1record_id,doi,title,year,venue,authors,abstract,citations,oa_pdf2c4ffdbb331463e1d17c21a3672b523d5373bb545,10.23889/ijpds.v11i4.3791,Developing a Geo-intelligent Housing Submarket Classification Framework for England and Wales,2026.0,International Journal of Population Data Science,Ayisha Dubi; Evgeniya Lukinova; Nick Clare; James Goulding,"Introduction & BackgroundAmid escalating housing affordability pressures, housing shortages, and widening spatial inequalities, with 1.33 million households on social housing waiting lists in England in 2024 (UK Government, 2025), there is growing demand for evidence-based housing policy (Rodríguez-Pose and Storper, 2020; Florida, Rodríguez-Pose and Storper, 2023). Yet housing markets are still commonly analysed using fixed administrative boundaries that often fail to reflect lived housing experiences. 3Advances in urban analytics highlight the need for data-driven approaches to capture dynamic socio-spatial change (Batty, 2021). Housing markets are known to be internally segmented by dwelling, neighbourhood, and accessibility characteristics (Maclennan and Tu, 1996; Leishman, 2009). 4Objectives & ApproachThis research develops a framework to explore housing submarkets across England and Wales using digital footprints, spatial analytics, and housing data. It integrates HM Land Registry Price Paid Data (2018–2025), Energy Performance Certificate records (2008–2025), Indices of Multiple Deprivation, ONS geography, Census indicators, and OpenStreetMap amenities. 5A pilot study in Donc",0.0,https://ijpds.org/index.php/ijpds/article/download/3791/79806cc1b21966412c5d3c3858a50f0216cd12654c31e,10.1080/10511482.2022.2037885,Editor’s Introduction,2022.0,Housing Policy Debate,Vincent J. Reina,"This issue of Housing Policy Debate brings together five articles related to questions of racial, ethnic, and income inequality in access to housing in the United States. It is not a special issue; these articles were not specially invited or guest edited. Yet they speak to each other well and show how mortgage lending and rental assistance practices, as well as metropolitan-level realities, often create and reinforce racial and spatial inequality. Three of the articles featured—Wenfei Xu’s “Legacies of Institutional Redlining,” Jaya Dey and Lariece M. Brown’s “The Role of Credit Attributes in Explaining the Homeownership Gap,” and Tyler Haupert’s “The Racial Landscape of Fintech Mortgage Lending”—explore different aspects of home mortgage lending and their effects on inequality. Xu offers historical context by measuring the detrimental effects of FHA and HOLC mortgage maps on homeownership, home values, and racial segregation in Chicago, Illinois, finding that these effects lasted decades beyond the maps’ creation in the 1930s and 1940s. Dey and Brown analyze the persistent White–minority gap in transition to homeownership, and especially the unequal recovery across Black and Hisp",0.0,https://www.tandfonline.com/doi/pdf/10.1080/10511482.2022.2037885?needAccess=true729195fe915e2991fc59708977a3f5ba92e3db88e,10.30933/kpllr.2022.99.299.,A Study on Property Tax Reform by Adjusting the Officially Announced Price of Representative Land,2022.0,Korean Public Land Law Association,Jun-Seo Lee,"Among the policies of ‘Real estate normalization’ of the ‘Yoon Suk-Yeol Government’s 120 national tasks’ announced on July 7, 2022, the adjustment of the announced real estate price has an important meaning. This is because this is a common factor related to the reconsideration of the reconstruction surplus profit recovery system and the real estate tax system, and is related to the downward revision of the reconstruction surplus profit recovery system and the reduction of the comprehensive real estate tax. The current government's policy is contradictory to the 'Plan for Realization of Official Real Estate Price' and 'Property Tax Relief Plan' announced by the Moon Jae-In government on November 11, 2020. The real estate price realization plan of the Moon Jae-In government reflects the 50-70% level to 90% of the market price by raising the announced real estate prices such as houses, buildings, and land by 3-4%p a year over 10 to 15 years. it will become a reality. In order to alleviate the burden of property tax while realizing the published price, the property tax rate of houses with a published price of 600 million won or less owned by a single homeowner should be reduced by 0.0",0.0,8a547082f94caf20169972825982bb64b818d7b58,10.1093/schbul/sbag003.147,148. Depressive emotions and psychological mechanisms triggered by falling housing prices in family financial management,2026.0,Schizophrenia bulletin,J. Qiu,"9 10 11 Housing property is a major part of family assets, and its value changes affect financial conditions and mental health. In recent years, falling urban housing prices have become common, leaving many families facing asset shrinkage and financial pressure. Studies show that major property loss greatly increases depression risk, yet most research focuses on acute crises like unemployment or bankruptcy. Little work examines depressive emotions triggered by falling housing prices as a chronic stressor. From a psychological perspective, property loss influences self-worth, security, and expectations. Cognitive appraisal theory holds that subjective evaluation of stressors shapes emotional reactions. This study examines how falling housing prices affect depressive emotions among family financial managers and explores the roles of cognitive appraisal, coping styles, and social support.12 13 14 15 The study uses a cross-sectional and longitudinal design. Participants are primary financial decision-makers from 800 families who purchased homes in a first-tier city from 2021 to 2023 and experienced at least a 15% price drop. Participants are 25–55 years old and are recruited through the hous",0.0,1641cb0f5b7f82ad632617acde306708968192cf67,10.26686/wgtn.17003323.v1,"Housing and Consumption in New Zealand: 17A Financial Accelerator DSGE Model 18and SVAR Analysis",2012.0,,Andrew D Fung,"This thesis examines the role of a financial accelerator mechanism for housing in the context of a small open economy. Following the seminal financial accelerator framework in a Dynamic Stochastic General Equilibrium (DSGE) model set out by Bernanke, Gertler and Gilchrist (1999) (BGG), Aoki, Proudman and Vlieghe (2002, 2002a, 2004) (APV) examine the role of the financial accelerator for the housing market. In my basic model (Chapter 2), I extend the analysis of APV from a closed economy to a small open economy in which imports are used as intermediate inputs into the production process and foreign demand for domestically produced goods is influenced by the real exchange rate. Unlike APV, I set the endowment of housing to be consistent with the nature of consumer behaviour, in that “rule of thumb” (ROT) consumers (who do not save) are renters, further differentiating them from “permanent income hypothesis” (PIH) consumers. I find that in contrast to APV, the financial accelerator effect does not increase the responsiveness of consumption and output to various shocks. This is due in part to the endowment of housing being restricted to PIH households. I find that the presence of a fin",0.0,https://openaccess.wgtn.ac.nz/articles/thesis/Housing_and_Consumption_in_New_Zealand_A_Financial_Accelerator_DSGE_Model_and_SVAR_Analysis/17003323/3/files/31454347.pdf1929e74b784ed235cd43f6373e8c71882c067a98b0,10.35850/kjtr.41.4.11,A Comprehensive Study on Real Estate Valuation for Tax Purposes under the Inheritance and Gift Tax Act: Focusing on Appraisal-Based Taxation by the National Tax Service,2024.0,THE KOREAN TAX ASSOCIATION,SuKyung Kim,"As of the present time, four years have elapsed since the National Tax Service (NTS) conducted appraisals of non－residential real estate. Despite ongoing controversies in academic and practical circles regarding whether the NTS may lawfully conduct appraisals or retroactive appraisals for taxation purposes, lower court rulings issued since 2022 have consistently upheld the legality of retroactive appraisals－where the price determination date aligns with the appraisal reference date, excluding initially conducted appraisals. These rulings rely on the Supreme Court’s holding in Decision 2000Du5098 (rendered on August 21, 2001), which interpreted Article 49 of the Enforcement Decree of the Inheritance and Gift Tax Act (hereinafter, the “Enforcement Decree”) as an illustrative provision. The NTS has further announced plans to expand the scope of retroactive appraisals to include residential real estate, which is anticipated to give rise to increased disputes. 20This study examines the legislative characteristics of Korea’s fair market value evaluation framework, the legislative history of recognizing appraised values as fair market value, judicial interpretations of retroactive appraisa",0.0,2143b1f8970ccc9c14887e1af43cd428cbce747f3e,10.29313/bcses.v6i2.25496,Faktor yang Menentukan Persepsi dan Preferensi Generasi Milenial terhadap Pembiayaan Griya SiMuda BSI di Kota Bandung,2026.0,Bandung Conference Series Economics Studies,V. Aulia; Ima Amaliah,"Abstract. Home ownership is an increasingly difficult primary need for the millennial generation to fulfill due to rising property prices and limited financial capacity. PT Bank Syariah Indonesia (BSI) introduced the Griya SiMuda financing program as a sharia-based housing solution for this segment. This study analyzes the factors that determine millennials’ perception and preference toward Griya SiMuda financing in Bandung City, the role of respondent characteristics in shaping them, and the most dominant factor. A descriptive quantitative method was used, with primary data collected from 100 millennial respondents in Bandung City who need housing financing, selected through purposive sampling. The variables measured were perceived benefit, perceived affordability, perceived value, internal preference, and external preference, using a four-point Likert scale. Data were analyzed descriptively (minimum, maximum, mean, standard deviation) and presented in frequency distributions, supported by validity and reliability tests. The results show that millennials’ perception and preference toward Griya SiMuda financing fall into the very good category. Perceived value is the dominant facto",0.0,https://doi.org/10.29313/bcses.v6i2.25496225c2615c6ffd113b04db9ecbf8743c5014da50c4c,10.26102/2310-6018/2018.22.3.007,TIME SERIES AND NEURAL NETWORK ALGORITHMS IN REAL ESTATE VALUATION,2018.0,"МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ",Ф. А. Сурков; Наталья Винедиктовна Петкова; Сергей Федорович Суховский,"В данной статье рассмотрена проблема прогнозирования цен на объекты недвижимости в долгосрочной и среднесрочной перспективе для принятия управленческих решений. Рынок недвижимости является одной из самых динамичных сфер российской экономики. По этой причине требуется проводить прогнозирование стоимости недвижимости, поскольку без планирования невозможно оценивать будущие расходы или строить экономические планы развития. Ценовая ситуация, описываемая средними ценами на жилом рынке недвижимости, является основополагающим объектом для оценки и прогнозирования в исследовании рынка жилой недвижимости. Стоимость на рынке недвижимости зависит как от средних цен, так и признаков объектов недвижимости. Данные показатели учитываются при прогнозировании рыночной цены недвижимости, которая важна в разработке субъектами рынка недвижимости вспомогательных техник выбора стратегических действий для развития и совершенствования жилищной сферы. В соответствии с вышесказанным, параметры объектов и динамика цен требуют пристального изучения новыми прогрессивными методами с использованием инновационных технологий. Массовая оценка недвижимости является сложной системой и требует не только определения па",0.0,239e3527d99811d3be32fdffc849c2ea9908e83e4e,10.3390/ijerph23040450,"Indoor Air Radon Testing Rate and Its Relationships with Various Socioeconomic and Public Health Factors in Georgia, USA",2026.0,International Journal of Environmental Research and Public Health,Uttam Saha; Kushajveer Singh; Derek Cooper; Pamela R. Turner; Rebecca Cantrell,"Highlights Public health relevance—How does this work relate to a public health issue? Radon is a radioactive gas that is the leading cause of lung cancer among non-smokers and the second leading cause of lung cancer overall, claiming more than 21,000 lives annually in the USA; yet, the radon testing rate is generally low despite its significant public health consequences. Evaluating the association of radon testing rates with a broad range of demographic, socioeconomic, housing, literacy, and health indicators addresses a critical knowledge gap at the intersection of indoor air quality, environmental health, and social determinants of radon-related health consequences. Public health significance—Why is this work of significance to public health? Identifies the important socioeconomic and public health factors that are primary determinants of indoor air radon-testing activities. Provides tools for identifying communities with systematically lower radon testing rates to support targeted, socio-culturally relevant public health outreach and indoor air quality interventions. Public health implications—What are the key implications or messages for practitioners, policy makers and/or re",0.0,https://www.mdpi.com/1660-4601/23/4/450/pdf?version=1775098539247067e7b44aaf5160e4b76a5bcd2206e158e529c7,10.1186/s12939-026-02772-8,Supporting minority youths’ mental health during COVID-19: the role of neighborhood composition,2026.0,International Journal for Equity in Health,S. Tan,"The COVID-19 pandemic has been shown to worsen mental health outcomes, particularly among minority youths. However, few studies have examined whether neighborhood composition—specifically greater co-ethnic density, which has been linked to better mental health in other contexts—might buffer against these negative effects. Furthermore, existing studies linking neighborhood ethnic composition to health tend to be based in the U.S., where racial/ethnic clustering is intrinsically intertwined with racial discrimination, making it hard to unpack the impact of neighborhood composition on mental health. This study examines how neighborhood composition, as estimated by a spatial measure of neighborhood-scale residential segregation, relates to mental health outcomes of youths of different ethnicities before and during the COVID19 pandemic, in Singapore, a multi-ethnic city-state with a large Chinese majority and long-standing policies to ensure sociospatial ethnic mix within neighborhoods. Four waves of survey responses of over 3000 youths were collected between 2019 and 2022, and joined to estimates of their neighborhood characteristics, include residential ethnic and socioeconomic segreg",0.0,https://doi.org/10.1186/s12939-026-02772-8253a9a770acc7e5e19d049619390908840317a409b,10.1158/1538-7755.disp23-a128,Abstract A128: A novel approach to understanding disparities in cancer clinical trial enrollment,2023.0,"Cancer Epidemiology, Biomarkers & Prevention",W. Dong; Jamie Shoag; Tamila Kindwall-Keller; Ali Kara; John Shanahan; Debora S. Bruno; J. Rose; S. Koroukian; Richard S. Hoehn,"26 Background: Cancer patients who participate in clinical trials have access to novel therapies, increased monitoring, and potentially improved survival. However, this access is not universal. Striking disparities in clinical trial enrollment have been linked to race, sex, and geography. However, studies on this topic are limited due to a lack of patient-specific socioeconomic data. Methods: This analysis used a novel linkage of administrative patient data from University Hospitals Seidman Cancer Center (Cleveland, Ohio) linked to the LexisNexis Socioeconomic Health Attributes, a robust dataset containing 442 patient-specific attributes gathered from public records regarding income, education, housing stability, property ownership and value, social support, and many others. We included cancer patients aged 18-75 years at the time of the latest cancer diagnosis (before trial enrollment if patient was enrolled) during 2007-2022. Because of the substantial differences in the demographic and clinical characteristics between the trial and non-trial patients, we applied the propensity score matching technique to one-to-one match all trial patients to non-trial patients by age, sex, race/",0.0,27d747b6191cf3416bea83d85e62acdb5d94570aa0,10.15396/eres2019_189,AVMs: An international comparison of the opportunities and challenges,2019.0,26th Annual European Real Estate Society Conference,Lynn Johnson,"The definition of AVM has been disputed across academia with a number of references excluding the inclusion of human interaction suggesting that models need to work independently from a professional. RICS describe an AVM (2008) ‘as one or more mathematical techniques to provide an estimate of value of a specified property at a specified date, accompanied by a measure of confidence in the accuracy of the result, without human intervention post initiation.’ An AVM is a mathematical model operated through computer software to determine Market Value or Market Rent of a property. There are several types of AVM including Artificial Neural Networks and Multiple Regression Analysis. According to Susskind and Susskind (2015) the professions are becoming antiquated, opaque, no longer affordable and unsustainable in an era of increasingly capable expert systems. This sentiment would appear to be spreading within the real estate world Property Technology or Proptech is now a term which features heavily in real estate press, professional bodies and to some extent academia. RICS, in their 2017 publication on the impact of emerging technologies on the surveying profession and subsequent (2017) pa",0.0,28b1703a1fbe6e9b59a3c163e75b72baf482942d98,10.24912/stupa.v6i1.27509,FAKTOR – FAKTOR LOKASI YANG MEMPENGARUHI HARGA JUAL RUMAH DI KECAMATAN SAWANGAN KOTA DEPOK,2024.0,"Jurnal Sains, Teknologi, Urban, Perancangan, Arsitektur (Stupa)",Nadia Vinieta Setia; Priyendiswara Agustina Bella; Liong Ju Tjung,"Sawangan is one of the districts with the lowest population density in the city of Depok. The rapid development in Sawangan is attributed to numerous residential areas constructed by renowned developers such as Sinarmas Land and Ciputra Group. Residential property prices in this location also experience annual increases. Despite being quite distant from the city center, reaching the nearest MRT station from Sawangan takes approximately 45 minutes by motorcycle. This research aims to determine how location factors influence the sale prices of houses in the Sawangan district based on these issues. The study involves multiple regression analysis and quantitative analysis. Using SPSS test results, it is indicated that location significantly affects the selling prices of residences in Sawangan. This is evident from the positive correlation observed in the sale prices of properties in Sawangan, Depok, where the positive constant value (a) of 52498.678 indicates a positive correlation between independent variables and property sale prices. Furthermore, the dominant location factor affecting house prices in Sawangan is the accessibility to public transportation, with a regression coefficie",0.0,https://journal.untar.ac.id/index.php/jstupa/article/download/27509/179772975acd9f9bd05765f914d15ddf6bd021b6e13ff35,10.35850/kjta.24.4.05,The Changes in Tax Rates and Housing Gifts,2023.0,THE KOREAN TAX ASSOCIATION,S. H. Kim; Sung Ook Park; Yong Won Choi,"As the real estate market has overheated since 2018, the government has significantly increased the comprehensive real estate holding tax rate, capital gains tax rate and the acquisition tax rate for housing gifts in consideration of tax policy aspects to block speculative demand. As the burden of comprehensive real estate holding tax, capital gains tax, and acquisition tax increased rapidly, some real estate owners chose gifts as a tax-saving strategy. Therefore, this study analyzes whether the number of housing gifts actually increased just before the tax rate of comprehensive real estate holding tax and capital gains tax centered on multiple homeowners and the acquisition tax rate increase for housing gift acquisition. 