless, young people across many societies face growing problems in achieving this transition, which can be attributed to rising house prices and the lack of affordable alternatives to homeownership. Moreover, young people are likely to have vastly divergent experiences and outcomes depending on their tenure. This paper explores these issues in relation to young people in Beijing. The paper argues that the housing issues in Beijing have distinct local characteristics. Drawing on a unique dataset of 83 housing stories, this paper explores housing differentiation and experiences amongst young, promising middle-class people in Beijing. This paper attempts to answer the following research questions: What factors contribute to young people’s access to homeownership in light of the rising house prices in Beijing? How is homeownership differentiated by different classifications? The answers to the research questions are the key to understanding the differentiation of homeownership amongst young, promising ",3.0,73248da620611c9e14613e6f9906c1f7978dec15f44,10.5430/ijfr.v6n2p84,A Study on the Main Determination of Mortgage Risk: Evidence from Reverse Mortgage Markets,2015.0,,Chien‐Chiang Lee; Kuo-Shing Chen; David Shyu,"The main determination of mortgage risk factors is undoubtedly related to the housing price .In this article, we employ threshold GARCH process in practical analysis, to capture the house price dynamic on the logarithm return. This study also estimates the housing price volatility in the presence of stationary variance property from the threshold GARCH model and its implied volatility can serve as a benchmark for the pricing reverse mortgage derivatives. Our results have important implications for hedging risk of reverse mortgages. To our best knowledge, this paper is the first study employing Poisson Regression approach to look at the housing prices risk of reverse mortgage incorporated with its number of loans.",3.0,https://www.sciedupress.com/journal/index.php/ijfr/article/download/6749/4043733c50373c97b45aaefb98c0bbccbd9b045551af8a0,10.1080/21606544.2023.2287190,The externality cost of environmental (dis)amenities in the urban housing market: an emerging evidence from Pakistan,2023.0,Journal of Environmental Economics and Policy,Tanzila Akmal; Faisal Jamil; Muhammad Haseeb Raza; Cosimo Magazzino,"ABSTRACT Rising urbanization on abiotic environmental conditions in housing units is one of the most severe difficulties that city municipality authorities face, all over the world. Waste production has increased as a result of households failing to implement waste management strategies that ensure sustainability. Thus, property values in the housing market have regularly deteriorated as a result of environmental (dis)amenities. The study examines the impact of a waste dump site on proximate property values in Pakistan’s twin cities. Using a systematic random sampling technique, questionnaires were distributed to 849 households. 35 dump sites were chosen from 100 metres to 500 metres. The dump sites were chosen based on their size and proximity to residential homes in the study area. The empirical results show that the distance between a residential property and a waste dump site significantly impacts rental values. Moreover, rents exhibit a negative relationship with increasing distance from the dump sites. Based on applied findings, more proactive enforcement of sanitary laws and regulations, such as removing all irregular dump sites from residential areas, is recommended.",3.0,73468e61ee03aa1fc5b3fb90df84383bb9ceb84edc6,10.1051/e3sconf/20199108049,Residential real estate value assessment considering the influence of various factors,2019.0,E3S Web of Conferences,A. Minnullina,"Interest in modern methods of assessing the value of real estate in the housing market is fuelled by the increased business activity, increased competition and changes in the taxation system. In this regard, the article proposes a method for assessing the value of real estate based on the analysis of various factors influence degree. The proposed recommendations are based on correlation and regression analysis. They allow for an absolute and relative adjustment of the residential property value. In order to confirm its practical application, a possible adjustment of the property considering the influence of the “quality of repair” factor is provided.",3.0,https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/17/e3sconf_tpacee2019_08049.pdf7358f2d132ac38de32acc3bf7dd0562a074822f89bf,10.1109/icssas57918.2023.10331796,Metro Cities House Price Prediction Using ML,2023.0,2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS),Sonal G. Naik; Aarti Puthran,"Due to changes in local infrastructure as well as real estate values, home prices fluctuate every single year. Customers can figure out the value of the house with the aid of a centralized system that predicts house prices and has infrastructure. It aids the consumer in making decisions on where and when to buy a home. When forecasting a property's value, several factors are taken into account, such as location, neighborhood, size, etc. The intention of this study is to predicate house prices for individuals depending on financial situation. This study matched the outcomes of many machine learning methods including linear, XGB, decision tree, lasso regression and random forest regression, due to its outstanding accuracy. House value forecast on a data set will be performed by utilizing all of aforementioned methodologies and selecting the best among them. This study establishes the application of machine learning algorithms in the prediction of house values using datasets collected from Kaggle. This study can construct a regression model that can precisely estimate the cost of a property created on its characteristics.",3.0,736611fd294892b8113ae2acaf0a5c30476fd4460e9,10.5089/9781484367513.001,"Housing Price, Credit, and Output Cycles: How Domestic and External Shocks Impact Lithuania's Credit",2018.0,Social Science Research Network,Iacovos I. Ioannou,"Lithuania’s current credit cycle highlights the strong link between housing prices and credit. We explore this relationship in more detail by analyzing the main features of credit, housing price, and output cycles in Baltic and Nordic countries during1995-2017. We find a high degree of synchronization between Lithuania’s credit and housing price cycles. Panel regressions show a strong correlation between a credit upturn and housing price upturn. Moreover, panel VAR suggests that shocks in housing prices, credit, and output within and outside Lithuania strongly impact Lithuania’s credit.",3.0,https://www.elibrary.imf.org/doc/IMF001/25338-9781484367513/25338-9781484367513/Other_formats/Source_PDF/25338-9781484368428.pdf7373bbb62c338a3d232c2aacd7765ca5dff12d73828,10.38197/2072-2060-2022-237-5-34-57,COMMERCIAL REAL ESTATE VALUATION BASED ON MACHINE LEARNING MODELS,2022.0,Scientific Works of the Free Economic Society of Russia,I. Astrakhantseva; N. Smirnova,"Based on the interests of users and compliance with the legislation in the field of valuation, the authors propose the use of machine learning methods in valuation practice for the valuation of commercial real estate. Five regression methods were chosen as the algorithms on which the model works. The proposed machine learning model can be trained and improved based on its own experience in an automatic mode without explicit human participation. The target indicator of the model is the “price per sq. m” and “rental rate” of commercial real estate.",3.0,https://doi.org/10.38197/2072-2060-2022-237-5-34-5773801ae6787ad8b253b6b747d479ace90eab013fd7e,10.1016/j.ijforecast.2019.11.007,Forecasting using heterogeneous panels with cross-sectional dependence,2020.0,International Journal of Forecasting,Oguzhan Akgun; A. Pirotte; G. Urga,"Abstract In this paper, we focus on forecasting methods that use heterogeneous panels in the presence of cross-sectional dependence in terms of both spatial error dependence and common factors. We propose two main approaches to estimating the factor structure: a residuals-based approach, and an approach that uses a panel of auxiliary variables to extract the factors. Small sample properties of the proposed methods are investigated through Monte Carlo simulations and applied to predict house price inflation in OECD countries.",3.0,https://openaccess.city.ac.uk/id/eprint/23247/1/APU_RR1_Manuscript.pdf7392ff905c52d134ca4c521ebb2eb4fb7bf5103e7ba,10.1109/raics61201.2024.10689962,Real-Time House Price Predictions with Regression Analysis,2024.0,IEEE Recent Advances in Intelligent Computational Systems,K. Purushotham; Bangarappa; Ashwini Kodipalli; Trupthi Rao,"Accurate property assessment is essential in the dynamic and ever-changing world of real estate so that both buyers and sellers can make well-informed choices. We are aware that setting the appropriate asking price for a house can be a difficult undertaking. Property values are greatly influenced by factors including location, size, amenities, market trends, and numerous economic indicators. This system supports in determining a property's price based on geographic regions factors, the number of bathrooms, bedrooms, and area (square feet). Forecasting future costs relies on examining historical market trends and value ranges. The primary focus of this analysis is to efficiently utilize regression models in combination with other methodologies to achieve the highest possible accuracy in predictions. This study aims to forecast housing values in Bangalore. Our website is designed to give you reliable and data-driven insights on property values when you're a potential homebuyer looking to identify the best property.",3.0,74064e3af50f7bb76a98e350d2819494b6012f55f61,10.1109/iceccme57830.2023.10253192,Analysis of Housing Market to Predict Home Price,2023.0,"2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)",M. L. Ali; David Magrini; Michael J. Cangelosi; D. Ribeiro,"Predicting the house market and home prices is still a challenging task. The price of a house depends on several factors and it is important to find the key factors that affect the price of a house in a particular location. In this study, we conducted an experiment to observe how certain variables affect housing prices. Principal component analysis (PCA) and a correlation heatmap were developed to document which variables had the highest correlation with price. At first, these variables were isolated for further development. Using the scikit-learn and XGBoost libraries for Python several models were developed for predicting housing prices within the area of Kings County, California, United States. From these models it was determined that using the full range of variables, rather than the most highly correlated variables, yielded the most accurate results. Out of the three regression models developed, the XGBoost model yielded the most accurate predictions compared to linear regression and random forest.",3.0,7419ff58218ee11333d86981210b7f395da17d81184,10.1016/j.iref.2020.02.006,Asset price bubbles in a monetary union: Mind the convergence gap,2020.0,,A. Czerniak; J. Borowski; Jakub Boratyński; D. Rosati,"Abstract We estimate logit models of housing and stock price bubbles, using panel data for 15 EU countries for the time period 1995–2014, covering a wide set of determinants: monetary, macroeconomic, demographic, institutional and those arising from monetary integration. Our key finding is that higher degree of real convergence diminishes the probability of a bubble in the housing market caused by a fall in interest rates after joining the monetary union. Applying these results to three Central European countries (the Czech Republic, Hungary and Poland) we conclude that, given the existing economic distance in terms of per capita GDP between the three countries and the top eurozone performers, joining the monetary union would ceteris paribus significantly increase the probability of house price bubbles in those countries. This result suggests that the optimum currency area framework should be broadened to include the degree of real convergence between the monetary union and the acceding country.",3.0,7425a859a74d5e7dcc25888ad0f9443b321d349f34c,10.4018/ijagr.2021040104,A Community Area Perspective of Real Estate and Crime in Chicago,2021.0,,M. Samarin; Madhuri Sharma,"This paper examines the relationships between crime-types and property values in the community areas of Chicago. Using a variety of unconventional web-based data sources, the authors use correlations, mapping, and regression analyses to find that while crime generally associates negatively with property values, not all crime-types have similar effects. Lower incidence of violent crimes and sex offenders in neighborhoods can have pronounced positive impacts on property values whereas certain types of property crimes gravitate toward neighborhoods with expensive homes. Further, crime rates may be similar or even higher than those in cheaper/disadvantaged areas. These types of offenses do not necessarily follow the price-dropping effect like other crime-types do on housing values. However, property crimes such as thefts do not follow this trend. They, thus, recommend that property crime alone should not be a factor when making decisions concerning home buying and/or where to live.",3.0,7432870d5ac737a0a531b2020c4f03370dc98ed10a2,10.1080/12265934.2021.1925142,Features of the ripple effect in the US regional housing markets: a viewpoint of nonsynchronous trading,2021.0,International Journal of Urban Sciences,I. Tsai,"ABSTRACT The present study used a new perspective that integrates the commonly observed market characteristics to analyse why the ripple effect is formed. This study proposed a model to infer that in a regional housing market with a relatively low trading probability, low trading volume, high price volatility, and low price efficiency coexist and cause the market to lag behind other regions in information response, forming a ripple effect. The empirical tests used data cover four regions of the United States (Northeast, Midwest, South, and West) between January 1999 and May 2020, and showed that the housing market in the South had the greatest liquidity and the lowest housing price volatility, however, housing prices were more informative in the South region. The empirical results also revealed the characteristics of the ripple effect in the US housing market. Changes in house prices occur earlier in the South, and information is transmitted from the South to the Northeast.",3.0,744c9974a81588d96825ff05507e6df8d723c5e4764,10.1109/icac3n60023.2023.10541549,Prediction of House Pricing Using Machine Learning,2023.0,"2023 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)",Kavita Saini; Ramvachan; Piyush Kashyap; Praveen Yadav,"Data mining is increasingly frequently used in the housing market. In order to estimate house prices, important housing qualities, and many other things, data mining is quite helpful since it can extract pertinent knowledge from raw data.According to research, the real estate market and home owners are frequently concerned about price variations in housing. a study analysis of the pertinent characteristics and the best models for predicting property values have been developed. The analysis’s findings demonstrated that, when compared to other models, ANN, can SVM, and XGBoost. After going through the data in this paper we came across that characteristics structures are the deciding factors for predicting house prices. This research will be Finding the most important factors that influence housing prices and identifying the most effective machine learning model to utilise in this project area will be of enormous help, particularly to housing developers and academics.",3.0,745bc9e4acf4a53e0562ac6aed2849e9700d8c1e5aa,10.1080/03081060.2023.2252397,Train stations and house prices: a local perspective,2023.0,Transportation planning and technology (Print),Changro Lee,"ABSTRACT Opening a new train station is considered a way to generate amenity in a neighborhood. However, as train lines extend from a central city to suburbs and remote places, train stations may generate disamenity depending on the local context. This study examines the externality of stations from a house valuation perspective. A mixed-effects model is employed to capture the varying relationship between house prices and distance to a nearby station. The results show that this relationship significantly varies by county, which leads to a house price premium in some counties and price discount in others. This study attributes the price discount (disamenity) to low ridership, seasonality of ridership, passenger traits, and long distance from a central city. The study results are expected to provide policymakers with balanced insights on establishing a new station so that train stations can serve as a local amenity, not a harmful facility in the neighborhood.",3.0,7464875877da6ff707e89b8f26cc7798a6c3d473445,10.1109/inocon60754.2024.10511592,House Price Prediction Using Linear and Lasso Regression,2024.0,2024 3rd International Conference for Innovation in Technology (INOCON),Mayank Sharma; Rahul Chauhan; Swati Devliyal; K. R. Chythanya,"This research paper aims to predict house prices with more efficiency and accuracy. The research paper involves three machine learning algorithms, ""linear regression, lasso regression, and ridge regression,"" that are applied to the data set of Bangalore and have various information about different places in Bangalore, like BHK, number of bathrooms, area, etc. The house price prediction is quite a difficult task, as predicting the price of the house involves many factors like area, number of bathrooms, climate condition, age of the property, and many other factors. To make predictions according to all the factors, the author has applied three algorithms, one by one. From these machine learning algorithms, we have developed a model that helps predict the prices of houses with more accuracy and a lower error percentage. This model can be very useful for both buyers and sellers, as it can justify the price of the house for both of them.",3.0,747661d15246ac95592a1e0b967698186341a68a380,10.1177/152397211901900201,Capitalization of Property Tax Relief: Some Equity Implications and Relevance to Policy Design,2019.0,Public Finance and Management,Geoffrey Propheter,"Empirical research in the property tax capitalization literature focuses on estimating the degree to which property tax relief is capitalized into property prices on average; however, economic theory suggests that capitalization will vary across homeowners within a jurisdiction in systematic ways. By focusing on the extent of tax capitalization on average, then, there is a tendency to overlook that in the case of property tax relief, people will benefit differently from the same amount of statutory relief. This paper discusses some determinants of capitalization that contribute to property tax relief being enjoyed differently. Recent empirical research is drawn upon to support the presented arguments, and when such research is not available, analysis of novel data is offered instead. the paper also discusses tax relief policy design strategies for mitigating inequities, to the extent differential outcomes are undesirable.",3.0,748306fa52033d78a115371da2474dfd2fa7bc5e339,10.1080/13547860.2020.1729930,Real estate prices and bank risk-taking in Japan,2020.0,,Rui Wang; Hang Luo,"Abstract This study examines the impact of real estate prices on bank risk-taking using a sample of 133 commercial banks in Japan from 2007 to 2016. Having controlled for other bank-risk determinants, we find that house price growth, house price deviation, and its cyclicality have significant positive (negative) impacts on bank risk-taking (stability). This finding is robust to using different bank risk measures and various econometric methods. Moreover, rising property prices decrease bank stability mainly by weakening bank capitalization and increasing banks’ volatility of returns. In addition, we find that the adverse effect of rising house prices on the stability of a bank becomes more pronounced when house prices rise abnormally and when the market structure reflects a monopoly. Our results also indicate that increasing house prices exacerbate the destructive effects of abnormal credit growth on bank stability.",3.0,749dc152e6248c388ae7eb58b017cbc929aeb6050bc,10.48550/arxiv.2405.06553,Scalable Property Valuation Models via Graph-based Deep Learning,2024.0,arXiv.org,E. Riveros; Carla Vairetti; C. Wegmann; Santiago Truffa; Sebastián Maldonado,"This paper aims to enrich the capabilities of existing deep learning-based automated valuation models through an efficient graph representation of peer dependencies, thus capturing intricate spatial relationships. In particular, we develop two novel graph neural network models that effectively identify sequences of neighboring houses with similar features, employing different message passing algorithms. The first strategy consider standard spatial graph convolutions, while the second one utilizes transformer graph convolutions. This approach confers scalability to the modeling process. The experimental evaluation is conducted using a proprietary dataset comprising approximately 200,000 houses located in Santiago, Chile. We show that employing tailored graph neural networks significantly improves the accuracy of house price prediction, especially when utilizing transformer convolutional message passing layers.",3.0,7508725f2f9ffd3e231100c66aaf901fb41db7ec22d,10.1109/i4tech55392.2022.9952397,Linear Regression Models based Housing Price Forecasting,2022.0,2022 International Conference on Industry 4.0 Technology (I4Tech),Vulligadla Amaresh; R. R. Singh; Rajeev Kamal; Anant Kulkarni,"Investigation for the house price forecasting is necessary to explore the machine learning based mode of precise estimation for housing units. The data available in the literature is adopted for this investigation. In connection to this, the data analysis is an important aspect. The Linear regression (LR) is one of the preferred techniques due to its superior capability for the data analysis. In this paper, subsequently, the data visualization is also deployed. The results obtained show that the forecasted prices of houses based on various parameters such as average area house income, average area house age, average area of number of rooms, and average area of number of bed rooms. The Linear regression model presented in this paper facilitates accurate estimation of the housing cost with ease. The model can be extrapolated for housing schemes across the globe by customizing it to the regional or local needs.",3.0,751555364da2d7d2e111677de1f9cac779f8e248bc4,10.1109/icdabi60145.2023.10629399,A Comparative Study For Predicting House Price Based on Machine Learning,2023.0,2023 4th International Conference on Data Analytics for Business and Industry (ICDABI),E. Eze; S. Sujith; M. S. Sharif; W. Elmedany,"This study presents the development and comparison of four machine learning approaches, namely, Random Forest, Decision Tree, Linear Regression and k-Nearest Neighbours (k-NN), to perform house prices prediction based employing the Boston Housing Dataset. The approaches performance was evaluated using root mean square error as well as R2, with the aim of identifying the model that best predicts housing prices. The dataset was thoroughly analyzed for features, correlations, multicollinearity, and overfitting. Results indicate that the RF model outperformed the other models in predicting house prices, as it has ability to address non-linearity and complex interactions among variables and reduce the impact of outliers. The DT model also performed well but may have been more prone to overfitting. LR, on the other hand, may have been limited by its assumptions of linearity and independence among variables.",3.0,7527598cb6436340e69d3f178a41fad1b449837e10b,10.1080/02673037.2022.2074969,"Does housing market segmentation ensure families a rental price benefit? The example of Frankfurt, Germany",2022.0,Housing Studies,Andreas Hartung,"Abstract Housing market segments are commonly differentiated based on the structural and spatial attributes of their dwellings. A sociological perspective highlights the fact that housing market segments can also arise along socio-economic characteristics of demanders. This can explain why and how particular groups of tenants are disadvantaged or favoured in terms of the rental prices they pay for comparable rental properties. This article illustrates this idea using the example of families with minor children. Exceptionally rich data collected in Frankfurt am Main, Germany, provides detailed measurements for housing conditions. The findings indicate that families in Frankfurt are beneficiaries of segmentation, with respect to the rental prices they pay. However, the extent of the benefit varies between local and specific structural submarkets.",3.0,75397b2d86c76ce9d0b7273c873c0269e7531a71f81,10.1109/iccrd54409.2022.9730190,Random Forest Model for the House Price Forecasting,2022.0,International Conference on Computer Research and Development,Yeqi Fang; Tian-Teng Li; Hongyuan Zhao,"Accurately predicting housing prices is of great significance to improving housing equity. In this work, we first analyzed the factors that affect housing prices. The experimental results show that income is the most important influencing factor, and its correlation with housing prices is as high as 0.69. In addition, room distribution and geographical location are the remaining important image factors. In order to achieve accurate housing price prediction, we designed a random forest to achieve housing price prediction. Compared with support vector regression (SVR), linear regression (LR) and decision tree (DT), our method achieves the best root mean square error (RMSE) and goodness of fit. In contrast, the predicted values of LR and SVR tend to be higher than the true values, while the values of DT tend to be lower than the true values.",3.0,754ba5b8f99e8a03e99fcf8aba7bfd02eb17206f4d4,10.1109/icac3n53548.2021.9725772,Real Estate Price Prediction Model,2021.0,"2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)",S. Anand; Prince Yadav; Adarsh Gaur; Indu Kashyap,"Machine learning has been a major driving force in the past years in several aspects of our lives namely medical diagnosis, normal speech command, detection of an image, product recommendation, spam recognition, and Price prediction, etc. There has been a steep increase in House prices every year, hence an automated system is required to predict future house prices. This system of house price prediction will help the owner to determine the selling price of a house and can help the buyer to arrange the appropriate time to purchase a house. Supervised Learning algorithm namely Linear Regression has been applied in this paper for prediction and analysis of house prices which depends on various factors such as BHK, locality, and bath, etc, and these factors are considered as the independent variables.",3.0,7555577d4bd84f0ce436a5fe76d8264e6d182c27a29,10.1109/icesc57686.2023.10193369,An Innovative Method for Housing Price Prediction using Least Square - SVM,2023.0,"International Conference Electronic Systems, Signal Processing and Computing Technologies [ICESC-]",Yasha Goel; A. N. Swaminathen; Rishika Yadav; B. Kanthamma; Ravi Kant; Amit Chauhan,"The House Price Prediction is often employed to forecast housing market shifts. Individual house prices cannot be predicted using HPI alone due to the substantial correlation between housing price and other characteristics like location, area, and population. While several articles have used conventional machine learning methods to predict housing prices, these methods tend to focus on the market as a whole rather than on the performance of individual models. In addition, good data pretreatment methods are intended to be established to boost the precision of machine learning algorithms. The data is normalized and put to use. Features are selected using the correlation coefficient, and LSSVM is employed for model training. The proposed approach outperforms other models such as CNN and SVM.",3.0,756806dd2fbc3ad91cda22fd4d7fd18c6f32ee2acba,10.1109/icseiet58677.2023.10303034,House Price Prediction Using Gradient Boosting and Linear Regression,2023.0,2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology (ICSEIET),Utkarsh Gupta; Himadri Vaidya; Rahul Chauhan; Chandradeep Bhatt,"Everyone dreams of buying and living in their own house, which is suitable for their lifestyle and suits their personality. The main objective for which people look at a house is the square footage of the house, the number of bedrooms and bathrooms, the location of the house, and the year it was built. This qualitative research helps to get an accurate price for the house that is suitable for their budget and will not affect them financially. It also helps a person buy a house according to his or her requirements. There are many machine learning algorithms out there, like Gradient boosting regression, linear regression, polynomial regression, and random forest regression. After researching on every algorithm, the best accuracy algorithm will be chosen to predict the house price.",3.0,757fcabe7d239a0aab3a1cae687e93e1bda2c3b67bf,10.1109/icscds53736.2022.9760832,Price Prediction of House using KNN based Lasso and Ridge Model,2022.0,2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS),N. H; P. R.; R. R; M. P.,"Getting a house of our wishes within our budget in a residential area of our customization is quite a tedious process. In order to overcome this, we have developed a model to get a house of our interest with religious belief and budget this Implemented model is of linear regression and k nearest neighbor's algorithm with gradient descent optimization to make an optimal model for predicting house prices using the dataset. Performed feature engineering and selection using lasso and ridge penalties to eliminate features that had little or no impact on the residual sum of squares error. Then exposes Jupyter notebook cells as REST Endpoints to make predictions with new information. Finally, we are trying to send an email alert to the concerned user to give an alert of the house price",3.0,7582a517f73e3d5a6381745d79d510d7408116b0b67,10.5260/chara.21.3.44,PropertyShark,2020.0,Charleston Advisor,Harold Gee,"PropertyShark is a searchable web-based database providing reports on residential and commercial properties in nearly all 50 U.S. states. For each property, the report contains the name of the property owner, building characteristics, title, sales history, estimated property value,759 liens, mortgage, foreclosure, zoning, building photos, and other information. There are lists of data on foreclosures, sales comparable, and for-sale properties. Dynamic interactive maps with select layers of information are available. PropertyShark is widely used by real estate industry professionals,760 developers, investors, home buyers, insurance agents, researchers, and college students. It is a resource to enable users to research and evaluate real estate properties to make informed decisions.",3.0,761b711d76963a5c536028ff275f212f25c22ed047a,10.1109/icecaa55415.2022.9936419,An Efficient System for the Prediction of House Prices using a Neural Network Algorithm,2022.0,2022 International Conference on Edge Computing and Applications (ICECAA),N. Patankar; K. Vengatesan; Y. Deshmukh; R. Chintamani; N. L. Shelake,"The process of projecting house prices involves making estimations regarding how house prices will vary in the future. This is an essential aspect of the process. The price of a home is determined by a variety of different aspects, some of which include the location of the property, the overall land area, and the population of the surrounding area. In order to estimate the range of possible costs associated with purchasing a house, the Deep Learning methodology is used. In addition to this, alternative approaches are used for estimating, such as linear regression, key-value network analysis, and random forest. In the final stage of this process, the results obtained from the models utilised are evaluated and contrasted.",3.0,76219353d1fc066f88950bba89737a52fde52f547a3,10.1109/prai53619.2021.9551047,Research and Application of Second-Hand Housing Price Prediction Model Based on LSTM,2021.0,2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI),Shunshun Ma; Gang-Wu Jiang; Jian-Hui Liu; Xin Wang,"The price of second-hand houses has always been a matter of great concern to people. This paper collects the sales data of second-hand houses in Jinshui District of Zhengzhou City, assigns spatial sequence to the data through spatial autocorrelation analysis and design algorithm, constructs LSTM model combining spatial sequence and attention mechanism, and forecasts the price of second-hand houses in Jinshui District. Finally, three schemes are designed and compared based on LSTM neural network. Experimental results verify that the LSTM model combining spatial sequence and attention mechanism constructed in this paper has better prediction effect.",3.0,763fa4b7cc67d8ba4b82555aae05355069bdc18f6df,10.1109/icemme49371.2019.00046,Residential Asset Pricing Prediction using Machine Learning,2019.0,2019 International Conference on Economic Management and Model Engineering (ICEMME),Yi-Yang Luo,"Residential asset price prediction and analysis are prevalent research topics in economy. Most researches focus on macroeconomy perspectives to explain the factors affecting residential asset prices. In this paper we examine some micro factors, like lot area, pool area, that can be used as features to predict house price. We fit a rather simple regression model which contains a few characteristics of a residential asset, and we are able to reach a fairly good result. Some machine learning algorithms such as random forest and support vector machine are also implemented to predict asset pricing. All regression models have a R squared over 0.9.",3.0,764a1288e8cf65012ea03579489b9345c74c09afe37,10.1109/iccst53801.2021.00090,A Price Prediction Method Based on CatBoost,2021.0,International Carnahan Conference on Security Technology,Tieping Wang; Yutian Wang; Mingzhe Liu,"We have studied a price prediction method based on CatBoost, which can evaluate and predict the value of objects. In our approach, we applied many advanced and popular techniques of feature engineering. Also, we compared and analyzed different models, including linear models, tree-based models, and deep learning in the modelling part. Specifically, we have applied it to the housing price prediction, which has verified the effectiveness of our method. Our method can not only be used to predict housing prices, but also to evaluate or predict the value of other objects, such as cultural objects or artworks.",3.0,765e04f25d9c056ddf806ce09dbcd556c6f5b0e5dae,10.1016/j.jtrangeo.2026.104571,Unequal benefits of metro station proximity: Income-level differences in housing price effects in Medellín,2026.0,Journal of Transport Geography,H. Villada-Medina; Luisa Díez‐Echavarría,,3.0,https://doi.org/10.1016/j.jtrangeo.2026.1045717661b8f4bfaf04533099c28f6bb808c375e3a32be28,10.1016/j.asej.2023.102499,Why foreclosed housing devalues in China? An evaluation based on property-rights risks through hedonic pricing model,2023.0,Ain Shams Engineering Journal,Xing-Yu Qian,,3.0,https://doi.org/10.1016/j.asej.2023.102499767526bd8f69a3f59f499f54cf7859f7bdcf41dc177,10.1080/10835547.2017.12091770,House Price Determinants: The Roles of Fundamentals and Sentiments,2017.0,,Helen X. H. Bao; Steven Haotong Li,,3.0,https://www.tandfonline.com/doi/pdf/10.1080/10835547.2017.12091770?needAccess=true76855dcdb0f1397024f2ddb059a08d721bca5aa9a23,10.35349/tar.2014..40.001,Analysis of Regional Characteristics that affect Housing Prices Using a GWR Model －Focused on Busan Metropolitan City－,2014.0,,Y. Oh; Jongmin Kim; Jeon-sang Kang,,3.0,https://doi.org/10.35349/tar.2014..40.001769b72ade09ac2e54b80b64172cba43ede3c675da2d,10.2139/ssrn.2071199,Spatial Weights Configuration and Impact of Environmental Externalities on Housing Prices,2013.0,,Masha Maslianskaia-Pautrel; C. Baumont,,3.0,770e5b6b64d27473ca8546d56f8e32fbc8649576af3,10.1504/gber.2011.040729,"Urban amenities and dwelling house prices in Sao Paulo, Brazil: a hierarchical modelling approach",2011.0,,L. Fávero,,3.0,771eaa614b709f0d80a81abcdbb400c5a08f9277daf,10.1007/978-3-319-14938-7_9,"The economic value of cyclonic storm-surge risks: A hedonic case study of residential property in Exmouth, Western Australia",2015.0,,R. Rebecca; L. Beckley; M. Tull,,3.0,77261f14b23c6f19a1cfe319d36f6c29076c3667da5,10.1016/j.pacfin.2024.102661,Exploring the value of green: The impact factors on China's second-hand green housing prices based on geographically weighted Lasso regressions,2025.0,Pacific-Basin Finance Journal,Qian-Wen Li; Ting-Yu Qian; Hui Wang; Chuanwang Sun,,3.0,7738f620c956e8a9084e5788a8e437cb80fefab6910,10.1007/978-3-030-69431-9_1,From Traditional House Price Appraisal to Computer Vision-Based: A Survey,2021.0,,Naser T. Naser; Sertan Serte; F. Al-turjman,,3.0,7745a3b4fbc9165f703719b7567196be18a0756f4a0,10.2747/0272-3638.15.1.9,"Housing Prices in Master- Planned Communities: Are There Premiums? The Evidence for Collin Country, Texas, 1980-1991.",1994.0,,E. Alexandrakis; B. Berry,,3.0,7753ad37800defcd1676bc92485752dd8fbd940b6cf,10.1007/978-3-031-10542-5_15,Spatial Econometric Analysis of Multi-family Housing Prices in Turin: The Heterogeneity of Preferences for Energy Efficiency,2022.0,Communication Systems and Applications,Federico Dell’Anna,,3.0,7761aa681861940d9ca058ee66aa19a0f1a02d1b0c0,10.1142/s2424922x2550007x,Predictions of Residential Property Prices for Ningbo City of Zhejiang Province in China Using Machine Learning,2025.0,Advances in Data Science and Adaptive Analysis,Bing-Zi Jin; Xiao-Jie Xu,,3.0,77728da8306284d8d3d576bf60c09cca54850c34d9f,10.1007/s10901-025-10244-8,"Identifying influencers of housing prices: spatial heterogeneity and pattern analysis using machine learning, spatial regression, and GIS",2025.0,Journal of Housing and the Built Environment,Nihal Genç; H. E. Colak,,3.0,77853b6dec666a6943dd94855e9c9aed85e6ff8e321,10.1080/12265934.2025.2606073,Exploring the spatial heterogeneity and nonlinear impacts of TOD on housing prices using osp-GNNW-XGBoost model,2025.0,International Journal of Urban Sciences,Chenxu Liu; Xiaodong Xu; Ke Liu; Abudureheman Abuduwayiti,,3.0,7793257364313645698b1556b7f2e9600d4526402cd,10.1007/978-3-031-20716-7_8,House Price Prediction via Visual Cues and Estate Attributes,2022.0,International Symposium on Visual Computing,S. Vaddi; Amira Yousif; S. Baraheem; Ju Shen; Tam V. Nguyen,,3.0,780287a3c515b0818e2b5f522117e37a409f0e9dbf4,10.3818/jrp.1.2.1999.89,"Municipal Police Salaries as a Function of Community Home Values, Household Incomes, and Physical Housing Characteristics",1999.0,,Joseph E. Pascarella,,3.0,7818f09e0c9ea529fa07cff42c408d76b8fe35784dd,10.5821/ctv.7747,Why fallow land in core urban areas?: planning or market deficits for sustainable development in Norway,2011.0,International Conference Virtual City and Territory,B. Nordahl; E. Falleth,"All over Europe cities are facing high immigration rates and are growing. The bigger cities in Norway are no exception. In Norway, as in most other European countries, Compact City Development is regarded as the most sustainable urban growth model. For more than 20 years urban land transformation and the reuse of greyand brownfield urban land into urban residential areas has been the main tool in sustainable accommodating of the growing population. Despite many successful developments there are still attractive areas in the core of the cities staying undeveloped. It stays undeveloped despite being designated for redevelopment by the local planning authority, despite rising house prices in the neighbouring area and despite high land values. This paper outlines a theoretical approach aiming at explaining why such area stays underdeveloped. It addresses planning theory, market theory and collective action theory in order to better understand urban redevelopment processes. The ideal of the compact city runs parallel with another important change in urban planning in Norway. There has been a market orientation in urban planning since the 1980s [4] [6] [3]. This market orientation has tr",2.0,https://upcommons.upc.edu/bitstream/2099/15907/1/104_Why%20Fallow%20Land%20in%20Core%20Urban%20Areas.pdf78221ffe82ecd5a338012f5d5600c67f59c9d02f5e3,10.2991/icadce-16.2016.170,Analysis on the Problems in the Process of Implementing Architectural Programming,2016.0,,Yunfeng Zhong,"The architectural programming has developed quite mature in the US, Europe and Japan, while in our country it has just started. Firstly, this paper corrects the misunderstanding of architectural programming from the understanding, further points out the relationship between programming and architectural design, and the importance of programming, finally puts forward the measures that shall be taken currently in the face of the architectural programming, so as to guide the construction and architectural design of our country to develop in a more healthy and mature direction. Keywords—architectural programming; architectural design; architect; creativity I. THE AWARENESS RECTIFICATION OF ARCHITECTURAL PROGRAMMING The architectural programming is highly valued in the architectural engineering field of Europe, the US, Japan and other economically developed countries and regions, the theories and methods of architectural programming have been quite mature, which has become a relatively independent link in the architectural activities. The domestic research and practice about architectural programming is relatively in the initial stage. The vast majority of the architects is familiar wit",2.0,https://download.atlantis-press.com/article/25858329.pdf7838df48a4c533bf2d881fa062a8b713bed9067172d,10.2991/essaeme-16.2016.3,The Application Of Traditional Culture In Interior Design,2016.0,,Yu Zhang,"How to deal with the relationship between interior design and traditional culture, Confucianism, Taoism, Buddhism and other main ideas in traditional culture and its application in interior design are worthy of our thinking. This paper discusses how to link the traditional culture with the modern art design, in order to form an interior design style with the characteristics of traditional culture. The Essence of Chinese Traditional Culture and Its Application in Interior Design Chinese traditional culture in the course of its thousands of years of history, Confucianism, Buddhism, Taoism, three in the struggle, mutual integration, promote the prosperity and development of Chinese culture. Regardless of the historical or contemporary Chinese design works, all by its influence. Confucian Doctrine of the Mean. The core idea of the doctrine of Confucius and Mencius is a middle course. First, attach importance to the Confucian philosophy of man and heaven ""the person and the nature harmonious thinking mode and value orientation, and a Chinese designers with the design philosophy and constitutes the traditional Chinese design the basic philosophical connotation. Some brands of real estate",2.0,https://download.atlantis-press.com/article/25860172.pdf784bb347394fe1439f74ae7e2a88041b23f8b2b5064,10.26686/wgtn.17139713.v1,The performance of the New Zealand economy: Savings and housing,2019.0,,David Law,"Recent policy changes and looming pressures in New Zealand have the potential to significantly impact the living standards of those who will enter retirement in the coming decades. In particular, a voluntary subsidised savings scheme known as KiwiSaver was introduced in 2007. Population ageing will increase the costs associated with New Zealand Superannuation (NZS), a universal government-funded pension paid for out of general taxation. In addition, rapid house price growth has made home ownership difficult for many, yet home ownership is likely to improve the living standards of retirees. These developments raise a number of important policy questions, which this thesis addresses. A variety of empirical approaches are employed, ranging from descriptive analysis to the application of regression techniques, including those designed to address specific econometric problems such as sample selection bias and unobserved heterogeneity. Data is primarily sourced from longitudinal and cross-sectional surveys. However, when required this is supplemented with house price, life expectancy and administrative data. Chapters 2 and 3 of the thesis provide an evaluation of the performance of Kiwi",2.0,https://figshare.com/articles/thesis/The_performance_of_the_New_Zealand_economy_Savings_and_housing/171397137858361fd463be68fd23f0094abf8ee388289f61f44,10.30541/v62i4493-520,Revisiting Urban Immovable Property Valuation: An Appraisal of Spatial Heterogeneities in Punjab Using Big Data,2023.0,Pakistan Development Review,Shoaib Khalid; Fariha Zameer,"This study employed big data and spatial analysis to assess property values in two cities, Lahore and Faisalabad. Traditional housing price models overlook spatial nuances, focusing solely on structural attributes. To address this, we constructed valuation models using ordinary least square regression and Fast Geographic Weighted Regression (FastGWR), implemented through Python and MPI, based on spatial variables. The models explained up to 75 percent of variance in Faisalabad and around 85 percent in Lahore. Factors like floor area, proximity of health facilities, recreational sites, and marketplaces add a premium to prices, while the nearness of educational institutions, worship places, and solid waste transfer stations or dumping sites lessen the property values in both cities. However, the proximity of industrial units and graveyards affects property values negatively in Lahore but positively in Faisalabad. This study highlights the critical significance of spatial factors in urban immovable property appraisal. As a result, it is recommended to integrate these factors into the process of policy formulation and urban planning. 7861. INTRODUCTION 787Pakistan’s real estate market emer",2.0,http://thepdr.pk/index.php/pdr/article/download/3243/3812788b434c49b54853a5146cd799c228fb4727d30a281,10.1108/ijhma-04-2024-0056,Impacts of macroeconomic factors during COVID-19 pandemic on property loan impairments and overhang: case study of Malaysia,2024.0,International Journal of Housing Markets and Analysis,Woei-Chyi Chai; K. Tham; Chin-Tiong Cheng; Kim Wing Chong; Kai Yun Yeoh,"789Purpose790The COVID-19 pandemic has profoundly impacted the global economy, disrupting supply chains, causing job losses and altering consumer demand. In Malaysia, the real estate sector has been notably affected, with increased property impairments and overhang due to unprecedented uncertainty. Understanding these effects is crucial for policymakers and investors to prevent real estate and banking crises. This study aims to analyse the relationships between macroeconomic factors during the pandemic on property impairments and overhang, providing insights for maintaining macroeconomic stability. The findings will inform strategies for mitigating economic shocks, identifying opportunities, and guiding real estate policies in Malaysia and potentially globally.791792793Design/methodology/approach794This research article uses a time series ARDL regression analysis to examine pivotal macroeconomic factors including income, housing process, interest rates and unemployment on property loan impairments and property supply overhang in Malaysia. ARDL is effective to measure and analyse time series data, especially to understand the lagged impacts of macroeconomic factors. This can be seen by various ",2.0,7955e48b17d985c16a506519398309d2e11015119e2,10.33142/jsa.v2i4.18079,Spatial-temporal Differentiation and Influencing Mechanism of Accessibility of Park Green Spaces: A Case Study Based on Shenzhen City,2025.0,Journal of South Architecture,Yu-Xi Chen; Mengting Deng; Chunxiao Wang,"Park green space is an important part of the urban natural ecosystem and public service facilities. The accessibility index of park green space can characterize the difficulty of using park green space for residents. In addition, it is an important index that reflects the rational layout and fair use of park green spaces. With the rapid development of urbanization, the unrestrained expansion of construction land, and the continuously increasing demands of the population have led to a gradual imbalance between the supply and demand of park green spaces. In this study, the spatialtemporal differentiation characteristics of accessibility of multi-scale park green spaces in Shenzhen under multiple travel modes from 2013 to 2020 were analyzed using the Gaussian two-step floating catchment area (Ga2SFCA) method, which comprehensively considers both supply and demand. The influencing degree of each factor on the accessibility of park green space was studied with a geographical detector from the global scale, through which the dominant factors were screened. The spatial differentiation of the action intensity of each dominant factor on the local scale was investigated using the geographica",2.0,https://doi.org/10.33142/jsa.v2i4.18079796ee2ebfd1ab282930c643f43bc3e9a9273c1faa52,10.1108/ec-09-2024-0895,Enhancing real estate prediction with entropy-based pattern analysis and economic sentiment integration,2025.0,Engineering computations,Dat Le; Sutharshan Rajasegarar; Wei Luo; T. Nguyen; M. Angelova,"797 798 The purpose of this research is to improve real estate market forecasting by introducing an innovative entropy-based multivariate clustering technique, Jensen–Shannon Time Series Segmentation (JSTS). The approach is designed to be integrated with advanced deep learning models such as Transformer, TSMixer, N-HiTS and LSTM, while incorporating key economic features such as consumer price index (CPI), sentiment analysis from headline news and real estate price and volume data to capture complex market dynamics.799 800 801 802 This study applies the JSTS clustering technique on a real dataset comprising Australian real estate market data, including property prices, volumes, CPI and sentiment scores derived from headline news. Advanced deep learning models such as Transformer, TSMixer, N-HiTS and LSTM are used for forecasting, and performance is compared against traditional clustering methods like weighted dynamic time warping (WDTW) and standalone forecasting models such as Autoregressive Integrated Moving Average (ARIMA). The models are evaluated based on forecasting accuracy metrics, including MAPE, RMSE and MASE.803 804 805 806 The experimental results show that the integration of JSTS clusteri",2.0,https://publications.aston.ac.uk/id/eprint/48378/1/Enhancing_real_estate_prediction_with_entropy-based_pattern_analysis_and_economic_sentiment_integration_AAM.pdf80797eac2836ad267399eb4ef76090f96f4901d6f3c,10.21272/mmi.2021.2-18,"Marketing, management and financial providing of affordable housing",2021.0,Marketing and Management of Innovations,S. Ianchuk; Olga Garafonova; Y. Panimash; D. Pawliszczy,"Today’s rising housing prices in most countries worldwide have caused increasable attention to the problem of affordable housing. It is a social or ethical issue and an essential economic direction. Thus, affordable housing has great potential, influencing economic growth, labor forces, innovation, sustainable development, and an inclusive economy. Systematization of informational sources, theoretical and practical approaches for providing affordable housing, and assessing social housing needs indicated many views on this problem among scholars and policymakers. That is why marketing, management, and financial providing of affordable housing are significant mainstreams. The research aims to investigate marketing and management fundamentals of providing affordable housing in connection with funding aspects based on cross-country analysis. For achieving this target, key trends of housing market segmentation were analyzed, considering the distribution of the population by tenure status and analytical house price indicators using the data of the statistical office of the EU, the World Bank, and the OECD. The ways to promote more affordable housing by public and local authorities, priva",2.0,https://doi.org/10.21272/mmi.2021.2-188082805789526648bd22b92f7a85ce71f5ada04e503,10.1111/rsp3.12252,"Interregional migration, the housing market, and a spatial shift in the metro area: Interrelationships in the case study of809 M810 oscow",2020.0,,N. Kurichev; E. Kuricheva,"The mutual influence of interregional migrations, the housing market, and the spatial transformation of the Moscow metropolitan area are studied empirically using the database of housing development projects and the depersonalized database of the residence addresses of buyers in the primary housing market. We consider the influence of natural rent, agglomeration economy, interregional inequality, and distance on the differentiation of the activity of residents of various Russian regions and cities in the primary housing market of the Moscow metropolitan area compared to their migration activity. The main characteristics of the extensive development of the Moscow metropolitan area are the prevalence of large greenfield residential district projects, urban sprawl in the near suburban zone, and the concentration of construction along transport corridors in the middle suburban zone. Selective influence of buyers from other regions on various zones of the Moscow metropolitan area owes to their orientation at the most affordable proposals in the primary market. The near suburban zone is the main gate into the Moscow metropolitan area for migrants, and construction in this zone is a major",2.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/rsp3.1225281159ed641404fe8490fd826fd5e24334df297cad6b,10.31203/aepa.2024.21.1.001,The Spillover Effect of Chinese Monetary Policy on Real Estate Prices: Based on TVP-VAR Model,2024.0,Asia Europe Perspective Association,Hongyan Wang; K. Lee,"Since the reform and opening up, the Chinese economy has entered a stage of rapid development and people’s demand for housing has also risen in tandem. In particular, the real estate market has grown rapidly since the reform of the housing welfare system in 1998. In 2003, the real estate industry became the pillar industry of Chinese economic development and the real estate price rose quickly as it was driving the economy. The Monetary Authority has macroscopically controlled the real estate price for nearly 20 years since 2001, but the housing price still maintains a high-speed growth, and the irrational growth brings the housing price bubble, which is extremely unfavorable to the stable development of Chinese economy. Since the financial crisis, the government has called for tougher and better regulation of house prices. Monetary policy is the most common macro-control means. Monetary authorities generally use such tools as money supply, interest rates and exchange rates to control housing prices. However, how to control housing prices according to monetary policy and stabilize the outlook for housing prices are issues that need to be solved urgently. Therefore, it is important t",2.0,81290181ebd36f1fb96eadc83ab655c59fbd48089a7,10.1108/jerer-06-2023-0020,Key determinants of sales price in the residential developments in Prague,2025.0,Journal of European Real Estate Research,David Mazáček; Jiří Panoš,"PurposeThe real estate residential market plays a crucial role in the economy and personal savings of a significant portion of the population. In recent years, the pricing of new apartments in Prague and other major European cities has experienced rapid growth but also a sharp decline during the global financial crisis of 2008. The purpose of this paper is to describe the relationship between the selling price per square meter of new residential developments in Prague and the macroeconomic determinants, as well as real estate sector variables.Design/methodology/approachThe econometric model developed for this study is based on quarterly observations from 2005 to 2021 and utilizes statistical learning (SL) techniques of lasso regularization combined with Bayesian model averaging (BMA).FindingsThe primary objective is to employ the model to identify the main determinants and provide insights into the price dynamics of new residential units. The outcomes of the model suggest that the set of variables selected in this paper – Net disposable income per household, Prague GDP per capita, Unemployment rate, Mortgage Interest rate and Covid dummy variable – provides a good explanation of th",2.0,https://doi.org/10.1108/jerer-06-2023-0020813bb99ca8c646d9e926311f7092af603bcebaa15ba,10.1109/oceans47191.2022.9977295,Assessing the Capacity of Pelagic Sharks to Serve as Ocean Observing Platforms in the Mid-Atlantic Bight and Beyond,2022.0,"OCEANS 2022, Hampton Roads",C. Wiernicki; T. Curtis; B. Block; M. Oliver; A. Carlisle,"The use of marine animals as ocean observing platforms (OOPs) has been identified by scientists and managers as having the ability to significantly improve existing oceanographic models and data resources. As OOPs, animals are fit with oceanographic sensors with the capacity to measure in situ temperature and conductivity with an accuracy comparable to ship-based conductivity-temperature-depth (CTD) casts or autonomous drifters. While initial applications of this approach focused on regularly surfacing marine mammals, there is significant unrealized potential for non-air-breathing species that still surface regularly (specifically sharks) to sample a wider range of global habitats. Sharks occupy habitats across the globe, in particular habitats that are coincident with hurricanes. One region where sharks have enormous untapped potential to serve as OOPs and directly benefit coastal communities is the Mid-Atlantic Bight (MAB). Using pelagic shark species that occur in the MAB to generate high-quality water column CTD profiles has the potential to greatly improve regional oceanographic and hurricane models by providing real-time oceanographic data to the Integrated Oceanographic Obse",2.0,814bfa0feda53884b8e548d74f59143f759e57f314e,10.31685/kek.v3i3.527,Pengaruh KPR terhadap Keterjangkauan Harga Properti Residensial,2019.0,,Pihri Buhaerah,"Abstract This paper describes and examines the linkage of house mortgages on residential property price growth in Indonesia by using qualitative and quantitative research methods. The qualitative research approach is used to elaborate descriptively the role of house mortgages on residential property prices. To strengthen it, this study then employs one of time series regression analyses namely autoregressive distributed lag (ARDL) model for the period of 2002Q1-2017Q4. To achieve the objective of this study, data was collected from secondary sources such as Bank for International Settlements (BIS), Bank Indonesia (BI), and Central Statistics Agency (BPS). The qualitative approach shows that under lack of land banking and public housing zones, the expansion of house mortgages affect positively residential property prices both for private and public housing. The argument has been confirmed from regression analysis by using the ARDL model. The estimation results using the ARDL model show that there is a positive and significant relationship between house mortgage on residential property price growth both in the long-run and in the short-run. Keywords: house mortgage, property residen",2.0,https://doi.org/10.31685/kek.v3i3.527815aeafe92064511298a34d305401ac623476344f9a,10.6007/ijarbss/v8-i11/4887,Macroeconomic Determinants and Mortgage Debt Accumulation: Evidence from Malaysia,2018.0,International Journal of Academic Research in Business and Social Sciences,Aqilah Nadiah Md. Sahiq; Siti Nursyafiqah Sukardi; N. Haron; S. Ismail; R. Othman,"The statistics of the household debt in Malaysia is among the highest in the ASEAN region with the biggest portion of the household debt goes to pay off the mortgage debt. As accommodation is one of the basic needs required by all households regardless of the price offered, concerns are given to the household affordability to live in a decent economy. The escalating trend of household debts related to mortgage is due to the increase in house prices, fluctuation in interest rates and speculative activities taken by investors. Thus, the aim of the study is to examine the relationship of principal drivers on mortgage debt accumulation using macroeconomic determinants. The macroeconomic determinants were represented by house price index (HPI), interest rate (IR), gross domestic product (GDP) and living cost (LC). The data set was analyzed between the period International Journal of Academic Research in Business and Social Sciences Vol. 8 , No. 11, Nov, 2018, E-ISSN: 2222-6990 © 2018 HRMARS 99 of 2006 till 2016 on a quarterly basis. Multiple regression analysis was applied to test the statistical relationship between the macroeconomic determinants and the mortgage debt. Several diagnost",2.0,https://doi.org/10.6007/ijarbss/v8-i11/488781652c5a1dfb38c5a2321b91bcae5521ffd491ce079,10.5194/isprs-archives-xlii-3-w10-1281-2020,EXPLORING THE IMPACT OF REAL ESTATE POLICY ON REAL ESTATE TRADING USING THE TIME SERIES ANALYSIS,2020.0,"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences",Shi-Wei Shao; Xin Huang; Li-Xia Xiao; Hui Liu,"Abstract. Housing price is a major issue affecting people's lives, but also closely related to the interests of the people themselves. Housing prices are affected by various factors, such as economic factors, population size factors, social factors, national policy factors, the internal factors of real estate and environmental factors. With the deepening of urbanization and the agglomeration of urban population in China, housing prices have been further accelerated. The Chinese government has also introduced a series of policies to limit real estate transactions and affect property prices. This paper also aims to explore a time series analysis method to analyse the impact of real estate policies on real estate prices. Firstly, the article searches for policy factors related to real estate through government official channels such as state, Prefecture and city, and analyses key words related to policy by means of natural language processing. Then, the real estate registration volume, transaction volume and transaction house price data which are arranged into time series are modelled using ARIMA time series model, and the data are processed according to scatter plot, autocorrelation ",2.0,https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W10/1281/2020/isprs-archives-XLII-3-W10-1281-2020.pdf817dcdf4f7c486975b3792f110ae8b624123dc79968,10.1371/journal.pone.0308484,Predicting tropospheric nitrogen dioxide column density in South African municipalities using socio-environmental variables and Multiscale Geographically Weighted Regression,2024.0,PLoS ONE,S. Hlatshwayo; Solomon G. Tesfamichael; M. Kganyago,"Atmospheric nitrogen dioxide (NO2) pollution is a major health and social challenge in South African induced mainly by fossil fuel combustions for power generation, transportation and domestic biomass burning for indoor activities. The pollution level is moderated by various environmental and social factors, yet previous studies made use of limited factors or focussed on only industrialised regions ignoring the contributions in large parts of the country. There is a need to assess how socio-environmenral factors, which inherently exhibit variations across space, influence the pollution levels in South Africa. This study therefore aimed to predict annual tropospheric NO2 column density using socio-environmental variables that are widely proven in the literature as sources and sinks of pollution. The environmental variables used to predict NO2 included remotely sensed Enhanced Vegetation Index (EVI), Land Surface Temperature and Aerosol Optical Depth (AOD) while the social data, which were obtained from national household surveys, included energy sources data, settlement patterns, gender and age statistics aggregated at municipality scale. The prediction was accomplished by applying ",2.0,https://doi.org/10.1371/journal.pone.0308484818c6aeb265b082f498b79a459ea7c4cd4e657e662c,10.1007/s10901-011-9263-4,A. Hurley: Beyond preservation: using public history to revitalize inner cities,2012.0,,J. Rouwendal,"In the last few decades there has been a remarkable revival of cities as the focal points of economic and cultural life. Many cities that seemed to be dying in the 1970s turned out to be focal points of economic and cultural life four decades later. One aspect of this remarkable turn of events has been the increasing appreciation of cities as meaningful places. The identity of cities has been determined in the course of history and the remnants of the past are therefore often cherished. It is not just that these old buildings and other objects are preserved, or restored in their former state, but often they receive a place in today’s society. The past is made useful for the present by integrating cultural heritage in our present-day society. Andrew Hurst works as a historian at the University of Missouri in St. Louis and he was involved in several projects in that city that were aimed at reviving older dilapidated areas of the town. These projects, as well as some others, are discussed at length in the book, and the author argues that they can serve as examples of what can happen, and sometimes does happen, in other neighborhoods. He places the St. Louis experience in the context o",2.0,https://link.springer.com/content/pdf/10.1007/s10901-011-9263-4.pdf8197f45c7e0c2ce00ee7b45a396be33c3ab863fa9fb,10.24203/ijcit.v10i3.98,Deep Learning Classification of Building Types in Northern Cyprus,2021.0,,Mubarak Muhammad; Sertan Serte,"Among the areas where AI studies centered on developing models that provide real-time solutions for the real estate industry are real estate price forecasting, building age, and types and design of the building (villa, apartment, floor number). Nevertheless, within the ML sector, DL is an emerging region with an Interest increases every year. As a result, a growing number of DL research are in conferences and papers, models for real estate have begun to emerge. In this study, we present a deep learning method for classification of houses in Northern Cyprus using Convolutional neural network. This work proposes the use of Convolutional neural networks in the classification of houses images. The classification will be based on the house age, house price, number of floors in the house, house type i.e. Villa and Apartment. The first category is Villa versus Apartments class; based on the training dataset of 362 images the class result shows the overall accuracy of 96.40%. The second category is split into two classes according to age of the buildings, namely 0 to 5 years Apartments 6 to 10 years Apartments. This class is to classify the building based on their age and the result shows ",2.0,https://doi.org/10.24203/ijcit.v10i3.988202b696f23d1348cade6eaa50942782b966af21ef6,10.1108/ajeb-06-2020-0013,The impact of foreign capital shifts on economic activities and asset prices: a threshold VAR approach,2020.0,Asian Journal of Economics and Banking,Boubekeur Baba; Guven Sevil,"821Purpose822The purpose of this paper is to investigate the impact of foreign capital shifts on economic activities and asset prices in South Korea.823824825Design/methodology/approach826The authors in this paper apply the Bayesian threshold vector autoregressive (TVAR) model to estimate the regimes of large and low inflows of foreign capital. Then, structural impulse-response analysis is used to check whether the responses of the variables differ across the estimated regimes. The model is estimated using quarterly data of foreign capital inflows, gross domestic product (GDP), consumer price index, credit to the private non-financial sector, real effective exchange rate (REER), stock returns and house prices.827828829Findings830The main findings suggest that large inflows of gross foreign capital, foreign direct investments (FDI) and foreign portfolio investments (FPI) are ineffective to boost economic growth, but large inflows of other foreign investments (OFIs) significantly contribute to GDP. The decreases in the foreign capital inflows are associated with larger depreciation of REER. The large inflows of gross foreign capital, FDI and OFIs are associated with further expansion of credit supply to",2.0,https://www.emerald.com/insight/content/doi/10.1108/AJEB-06-2020-0013/full/pdf?title=the-impact-of-foreign-capital-shifts-on-economic-activities-and-asset-prices-a-threshold-var-approach83199f50b9d040d39f9dcaf83f63d045316b5cbdd0f,10.5772/16224,An AND-OR Fuzzy Neural Network,2011.0,,Jiang-Hua Sui,"Fuzzy neural network combines the theories of fuzzy logical and neural network, including learning, association, identification, self-adaptation and fuzzy information process. The logic neurons have received much concern all the time as the important components of neural networks. From the models designing to the algorithms studying, there are many achievements. Glorennec[1]proposed a general artificial neuron to realize Lukasiewicz logical operate. Yager[2] employed a group of OWA fuzzy Aggregation operators to form OWA neuron. Pedrycz and Rocha [3] proposed aggregation neurons and referential neurons by integrating fuzzy logic and neural network and discuss the relation about the ultimate network structure and practical problem; Pedrycz i.e. [4],[5],[6] constructed a knowledgebased network by AND, OR neurons to solve classified problem and pattern recognition. Bailey i.e. [7] extended the single hidden layer to two hidden layers for improve complex modeling problems. Pedrycz and Reformat designed fuzzy neural network constructed by AND, OR neurons to modeling the house price in Boston [8]. We consider this multi-input-single-output (MISO) fuzzy logic-driven control system based o",2.0,https://www.intechopen.com/citation-pdf-url/1358683264432d100a0281346a4b9a09426b532ffa872438,10.24247/ijmperdjun2020656,GIS based Temporal Analysis of Land use Pattern and its Impact - Proposed Gurgaon Manesar Urban Complex,2020.0,,Manuj Dev; S. M. Veerabhadrappa,"Objectives: The objective of this study is to analyse the rapidly changing land use pattern for past several years around proposed Gurgaon Manesar urban complex and its socioeconomic impact on the residents. Material and Methods: GIS based temporal analysis of land use pattern was done using Landsat satellite Imagery for the year 1987, 2011, 2014 and 2019. Georeferencing of the satellite imagery was done using Ground control Point in the first phase and then Land use pattern was digitised using Arc GIS software in the second phase. The distinct land use for past years was then analysed using population and agriculture data to understand the socio-economic impact. Findings: The infrastructure projects and Immigrants Population has resulted in an increase of settlement area which has increased from 2% in the year1987 to 15% in 2011 of total Manesar tehsil area which grows up to 21% in 2014 and currently occupies 26% of the total area. The increasing settlements area has direct impact on the count of household which has increased from 13817 in 2001 to 44074 in 2011 and is expected to grow up to 57476 by the year 2021. Result suggests that Manesar Township as