30According to the empirical analysis results of Hypothesis 1 of this study, in the negative binomial regression, all gifts increase significantly due to an increase in the comprehensive real estate holding tax rate for multiple homeowners, but in the results of the ordinary least squares, gifts do not increase significantly. On the other hand, due to a significant increase in the comprehensive real estate holding tax rate for multiple homeowners and the increase i",0.0,31f6d738d1b12cf35952e5fdc812496f37c4bb9c96,10.1158/1538-7755.disp24-b138,Abstract B138: The association of neighborhood-level socioeconomic status and treatment outcomes in dermatofibrosarcoma protuberans: A United States cohort analysis,2024.0,"Cancer Epidemiology, Biomarkers & Prevention",F. N. Mirza; M. Hoang; H. Goldbach; O. Wisco; T. Libby; M. Weinstock; Abrar A. Qureshi; E. Cho,"32 Dermatofibrosarcoma protuberans (DFSP) is a rare, locally aggressive soft tissue sarcoma predominantly affecting the dermis and subcutaneous fat. There has been a lack of research on the intersection of socioeconomic status (SES) and DFSP. Therefore, this study aims to investigate the impact of SES on DFSP outcomes, specifically with differing treatments, an area previously underexplored due to the disease’s rarity. Cases of malignant DFSP from 2006-2018 in the Surveillance, Epidemiology, and End Results Census Tract-level SES and Rurality Database were identified to conduct a retrospective cohort analysis. SES was categorized into 5 groups using the Yost Index, a composite census-tract level measure of SES which incorporates factors like household income, educational level, and home value. Multivariable Cox-proportional hazards regression was conducted to assess relationships of reported characteristics with overall survival. Multivariable logistic regression was used to assess for independent predictors of receipt of Mohs surgery. The associations were also stratified by race/ethnicity. Among the 4995 DFSP cases identified, patients were mostly female (53%), white (52%), and of",0.0,33ab98f8c0628bc3dbd16883c3f29cde05ef98b42e,10.21686/1818-4243-2023-3-17-26,Big Data Intelligent Analysis Technology for the Study of Spatial and Time Trends in the Development of Large Cities,2023.0,Open Education,K. Mulyukova; I. V. Mulyukov; V. Kureichik,"The purpose of this research is to study modern problems and prospects for solving the processing of big data containing information about real estate, as well as the possibility of practical implementation of the methodology for processing such data arrays by designing and filling a special graphic abstraction «metahouse» on a practical example.Materials and methods. The study includes a review of bibliographic sources on the problems of big data analysis and their application in the modern field of construction of large cities. During the study, a technique for presenting data in a graphical form – abstraction was used. The mathematical basis of the technique is the use of multidimensional spaces, where measurements are the characteristics of individual objects. Computer simulation of a practical problem was applied using the C# programming language. Big data storage is based on the MongoDB server. To visualize data, a Web interface based on HTML and CSS is used.Results. In the course of the work, the main characteristics of big data were identified, and the specifics of data arrays consisting of information about real estate objects in a large city were described. When processin",0.0,https://openedu.rea.ru/jour/article/download/955/595348d7a4fb8ea8b050402b49e0e1161aa7ab0c3767d,10.26686/wgtn.16552782.v1,Risk from extreme precipitation and climate change on New Zealand’s residential property,2021.0,,Jacob Pastor Paz,"Three manuscripts form the basis of this dissertation exploring the effect of extreme precipitation and climate change on residential property in New Zealand. The first manuscript investigates the public insurer’s expected future liabilities, given future climate projections. Specifically, it examines the effect of extreme precipitation on direct property damage associated with rainfall-induced landslides, storms and floods. This study applies a fixed-effects panel regression model using claim data linked to extreme precipitation data over 2000-2017 and future climate change scenarios until 2100. The results show that liabilities will increase more if future greenhouse gasses emissions are higher. At the aggregate level, the percent change between past and future liabilities ranges between an increase of 7 to 8% higher in the next 20 years, and an increase between 9 to 25% increase by the end of the century, depending on the greenhouse gases emissions scenario.35The second manuscript examines the risk of property damage from landslides associated with extreme precipitation. The focus is on the Nelson region as it displays the highest number of claims and pay-outs relative to its pop",0.0,https://openaccess.wgtn.ac.nz/articles/thesis/Risk_from_extreme_precipitation_and_climate_change_on_New_Zealand_s_residential_property/16552782/1/files/30607218.pdf36a32da664bdfbb4e948cdcebe3a38b512ae4db40a,10.7251/emc2402408s,APPLICATION OF THE CAMA ALGORITHM IN MASS PROPERTY VALUATION IN THE REPUBLIC OF SRPSKA,2024.0,APEIRON: a journal for ancient philosophy and science,Dragan Stanković,"The real estate market is an integral part of the economic development of every country and contributes to promoting investments, production, increasing income, as well as socio-economic stability. Every country strives to achieve stability in the real estate market. The most important factor in the stability of the real estate market is real estate prices. Real estate prices determine the general well-being of citizens. They also affect the operations of banks (through mortgages, sales, etc.). It is extremely important to stabilize real estate prices. Only in this way is it possible to implement policies and macroeconomic measures that will make the real estate market an efficient segment of the overall socio-economic system. For all these reasons, it is very important to accurately assess real estate prices. Mass appraisal or Computer-Assisted Mass Appraisal (CAMA) are terms usually applied for the valuation of real estate for property taxation purposes. The development of computational technologies enables computer-supported methods for valuing all properties. CAMA represents a computer algorithm for the mass and individual calculation of real estate values. The use of the term ",0.0,https://doisrpska.nub.rs/index.php/economyandmarket/article/download/11412/109983765e29a878118f88d4608defe0403776aa57bc7d0,10.15396/eres2019_39,Why do Natives and non-Natives in the United Kingdom have different Spatial Patterns?,2019.0,26th Annual European Real Estate Society Conference,Anupam Nanda; Olayiwola Oladiran,"The United Kingdom, like other OECD countries, has attracted a high number of immigrants from around the world in the last half-century. The liberalisation of immigration for commonwealth citizens between 1948 and 1972 and subsequent accession to the European Economic Community afterward set the stage for the heterogeneity of the UK population. Housing is an intrinsic element of an immigrant’s voyage and is typically linked with local and regional spatial patterns. Thus, the UK regional and local spatial dynamics may also be linked to the dynamic forces of immigration waves to the UK in the last half-century. Anecdotal evidence suggests that immigrants in the UK prefer to settle in the South-east of England compared to other regions of the UK. London particularly houses approximately 2.8 million immigrants, which is over 40% of the total immigrant stock, thus, immigrant and ethnic clusters are entrenched and expanding. Literature reveals that factors such as regional economics, regional housing markets, regional labour markets, and urban dynamics are key determinants of regional and metropolitan spatial patterns. Furthermore, individual taste and preferences, socio-economic factors",0.0,https://eres.architexturez.net/system/files/P_20190628015559_2884.pdf384ef773d0acf70d26a0e78eaeaa9eef98639298e9,10.1108/ijhma-07-2026-0232,"Accessibility and investment returns: evidence of yield compression in transit-oriented condominium markets in Bangkok, Thailand",2026.0,International Journal of Housing Markets and Analysis,Chunyarat Nititerapad; Terdpong Boonpan,"39 40 This study aims to examine the relationship between accessibility and investment returns in transit-oriented condominium markets, focusing on rental yield as a key indicator of housing market performance. Previous studies have emphasized property price premiums associated with transit accessibility. Their impact on rental yield remains insufficiently understood.41 42 43 44 A quantitative cross-sectional research design was adopted using a structured dataset of 300 condominium units located within an 800-meter radius of transit stations along three rail mass transit lines in the Bangkok Metropolitan Region, which was collected from five major real estate platforms in Thailand between June 2025 and December 2025. The analysis focuses on the mid-market segment, meaning properties with prices not exceeding THB 150,000 per square meter or USD 5,000 per square meter. Multiple regression analysis based on ordinary least squares with stepwise regression used for variable selection technique to identify significant locational and structural determinants of rental yield. It also serves as an explanatory tool to identify and interpret the relationships between rental yield and key housing mar",0.0,45a9b2be288b859e2239a8d1164204be3c81c7cfde,10.69913/udj.2026.08.8.2.116,The Urban Revitalization Effect of Local Creator Hubs in Idle Spaces : Focusing on the Cases of F1963 in Busan and the Incheon Art Platform,2026.0,PLUS URBAN DESIGN ASSOCIATION,Chae-Yeon Ryu; Seok-Hyun Lee,"(Background and Purpose) As urban idle spaces increase amid population decline and regional extinction, local creator hubs that regenerate such spaces have drawn attention as key agents of regional revitalization. However, prior studies have largely confined their analysis to the internal operation and governance of these hubs or to the specific district in which a hub is located, leaving the path and ripple effect through which a hub's meaning spreads to surrounding areas after its formation empirically underexplored. Accordingly, this study defines the degree to which a local creator hub functions as a social anchor that transforms the perception and discourse of surrounding areas as a measure of its urban revitalization effect, and aims to clarify how the meaning of a hub diffuses into its surroundings. 46(Method) F1963 in Busan and the Incheon Art Platform, both of which converted idle modern industrial facilities in port cities into complex cultural spaces, were selected as cases. News texts were collected through BIGKinds and blog texts through Textom, covering the periods before and after each hub's formation, and all texts were analyzed using the text mining tool Textom. The",0.0,47658d90ef0d3258314450bd96c51f0fddfe97d6c8,10.12794/metadc2615,The Home Mortgage Interest Deduction for Federal Income Tax: A Federalist Perspective,2000.0,,Dennis Ortiz,"The debate over federal income tax treatment of home mortgage interest (HMI) has largely overlooked an important, and possibly unintended political and economic consequence of our federal income tax system. The distribution of the for home mortgage interest deduction tax benefit across states is a possible missing consideration. Specifically, this study offers a federalist1 perspective on the federal income tax benefit from the deduction for HMI - one of the largest personal federal tax expenditures on the books. This dissertation analyzes current national political rhetoric from a federalist perspective. Discussion also includes background, current status, and proposed changes to the tax code for of the HMI deduction. First, a Tobit regression is used to analyze the distribution of the HMI tax benefit across states and to test for disproportionate distribution across states in benefit derived from the federal income tax deduction for home mortgage interest beyond that which is explained by income. This initial part of the study is also the precursor to a hierarchical analysis seeking to identify significant factors affecting the distribution of the benefit of the HMI deduction acr",0.0,https://digital.library.unt.edu/ark:/67531/metadc2615/m2/1/high_res_d/Dissertation.pdf48d77a044aa849e0c3b289ca46205d433e83a5364e,10.22452/ijps.vol2no1.4,Housing speculation and housing price bubbles in Korea,2009.0,,Hee-Soo Chung; Hyuk-Il Kwon,"One of the major social and economic problems in Korea in recent years has been and still is the sustained housing prices spiral, especially since 2000. In Seoul, the ratio of housing prices to household annual income is about 10 times. This is three or four times higher than the corresponding ratios in advanced countries. To make the matter worse, housing prices continue to rise rapidly in large cities including Seoul (up to twenty per cent per year lately). The excessively high housing prices results in worsening housing affordability, higher wage rates and worker's weakening desire to work. It is a common belief in Korea that speculation is the main reason for such high housing prices, thus the government has applied a series of anti-speculative measures including major increase in capital gains tax and property tax. However, these measures have not been able to eliminate speculative housing prices bubbles. This paper is designed on one hand to evaluate the contribution of speculative activities to housing prices hike and, on the other to verify the presence of bubble in housing prices. In order to quantify the impact of speculation on housing prices, a simple devise is applied;",0.0,https://ejournal.um.edu.my/index.php/IJPS/article/download/4608/239849a27f32521298ba5701c9d495b9104c484b36539c,10.31732/2663-2209-2026-83-101-109,SPATIAL MODELING OF RESIDENTIAL REAL ESTATE VALUE BASED ON COMPREHENSIVE CONSIDERATION OF SOCIO-ECONOMIC FACTORS,2026.0,"""Scientific notes of the University""KROK""",O. Pomortseva; Володимир Паньків; Віктор Козиренко,"The relevance of the study is driven by the need to improve approaches to modeling residential real estate value in wartime conditions, when, along with traditional socio-economic factors, spatial characteristics of the territory and the consequences of military actions become significantly important. The subject of the study is the spatial patterns of value formation for one-room apartments in the secondary market within the vernacular districts of the Saltivka residential area in Kharkiv. The purpose of the article is to substantiate a spatial approach to modeling residential real estate value, according to which pricing is considered a result of the combined impact of several socio-economic factors, rather than a single dominant factor. To achieve this goal, a list of factors characterizing the studied territory was determined, the exclusion of parameters whose impact within the residential area is practically homogeneous from quantitative analysis was justified, a correlation analysis of the impact of individual factors on housing value was performed, and a complex multiple regression model was built. The methodological basis of the study includes methods of spatial, correlatio",0.0,https://snku.krok.edu.ua/index.php/vcheni-zapiski-universitetu-krok/article/download/1308/1341503eb30c0bafc79005329b63308af2ca9de8e8422e,10.9790/5933-170301120195,"Infrastructure, Connectivity and Residential Price Dynamics in India’s Tier-II Cities: A Comparative Longitudinal Analysis",2026.0,IOSR Journal of Economics and Finance,Adarsh Raj,"India’s Tier-II cities are becoming increasingly important components of the country’s urban and residentialdevelopment landscape, yet their housing markets do not necessarily follow a common trajectory. This study51examines how infrastructure development and local economic conditions interact with residential housing-price52movements across five selected Tier-II Indian cities: Jaipur, Indore, Lucknow, Coimbatore and Nagpur. The53quantitative analysis uses 140 city-quarter observations from the National Housing Bank’s NHB RESIDEX54Housing Price Index at Assessment Prices, covering Q2 2018 to Q1 2025. The quantitative panel is complemented55by national monetary and inflation indicators, infrastructure-event chronologies, airport connectivity evidence,56employment and Global Capability Centre (GCC) developments, housing-market research, and a qualitative57comparative case study of Siliguri.58The results demonstrate substantial heterogeneity in residential-price performance. Coimbatore recorded the59highest cumulative HPI appreciation over the study period at 57.43% (CAGR 6.95%), followed by Lucknow at6049.50% (CAGR 6.14%), Indore at 43.69% (CAGR 5.52%), Jaipur at 42.00% (CAGR 5.33%) and Nagpur",0.0,https://doi.org/10.9790/5933-170301120195618e0cff70d0ba33e58f0b88d60d9d9dff44479caf,10.1080/09599916.2021.1917885,Introduction to special issue,2021.0,,Rainer Schulz; Martin Wersing,"In 2019, as guest editors of the Journal of Property Research, we called for contributions to the special issue Automated Valuation Services (AVSs). We were interested in particular in case studies that discuss the development, implementation, and operation of an AVS. We are very grateful to Bryan MacGregor, the editor of the journal, and to the many reviewers, who assessed the submissions and helped us with the selection of the four papers that have been included in the special issue. While there are already many papers that examine the performance of different statistical models for market value predictions of residential properties, only a few papers examine how to implement such models as a service for users on an ongoing basis. Users expect that such a service is easy to use, and they also expect that it is timely and robust. A service should provide a prediction of the market value, but should also indicate the uncertainty of this prediction in a manner that the user can understand. Methods from machine learning are increasingly used for these tasks and it can be difficult to explain these methods to non-experts. If it is important that details on the methods should be commun",0.0,https://doi.org/10.1080/09599916.2021.191788562f4cdeccc990457ccab2efca6e10e923720451a86,10.3390/realestate3020003,The Assessment of Property Value Under EU Regulation 575/2013: An Operational Model for Italian Residential Market,2026.0,Real Estate,Paolo Rosato; Giovanni Florian; Matteo Galante,"The correct valuation of collateral supporting real estate loans has always been a key issue for the stability of the credit system. Substandard lending practices and the absence of uniform valuation approaches have historically contributed to the accumulation of non-performing loans. In recent years, several regulatory measures operating at both the European and national level have introduced principles, rules and procedures aimed at standardizing the valuation of properties pledged as collateral for credit exposures. These interventions seek to promote greater transparency, consistency, and prudence in property appraisals, thereby enhancing the soundness and resilience of the financial system. In January 2025, the updated Regulation (EU) 575/2013 came into force, incorporating the Basel III reform (also referred to as Basel 3+ or Basel IV). Among the innovations introduced, the concept of property value (PV) is particularly relevant, a prudential value that excludes expectations of price growth and considers the sustainability of the value over time in relation to the duration of the loan. PV is defined as a derived value with respect to market value (MV), determined by