well as other villages acr",2.0,https://doi.org/10.24247/ijmperdjun2020656833e3730ac33404b0fddef77607f5782caefdc6a1cb,10.3390/buildings14124024,Urban Built Environment as a Predictor for Coronary Heart Disease—A Cross-Sectional Study Based on Machine Learning,2024.0,Buildings,Dan Jiang; Fei Guo; Zi-Teng Zhang; Xiao-Qing Yu; Jing Dong; Hong-Chi Zhang; Zhen Zhang,"The relationship between coronary heart disease (CHD) and complex urban built environments remains a subject of considerable uncertainty. The development of predictive models via machine learning to explore the underlying mechanisms of this association, as well as the formulation of intervention policies and planning strategies, has emerged as a pivotal area of research. A cross-sectional dataset of hospital admissions for CHD over the course of a year from a hospital in Dalian City, China, was assembled and matched with multi-source built environment data via residential addresses. This study evaluates five machine learning models, including decision tree (DT), random forest (RF), eXtreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and support vector machine (SVM), and compares them with multiple linear regression models. The results show that DT, RF, and XGBoost exhibit superior predictive capabilities, with all R2 values exceeding 0.70. The DT model performed the best, with an R2 value of 0.818, and the best performance was based on metrics such as MAE and MSE. Additionally, using explainable AI techniques, this study reveals the contribution of different built en",2.0,https://www.mdpi.com/2075-5309/14/12/4024/pdf?version=1734532505834e2d1b2122c529dbb08c71354866533bb7b2160be,10.3390/land15030484,Accessibility and Social Equity of Urban Park Green Spaces in Megacities from an Environmental Justice Perspective: A Case Study of the Six Central Districts of Beijing,2026.0,Land,Ting-Ting Ding; Chang Wang; Bo Zeng; Yu-Qi Li; Yun-Yuan Li,"Against the backdrop of rapid development in megacities, urban park green spaces serve as essential public resources whose accessibility and equity directly affect residents’ quality of life and broader social justice. This study addresses the imbalance between the spatial distribution of green space resources and the socio-demographic characteristics of different population groups in megacities. It takes the six central districts of Beijing as the study area and integrates data from 457 urban parks. The research applies the Gaussian two-step floating catchment area (G2SFCA) method and bivariate spatial autocorrelation analysis (Moran’s I) to systematically evaluate the equity of urban park green space provision across multiple social dimensions, including economic status, educational attainment, and vulnerable groups. The results indicate that urban park green spaces in Beijing’s six central districts exhibit a pronounced central and northern advantage, with significant deficits in southern and peripheral areas. High accessibility and greater per capita green space are concentrated in core and high-housing-price districts, overlapping with high-income and highly educated populatio",2.0,https://www.mdpi.com/2073-445X/15/3/484/pdf?version=17737584558354d20fc4735a8cac30673d326c2852754e7f8636d,10.11648/j.ajtas.20150405.15,Spatial Modelling of Disparity in Economic Activity and Unemployment in Southern and Oromia Regional States of Ethiopia,2015.0,,Berisha Mekayhu Gelebo; A. Goshu,"Growth of productivity is the precondition to improve the living standard of people and maintain competitiveness in the globalized economy. However, wide regional deferential in labor force implies inefficiency as whole and might affect both aggregate unemployment and national output. The basic goal of this study was to model disparity in economic activity and unemployment in Southern and Oromia Regional States of Ethiopia, by incorporating spatial effects. Population and Housing Census data for 381 districts were used. The exploratory spatial data analysis, OLS regression model, and spatial econometric models were employed. The exploratory spatial data analysis results revealed that both economic activity and unemployment rates in a given district were directly affected by those of its neighbors. Economic activity and unemployment rates for males and females also spatially depended on that of neighboring districts. Spatial autocorrelations between unemployment and economic activity rates is negative. In modeling aspect, relying on specification diagnostics and measures of fit; spatial lag model was found to be the best model for modelling both economic activity and unemployment ra",2.0,http://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150405.15.pdf836e8c493f8f8386b7fcf31eddecf271196f60e8d72,10.1111/1475-6773.14342,Sampling coverage of the Arkansas all-payer claims database by County's persistent poverty designation.,2024.0,Health Services Research,Chenghui Li; Cheng Peng; Peter DelNero; Mahima Saini; Mario Schootman,"OBJECTIVES837To evaluate the quality of Arkansas All-Payer Claims Database (APCD) for disparity research in persistent poverty areas by determining (1) its representativeness of Arkansas population, (2) variation by county, and (3) differences in coverage between persistent poverty and other counties.838839840DATA SOURCES841Cross-sectional study using 2019 Arkansas APCD member enrollment data and county-level data from various agencies.842843844DATA COLLECTION/EXTRACTION METHODS845An alias identifier linked persons across insurance plans. County FIPS codes were used to extract county-level variables.846847848STUDY DESIGN849Cohort 1 included individuals with ≥1 day of medical coverage in 2019. Cohort 2 included individuals with medical coverage in June, 2019. Cohort 3 included individuals with continuous medical coverage in 2019. Sampling proportions of a county's population in the three cohorts were compared between persistent poverty and other counties. Inverse-variance weighted linear regression was used to identify county-level socioeconomic and demographic characteristics associated with inclusion in each cohort.850851852PRINCIPAL FINDINGS853In 2019, 73.6% of Arkansans had medical coverage for ≥1 day (Cohort 1)",2.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1475-6773.14342854a809d0ed65b713714fabbd8c0c889936ee2c83a4,10.1108/jerer-04-2023-0014,International retiree migration and housing markets. Evidence from Spain,2023.0,Journal of European Real Estate Research,Paloma Taltavull de La Paz; K. Gibler,"PurposeLarge numbers of Northern European retirees have migrated to Southern European countries. A relevant part of this migration is not driven by work purposes but rather the desire to establish residence in a warmer country. These migrants come from different countries and exhibit diverse socioeconomic characteristics and preferences, including varying income levels, housing tastes and cultural habits, which could potentially influence the housing market in their host countries. This paper aims to examine the permanent impact of retiree migrant flows on house prices in Alicante, Spain, from 1988 to 2019, explicitly considering the impact related to the country of origin.Design/methodology/approachThis paper examines the permanent impact of retiree migrant flows on house prices in Alicante, Spain, from 1988 to 2019, explicitly considering the impact related to the country of origin using panel cointegration – Dynamic Ordinary Least Squared (DOLS) models.FindingsResults indicate that the long-term relationship captures the entire effect on house price change and that prices react immediately to the immigrants' presence with permanent effects. The results also suggest that the stro",2.0,http://rua.ua.es/dspace/bitstream/10045/137599/1/Taltavull_Gibler_2023_JEuroRealEstateRes_accepted.PDF8557e0215d7651641787bd00707421b43b7fde0332b,10.1186/s13705-025-00523-1,"Examining spatial variations in the relationship between domestic energy consumption and its driving factors using multiscale geographically weighted regression: a case study in Nottingham, England",2025.0,"Energy, Sustainability and Society",Yuan Feng; Ying Miao; Ed Turner,"Domestic energy consumption contributes to over a quarter of the UK’s carbon emissions, understanding how it is driven can be helpful for delivering a fair energy transition to net zero. Energy usage is noted as a spatial phenomenon, however, the spatial variability of how it is driven is rarely considered in existing UK studies. To contribute to this research gap, this study examines the spatial variations in the relationship between domestic energy consumption and its driving factors using the local spatial statistical modelling technique multiscale geographically weighted regression (MGWR). With explanatory variables on dwelling and household characteristics, this study analyses data at Lower Layer Super Output Area (LSOA) level on the study area, Nottingham, a somewhat socio-economically deprived city that also has the UK’s largest district heating (DH) system supplying low-carbon residential heating. The study reveals domestic energy consumption is driven by factors at different spatial scales with spatially varied or even spatially heterogeneous patterns. Specifically, higher domestic energy consumption is affected differently across local areas by larger percentages of dwell",2.0,https://energsustainsoc.biomedcentral.com/counter/pdf/10.1186/s13705-025-00523-1856c1d21684ae344f91787d890ea9f2ee65e1c2a777,10.1080/12265934.2025.2517097,Housing price dynamics within an integrated urban model: testing the spatial and temporal transferability,2025.0,International Journal of Urban Sciences,M. Orvin; M. Fatmi; Ifratul Hoque,"ABSTRACT This study evaluates spatio-temporal transferability of housing price models within an integrated urban modelling framework. Multi-year housing sales data for the city of Vancouver is used to develop models in pre-, during-, and post-pandemic periods. The models adopt a random parameter autoregressive method to accommodate autocorrelation and random parameters to capture unobserved heterogeneity. Results confirm the influence of temporal, dwelling, land use, neighbourhood and accessibility attributes on housing prices. For instance, in the case of land use attributes, prices are more likely to be higher in diverse land mix areas pre-pandemic. Results also confirm several differences among three phases of the pandemic and variations in effects across observations. For example, prices are less likely to be higher in diverse land mix areas during and post-pandemic for a certain number of observations. Results further reveal that housing prices tend to be influenced by nearby dwelling prices. For spatial transferability, the Vancouver model, developed using pre-pandemic data, was applied to another city within the same province – Kelowna, which features different geographic an",2.0,857e5b5adaafa91f1dd4e000a997705be716b4a5516,10.1108/jerer-11-2023-0043,"Investigating the impact of housing price increases on consumption: heterogeneity by age, tenure and housing quality",2024.0,Journal of European Real Estate Research,Alejandro Fernández,"PurposeThe purpose of this paper is to understand the distributional impact of house price increases on consumption in the context of the energy transition.Design/methodology/approachThis study draws from two micro cross-sectional datasets, the English Housing Survey (EHS) and the Living Costs and Food Survey (LCFS) to study the Marginal Propensity to Consume (MPC) out of changes in house prices. By employing pseudo-panel regressions, the paper examines the impact of house price changes on consumption among diverse household types.FindingsThis paper finds varying consumption responses to house price changes across age and tenure groups. Older homeowners tend to increase consumption when house prices rise. In contrast, middle-aged individuals, often renters or mortgage holders, reduce consumption in response to price increases. The youngest age group also experiences increased consumption but to a lesser degree than the oldest group. Energy-efficient homes are related to lower consumption across all tenure levels. However, when interacted with house prices and age, the estimates are positive, pointing to an unequal accrual of property premiums depending on housing market positions.R",2.0,https://www.emerald.com/jerer/article-pdf/17/2/232/9516093/jerer-11-2023-0043.pdf858c2bab3834aab7bdca28c3254c94999276b2e89ef,10.4314/ejhs.v31i4.7,"Spatial Modelling of Risk Factors for Malaria Prevalence in SNNP Regional State, Ethiopia",2021.0,Ethiopian Journal of Health Sciences,Shammena Aklilu Toma; Baleh Wubejig Eneyew; G. Taye,"Background Malaria is one of the most severe public health problems worldwide with 300 to 500 million cases and about one million deaths reported to date of which 90% were from world health organization (WHO) Sub Saharan Africa (SSA) countries. The purpose of this study was to explore the spatial distribution of malaria parasite prevalence (MPP) among districts of Southern Nations Nationalities and Peoples Regional State (SNNRS) in Ethiopia by using 2011 malaria indicator survey (MIS) data collected for 76 districts and to model its relationship with different covariates. Method Exploratory spatial data analysis (ESDA) was conducted followed by implementation of spatial lag model (SLM) and spatial error model (SEM) in GeoDa software. Queen contiguity second order type of spatial weight matrix was applied in order to formalize spatial interaction among districts. Results From ESDA, we found positive spatial autocorrelation in malaria prevalence rate. Hot spot areas for MPP were found in the eastern and southeast parts of the region. Relying on specification diagnostics and measures of fit, SLM was found to be the best model for explaining the geographical variation of MPP. SLM analy",2.0,https://www.ajol.info/index.php/ejhs/article/download/214443/202255859df3b4adf69c8b83194ce00dc890b5f9535609557,10.15396/eres2013_44,Unraveling the Inflated House Price in the New Housing Supply,2013.0,,R. Ismail,"There are a lot of concern both by public at large and the government on the inflated house price in the housing market, fearing that it could lead to property bubble and raises the issue of affordability. On a closer look at the property transactions data, it was observed that the main increase in the house price is often related to the primary housing market, i.e. new supply in the housing market whereas the sales price of existing supply, or the secondary market, remains stable or increase marginally. What is more baffling is that the two markets are sometimes in the same location, with same accessibility, with units available for sale but the sales price of houses in these market shows a marked difference. It is grounded in theory that price is a function of demand and supply. However, if supply is in abundance in the secondary market, what causes the price differentiation between these two markets. This research attempts to unravel the inflated house price in the primary market. First, the research investigates on market efficiency i.e. the extent of dissemination of sales information of both housing in the primary and secondary market. Second, it explores the level of complex",2.0,https://eres.architexturez.net/system/files/pdf/eres2013_44.content.00129.pdf860ec5067fce7f7bd0e21ffb50db49bc14b4a59cc3a,10.1108/jerer-01-2024-0001,Exploring the impact of renovation subsidies on housing markets – evidence from the Swedish property market,2024.0,Journal of European Real Estate Research,Mats Wilhelmsson; Abukar Warsame,"PurposeThe primary aim of this research is to examine the effects of the Renovation, Conversion, and Extension (ROT) tax deduction for renovations on the scope and quality of renovations and its subsequent impact on house prices across various Swedish municipalities.Design/methodology/approachThis study utilises a two-way fixed effect instrument variable (IV) spatial Manski approach, analysing balanced panel data from 2004 to 2020 at the municipal level (290 municipalities) in Sweden. The methodology is designed to assess the impact of the ROT subsidy on the housing market.FindingsThe study reveals that the ROT subsidy has significantly influenced house prices, with noticeable variations between municipalities. These differences are attributed to the varying amounts of tax reductions for renovations and the extent to which property owners utilise these subsidies.Research limitations/implicationsThe research is limited to the context of Sweden and may not be generalisable to other countries with different housing and subsidy policies. The findings are crucial for understanding the specific impacts of government subsidies on the housing market within this context.Practical implicatio",2.0,https://doi.org/10.1108/jerer-01-2024-000186108fa75f12fde9dd3923ca96b272dbf131693e06f,10.59782/sidr.v1i1.53,Research on CPI prediction based on LSTM model with double-layer attention mechanism,2024.0,Scientific Insights and Discoveries Review,Man-Ru Dong; Xiaobin Tang,"With the increasingly complex and changing economic and political environment at home and abroad, timely and accurate forecasting of the consumer price index (CPI) plays an important role in boosting consumer confidence and implementing the strategy of expanding domestic demand. Aiming at the multidimensional characteristics of CPI dynamic changes and the lag problem of release, this paper constructs a CPI prediction dataset by combining natural language processing technology, introduces a two-layer Attention mechanism into the LSTM neural network structure, and constructs an ATT-LSTM-ATT model for CPI prediction. At the same time, multiple machine learning models (ATT-LSTM, LSTM, SVR, RF, XGBoost and LGBM) are introduced for comparison and cross-validation analysis. The study found that: (1) The two-layer Attention mechanism can dynamically focus on key information in the two dimensions of features and time series, strengthen the LSTM model's attention allocation to real estate policies, Double Eleven and holidays, highlight the impact of important features and important time points on CPI changes, and effectively improve the model's accuracy in predicting CPI; (2) Compared with t",2.0,https://doi.org/10.59782/sidr.v1i1.538628526c1013a72233c492c626d574d3c6befb63399,10.1177/14738716261434842,Evaluating the utility of feature importance visualizations in SHAP,2026.0,Information Visualization,Dennis Collaris; Yunhao Liang; M. Willemsen; Angelos Chatzimparmpas; J. V. van Wijk,"Feature importance is a technique that helps users understand machine learning models by showing how much each feature contributed to the model’s predictions. For example, it can be used for housing price prediction to explain why certain features lead to higher or lower prices. Different visualizations are used to convey feature importance to users: standard bar charts, but also advanced waterfall plots and force plots as provided by SHAP. These advanced visual representations convey more information (e.g., about the additivity property of the technique). However, this may come at the expense of the figure’s simplicity. Both the trade-off between these properties and the added benefit of these advanced visualizations have yet to be formally studied. In this paper, we evaluate the effectiveness of three common SHAP visualization types for users to understand how machine learning-based prediction works in a housing price prediction scenario. Each participant answered a set of quiz questions aimed to measure their basic understanding of the feature importance (the absolute impact of features), the negative or positive impact of features, and the additivity property of feature importa",2.0,https://doi.org/10.1177/14738716261434842863f959e78776b586967ccf2d3c7aad8b07bf45b736,10.15330/jpnu.12.4.49-65,"Green Economics in Action: Exploring the Nexus Between Industrial Growth, Market Forces, and Carbon Credit Movements in Emerging Economies – With Reference to India",2025.0,Journal of Vasyl Stefanyk Precarpathian National University,S. A. Sait; P. Vijesh,"This study examines the impact of key economic growth indicators on the carbon credit market in India, highlighting how selected macroeconomic variables shape its dynamics in an emerging economy. Using secondary data covering 11 years from the Reserve Bank of India (RBI) and BSE India, the research applies rigorous econometric methods, including the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) Unit Root Tests, the Vector Error Correction Model (VECM), and Ordinary Least Squares (OLS) regression, to explore both short- and long-run relationships among variables. The analysis considers crude oil prices, automobile sales (AMS), the Housing Price Index (HPI), GDP, the Index of Industrial Production (IIP), and the Green Exchange Index—an indicator of sustainable finance performance and investor confidence in carbon markets. These variables capture economic activity, market sentiment, and energy dependence, all of which influence carbon credit pricing and demand. Empirical results indicate that crude oil prices and AMS negatively affect carbon credits, whereas HPI, GDP, and IIP positively impact them. The Wald test indicates no long-run relationship between carbon credits and c",2.0,https://doi.org/10.15330/jpnu.12.4.49-658642541630c94ddbe3cdbd9307dc7fa0aacfca77634,10.1353/jda.2025.a952656,Demand for Electricity and its Determinants in Australia: Policy Implications,2025.0,Journal of Developing Areas,Albert Wijeweera; Clevo Wilson; W. Athukorala; D. Rajapaksa; Shunsuke Managi,"ABSTRACT:The electricity industry has emerged as a crucial driver of the Australian economy, undergoing significant transformations shaped by industry reforms initiated in the early 1990s. These reforms include the introduction of full retail contestability in the electricity markets to stimulate competition by encouraging new market entrants in the retail sector. This paper investigates the response of residential electricity consumers to these reforms by estimating residential electricity demand as a function of consumer income, electricity price index, gas price index, and two temperature variables. The analysis employs annual data spanning from 1989 to 2019 and applies the Autoregressive Distributed Lag (ARDL) framework, a bound testing approach to cointegration, to estimate both long-run impacts and short-run dynamics. The ARDL framework is particularly advantageous in this context, as it accommodates variables with different orders of integration, includes lagged variables to capture the dynamics and time lags in electricity demand and its determinants, and provides consistent long-run demand elasticities with superior sampling properties especially in short time series spans",2.0,86552bf3bcda1fac24811bd4c677326a9debe5a3dea,10.14744/megaron.2026.60486,Determining spatial heterogeneity and influencing factors in housing prices with geographically weighted regression method: A case of Erzurum,2026.0,"MEGARON / Yıldız Technical University, Faculty of Architecture E-Journal",Cansu Güller,"Understanding the dynamics of the real estate market has emerged as a pivotal concern in urban economics for ensuring sustainable land management and effective investment strategies. The spatial heterogeneity of housing market determinants gives rise to variations in market activity and significant spatial differences in property values. However, global regression models are constrained in their ability to capture this heterogeneity and the spatial autocorrelation of housing prices. The aim of this study is to identify the spatial variability of housing prices and the potential factors influencing them. To this end, the study employs the Geographically Weighted Regression (GWR) model, which enables the analysis of spatial heterogeneity. In addition to conventional structural variables (floor area, age, heating type, number of floors, and floor level), measurable indicators, including network-based accessibility metrics (connectivity, betweenness, and closeness), distance to the central business district, and the remotely sensed Normalized Difference Vegetation Index (NDVI), are integrated into the model. The findings reveal the complexity of the housing market in Erzurum, showing t",2.0,https://jag.journalagent.com/z5/download_fulltext.asp?pdir=megaron&plng=eng&un=MEGARON-6048686631395c14c75a4e8f57d4bdf78b6b6a9413f14172,10.35595/2414-9179-2020-1-26-503-515,Approaches to mapping the regional housing market to provide visualization of the National Project “Housing and Urban Environment”,2020.0,InterCarto. InterGIS,S. Gurov,"The article shows various ways of mapping the regional residential real estate market, which can be used in the future to visualize the National Project “Housing and Urban Environment”, implemented until 2024. The author demonstrates different types of cartodiagrams. It is shown that the equipment of residential real estate is more convenient to display a petal cartodiagram. Attention is paid to the use of cartodiagram with stroke, the semicircular and mesh cartodiagram.867868The author gives a significant place in the publication to the method of mapping housing affordability for citizens, as one of the important tasks of the National Project “Housing and Urban Environment” is to create conditions for increasing the level of affordability of residential real estate acquisition. There are 4 availability indicators that can be displayed on the map using special cartograms and the cartogram method. The most successful option, according to the author, is a combination of two techniques for the image of a cartogram on one map.869870Attention and rather rare means of socio-economic mapping, in particular the pseudoisolines (of isopleth). The author notes that this method is one of the most obvi",2.0,https://doi.org/10.35595/2414-9179-2020-1-26-503-515871a9a30913a0c18b842080395cc26566e470b36da1,10.3390/su15054205,Big Data Application in Urban Commercial Center System Evaluation,2023.0,Sustainability,Xinyu Liu; Yi Guan; Zihan Wu; Lufeng Nie; Xiang Ji,"Big data has provided new opportunities, directions, and methods for research on urban commercial center systems. Based on a quantitative assessment of big data and public participation, the “big data + public feedback” evaluation model can objectively and scientifically quantify the scale and structural characteristics of urban commercial center systems. In this paper, socioeconomic and material spatial attributes were considered in the selection of four indexes, including commercial agglomeration centrality, commercial facility service level, commercial industry status, and industry attraction. Specifically, we based our selection on the big data of the point-of-interest network, housing price, and population. ArcGIS, SPSS, and other analytical tools were employed to conduct a comparative analysis, cluster analysis, spatial network analysis, and correlation analysis. Using these data, we constructed an assessment index system, which was then utilized to comprehensively evaluate the current commercial land use in Nanjing’s main urban area and measure the degree of commercialization. The commercial center system in the main urban area of Nanjing was found to be consistent with the ",2.0,https://www.mdpi.com/2071-1050/15/5/4205/pdf?version=16776519058726dd20fec1617eb7d3a26ca5d0e5b1f6637319a89,10.1177/21582440251363789,Research on the Mechanism of Urban Population in the Process of Urban Housing Price Differentiation,2025.0,SAGE Open,Chengming Yu; Shubin Miao,"In recent years, the dynamics of housing prices have garnered significant attention in China, particularly the inter-city relationships of these prices. Despite its relevance, scholarly discourse has been somewhat constrained by the limitations of methodologies and the depth of discussion, leaving the underlying mechanisms inadequately elucidated. Anchored in the theoretical framework of new economic geography, this study embarks on a comprehensive examination of urban population, integrating pertinent macroeconomic variables to refine the theoretical model concerning the inter-urban housing price dynamics from the vantage point of urban population. Methodologically, the investigation employs benchmark regression analyses and incorporates various interaction terms to assess the impact of urban population on housing price disparities and to elucidate the transmission mechanisms through which macroeconomic indicators, such as household financial behaviors, influence housing price variations via household income. Findings suggest that population plays a pivotal role in driving the disparities in housing prices across cities. Specifically, variables such as the growth rate of deposits,",2.0,https://doi.org/10.1177/2158244025136378987390d46def38251fdef35ff5fee1a09f532b2f89e9,10.33763/finukr2022.02.072,Methods and models of real estate mass appraisal,2022.0,Fìnansi Ukraïni,M. Bondar; A. Kulyk,"This article examines the approaches, methods and models of mass appraisal. The article also considers features of application of valuation approaches and methods when conducting mass appraisal taking into account the type of real estate, the impact of price-forming factors of demand and supply and etc. Based on the analysis of scientific works of foreign and domestic scientists, the classification of mass appraisal models is given, which includes parametric multiple regression models, spatial and nonparametric models, as well as more modern methods, in particular, artificial neural networks, genetic algorithms, rough set theory and etc. Hedonistic models as the most common type of parametric multiple regression have been studied. In particular, the mathematical interpretation of this model is given, the key pricing factors that are used as explanatory variables in the construction of the model are analyzed, as well as different types of hedonistic models, their advantages and disadvantages are considered. As complementation of the traditional hedonistic model, spatial models are investigated , while nonparametric models are studied as an alternative. In particular, foreign scienti",2.0,https://finukr.org.ua/?page_id=17&year=2022&issueno=2&begin_page=72&mode=get_art&flang=uk8742553bb293b5e468b8167885e9945acea018509e9,10.29244/jp2wd.2022.6.2.136-145,Integrasi Ekonometri dalam Efektivitas Perencanaan menuju Penyediaan Perumahan yang Berkelanjutan,2022.0,Journal of Regional and Rural Development Planning,D. Rahmawati; Ikhfadhulhikmy Kurnia; Mulyono Sadyohutomo,"In accelerating spatial planning, an effective instrument is needed in planning so that the implementation of space utilization is in accordance with the expected goals, so that resources remain efficient, investment calculations become more real, and environmental balance in sustainable urban development is maintained. Housing development is often delayed (planning delay) due to the inefficient land provision process. On the other hand, the housing backlog is increasing to 800,000 units per year, thus it is necessary to investigate the effectiveness of planning, especially in the housing sector that has been running in Indonesia. Econometry as a quantitative approach with various variances of analysis techniques has many benefits but has not been widely integrated in planning, especially in calculating the effectiveness of instruments in achieving spatial planning goals. This article aims to examine the effectiveness of planning, especially related to land value and housing development through an econometric model approach by taking samples in Surabaya. The technique used is multiple regression analysis which shows the level of influence between six independent variables, namely l",2.0,https://journal.ipb.ac.id/index.php/p2wd/article/download/37899/23601875d2ff72085d2f6fc1ecd7c69df48f5cc943b1ef36,10.3390/land14020328,"Wealth and Altitude Explain Urban Plant Diversity in Residential Areas of Hainan, China",2025.0,Land,Lin-Ke Su; Huiting Tang; Gong He; M. M. Nizamani; Hua-Feng Wang,"Rapid urbanization worldwide poses significant challenges to biodiversity, as urban habitat fragmentation coexists with diverse landscape forms. Residential areas, a critical component of urban ecology, are essential for understanding the mechanisms that drive biodiversity conservation and the harmonious coexistence of humans and nature. Additionally, the gradient distribution of biodiversity remains a focal point in ecological research, aiding in the comprehension of fundamental species–environment interactions. In this study, we sampled 269 residential areas across fifteen counties and municipal districts on Hainan Island to investigate biodiversity alongside residential characteristics, as well as socio-economic and environmental variables. Utilizing the Generalized Linear Model (GLM), we analyzed the differences and commonalities of plant driving factors through horizontal and vertical two-dimensional gradient models with box plots, Principal Component Analysis (PCA), Principal Coordinate Analysis (PCoA), and path models to examine the existence, distribution, and nature of these gradients. Our findings indicate the following: different plant types are driven by distinct mechan",2.0,https://www.mdpi.com/2073-445X/14/2/328/pdf?version=1738832792876ada2532b1c7839371110b0481dd127be979b6834,10.69968/ijisem.2025v4i1378-383,A Holistic Measure of Socioeconomic Status for Municipal Aid Allocation in Connecticut,2025.0,International Journal of Innovations in Science Engineering And Management,Pranjali Jain,"Economic disparities between municipalities in the state of Connecticut remain a pressing concern, often complicating efforts to allocate government aid effectively. Although Connecticut boasts one of the highest per capita incomes in the United States, the distribution of wealth and access to resources across its 169 towns is far from equitable. This paper introduces a comprehensive, data-informed approach to measuring socioeconomic status (SES) that seeks to address this challenge. By combining five carefully selected indicators—median home value, unemployment rate, median household income, participation in the Supplemental Nutrition Assistance Program (SNAP), and poverty rate—a composite SES index was developed for each municipality. These indicators were normalized and weighted to ensure consistency and comparability across towns of varying population sizes and economic characteristics. The resulting SES scores allowed for the classification of municipalities into high, medium, and low SES groups, offering a clearer picture of where disparities lie. The methodology is designed to serve as a practical framework for policymakers, particularly in decisions regarding the allocation",2.0,https://ijisem.com/journal/index.php/ijisem/article/download/361/3358776c76961a7d421dbee784b361a83961b152781273,10.3390/su18031500,Spatial Disparities in Housing Values in the United States During the Great Depression: A Place-Based Sustainability Perspective,2026.0,Sustainability,Xinba Li; Chuanrong Zhang,"Spatial disparities in housing values during the Great Depression reflect not only regional housing market conditions but also deeper inequalities in economic opportunity, social infrastructure, and environmental resilience that are central to place-based sustainability. Despite extensive research on housing inequality during this period, spatial disparities in housing values—particularly in relation to race beyond the neighborhood level—remain underexplored. This study examines county-level spatial disparities in housing values in the United States between 1930 and 1940, framing housing values as an indicator of place-based sustainability. Using spatial visualization, global and local spatial econometric models, and Multi-Scale Geographically Weighted Regression (MGWR), we analyze how economic shocks, environmental stressors, and socioeconomic and demographic factors jointly shaped uneven housing outcomes across space. Our findings reveal distinct regional trends: higher housing values were concentrated in the Northeast, Midwest, and West Coast, while lower values prevailed in the Mountain and Southern regions. Housing values declined from 1930 to 1940, with the Dust Bowl intensif",2.0,https://doi.org/10.3390/su180315008782e360f77caee23c4aea32f4046a7c61915f50221,10.1007/s10980-024-01998-7,"Scale-specific effects of urban landscape pattern on the COVID-19 epidemic in Hangzhou, China",2024.0,Landscape Ecology,Jun-Bing Chen; Zhou Sun; Yu Wu; Guang Hu; Yanping Wen,"Understanding the scale-specific effects of different landscape variables on the COVID-19 epidemics is critical for developing the precise and effective prevention and control strategies within urban areas. Based on the landscape epidemiology framework, we analyzed the scale-specific effects of urban landscape pattern on COVID-19 epidemics in Hangzhou, China. We collected COVID-19 cases in Hangzhou from 2020‒2022 and combined the datasets of land use and land cover (LULC) and social gathering point (SGP) to quantify the urban landscape pattern. Optimal general linear model with stepwise regression was applied to explore the dominant landscape factors driving the COVID-19 transmission in the city. Furthermore, multi-scale geographically weighted regression illustrated the spatial heterogeneity and scale specificity of these landscape variables’ effects to COVID-19 epidemic. Eight landscape variables of LULC and SGP patterns were identified which explained 68.5% of the variance in spatial risk of COVID-19. Different optimal bandwidths across these variables in MGWR indicated their scale-specific effects. LSI of green space enhanced the spatial risk across the entire region. The effec",2.0,http://dx.doi.org/10.1007/s10980-024-01998-7879afd98fdb7ac97b19e0f7b50ef5fd3c13d7d48881,10.3934/nar.2024004,Housing price dynamics and elasticities: Portugal's conundrum,2024.0,National Accounting Review,A. M. Cunha; R. Loureiro,"880 We aimed to estimate the housing price determinants and elasticities in Portugal's metropolitan areas to help understand the dynamics of the abnormal price increase of the last decade, one of the highest in Europe and the World. 