consideri",0.0,https://doi.org/10.3390/realestate302000363e2415ed3f5cc748e15f44eef43b10642a8d35ec3,10.5821/siiu.12165,Segregación Urbana Contemporánea: Análisis de enclaves fortificados en dos distritos de Londrina-PR,2022.0,Seminario Internacional de Investigación en Urbanismo,Fernando Zuin; L. Lino,"Studies on contemporary urban segregation are related to condominiums, linked to security, violence, and real estate interests. Social inequality and status distinction is fertile ground when it comes to the fear industry, as it generates profit for different industries. The objective of the study was to develop the theme ""Fortified enclaves: the new urban segregation, by Tereza Pires do Rio Caldeira (1997)"", transposing her concepts about fortified enclaves in two neighborhoods in the city of Londrina - PR. The selected neighborhoods were Gleba Palhano and Terra Bonita, as they have different characteristics in their condominiums, with the aim of translating the phenomena of enclaves into building elements on a meso and micro-scale. The analysis consists of simplified axial maps, interpreting its layout and morphology that translate the identity of the area, as well as the study of images of the condominiums for systematic reading. The article points out that the typologies of gated communities show the distinction on the meso scale, however, they indicate several similarities between the concepts of fortified enclaves on the micro-scale. And vertical condominiums tend to mask hos",0.0,https://upcommons.upc.edu/bitstream/2117/395659/2/ANAIS_XIV_SIIU_SC1435.pdf64a133c2b8b2eb382b25c0d9fe310b7bafe20a31dd,10.47941/ijf.136,FACTORS AFFECTING THE MARKET PRICE OF COMMERCIAL REAL ESTATES IN NAIROBI. A CASE STUDY OF HURUMA ESTATE,2017.0,,Dan Nduti; Wambugu Mr.Wambugu,"Purpose: The study sought to determine the factors affecting the market price of commercial real estates in Nairobi. A case study of Huruma estate Methodology: This study employed descriptive survey design. The target population comprised of both managerial rand non-managerial employees in the real estate agencies. The real estate agencies registered by Kenya Property Developers Association (KPDA) and operating in Huruma area are 69 in number. The study sample comprised of 30% of all real estate firms registered by Kenya Property Developers Association (KPDA) in the region. Therefore, the sample size is 21 firms. The use of census survey was employed for statistical research, population count or business marketing purposes. Primary information was gathered by use of a Likert scale questionnaire Results: The result displays results of responses regarding the first variable which was rent as determinant of market price of commercial real estate. This finding was supported by an overall responses mean of 3.94 which means that more respondents were agreeing with the statements on this objective in the questionnaire. The responses were also spread from the mean at a 0.779 standard devi",0.0,https://doi.org/10.47941/ijf.13665888b54865cf336a2d995da1434ddc17752f08b25,10.1177/00943061221142073,Buyers “Always Spend More Than They Think They Will; They Just Don’t Know It Yet”,2022.0,Contemporary Sociology,G. Squires,"Consumer preferences are made, not chosen. At least in housing markets when buyers work with real estate agents, as most do, those agents often shape or at least significantly change the units their clients ultimately buy and the neighborhoods they land in. One prominent effect, as Max Besbris shows in Upsold: Real Estate Agents, Prices, and Neighborhood Inequality, is that buyers routinely pay more, often considerably more, than their initial price ceiling as a direct result of interactions with their agents. A more important outcome is that neighborhood inequalities are heightened, as are racial wealth disparities. This provides further evidence that market transactions are not the result of rational, utility-maximizing decision-making proffered by the neoclassical model expounded by most economists. Particularly where informational asymmetries exist—as is the case in housing, where most households may purchase two or three homes in a lifetime, but agents deal with such transactions daily—buyers are dependent on the ‘‘experts’’ they are hiring to help them make the best decision. Buyers depend on agents primarily because they believe their agent knows details of properties they v",0.0,6691f9f13144d287c244d378ca7e9c888de7285903,10.1177/00943061221129660e,The Fight against Supergentrification in Aspen,2022.0,Contemporary Sociology,Laura Backstrom,"Western U.S. states are now popular destinations for people seeking to live in smaller communities near ski slopes, lakes, or national parks. During the COVID-19 pandemic, a rise in demand caused housing prices to soar in Montana, Wyoming, Idaho, and Colorado where affordable housing was already limited, and long-time residents reported feeling physically and culturally displaced by wealthier newcomers. To make sense of the changes occurring in fast-growing western states and how small communities can respond, Jenny Stuber’s book Aspen and the American Dream: How One Town Manages Inequality in the Era of Supergentrification is a timely and valuable contribution for sociologists, urban planners, and community leaders. Locals commonly describe Aspen as a place where the people who visit have three houses and the people who live there have three jobs. In her perceptive analysis, Stuber interrogates the ‘‘impossible math’’ of Aspen where the median income is $73,000 per year but the median home price is $4 million. Drawing on ethnographic fieldwork, over 75 interviews, and document analysis, Stuber describes the community’s fight to stem supergentrification in Aspen. Supergentrificatio",0.0,67cb581c1c03d8ad1cd9bd84f4a48673977c11e0a0,10.3390/land15071186,"Exploring the Relationships Between Residential Green Spaces and Childhood Allergic Diseases in Chengdu, China",2026.0,Land,Shuyuan Li; Jintian Hui; Sangdi Yang; Ling-Lan Bi; Mengmeng Li,"Studies on the association between green spaces and childhood allergic diseases are limited and have yielded inconsistent results across different regions. Furthermore, this relationship remains underexplored in Chengdu, China. In this study, we aimed to investigate the association between the residential greening rate, Normalized Difference Vegetation Index (NDVI), proportion of allergenic plants (proportion of allergenic woody plants, proportion of allergenic herbaceous plants), the closeness-to-nature characteristics of children’s playgrounds, and childhood allergic diseases in Chengdu. Seven representative neighborhoods were selected based on a 2018 database of pediatric patients from West China Hospital of Sichuan University. Through questionnaires in 2025, data on allergic diseases (e.g., atopic dermatitis and allergic rhinitis) were collected for 210 children aged 0–6 years. Logistic regression models were employed to analyze the data. The results indicate that in the overall sample, residential greening rate, NDVI, proportion of allergenic plants and closeness-to-nature characteristics of children’s playgrounds showed no significant association with allergic diseases. Howev",0.0,https://doi.org/10.3390/land1507118668708cb5206c99bee3bd1a751edfcbb06262935043,10.24912/ijaeb.v3i4.1902-1913,"THE IMPACT OF TSMC’S ESTABLISHMENT IN TAIBAO, CHIAYI ON LOCAL SMALL-SCALE DEVELOPERS: CHALLENGES AND STRAGEGIC RESPONSES",2025.0,International Journal of Application on Economics and Business,Kan-Xuan Huang; Yu-Ting Hung; Yao-Lin Chang; Hsing-Chiao Chiu; Chien-Chung Yang,"This study focuses on Chiayi County and explores the effects of TSMC’s factory establishment on the housing market in Chiayi’s core area and examines the land acquisition and marketing strategies adopted by local small-scale developers in response. Following TSMC's announcement to set up a plant, the local government actively promoted public construction and infrastructure upgrades— enhancing roads, school districts, and overall living amenities—which gradually transformed Chiayi County into a region with growing development potential, stimulating both housing demand and supply momentum. First, this study reviews the literature to explore how TSMC's presence influences regional housing prices, land use, and residential supply. Then, it analyzes the characteristics of new housing projects in recent years, including the number of projects, floor area planning, building types, and developers’ marketing focuses. To gain deeper insight into the current market and developer strategies, the study builds on the above findings and adopts a qualitative approach, interviewing six professionals—including architects, appraisers, developers, and scholars—to analyze the current state and future t",0.0,https://journal.untar.ac.id/index.php/ijaeb/article/download/36523/22099698eab2baa96bf028ef5075303dd265654b52cafbd,10.3897/jucs.125268,Editorial,2024.0,Journal of universal computer science (Online),Christian Gütl,"Dear Readers, 70 It gives me great pleasure to announce the fourth regular issue of 2024. I would like to thank all the authors for their sound research papers and the editorial board and our guest reviewers for their extremely valuable reviews and suggestions for improvement. These contributions and the generous support of the consortium members enable us to run our journal and maintain its quality. I would also like to thank our broader community for reading and incorporating sound J.UCS papers into their research. I am also very proud to announce that our journal has been included in the Free Journal Network as one of the listed high-quality journals in this field.71 Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends.72 In this regular issue, I am very pleased to introduce six papers by 22 authors from nine countries: Brazil, Czech Republic, Germany, Iran, Jordan, Portugal, Thailand, Turkey, and the USA.73 In a collaboration between researchers from t",0.0,http://dx.doi.org/10.3897/jucs.12526874e451a0191069d1141126c4315fe945aa4b0baff2,10.2196/74195,"Changes in the Neighborhood Built Environment and Chronic Health Conditions in Washington, DC, in 2014-2019: Longitudinal Analysis",2025.0,JMIR Formative Research,Q. C. Nguyen; Riki Doumbia; Thu T. Nguyen; Xiaohe Yue; H. Mane; Junaid S. Merchant; T. Tasdizen; Mitra Alirezaei; Pankaj Dipankar; Dapeng Li; Penchala Sai Priya Mullaputi; Amrutha S Alibilli; Y. Hswen; Xin He,"Background Google Street View (GSV) images offer a unique and scalable alternative to in-person audits for examining neighborhood built environment characteristics. Additionally, most prior neighborhood studies have relied on cross-sectional designs. Objective This study aimed to use GSV images and computer vision to examine longitudinal changes in the built environment, demographic shifts, and health outcomes in Washington, DC, from 2014 to 2019. Methods In total, 434,115 GSV images were systematically sampled at 100 m intervals along primary and secondary road segments. Convolutional neural networks, a type of deep learning algorithm, were used to extract built environment features from images. Census tract summaries of the neighborhood built environment were created. Multilevel mixed-effects linear models with random intercepts for years and census tracts were used to assess associations between built environment changes and health outcomes, adjusting for covariates, including median age, percentage male, percentage Hispanic, percentage African American, percentage college educated, percentage owner-occupied housing, and median household income. Results Washington, DC, experienc",0.0,https://formative.jmir.org/2025/1/e74195/PDF75e2e8ce177e62fd834cbefc0f30a5b8ee655ad5e9,10.3390/futuretransp6040139,Retailer-Managed Home Delivery and Active Travel for Grocery Shopping: Evidence from Urban Italy,2026.0,Future Transportation,John Omwamba; Chiara Ricchetti; Lucia Rotaris; G. Longo,"Grocery shopping remains a heavily car-dependent activity in urban areas, even for short-distance trips within residential neighbourhoods. A primary barrier to shifting toward active travel (walking or cycling) is the physical burden of carrying heavy or bulky goods. This study investigates whether a retailer-managed home delivery service could encourage consumers who currently rely on motorised modes for grocery shopping to shift towards active travel while preserving the in-store shopping experience. The analysis focuses on urban Italian consumers who currently use motorised modes for grocery shopping. Using a Stated Preference (SP) experiment and a Mixed Logit (MMNL) model (n = 88), we analyse the conditions under which such a service may encourage the adoption of active travel modes and support proximity-based shopping patterns. Given the exploratory nature of the study and the small, non-representative sample, the findings should be interpreted as preliminary evidence for urban motorised grocery shoppers rather than as representative of the Italian population. The results indicate a substantial willingness among respondents to adopt the proposed service configuration. Delivery",0.0,https://doi.org/10.3390/futuretransp60401397671a26ac4937e405ad5fd57ff4b175a7bd3db082f,10.46914/1562-2959-2026-1-2-442-458,Mortgage lending and the impact of financial volatility on the economy of Kazakhstan,2026.0,"Bulletin of ""Turan"" University",M. B. Karabayev; L. Baitenova; F. Gasimov; M. S. Orazalinova,"The article examines the development of the mortgage lending market in Kazakhstan and the impact of financial volatility factors on mortgage loans. The main objective of the study is to assess the influence of inflation, the policy interest rate, and the exchange rate on the volume of mortgage lending, as well as to identify directions for ensuring housing market stability. Official data from the National Bank of the Republic of Kazakhstan, the Bureau of National Statistics, and second-tier banks were used in the study. The study employed statistical, correlation, regression, vector autoregression (VAR), and cointegration methods of analysis. The econometric modeling results showed that increases in inflation and interest rates have a negative impact on mortgage lending volumes. It was found that a 1% increase in inflation reduces mortgage loan volumes by an average of 0.7–0.9%. In addition, exchange rate instability was proven to increase construction costs and contribute to rising housing prices. The study found that the Otbasy Bank housing savings system is a relatively stable model under conditions of financial volatility. Based on a deposit-based savings mechanism, this system",0.0,https://vestnik.turan-edu.kz/jour/article/download/5603/19887799d862e1e682753af72303ea1e8da7c9c933129d,10.1007/978-981-15-8848-8_8,Introduction: Real Estate Tax System and Real Estate Market in Japan,2021.0,New Frontiers in Regional Science: Asian Perspectives,Y. Higano,"This introduction summarizes chapters of Part II. In Chap. 10.1007/978-981-15-8848-8_9, Yamamoto (Jpn J Real Estate Sci 31:88–96, 2018) has compared between the street method, the asset valuation adopted for the imposition of property tax in Japan, and the computer-assisted mass appraisal (CAMA) method generally adopted in North America focusing on education and training of valuators. In Chap. 10.1007/978-981-15-8848-8_10, Yamazaki (Jpn J Real Estate Sci 31:97–101, 2018) argues that one of the major causes for relatively low density use of land in the city in Japan is the land tax system. He focuses on property tax and examines defects of the tax from view of economist. In Chap. 10.1007/978-981-15-8848-8_11, Kobayashi (Jpn J Real Estate Sci 31:129–138, 2018), taking an actual example, has examined difference between precise legal interpretation of ‘exemption from real estate acquisition tax due to purpose of use’ and taxation practices conducted by local administrative bodies. In Chap. 10.1007/978-981-15-8848-8_12, Shirakawa and Okoshi (Jpn J Real Estate Sci 31:88–96, 2017) have shown that the real estate companies were committed to transactions as dual agencies to whatsoever degre",0.0,https://link.springer.com/content/pdf/10.1007/978-981-15-8848-8_8.pdf789ebd36a42a24db7dcda1662acf0b0079e5efbeda,10.26686/wgtn.17011946,"The New Eastside: Re-populating East Christchurch Through Diverse, Contextualised, Medium Density Housing",2014.0,,Brett Wines,"At the conclusion of the 2010 and 2011 Canterbury earthquakes more than 5100 homes had been deemed unsafe for habitation. The land and buildings of these were labelled “red zoned” and are too badly damaged for remediation. These homes have been demolished or are destined for demolition. To assist the red zone population to relocate, central government have offered to ‘buy out’ home owners at the Governmental Value (GV) that was last reviewed in 2007. While generous in the economic context at the time, the area affected was the lowest value land and housing in Christchurch and so there is a capital shortfall between the 2007 property value and the cost of relocating to more expensive properties. This shortfall is made worse by increasing present day values since the earthquakes. Red zone residents have had to relocate to the far North and Western extremities of Christchurch, and some chose to move even further to neighbouring towns or cities. The eastern areas and commercial centres close to the red zone are affected as well. They have lost critical mass which has negatively impacted businesses in the catchments of the Red Zone. This thesis aims to repopulate the suburbs most affect",0.0,https://openaccess.wgtn.ac.nz/articles/thesis/The_New_Eastside_Re-populating_East_Christchurch_Through_Diverse_Contextualised_Medium_Density_Housing/17011946/1/files/31466663.pdf7954d7798e6d77093a904a5aceb753929b8f6700de,10.5194/egusphere-egu21-14861,"Integrated methodologies for seismic risk mitigation in Durrës (Albania) after the seismic event of November 26th, 2019",2021.0,,E. Shehu; K. Skrame,"<p>Albania, the small country in the western Balkan, is a disaster-prone country. It ranks as one of the countries in the world with the highest economic risk from natural hazards events. During the past several decades, in average, Albania has been hit by about one major geological event per year. The impact of disasters in Albania are significantly compounded by a relatively high degree of poverty, lack of infrastructure maintenance, unsafe building and land use practices, linked to rapid urbanization, exploitation of natural resources (overgrazing of pasture, overexploitation of forests and riverbeds, etc.) as well as some other consequences of the transition from a centralized to an open marked economy.</p><p>From a geological point of view, Albania is a young and very dynamic territory and is very vulnerable to the geological and hydro-geological hazards as: earthquakes, landslides, flooding, torrential rains, river erosion, coastal erosion and avalanches that cover almost the entire territory. Due to these conditions its average annual losses count for about 2.5% of its GDP.</p><p>The Durrës earthquakes of 2019 had a huge impact on the Albanian economy. The city of Durr&",0.0,https://doi.org/10.5194/egusphere-egu21-1486180b3d62045b0df9370163d4c3582805e4dbafa60b9,10.57017/jorit.v3.1(5).05,Predicting UK Housing Price using Machine Learning Algorithms,2024.0,"Journal of Research, Innovation and Technologies (JoRIT)",M. Ghafoor; Gbadebo A. Ogundeji; Dilkushi de Alwis Pitts,"The development of reliable predictive algorithm for house price as the housing market is a stand-out among the most involved regarding valuing the price and continues to fluctuate, is constantly a need for socio-economic advancement and welfare of citizen. In this paper, we develop machine learning algorithms for forecasting UK housing Price, and find an optimal algorithm that forecasts housing price accurately on the premises of the presence of many features or covariates. After applying correlation analysis to remove correlated variables in order to avoid multicollinearity, thereby increasing the statistical power, a novel method of using regression analysis to first of all understand and select statistically significant features for the various regions in England based on North South divide is adopted. These features are then used in the machine learning algorithm to further increase the statistical power of the algorithm, increase the level of accuracy for each of them and ultimately increase the predictive values for the algorithms.The model construction involves 3 stages: 1- correlation analysis to identify and remove correlated variables thereby avoiding multicollinearity a",0.0,https://library.acadlore.com/JORIT/2024/3/1/JORIT_03.01_05.pdf81e5af3858c1d341e8916c7d2c324262df6722dac1,10.36643/mjrl.28.2.bankruptcy,Bankruptcy in Black and White: The Effect of Race and Bankruptcy Code Exemptions on Wealth,2023.0,Michigan Journal of Race & Law,M. Bruckner; Raphaël Charron-Chénier; Jevay Grooms,"Bankruptcy law in the United States is race-neutral on its face but, in practice, race matters in bankruptcy outcomes. Our original research provides an empirical look at how the facially neutral laws that allow debtors to retain assets in bankruptcy cases result in disparate outcomes for Black and white debtors. Racial differences in asset retention in bankruptcy cases play a role in perpetuating wealth inequality between Black and white debtors.8283Existing bankruptcy data lacks individual-level characteristics such as race, which inhibits researchers’ ability to adequately assess biases or unintended consequences of laws and policies on subsets of the population. Thus, we construct a novel data set using bankruptcy data from Washington D.C. in 2011 and imputing race. The data demonstrates that facially race-neutral bankruptcy laws contribute to racially disparate outcomes by allowing white debtors to keep larger amounts of both personal and real property.8485First, exemption laws allow every bankruptcy filer to retain some personal property even if they do not repay their creditors in full. At the median, white filers in the District of Columbia claimed $10,150 in exemptions, relati",0.0,https://repository.law.umich.edu/cgi/viewcontent.cgi?article=1401&context=mjrl8635c42ebc1505d4785818d8638a58e4e0645e2136,10.56334/sei/9.9.5,An Analytical Study of the Informal Housing Problem in Al-Kut City (2010- 2025),2026.0,"Science, Education and Innovations in the Context of Modern Problems",Nada Jamil Shujair,"Studying informal settlements in the city of Al-Kut is of paramount importance, given the significant impact this issue has on the87lives, living standards, and social conditions of the residents. This is particularly relevant to the study period spanning 2010 to 2025,88during which cities across Iraq—including Al-Kut—witnessed a widespread proliferation of informal settlements. These settlements89were driven by low-income individuals unable to purchase real estate or residential land due to population growth and high market90demand, which caused property prices to soar, Given the disparity between the cost of real estate and residential land in the city of AlKut and the residents' meager incomes—which are insufficient to meet their housing needs—and in the absence of long-term urban91planning to accommodate population growth, informal settlements have emerged. These settlements have sprung up in vacant spaces92between city neighborhoods, along the banks of small streams situated amidst residential areas, and on empty lots owned by93government agencies. Consequently, as this phenomenon represents one of the most significant urban planning challenges facing AlKut, he studies addressed the ",0.0,https://doi.org/10.56334/sei/9.9.5944fd963990fdf0dcc148d1cb09d4672dfe7bbc269,10.31617/1.2023(151)08,Factor analysis of the housing market in Ukraine (2003–2023),2023.0,SCIENTIA FRUCTUOSA,Alona Shmygel; Natalia Shulga,"The residential real estate market plays a key role in the development of global and national economies, since it has acted as a powerful driver of GDP growth, increasing the level of employment, household incomes, and profitability of financial institutions, construction organizations and trading enterprises. The house price growth has two dimensions, threat to financial stability and source of economic development. The aim of this research is to formulate a list of criteria, based on which one can decide whether the house price at a given stage of housing market development needs to be constrained or not. The aim is achieved by a multi-perspective analysis of residential real estate market in Ukraine and its evolution. This research is based on general scientific and special methods of cognition, including abstract logical reasoning, grouping, and statistical analysis. The factors that contribute to the growth of house prices are formed using the method of principal component analysis, and the effect is estimated using least squares regression. Five stages of the evolution of the Ukrainian residential real estate market are identified according to two general indicators: housing",0.0,http://dx.doi.org/10.31617/1.2023(151)08952e3de6ec721f6bc58be2bb6adc284a2e964b3ff6,10.2478/joim-2026-0003,The Role of Economic Factors and Government Policies in the Development of the Polish Housing Market,2026.0,Journal of Intercultural Management,Ewa Werpachowska; Tetiana Paientko,"Abstract Objective The objective of this study is to empirically examine the combined effects of macroeconomic demand-side determinants and policy interventions within a unified analytical framework, focusing on the housing markets of Warsaw, Kraków and Wrocław. Methodology The analysis employs a quantitative research design based on a fixed-effects panel data regression model using quarterly data from 2014Q1 to 2024Q4. Housing prices on the secondary market serve as the dependent variable, while income, interest rates, unemployment and policy implementation are included as explanatory variables. Data are sourced from the National Bank of Poland (NBP) and the Central Statistical Office (GUS). Model diagnostics address multicollinearity, serial correlation and cross-sectional dependence, with Driscoll–Kraay standard errors applied to ensure robustness. Findings The results indicate that income is the primary driver of housing prices in major Polish cities, confirming the importance of demand-side economic fundamentals. Unemployment also exhibits a statistically significant positive relationship with housing prices, reflecting atypical market dynamics during periods of economic shock",0.0,https://reference-global.com/download/article/10.2478/joim-2026-0003.pdf964fade3b24272d539442fa7526c33528776eae584,10.32782/1814-1161/2025-4-2,FACTORS INFLUENCED ON THE REAL ESTATE MARKET OF UKRAINE DURING THE PERIOD OF RUSSIAN MILITARY AGGRESSION,2025.0,State and Regions. Series: Economics and Business,Yurii Gudz,"The article examines the main factors influencing the real estate market of Ukraine during the period of full-scale military aggression by Russia. The relevance of the study is due to the need for a scientific understanding of the transformation processes taking place in the housing market under martial law, and the need to form effective mechanisms for state regulation of the real estate market in extreme conditions. Based on the analysis of Ukrainian scientific sources, statistical data of the National Bank of Ukraine, the State Statistics Service of Ukraine and specialized market surveys, the key determinants of the transformation of the housing market have been identified. It has been established that the war forms fundamentally new conditions for the functioning of the real estate market through a complex of interrelated factors: security risks and threats to the life of the population, mass internal displacement of the population from occupied and front-line territories, significant damage and destruction of the housing stock as a result of hostilities, macroeconomic instability and inflationary processes, as well as cardinal changes in state housing policy and mechanisms for",0.0,97b6d7ebe69a7b26ea7465c0215b2e3a7970639e62,10.3390/land15081500,Siting Versus Capitalization of Disamenity and Amenity Facilities in Housing and Land Prices Using Integrated Multi-Source Spatial Data in South Korea,2026.0,Land,Solhee Kim; Yunhee Park,"Disamenity and amenity facilities pull property values in opposite directions, yet prior studies have treated disamenities as a homogeneous category and have been constrained by data scattered across agencies and by mismatched spatial scales. This study integrates multi-source spatial data, comprising 3426 sub-districts (eup-myeon-dong) nationwide, a 500 m population grid of 376,668 cells, and the actual point locations of individual facilities, into a single spatial framework and applies a multi-method design that combines a spatial Durbin model (SDM), explainable machine learning (XGBoost–SHAP), an environmental-justice analysis, and a micro-siting analysis. Four findings emerge. First, not all disamenities are associated with lower prices: only pollution-emitting facilities with a clear emission signature, namely, sewage treatment plants and incinerators, capitalize robustly as cross-sectional associations, into both housing prices and officially assessed land values, with the effect extending into neighboring sub-districts rather than staying purely local. The other disamenities show no robust total effect on either price; their estimates are small or unstable across specificat",0.0,https://doi.org/10.3390/land150815009869c0e6c1b5774c1329ed0379af416457f348bdf4,10.3390/sci8080200,Evaluating the Effects of Urban Regeneration Initiatives Through Market-Based Approaches: The Case Study of the Esquilino District in the City of Rome (Italy),2026.0,The Scientist,F. Tajani; P. Morano; F. Di Liddo; M. Locurcio,"The present research investigates the relationship between urban regeneration initiatives and residential real estate market dynamics by assessing market price appreciation associated with the factors most commonly considered in housing transactions. The study focuses on the Esquilino district in the city of Rome (Italy) with particular attention to the redevelopment of Piazza dei Cinquecento, the major public space located in front of Roma Termini railway station. The intervention aims to improve urban accessibility, reduce traffic congestion, and enhance public space quality through a new spatial configuration and the creation of a tree-lined area. The objective of the study is to verify whether, and to what extent, the ongoing regeneration project has influenced residential property values. To achieve this goal, an econometric analysis is implemented to quantify the contribution of different housing and locational attributes to residential asking prices and to identify the variables that significantly affect value formation within the local market. Given that the initiative is still in progress and approaching completion, the analysis adopts a diachronic perspective by comparing",0.0,https://doi.org/10.3390/sci808020099e9ee3d7fa5a8ac884e1b11dbc6c470bedbdf47f8,10.1161/circ.149.suppl_1.p104,Abstract P104: Geospatially Varying Relationships of Green and Blue Spaces With Biomarkers of Stress and Cardiometabolic Risk in Racially and Ethnically Diverse Children,2024.0,Circulation,Junia N. de Brito; Lan Mu; A. Tate; A. Fertig; Mark A. Pereira; J. Berge,"100 Introduction:101 Neighborhood green (e.g., parks) and blue (e.g., lakes) spaces have been associated with reduced stress and cardiometabolic risk (CMR), but research is limited in socially vulnerable urban communities affected by structural racism and redlining. This study explored the spatial correlation between green and blue spaces and cortisol levels, BMI percentile, waist circumference (WC), pulse, and systolic (SBP) and diastolic blood (DBP) pressure in a sample of racially/ethnically diverse children.102 103 104 Methods:105 We used cross-sectional data from the Family Matters cohort study, consisting of 1307 racially/ethnically diverse families with children (age 7±1.5 yrs). Participant addresses were matched to the US 2010 Census tract attributes. We used 246 sample locations across 149 census tracts in the 7-county Twin Cities, MN, metro area. Green and blue spaces were retrieved from the Twin Cities Metropolitan Council. Hair cortisol (chronic stress biomarker), BMI, WC, pulse, SBP and DBP were objectively measured in a subsample of 246 children. We used Decision Tree and Geographically Weighted Regression (GWR) to examine the spatially varying associations of CDC’s Social Vulner",0.0,10697a29415219c0839e3490f475d09ae9e22af5eaf,10.33841/1819-1339-1-49-242-255,The impact of war on the modern status of the real estate market in Lviv,2025.0,Modern achievements of geodesic science and industry,Y. Hubar; Y. Khavar,"Purpose. The purpose of the study is to analyse the land plot market, secondary and primary real estate markets in Lviv. The aim of the analysis is to identify the factors that influence the pricing policy of the real estate market, as well as to provide potential buyers and investors with an informative basis for making management decisions. Methodology. The analysis of the real estate market and information on transactions and objects in respect of which these transactions are concluded allows to calculate and analyse the typical price level prevailing in the market for a certain group of objects. To solve this problem, statistical indicators are used that characterise the entire set of observation units by measures of central tendency and variability (variation). The values of statistical indicators help to form a general idea of the market size, intensity of market processes and their change depending on specific conditions of place and time. These indicators provide the necessary basis for identifying the relationship between the price level and the characteristics of transactions and objects, respectively. Results. The analysis of the real estate market in Lviv has revealed a",0.0,107702eacc55336758fc2858adc3357595986ed1054,10.70382/hijedcm.v07i4.030,"EFFECT OF STREET TRADING ON COMMERCIAL PROPERTY VALUES: A CASE STUDY OF OSHODI, LAGOS STATE",2025.0,International Journal of Environmental Design and Construction Management,Latona Grace Eyewuoma; Adesanya Folasade Olubukola,"Street trading, often described as the “informal occupation of urban space” (Bromley, 2000, p. 1), is a defining feature of developing cities, particularly in Nigeria where rapid urbanization has outpaced formal planning structures. In Lagos State, Oshodi serves as one of the busiest transport and commercial hubs, attracting thousands of informal traders daily. While this activity provides livelihoods and affordable access to consumer goods, it also generates urban externalities such as congestion, environmental degradation, and insecurity (Adegun, 2013; Chen, 2016). These externalities are particularly relevant to commercial property markets, as property values are highly sensitive to locational and environmental attributes (Ogunba, 2015). This study investigates the effect of street trading on commercial property values in Oshodi, Lagos State. Using a mixed-methods approach that combined structured questionnaires administered to traders, property owners, and estate professionals, as well as field observations and interviews, the study assesses how patterns of trading activity affect rental and capital values of commercial properties. Regression analysis indicates that intense str",0.0,https://harvardpublications.com/hijedcm/article/download/441/450108bf5f82c642c0b6ec62378894155e0a1d728267b1,10.4038/bhumi.v5i2.31,A Study on Impact of Residential Property Value Based on Designated Heritage Property with Special Reference to the City of Kandy-Sri Lanka,2017.0,,Uditha Jayampath; P. Bandusena,"Real properties are fixed assets with a bundle of rights. Thus, the freehold property owners could possess, use, abuse and even destroy his real property. However, the rate of re-development and high demand has directly affected the increase of a clear site value of the property than the present capital value of the property. Further, the property owner always considers the highest and the best use of the property. In this situation, there is a less possibility for redevelopment of the property as there is reason for legal restraints of the heritage-designated property. Harvey & Jowsey (2004)) introduces methods of preserving a historic building, when a period of time the clear site value has been increasing than the present capital value of the property. In addition, after a period, ‘value as a historic house’ the heritage property value increases than the clear site value. However, heritage properties generally performs well and has a significant demand in the market. Few researchers had identified that there were less/no-observable impact on the property value of heritage-designated property. Therefore, this research focuses on the investigation of the impact of heritage designa",0.0,https://doi.org/10.4038/bhumi.v5i2.311090e2560e08d4be50aab661de176cad15b0cb5c316,10.1017/sus.2025.10034,Identifying the determinants of the energy transition in departments in Colombia: an analysis with spatial econometrics,2025.0,Global Sustainability,Clara Inés Pardo-Martínez; Alexander Cotte Poveda,"Abstract Non-technical summary This study aimed to analyse the advantages and challenges of the energy transition in an emerging economy such as Colombia via quantitative spatial panel data models using Colombian regions, which included departments from 2015 to 2023, to determine the main relationships between the energy transition and other variables, such as housing features, energy consumption and costs, fossil fuel use, mining, transportation activities, deforestation and livestock activity. Technical summary Energy transition is closely related to climate change and is helpful for achieving the main initiative in a broader strategy adopted by governments to contain global warming to 1.5°C above preindustrial levels by the middle of the century. This study uses different empirical methods as quantitative spatial panel data models to determine variables that impact energy transition considering that the limitations of this study are related to the availability of data in every region and information on specific actions to promote energy transition in the regions. The results revealed that regions with higher levels of households, electricity coverage, energy, gasoline and diesel",0.0,https://doi.org/10.1017/sus.2025.100341104515febc5d5fca65e3a36e6f6233d5458cb7fad9,10.3390/land15101834,"Inspecting Transport–Land Synergy in Transit-Oriented Station Areas from the Perspective of Jobs–Housing Relationship Typology: A Case Study of the Highest-Density Built-Up Zone of Shenzhen, China",2026.0,Land,Hao Geng; Zhi-Tao Zhong; Fang Liu; Yu-Song Zhu; Jing-Yi Zhang,"Jobs–housing balance is an important goal of urban sustainable development, and Transit-Oriented Development (TOD) is widely regarded as an effective pathway to optimize the jobs–housing relationship. However, station-area-scale jobs–housing studies remain limited, and existing jobs–housing typologies have not been linked to regulable TOD characteristics, leaving jobs–housing optimization without a basis for differentiated regulation. Using 72 built subway station areas in Shenzhen’s Density Zone 1 as samples, this study draws on three dimensions, namely transport supply (Node), land use (Place), and jobs–housing, and applies hierarchical clustering, multiple linear regression, and Lasso regression to examine jobs–housing typologies, land use and building distribution characteristics, and the effects of transport supply and land use on the jobs–housing relationship in TOD station areas. Regression analysis uses jobs–housing value, a weighted score derived from the employment–residential area ratio (JH1) and the employment–residential population ratio (JH2), as the dependent variable. The main findings are as follows. (1) TOD station areas can be classified into four types, which di",0.0,https://doi.org/10.3390/land1510183411186b8189c7d223987e69a64adcbd01d10c8a4a8d9,10.5194/isprs-archives-xlviii-4-w6-2022-87-2023,SPATIO-TEMPORAL