881882We followed a three-step methodology applying panel data and time series regression estimation. First, we estimated the determinants of housing prices at the national and metropolitan area levels. Second, we split the sample by coastal and inner metropolitan areas and estimated the determinants of housing prices and the supply elasticities of each group. Third, we estimated the correlations between housing price growth and elasticities to find whether these determinants correlate. 883884The results showed that at the national level, housing prices are inelastic to aggregate income (0.112). Momentum is the most significant determinant of housing prices (0.760). At the metropolitan areas level, we found an inelastic housing supply, a price-to-income elasticity close to zero, and a more inelastic supply in coastal areas. We found no significant correlation between housing price growth, price-to-income, and supply elasticity. The coastal areas registered housin",2.0,http://www.aimspress.com/aimspress-data/nar/2024/1/PDF/NAR-06-01-004.pdf885b040b216a50fa3ef33553b69f02d456baa0617a3,10.21033/wp-2025-01,"Skills, Migration, and Urban Amenities Over the Life Cycle",2025.0,Social Science Research Network,David Albouy; R. J. Faberman,"We explore how much sorting across cities based on worker skill is driven by a desire to live in high-amenity cities, and if so, whether this sorting has a life-cycle component. We use data from the restricted-access geocode files for three cohorts of the National Longitudinal Surveys of Youth (NLSY), supplemented with data on time use and household expenditures. We estimate a quality of life index, which captures the degree of local amenities and labor productivity within a metropolitan area, using the methodology from Albouy (2012, 2016). We find that, like consumption expenditures, the average quality of life of an individual’s metropolitan area is hump-shaped over the life cycle, though it peaks much earlier. The observed life-cycle changes in average quality of life reflect migration decisions. Variations in average quality of life are driven primarily by changes in local amenities (and are reflected in changing housing prices across metropolitan areas) and are largest for the high skilled. We argue that the early hump-shape in quality of life reflects a bunch of amenity demand towards the earlier stages of the life cycle, and that this bunching is driven by expectations of ch",2.0,https://doi.org/10.21033/wp-2025-01886661996919e34459bef41ac40a41f418dfc4005d7,10.36348/gajeb.2024.v06i06.001,"Impact of Housing Supply, Income Levels and Macroeconomic Factors on Housing Prices in Nigeria",2024.0,Global academic journal of economics and business,Isaac Ukaoma Udensi; Ojong Sunday Oyama; M. Abbey,"Housing affordability remains a critical challenge in Nigeria, driven by rapid urbanization, inadequate housing supply, economic disparities, and fluctuating macroeconomic conditions. This study investigates the impact of housing supply, income levels, and key macroeconomic factors, specifically inflation, interest rates, and GDP growth, on housing prices in Nigeria. Using a quantitative research design, secondary data from the Central Bank of Nigeria, the National Bureau of Statistics, and the World Bank were analyzed over the period from 2013 to 2023. Statistical analyses, including trend analysis, correlation, multiple regression, and Granger causality tests, were conducted to determine the relationships between housing prices, housing supply, income levels, and macroeconomic indicators. Findings indicate that increased housing supply plays a stabilizing role in moderating housing prices, while rising income levels drive demand elevates prices. Inflation is identified as a key driver of housing costs, primarily due to escalating construction expenses, while interest rates show minimal impact on housing prices due to limited mortgage accessibility in Nigeria. The study among othe",2.0,https://doi.org/10.36348/gajeb.2024.v06i06.001887e9e869e3a1bc8a420e3f58225eeefc997ce6c842,10.2139/ssrn.4377406,"Estimation of Property Value Changes from Nearby Carbon Capture, Utilization, and Storage Projects in the United States",2024.0,Social Science Research Network,Kai-Fang Luo; Yue-Ming Qiu; P. Liu; Ying-Dan Mei,"Carbon capture, utilization, and storage (CCUS) techniques are vital to reaching decarbonization goals. Using nationwide CCUS data and property-level housing transaction records in the U.S., we quantify the impact of CCUS projects on nearby property values. Our research reveals three main findings. First, within 15 km of a CCUS project, property values decrease by 10.18% on average and the impact is heterogeneous depending on the property proximity. Second, a CCUS project’s negative impact varies across the project types and facility conditions. The value of a property decreases by 9.88% and 6.42% after a carbon capture-only and a carbon storage-only project is operational within 15 km of the property, respectively. Retrofit CCUS facilities have no significant impact on nearby housing prices, whereas new-built CCUS facilities significantly reduce nearby house prices by 10.68%. Third, we apply a DDD approach to distinguish between CCUS effects and oil basin effects on housing prices and find that the CCUS operation has a net impact of -17.84% on nearby housing prices when compared to similar houses in oil basins without CCUS operations. Our mechanism analysis indicates that the incr",2.0,https://doi.org/10.2139/ssrn.43774068884021c828bed1ed9f674957eff534f1c7b7277a47,10.1088/1755-1315/473/1/012066,Young consumer preferences of the housing environment in Makassar City,2020.0,IOP Conference Series: Earth and Environment,S. A. Yanti; S. Trisutomo; M. Arifin,"The increasing number of residents (youth) who have migrated in urban areas triggers various problems, one of which is the problem of providing housing. This study aims to look at the preferences of young consumers towards the residential environment residing in the city of Makassar. The research approach used is a quantitative approach with the analytical method used is factor analysis to reduce several indicators in several variables such as the physical condition of housing, accessibility, facilities, infrastructure, and social environment around the housing into a set of indicators that represent the preferences of young consumers. This research was conducted in Makassar City with a sample of 350 respondents. Sources of data obtained from library research and interviews using data collection tools, namely questionnaires. The results showed of 23 indicators (indicators of type of house, house design, house price, type of house, location of house, availability of public transportation, proximity to the city center, proximity to workplaces, educational facilities, health facilities, trade facilities, sports facilities, facilities green open space, availability of water, availabili",2.0,https://iopscience.iop.org/article/10.1088/1755-1315/473/1/012066/pdf8894e3711cec97cc562f45b0e7df2afa12f9d4347b3,10.18306/dlkxjz.2021.02.009,Influencing factors of housing price differentiation based on the spatial quantile model: A case study of Wuhan City,2021.0,Progress in Geography,Xin-Hai Lu; Dawei Cai; Chen Zeng,": The change of housing price is an important issue related to urbanization and high-quality social and economic development in China. In order to explore the influencing factors in terms of the quantiles of housing price, this study took Wuhan City as a case, which is the central city of central China, and used the spatial quantile regression (SQR) model for quantitative analysis. The two-stage least squares (2SLS) model result was compared with the SQR model output to reveal its superiority. The research shows that: 1) The SQR model not only can consider the spatial autocorrelation of the housing price, but also have the capability to embed the conditional distribution characteristics, which helps to better describe the driving effects of micro- factors on different housing prices. 2) In view of the quantiles, the spatial autocorrelation of the high prices is stronger than the low prices. The influencing factors show volatility and heterogeneity. Compared with the mean result of the 2SLS model, the influencing degree of each factor in the SQR model increases or decreases with the change of the quantile. There is a significant difference in the degree of influence on housing price",2.0,http://www.progressingeography.com/CN/article/downloadArticleFile.do?attachType=PDF&id=492248902ec2d3e0a6d0261de22a62ad28a4f2ce47db01fd,10.1007/s41324-025-00608-1,"Quantifying the economic impact of nature, recreation and heritage: a spatial assessment of land prices in alpine tourism regions",2025.0,Spatial Information Research,Miroslav Despotovic; David Koch,"The dynamics of land prices in Alpine tourism regions are shaped by natural amenities, limited buildable land, and demand from second-home owners, posing significant challenges for sustainable regional development. This study examines the spatial impacts of alternative determinants of land value in Tyrol, Austria, including recreational activities, cultural heritage, and environmental features, using a Spatial Durbin Error Model (SDEM) while controlling for primary land value drivers. The results reveal skiing as the most influential recreational driver of land prices, with strong direct and spillover effects, while running and cycling activities show significant localized positive impacts, with the latter exhibiting nonlinear effects influenced by topographical and infrastructural variations across municipalities. Environmental features such as Natura 2000 protected areas and water areas consistently exhibit negative effects, reflecting restrictions on buildable land use. Cultural heritage positively impacts land values; however, its influence diminishes when control variables are introduced. The inclusion of controls enhances the model’s explanatory power, confirming the critical",2.0,https://doi.org/10.1007/s41324-025-00608-1891b14e9acf98546e9e7ca5da1145a108c1b334954f,10.1007/s10708-023-10911-9,The dynamics and uniqueness of semigration to hermanus from across South Africa,2023.0,GeoJournal,L. Willemse,"Covid-19 significantly impacted the South African economy, particularly through geographical flexibility in workspaces, made possible by improvements in smart office technology. The ability to participate in hybrid forms of work (and ‘work from home’) has increased patterns of decentralised living, moving away from larger cities towards peripheral suburban locations and smaller towns, usually with the aim of improving living conditions. This trend has been described as ‘semigration’, and towns in the Western Cape seem to be the prime locations. The aim of this study is to investigate the extent and characteristics of semigration to Hermanus, in the Western Cape of South Africa. The data for this study was collected from two companies (Lightstone and Property24). Sampled data from Lightstone was analysed through frequency counts to provide a trend analysis of semigration to Hermanus. Property24’s open-source data was analysed through frequencies, crosstabulations, and various spatial statistical techniques in ArcMap. Results indicate that Covid-19 slowed the pace of property purchases in Hermanus but did not necessarily increase this pace drastically in comparison to previous years.",2.0,https://link.springer.com/content/pdf/10.1007/s10708-023-10911-9.pdf892b9bb7d2c88ca67b39ea5173df3df120cce080b20,10.29244/jp2wd.2020.4.2.113-133,Pola Interaksi Spasial serta Karakteristik Individu dan Rumah Tangga Komuter antar Kabupaten/Kota di Bandung Raya,2020.0,,Riya Supriyatin; A. E. Pravitasari; Didit Okta Pribadi,"Spatial Interaction Pattern and Characteristics of Individual and Household Commuters between Regencies/Cities in Greater Bandung 893Differences in regional development in Greater Bandung caused spatial interaction in order to meet the shortcomings in origin areas. One of the form spatial interaction is commuting. This study aimed to analyze the pattern of spatial interaction between regencies/cities in Greater Bandung and determine individual and household commuter characteristics. Variables used to analyze the pattern of spatial interactions were number of commuters, distances, regional development index, built up area ratio, and population density of regency/city units in Greater Bandung. Method used were Entropy Index and Augmented Doubly Constrained Entropy Model. Variables used to determine individual and household commuter characteristics were age, marital status, highest education completed, number of household members, monthly household expenditure, and the main reason for choosing settlement location. Data were originated from 2017 Greater Bandung Commuter Survey held by Statistics Indonesia (BPS), which consists of 15,082 individuals and 4,531 households. The method used w",2.0,https://journal.ipb.ac.id/index.php/p2wd/article/download/29825/2006489484ad4582441dc4d4bc35704555200680e2777264,10.1145/3767052.3767068,Machine Learning Models for Predicting Second-hand House Prices: A Comparative Study,2025.0,"Proceedings of the 2025 International Conference on Big Data, Artificial Intelligence and Digital Economy",Haoran Jiang,"This paper presents a comparative analysis of the application of machine learning models in predicting the prices of second-hand houses. It underscores the importance of the real estate market within the economy and points out the advantages of precise predictions for various stakeholders, including homebuyers, real estate agents, and financial institutions. Traditional methods frequently fail to grasp the intricate interactions that impact house prices, whereas machine learning techniques have garnered attention for their capability to handle such complexities. The study made use of the “Shanghai Second-hand Housing Prices from 2024 Anjuke Website” dataset from Kaggle, which comprises 175,135 records, each having nearly 30 features. After undergoing preprocessing procedures like duplicate removal, feature transformation, outlier removal, missing value imputation, feature encoding, feature engineering, and data standardization, the data was divided into training and testing sets. Four machine learning models were compared: Linear Regression, Ridge Regression, Random Forest, and XGBoost. The findings revealed that Random Forest and XGBoost surpassed the linear models, with Random Fo",2.0,https://dl.acm.org/doi/pdf/10.1145/3767052.376706889549ff75ed86d0bf1bea5209dc9f765cb191e53267,10.1007/s11222-025-10683-x,Improving the prediction accuracy of statistical models: A new hierarchical clustering approach,2025.0,Statistics and computing,Ángel López-Oriona; Ying Sun; J. A. Vilar,"Statisticians and machine learning practitioners frequently encounter datasets originated from multiple populations but containing the same type of measurements. In such cases, predictive analytics is typically carried out by either fitting a separate model to each dataset independently or by merging the datasets and fitting a single model to the combined data. These approaches overlook the potential existence of multiple groups of datasets associated with different underlying models, and, therefore, fail to exploit the inherent similarity between datasets to improve predictions. A third alternative is to perform pairwise comparisons between the populations before fitting the models. However, this is not always feasible, can become a very challenging task with complex models, and often does not rely on predictive accuracy. To address these issues, we propose a clustering approach designed to improve predictions in general databases. The method is based on a novel type of objective function that represents the total by-group prediction error. The clustering problem is solved using a hierarchical-type algorithm of agglomerative nature that automatically obtains the resulting clusteri",2.0,https://link.springer.com/content/pdf/10.1007/s11222-025-10683-x.pdf89628c5e0e2dd944fb912aff80378ed5fb248c2b370,10.3390/buildings15173177,Multidimensional Urbanization and Housing Price Changes: Evidence from 35 Large- and Medium-Sized Cities in China,2025.0,Buildings,Jie-Ning Meng; Peng-Fei Liu; Mei-Jie Li,"To address the issue of spatial mismatch of real estate resources at its source, we incorporate the multidimensional urbanization speeds into the real estate market stock-flow model, simulate the regional real estate resource allocation process from a dynamic perspective, and explore the impact of urbanization on housing prices, as well as the characteristics that a coordinated multidimensional urbanization should possess. Utilizing data on population flow, economic development, and the relative increment of newly built housing units that meet delivery standards from 2008 to 2022 in 35 large- and medium-sized cities in China. We employ the dynamic panel system GMM approach to estimate the direct effect of single-dimensional urbanization on housing prices, and utilize the threshold effect model to examine the comprehensive effect of multidimensional urbanization on housing prices. The findings reveal that population, economic, and spatial urbanization influence housing prices by altering the flow of real estate supply and demand, with their effects being significantly shaped by the scarcity of stock real estate resources. The dynamic coordination of multidimensional urbanization ρ h",2.0,https://www.mdpi.com/2075-5309/15/17/3177/pdf?version=17569772118977cfdf300e323f2dbc3370f6bfec2c94dd7e8ce32,10.2139/ssrn.3727743,Limited Attention in the Housing Market: Threshold Effects of Energy-Performance Certificates on Property Prices and Energy-Efficiency Investments,2020.0,Social Science Research Network,Rodolfo Sejas-Portillo; David A. Comerford; Mirko Moro; Till Stowasser,"We study the effects of limited attention on property prices and energy efficiency (EE) investments in the housing market. Using a novel dataset, we analyse over 5 million residential property sale transactions in England and Wales, each containing information about sale price, property and location characteristics, and a mandatory energy performance certificate (EPC). The EPC includes a continuous energy cost rating (SAP rating) which is mapped into seven colour-coded rating bands (ranging from green A to red G). Applying a Regression Discontinuity Design (RDD), we document significant price discontinuities at the rating band thresholds. We estimate that - holding the underlying SAP score equal - being in a higher rating band increases the final sale price of a property between 0.8% and 2.5% ($2,000 and $6,625 based on average sale prices) depending on the threshold crossed. The presence of price discontinuities suggests that individuals are attentive to the simpler colour-coded rating band and partially inattentive to the more precise SAP rating. We present a simple model for estimating the degree of inattention and show that, for a given level of attention, rating bands reduce a",2.0,https://doi.org/10.2139/ssrn.37277438989430444155a33bf9bb5a8a2fcdcb82fe2b3aeb1f,10.5120/ijca2020920303,A Machine learning based Advanced House Price Prediction using Logistic Regression,2020.0,International Journal of Computer Applications,Ravula Bala; K. Surya; Tadiparthi Chandravas; M. J,"Machine learning has played a bigger role than in previous years’ image detection, spam changes, normal conversation orders, product recommendations, medical diagnoses. Current machine learning algorithms help to improve security alarms, public safety and medical improvement and other advantages using machine learning penetration in to several global implementations and their improvement. The machine learning system provides better opportunities customer service and safe car system. Currently A paper on which we talk about future housing price forecasts machine learning algorithms. As for Choose from our comparative and various study hypotheses forecasting methods. We use refined-lasso regression as our model due to the method of adaptation and verification on the model selection. Research shows that we need a problem-solving approach able to succeed and develop assumptions It will be possible to compare the cost of the house with other models. On the other hand, the housing price index and progress forecast of housing costs that tend to make real progress real estate policy schemes. This study uses machine learning The algorithm is used as a research method to develop housing pric",2.0,https://doi.org/10.5120/ijca2020920303899467b96f0a287c2c326f70492b72223bc08a7bf52,10.1108/ijhma-12-2023-0169,A modified spatial house price to income ratio and housing affordability drivers study: using the post-LASSO approach,2024.0,International Journal of Housing Markets and Analysis,Qifeng Wang; Bofan Lin; Consilz Tan,"Purpose900The purpose of this paper is to develop an index for measuring urban house price affordability that integrates spatial considerations and to explore the drivers of housing affordability using the post-least absolute shrinkage and selection operator (LASSO) approach and the ordinary least squares method of regression analysis.901902Design/methodology/approach903The study is based on time-series data collected from 2005 to 2021 for 256 prefectural-level city districts in China. The new urban spatial house-to-price ratio introduced in this study adds the consideration of commuting costs due to spatial endowment compared to the traditional house-to-price ratio. And compared with the use of ordinary economic modelling methods, this study adopts the post-LASSO variable selection approach combined with the k-fold cross-test model to identify the most important drivers of housing affordability, thus better solving the problems of multicollinearity and overfitting.904905Findings906Urban macroeconomics environment and government regulations have varying degrees of influence on housing affordability in cities. Among them, gross domestic product is the most important influence.907908Research limitatio",2.0,909427e201e50d20c1dd309a846c778701455f25b5e,10.15242/iicbe.dir1216420,"Research on Urban Low Income Housing Policy Delivery in Tamale , Ghana",2017.0,,Eunice Yorgri; Leng Hong; Cheng Wen; J. Patience,"Housing among other amenities is a basic right entitled to every individual in any human enclave and proper and decent housing is of great value in all spheres of life because it enhances safety, security and honour. Generally, access to decent housing is a global problem and a tremendous magnitude both in quantity and quality. Ghana has a housing deficit of 1.7million which has become a major challenge to successive governments. Rapid urbanization among other factors have been blamed for the acute housing shortages which have resulted in escalating housing prices and rent in most urban areas, especially areas where there are perceived job opportunities resulting in shanty towns and slums development. This has made the provision of affordable housing one of the daunting challenges in the socio-economic development of Ghana. Despite efforts made by successive governments in addressing housing problems, housing deficits still paddles on. This study employed quantitative and qualitative methods of data collection from both primary and secondary sources. It does so by surveying literature from policy documents, reports, journals, books and through questionnaires and interviews. The stu",2.0,https://doi.org/10.15242/iicbe.dir121642091041ef3ce62cea55327ba05e5ae1fd5f66c1569b42,10.3390/buildings15091405,"Housing Price Modeling Using a New Geographically, Temporally, and Characteristically Weighted Generalized Regression Neural Network (GTCW-GRNN) Algorithm",2025.0,Buildings,Saeed Zali; P. Pahlavani; O. Ghorbanzadeh; Ali Khazravi; Mohammad Ahmadlou; Sara Givekesh,"The location of housing has a significant influence on its pricing. Generally, spatial self-correlation and spatial heterogeneity phenomena affect housing price data. Additionally, time is a crucial factor in housing price modeling, as it helps understand market trends and fluctuations. Currency market fluctuations also directly affect housing prices. Therefore, in addition to the physical features of the property, such as the area of the residential unit and building age, the rate of exchange (dollar price) is added to the independent variable set. This study used the real estate transaction records from Iran’s registration system, covering February, May, August, and November in 2017–2019. Initially, 7464 transactions were collected, but after preprocessing, the dataset was refined to 7161 records. Unlike feedforward neural networks, the generalized regression neural network does not converge to local minimums, so in this research, the Geographically, Temporally, and Characteristically Weighted Generalized Regression Neural Network (GTCW-GRNN) for housing price modeling was developed. In addition to being able to model the spatial–time heterogeneity available in observations, this",2.0,https://www.mdpi.com/2075-5309/15/9/1405/pdf?version=1745323671911bb56415eb6cff0ffd2b05f6e4bafd6cec47cd182,10.3846/mla.2023.16969,IMPACT OF GREEN SPACES ON HOUSE PRICES,2023.0,Mokslas: Lietuvos Ateitis,Aistė Gaižauskienė,"Studies around the world have shown that urban green spaces have a positive effect on house prices. The economic benefits of green spaces are an issue that still needs to be addressed. When analyzing scientific literature, it is problematic to compare studies on the influence of green spaces on house prices, due to the absence of a unified assessment system. Working on studies examining a similar categorisation of green spaces, the application of the same assessment methods and the use of a common set of variables would allow for more detailed and accurate analyses. Therefore, the aim of the work is to identify the categories of green spaces and the method of evaluation, which most accurately describes the impact of urban green spaces on housing prices. This work examined 8 studies, most of which were carried out in Europe. An analysis of studies has shown that the most commonly used method for determining the impact of green spaces on house prices is the hedonistic pricing method. When analyzing literature it was noticed that quite often the influence on the price of housing was measured only from a few types of green spaces, although the classification of green spaces is very wid",2.0,https://journals.vilniustech.lt/index.php/MLA/article/download/16969/1165991261421beaba5f0fc29d32b1a3f8024660613c2195,10.37134/jcit.vol13.2.5.2023,Forecasting on House Price Index using Artificial Neural Network,2023.0,Journal of Contemporary Issues and Thought,Gangaieisvari Gobalkrishnan; Zahayu MD. Yusof,"Forecasting the residential property sector is a crucial component in the decision-making process for investors and government in supporting asset allocation, developing property finance plans and implementing a relevant policy. The purpose of this study is to examine the determinants of Penang house price index and to develop a model to forecast Penang house price index in Malaysia. Estimation is done by using ordinary least square and artificial neural network method. Relevant data sets were obtained from the Monthly Statistical Bulletin, Bank Negara Malaysia and National Property Information Centre. The empirical analysis of this research is based on quarterly time series data which cover the periods from 2005Q1 to 2022Q1. The main findings reported that base lending rate and unemployment rate are negatively associated with and have significant impacts on Penang house price index. Meanwhile, gross domestic product is positively related to and has a significant impact on Penang house price index. Consumer price index shows a positive sign; however, it recorded an insignificant impact on Penang house price index. Even though there are three independent variables recorded significa",2.0,https://ejournal.upsi.edu.my/index.php/JCIT/article/download/7866/4375/445979132ca60d2f9386cb6db3d3ba979928484d1068046c,10.35414/akufemubid.933675,Metro İstasyonları ve Metro Hatları Çevrelerinin Konut Alt Piyasasına Etkisi: İstanbul Kadıköy-Tavşantepe Metro Hattı,2021.0,Afyon Kocatepe University Journal of Sciences and Engineering,Birol Alas,"In this study, it is aimed to examine the sub-markets of real estate in terms of both metro station regions and regions between the metro stations in terms of both residential prices for sale and rental prices. Therefore; the answer to the question of whether there is a difference between the prices of the apartments (houses) close to metro stations and prices of the houses located in the same proximity to the metro lines. For this purpose, the prices of the houses on 26.5 k ilometers long Kadıköy - Tavşantepe Metro line on the Anatolian side of Istanbul were examined. The prices of the houses located around the metro stations and those between the two metro stations are mutually compared with all the data collected along the line for both sale and rental housing prices. Then the whole line is divided into a total of 17 regions as two stations and between these stations. In each of 17 zones, two station zones and the zone between these stations were compared for both sale and rental housing prices. As a result, in the comparison of all station region data and inter-station region data, no difference was found in both the prices for sale and rental prices. In the examination of two ",2.0,https://doi.org/10.35414/akufemubid.93367591411a25211aa932174ce6741e534519c81a574b270,10.31705/wcs.2022.9,Analysis of the current housing market in Colombo metro region to enhance the prospective consumer satisfaction,2022.0,Proceedings of 10th World Construction Symposium 2022,SR Dunuwila; K. Devapriya,"Housing is an essential element of social development. One consumer may purchase such a house once for his/her entire lifetime. Therefore, when making a housing purchasing decision, it is intensively explored more than it is considered in purchasing other consumable products. Property developer has the responsibility of understanding the consumer behaviour. The main research problem is the lack of attention towards the analysis of the current housing market of the Colombo Metro region from the consumer perspective. An extensive literature synthesis was carried out to gather information on the general attributes of the housing market and determinants of housing supply. Furthermore, investigation on the housing demand and consumer behaviour reference to Colombo Metro region were conducted through the literature synthesis. Subsequently, case studies and a survey have been adopted to proceed with the study following the mixed research approach. Semi-structured interviews for case studies and questionnaire survey were employed as the primary data collection. The primary data analysis was conducted through manual content analysis, Relative Important Index (RII) techniques and elementary ",2.0,http://dl.lib.uom.lk/bitstream/123/19955/1/WCS2022_Proceedings_9.pdf9154b684dbcae4b5197fc76f6581e12097da9ec07d9,10.22201/cisan.24487228e.2019.1.360,International Mobility of the Wealthy in an Age of Growing Inequality,2018.0,Norteamérica,Andrés Solimano,"The global economy and society are affected by the rising inequality in income and wealth along with an increasing frequency and severity of financial crises, tendencies for protectionism, and fragmented globalization. A largely unexplored topic in migration analysis is the international mobility of the wealthy and their assets, looking for countries that offer financial security, lower taxation, good educational facilities, safe cities, and other amenities. This mobility is largely motivated by [an] increasing concentration of wealth and incomes toward the top 1 or 0.1 percent in several economies and the search for diversification of newly created wealth. Some economies that are home of the wealthy are affected by political instability insecurity and weak property rights, prompting them to leave. Outflows of the wealthy and their assets have various consequences on both home and receiving nations such as a reduction of reduce tax revenues in the home country, an increase in property prices in the receiving nations, and is creating as well as the creation of a whole (legal) industry granting passports, residence, and citizenship oriented to the wealthy. This paper provides an over",2.0,https://www.revistanorteamerica.unam.mx/index.php/nam/article/download/360/494916b5d87c8de61eb04cf4f8cb5f22cbbc123d384bb4,10.36108/ujees/9102.10.0160,Impact of Landscaping on Residential Property Value in Lekki Phase 1 Lagos,2019.0,UNIOSUN Journal of Engineering and Environmental Sciences,J. O. Oyedeji,"Previous studies established that landscaping is a significant factor with impact on residential property value. However, there is absence of similar study in the study area. This study filled the gap by examining impact of landscaping on residential property value at Lekki Phase 1, Lagos. Population for the study are Estate Surveyors and Valuers managing residential properties in the study area. Simple random sampling technique was employed to arrive at a sample size of 94 out of 124 study population. Out of the 94 questionnaire administered, 90 questionnaire were retrieved. Data gathered were analyzed using descriptive statistics like frequency distribution table and relative importance index. The hypothesis was tested using correlation and regression. Findings from the study revealed that fence is the most prominent landscaping