ANALYSIS AND MODELLING OF DENGUE INCIDENCES IN QUEZON CITY USING ORDINARY LEAST SQUARES AND SPATIAL REGRESSION,2023.0,"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences",A. Blanco; B. J. Harder; I. A. R. Teh,"Abstract. This research examined monthly dengue incidences per barangay (village) in Quezon City, Philippines over a period of six years (2010–2015) to determine the relative significance of environmental variables on dengue prevalence. The data were subjected to correlation analysis, spatial autocorrelation assessment, multiple factor analysis, ordinary least squares (OLS) and spatial regression. Local Indicators of Spatial Autocorrelation (LISA) Moran’s I cluster maps indicate significant (p=0.05, 0.01) High-High clustering and Low-Low clustering in the northern and southern parts of the city, respectively. Monthly total cases indicated increasing trend staring from May/June, peaking at around August/September, and declining afterwards to lower levels in November/December. This corresponds to the typical temporal rainfall pattern. Dengue cases were found to be positively correlated (α=0.05) with Population (R=0.84), Informal_Settlements (IS) (R=0.716), Very_Low_Density_Residential (VLDR) (R=0.512), Open_Spaces (OS) (R=0.339), Mean_Rainfall (RFM) (R=0.637), and Mean Elevation (EM) (R=0.498). Dengue incidence (DI) was negatively correlated with Mean Air Temperature (ATM) (R=−0.3 to",0.0,https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W6-2022/87/2023/isprs-archives-XLVIII-4-W6-2022-87-2023.pdf1122eccc0161d4e54491f7121341f0a924554123dd5,10.48175/ijarsct-29516,House Price Prediction Using Machine Learning,2025.0,"International Journal of Advanced Research in Science, Communication and Technology",Rohan Bamane; Diganta Gavhane; Paras Igave; M. M. Zade,"Abstract: In today’s real estate market, predicting the accurate price of a house has become an important task, as property rates are continuously changing based on various factors. Every buyer or seller expects to know the correct market value before making any decision, but manual estimation often leads to confusion and uncertainty. To solve this problem, our project titled “House Price Prediction using Machine Learning” is designed to estimate the selling price of a house based on its features and specifications. With the growing demand for real estate data analysis, this project helps users make better financial decisions using Artificial Intelligence. The system takes various input features such as total area (in square feet), number of bedrooms, number of bathrooms, stories, parking spaces, availability of main road, presence of a guest room or basement, hot water heating, air conditioning, furnishing status, and preferred area. These features directly influence the house price, and our model learns their relationships from the dataset.113In this project, we used Python as the core programming language along with libraries like Pandas, NumPy, and scikit-learn. The data preproce",0.0,https://doi.org/10.48175/ijarsct-295161142ed07a1810630a3e0ed3df90720216a210570044,10.14738/abr.86.8399,Eddison Walters Modern Economic Analysis Theory: Building on the Eddison Walters Risk Expectation Theory of The Global Financial Crisis of 2007 and 2008,2020.0,,Eddison T. Walters,"The results of several recent studies called the analysis of economic data from the United States real estate industry preceding the Global Financial Crisis of 2007 and 2008 into question. Advancement of technology is a disruptive force throughout the economy today. The speed at which technology continues to evolve has resulted in significant changes throughout the entire economy around the world. As technology continue to evolve, there is no question technology disruption will continue to occur. Economist can no longer rely solely on backward looking historical data when conducting economic analysis. The evolution of the global economy require an understanding of the impact of advancement in technology occurring throughout the economy as researcher conduct analysis of data to draw conclusions. The result of recent studies led to the development of “Eddison Walters Modern Economic Analysis Theory”. Data analysis suggesting the false conclusion of the existence of a real estate bubble in the United States real estate market preceding the Global Financial Crisis of 2007 and 2008 was presented in Walters and Djokic (2019). Nonlinear regression analysis for correlation of variables in ",0.0,https://doi.org/10.14738/abr.86.8399115b34db013cb7a00c0cc4dd3791fd023ed0c885e45,10.3390/land15061099,Regional Institutional Capacity as a Potential Mediator of Infrastructure Capitalization: A Conceptual and Geospatial Framework,2026.0,Land,Eleni Kyriakidou; Nikolaos Karanikolas; Eleni Athanasouli; D. Kourkouridis; A. Xifilidou,"Major infrastructure investments alter accessibility and urban development patterns, yet their impact on housing prices varies significantly across regions. The prevailing interpretation attributes this heterogeneity to supply differences or regulatory constraints, treating land use regulations as exogenous variables. Nevertheless, even two regions with a nominally similar regulatory framework may produce substantially different outcomes in the housing market, depending on the effectiveness of rule implementation. This paper argues that this approach overlooks a critical variable: the ability of regional authorities to coordinate, regulate, permit, and implement spatial development in a predictable and timely manner. In line with this, a conceptual framework is developed, grounded in the literature on spatial and multi-level governance, in which regional institutional capacity is proposed as a potential mediator of capitalization around project milestones (announcement, funding, construction, operation), rather than as a backdrop. This capacity shapes outcomes through three interrelated dimensions: the responsiveness of supply, which depends on administrative capacity and regulator",0.0,https://doi.org/10.3390/land15061099116e24585433dd158c6d584ad0fc8c841a10b451bcf,10.3389/fpubh.2026.1784078,Heat and socioeconomic deprivation compound to drive coronary heart disease in Los Angeles,2026.0,Frontiers in Public Health,Shu-Tong Huo; Tessa R. Pulido; R. Archer; Joshua B. Fisher; Jason A Douglas,"Background Socioeconomic deprivation and environmental heat exposure each increase cardiovascular risk, yet evidence is limited on how these stressors co-occur and jointly shape disease burden within cities. Mapping their overlap can inform equity-oriented planning and needs-based allocation of health and social protection resources. Methods We conducted an ecological geospatial analysis of 2,513 census tracts in Los Angeles County. Adult coronary heart disease (CHD) prevalence was obtained from CDC Population Level Analysis and Community Estimates (2021). Socioeconomic deprivation was measured using the Social Deprivation Index (SDI), and heatwave surface heat hazard was measured using land surface temperature (LST) retrieved from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station. We identified hot spots and overlaps using Getis–Ord Gi* statistics. Associations between SDI, LST, and CHD were estimated using z-score–standardized OLS regression with tract-level sociodemographic controls; we assessed spatial dependence and applied geographically weighted regression (GWR) with adaptive bandwidths to characterize spatial heterogeneity. Results CHD hot spots overla",0.0,https://public-pages-files-2025.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1784078/pdf11782c1353f17273a5817493bea818c75ac96f8a626,10.35483/acsa.am.109.89,Medium Density Beyond the Missing Middle,2021.0,Expanding the View,Martin Haettasch,"The question of housing in America’s growing urban centers has gravitated towards extremes in recent years: efforts at densification have sparked massive developments of multi-story apartment blocks, on the other hand the free standing single family house remains to date the unchallenged ideal of many Americans. Austin, TX is no exception to this trend: Single-family homes continue to make up by far the largest share of housing while large multifamily structures have seen a steady increase by about 40% over the last decade1. This development has led to spatial and social disparities. While multi-unit structures have accelerated the urbanization of a few neighborhoods and corridors, and cater to a transient population of young professionals, rising property values have made the “house” an increasingly unattainable dream for many middle class families. This lack of a middle ground has been aptly identified within the discourse of New Urbanism as the “Missing Middle,”2 referring to the density range between the apartment block and the single family house as much as a vanishing “middle class”. The Missing Middle promotes walkable neighborhoods with housing densities able to sustain loc",0.0,https://www.acsa-arch.org/proceedings/Annual Meeting Proceedings/ACSA.AM.109/ACSA.AM.109.89.pdf118a19c363ce56860e0d288d70c17898559b8241012,10.1108/ijhma-08-2026-0318,Predicting metropolitan house-price downturns: a machine learning panel study across forecast horizons,2026.0,International Journal of Housing Markets and Analysis,Kala Nisha Gopinathan; Hari Hara Krishna Kumar Viswanathan,"119 120 This paper aims to evaluate whether metropolitan house-price downturns can be predicted out-of-sample, how predictability varies with the forecast horizon and whether machine learning classifiers improve on a naive persistence benchmark.121 122 123 124 Using a panel of 94 US metropolitan areas drawn from the Federal Housing Finance Agency (FHFA) All-Transactions House Price Index (1978–2026; 18,083 metro-quarters), a downturn is defined as a metropolitan index that is net lower h quarters ahead. Eight classifiers (four support-vector-machine kernels, random forest, gradient boosting, a feedforward neural network and logistic regression) and a persistence rule are compared across horizons of 1, 4 and 8 quarters. Models are evaluated with a leakage-safe expanding-window panel protocol, tuned on the area under the receiver operating characteristic (ROC) curve and assigned a decision threshold that maximizes the training geometric mean of sensitivity and specificity. Inference uses a quarter-blocked bootstrap.125 126 127 128 Metropolitan downturns are predictable out-of-sample, but a persistence benchmark tuned to the same operating criterion as the models captures most of that predictability. On ",0.0,12987691f8649483df9b96b24df76880b3bae3b1602,10.7256/2454-0714.2026.2.80191,A Method for Detecting Potentially Undervalued Real Estate Assets Using a CatBoost model,2026.0,Программные системы и вычислительные методы,Daniel Gennadjevich Gnitetskiy; M. L. Rysin,"The article proposes a new approach to residential real estate valuation based on intelligent analysis of listing data using gradient boosting. The method is aimed at identifying presumably undervalued offers based on a proxy-valuation model, that is, a model estimate obtained from the parameters of the object. Unlike traditional methods of real estate data analysis that use static rules or limited regression models, the approach proposed by the authors uses the capabilities of CatBoost gradient boosting to identify complex dependencies between the parameters of a residential real estate object. Using the CatBoost model, it is proposed to calculate an estimated valuation of an object relative to offers in the residential real estate market (proxy estimate) and use its deviation from the listing price as the main metric. The method is implemented in a software system that includes the following modules: a web application, a valuation module, a database, and a geoservice. The software system focuses on building a regression model that includes the calculation of the absolute and relative deviation of the listing price from the expected price of the object. These variables are key ind",0.0,https://doi.org/10.7256/2454-0714.2026.2.80191130849ffb5051e497d792b4d29d7be4cda84b4660d3,10.1108/ijhma-12-2023-0179,The effect of the COVID-19 pandemic on TOM in the housing market,2024.0,International Journal of Housing Markets and Analysis,Askar H. Choudhury,"131Purpose132The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.133134135Design/methodology/approach136Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opte",0.0,137f0dfd97e512aee1bfac9c0c89e88d3f3e332b5c5,10.9734/jerr/2026/v28i51905,Machine Learning Algorithms for House Price Prediction: A Comparative Study,2026.0,Journal of Engineering Research and Reports,Mirali Mammadzade,"Background: Accurate house price prediction is important for supporting real estate valuation, investment planning, banking decisions, and urban development through data-driven analysis of complex housing factors.138Aims: The aim of this study is to compare the predictive performance of selected machine learning algorithms for house price prediction using the Ames Housing dataset. The study also investigates whether target transformation and feature importance analysis can improve both model performance and interpretability.139Study Design: This study follows a quantitative experimental research design based on supervised machine learning regression methods.140Place and Duration of Study: The study was conducted using the Ames Housing dataset during the period from January to March 2026.141Methodology: The dataset contained 2930 residential property records and more than 80 explanatory variables. The target variable was SalePrice. Data preprocessing included removal of identifier columns, missing value imputation, categorical encoding, numerical scaling, and logarithmic transformation of the target variable. Four machine learning models were implemented using Python and scikit-learn: Linea",0.0,https://doi.org/10.9734/jerr/2026/v28i5190514244ae73e772a75ef14a7c49aedea3f6f0d71e063c,10.29015/cerem.540,The Effects of Monetary Policy on House Prices in Spain: the role of the economic and monetary union membership in the housing bubble prior to the great recession,2018.0,The Central European Review of Economics and Management,Tibor Pál,"Aim: This paper aims to discover the evolution of monetary transmission in Spain by focusing on the short-term interest rate, credit aggregates and house prices through different stages of economic development and European integration between 1975 and 2008. In addition, the analysis devotes special attention to the interval of the last housing boom, in order to reveal the importance of the interest rate policy of the ECB.Design / Research methods: The study applies a tri-variate autoregressive model assigned to three overlapping periods outlined by regime shifts in the Spanish economy. The estimation output determines the strength and persistency of the links between interest rates, credit aggregates and house prices. Consequently, the results of the econometric analysis provide proper base for comparison in order to identify the dominating channels of monetary transmissions through a prolonged period.Conclusions / findings: It is found that the transmission mechanism in Spain essentially altered over time since 1975. At the beginning of the full analysed interval the role of the credit channel was dominant, then its importance gradually diminished. After the EMU accession the trad",0.0,https://ojs.wsb.wroclaw.pl/index.php/WSBRJ/article/download/540/38214391d2fc62c465f1dd8466038ebbf2da1efb2ae53a,10.3390/ijgi15070286,"Deciphering Mobility in ""Strip Cities"": Multiscale Mechanisms and Spatial Fusion of Ride-Hailing Demand Under Topographical Constraints",2026.0,ISPRS Int. J. Geo Inf.,Di Wang; Shu-Xin Jin; Lin Lin,"Understanding the spatial generation mechanisms of ride-hailing demand is crucial for sustainable urban mobility. However, existing literature largely assumes monocentric urban layouts and globally stationary spatial scales, often overlooking the severe topographical constraints inherent in “strip cities”. To bridge this gap, the present study proposes a novel dual-level analytical framework coupling the Spatially Embedded Laplacian Graph Partition (SE-LGP) algorithm with a Log-Gaussian Multiscale Geographically Weighted Regression (MGWR) model. Taking Jinan, China, as a quintessential strip city, we incorporate spatial penalties to decode its mobility dynamics. Macroscopically, we reveal that substantial topographic friction fragments the workday mobility network into a chain of 23 highly localized micro-circulations. This anisotropic friction results in a notable 41.70% intra-community retention rate, demonstrating that flexible mobility operates within confined functional basins rather than a unified citywide market. Microscopically, the MGWR uncovers significant multiscale spatial heterogeneity: the jobs–housing mismatch is strongly associated with demand at a global macro scal",0.0,https://doi.org/10.3390/ijgi1507028614464ba5718cab9579722dd7109c012d4c226b91c07,10.56318/eem2026.01.020,Research on the Warsaw real estate market under conditions of post-pandemic recovery and migration processes,2026.0,Economics Entrepreneurship Management,O. Shandrivska; Sofiia Bohaychyk; Diana Skrypchenko,"The transformation of the Warsaw real estate market under the influence of global macroeconomic instability, pandemic-related restrictions, and large-scale migration waves necessitates a scientific analysis of structural changes and adaptive mechanisms within the housing sector. The aim of the study was to conduct a comprehensive analysis of the Warsaw real estate market conditions during 2021-2025, to assess the level of its market concentration, and to identify the determinants of demand transformation in the context of post-crisis recovery. The methodological framework of the study was based on a systems approach, incorporating statistical analysis of time series data, comparative methods, SWOT analysis, and mathematical tools for assessing market concentration, including the Herfindahl-Hirschman Index, the Linda Index, and the Gini coefficient. The results indicated that price dynamics in both the primary and secondary housing markets in Warsaw over the period 2021-2025 demonstrated an upward trend, with peak growth recorded in 2024, driven by inflationary pressures and supply shortages. It has been established that the spatial distribution of housing prices across the district",0.0,https://eem.com.ua/web/uploads/pdf/ЕПМ_13_1_2026-20-32+.pdf145d30d6f191d9ec323f7113f14bf7405b1c54a56b0,10.62051/ijgem.v4n1.54,Research on Influencing Factors of China Real Estate Price Based on Multiple Linear Regression,2024.0,International Journal of Global Economics and Management,Wuyang Wang,"Employing the methodology of multiple linear regression, this study delves into the primary determinants influencing real estate valuations in China in a systematic manner. In the midst of China's swift economic expansion, the real estate sector has evolved into a pivotal component of the economy, with fluctuations in property prices exerting a substantial influence on the stability of the socio-economic landscape. This paper aims to reveal the deep-seated reasons of China real estate price fluctuation through comprehensive analysis of various potential factors, and provide scientific basis for policy makers. The study selected data from 2010 to 2020. The empirical analysis results show that there is a significant negative correlation between the completed residential area and the real estate price, that is, the increase of supply will lead to the price decline, which is in line with the expectation of supply and demand theory. Housing cost has a significant positive impact on real estate prices, indicating that rising costs are an important factor in pushing up housing prices. The cost of land acquisition also has a positive impact on housing prices. Although its significance is s",0.0,http://dx.doi.org/10.62051/ijgem.v4n1.5414658617f408330c8d7db78d85620c61a77e43a8cff,10.34680/beneficium.2026.2(59).44-53,Risk Analysis for Granting Loans Secured by Residential Real Estate,2026.0,Beneficium,M. Izmaylov; T. N. Izmaylova,"This study develops a comprehensive methodology for assessing risks in residential real estate-secured lending in contexts of high national economic volatility. The relevance of this work is underscored by the vulnerability of mortgage systems to crises, as evidenced by the 2008 financial crisis and the COVID-19 pandemic, alongside the limitations of existing models that often overlook operational costs and the synergy of risks. The goal is to create an integrated approach that combines borrower creditworthiness analysis, dynamic collateral valuation, and operational risk modeling in collections. Key objectives included systematizing specific risk factors for the national market, developing a model to assess the interconnection between probability of default (PD) and loan-to-value (LTV) risk, considering macroeconomic scenarios, quantitatively assessing operational risks (LGD) based on empirical data regarding collateral realization timelines, integrating PD, LGD, and EAD into a unified structure for loss calculation, and formulating recommendations for banks and regulators. The methodology is based on econometric analysis of data from the Central Bank of Russia (CBR) and the State",0.0,https://beneficium.pro/index.php/beneficium/article/download/BENEFICIUM.2026.2%2859%29.44-53/430147ab075d7f7252fbca925be25b91b344884cd55516,10.2139/ssrn.3614085,Superintending Local Constraints on Housing Development: How California Can Do It Better,2020.0,,Christopher S. Elmendorf; E. Biber; Paavo Monkkonen,"Over the past few years, the California legislature has substantially reformed the state’s ambitious, but questionably effective, “housing element” law. The law requires local governments to periodically adopt a state-approved plan, called a housing element, through which the local government both identifies sites that can accommodate the locality’s share of regional housing need, and mitigates or removes constraints on the development of housing. This paper offers recommendations for how the Department of Housing and Community Development (HCD) could use its new authority to improve housing elements’ analysis of constraints and the associated programs to mitigate or remove them. 148 149A fundamental challenge is that HCD has no reliable data about the severity or prevalence of local governmental constraints, data which would allow cities to be compared to one another and benchmarked against the (few) standards that already exist in state law. 150 151Our response runs on two principal tracks. First, we recommend that HCD rebuttably presume that local governments in expensive areas have substantial governmental constraints if their rank by housing price (rent) exceeds their rank by rate of ",0.0,https://doi.org/10.2139/ssrn.3614085152e8100671e6f01234b6db66d90576c2f0dc378523,10.3390/buildings16153000,Interpretable Machine Learning for Classifying Expansion and Deceleration Regimes in the U.S. Housing Market Using Construction Cost and Supply Indicators,2026.0,Buildings,Minsoo Baek; Jung-Hyun Lee,"Housing market research has traditionally emphasized forecasting continuous price levels, often overlooking the discrete regime shifts that more directly capture cyclical risk and market turning points relevant to construction planning and investment decisions. This study develops an interpretable machine learning framework to forecast monthly U.S. housing market expansion and deceleration regimes one month ahead by integrating macroeconomic, financial, and construction-related indicators with national housing price data spanning January 1993 through March 2025. Feature selection and hyperparameter tuning are conducted entirely within the training sample using time-series cross-validation, ensuring that all reported performance metrics reflect genuine out-of-sample generalization. Recursive feature elimination combined with variance inflation factor screening yields a compact, seven-variable predictor set, with no macro-financial variable contributing an incremental discriminatory signal. Five classification models spanning linear and tree-based ensemble families are benchmarked under a strict temporal 80%:20% train–test split. Tree-based ensemble models consistently outperform the",0.0,https://www.mdpi.com/2075-5309/16/15/3000/pdf?version=1785248311153a2b536ec04f0dac5420d838986e4971f2488614e,10.65955/febe-09-2026-00096,Exploring Key Infrastructure Indicators Influencing Residential Property Values: A Delphi Analysis in the National Capital Region of Delhi,2026.0,Frontiers in Engineering and Built Environment,V. Gautam; Srishti Sagar; S. Atreya,"Normal 0 false false false false EN-IN X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:""Table Normal""; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:""Calibri"",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:""Times New Roman""; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;} This paper aims to explore and finalize the critical infrastructure variables and indicators comprising physical and social elements on residential property values in urban areas through a Delphi analysis involving experts from various fields such as real estate development, urban planning, civil engineering, sociology, and public policy. Integrated infrastructure encompasses a wide range of factors, including transportation networks, public amenities, educational insti",0.0,https://doi.org/10.65955/febe-09-2026-00096154ad4880c1c13209bda1dc3f98e5da148188d920fa,10.34229/2707-451x.21.1.7,Using Machine Learning Methods to Estimate the Cost of Housing,2021.0,Cybernetics and Computer Technologies,V. Tretynyk; А. Voznyak; V. Domrachev,"Introduction. Nowadays, the state has enshrined at the legislative level the definition of appraised value for tax purposes in sales of real estate as mandatory. The comparative approach most often used by appraisers has disadvantages such as the inability to find analogues in some cases and the need to make corrections, which affects the reliability of the results. The module of electronic determination of appraisal value (Module) similar to the object of property appraisal of the Unified database of appraisal reports works on the same approach and quite often overestimates appraisal value that leads to increase in the size of the tax during sales as the real estate cannot be sold for the price less than the estimated cost.155156Today to determine the price of an automated system correctly, it is necessary to fill the Unified Valuation Database in the State Property Fund with large knowledge bases - a huge IT system. So far, the thoughtless machine still determines the price by the average value. Currently there are often situations when the appraised value of real estate, determined by the Module, exceeds its real market value. Given that the approach used by the Valuation Module doe",0.0,https://doi.org/10.34229/2707-451x.21.1.715745fe6b5c71fc49620566500bc157967a77b6410a,10.1108/ijhma-06-2025-0145,Driven by regeneration? New geographic dynamics of foreign demand in English housing market,2025.0,International Journal of Housing Markets and Analysis,Cornelia Agyenim-Boateng,"158 159 Recent changes in how locations and site features are selected are reshaping foreign investment patterns in housing markets. Factors such as urban regeneration, connectivity, and investment security are attracting international buyers, leading to a rise in overseas property acquisitions. This trend raises key questions: Does foreign investment promote economic growth, or does it strain local markets by inflating prices and reducing affordability? The challenge for policymakers and planners is to weigh the benefits – such as development and tax revenue – against potential drawbacks such as displacement and inequality, to manage housing markets sustainably in an increasingly globalised context.160 161 162 163 Propensity score matching is used to construct a comparable group, minimising selection bias by accounting for observable characteristics. The nearest neighbour matching method pairs locations in the treatment group with the most similar counterparts in the comparison group. Statistical tests used to evaluate the quality and reliability of the matching process indicate that the mean differences between the treated and control groups are not statistically significant, as reflected by",0.0,16451a11d3b2f52781176626d01611a9735a5fb4e02,10.1108/ijhma-07-2025-0173,"From rates to real estate: a sentiment analysis of buy, sell, and rent decisions",2025.0,International Journal of Housing Markets and Analysis,Jayden Bielkiewicz; Shaomeng Jia; Wei He,"165 166 The study aims to investigate how US households form expectations about housing market variables, particularly mortgage rates, home prices and rent, and how these expectations influence their buying, selling and renting decisions. It also seeks to understand how broader macroeconomic factors like inflation and interest rates shape consumer sentiment and behavior in the housing market.167 168 169 170 The study uses a behavioral economics lens and relies on survey-based consumer sentiment data. It examines the relationships between perceived inflation, mortgage rates and housing decisions using comparative analysis across different decision types - buying, selling, and renting. Econometric tools such as regression analysis are used to interpret these relationships.171 172 173 174 Households generally believe that persistent inflation will lead to higher mortgage rates, which in turn are expected to put downward pressure on home prices. Among the factors shaping home price expectations, mortgage rates exert the strongest influence. Buyers tend to be most responsive to recent home price trends, whereas sellers are more sensitive to changes in interest rates. In the rental market, inflation emerges",0.0,17589e3913b2b05167413a08dedf6ab3c624ae9ac99,10.48014/fdg.20231115001,Study on the Spatial Correlation Characteristics of Residential Land Prices in Counties of the Urban Agglomeration in the Middle Reaches of the Yangtze River and Their Influencing Factors,2023.0,FRONTIERS OF DEVELOPMENT GEOGRAPHY,Jinming Ren; Chen Zeng; Yu-Zhe Wu,"The role of residential land price in regulating local economic development and optimizing urban land spatial layout is becoming more and more prominent. This paper takes the residential land price of 229 counties (districts, cities) of urban agglomeration in the middle reaches of the Yangtze River in 2017 as the research object, and establishes a theoretical model of the influencing factors of county residential land price based on equilibrium price and regional economic theory; and uses methods of spatial autocorrelation analysis and spatial lag model to explore the spatial distribution characteristics and influencing factors of residential land price in the research area and the influencing factors. The research results indicate that: (1) There are significant overall differences in residential land price among different counties within the urban agglomeration in the middle reaches of the Yangtze River, and the overall distribution of residential land price shows a ""pyramid"" pattern from low to high. (2) The spatial distribution pattern of residential land price in counties within urban agglomerations is greatly influenced by the location conditions and the administrative level ",0.0,http://www.scifootprint.com/Contribution/download.aspx?filetype=PeriodicalDirectoryFile&muLuId=f214cdf8-d4fa-4543-a837-fca84fb40a1f176e048ddf80383023f75cf185779ccdfd2a59f7c64,10.51867/ajernet.7.2.59,"Macroeconomic policies, house prices and housing supply in Ghana: The role of economic growth",2026.0,African Journal of Empirical Research,Naana Amakie Boakye-Agyemang; Benjamin Kwakye; S. Ameyaw; Ebenezer Afrane; Jannat Abbas; Tahiru Alhassan,"In many developing countries, housing debates and policies are focused on making prices affordable for consumers and increasing the supply. Despite the ongoing efforts by governments, there is compelling evidence that the informal sector drives the housing markets in developing economies. Hence, sound macroeconomic policies that affect consumers may serve as a lever to reduce prices and increase supply. This paper, therefore, examines the efficacy of monetary, fiscal, and debt policies on house prices (HP) and housing supply (HS), while underscoring the inclusive and exclusive roles of economic growth in Ghana. We utilized the Fully Modified Ordinary Least Squares (FMOLS) and the Canonical Cointegrating Regression (CCR), supported by annual data from the period 2000 to 2023. The study establishes that, regardless of whether economic growth is included or excluded from the model, house prices and housing supply respond to macroeconomic policies. HP and HS are marginally affected by the monetary policy rate (MPR), fiscal policy (FP), debt policy (DP), and economic growth (RGDP). Moreover, both HP and HS have a positive influence on each other. This study has therefore expanded the sc",0.0,https://ajernet.net/ojs/index.php/ajernet/article/download/1751/1171177bfd9b70164a2d1a335b6b31e7fa743980a9008e2,10.52260/2304-7216.2025.3(60).17,ВЛИЯНИЕ ИПОТЕЧНОГО КРЕДИТОВАНИЯ НА ДОХОДЫ НАСЕЛЕНИЯ,2025.0,"Вестник Казахского университета экономики, финансов и международной торговли",G. Rakhimzhanova; Г. А. Болсынбекова; R. Mugauina; А. А. Орынбасарова; G. Rakhimzhanova; G. Bolsynbekova; R. Mugauina; A. Orynbassarova,"В статье представлен комплексный анализ влияния ипотечного кредитования на доходы населения. Показана динамика и доля ипотечного кредитования в общем объеме кредитования по стране. Изучена структура ипотечного кредитования и проведен расчет ипотечной нагрузки домохозяйств, имеющих жилищную ипотеку. С помощью регрессионного анализа выявлено, что на доходы домохозяйств, имеющих жилищную ипотеку, влияют такие факторы, как процентная ставка банка на ипотечный кредит, цены на жилье и курс валют. Представлена регрессионная взаимосвязь данных переменных и получены результаты такой взаимосвязи. Определено, что сокращению ипотечной нагрузки, влияющей на размеры доходов домохозяйств с ипотечными кредитами, способствует реализация механизма долгосрочных инструментов фондирования кредитов, развития и расширения программ альтернативных форм финансирования жилья, программ защиты должников в кризисные периоды, роста конкуренции на банковском рынке и ужесточения макропруденциального регулирования. В работе использованы методы наблюдения, измерения и регрессионного анализа, позволивших провести количественную оценку влияния ипотечной нагрузки на доходы домохозяйств и дать объективные выводы на осно",0.0,178bfe777ca1620f308f42c0885023e87bc5e76dc81,10.36877/jwer.a0000516,Determinants of Home Ownership Among the Urbanites in Melaka,2024.0,Journal of Workforce Education & Research,A. Mohamed; Mashahruni Md. Salin,"Over the past few years, the real estate market has been unbalanced in terms of supply and demand when supply exceeds demand, especially in urban areas. Although there is a high demand for affordable housing, the issue of house dumping is still a public debate. The difficulty of owning a house has always been a public issue debated by the government and the private sector in finding a solution for all stakeholders. With the current economic situation, buying a house is almost impossible due to the drastic increase in house prices in urban areas. In reality, urban communities need affordable housing to overcome the problem of renting a house, where the cost of renting is increasing. This study aims to identify whether factors such as market price, socio-economics, and community well-being affect home ownership, especially in Melaka urban areas. Data and information were obtained from house renters in some selected housing estates. The data collected from questionnaires was analysed using Statistical Package for Social Science (SPSS) 23 and Microsoft Excel software. The regression analysis method was employed for this study. The study results found that the increase in house prices a",0.0,https://journals.hh-publisher.com/index.php/JWER/article/download/1033/5471793a82ed9242cd6946d4d6214dd216e73caa84f336,10.4324/9781003173670-8,Housing Market Dynamics,2022.0,Housing in Turkey,Leyla Alkan Gökler,"The dynamics of housing markets constitute a complex phenomenon. The body of research is vast and includes thousands of studies done on the national and international level. Empirical evidence indicates that housing markets differ in local institutional arrangements and that these might affect the ways they develop over time. To analyze housing market dynamics in various institutional environments and to increase our understanding of them is the main objective of this thesis. The scope of research includes analysis of relevant theories and empirical models that might explain development of house prices over the long run. Given that prices affect housing construction, this thesis also aims to deepen understanding of the impact of the relationship between new construction and underlying fundamentals, together with various institutional arrangements that might differ between countries. In particular, they include bank lending policies, valuation methods for mortgage purposes, different regulatory measures like rent control, as well as land and building policies. The Swedish apartment market was chosen for empirical analysis. The analysis is carried out by a combination of qualitative ",0.0,180a7c2b619697b5a90d1acf6b7df4ea0f4f9d3f745,10.7176/jesd/10-2-03,Comparative Analysis of the Property Negotiation Outcome Between Professional Estate Agents and Non-Professional in Akure,2019.0,Journal of Economics and Sustainable Development,R. A. Oladapo; I. Egunleti,"The paper compares the already established characteristics of the property market negotiator between professional property negotiators (agents/mangers) and non-professional property negotiators to property transactions in order to assess significant difference in their negotiation outcome (final price) from their Principals viewpoints. The target populations in the study were the land and real property owners in the three selected low, medium and high residential densities of Akure city, Nigeria. Their sample size were proportionately selected from their varying sampling frame and stratified random sampling adopted led to identification of distinct types of the residential properties in each densities. Structured close-ended questionnaire was administered on these owners to elicit their perception on the characteristics of their property negotiators at the point of purchase of land or letting of the residences and the negation outcomes. Data analysis was both descriptive and inferential statistics of mean scores with Kruskal Wallis Test and independent sample t-test respectively. Findings showed little significant differences in the ranking for both professional and non-professiona",0.0,https://www.iiste.org/Journals/index.php/JEDS/article/download/46203/47700181654004961a5a2cbe10a8d52513219e45d59b7258,10.31530/cjnst.2025.1.1.2,A Newly Proposed Hybrid Model for Predicting Real Estate Stock Prices in Kurdistan Region of Iraq,2025.0,Charmo Journal of Natural Sciences and Technologies,Y. Juma; Bryar Hassan,"Background: Accurate prediction of real estate stock prices plays a significant role in enabling investment choices, econometric forecasting, and strategic investment decisions in emerging markets. The Kurdistan Region of Iraq is among the emerging markets that are marked by non-homogeneity of data, limited representativeness, and non-availability of standardized appraisal methods.182Aims: This study aims to develop and test a stable hybrid machine learning model for predicting real estate stock prices in the Kurdistan region of Iraq. The new approach highlights methodological quality, representativeness of the dataset, and replicability in a bid to fill the gaps observed in the existing literature.183Methodology: The research makes use of the Kurdistan House Price Prediction (KHPP) data, which has various property features. Preprocessing of the data involved