feature in the study area, while shrubs and trees is the least prominent landscaping feature in the study area. The research also revealed that the residence have the highest preference for fence as a landscaping feature and have the least preference for shrubs. Findings from the research established that the most prominent impact of landscaping on reside",2.0,https://doi.org/10.36108/ujees/9102.10.0160917a9f96b8d4c09e07ada7234708ff37abf3f7f9743,10.1017/s1742170523000376,Geospatial characteristics of allotment garden provision in Tokyo,2023.0,Renewable Agriculture and Food Systems,Keidai Kishimoto; Wanglin Yan,"Abstract Allotment gardens (AGs), one of the most popular forms of urban agriculture (UA), have attracted social attention because of the ecosystem services they provide to citizens. However, the services and availability of AGs may be unevenly distributed, owing to their geographic location. The patterns underlying the provision of AG plots and facilities to users in Tokyo are unclear. Thus, this study quantitatively examines the characteristics of different types of AG provision and their determinants in the metropolitan region of Tokyo. We classified a sample of 313 AGs gathered from governmental open data via a non-hierarchical cluster analysis of AG provision patterns based on their properties, including number of plots, plot size, contract price and duration, and facilities such as agricultural equipment and access to instructors. Moreover, we examined the influence of urban development and residential characteristics on these classes using the Kruskal–Wallis test. The analysis identifies six AG provision patterns based on their properties. It also revealed that AG provision in Tokyo was differentiated by the percentage of agricultural land and the socio-demographic character",2.0,https://www.cambridge.org/core/services/aop-cambridge-core/content/view/D98CC729FA76F47F86AC9F0AAC51CC8A/S1742170523000376a.pdf/div-class-title-geospatial-characteristics-of-allotment-garden-provision-in-tokyo-div.pdf918288d5c5a2e7a478994e143701f4cc724e11c890e,10.54097/bd459218,Multiple Machine Learning Models in House Price Prediction: Performance Evaluation and Comparison,2024.0,"Highlights in Business, Economics and Management",Jiahao Wu,"House pricing is one of the most critical concerns for people. Accurately predicting house prices is essential for participants and investors in the real estate market. This study delves into the realm of predictive modeling for house prices using a multimodal approach in machine learning. The research methodology spans various stages including data collection, preprocessing, feature engineering, and model selection. Based on a dataset sourced from Kaggle, housing data from Iowa comprising 79 pertinent features influencing property prices is utilized. Performance evaluation is conducted using the root mean square error (RMSE), comparing the efficacy of linear regression models such as Lasso regression, Kernel Ridge regression, and ElasticNet regression against tree-based models including XGBoost, Gradient Boosting, and LightGBM. Additionally, the study implements two stacked ensemble models to identify the most optimal predictive model for house prices. Experimental procedures involve training and evaluating these models on specific datasets to gauge their predictive accuracy. Findings and discussions underscore the comparative analysis of diverse machine learning models within the",2.0,https://drpress.org/ojs/index.php/HBEM/article/download/24522/240269194c47c7b6380a7f0f01af34532718b9cfbf806c27,10.1200/cci-24-00310,Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States,2025.0,JCO Clinical Cancer Informatics,S. Hashtarkhani; B. White; Benyamin Hoseini; D. Schwartz; A. Shaban-Nejad,"PURPOSE Lung cancer (LC) is a leading cause of cancer-related mortality in the United States. Accurate prediction of LC mortality rates is crucial for guiding targeted interventions and addressing health disparities. Although traditional regression-based models have been commonly used, explainable machine learning models may offer enhanced predictive accuracy and deeper insights into the factors influencing LC mortality. METHODS This study applied three models—random forest (RF), gradient boosting regression (GBR), and linear regression (LR)—to predict county-level LC mortality rates across the United States. Model performance was evaluated using R-squared and root mean squared error (RMSE). Shapley Additive Explanations (SHAP) values were used to determine variable importance and their directional impact. Geographic disparities in LC mortality were analyzed through Getis-Ord (Gi*) hotspot analysis. RESULTS The RF model outperformed both GBR and LR, achieving an R2 value of 41.9% and an RMSE of 12.8. SHAP analysis identified smoking rate as the most important predictor, followed by median home value and the percentage of the Hispanic ethnic population. Spatial analysis revealed sig",2.0,https://doi.org/10.1200/cci-24-003109205db24a0cf95c50ad2386ce5d531ac7806525cdab,10.1109/iconstem56934.2023.10142407,Leveraging Affordable Business Innovations to Tackle House Price Rising: Applications of Machine Learning Algorithms,2023.0,2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM),P. P. Sri; Vaddi Naga Padma Prasuna; R. Murugesan; ShilpaManjunath Nilugal,"Affordable housing as the most significant Sustainable Development Goal of the United Nations, is a burgeoning social problem across various nations globally. A lack of reasonable lodging to purchase or lease is fuelling the worldwide accommodation emergency. By 2025, 1.6 billion people are supposed to be unfavorably impacted by this. Starting from the beginning in the historical landmark, the housing market has grown quickly and land costs have been soaring. The land business has ended up being the support point industry of our general economy. Nonetheless, verifiable experience has repetitively demonstrated that, as financial advancement depends too significantly on land, these costs proceed to upsurge quickly there by outstripping demand over supply of land. Covid Pandemic is considered to have made the crisis much worse. The Indian housing market remains to skirmish with supply-side management for housing. Scarcity of technologically advanced and encumbrance-free urban land, the amplified cost of construction, absence of a feasible rental market and master-plan restraints have decreased the latent growth of the formal housing market in India.Taking cue from this, the research p",2.0,92143aa247214260b5b0daa43f6a5388eb513b3b9bc,10.2139/ssrn.3507803,The Zoning Strait-Jacket: The Freezing of American Neighborhoods of Single-Family Houses,2020.0,Social Science Research Network,Robert C. Ellickson,"Municipal zoning practices profoundly shape urban life in the United States. In regions such as Silicon Valley, regulatory barriers to residential construction have helped raise house prices to roughly ten times the national median. These astronomic prices have prompted some households to move to places, such as Texas, where housing is far less expensive. I have been engaged in an empirical study of zoning practices in Silicon Valley, Greater New Haven, and Greater Austin. This Article presents one of my central findings, induced from those metropolitan areas and elsewhere: local zoning politics typically freezes land uses in an established neighborhood of detached houses. The consequences are profound. Single-family neighborhoods constitute a solid majority of urban land in the United States. Within these frozen neighborhoods, real estate markets cannot respond to changes in supply and demand conditions. 922 923The Article marshals a variety of evidence to prove that the zoning strait-jacket exists. It also discusses possible exceptions to it. The most plausible is proximity to a newly opened transit node, an event that may transform zoning outcomes, even in a neighborhood of houses. ",2.0,https://doi.org/10.2139/ssrn.350780392492b1fdca56c779809c7c411209a0b789c702ffc5,10.3390/land14112205,Unveiling the Spatial Mismatch Between Green Space Equity and Residents’ Subjective Well-Being: An Integrated Approach Based on Machine Learning and Social Media Data,2025.0,Land,Hao Gong; Leilei Sun,"The limited capacity of urban green spaces to equitably satisfy the well-being needs of populations in urbanized areas is a global challenge. However, research on the spatial mismatch between green space equity and residents’ subjective well-being (SWB) remains inadequate. Using Shanghai as a case study, this research integrates social media data with an improved GA2SFCA method to evaluate SWB and UPGS accessibility and analyzes and compares the geographical spatial distribution differences of UPGS accessibility across different travel modes. This study employs machine learning to reveal the potential drivers of the mismatch between SWB and UPGS accessibility (note that this study does not explore causal relationships). The results indicate that: (1) UPGS accessibility in Shanghai exhibits pronounced spatial heterogeneity, the equity results derived from the Lorenz curve and Gini coefficient indicate that public transit (Gini = 0.579) < walking (0.427) < driving (0.149), and community parks effectively mitigating disparities among other urban park types; (2) UPGS accessibility and SWB are spatially correlated (r = 0.013, p < 0.01, z > 2.58), with a distinct High-High clustering pat",2.0,https://doi.org/10.3390/land14112205925045400dd53715292187d802819b5c367a492c19f,10.25148/etd.fi11120810,Scaling geospatial searches in large spatial databases,2011.0,,A. Cary,"OF THE DISSERTATION SCALING GEOSPATIAL SEARCHES IN LARGE SPATIAL DATABASES by Ariel Cary Florida International University, 2011 Miami, Florida Professor Naphtali Rishe, Major Professor Modern geographical databases, which are at the core of geographic information systems (GIS), store a rich set of aspatial attributes in addition to geographic data. Typically, aspatial information comes in textual and numeric format. Retrieving information constrained on spatial and aspatial data from geodatabases provides GIS users the ability to perform more interesting spatial analyses, and for applications to support composite location-aware searches; for example, in a real estate database: “Find the nearest homes for sale to my current location that have backyard and whose prices are between $50,000 and $80,000”. Efficient processing of such queries require combined indexing strategies of multiple types of data. Existing spatial query engines commonly apply a two-filter approach (spatial filter followed by nonspatial filter, or viceversa), which can incur large performance overheads. On the other hand, more recently, the amount of geolocation data has grown rapidly in databases due in part to a",2.0,https://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=1648&context=etd926bc2591a24dd8cd1c90b704f48e380e2e38115aae,10.14710/tataloka.22.4.573-585,Identifikasi Spasial Faktor-Faktor yang Mempengaruhi Permukiman Kumuh di Kota Bekasi,2020.0,,R. Wijayanti; Atang Sutandi; A. E. Pravitasari,"Bekasi City is buffer city for Jakarta City which have high attraction for people to urbanize. It have consequences of the emergence of many urban areas issues such as slum area. The slums handling by the government has been running intensively since 2016. Objectives of the study were 1) Analyze of spatial distribution patterns in slum area in Bekasi City 2) Analyze of key factors which affects slum area in Bekasi City. Data were analyzed using spatial analysis, i.e Moran Index analysis, spatial mean center analysis, spatial standart deviation analysis, multiple linear regression and Geographically Weighted Regression (GWR). The result of the analysis showed that the spatial pattern of slum areas in Bekasi City in 2016 and 2018 has changed. The Morans Index analysis in 2016 showed that the spatial pattern of the slum distributuon was clustered pattern, whereas in 2018 was random pattern. The spatial mean center and standart deviation ellips analysis showed that the slums moving towards southern of Bekasi City. The influenced factor of slum in 2016 was the number of low income people. Meanwhile in 2018, it were the number of of slums in each district, the percentage of poor children",2.0,https://doi.org/10.14710/tataloka.22.4.573-585927b122e01fa924e3d1d468416a3a4491090b413f43,10.30560/jems.v8n2p117,Gradient Boosting Decision Tree for House Price Prediction with Google Trends,2025.0,Journal of Economics and Management Sciences,Faye F.F. Jiang,"Predicting house price accurately can reflect the popularity of the housing market and help making decisions for investors and policymakers. Statistics of macro factors are commonly used for house price forecasting; however, macro factors obtained from government reports have defect of time lag and may impair the prediction performance. Google Trends data can serve as a leading sentiment indicator of people’s attitudes and expectations toward the housing market and help improve house price prediction. Therefore, this study proposes a new methodology framework for house price prediction with Google Trends data. Recursive Feature Elimination (RFE), a feature selection method, is utilized to remove noisy data and improve feature quality. Gradient Boosting Decision Tree (GBDT) is adopted to establish models for house price forecasting. Real estate-related Google Trends data, along with the fundamental house price index (HPI) data are collected to predict the growth rate of HPI in the United States. Results show that RFE can effectively remove irrelevant features and improve the model performance. GBDT has higher and more stable prediction accuracy than other prediction models, especial",2.0,https://j.ideasspread.org/index.php/jems/article/download/1542/1394928757bd683e867096bd3de6214d8e05c02d711dbcc,10.11628/ksppe.2021.24.5.529,Effects of Educational and Cultural Facilities on Housing Prices in Seoul from an Accessibility Perspective,2021.0,"Journal of people, plants, and environment",Minki Sung; Junghoon Ki,"Background and objective: A great deal of previous research has highlighted the value of educational and cultural facilities embedded in housing prices, by taking a large spatial area as the focus, such as the city or district level. However, few studies have investigated the extent to which educational and cultural facilities influence the formation of housing prices from an accessibility perspective. This study aims to identify the value of educational and cultural facilities embedded in the housing prices in Seoul Metropolitan City with a focus on the concept of the residents’ neighbourhood and accessibility. Methods: To this end, this research used a spatial regression model with educational and cultural facilities as the independent variables and housing prices as the dependent variable. The model assessed the accessibility of cultural and educational facilities by considering geographic effects. Results: The findings are as follows. First, the spatial error model was found to be the best fit for multi-unit housing, while the spatial lag model was more appropriate for single-unit housing and apartments. Second, private educational facilities and art museums had positive effect",2.0,https://jppe.ppe.or.kr/upload/pdf/ksppe-2021-24-5-529.pdf9292ad4167cdcf866f45a87623c9c0088832b3d3962,10.4000/cybergeo.32555,The Middle Classes in the west Paris Periurban Fringes,2019.0,CyberGeo,C. Aragau; Martin Berger; L. Rougé,"Paris region is confronted to an important phenomenon of social polarization. Periurban rings surrounding the city are today the only metropolitan areas in which mid-management jobs are the most numerous among active socio-professional group, although their importance tends to decrease due to the strong growth and spread of executive jobs. The ubiquity of spatial distribution of the middle class hides however a great diversity of social positions, statuses, residential strategies within the category. According to the social milieu and the residential proximities but also the residential and daily mobility strategies, a fracture takes shape between a fraction of the middle class assimilated to executives belonging to a “high middle class”, and the “low middle class”. The latter being composed of foremen and supervisors, often assimilated to white-collars and laborers, often constrained to become owners further away from town. Statistical analysis based on detailed files from the census and households inquiries conducted in the Parisian great west (Grande Couronne and adjoining administrative departements) highlight the integration of the middle class in the periurban space, making i",2.0,https://doi.org/10.4000/cybergeo.325559309d3d35f4c32d810f478070d970d3155eb9b7dc3e,10.1007/s44282-024-00073-5,Institutions' dynamics and homeownership aspirations in Ethiopia,2024.0,Discover Global Society,Efa Tadesse Debele; Taye Negussie; Solomon Mulugeta,"The desire to own a home is a common aspiration amongst many. The role of institutions either by enabling or constraining the achievement of the aspirations is very crucial. Unfortunately, the study investigating the impact of these institutional factors on homeownership aspirations was not adequately adequate. Thus, the researchers motivated to analyze the role of institutions for homeownership aspirations. Researchers used qualitative and quantitative data analysis methods to explore the relationship between mortgage access, housing market prices, and homeownership in Sheger City, Ethiopia. The study involved 384 participants, and the results were analyzed using descriptive statistics and multinomial regression. The findings suggest that mortgage access and housing prices play a significant role in shaping homeownership aspirations. However, it was apparent that banks prioritize the wealthy, resulting in the housing market neglecting the needs of low-income individuals and families, leading to unaffordable housing options and findings revealed institutions’ dynamics are accompanied by dysfunctions. The multinomial regression analysis revealed that the likelihood of owning a home ",2.0,https://link.springer.com/content/pdf/10.1007/s44282-024-00073-5.pdf931ba41e6776108363c9b7ff3b89a68d8e64fe7a5fa,10.1016/j.proenv.2014.11.035,Impact of Economic Crisis on the Intention to Move House,2014.0,,G. Dane; Ab Anna Grigolon; S. Rasouli; H. Timmermans,"As a result of the economic crisis in 2008, the price of petrol, goods and agricultural products has rapidly increased and cities all over the world started to suffer from high levels of unemployment and lower business survival rates. Due to the economic downturn, the Dutch housing market also started to weaken in 2008. Moreover, it became more difficult to obtain mortgages and loans from the banks. As a result of these processes, housing prices dropped substantially, the percentage depending on housing type, location and market. To better understand underlying processes, it is of interest to examine the effects of the economic crisis on housing choices, particularly because housing choices impact other long-term choices such as employment and short-term decisions such as where activities will take place in the city. In this paper, we discuss the results of a model of households’ intentions to move house, comparing the situation before and during the economic crisis. We investigate whether housing expenditures influenced the probability of the intention to move. For that purpose, we use the Dutch Housing Survey (WoningOnderzoek) from 2006, 2009 and 2012. This data contains informat",2.0,https://doi.org/10.1016/j.proenv.2014.11.0359327805f44694ca72d869d8ddf872999c358bd12c46,10.52783/jisem.v10i30s.4899,Predicting Housing Prices Using Advanced Analytics: A Study on Key Property Features and Market Dynamics,2025.0,Journal of Information Systems Engineering & Management,M. Iftikhar,"Effective forecasting of housing prices becomes essential for buyers, sellers, developers, and politicians to make informed real estate decisions. This study seeks to create a forecast model for real estate prices by examining essential property characteristics including living area, property quality, furnishing status, geographic location, and coastal proximity. The study employs a comprehensive dataset and utilises statistical techniques, such as correlation analysis, multiple linear regression, and ANOVA, to ascertain the primary factors affecting property valuation. The results indicate that property quality, living space, and cost per square foot are the strongest predictors of housing prices. At the same time, location-based factors, including coastal proximity, significantly influence decision-making. Interestingly, total land area exhibits a weaker correlation with price, suggesting buyers prioritize functional living space over expansive lot sizes. Furnished properties also command a significant premium over unfurnished ones, emphasizing the role of interior readiness in property valuation. These results integrate conventional statistical methods with data-driven insights,",2.0,https://jisem-journal.com/index.php/journal/article/download/4899/23109335ca6dc795ae78bdf815826faf40c1acab6f2798c,10.34312/jgej.v4i1.18881,"Faktor yang Mempengaruhi Perkembangan Perumahan Di Pinggiran Kota (Studi Kasus : Kecamatan Boja, Kabupaten Kendal)",2023.0,JAMBURA GEO EDUCATION JOURNAL,Vinda Adeliana; Zukruf Novandaya; Hafzhi Nur Azmi,"Population growth and the activities it carries out make the need for land increase, especially in the city center. Land in the city center which is increasingly limited and expensive is no longer able to be accessed by the community to support its activities so that these activities develop into suburban areas. Boja District is one of the suburban areas affected by the development of the City of Semarang, which is indicated by the growth of residential areas since 2010-2020. The purpose of this research is to find out what factors influence the development of suburban housing, Boja District, Kendal Regency. The analysis technique uses quantitative descriptive. Respondents are people who live in housing in the District of Boja. There are 9 variables used for factor analysis and 100 respondents to be surveyed. The research variables consist of livelihood, level of expenditure, length of stay, area of origin, number of family members, house prices, ease of buying a house, installment costs, and credit terms . This research is studied based on the pattern of housing development in suburban areas and the characteristics of housing residents. The results of this study are that there are",2.0,https://doi.org/10.34312/jgej.v4i1.1888193469ec6b6c759d7dd0b7b2c18469e28061514dcccf,10.5198/jtlu.2024.2456,"Employment concentration, dispersion, and the changing commute in the San Francisco Bay Area",2024.0,Journal of Transport and Land Use,Evelyn Blumenberg; Samuel Speroni,"In the first decade and a half of the twenty-first century, the San Francisco Bay Area experienced rapid job growth (17% from 2002 to 2015). Employment growth greatly exceeded housing production, resulting in rising housing prices. The mismatch between jobs and housing potentially contributed to an increase in commute distance, as workers relocated to outlying neighborhoods in search of affordable housing. In this paper, the authors analyze changes in commute distance over time, with a focus on the spatial location of employment and, in particular, downtown job growth. They find that commute distance increased slightly between 2002 and 2015 throughout the Bay Area (from 17.2 to 17.8 mi.), with the greatest increase among workers in job centers located in outlying parts of the region (from 19.1 to 20.8 mi.). Increases in census tract jobs was by far the strongest predictor of commute distance increase, though this overall relationship in the region was likely moderated by the increase in employment in downtown San Francisco (44%) where, all else being equal, workers travel shorter distances (14.4 mi. in 2002 and 15.4 mi. in 2015) relative to other workers. This relationship may be d",2.0,https://jtlu.org/index.php/jtlu/article/download/2456/1756935a2ba159dfd8cc7c5086646e06d2b0261fbb98f67,10.1051/matecconf/201926602005,Determining the Impact of Aircraft Noise towards Residential Property Price,2019.0,MATEC Web of Conferences,A. Yassin; Mohd Lizam Mohd Diah; Edie Ezwan Mohd Safian; Mohd. Yamani Yahya; Sulzakimin Mohammad; Lim-Shin Cheng,"The objective of this paper is to examine the effect of aircraft noise on residential property price within the case study area, and the main focus of this research was the distance of selected residential housed from Kuching International Airport (KIA). Aircraft noise is a source of noise pollution and act as environment factor that affect the house prices. Environmental disamenities from water and noise pollution will caused the houses to sell at lower price, accounted 20.8% less than houses located in area without noise interference. Apparently, the noise produced by the aircraft has even larger negative impact on house prices as compared to road traffic noise and railway noise. This study adopted quantitative approach in answering the objective of the paper. The findings were based on the secondary data which including 210 property transaction data within year 2015. The range of areas for this study was limited to selected residential terrace houses that located within 10.0 km from Kuching International Airport (KIA). The findings from Multiple Regression Analysis (MRA) shows that the property prices located nearer to the airport (<2.5 km from KIA) in selected case study areas ",2.0,https://www.matec-conferences.org/articles/matecconf/pdf/2019/15/matecconf_iconbee2019_02005.pdf936c86112148f4bfac2bb0fdeb7d1d0fbf3759ad63a,10.3390/ijgi14050202,Spatio-Temporal Paths and Influencing Factors of Residential Mobility in Guangzhou: A Micro-Level Perspective of Newly Employed College Graduates,2025.0,ISPRS Int. J. Geo Inf.,Xiangjun Dai; Chun-Shan Zhou; Xiong He,"Residential mobility within cities reflects the spatio-temporal patterns of individual or household relocation behaviors and serves as an effective tool for interpreting urban socio-spatial differentiation from a micro-level perspective. Newly employed college graduates (NECGs) have become the second-largest migrating population in China. This study selects Guangzhou, a megacity, as the study area and utilizes data from the “Guangzhou New Citizens’ Residential Mobility Survey” conducted in 2023. It applies spatio-temporal systems and the spatio-temporal path method based on time geography to explore the residential mobility trajectories of NECGs in Guangzhou. In addition, the study uses a logistic regression model to explore the influencing factors. The findings indicate that NECGs frequently move across districts, showing no significant patterns of concentration or dispersion. However, residential location choices vary considerably across educational levels and household registration natures (Hukou), and as the duration of residence in Guangzhou increases, the probability of residential mobility among NECGs across all educational levels shows a declining trend. Specifically, marit",2.0,https://doi.org/10.3390/ijgi14050202937b1f0fbb7937f509dfd03f3d9ead7087deb461154,10.68128/jocrebe.2017.lddp31nx,OIL PIPELINE VANDALISM AND RESIDENTIAL PROPERTY RENTAL VALUE: EMPIRICAL EVIDENCE FROM NIGERIA,2017.0,Journal of Contemporary Research in the Built Environment,V. Ige; A. F. Audu,"Purpose: The incidence of oil pipeline vandalism in Nigeria is on the increase and often a threat 938to the host communities. Environmental degradation, hazard to human livelihoods and health 939amongst others are consequential effects of oil pipeline vandalism which oftentimes generate 940stigma to the surrounding properties and dis-amenity to property values. The purpose of this 941study is to examine the effect of oil pipeline vandalism on residential rental values. 942Design/methodology/approach: To achieve this, questionnaires was distributed to 400 tenants 943drawn from four purposively selected residential estates in the case study areas. Data gathered 944was analysed using both descriptive (relative importance index) and inferential statistics (linear 945regression model) to identify factors that motivates tenant’s choice of settling down in the study 946area and the factors that determines residential property rental value in the area. 947Findings: The findings show ‘loss incurred to vandalism in monetary terms’ alongside six other 948explanatory variables as statistically significant to residential rental value in the study area. 949Originality/value – The outcome of the paper will assist proper",2.0,https://jocrebe-uu.com/article/oil-pipeline-vandalism-and-residential-property-rental-value-empirical-evidence-from-nigeria.pdf9504a47dceb98c2cf3235fbd6fdc303bbf244c73470,10.56397/fms.2024.08.06,The Role of Road Transport Infrastructure in Shaping Property Values in West African (Ghana and Nigeria): A Review of Literature,2024.0,Frontiers in Management Science,Chinago Budnukaeku Alexander; Ikechi Okpakam,"This study investigates the multifaceted impacts of road transport infrastructure on property values in West African cities, emphasizing urban and peri-urban settings. The research integrates a comprehensive literature review with empirical analyses to explore the factors affecting property value dynamics, including proximity to roads, traffic congestion, noise pollution, and socio-economic characteristics. Empirical evidence from countries such as Ghana and Nigeria reveal that well-planned road improvements generally enhance property values by improving accessibility and connectivity. However, challenges such as increased noise levels and traffic congestion can detract from residential desirability, particularly in densely populated urban areas. The study underscores the importance of integrated urban planning strategies that balance infrastructure development with environmental sustainability and community well-being. Recommendations for policymakers and urban planners include investment in infrastructure maintenance, the promotion of sustainable transport modes, and enhanced community engagement to align road transport projects with local development priorities. By addressing th",2.0,https://www.paradigmpress.org/fms/article/download/1261/111595162b24ec957881d4d472471680cc3cadd3aff5709,10.1108/ijhma-12-2024-0189,Determinants of single-family residential property values in Ethiopia: a comparative analysis of willingness to pay or receive and real transaction data,2025.0,International Journal of Housing Markets and Analysis,Masresha Belete Asnakew; Melkam Ayalew Gebru; Wuditu Belete; Takele Abebe; Yeshareg Baye Simegn,"Purpose952This study aims to identify determinants of single-family residential property values and fill the gap by analyzing respondents’ willingness to pay/receive data alongside real transaction data. Ordinal logistic regression and ordinal least square regression were used.953954Design/methodology/approach955Ordinal logistic regression effectively analyzes willingness-to-pay/receive data, accommodating the ordered nature of property value responses while incorporating multiple influencing factors. Ordinal least square regression quantifies the impact of continuous and categorical predictors on real transaction data.956957Findings958Findings revealed strong associations between property values and several variables. Analysis of willingness-to-pay/accept data from 232 respondents showed significant impacts of factors such as the number of rooms, site area, construction material, property orientation, property age and proximity to bus stations and the central business district (p < 0.05). Similarly, ordinal least square regression analysis of transaction data confirmed the significance of most of these factors, except for property orientation, which indicates the difference of preference in the",2.0,959a6e470934ada30f392d8208451f6bdb8493c8397,10.4236/jss.2020.84003,Evolution of Spatial Pattern and Influencing Factors of Population in Guangdong-Hong Kong-Macao Greater Bay Area,2020.0,,He Yao,"This paper takes the population of Guangdong-Hong Kong-Macao Greater Bay 960Area as the research object, and analyzes the evolution of population spatial 961pattern of Guangdong-Hong Kong-Macao Greater Bay area. According to the data, 962eleven areas of “two districts and nine cities” in Guangdong-Hong Kong-Macao 963Greater Bay area district are divided into three levels; the relative migration 964index is used to divide them into relative moving in areas and relative moving 965out areas, on the other hand, using the spatial panel data regression model to 966analyze the influencing factors of the evolution of the spatial pattern of 967population in Guangdong, Hong Kong and Macao. The main conclusions are as 968follows: 1) from 2005 to 2017, the spatial distribution of population in 969Guangdong-Hong Kong-Macao Greater Bay area shows a trend of continuous 970aggregation. The geographic concentration of population in Shenzhen, Dongguan, 971Hong Kong and Macao is relatively high. Guangzhou, Foshan, Shenzhen and 972Zhongshan belong to the region of relative migration. 