leakage-secure feature encoding, missingness checks, and deduplication verification. A combined model of Random Forest (RF) and Extreme Gradient Boosting (XGB) was used, alongside nested cross-validation for hyperparameter selection and testing. Statistical significance testing, bootstrapped confidence intervals, and feature ablat",0.0,https://cjnst.chu.edu.iq/index.php/cjnst/article/download/15/3184417922b800641d07da880054906a0c2808e963e6,10.1080/10095020.2025.2543496,Integrating remote sensing and deep learning for mapping urban housing wealth patterns,2025.0,Geo-Spatial Information Science,Emmanuel Nyandwi; M. Gerke; P. Achanccaray; Christian Lessmann,"ABSTRACT Disaggregated data on housing and household economic conditions, particularly in developing countries, are often inaccessible to urban planners and local users, and conducting independent in-person surveys is prohibitively expensive. This impedes the development of precisely informed policies and targeted infrastructure investments, services, and incentives that are essential for fostering equitable and sustainable cities. Using Kigali and Musanze cities in Rwanda, this study introduces a novel deep learning-based approach that combines limited expert annotation, self-training, and instance image segmentation to generate building-level housing wealth data. Our experiment included state-of-the-art instance segmentation based on You Only Look Once (YOLO) and Masked-attention Mask Transformer (Mask2former) models. To check the validity of the predictions, over ten thousand samples from the official cadastre-based property taxation database were used to evaluate whether the observed patterns in actual property values align with the model’s predictions. Importantly, our method successfully detected approximately 70% of the existing buildings within their respective perceived we",0.0,https://doi.org/10.1080/10095020.2025.2543496185fadf2a17c179d4c68f2a374a3c64fffcb2863fc9,10.54941/ahfe1003682,Determinants influencing the current real estate market in the Slovak Republic,2023.0,"Human Factors in Architecture, Sustainable Urban Planning and Infrastructure",D. Spirkova; Julius Golej; Monika Zatrochova,"Increasing mortgage loan prices, interest rates, the decline in the attractiveness of investment housing, inflation, and the energy crisis are the determinants that significantly affect real estate, especially the residential market. Currently, in December 2022, the European Central Bank (ECB) has raised key interest rates by 50 basis points and is expected to raise them further. According to the ECB's latest forecasts, the average inflation rate will reach 8.4% this year and fall to 6.3% in 2023. In the following years, the ECB expects inflation to average 3.4% in 2024 and 2.3% in 2025. Thus, current developments bring turbulence to the property market, but radical changes in the residential property market cannot be expected. In the past, investment properties accounted for up to half of all transactions in the real estate market in Slovakia. Those intended for rent are suffering under the impact of sharp price increases, as several analyses have shown. Another interesting fact is the payback period for such an investment. For example, in the centre of Slovakia's capital Bratislava, the return on a one-room flat has been extended by more than four years to 30.5 years from January",0.0,18673afbaf400205c273a68a83ecbf5824c7040910b,10.37871/jbres2127,"Integrating Environmental, Social, and Governance Factors in Megaproject Front-End Design: A Quantitative Framework with Prediction and Uncertainty Assessment",2025.0,Journal of Biomedical Research & Environmental Sciences,Henry Efe Onomakpo Onomakpo,"Megaproject Front-End Engineering Design crucially impacts lifecycle value, yet often lacks systematic methods to integrate multi-dimensional value drivers, including Environmental, Social, and Governance factors, using advanced analytics. This research aimed to develop foundational knowledge and a methodological framework to address this gap. This study employed a quantitative approach using panel data (circa 2009-2023), merging country-level Environmental, Social, and Governance indicators from the World Bank and house price indices from Organization for Economic Co-operation and Development countries as an economic performance proxy. Analyses included multicollinearity assessment using the Variance Inflation Factor, panel data regression (Pooled Ordinary Least Squares, Fixed Effects, Random Effects with cluster-robust errors), and the development of a machine learning-based Automated Valuation Model using Random Forest with lagged predictors. Uncertainty quantification for the Automated Valuation Model was performed using Conformal Prediction. The Fixed Effects model (preferred via diagnostic tests; within-coefficient of determination = 0.59) identified significant within-countr",0.0,https://doi.org/10.37871/jbres21271876ad1c8cd3b62de8723af9d4ca34085ba5fa7dec2,10.22395/seec.v21n49a7,"Financiamiento especulativo de la vivienda en Colombia: un análisis de la dinámica de los precios, 2000-2016",2018.0,,Gonzalo Cómbita Mora; O. P. Rodríguez; María Gabriela Ramos Barrera,"espanolEl presente trabajo estudia la dinamica de los precios de la vivienda en Colombia para el periodo 2000 a 2016. Se presentan los fundamentos teoricos no-convencionales para la determinacion del precio, junto con dos estrategias cuantitativas: 1) un estudio descriptivo del mercado y la posible fragilidad financiera que ha permitido el incremento de los precios de la vivienda nueva; y 2) un modelo econometrico VEC que captura los determinantes de corto y largo plazo del precio. Se concluye que la dinamica del precio de la vivienda esta influenciada por el credito hipotecario y los costos de construccion, y presenta una fuerte relacion de largo plazo entre el comportamiento actual y previo de los precios, evidentes en los escenarios financieros especulativos. EnglishThis paper studies the dynamics of housing prices in Colombia from 2000 to 2016. The non-conventional theoretical foundations for price determination are presented, along with two quantitative strategies: 1) a descriptive study of the market and the possible financial fragility that has allowed the increase in prices of new housing; and 2) a VEC econometric model that captures the short and long term price determinan",0.0,https://revistas.udem.edu.co/index.php/economico/article/download/2829/2600188b5921ce2349c3e93f1716870fe42a28b6a38d5da,10.1108/ijhma-08-2025-0186,Regional-level analysis of housing price dynamics in the United Kingdom: a multivariate causality approach,2025.0,International Journal of Housing Markets and Analysis,I. Daniel; T. Fateye; F. Abanda,"189 190 This study aims to examine the dynamic causal relationships between regional housing markets and the national house price index in the UK between 2005 and 2024, a period spanning the global financial crisis, Brexit, the COVID-19 pandemic and subsequent inflationary pressures. It aims to identify whether particular regions act as persistent “signal transmitters” of housing price movements and to assess the implications for market integration, segmentation and housing policy.191 192 193 194 The analysis uses monthly housing price indices disaggregated across the devolved nations and the nine English NUTS1 regions. Using Granger causality testing and robust regression estimators (M-estimator, S-estimator and MM-estimator), the study evaluates both the direction and stability of interregional price leadership. Structural break diagnostics are incorporated to account for volatility and regime shifts over the study horizon.195 196 197 198 The results reveal that three English regions (the Northeast, Northwest and Southwest) consistently act as signal regions, Granger-causing movements in the national index. Conversely, London and the Southeast display diminishing bidirectional influence, signalling ",0.0,https://centaur.reading.ac.uk/124830/1/ACCEPTED%20MANUSCRIPT%20ON%20REGIONAL%20LEVEL%20ANALYSIS%20OF%20HOUSING%20PRICE%20DYNAMICS.pdf1998649a73d38d76a00218a85416c4ac2beaa8f63ee,10.1186/s13033-026-00695-z,Impact of housing price increases on depression and stress in urban Iran: a case study from district 18 of Tehran,2026.0,International Journal of Mental Health Systems,S. Pirasteh; Esmail Isazade; V. Isazade; Fardin Mostafaei; Sediqe Azadi Bakhshayesh; Zahra Salimi,"Background Unaffordable housing has become a critical public health concern in developing countries, where rapid urbanization and inequality increase mental health risk. While existing research has explored economic and infrastructural dimensions of housing, limited attention has been given to how rising housing prices impact psychological well-being in low-income urban neighborhood. This study investigates the relationship between housing price increases and two key mental health outcomes depression and perceived stress within District 18 of Tehran, Iran. Methods A cross-sectional study was conducted among 180 households across seven neighborhoods in District 18, using standardized psychometric tools: the Perceived Stress Scale (PSS-14) and Beck Depression Inventory-II (BDI-II). Housing indicators, including apartment and villa prices, household density, and building density, were obtained from municipal datasets. Spatial clustering and regression analyses were applied to examine the spatial and statistical relationships between housing and mental health patterns. Results Housing price increases showed a strong positive correlation with both depression and perceived stress. The re",0.0,https://doi.org/10.1186/s13033-026-00695-z200dbed2746b9ffc50026f6a2d6fb3f02b6a616b278,10.3390/su18168440,Spatial Differentiation and Sustainable Development in Urban Housing Choice Among China’s Floating Population: A Geospatial Heterogeneity Perspective,2026.0,Sustainability,Wang-Bao Liu; Jinglin Zhang,"Geospatial heterogeneity plays a critical role in shaping housing tenure among China’s floating population. Using three binary indicators—homeownership, social housing, and informal housing—this study measures migrants’ housing affordability, access to public housing resources, and residential stability. Based on nationally representative data from the 2017 China Migrants Dynamic Survey (CMDS), we examine how geospatial characteristics influence migrants’ housing tenure. The results show that both origin and destination characteristics significantly affect housing outcomes. Migrants moving to higher-tier cities are more likely to obtain social housing because these cities provide better public services and greater opportunities to accumulate human and social capital. However, high housing prices in megacities substantially reduce the likelihood of homeownership. A clear birthplace effect is also observed: migrants originating from eastern China and megacities have significantly higher probabilities of obtaining both social and owner-occupied housing, reflecting the advantages associated with more developed places of origin. In addition, marital status, duration of migration, educat",0.0,https://doi.org/10.3390/su18168440201772a5463bb41cadba99701f0efa9c41e35defef8,10.37355/acta-2024/2-04,Correlation between Wages and House Prices: an Analysis of Regional Differences in the Czech Republic,2024.0,Acta VŠFS,Oskar Crnadak,"Background: The real estate market in the Czech Republic exhibits significant differences among regions, especially in terms of the influence of economic factors such as wages on property prices. Wages are one of the key determinants of house prices, but their influence may vary across regions and over time. It is important to further understand the dynamics between wages and house prices at the regional level.202Objective: This study seeks to investigate whether and how the impact of wages on house prices varies among regions and how it changes over time.203Methods: The fixed effects panel regression with interaction terms was used to account for regional and time effects. The model includes lagged house price values to better capture market dynamics over time. Interaction terms between wages and regions allow for the detection of region-specific effects. The Newey-West correction was used to control for heteroskedasticity and autocorrelation.204Results: In some regions, such as Prague, factors other than wages (e.g. lack of supply and high demand) may play a more significant role. The analysis also confirmed that house prices exhibit time inertia, which means that past price developmen",0.0,https://doi.org/10.37355/acta-2024/2-042056504accccc4e38141d966f0196a8c068115c96fd,10.17261/pressacademia.2023.1703,Do hot money movements drive Turkey s housing prices,2023.0,Pressacademia,Tuncer Yılmaz,"Purpose- The aim of this study is to investigate the existence and extent of the impact of ""hot money"" inflows on Turkey's real estate market, which causes the expansion of the monetary base in the markets. The use of data specific to the Turkish market, which has an emerging market economy and where housing production and sales are significant as an important macroeconomic indicator in this market, where the right to acquire housing has been stretched to foreigners in recent years and where excessive short-term capital flows are preferred, reveals the importance of the study. For this purpose, the ""Housing Price Index"" indicators created by the CBRT to monitor price changes in the housing market were used to measure the situation of housing markets in Turkey in recent years and to measure the possible effects of short-term international capital inflows on real estate prices.206Methodology- As a method in the study, firstly, the changes in the monthly data between January 2010 and August 2022 belonging to both hot money and the Housing Price Index compared to the previous period were calculated, and then these data were seasonally adjusted. Then, the Dynamic Ordinary Least Squares (D",0.0,https://doi.org/10.17261/pressacademia.2023.17032074bb87ee76bfba6acac357cc4755f4b6a932609dc,10.33293/1609-1442-2026-29(3)-123-132,Analysis of factors of mortgage lending availability in Sevastopol,2026.0,Economics of Contemporary Russia,Svetlana V. Tselio,"208 With rising interest rates and an accelerated rise in housing prices, the problem of mortgage lending availability is becoming particularly relevant. For the city of Sevastopol, where the real estate market is developing under the influence of resort and investment demand, limited land resources and high migration activity, a decrease in mortgage availability is becoming a factor constraining the socio-economic development of the region. The purpose of the study is to identify and quantify the factors influencing the low availability of mortgage loans in Sevastopol in 2020–2024. Materials and methods: the information base is based on the official statistics from Rosstat, the Bank of Russia, and DOM.RF, EMISS, and Rosreestr. To ensure the completeness and reliability of the analysis, the work used indicators reflecting key trends in the development of the mortgage market and the income level of the population. The methodological framework of the research includes comparative and structural-dynamic analysis, as well as correlation and regression modeling. To assess accessibility, the debt burden indicator (PD) was used, reflecting the ratio of the average monthly mortgage payment a",0.0,https://www.ecr-journal.ru/jour/article/download/1177/696209b5c231d5ebe30d896c5855b61fb85f74545eeaa3,10.18500/1817-7115-2024-24-2-147-152,Categorical modeling of the sublanguage of homeopathy,2024.0,Izvestiya of Saratov University. Philology. Journalism,A. E. Buzheninov,"The article considers the categorical modeling of the sublanguage of homeopathy. The sublanguage of a professional area of knowledge and/or activity is a means of verbalizing its notions, concepts and categories, and serves as a form of creation, interpretation, storage and communication of special knowledge. On the material of 1258 terms and general literary words from homeopathic dictionaries, monographs and tutorials on homeopathy, 8 universal ontological and epistemological categories are distinguished (categories of agent, state, processes, occupations, properties, values, sciences and branches, tools) and one category, characteristic for this sublanguage, – “Medication”. These particular categories are conceptually significant for this sublanguage, they form the “framework” of homeopathic knowledge. Universal categories are inherent in any scientific field, while the specific category “Medication” is a unique mental construct due to the distinctive characteristics of the concepts constituting this category and the prevailing means of their verbalization. From the point of view of verbalization of homeopathic concepts that form the identified categories, the sector-specific (g",0.0,https://bonjour.sgu.ru/sites/bonjour.sgu.ru/files/text-pdf/2024/05/filologiya_20024_2_4_buzheninov.pdf2104e1e0ae815cd9229e39e333a86f0764ee643f300,10.3390/urbansci10040188,"Study on the Distribution Patterns and Driving Mechanisms of Urban Plant Diversity in Green Building Demonstration and Non-Demonstration Areas of Jinan, China",2026.0,Urban Science,Hai-Li Zhang; Zong-Shan Zhao; Zong-Jin Zhao; M. M. Nizamani; Xiuyu Bian; Xiujun Liu,"Urban street greenery plays a crucial role in enhancing biodiversity, environmental quality, and human well-being. However, how different street greening strategies shape urban plant diversity across functional urban contexts remains insufficiently understood. Taking Jinan, a rapidly urbanizing city in China, as a case study, this research investigates the spatial patterns, compositional differences, and driving mechanisms of plant diversity between Green Streets (GS) and Non-Green Streets (NGS) across various Urban Functional Units (UFUs). A 1 km × 1 km grid was used to delineate UFUs, combined with field-based plant surveys, linear regression analyses, and the public space assessment framework of Sustainable Development Goal (SDG) 11.7.1. Results indicate that plant diversity is strongly dependent on urban functional types, with higher species richness observed in residential and recreation/leisure districts, and lower levels in industrial, commercial, and transportation districts. The ecological effects of GS exhibit clear context dependence, being more pronounced in residential, educational, and public service areas, but limited in commercial and industrial zones. NGS recorded ",0.0,https://www.mdpi.com/2413-8851/10/4/188/pdf?version=17750252212112768387db18e250c8c615f2279cbe12bdacc3d2a,10.3390/urbansci10070402,Modeling the Built Environment’s Role in Shaping Innovation-Oriented Productivity Through a Spatially Heterogeneous Lens,2026.0,Urban Science,Yan Gu; Yifei Hou; Yu-Die Zhang; Ruoxi Zhang; Le-Min Zhang,"Innovation-oriented productive forces are increasingly concentrated in cities, but the multiscale mechanisms through which the built environment shapes these forces remain insufficiently understood. This study develops a spatial analytical framework linking firm-level new quality productive forces (NQPF) to fine-grained urban spatial structures. Using 89 A-share listed firms in the Xiamen–Zhangzhou–Quanzhou (XZQ) urban agglomeration, we first construct an entropy-weighted NQPF index from eleven financial indicators related to R&D human capital, advanced capital stock, intangible assets, and operational efficiency. Kernel density estimation is then used to transform discrete firm-level NQPF values into a continuous 600 m × 600 m grid surface as the dependent variable. On the explanatory side, 27 built-environment variables are organized into an integrated indicator system covering urban form, natural conditions, jobs–housing structure, and service infrastructures. We combine cross-validated recursive feature elimination (RFE-CV) with multiscale geographically weighted regression (MGWR) to construct two model specifications: a 7-variable parsimonious subset and a 14-variable highest-",0.0,https://doi.org/10.3390/urbansci100704022122a60ad65cd6993d2bcd461d2c0087e19f7638970,10.55959/msu0579-9414.5.80.1.9,Incomes