2) This 973paper selects the quality of basic education, housing price, industrial structure, 974medical service level, traffic convenience and incom",2.0,https://www.scirp.org/pdf/jss_2020040115483591.pdf9756efc3d6e07e62ff23643020c1ae408bac8a84989,10.1007/s11113-023-09826-7,"The Structure of Pandemic Vulnerability: Housing Wealth, Residential Segregation, and COVID‑19 Mortality",2023.0,Population: Research and Policy Review,Chinyere O. Agbai,"The COVID-19 pandemic has been particularly devastating for those with limited economic resources. Extensive research demonstrates the negative relationship between wealth and mortality at both the individual and area levels. In addition, residential segregation has been linked to poor health and greater mortality. Home equity is the largest asset that many Americans own, but residential segregation devalues homes located in Black neighborhoods. Despite the interlocking relationships between wealth, residential segregation, and mortality, it remains unclear how wealth and residential segregation work to predict COVID-19 deaths. Using U.S. Census data and county-level COVID-19 data from Johns Hopkins University (n = 1164), I deploy median home value as a wealth proxy and negative binomial regression models to interrogate two questions. (1) What is the relationship between home value and COVID-19 deaths? (2) How does the relationship vary by level of residential segregation? Results indicate that COVID-19 mortality is 64 percent greater in the lowest wealth counties than in the wealthiest counties. At average median home value, the most segregated counties with the largest Black popu",2.0,https://link.springer.com/content/pdf/10.1007/s11113-023-09826-7.pdf97698c2b81dcb6b2819536f9a1c1a5c0b46ad7f7fe3,10.1371/journal.pone.0323405,Understanding disappearances in Mexico City: A data-driven analysis,2025.0,PLoS ONE,Daniel Aguilar-Velázquez; Ruben Calvario Peréz; Carlos Mondragón Mendoza; Alfonso Valenzuela-Aguilera,"Mexico is facing an escalating crisis of violence, marked by a sharp increase in disappearances. However, key information remains unknown, such as the typical profile of victims, geographic hotspots, and the relationship between disappearances, economic conditions, and public security. We used a government database of 3,450 disappearances in Mexico City, together with scraped data, to analyze the phenomenon of missing persons. We found that disappearances are not homogeneously distributed along Mexico City, the central district has the highest disappearance rate per capita, which can be attributed to city mobility for job locations. Men account for 62.5% of missing persons, but women aged 15-19 are the most vulnerable group. There is a strong correlation (r=0.95) between reports of drug dealing and disappearances, both of which may be related to the presence of organized crime. Furthermore, when disappearances are normalized to account for mobility related to job locations, a strong negative correlation (r=–0.7) emerges between disappearances and housing prices. This suggests a pattern of socio-economic segregation in disappearances, with higher rates in areas with lower housing pr",2.0,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0323405&type=printable9774b5606b2af40f4fab74129cf6fff570173518079,10.5220/0013700300004670,"Using Linear Regression, Ridge Regression, Lasso Regression, and Elastic Net Regression for Predicting Real Estate Price",2025.0,Proceedings of the 2nd International Conference on Data Science and Engineering,Zhaorui Zeng,": In the contemporary economic landscape, the real estate market wields significant influence, with housing prices being a crucial factor affecting individuals, industries, and the overall economy. Fluctuations in housing prices can impact people's living standards, investment decisions, and the stability of related industries. Thus, accurately predicting housing sales prices is of utmost importance. This paper focuses on predicting housing sales prices using linear regression, ridge regression, Lasso regression, and elastic net regression. The principles and applications of the four regression models in price prediction are explored in detail. By calculating evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), the performance of each model is compared. The results show that linear regression has an MAE of 0.092129, indicating normal performance. Ridge regression can address multicollinearity issues with an MAE of 0.091513, but may overfit. Lasso regression and elastic net regression, with MAEs of 0.087802 and 0.087756 respectively, can simplify models through feature selection, reducing overfitting risks and improving ",2.0,https://doi.org/10.5220/001370030000467097832efd0ebb04018868b7084b47a6cd49902d56cf6,10.54254/2755-2721/55/20241483,"House price prediction using machine learning for Ames, Iowa",2024.0,Applied and Computational Engineering,Qiong-Wei Ye,"The real estate sector is pivotal to economic growth and plays a substantial role in the global GDP. In this technologically advanced age, the adoption of machine learning for accurate house price prediction is crucial. These models optimize decision-making for homeowners, sellers, and investors alike. This study represents a comprehensive exploration into the field of house price prediction within the context of Ames, Iowa, United States. The primary objective of this research is to construct a reliable and highly accurate predictive model, empowering individuals to estimate property values with unprecedented precision. The research encompasses three different machine learning algorithms: linear regression, random forest, and XGBoost. The use of the dataset from the reputed website helps improve the reliability of the result. Furthermore, the investigation extends to a detailed examination of the multifaceted determinants exerting a profound influence on house prices in the dynamic Ames real estate landscape, and determined that the factor that will influence the house price most is the total area of the house. Among all models, XGBoost produces the best result, which achieved an ",2.0,https://ace.ewapub.com/article/view/11040.pdf979d7a48acfcb2f296febe6b6e3ab5f16572f2a8354,10.6007/ijarbss/v4-i9/1129,Effect ofMunicipal Toll and Connections Cost on Housing Prices in Different Areas of the Isfahan City,2014.0,,Hossein Ostadi; Vahid Mahnam,"The growing urban population in developing countries with restrictions on the provision of housing becomes one of the major problems of municipalities to provide services required and urban infrastructure such branches are different. In this study the municipal toll and connections cost of various organs such as water, electricity, gas, telephone over the period 1390-1380 based on ordinary least squares regression has been evaluated using a cost function models and are examined inequalities in housing prices in different areas of the city. According to the results of the study variables, building materials prices, fire insurance and labor prices in the housing sector have positive and significant impact on housing prices in different areas of the city. In other words, the rise in prices of building materials in housing prices, wages, all prices in variable tariffs and duties to permit residential construction will increase the cost of housing facilities granted by banks in different regions that all of which leads to a decrease in supply and an increase in housing prices in these areas. On the other hand the increase in population has led to an increase in housing demand in the reg",2.0,http://hrmars.com/hrmars_papers/Effect_of_Municipal_Toll_and_Connections_Cost_on_Housing_Prices_in_Different_Areas_of_the_Isfahan_City.pdf9805d60005895c0bc35511c8d6aefdfae4a6b82af90,10.24018/ejbmr.2021.6.4.1002,Determinant Influencing Factors for Subsidized House Preferences in Bandung Regency,2021.0,European Journal of Business and Management Research,M. Andrianto; R. Rahadi,"The increasing population with a limited area of land makes house prices in Bandung City becoming expensive. Many people looking for a cheaper price in the outer city such as in the area around Bandung Regency. It is the largest region and the most populated region that bordered Bandung City. One type of house that is widely offered in the region is subsidized house. It is a house that some of the payment is assisted by the government. The data shows that the subsidized house realization is increasing every year. This research aims to investigate the influencing factor that influences people's preference for the subsidized house in Bandung Regency. Several pieces of literature concludes that the influencing factor for subsidized house preference is price, physical attributes and qualities, accessibility, location, and facility. The methodology used for this research is using quantitative and qualitative studies. The study is using mixed method between quantitative and qualitative method by conducting questionnaires, and interviews. The findings of this research could be useful for the stakeholders such as consumers, governments, and real estate developers. For consumer, this resear",2.0,https://www.ejbmr.org/index.php/ejbmr/article/download/1002/545981deda694998b7ab47a18fd94ec93bbe30ab59b3b0,10.52339/tjet.v42i3.802,Modelling Prediction of Cities Real Estate Price Trend Using Recurrent Neural Network: A Case of Dar es Salaam City,2023.0,Tanzania Journal of Engineering and Technology,E. Kalinga; Ezekiel Nyamle; Abdi T. Abdalla,"Real estate refers to a class of real property such as land and its associated infrastructure. The prediction of real estate prices in cities, which is affected by a number of parameters, is an open research problem. The lack of reliable and effective tools for price forecasting in real estate, especially in residential housing, can adversely affect investment flows and the growth of the real estate sector. Taking Tanzania as an example, the price prediction practices rely on human suggestions that are prone to personal bias and subjective to price hysteria for personal gain and impact consumer expectations. To address the challenge, this paper designed a real estate price trend prediction model for the cities using Recurrent Neural Networks (RNN) with a Long Short-Term Memory (LSTM). The study identified the factors influencing real estate property prices, including size, location, time, property quality, accompanied services, market nature, price of land, cost of building materials, and value for money. However, the study spotted the size, price, location, and time as key factors in predicting price trends when using RNN-LSTM. The results show that the proposed RNN-LSTM model per",2.0,https://tjet.udsm.ac.tz/index.php/tjet/article/download/802/731982b7cd07b4d41422f51f12a98dcc70a74d36cf55f2,10.30902/jrea.2018.4.2.1,"The Real Estate Tax Policy and the Housing Market983 Stabilization - Focused on the Property Possession Tax and the Transaction Tax984 -",2018.0,Journal of Real Estate Analysis,Jin Baek Park; Young Lee,"This paper investigates the effect of real estate tax policy on the stabilization of housing prices. The real estate taxes are imposed at the stages of the acquisition, possession, and disposition of real estate, and are used for the government revenue. And the government implements the policy of raising or lowering tax rates at each of the above stages for real estate cycle control. Raising the property possession rate is expected to have the effects of housing price stabilization by lowering the expected rate of return and restraining demand for housing, and the effects of shifting tax burden onto market price. Raising the transaction tax acts as an incentive for reducing housing supply and raising housing price due to delayed housing transaction. The taxation is probable to have different effects according to tax rate levels. This study analyzes the impact of property possession taxes and transaction taxes on housing prices, using unbalanced panel data from 1980 to 2015 in 35 OECD countries. Regression results show that raising the property possession tax rate in countries with low property possession rates leads to stabilization of housing prices, while raising it in countries ",2.0,http://www.ejrea.org/download/download_pdf?pid=jrea-4-2-19854fc107fab4a3c979bffb004a94026f88a10bb4a7,10.17509/seict.v5i2.75945,Comparative Analysis of Artificial Neural Network (ANN) and Random Forest in House Price Prediction,2024.0,"Journal of Software Engineering, Information and Communication Technology (SEICT)",Y. Retnowati; Abid Mafahim; Indira Syawanodna,"With the advancement of information technology, the application of machine learning in the property industry, particularly for house price prediction, has become increasingly important. Technology plays a crucial role in speeding up and enhancing the accuracy of property buying and selling processes. Therefore, the role of machine learning technology can be utilized to meet the need for improving the accuracy of house price predictions in major cities of developing countries, such as Bandung. This research aims to analyze the effectiveness of the Artificial Neural Network and Random Forest algorithms in predicting house prices in Bandung. The data used includes house sales data in Bandung, covering land area, building area, number of bedrooms, number of bathrooms, number of parking spaces, and the subdistrict location. The analysis of the algorithms is conducted by comparing the performance testing results of both algorithms using performance metrics for regression models such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-Square (R2). Additionally, this research analyzes which data ratio among the training, validation, and test data y",2.0,https://ejournal.upi.edu/index.php/SEICT/article/download/75945/302739867dedaa34606eca8693e9ef92edab742ca54c4681,10.2478/fiqf-2025-0029,Effects of eurozone and schengen area accession on real estate prices,2025.0,Financial Internet Quarterly,Josip Visković; Danijela Čipčić,"Abstract The real estate sector plays an important role in economic cycles and macroeconomic stability, as shown by various studies that establish a link between real estate prices and economic activity. While the traditional determinants of real estate prices have been extensively studied, the impact of institutional factors such as Eurozone membership and Schengen accession has not yet been sufficiently researched. Monetary integration promotes economic growth by boosting trade, investment and financial integration, while lower interest rates improve access to credit, which drives demand for real estate. Similarly, Schengen accession increases labor mobility, cross-border economic activity and the acquisition of real estate, further fueling demand for housing. Therefore, this study aims to examine the role that these institutional changes have on the real estate markets in the EU member states. A panel data analysis for the period from 2000 to 2023 using a fixed effects model confirms that joining the Eurozone has a statistically significant impact on house prices, primarily due to lower interest rates and economic growth. Schengen membership also contributes to rising real estat",2.0,https://reference-global.com/2/v2/download/article/10.2478/fiqf-2025-0029.pdf987ebc0e942a4fc7bb260d242c57aba376771e19926,10.1088/1755-1315/272/3/032155,Spatial and Econometric Modeling of the Demographic Situation in the Kemerovo Region,2019.0,IOP Conference Series: Earth and Environment,M. Novoseltseva; S. Gutova; K. Glinchikov,"The article presents an analysis of the demographic situation in 34 municipal formations in the Kemerovo Region of the Russian Federation (16 urban districts and 18 municipal districts). The methods of spatial-econometrics modelling are used to study the factors affecting depopulation in the region and problems of the spatial arrangement and heterogeneity of the 24 single-industry cities. Calculating the local and global Moran’s index revealed positive autocorrelation between the population of the regions and districts, which makes it necessary to consider spatial interconnections while creating prediction models. In order to downweight the attribute space, four integrated indexes, that characterize integrated assessments of the levels of education, ecology, healthcare and housing conditions were created. The SAR-model was built, reflecting the effect complex assessment of the social-ecological infrastructure development has on the relative population growth in municipal formations of the Kemerovo region. The assessment of factors, affecting population in the territorial units, have shown that the most influential are healthcare and housing. Based on the data obtained, it was concl",2.0,https://iopscience.iop.org/article/10.1088/1755-1315/272/3/032155/pdf988ec0abab86782559bebc60e4610aa10431c1fe804,10.1109/icoeca66273.2025.00118,Optimizing House Price Prediction Models: A Hybrid Approach using GWO-Feature Selection and CatBoost Tuning,2025.0,2025 5th International Conference on Expert Clouds and Applications (ICOECA),V. P. Pavan Kumar; B. K; V. S. Divya Sundar; N. S. K. M. K. Tirumanadham; Praveen Kumar Vadapally; K. V. Sunil Varma,"This function is a very important function in real estate, as stakeholders such as investors, buyers, and sellers make the right decisions. Correct property values are thus required for understanding market dynamics and optimizing investment strategies, especially for fair pricing. However, remains a critical challenge because of the multiplicity of housing data-the location, attributes of a property, and economic conditions among others. This study aims to fill these gaps by developing an advanced predictive model for house price estimation. Here, the focus is on improving the feature selection process so that more impactful variables are identified and optimized for more accurate predictions. In this section, it makes use of advanced computational techniques, such as the Grey Wolf Optimizer (GWO) for feature selection and CatBoost with RandomizedSearchCV for model building. The key findings include the efficiency of GWO in selecting the most relevant features, which enhance model interpretability and decrease overfitting. Besides, CatBoost with the RandomizedSearchCV yielded a considerable improvement in accuracy of prediction, thus giving proof of the model’s ability to be robus",2.0,989c453bf1ff4e4bafc381ec691c9f45bec5d00f2be,10.51713/jarac.v3i1.46,"PENGARUH RASIO LIKUIDITAS, SOLVABILITAS, PROFITABILITAS DAN AKTIVITAS, TERHADAP KEBIJAKAN DIVIDEN PADA PERUSAHAAN PROPERTY, REAL ESTATE, AND BUILDING",2021.0,Journal Research of Accounting (JARAC),Ni Komang Sariasih; K. F. Adnantara; Laras Oktaviani,"This study aims to determine the effect of the Liquidity Ratio, Solvency, Profitability and Activity on Dividend Policy at Property, Real Estate, and Building Companies Listed on the Indonesia Stock Exchange for the 2017-2019 Period. The reason for choosing property, real estate and building companies as objects of research is because the property sector is considered to be still an attractive investment in the midst of a new type of corona virus pandemic that causes Covid-19. Travelio Property Management Business Development Manager Vincentius Christopher considers that currently the benefits that can be taken from property investment are in terms of yield (profits calculated from the annual rental value compared to property prices), not capital gains. The population in this study are property, real estate, and building companies that already exist and are still listed on the Indonesia Stock Exchange for the 2017-2019 period, which consists of 91 companies. The sampling technique used in this study was purposive sampling so that it consisted of 87 companies as the research sample. The data analysis technique used in this study was multiple linear regression. The test results show ",2.0,https://doi.org/10.51713/jarac.v3i1.469907cbf4d0a5c2c882e94dee2581f2f45ccd5e065d4,10.68479/001c.121128,Distributional Responses to Property Tax Changes,2024.0,Journal of Regional Analysis & Policy,Kyle A. Kopplin,"Local property tax capitalization into house prices is an ongoing empirical debate, and county-level responses are under-studied in this vein. This paper contributes to the empirical literature on property tax capitalization by demonstrating varying responses to average nominal property tax rate increases and decreases along the distribution of house prices in counties containing large U.S. cities. The empirical setting uses data on county-level average nominal property tax rates for almost one hundred counties, and within-county standardized tax rate changes are used as to study house price changes in a capitalization framework. Decreases in the average nominal property tax rate tend to increase house prices around 1.26%, and increases tend to decrease house prices around 0.75%. Using recentered-influence functions (RIFs) that analyze unconditional quantile regressions, the responsiveness of households is shown to vary across the distribution of house prices. Specifically, house prices respond to increases in property tax rates by larger percentages on the lower end of the house price distribution, and house prices are unaffected by rate hikes on the high end of the distribution. ",2.0,https://jrap.scholasticahq.com/article/121128.pdf991f9da086dd495e67f751b35f6a882b164a01e85bf,10.1108/ijbpa-11-2023-0172,"Examining the nexus of building regulations, urban planning, and housing prices and affordability: insights from Prishtina, Kosovo",2024.0,International Journal of Building Pathology and Adaptation,Visar Hoxha,"PurposeThe present study aims to investigate the relationship between building regulations, urban planning, and perceptions of housing affordability and prices in Prishtina, Kosovo.Design/methodology/approachA self-report survey with 1,000 respondents, selected through stratified probability sampling, provided the necessary data. Principal component analysis was applied to the questionnaire's internal structure, while regression analysis helped uncover housing affordability and housing prices perception predictors.FindingsThe study found that building regulation standards and zoning/land-use regulations reveal positive relationships with housing prices and housing affordability perception. Among these components, building regulations and standards show a stronger connection with housing affordability and price perception in comparison to urban planning and development.Research limitations/implicationsBy investigating the relationship between building regulations, urban planning, and housing affordability and price perception in Prishtina, the present research makes a valuable contribution to the existing literature. The findings of this research hold significant implications for po",2.0,99260db02d7a8ed6bd914b476c6295ef71f7bdfd000,10.54254/2755-2721/96/20241281,Application and Performance Comparison of Compound Neural Network Model based on CNN Feature Extraction in House Price Forecast,2024.0,Applied and Computational Engineering,Jia-Yu Zhang,"Abstract. This study used a total of eight machine learning algorithms to forecast property prices, it not only provides a robust comparison of the predictive power of different algorithms but also significantly advances our understanding of the factors that influence property prices. In this paper, four traditional machine learning algorithms and four neural network models are selected for comparative study and analysis, of which the neural network models include fully connected neural networks (FCNN), convolutional fully connected neural networks (FCNN+CNN), generative adversarial fully connected networks (FCNN+GANs) and generative adversarial convolutional fully connected neural networks (FCNN+GANs+CNN). This study applied to a Kaggle's sample. The results reveal that the models based on FCNN+CNN and FCNN+GANs+CNN perform relatively well in house price prediction, with both obtaining an explanatory power of R as high as 0. 96 and 0. 97, respectively and significantly outperforming traditional machine learning algorithms. It is worth mentioning that the FCNN+CNN model is slightly stronger in terms of error minimization, but both perform better in terms of stability and generaliza",2.0,https://doi.org/10.54254/2755-2721/96/2024128199351c83440aecf46b1eea286f1cee78e34d6e1fd6f,10.1016/j.scitotenv.2024.173542,Assessing the impact of mining on cyclic and linear methylsiloxane distribution in Tibetan soils: Source contribution and transport pattern.,2024.0,Science of the Total Environment,Lewei Li; Ruwen Chang; Ji-Ping Li; He Zhang; Xinyu Du; Jun Li; Guo-Li Yuan,"The pervasive presence of methylsiloxanes (MSs), comprising linear and cyclic congeners, in the environment poses significant ecological risks, yet the understanding of their transport mechanisms and deposition patterns remains limited. This study analyzed the concentrations of 12 linear-MSs (L3-L14) and 7 cyclic-MSs (D3-D9) in 29 surface soil samples collected across varying altitudes (3726 to 4863 m) near the Jiama mining sector in Tibet, aiming to investigate the distribution and transport dynamics of MSs from the emission source. The distribution of total MS concentration (ranging from 50.1 to 593 ng/g) showed a remarkable correlation with proximity to the mining site, suggesting the emergent source of mining activities for the MSs in the remote environment of the Tibetan Plateau. Employing the innovative model of robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR), the analysis predicted that the mining operations contributing 57.1 % of the total soil MSs, would significantly surpass contributions from traffic emissions (14.7 %), residential activities (13.2 %), and the environmental factor of total organic matter content (14.9 %).",2.0,9946e7c4c2a8bba183e6c86e6c8636efbf988f6a69c,10.22214/ijraset.2023.56232,House Price Prediction: Comparative Analysis of Regression-Based Machine Learning Algorithms,2023.0,International Journal for Research in Applied Science and Engineering Technology,D. E. Aniobi; Chukwuemeka Oluebube Ochuba; Saater Benedicta Nguideen,"Abstract: Advancement in technology has revolutionized the ways of doing things in contemporary time. One of such is Artificial intelligence which has given birth to myriads of methods/techniques employed in solving real life problems. Many Machine learning techniques can be used in predicting house prices with several factors in consideration. House prices rise on annual basis, triggering the need for house price prediction models. Predictive models enable families to acquire a house of their choice when accurately developed. There have been a significant number of articles that adopt traditional machine learning algorithms to successfully estimate house prices, but they rarely compare the performance of individual models. This study will extensively test and compare numerous machine learning technique and present an optimistic model that will be used in developing a house pricing prediction system. Several models were developed and compared using Linear Regression (LR), Least Absolute Shrinkage & Selection Operator Regression (LASSO-R), Ridge Regression (RR), K-Nearest Neighbours Regression (KNN-R), Decision Tree Regression (DTR) and Extra Trees Regression (ETR) algorithms. Imple",2.0,https://doi.org/10.22214/ijraset.2023.56232995b6cd32c1500330185bab167bef9ea88a3bd38845,10.1021/acsestair.5c00277,Evaluation of Smoke Exposure Risk from January 2025 Los Angeles Wildfires Using Crowdsourced Data,2026.0,ACS ES&T air,Yi Ji; Christopher Devlin; C. Ivey,"Wildfire smoke is an increasingly significant contributor to air pollution in the western United States, posing serious health risks and complicating efforts to assess personal exposure, particularly indoors. The January 2025 Palisades and Eaton Fires in Los Angeles County caused elevated levels of PM2.5 in the downwind cities. This study leverages a high-resolution network of crowdsourced PurpleAir sensors to evaluate indoor and outdoor PM2.5 levels before, during, and after the wildfire smoke events. We matched indoor–outdoor sensors and analyzed disparities in smoke exposure across communities with different CalEnviroScreen (CES) vulnerability scores, ventilation types, and home values. Results indicate that outdoor PM2.5 increased substantially during smoke days, with the highest CES-burdened communities experiencing the greatest ambient concentrations. Indoor PM2.5 also increased across all neighborhoods but indoor/outdoor (I/O) ratios declined during the smoke period, indicating partial indoor protection and likely occupant behavior changes. Infiltrated PM2.5 increased during the smoke period and varied across the CES groups. Building attributes showed limited predictive powe",2.0,https://doi.org/10.1021/acsestair.5c0027799653bb7bb713ed608d57fea08775644003beb501b0,10.3390/ijgi15010046,A Machine Learning Approach Using Spatially Explicit K-Nearest Neighbors for House Price Predictions,2026.0,ISPRS Int. J. Geo Inf.,Meifang Chen; Changho Lee; Yong-Wan Chun,"Spatial data has distinctive properties that differentiate it from non-spatial data. One prominent characteristic is spatial autocorrelation (SA). When machine learning techniques are applied for spatial data modeling, they require spatially explicit consideration. If these inherent spatial structures are ignored, models may produce biased predictions. However, integrating this property into the model yields additional spatial insight, thereby enhancing learning and improving predictive accuracy. This study examines spatially explicit K-nearest neighbors (SE-KNN) by integrating SA as a spatially explicit property, λ, into the learning algorithm. The innovation of SE-KNN lies in its alignment with the principle of spatial autocorrelation, as KNN’s core learning assumption—that similar observations tend to have similar outcomes—naturally parallels spatial dependence. The proposed SE-KNN is applied to a house price prediction model using house sales data from Franklin County, Ohio to demonstrate a spatially dependent, data-rich, and real-world problem. The results show that SE-KNN achieved the best prediction accuracy compared to mean of absolute error (MAE) of three other machine lea",2.0,https://www.mdpi.com/2220-9964/15/1/46/pdf?version=1768991780997ddb99eec8fa003ea288addae791ca68778d13e7d,10.1177/23998083251342626,The allometry of housing prices in urban scaling laws and an equalised dwellers’ utility across settlement sizes,2025.0,Environment and Planning B Urban Analytics and City Science,Luca S. D'Acci,"Large cities have benefits offset by costs. Since the inter-urban Rosen–Roback model, economists interpret the attractiveness of a city in terms of spatial equilibrium: a cost-benefit balance across locations. High incomes are counterbalanced by high prices or disamenities. A cross-sectional regression analysis on the ∼ 8000 Italian municipalities whose population sizes range from around 30 to 3 million, found the following association: by doubling the population of a settlement, holding constant its per capita income and touristic presences, its house price rises by 9%. House price increases are not offset by an increase in income. Bigger cities are more expensive than smaller. This opens reasoning upon indirect economic valuations of the value - or cost if you see it differently - (opportunities, services, vibrancy…) that urban size has for people who choose - or must choose - bigger settlements. At the best of my knowledge, this is the first work, in this form, finding a scaling coefficient for house price. It resulted around 0.13, namely, by passing from a town of 10˙000 inhabitants to a city of 1 million inhabitants (even if holding constant tourism and income), the average ho",2.0,https://doi.org/10.1177/23998083251342626998b60e091527b31895486d53992e075e00d3998369,10.4025/actascitechnol.v43i1.48912,"Property value map updating by mass appraisal method – a case in the city of Pato branco, state Paraná",2020.0,,L. Miotti; C. Loch,"This study addresses Property Value Map updating, specifically when it comes to the absence of methods that allows for efficiency, agility and transparency in estimating property value in Brazilian municipalities. In many cities, the Property Value Map is not being updated due to use of complicated methodologies that require several people and a great amount of time to be applied. For the cities, all properties have a significant importance, since they provide financial resources for city maintenance, considering that the value of the property serves as reference for the Municipal Property Tax and the Property Conveyance Tax to be collected. A failure to update the Property Value Map has been favoring the establishment of a condition of maladjustments in the sphere of public administration concerning property value, as well as tax collection, triggering situations intrinsic to this condition, such as the updating need for promotion of social equity and fair taxation. The present investigation also questions the use of single-family building values, resulting from Mass Appraisal, in substitution of market values, to update the Property Value Map. The modelling was designed through t",2.0,https://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/download/48912/7513751505599993de036dfddcce85a957ed7bf307b09a73c9ba574,10.1051/e3sconf/202560501007,Enhancing real estate price prediction using optimized least squares moment balanced machine,2025.0,E3S Web of Conferences,Riqi Radian Khasani,"Real estate price prediction is crucial for urban planning and economic forecasting, as property values are influenced by factors such as location and infrastructure. Traditional methods often struggle to capture the complex relationships between these variables, resulting in inefficient land use and suboptimal resource allocation. To address this challenge, this study introduces the Optimized Least Squares Moment Balanced Machine (OLSMBM), an advanced machine learning model designed to enhance the accuracy of real estate price predictions. The model incorporates key features such as transaction date, house age, proximity to MRT stations, number of convenience stores, and geographic location. The OLSMBM was benchmarked against five other machine learning models, including LSSVM, BPNN, ELSIM, Decision Tree, and Linear Regression. The results from a 10-fold cross-validation demonstrate that OLSMBM consistently outperforms other models across five evaluation metrics, including RMSE (6.977), MAE (4.752), MAPE (13.76%), R (0.846), and R² (0.728). The comprehensive evaluation, summarized by the Reference Index (RI), showed that OLSMBM achieved a perfect RI score of 1.000, highlighting it",2.0,https://doi.org/10.1051/e3sconf/2025605010071000967444c72972f67d3dbbd549f87a2da46efb492a,10.1080/22797254.2018.1459209,"Spatial non-stationarity analysis to estimate dwelling units in Riyadh, Saudi Arabia",2018.0,,M. Alahmadi,"ABSTRACT Dwelling unit (population) data at finer zones are important for different applications. The census data are not available every year and published at coarse zones. While the remotely sensed data have addressed this limitation, analysis of spatial non-stationarity in the relationship between dwelling and detailed urban remote-sensing covariates for dwelling estimates is almost novel. Riyadh, Saudi Arabia, is chosen as a case study. The remote-sensing variables have been derived from QuickBird data using an object-based image analysis. To analyse the spatial non-stationarity, ordinary least squares (OLS) and geographically weighted regression (GWR) approaches are applied. The OLS models suggest that utilizing three-residential classes provides more accurate results than built area and residential built area. The GWR models are more accurate than the OLS model. This research proves that the GWR approach cannot completely handle the spatial non-stationarity problem without efficient explanatory variables. For example, the GWR model that uses three-residential classes is the best model to estimate dwelling compared with the GWR model that uses residential built area. This arti",2.0,https://doi.org/10.1080/22797254.2018.14592091001cadf39dc64444835f3422e3df9da25f389fa1d04,10.3390/su162210102,"Relationship Between Spatial Form, Functional Distribution, and Vitality of Railway Station Areas Under Station-City Synergetic Development: A Case Study of Four Special-Grade Stations in Beijing",2024.0,Sustainability,Yuhan Sun; Bo Wan; Qiang Sheng,"The integration of railway stations into urban environments necessitates a detailed examination of their vitality and influencing factors. This study assesses urban vitality around four major railway stations in Beijing utilizing a variety of analytical models including Ordinary Least Squares, Geographically Weighted Regression, Multi-Scale Geographically Weighted Regression, and machine learning approaches such as XGBoost 2.0.3, Random Forest 1.4.1.post1, and LightGBM 4.3.0. These analyses are grounded in Baidu heatmaps and examine relationships with spatial form, functional distribution, and spatial configuration. The results indicate significant associations between urban vitality and variables such as commercial density, average number of floors, integration, residential density, and housing prices, particularly in predicting weekday vitality. The MGWR model demonstrates enhanced fit and robustness, explaining 84.8% of the variability in vitality, while the Random Forest model displays the highest stability among the machine learning options, accounting for 76.9% of vitality variation. The integration of SHAP values with MGWR coefficients identifies commercial density as the mo",2.0,https://doi.org/10.3390/su1622101021002bf1b5947ca826931e3e0455435b5797d306e57fb,10.1108/ijhma-07-2023-0095,How the single-family residence housing market capitalizes green property upgraded features: evidence from city of Austin,2023.0,International Journal of Housing Markets and Analysis,Jun-Feng Jiao; Xiaohan Wu; Ye-Fu Chen; A. Farahi,"1003Purpose1004By comparing regression models, this study aims to analyze the added home value of green sustainability features and green efficiency characteristics, rather than green certifications, in the city of Austin.100510061007Design/methodology/approach1008The adoption of home green energy efficiency upgrades has emerged as a new trend in the real estate industry, offering several benefits to builders and home buyers. These include tax reductions, health improvements and energy savings. Previous studies have shown that energy-certified single-family homes command a premium in the marketplace. However, the literature is limited in its analysis of the effects of green upgrades and certification on different types of single-family homes. To address this gap, this research collected data from 21,292 multiple listing services (MLS) closed home-selling listings in Austin, Texas, over a period of 35 months.100910101011Findings1012The analysis results showed that green efficiency features could generally increase single-family housing prices by 11.9%, whereas green sustainability upgrades can potentially bring a 11.7% higher selling price. Although green housing certification did not have significant effects on",2.0,101337d3e1489386e809f9159a6c381cd6cdbb7794f2,10.1051/itmconf/20246701042,Prediction of residential property prices using machine learning algorithms,2024.0,ITM Web of Conferences,S. Oh; See Pheng Hang; Jasmine Thye Wei Wang,"Residential property prices prediction is essential for evaluating market value and identifying over-pricing or under-pricing. This study investigates the performance of various machine learning algorithms, including Decision Tree (DT), Random Forest (RF), and Multilayer Perceptron (MLP) in predicting residential property prices. The study performs exploratory data analysis and principal components analysis (PCA) to reduce the dimensionality of the variables and extract the most useful variables affecting terrace house prices in Kuala Lumpur, Malaysia. A publicly available dataset is used for training and testing the algorithms, with a 70:30 proportion after pre-processing procedures. Performance indicators such as Kappa statistics, r-squared, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are used to evaluate the algorithms. The results show that RF outperforms DT and MLP, achieving the highest accuracy score of 85.82%, and highest Kappa statistics of 0.8307. The study also finds that the predicted data by RF algorithm are reliable from the train set. After performing exploratory data analysis and PCA, RF-PCA demonstrated the b",2.0,https://doi.org/10.1051/itmconf/202467010421014ac4d9672f30ffdb9281324b208b781d461a1e66c,10.3846/ijspm.2026.26144,Is digital trade affecting city house prices? An artificial intelligence perspective,2026.0,International Journal of Strategic Property Management,Hui-Ying Yang; Xiaohuan Hou,"As a new engine of economic growth, digital trade is playing an increasingly pivotal role in shaping urban dynamics, including housing markets. This study investigated how the development of digital trade influenced city-level house prices in China. A digital trade index and an urban AI index were constructed using the entropy-TOPSIS method and text mining techniques, respectively. Empirical results showed that digital trade exerted a significant and robust positive linear effect on urban house prices, with no evidence of a nonlinear relationship. AI significantly strengthened this effect, acting as a positive moderator, while trade openness weakened it. Further heterogeneity analysis revealed that the impact of digital trade was more pronounced in coastal and high-income cities, yet AI integration substantially boosted this effect in non-coastal and low-income cities, suggesting strong potential for digital catch-up in underdeveloped regions. These findings indicated that digital trade, AI adoption, and regional characteristics jointly shape urban housing outcomes. Therefore, beyond advocating for stronger governmental support for digital infrastructure and emerging technologies, ",2.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/26144/13333101572de95ddf3ce5aafb6434f937ec785835965db4e,10.3390/land10101077,Reconstruction of Residential Land Cover and Spatial Analysis of Population in Bursa Region (Turkey) in the Mid-Nineteenth Century,2021.0,Land,E. Ustaoğlu; M. E. Kabadayı,"The historic reconstruction of residential land cover is of significance to uncover the human-environment relationship and its changing dynamics. Taking into account the historical census data and cadastral maps of seven villages, this study generated residential land cover maps for the Bursa Region in the 1850s using a model based on natural constraints, land zoning, socio-economic factors and residential suitability. Two different historical reconstructions were generated; one based on a high density residential model and another based on a low density model. The simulated landcover information was used as an ancillary data to redistribute aggregated census counts to fine scale raster cells. Two different statistical models were developed; one based on probability maps and the other applying regression models including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The regression models were validated with historical census data of the 1840s. From regression models, socio-economic and physical characteristics, accessibility and natural amenities showed significant impacts on the distribution of population. Model validation analysis revealed that",2.0,https://www.mdpi.com/2073-445X/10/10/1077/pdf?version=163473453310166143c8ceb7d62a582d3647cbb5865355bc07471a,10.21837/pm.v21i30.1402,DETERMINANTS OF INVESTMENT TO ATTRACT INVESTMENT IN AFFORDABLE HOUSING PROJECTS IN MALAYSIA,2023.0,PLANNING MALAYSIA,Mohd Ariff Mohd Daud; Saiful Azhar Rosly; Zulkarnain Muhamad Sori,"The provision of shelter is a fundamental human right as defined by the maqasid al-shariah hence, the Islamic financial system should give undivided attention by assisting to provide affordable housing. This, however, is relatively hard to do given that market forces dictate property prices that many cannot afford. Unaffordable housing is a serious issue being faced by major cities around the globe, including Malaysia. Naturally, most individuals resort to government assistance, as the issue warrants social and political attention. With limited fiscal capacity, governments have fewer options to build affordable housing. Similarly, private developers will not build houses that are not profitable. One possible solution is to seek investments from institutional investors who, with deep pockets, could resolve this conundrum. This study attempts to investigate factors that motivate institutional investors to invest in affordable housing projects in Malaysia. A qualitative approach was applied to identify six attributes of investments, namely fund structure and mandate, market return, social return, risk mitigation, governance and transparency, and government support. These findings prov",2.0,https://www.planningmalaysia.org/index.php/pmj/article/download/1402/108210171e7f41e0d76bc1afe3439bc1d6d5b27d93c41f1a,10.1080/07352166.2024.2415936,"Race, profit, and algorithms: Neighborhood-level analysis of iBuyers’ profit margin",2024.0,Journal of Urban Affairs,Wonyoung So,"ABSTRACT iBuyers are firms that use automated valuation models (AVMs), streamline home buying processes, and provide all-cash offers to purchase homes. Although the previous literature has explored the roles and limitations of iBuyers in the housing market, empirical research on the racial implications of these algorithmic home buying processes remains understudied. Using a spatial lag model, this study shows the spatial clustering of iBuyer profit margins, that iBuyers gain more profits when they resell to individuals than institutions, and that some iBuyers have a statistically significant correlation between their profit margins and the proportion of marginalized racial groups within a census tract, while controlling for individual housing characteristics, neighborhood housing quality and demand, and neighborhood amenities and socioeconomic factors. These findings suggest that the more adeptly iBuyers can forecast housing values, the greater the potential to maximize profits from homeowners in communities of color. Consequently, this research contributes to the understanding of how technological mechanisms operate within a purportedly race-neutral framework and advocates for the",2.0,https://doi.org/10.1080/07352166.2024.241593610187400810c23d0417fa014137343444f1b040cf487,10.3390/jrfm16100439,Predicting the Non-Return of Chonsei Lease Deposits in the Republic of Korea,2023.0,Journal of Risk and Financial Management,J. O. Park; Jinhee Choi; Guy Ngayo,"Chonsei, a Korean housing lease system, enables landlords to acquire direct housing purchase funds without mortgages and offers tenants a cost-effective rental option. However, public concerns have arisen about potential landlord defaults, causing financial distress for tenants. This study examined the risk of non-return of the Chonsei deposit and developed a default prediction model using Chonsei contract data from the Korea Housing and Urban Guarantee Corporation. Starting with the components from Merton’s bond pricing model, we included variables that reflect contract-specific factors, macroeconomic conditions, and the Korean Chonsei practices. The findings revealed that higher house price volatility, elevated debt-to-house value, and risk-free interest rates positively correlate with non-return risk. Meanwhile, certain factors, such as longer remaining maturity, favorable macroeconomic conditions, and rising market Chonsei price trends, demonstrated negative correlations with non-return risk. Consequently, a logistic regression-based default prediction model, with eight risk factors that predict the deposit non-return, was suggested. By identifying risk factors and predicting t",2.0,https://www.mdpi.com/1911-8074/16/10/439/pdf?version=16968435541019f69a5bd1259a127997971ab7665e226f6509dc99,10.36948/ijfmr.2023.v05i06.11519,House Price Prediction Using Linear Regression Model,2023.0,International Journal For Multidisciplinary Research,J. Parekh,"Machine learning is a subset of Artificial Intelligence. Artificial intelligence (AI) and machine learning (ML) are key technologies in solving problems and addressing a wide range of issues in many different fields. Due to its capacity to automate processes, analyze massive volumes of data, and make precise judgments. Generally, it is used in voice assistants, recommendation systems, autonomous vehicles as well as in fraud detection. It also plays a vital role in the real estate sector, accurate house price prediction helps buyers, sellers, and investors make accurate decisions. There is a need for technology to predict housing values because they rise annually. Predicting house prices can assist developers in setting a property's selling price as well as buyers in scheduling the ideal time to buy a home. Four factors influence the price of a house which are area, bedrooms, bathrooms, and location. This study uses a methodology to forecast the price of houses based on relevant features, specifically by applying a linear regression model. Through the use of machine learning methods such as Random Forest, K-Means, Decision Tree, and Linear regression. This strategy will make it easi",2.0,https://www.ijfmr.com/papers/2023/6/11519.pdf1020bb333ded7cf46af47da2d5ef3020d3486c45bde1,10.30902/jrea.2020.6.1.19,"Effects of Neighborhood Environmental Factors on Apartment Housing1021 Prices",2020.0,Journal of Real Estate Analysis,So-Young Lee; M. Kim; Eun Jung Kim,"The purpose of this study is to examine correlations between neighborhood environments and housing prices in Gangnam-gu, Seoul, and Suseong-gu, Daegu. The study covers apartment complexes where transaction took place in 2017, and the total numbers of the complexes are 469 and 258 in Gangnam-gu and Suseong-gu, respectively. The dependent variable is the average transaction price per m2 of apartment, and independent variables comprise complex characteristics and neighborhood environment characteristics. The results of analysis showed that there were differences in environmental factors affecting housing prices between the two regions. The housing price was correlated with normalized difference vegetation index (NDVI) (+), mean slope (‒), and distance to river (‒) in Gangnam-gu, while it was associated with distance to high school (‒), number of private academies (+), high school quality (+), distance to subway station (‒), and the number of public sport facilities (+) in Suseong-gu. This study is differentiated in that it comparatively analyzed neighborhood environment factors affecting apartment prices between Gangnam-gu in Seoul Capital Area and Suseong-gu in non-Seoul Capital Area",2.0,http://www.ejrea.org/download/download_pdf?pid=jrea-6-1-1910227c883f686a3ca8e6532e07d5471149dd009a25d7,10.1080/07352166.2025.2520237,Rail accessibility and residential property values: Estimating value capture potential,2025.0,Social Science Research Network,Laurence Carleton; Roselyne Joyeux; George Milunovich,"ABSTRACT Developing urban rail infrastructure is crucial to improving connectivity and supporting productivity, but the substantial costs associated with new infrastructure projects present a major challenge to such endeavors. This leads to the consideration of value capture as a means of funding new rail investments. However, a key input into the analysis of such policies is the value of the property premiums induced by rail accessibility, which are difficult to estimate. We address this issue by investigating rail-induced property premiums in Sydney using an approach that extends previously used methods. Our results support the notion that rail stations are associated with neighborhood property price premiums, which in turn are moderated by the degree of accessibility. Cross-sectional analysis indicates that this access—premium effect is related to distance from the station, however broader spatial analysis reveals considerable variation in the premia across metropolitan Sydney. There are also a small number of cases where the role of rail access, as an attractor, is challenged. Thus, while in most cases publicly funded transit improvements increase property values, our findings ",2.0,https://www.tandfonline.com/doi/pdf/10.1080/07352166.2025.2520237?needAccess=true10239174832e2a1ff51c59bdcacb26425d13947f6bd3,10.3390/land11091532,"How Does the Quality of Junior High Schools Affect Housing Prices? A Quasi-Natural Experiment Based on the Admission Reform in Chengdu, China",2022.0,Land,Xiaohu Tian; Jin Liu; Yong Liu,"We exploit an admission reform of junior high schools in Chengdu, China, to identify the capitalization effect of high-quality educational facilities on housing prices. Since 2013, some elite junior high schools have conducted an experimental policy called the four-year junior high school project (hereinafter referred to as FJHP). The FJHP reduced the admission chances to elite junior high schools within the FJHP school districts via lottery. Based on 88,745 resale housing transaction records from Chengdu during 2010–2018, we used the difference-in-difference (DID) methodology to estimate the average price effect of the FJHP. Furthermore, we established a DID model with quantile regression to estimate the heterogeneous effect of the FJHP on housing prices. The empirical results show that the implementation of the FJHP reduced the housing prices in the relevant school districts by at least 5.5%, and its price reduction effect increased over time. The quantile regression results show that households with high-priced housing are more sensitive to the change of admission chance to elite junior high schools, which indicates the inequality in accessing high-quality school facilities unde",2.0,https://www.mdpi.com/2073-445X/11/9/1532/pdf?version=1663132536102467fe71c05322ce8183b91e3dd920031293f7b18b,10.34288/jri.v6i1.262,House Price Prediction Using Data Mining with Linear Regression and Neural Network Algorithms,2023.0,Jurnal Riset Informatika,E. Palupi,"The need for housing in big cities is very high because most offices and economic centers are in big cities. Limited land and high demand cause house prices to rise. Many developers build housing on the outskirts of big cities with access to trains and toll roads to make transportation easier. Property developers compete by providing the best prices, various choices of house specifications, ease of the mortgage process, and attractive promotions such as no down payment. A house is a long-term investment whose price increases yearly, so proper analysis is needed to buy a place to live in. Several factors influence the price of a house, including location, land area, building area, building type, and so on. This research aims to create a house price prediction model using the Linear Regression Algorithm and Neural Network so that the results can be useful for property agents in predicting house sales or from the buyer's side in predicting house prices. The results of this research use the Linear Regression Algorithm RMSE 0.775, while the Neural Network Algorithm uses RMSE 0.645. From this research, modeling using the Linear Regression Algorithm has better results. Still, the Linear R",2.0,https://ejournal.kresnamediapublisher.com/index.php/jri/article/download/262/2651025a8155bcc0ea79c46be753f6e85a43eb59790198c,10.3934/qfe.2017.1.94,The Price Concessions of High- and Low-Priced Housing in a Period of Financial Crisis,2017.0,,I. Tsai; Huey-Cherng Tsai,"This study uses the data from the Taiwan housing market and three methods to estimate the liquidity of high- and low-priced housing markets. The first method in this study used quantile regressions to estimate the relationship between time on the market and final transaction price. The second method involved adding variables representing housing price characteristics to the quantile regression model to control the influence of other housing characteristics and estimate the relationship between time on the market and transaction price. Lastly, this study estimated the price concessions that individual sellers accept to realize an asset. All empirical results showed that lower housing prices imply higher liquidity costs in the final transaction, and the average rate of price concession for low-priced housing was greatest at approximately 7%. The results of this study demonstrate that in an emerging market with a significant divide between rich and poor, when a financial crisis occurs, the most vulnerable sellers of real estate are the poor. The government should pay particular attention to the liquidity of the low-priced housing market and provide adequate avenues of funding to low-i",2.0,https://doi.org/10.3934/qfe.2017.1.9410265728d58aa7da7a60db0199921334a4476041a140,10.54518/rh.5.3.2025.666,Determination of Home Purchase Decisions with Technology Adoption as a Moderating Variable,2025.0,Research Horizon,Nurul Linda Wati; B. W. Otok,"The residential property market in Indonesia has experienced significant growth, with a 1.89% increase in the Residential Property Price Index and a 31.16% rise in sales in the first quarter of 2024, though Surabaya recorded the lowest price growth in Java at 0.34%. This study aims to examine the determinants of home purchase decisions, including purchasing power, price, location, marketing advertisements, and developer brand image, with technology adoption as a moderating variable. Using a quantitative approach, data were collected from 225 respondents across four housing projects in Gresik, East Java, through questionnaires analyzed with Moderated Structural Equation Modeling. The findings reveal that all determinants significantly influence home purchase decisions, with developer brand image having the strongest effect. Technology adoption enhances these relationships by improving information access and consumer trust through digital platforms. The study concludes that developers should prioritize digital marketing strategies, such as virtual tours and social media campaigns, to boost consumer engagement and address declining sales trends. These insights offer strategic guidance",2.0,https://journal.lifescifi.com/index.php/RH/article/download/666/52810279ae950aa30bc499542854f7929c9d043470dc853,10.1108/ijhma-01-2025-0012,The impact of financial stress on house price expectations: evidence from survey data,2025.0,International Journal of Housing Markets and Analysis,D. Rooj; Asish Saha; R. Sengupta; Anurag Banerjee,"Purpose1028This paper aims to investigate the impact of financial stress on house price expectations using unit-level observations from novel survey data.10291030Design/methodology/approach1031The authors use household-level data from the Inflation Expectation Survey of Households of the Reserve Bank of India and the Financial Stress Index (FSI) released by Tracking Asian Integration of the Asian Development Bank. The authors exploit the variations in household price expectations and estimate the impact of lagged FSI on house price expectations.10321033Findings1034The authors find that increased financial stress results in lower house price expectations and has a stronger impact for the near term vis-à-vis a year ahead. Heterogeneity analysis reveals that a rise in FSI leads to increased expectations of house prices among women. Expectations are lower for the older population and people with less income stability. Asymmetric analysis reveals that house price expectations are more sensitive to high financial stress, offering new insights into the cyclical nature of housing sentiment. Moreover, the equity market and the foreign exchange market have the highest negative impact on house price expectations ",2.0,10359a39aa4edaea644cd3e96f453a2f7aecd3efdda7,10.1016/j.dib.2025.111505,From archives to AI: Residential property data across three decades in Brunei Darussalam,2025.0,Data in Brief,Haziq Jamil; Amira Barizah Noorosmawie; Hafeezul Waezz Rabu; Lutfi Abdul Razak,"This article introduces the first publicly available data set for analysing the Brunei housing market, covering more than 30,000 property listings from 1993 to early 2025. The data set, curated from property advertisements in newspapers and online platforms, includes key attributes such as price, location, property type, and physical characteristics, enriched with area-level spatial information. Comprehensive and historical, it complements the Brunei Darussalam Central Bank's Residential Property Price Index (RPPI), addressing the limitations of restricted access to raw RPPI data and its relatively short timeline since its inception in 2015. Data collection involved manual transcription from archival sources and automated web scraping using programmatic techniques, supported by innovative processing with Large Language Models (LLMs) to codify unstructured text. The data set enables spatial and temporal analysis, with potential applications in economics, urban planning, and real estate research. Although listing prices are only a proxy for market values and may deviate from actual sale prices due to negotiation dynamics and other factors, this data set still provides a valuable reso",2.0,https://doi.org/10.1016/j.dib.2025.1115051036eb406ca5beb07c95842f722c35ad449803cc8ef9,10.2478/bjreecm-2025-0008,Energy Efficiency Trend in the Latvian Residential Property Market,2025.0,Baltic Journal of Real Estate Economics and Construction Management,Don Hasitha Nirmal Welikala; Sanda Lapuķe; A. Kundziņa; Medha Sharma; R. Sanchaniya,"Abstract This research examines the influence of energy efficiency on the residential real estate market in Latvia, highlighting its environmental and socioeconomic aspects. The impact of laws, regulations, and market dynamics on energy efficiency enhancements is also highlighted, outlining current trends, regulatory structures, and economic elements that facilitate or obstruct the implementation of energy-saving initiatives. Special emphasis is placed on the impact of these policies on residential property prices, market dynamics, and the relationship between supply and demand. This research analyzes energy-related regulations and identifies deficiencies in the regulatory framework, elucidating the intricate relationship among law, market dynamics, and consumer behavior. The research mainly relies on empirical study methods, outlining the basis for a well-defined approach to evaluation, performance, and energy-saving solutions in the Latvian residential sector. The data was acquired by circulating a detailed survey among industry experts and residents of Latvia. The data was thoroughly evaluated using multiple regression analysis to estimate the property values in Latvia, which is",2.0,https://doi.org/10.2478/bjreecm-2025-0008103726506b992e1429ab5aa6b8f08b27267db6c93321,10.1145/2835022.2835028,Exploring New Data Sources to Improve UK Land Parcel Valuation,2015.0,UrbanGIS@SIGSPATIAL,H. Crosby; Paul Davis; S. Jarvis,"The paper describes a novel approach for building a UK-wide Automated Land Valuation Model and its implementation into commercial online software. We examine existing approaches to land valuation used in the UK, notably Trade Area Analysis, Spatial Interaction and Comparable Sales. We make the case that land use analysis, demographics and societal preferences affect the potential income and optimal use of parcels of land and hence the value of those parcels. This hypothesis leads to the introduction of a number of additional factors required to facilitate estimated land value, including traffic flow, population and site suitability. A number of artificial intelligence (AI) and machine learning spatial-temporal techniques are introduced to predict the value of all land parcels sold since 1995. We introduce a new technique, which includes (i) the application of Support Vector Machines to land use analysis; (ii) the use of predictive techniques for macro-environmental factors; (iii) the use of large, open-source data sets to improve valuation; (iv) industry alignment in predefined industrial tool. A number of different mathematical techniques are used to validate the proposed model an",2.0,https://doi.org/10.82444/warw.337295291038dc1a9cd92c38b3541fc97ce012fd7a15bd7f98f8,10.3390/su13020455,Effect of Hierarchical Parish System on Portuguese Housing Rents,2021.0,Sustainability,Sofia Vale; Felipa de Mello-Sampayo,"This manuscript analyzes an inter-parish housing rents gradient with respect to surrounding parishes. Using data on housing rents for 4049 Portuguese parishes in 278 municipalities, the paper explores the spatial patterns of housing rents using the geographically weighted regression (GWR) methodology. The housing rents can be explained by socio-economic factors comprising the effects of unemployment, sustainability, social diversity, elderly dependency, and population density. The proportion of overcrowded dwellings reflecting how poor living conditions affect housing rents was also included in the spatial analysis. On the structural side, characteristics of the dwellings were also included such as the area of the home and the number of other homes available in the parishes. Locational factors reflect households’ valuation for access to other parishes. In order to capture location characteristics, besides considering mobility within municipalities, the GWR allowed using distances to nearby parishes, i.e., parish hierarchy distance effect. The results suggest that the Portuguese rental housing market exhibits a heterogeneous pattern across the territory, displaying spatial variabili",2.0,https://www.mdpi.com/2071-1050/13/2/455/pdf?version=16099227561039df6400af862f8114b070010652b1e08cfb6b4135,10.21837/pmjournal.v16.i7.524,IMPEDIMENTS TO HOME OWNERSHIP: PERCEPTION OF LOWAND MIDDLE-INCOME TENANTS IN SELANGOR,2018.0,PLANNING MALAYSIA JOURNAL,Kun Hing Yong; Kuppusamy Singaravelloo,"Home ownership affordability has been a serious issue among the low and middle income households in Malaysia. Main contributors to this issue are low income, high cost of living and growth in income that does not commensurate the rise in property price. This research intends to identify the level of home ownership affordability among those who are unable to have a house and are tenants. In addition, it intends to examine the factors that affect their home ownership affordability. The study focuses on people residing at rented units in Kajang, an urban city in Selangor, where primary data was collected using questionnaire method. The target group was low and middle income households, whose head of household aged between 21 to 50 years and total household income capped below RM7,500. The success rate was 74 percent based on 250 households met. The research findings indicate that the level of home ownership affordability varies by ethnicity, household income and educational level. Meanwhile, four main factors that influence home ownership affordability are price of house, affordable mortgage loan, proximity to workplace and government policy on affordable housing. Majority of the resp",2.0,https://doi.org/10.21837/pmjournal.v16.i7.52410406a40167a6a70cb9155b5be6185a463f8143b8c75,10.3390/ijfs12030074,Housing Price-Vacancy Dynamics—An Empirical Study of the Hong Kong Housing Market,2024.0,International Journal of Financial Studies,C. Yiu; T. Murray,"This study uses time series regression models and dynamic panel models of five-class housing to investigate the dynamics of the housing price-vacancy relationship in Hong Kong, offering insights distinct from previous cross-sectional analyses that take new housing completions as a supply proxy, without considering vacant homes as a source of housing supply. Two major contributions emerge: first, the results support the hypothesis that housing vacancies exert a negative impact on housing prices, holding other factors constant. Second, new builds supply is found to have a positive effect on housing prices, which is in line with many previous studies, but it contradicts the prediction. The results challenge the use of land supply or new housing completions as the proxy of housing supply and put forward a novel suggestion of including vacant homes in the housing price analysis. Advanced approaches to collecting housing vacancy data are also discussed. These findings have significant implications for policymakers, urban planners, and real estate investors, providing valuable insights for crafting targeted interventions and informing investment decisions. This is one of the first time se",2.0,https://www.mdpi.com/2227-7072/12/3/74/pdf?version=17222433791041d66dcd3e24d3aa705a9e17810be910446c18dce3,10.7819/rbgn.v23i3.4114,What is the effect of location on rental housing prices in Athens?,2021.0,Review of business Management,Jorge Chica-Olmo; Rafael Cano-Guervos; María-Despoina Moschovaki; Ivan Tamires-Turizo,"Abstract Purpose: The aim of this study is to quantify the effect of location on rental housing prices in the city of Athens. Theoretical framework: The right to adequate housing is a fundamental human right defended by democratic societies. Therefore, it is of interest to examine housing tenures for both owned and rented accommodation. Design/methodology/approach: Geostatistical methods (regression-kriging) were used to obtain the results, which are represented on an isovalue map of rental housing prices displaying the minor and major effects of location by zone. Findings: This study highlights the impact of location on rental housing prices by showing how the rent of a standard dwelling in the city of Athens varies depending on its location. Research Practical & Social implications: The main social implications of this work is it helps investors determine where to direct investments and it assists public authorities in deciding where to focus urban management policies, in order to control the undesirable effects of an excessive rise in rents caused by tourism. Originality/value: The main originality of this paper lies in its isovalue map of rental housing prices for standard dwel",2.0,https://rbgn.fecap.br/RBGN/article/download/4114/1724/3243710422fcf45aa2743e8132fb5777a73fd2173df413369,10.62051/d5x1ss82,Comparing Linear Regression and Random Forest for Housing Price Prediction: Insights from the Boston Housing Dataset,2025.0,Transactions on Computer Science and Intelligent Systems Research,Guo-Guo Chen,"Housing price prediction is a critical task in real estate and economic analysis, providing valuable insights for stakeholders such as homebuyers, sellers, and policymakers. This study focuses on the Boston Housing dataset, a benchmark with 505 samples and 14 features, to predict the median value of owner-occupied homes (MEDV) using Linear Regression and Random Forest Regression. Exploratory data analysis reveals non-linear patterns, such as the right-skewed distribution of MEDV (skewness = 1.11) and strong correlations with features like LSTAT (-0.74) and RM (0.70). The dataset was standardized and split into 80-20 training and testing sets for model evaluation. Results show that Random Forest outperforms Linear Regression, achieving an MSE of 7.58 and R² of 0.864 compared to 19.38 and 0.652, respectively. Feature importance analysis highlights LSTAT and RM as key predictors, emphasizing socio-economic and structural influences. While Random Forest excels in capturing non-linear relationships, Linear Regression offers interpretability for policy insights. However, the dataset’s historical context and small size limit its applicability to modern markets, suggesting future research ",2.0,https://wepub.org/index.php/TCSISR/article/download/5464/59981043be65f82c54bf36f354688dfdd8b0bee4ce22f492,10.1017/s0266466621000384,CONTINUOUSLY UPDATED INDIRECT INFERENCE IN HETEROSKEDASTIC SPATIAL MODELS,2021.0,Econometric Theory,Maria Kyriacou; Peter C. B. Phillips; Francesca Rossi,"Spatial units typically vary over many of their characteristics, introducing potential unobserved heterogeneity which invalidates commonly used homoskedasticity conditions. In the presence of unobserved heteroskedasticity, methods based on the quasi-likelihood function generally produce inconsistent estimates of both the spatial parameter and the coefficients of the exogenous regressors. A robust generalized method of moments estimator as well as a modified likelihood method have been proposed in the literature to address this issue. The present paper constructs an alternative indirect inference (II) approach which relies on a simple ordinary least squares procedure as its starting point. Heteroskedasticity is accommodated by utilizing a new version of continuous updating that is applied within the II procedure to take account of the parameterization of the variance–covariance matrix of the disturbances. Finite-sample performance of the new estimator is assessed in a Monte Carlo study. The approach is implemented in an empirical application to house price data in the Boston area, where it is found that spatial effects in house price determination are much more significant under rob",2.0,https://www.cambridge.org/core/services/aop-cambridge-core/content/view/11CABB59EA7FAA3CD8F3981ED8CD39BE/S0266466621000384a.pdf/div-class-title-continuously-updated-indirect-inference-in-heteroskedastic-spatial-models-div.pdf1044b7385ac652823a01b24714fd8024e76cd2de7ef3,10.3390/land14102008,Spatial Reconfiguration of Housing Price Patterns and Submarkets in Shanghai Before and After COVID-19,2025.0,Land,Yunjie Feng; Zihan Xu; Jiaxin Qi; Yao Shen,"Housing markets worldwide have undergone major disruptions during the COVID-19 period, raising questions about how systemic shocks reshape housing preferences and spatial structures. This study develops an integrated spatial framework to examine multi-dimensional housing market restructuring, combining global and local modelling with network-based submarket delineation. Using Shanghai as a case study, we compare pre- and post-pandemic conditions (2019 and 2023) to explore fluctuations in housing prices, shifts in attribute effects, and reconfiguration of submarkets. The results reveal highly differentiated market responses across space. A dual restructuring is observed: decentralisation within the urban core and reinforced integration of outer-peripheral areas into the metropolitan centre, suggesting a gradual transition from a monocentric system towards a more polycentric and context-dependent housing landscape. Methodologically, the study proposes a transferable framework for analysing spatial restructuring under systemic shocks. Empirically, it provides fine-grained evidence of housing market reconfiguration across spatial scales, offering practical insights for spatially inform",2.0,https://www.mdpi.com/2073-445X/14/10/2008/pdf?version=1759833887104526ecbfec0d9048eb2d7579f0e3e280f3a5825d9c,10.1051/e3sconf/202016504016,The Impact of Accessibility of Urban Central Parks on Housing Prices of Fuzhou,2020.0,E3S Web of Conferences,Wenhao Tan; Linfeng Liu; Yage Cui; Jintao Chen; Feng Lin; Ying-Ying Zhong; Hui Pan,"The paper analysed the impact of accessibility of urban parks within the third ring on housing prices of Fuzhou through network analysis and SPSS correlation analysis based on data of sources like remote-sensing image, web crawling and urban road network, discovering that: (1)The density of residence within the third ring of Fuzhou decreases from the centre to the edges; (2) The housing price, ranging between 18000 and 28000 RMB/ m2, peaks in the city centre; (3) For houses within the third ring of Fuzhou, Gulou District enjoys the greatest access to urban parks while Cangshan District is the poorest in this regard. (4) The residence within the third ring of Fuzhou could be rated as A or A- in terms of access to urban parks, with an overall excellence performance; (5) The walking distance to the parks is significantly correlated with the housing price. The shorter the distance, the higher the price. Regarding this, the paper proposed the following suggestions: (1) Revise the routes to Gaogai Mountain Park by increasing entrances and exists to improve its accessibility; (2) Improve the transportation network and increase footpaths to the park, thus shortening the distance between th",2.0,https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/25/e3sconf_caes2020_04016.pdf1046787ba5a84faf66a799bc0e0eb2cd65dde00952df,10.1108/ijhma-05-2025-0115,Factors affecting housing prices in Kadiköy (Istanbul): integration of MGWR and Space Syntax analysis,2025.0,International Journal of Housing Markets and Analysis,Mustafa Kahraman; Furkan Taha Çobanli; Emre Akadal,"1047 1048 The purpose of this study is to examine how street network morphology, measured by spatial configuration metrics, affects housing prices in Istanbul, beyond traditional factors such as structure and location. While numerous studies have investigated the determinants of property values, the relationship between street geometry and property prices remains to be studied.1049 1050 1051 1052 The data set was collected through web crawling. The area where for sale advertisements were concentrated and varied was selected as the study area. In addition to building data obtained through web scraping, environmental and road data were also used. Environmental data (distance to public transportation stops, parks, coastlines and business and commercial areas) were obtained from relevant municipalities and institutions. Street network data was obtained from OpenStreetMap, and Space Syntax metrics such as connectivity, choice, integration and total depth were calculated. Multiscale geographically weighted regression was used to investigate the relationship between these variables and housing prices.1053 1054 1055 1056 The results show that, in addition to common structure and location characteristics, street network",2.0,1057a0705accf0ce07f2e626786bfb106cf2b05dd9c7,10.32873/unl.dc.jade913,"Homeownership Rate and Other House Price Determinants1058Impacts on New House Prices in Colombia",2020.0,Journal for the Advancement of Developing Economies,Héctor Alberto Botello-Peñaloza,"Homeownership remains a preferred form of tenancy in different parts of the world. The1059attractions of security, stability, investment potential and a sense of pride outweigh the fear of1060price instability. For this reason, the Colombian government has encouraged in recent years,1061various demand policies that have sought to promote the increase in the number of homeowners.1062However, these ideas could have a severe impact on prices in the real estate market. Therefore,1063this study seeks to examine the effect of homeownership rate on new house prices in an emerging1064country with low real estate ownership, credit restrictions and average per capita income. The1065study uses panel data model to examine the influence of housing tenancy and other variables on1066the variation of housing prices in Colombia. Data were obtained from various sources including1067the Central Bank of Colombia, Financial Superintendence of Colombia, and National1068Administrative Department of Statistics of Colombia. The results show that homeownership rates1069have a positive effect on the price of new homes, which supports the hypothesis of the research.1070The population growth of the cities is the factor that is most relevant when",2.0,https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1044&context=jade1071e5b55a9f70835b13ca89361f43dccd19cc3ed5dc,10.24853/ijbesr.6.2.111-126,"A Preliminary Study of Housing Satisfaction, Mobility, and Adaptation of the Jakartans in 2020-2021",2022.0,International Journal of Built Environment and Scientific Research,Putra Bagus Khalis; J. Adianto,"This study aims to investigate the factors of housing satisfaction, mobility, and adaptation for providing suitable housing quality to the needs of the Jakarta residents. Although many housing provision programs are delivered in the capital city to accommodate the housing needs of the Jakartans, its result remains unsuccessful to attract interest from the designated homebuyers in the capital city, especially the middle and low-income groups, due to its unaffordable housing price and potentially unmeet housing preferences and satisfaction. According to the quantitative analysis of 400 respondents, it finds age and marital status are the prominent socio-demographic factors of housing satisfaction, mobility, and adaptation of the Jakartans, followed by various attributes of housing norms. This study encourages the city authority, planners, and architects must thoroughly plan the location of housing planning with sufficient proximity to the public amenities, which also implicates the investment opportunities, land, and building taxes. While adaptable housing design is suggested to enable self-help development by the residents for coping with the unforeseen needs in the future according",2.0,https://jurnal.umj.ac.id/index.php/IJBESR/article/download/12387/79931072dcbffdde7839da8bbe43ba5ae343cefa81974874,10.2139/ssrn.2664152,Systemic Risk Rankings and Network Centrality in the European Banking Sector,2015.0,Social Science Research Network,V. De Bruyckere,"This paper presents a methodology to calculate the Systemic Risk Ranking of financial institutions in the European banking sector using publicly available information. The pro- posed model makes use of the network structure of financial institutions by including the stock return series of all listed banks in the financial system. Furthermore, a wide set of common risk factors (macroeconomic risk factors, sovereign risk, financial risk and housing price risk) is included to allow these factors to affect the banks. The model uses Bayesian Model Averaging (BMA) of Locally Weighted Regression models (LOESS), i.e. BMA-LOESS. The network structure of the financial sector is analysed by computing measures of network centrality (degree, closeness and betweenness) and it is shown that this information can be used to provide measures of the systemic importance of institutions. Using data from 2005 (2nd quarter) to 2013 (3rd quarter), this paper provides further insight into the time-varying importance of risk factors and it is shown that the model produces superior conditional out-of-sample forecasts (i.e. projections) than a classical linear Bayesian multi-factor model. JEL Classification: ",2.0,https://doi.org/10.2139/ssrn.26641521073a3c9f45679aa7cf7c7400c4a0a5d77b394aa8170,10.54254/2754-1169/105/20241950,Investigation of Influential Factors of Housing Price,2024.0,"Advances in Economics, Management and Political Sciences",Shengjie Zhou,"The fluctuation of housing prices and its influencing factors are important research topics in economics. Under the current background of rapid globalization and urbanization, understanding the driving factors of housing prices is not only an important contribution to academic research but also has important guiding significance for policymakers and individual home buyers. This paper explores the key factors affecting housing prices, focusing on the impact of variables such as living space, land space, housing landscape, housing conditions, housing quality, and housing construction year on housing prices. This study uses multiple regression analysis to analyze the US housing price data set. The results show that living space, housing landscape, housing quality, and housing construction year have statistically significant effects on housing prices at a significance level of 1%. This study provides an effective reference to real estate developers, policymakers, and individual home buyers, which can help them understand market demand more deeply, make decisions that are more in line with market trends, and promote the dynamic and functional development of the real estate market.",2.0,https://doi.org/10.54254/2754-1169/105/202419501074a04bf32d4475bb5d167dbb9d49674ac4808835cc,10.1111/pirs.12413,"Real wages, amenities and the adjustment of working hours across local labour markets",2018.0,Papers in Regional Science,T. Schlüter,This article establishes a link between the traditional labour economics and the urban economics literature by analyzing differences in working hours across regional labour market areas in the UK. Using a real wage index reflecting skill adjusted earnings net of quality adjusted house prices in Britain and panel data on working hours the effect of regional real wages on labour supply is assessed. The identification strategy relies on workers who move across 157 labour market areas in Britain and includes individual fixed effects. The main finding is that working hours are significantly higher in labour market areas that offer lower real wages. Decreasing real wages by £1000 results in an increase of working hours of 0.3 %. Real wage differentials can be seen as a proxy for the local amenity level. I can replicate my finding including a set of amenities instead of the real wage index. The effect is mainly due to labour supply decisions of low skilled workers who work significantly longer hours in low real wage areas than high skilled workers. This indicates that low skilled workers are willing to increase their labour supply in order to afford living in high amenity areas.,2.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/pirs.124131075edfaae27995bfb08faa7b8f49f19025e174fb382,10.1017/s1365100524000476,A tale of two tails: 130 years of growth at risk,2023.0,Macroeconomic Dynamics,M. Gächter; Elias Hasler; Florian Huber,"Abstract We extend the growth-at-risk (GaR) literature by examining US growth risks over 130 years using a time-varying parameter stochastic volatility regression model. This model effectively captures the distribution of GDP growth over long samples, accommodating changing relationships across variables and structural breaks. Our analysis offers several key insights for policymakers. We identify significant temporal variation in both the level and determinants of GaR. The stability of upside risks to GDP growth, as seen in previous research, is largely confined to the Great Moderation period, with a more balanced risk distribution prior to the 1970s. Additionally, the distribution of GDP growth has narrowed significantly since the end of the Bretton Woods system. Financial stress is consistently associated with higher downside risks, without affecting upside risks. Moreover, indicators such as credit growth and house prices influence both downside and upside risks during economic booms. Our findings also contribute to the financial cycle literature by providing a comprehensive view of the drivers and risks associated with economic booms and recessions over time.",2.0,https://doi.org/10.1017/s13651005240004761076372cb799c1e39c41831d74ee1908fbc8fc1183bb,10.1109/comnetsat63286.2024.10862860,Significant Features for House Price Prediction Using Machine Learning,2024.0,"2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)",M. Saefudin; Mindi Richia Putri; Abdul Hadi; H. Wijayanto; Budi Irmawati,"Predicting house prices is a creative way to comprehend actual home costs. Therefore, changes in house prices affect both buyers and sellers. It also shows the state of the housing economy at the moment. The purpose of this study is to identify the features that have the largest impact on house prices. The methods used to produce the most relevant feature combinations are Chi-Square and Mutual Information. To determine which combination of features has the greatest impact, we will feed the feature selection results into several machine learning algorithms—including Linear Regression, Logistic Regression, and Random Forest Regression—and evaluate them using Mean Absolute Error (MAE). The Linear Regression model shows that the combination of features based on the Mutual Information method with $K=20$ produces the smallest MAE, namely $23,251.43$. The Logistic Regression model shows that the combination of features based on the Mutual Information method with $K=10$ produces the smallest MAE, namely $\mathbf{3 3, 6 5 4 . 8 5}$. The Random Forest Regression model shows that the combination of features based on the Mutual Information method with $\text{K}=15$ produces the smallest MAE, n",2.0,1077ae0059fec67f4d122f48d0f3ed5606d097df0ad1,10.3390/a16110526,A General Model for Side Information in Neural Networks,2023.0,Algorithms,Tameem Adel; M. Levene,"We investigate the utility of side information in the context of machine learning and, in particular, in supervised neural networks. Side information can be viewed as expert knowledge, additional to the input, that may come from a knowledge base. Unlike other approaches, our formalism can be used by a machine learning algorithm not only during training but also during testing. Moreover, the proposed approach is flexible as it caters for different formats of side information, and we do not constrain the side information to be fed into the input layer of the network. A formalism is presented based on the difference between the neural network loss without and with side information, stating that it is useful when adding side information reduces the loss during the test phase. As a proof of concept we provide experimental results for two datasets, the MNIST dataset of handwritten digits and the House Price prediction dataset. For the experiments we used feedforward neural networks containing two hidden layers, as well as a softmax output layer. For both datasets, side information is shown to be useful in that it improves the classification accuracy significantly.",2.0,https://www.mdpi.com/1999-4893/16/11/526/pdf?version=17000996111078dc01044cd8fb661043f38881e350ef4f12772f7b,10.32473/flairs.v35i.130668,A Comparison of House Price Classification with Structured and Unstructured Text Data,2022.0,The Florida AI Research Society,Erika Cardenas; C. Shorten; T. Khoshgoftaar; B. Furht,"1079 1080 1081Purchasing a home is one of the largest investments most people make. House price prediction allows individuals to be informed about their asset wealth. Transparent pricing on homes allows for a more efficient market and economy. We report the performance of machine learning models trained with structured tabular representations and unstructured text descriptions. We collected a dataset of 200 descriptions of houses which include meta-information, as well as text descriptions. We test logistic regression and multi-layer perceptron (MLP) classifiers on dividing these houses into binary buckets based on fixed price thresholds. We present an exploration into strategies to represent unstructured text descriptions of houses as inputs for machine learning models. This includes a comparison of term frequency-inverse document frequency (TF-IDF), bag-of-words (BoW), and zero-shot inference with large language models. We find the best predictive performance with TF-IDF representations of house descriptions. Readers will gain an understanding of how to use machine learning models optimized with structured and unstructured text data to predict house prices. 1082 1083 1084",2.0,https://journals.flvc.org/FLAIRS/article/download/130668/13386910855cd7c4cc4e12a3e5eb5ced3a240c0b684e34f4cd,10.54691/bcpbm.v38i.4014,Price Prediction of Ames Housing Through Advanced Regression Techniques,2023.0,BCP Business & Management,Yueting Han,"House price fluctuates at all times, and it is necessary to predict the future house prices for the reason that it is crucial for buyers, property investors and the economy as a whole. This article reveals the important features relating to house price and demonstrates the method of predicting house prices in Ames, Iowa through advanced regression techniques, such as correlation, feature engineering, and model building. The prediction is performed through machine learning methods based on the dataset containing almost all the features of residential houses in Ames, Iowa. The dataset was originally splitted into the ‘train set’ and the ‘test set’. Six models were chosen, LASSO Regression, Elastic Net Regression, Gradient Boosting Regression, XBoost, LightGBM, and Stacked Model, for evaluation under the five-fold cross-validation to check the performance of the algorithms and report the root-mean-square logarithmic error. Eventually, Gradient Boosting Regression Model is chosen by the lowest root-mean-square logarithmic error of 0.04615 and a low cross-validated value of 0.1174 to predict the house price to benefit buyers, investors, and the whole society.",2.0,https://bcpublication.org/index.php/BM/article/download/4014/390810868b43b5be5e40f59bf55fded710b3e8aa7b8fd20c,10.1109/icisaa62385.2024.10828645,"Evaluating the Effectiveness of Random Forest versus Decision Tree Algorithms in Predictive Analytics for Enhancing Sustainable Development Strategies in Real Estate, Infrastructure, and Construction: A Machine Learning Perspective",2024.0,2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA),D. M. Gala; Bhakti Pawar; Gayathri Band; A. Dalal; Arnab Chakraborty; Utpal Das; B. Patil,"This study explores the application of machine learning algorithms, specifically decision tree and random forest models, to predict house prices within the Indian real estate market. Utilizing a comprehensive dataset obtained from Kaggle, this research encompasses extensive data preprocessing, feature engineering, and exploratory data analysis to identify key predictors of house prices. The comparative analysis aims to assess the accuracy, efficiency, and applicability of both models in the context of predictive analytics for sustainable development goals (SDGs) in real estate. The findings reveal that the random forest model outperforms the decision tree model, demonstrating superior predictive accuracy with a lower Root Mean Squared Error (RMSE). Key predictors identified include living area, number of bathrooms, and the condition of the property, with the random forest model providing deeper insights into feature interactions and their collective impact on house prices. This research highlights the potential of advanced machine learning techniques in enhancing real estate market analysis and strategic decision-making. The superior performance of the random forest model underscor",2.0,1087e6b038a89b6b23dd58c83a2eaca0ac71f7893783,10.1016/j.jfi.2025.101181,Borrower expectations and mortgage performance: Evidence from the COVID-19 pandemic,2025.0,Journal of Financial Intermediation,W. Larson; C. Makridis; Chad Redmer,"We assess issues related to borrower beliefs and mortgage performance using new individual panel data that simultaneously cover borrower expectations, forbearance status during the COVID-19 pandemic, and a wide array of demographic characteristics. First, we establish the determinants of borrower expectations, with local experiences and those of social networks playing important roles. We then show that households who, at origination, were optimistic about future house price appreciation or pessimistic about the possibility of future unemployment were more likely to enter forbearance in 2020. However, by early 2021, appreciation-optimistic borrowers who were in forbearance were likely to have cured or prepaid their loan, while those who expected unemployment were likely to still be in forbearance. We offer three channels by which expectations affect forbearance behavior: choices of initial loan terms, associations with actual future events, and factors related to belief formation that are also plausibly associated with forbearance. Our findings highlight the crucial role borrower expectations play in both leverage choices and mortgage performance.",2.0,https://doi.org/10.2139/ssrn.47056531088c578bd87604d74daf8eff6ad5d7759e3988893fc,10.58317/eksen.1237410,EXAMINING VARIABLES EFFECTING HOUSING PRICES WITHIN THE SCOPE OF TRANSPORTATION INFRASTRUCTURE,2023.0,EKSEN Dokuz Eylül Üniversitesi Mimarlık Fakültesi Dergisi,Gülsüm Ecem Demirdağ; İrem Ayhan Selçuk,"There are many factors that people pay attention while choosing housing. In addition to the physical characteristics of the housing; spatial features such as the distance of the housing to the city center, work and educational areas, public transportation stops also play an important role in the selection of housing. Although the distance of the housing to these places does not change, access will be much easier within the facilities provided by the existing transportation infrastructure. In parallel with urbanization and population growth, as a result of the increases in housing demand, there have been significant increases in housing prices. In this context, the aim of the study is to examine the variables affecting housing prices and to examine the connection between transportation and accessibility and housing prices. In this context, 30 studies were examined in which variables affecting housing prices were examined by different disciplines. As a result, it was seen that the distances to public transportation stops, the city center, the working and educational areas and the accessibility of housing have an increasing effect on housing prices.",2.0,https://dergipark.org.tr/en/download/article-file/28982541089efad6116c146c1e96eddce148d29cd8024ba81c3,10.2478/subboec-2025-0003,Determinants of Rent Prices: An Analysis of European Cities,2025.0,Studia Universitatis Babes-Bolyai Oeconomica,Anuța Buiga; Mirela Popa,"Abstract Using the data of a sample consisting of 99 cities from 37 European countries (2024) this paper discusses the determinants of rent prices. The results indicate that rents are lower in areas with longer commute times and higher cost of living index, while high wages and high house price to income ratio (HPIR) lead to increase rent prices. In this paper, we constructed the Rent Index as average of four variables relating to the rents of one-room and three-room apartments in the city centre and in the suburbs. We identify the factors that explain the variation of the Rent Index using multiple linear regression. The overall conclusions of analysed models are mixed: salary is the strongest predictor in all models, if the city is a university one, we expect an average rent increase between 105.9 and 115.81 euro compared to non-university cities. If the city has a tourist attraction, the rent price will increase on average with a sum between 114.59 and 158.61 euro; the safety index and pollution index have significant effects on Rent Index; an increase of HPIR by one unit will lead to an increase in the Rent Index with 13.6 euros per month.",2.0,https://reference-global.com/2/v2/download/article/10.2478/subboec-2025-0003.pdf1090719ddda153b52e86649ce1056722027072dd1a5a,10.5194/isprs-annals-v-4-2022-49-2022,STACKING-BASED UNCERTAINTY MODELLING OF STATISTICAL AND MACHINE LEARNING METHODS FOR RESIDENTIAL PROPERTY VALUATION,2022.0,"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences",A. Jafari; M. R. Delavar; A. Stein,"Abstract. Estimating real estate prices helps to adapt informed policies to regulate the real estate market and assist sellers and buyers to have a fair business. This study aims to estimate the price of residential properties in District 5 of Tehran, Capital of Iran, and model its associated uncertainty. The study implements the Stacking technique to model uncertainties by integrating the outputs of basic models. Basic models must have a good performance for their combinations to have acceptable results. This study employs four statistical and machine learning models as basic models: Random Forest (RF), Ordinary Least Squares (OLS), Weighted K-Nearest Neighbour (WKNN), and Support Vector Regression (SVR) to estimate the price of residential properties. The results show that the integrated output is more accurate for the quadruple combination mode than for any of the binary and triple combinations of the basic models. Comparing the Stacking technique with the Voting technique, it is shown that the Mean Absolute Percentage Error (MAPE) reduces from 10.18% to 9.81%. Hence we conclude that our method performs better than the Voting technique.1091",2.0,https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-4-2022/49/2022/isprs-annals-V-4-2022-49-2022.pdf109244d36dfbef380c152755e72bf5cedaad74f84d30,10.15544/mts.2024.50,The Dynamics of the Real Estate Market: The Impact of Price Changes on Consumer Decisions to Purchase Housing,2024.0,Management Theory and Studies for Rural Business and Infrastructure Development,Deimena Montvydaitė,"Abstract The real estate market has recently undergone significant changes that influence consumer behavior. The aim of the study is to assess the dynamics of the real estate market and to investigate how changes in housing prices affect consumers’ intentions to purchase real estate in the near future, investigating the case of Lithuania. The research methods of scientific literature analysis, statistical data analysis, regression analysis, and logical conclusion formulation are employed. The study’s results revealed trends in the growth of real estate market prices in Lithuania. A positive relationship was established between the increase in housing prices and Lithuanian consumers’ intentions to buy or build a home within the next 12 months. Although it may seem that rising housing prices could negatively impact intentions to purchase, the study’s findings suggest that the factor of increasing prices may encourage purchasing decisions, as consumers, seeing upward price trends, do not want to miss the opportunity to buy before prices rise further. They may view prices as an investment opportunity or trust the improving economic situation.",2.0,https://doi.org/10.15544/mts.2024.501093453d1b17234b96475f56db4fa25bfca2ebed495f,10.3390/axioms13010004,Robust Variable Selection with Exponential Squared Loss for the Spatial Error Model,2023.0,Axioms,Shi-Da Ma; Yiming Hou; Yun-Quan Song; Feng Zhou,"With the widespread application of spatial data in fields like econometrics and geographic information science, the methods to enhance the robustness of spatial econometric model estimation and variable selection have become a central focus of research. In the context of the spatial error model (SEM), this paper introduces a variable selection method based on exponential square loss and the adaptive lasso penalty. Due to the non-convex and non-differentiable nature of this proposed method, convex programming is not applicable for its solution. We develop a block coordinate descent algorithm, decompose the exponential square component into the difference of two convex functions, and utilize the CCCP algorithm in combination with parabolic interpolation for optimizing problem-solving. Numerical simulations demonstrate that neglecting the spatial effects of error terms can lead to reduced accuracy in selecting zero coefficients in SEM. The proposed method demonstrates robustness even when noise is present in the observed values and when the spatial weights matrix is inaccurate. Finally, we apply the model to the Boston housing dataset.",2.0,https://doi.org/10.3390/axioms1301000410942b12c21c7ae7b6d07fd9a137ce1e46c604fed807,10.21098/jimf.v5i1.1044,HOUSE PRICE CHANGES AND ISLAMIC BANK STABILITY: EVIDENCE FROM MALAYSIA,2019.0,Journal of Islamic Monetary Economics and Finance,Shi-Au-Hui Kok; N. Ismail,"In this paper, we examine the relationship between house price and Islamicbank stability in Malaysia. In particular, in relating to Islamic bank stability toMalaysian house price changes, we evaluate the nature of the relationship from theperspective of nonlinearities. The Autoregressive Distributed Lag (ARDL) modelis applied to a sample that consists of 9 Islamic banks in Malaysia for the periodof 2000-2016. Our results indicate that there is an inverted U-shaped relationshipbetween house price and Islamic bank stability in the long run. Meanwhile, therelationship is insignificant in the short-run. To put it differently, initially, thehigher house prices, the more stable the bank. Then, the impact of house prices onbank stability becomes negative when house prices surpass the threshold point.As far as the bank-specific characteristics are concerned, the cost to income ratiois found to significantly and negatively related to the bank stability. Such a resulthas policy implications in which it is crucial for achieving balance in the housingmarket, and efficiently managing the cost is equally important to ensure Islamicbank soundness.",2.0,http://jimf-bi.org/index.php/JIMF/article/download/1044/7391095ad8831bf6a3a889cc22205fbdb825e0fbfa91b27,10.54254/2754-1169/2024.ga18866,Forecasting House Prices in Shanghai Using Common Factor Analysis and Multiple Linear Regression Analysis,2024.0,"Advances in Economics, Management and Political Sciences",Zhewei Deng,"Predicting housing prices is critical for understanding economic trends and making decisions in real estate markets. This is accomplished by identifying important influencers and utilizing prediction models. Predicting these prices is essential for understanding broader economic trends and guiding decisions in real estate markets. The model was trained and evaluated using economic and financial data from the CEInet Statistics Database, with key variables representing major financial, demographic, and economic characteristics included. To estimate housing prices, a combination method combining common factor analysis and multiple linear regression was used. The KMO test result of 0.845 and the scores of Bartlett's Test of Sphericity (2=4296.29, p=0.000) confirmed the data's suitability for factor analysis. Factor analysis reduced the dimension of the dataset, revealing two important components responsible for 80.01% of the volatility in home costs. The multiple linear regression model based on these characteristics was tuned to attain good prediction accuracy. The model predicted housing prices in Shanghai with an accuracy of 94.54%.",2.0,https://www.ewadirect.com/proceedings/aemps/article/view/18866/pdf1096d96aa42438b8af2ebaf3bad8ecef5e6c9e583984,10.1007/s11067-024-09655-x,Unraveling Incommensurate Spatial Partitions: A Bipartite Graph Approach to School-Neighborhood Interactions and Their Impacts,2024.0,Networks and Spatial Economics,Sergio J. Rey; E. Knaap; Zachary P. Neal; Clio Andris,"This paper investigates the challenges and opportunities arising from incommensurate spatial partitions (ISPs) in regional science and spatial econometrics, focusing on how processes with overlapping yet distinct boundaries, interact and influence each other. ISPs are prevalent in various domains, including housing markets, employment centers, voting districts, and educational institutions, often complicating spatial econometric modeling and analysis. Using the intersection of school catchment areas and neighborhoods as a primary case study, the paper introduces a novel methodological framework utilizing bipartite graphs. This approach reframes the relationship between different spatial units, allowing for the analysis of multi-process spatial contexts without needing harmonization of spatial supports. The paper also develops new spatial weights derived from the bipartite graph, facilitating both exploratory spatial data analysis and confirmatory spatial econometric modeling. These methods are illustrated through a case study of San Diego, California analyzing 198 neighborhoods and 370 public elementary school catchments.",2.0,https://www.researchsquare.com/article/rs-3894643/latest.pdf10975fe5b3178a564f8b5db8d1f2e025b5f03f08ef25,10.3390/realestate3010002,"Peri-Urban Real Estate, Land-Use Changes, and Sustainability Challenges in Bangalore: Lessons from the Global South",2026.0,Real Estate,A. Varkey; Eby Johny; Jayakumar Chinnasamy,"Peri-urbanization in rapidly growing cities of the Global South is increasingly driven not only by demographic growth but by escalating inner-city land and housing prices that push households and developers toward peripheral zones. Bangalore exemplifies this transition, where housing affordability pressures, speculative real estate investment, and weak land governance interact to transform agricultural landscapes into fragmented built-up clusters. Using satellite imagery (1991–2024), census data, and GIS-based land-use classification, this study quantifies peri-urban expansion across eight clusters in the Bangalore Metropolitan Region. The results show rapid built-up growth, agricultural land decline, and increasing spatial fragmentation, reflecting processes of extended urbanization beyond formal city boundaries. These transformations produce environmental stress, infrastructure deficits, and socio-spatial inequalities. The paper situates Bangalore within planetary urbanization debates and argues that peri-urban sustainability depends on land market regulation, spatial planning capacity, and data-driven governance.",2.0,https://www.mdpi.com/2813-8090/3/1/2/pdf1098d7fd59f99b06c5ad31d606a3860ecaacffeeda20,10.58567/jea03010003,Interactive influence of house prices and the repo rate on household debt in South Africa,2023.0,Journal of Economic Analysis,J. Marire,"The recent interest rate policy decisions of the South African Reserve Bank have been criticized significantly by left-leaning political parties and civic society organisations for being anti-poor, anti-labour, and pro-capital because of their implications for household debt. Existing literature has established that interest rates and house prices are insignificant determinants of household debt dynamics in South Africa. Taking advantage of additional data for the period 2013-2022, and contrary to the previous studies, the paper maintains that house prices and the central bank policy rate play a crucial role in household debt dynamics. Applying a Markov Switching regression to quarterly data for the period 1981Q1 to 2022Q1, the paper finds that house prices and the policy rate have a significant influence on household debt dynamics. It establishes the existence of a ‘house price boom, low policy rate burden’ regime and a ‘high policy rate burden, low house price’ regime. The coexistence of the ‘house price boom’ and the ‘low policy rate’ explains the debt euphoria characterised by significant household leveraging.",2.0,http://anser.press/index.php/JEA/article/download/450/3901099