of residents in west European capitals and the immigrants’ settlement pattern,2025.0,Lomonosov Geography Journal,D. Shatilo,"The article studies the relationship between the immigrants’ settlement and the incomes in large European capitals using the examples of London, Madrid and Berlin. Cities were selected based on the importance of the immigration processes, the availability of regional statistical materials and their role in the countries. These cities have attracted large numbers of immigrants, especially in the last 30 years (and earlier in the case of London due to the influx of people from former colonies). The example of housing costs and income distribution shows the socio-ethnic stratification. As a result of the study, cities’ districts were classified based on the immigrants’ percentage, income levels and housing prices, which demonstrated a high degree of territorial mosaic and heterogeneity. For four European capitals, it was shown that a hypothesis of close relationship between the income and migrants microgeography is not always confirmed. The most complicated picture is in London. In Berlin and Madrid, migrant localization sectors are visible, but (not to mention Moscow) it is more difficult to identify their connection with income stratification. A most notable characteristic of the ci",0.0,https://vestnik5.geogr.msu.ru/jour/article/download/1451/9052137c14a2878738b04d2a224ace1996d3bffdbf1455,10.3390/buildings16153041,Stakeholder Cognitive Gaps in Residential Development Planning: Evidence from Low-Rise Housing Projects in Taiwan,2026.0,Buildings,Teng-Che Lu; Tsung-Chieh Tsai,"Low-rise terraced housing constitutes a major segment of Taiwan’s residential market, yet stakeholder perception differences during residential development planning remain insufficiently understood, particularly regarding sustainability considerations. In this study, we investigate cognitive gaps among developers, homebuyers, and construction professionals across six planning dimensions, including site selection, housing price, capital capacity, construction risk, building planning, and sustainability. A structured questionnaire survey was conducted in Changhua County, Taiwan, yielding 176 valid responses (37 developers, 92 homebuyers, and 47 construction professionals). Data were analyzed using Cronbach’s α reliability analysis, exploratory factor analysis (EFA), chi-square tests, one-way ANOVA, Fisher’s LSD post hoc comparisons, and robustness analyses using ANCOVA and Tukey’s HSD. Significant stakeholder perception differences were identified for 15 of the 19 planning factors (p < 0.05). Supply-side stakeholders consistently prioritized construction cost, financing capacity, and construction risk, whereas homebuyers placed greater emphasis on transportation convenience, living a",0.0,https://www.mdpi.com/2075-5309/16/15/3041/pdf?version=17855099822143e8a66d9c5f910183c046871189fdd16e8f86e99,10.17271/23188472149120266199,Uma revisão das tecnologias habilitadoras aplicadas à precificação de imóveis,2026.0,Revista Nacional de Gerenciamento de Cidades,Larissa Kelly da Silva França Barbosa; Emilia R. Kohlman Rabbani; Eliane Maria Gorga Lago; F. D. da Cruz,"Objective – To understand how enabling technologies have been applied to real estate pricing through a systematic literature review, mapping methods, applications, and trends in real estate appraisal engineering. 215Methodology – A Systematic Literature Review (SLR) was conducted following the PRISMA protocol. Searches were carried out in relevant databases such as Science Direct, Scopus, and Web of Science, using a search string with Boolean operators. A total of 55 articles published between 2014 and 2024 were analyzed. 216Originality/Relevance – This study systematizes scientific articles published over the last ten years on the use of emerging technologies in real estate appraisal, highlighting the contrast between traditional methods and new approaches based on artificial intelligence. 217Results – Most studies still rely on traditional valuation methods, but there is a growing use of enabling technologies such as machine learning and geoprocessing. China stands out as the country with the largest number of publications. Regarding applications, real estate investment studies predominate (49%), followed by urban planning. The application of artificial intelligence improves accuracy a",0.0,https://publicacoes.amigosdanatureza.org.br/index.php/gerenciamento_de_cidades/article/download/6199/6168218e2fea123d6417ddd51372afdcf0969732c2aa21f,10.11113/intrest.v18n1.367,"Effect of Micro Economic Factors on Residential Property Value in Birnin Kebbi, Kebbi State, Nigeria",2024.0,International journal of real estate studies,Isyaku Ibrahim; D. Daud; U. Sa'ad; H. Yaro; Adebayo Oyekanmi Oluwole; Rilwanu Bello; Shakirat Abdulazeez Remilekun; Shittu Wasiu Oyewale,"The research aims to assess the microeconomic factors affecting residential property values in Birnin Kebbi. Property serves as an asset for any individual, as real estate is one of life's primary goals. Besides the need to eat and drink, property also plays a crucial role in daily life. Property values are influenced by various factors, which can be either microeconomic or macroeconomic in nature, or both. Many microeconomic variables are related to property attributes and buyer demographic profiles. To achieve this aim, two objectives were formulated: (i) to examine the types of property and microeconomic factors affecting property values in the study area, and (ii) to assess the effect of microeconomic factors on residential property values. Both primary and secondary sources of data were used for data collection. Out of 377 questionnaires administered, 321 were retrieved. Simple random sampling and stratified random sampling methods were employed. Quantitative methods were used for data analysis, and all data gathered were analysed using descriptive statistics and the Likert scale. The study found that physical characteristics of residential properties, such as types of materia",0.0,https://intrest.utm.my/index.php/intrest/article/download/367/1702199ba26d9949fd0099a122b249e996b0417baecf4a,10.18822/byusu20260335-43,Socio-legal conditioning of criminal protection of the order of carbon units,2026.0,Yugra State University Bulletin,S. I. Rudnitsky,"The article examines the socio-legal conditions for establishing a criminal prohibition on violating the procedure for handling carbon units. The formation of a legal regime for carbon units in the Russian Federation, the development of registry accounting for the results of climate projects and the emergence of criminogenic risks in this area require an assessment of the limits of criminal legal impact. The carbon unit is considered as a normatively defined, accounted for and economically significant result of environmental activities, illegal impacts in this area can undermine the reliability of climate accounting and harm public and property interests. 220The subject of the research is the socio-legal, criminal-legal, and criminological prerequisites for establishing criminal liability for intentional violations of the procedure for the circulation of carbon units. 221The purpose of the research is to substantiate the need to ensure the criminal-legal protection of social relations that arise in the field of the circulation of carbon units. 222The research methods include dialectical, inductive, deductive, comparative legal, analysis, synthesis, concretization, and criminological fore",0.0,https://vestnikugrasu.org/byusu/article/download/713443/22737122361e5dfd6d6524d88e8256ff82a99dabb2a00bf05,10.59413/ajocs/v7.i2.39,"Beyond Loss Aversion and Liquidity Traps: A Behavioural Stated Choice Analysis of Speculation, Over-Leverage, and Residential Housing Prices in Kenya's Affordable Housing Agenda",2026.0,African Journal of Commercial Studies,C. Owuor,"The global residential housing crisis has increasingly been attributed to behavioural anomalies rather than to purely rational market forces. Yet, the empirical integration of loss aversion, over-leverage, and liquidity dynamics within emerging African housing markets remains critically underdeveloped. This study advances a comprehensive behavioural framework for understanding residential housing price determination in Kenya, drawing on stated choice (SC) methods and prospect theory to analyse how loss aversion interacts with over-leverage and liquidity constraints to drive speculative price distortions. Analysing data from 234 real estate investors through real estate agents in Kenya, by using a multi-stage approach, the research employs hierarchical regression and mediation analysis to examine how psychological expectations mediate the relationship between microeconomic indicators and residential housing prices. Findings reveal that locational attributes exert the strongest influence on housing prices (β = 0.846, p < 0.001), while psychological expectations mediate price formation, with emotional biases (ΔR² = 0.089) demonstrating stronger mediating influence than cognitive error",0.0,https://doi.org/10.59413/ajocs/v7.i2.39224fcec00fae70d014feb2920bdc21458721ab346c7,10.15663/651.rfe10,Countering gentrification in Tāmaki Makarau: Towards a framework for urban environmental justice,2025.0,,J. Terruhn; R. Simon-Kumar; Kim N. Dirks; Andrea Edwards,"This policy brief argues for urban policies that actively aim to end gentrification.225226Gentrification is intimately tied to state-led processes of urban renewal. Though historically understood as the displacement of low-income households as an unfortunate byproduct of socioeconomic and demographic neighbourhood change, contemporary gentrification is constituted by profit-driven investment decisions that erase undervalued neighbourhoods and their communities in the name of urban renewal (variously referred to as redevelopment, regeneration, renaissance, or revitalisation).227228These processes of gentrification fundamentally contribute to environmental injustice. At the same time as creating ‘zones of abundance’, neighbourhood regeneration projects simultaneously (re-)create ‘zones of neglect and sacrifice’, which are burdened with environmental risks. As low-income and ethnic minority households are more likely to either reside in or to be displaced into these neighbourhoods, they are disproportionately exposed to environmental risk factors.229230Moreover, gentrification enables and legitimises the exclusion of marginalised communities in multiple ways: 1) it devalues existing places and th",0.0,https://researchcommons.waikato.ac.nz/bitstreams/2e2aa5d9-16db-44f3-a6ab-ce4c02a9264f/download231e1befe8d470af5ab5b9ea98f2b17c6ece3c491a6,10.1108/ijhma-02-2026-0036,Housing value changes and consumption: the role of asymmetry,2026.0,International Journal of Housing Markets and Analysis,YuDi Feng; X. Ge; Ling-Zhen Yao,"232 233 Amidst China’s declining consumption rate and unprecedented downward pressure on housing prices, this study aims to critically explore the asymmetric impact of housing value fluctuations on household consumption scale and structure. Specifically, it questions the traditional life-cycle-permanent-income hypothesis by investigating whether housing depreciation suppresses consumption more significantly than appreciation stimulates it. The purpose is to understand the behavioral mechanisms that are rooted in prospect theory that drive households to alter spending habits during asset shrinkage, thereby addressing the paradox of stagnant consumption despite historical real estate booms.234 235 236 237 Based on microlevel data from five waves of the China Household Finance Survey spanning 2011–2019, covering 59,977 observations, this study uses fixed-effect regression models to analyze the asymmetric wealth effects. The approach uses prospect theory to operationalize loss aversion, separating the sample into appreciation and depreciation groups to test distinct coefficients. Furthermore, the study uses mediation models to examine transmission mechanisms, like specifically risk preference, deb",0.0,238d7a66e466405cf8ea995920db6395cc0c1cfa34d,10.52804/ijaas2024.515,Assessment of the impact of native tree species reforestation in Rwanda. A case study of Ibanda makera natural forest,2024.0,International journal of agriculture and applied sciences,Gaspard Ntabakirabose; Innocent Hakuzimana; Vital Mory Karinda; Jean Majuga; D. Mburu,"The study was conducted in Kirehe District, Mpanga Sector. The major objective of this study was to assess the impact of native tree species reforestation in Rwanda. A Case Study of Ibanda Makera Natural Forest. The study adopted a cross-sectional survey research design as its framework to guide the process of data collection. The target population was households living closer to the forest. The study adopted a purposive sampling approach to select four villages. Twenty (20) households were randomly selected from each village making a total sample size of 80 respondents. Descriptive analysis was done using SPSS version 20, and regression using STATA version 17. A logistic regression model was mostly adopted for this study to determine the factors influencing native tree species in the study area. The result of the regression analysis indicated that age, gender, occupation, household size, price of fuel, tree product use, and distance to the forest influenced negatively native tree species. This study revealed that the most common social-economic benefits of native tree species reforestation are community benefits, soil protection, carbon sequestration, increased research activities",0.0,http://dx.doi.org/10.52804/ijaas2024.515239be5723a829c3addd4a61fa05d236d49d40102166,10.30742/perspektif.v25i1.756,IMPLIKASI HUKUM PERBANDINGAN KONSEP CO WORKING SPACE DENGAN PERKANTORAN DAN PERINDUSTRIAN BAGI PELAKU USAHA RINTISAN DI INDONESIA,2020.0,Perspektif,Cita Yustisia Serfiyani,"Co working space merupakan inovasi terbaru bagi pelaku usaha di seluruh dunia termasuk Indonesia dalam menyiasati tingginya harga properti khususnya tempat kerja. Meskipun sama-sama berfungsi sebagai tempat kerja, gedung atau bangunan co working space tidak lantas dapat disamakan dengan gedung perkantoran dan perindustrian apalagi tempat hunian sehingga regulasi yang dirumuskan untuk mengatur co working space sepatutnya berbeda. Ijin pendirian bangunan yang dimanfaatkan sebagai co working space harus disesuaikan pula dengan pengaturan zonasi yang berbeda di setiap daerah karena belum adanya payung hukum di tingkat pusat. Di samping itu, hubungan hukum antara pemilik dan pengguna co working space tergantung dari jenis perjanjian hukum yang diberlakukan. Kesimpulan dari penelitian dengan metode yuridis normatif ini adalah bahwa dari perbandingan konsep co working space dengan konsep perkantoran dan perindustrian tersebut dapat dirumuskan karakteristik co working space serta implikasi hukum yang terjadi sehingga perlu diprioritaskan penyusunan regulasi yang mengatur co working space secara spesifik. Co working space is the latest innovation for businesses around the world including In",0.0,http://jurnal-perspektif.org/index.php/perspektif/article/download/756/pdf_18624012d6ee799e126130b2a765b45573c151950677ba,10.17072/1994-9960-2026-2-214-226,Decision support system with a Telegram bot and machine learning methods for the real estate market,2026.0,Вестник Пермского университета Серия «Экономика» = Perm University Herald ECONOMY,V. Gorelik; T. Zolotova,"Introduction. The problem of managing investments in residential real estate as a tool for generating income is considered; an investment option with mortgage lending and no additional funds is proposed. A Telegram bot was programmed to automate the investment decision-making process.241242Purpose. The aim of the study is to develop a decision support system as a Telegram bot, aimed at assessing the investment attractiveness of real estate objects with statistical data analysis and forecasting methods.243244Materials and methods. The data for the study is taken from the CIAN platform. The methodological framework includes methods for statistical data analysis, machine learning, and user interface design in decision support systems. Primary calculations and research are performed with Python programming language functions; the decision support system was implemented in Google Colab with the pyTelegramBotAPI library.245246Results. Information was collected, cleaned and pre-processed for cities in Russia, and rental and sales housing prices were studied. The CatBoostRegressor machine learning model was applied to forecast rental price for properties put up for sale, which made it possible to calc",0.0,https://doi.org/10.17072/1994-9960-2026-2-214-226247ab3bce93e97f8d91cc6717b6039636c6e26d5292,10.70917/ijcisim-2026-0370,Innovative Research on Rural Housing Renewal and Rehabilitation Models Driven by Rural Electricity Economy,2026.0,International journal of computer information systems and industrial management applications,Rong Li; Chao-Nan Liu; Bei Li,"Under the background of rapid development of e-commerce economy, rural housing renovation faces new opportunities and challenges. The traditional housing renovation model is difficult to meet the needs of modern rural development, with obvious spatial differentiation characteristics and complex and diverse influencing factors. Exploring the innovative model of housing renovation driven by e-commerce economy is of great significance for promoting rural revitalization, improving rural living environment, and promoting the coordinated development of urban and rural areas. This study adopts a combination of geographically weighted regression (GWR) model and random forest (RF) model to deeply analyze the spatial influencing factors and threshold effects of rural housing renovation driven by e-commerce economy, based on 800 rural housing renovation sample sites in County A. The spatial regression analysis was carried out by constructing the characteristic price model, using ArcMap 10.3 software, and producing partial dependency maps using Python 3.0 software. The results show that: the goodness-of-fit R² of the GWR model reaches 0.8314, which is significantly better than that of the OLS ",0.0,https://cspub-ijcisim.org/index.php/ijcisim/article/download/1719/13102483522685fb9c06f767c44d730b7b9fc832753ae36,10.1186/s12875-025-03124-1,"Implementing community-based heart failure screening in primary care settings: a population-based pilot study in Shanghai, China",2025.0,BMC Primary Care,Xin Gong; Jie Xu; Min Wang; Hao Hu; Y. Yan; Wei Han; Jiaoling Huang,"While risk-stratified heart failure screening demonstrates clinical value in enabling early intervention, current implementation remains limited globally. This study aims to develop and validate a multi-stakeholder collaborative screening framework based on primary healthcare system. We conducted the Pudong Heart Failure Screening Project (PHSP) in the medical consortium in Shanghai. The PHSP was delivered via five phases: (a) investigation, (b) screening, (c) individual invitation, (d) consultation in primary health care and (e) management in secondary health care. Data were collected by electronic data capture (EDC) system, office administrators and GPs. Univariate and multivariate logistic regression analysis was used to examine the relationship between the outcomes and factors. A total of 8022 individuals took part in the PHSP and 2473 participants (30.83%) were initially defined positive in the screening. Among the 2473 positive participants, 1553 responded to the invitation call, and 664 individuals accepted the invitation to visit the Cardiologist-driven GP joint clinic. Notably, house price was identified as a significant factor influencing their decision to accept the invi",0.0,https://doi.org/10.1186/s12875-025-03124-1249b6b4a5cf68aef27a8f49154f9ffe2468c67eac7e,10.1057/s41599-021-00741-w,Spatial-geographical analysis of urbanization in Iran,2021.0,Humanities and Social Sciences Communications,A. Pilehvar,"The development of urbanization and urbanism in Iran has created a wide socioeconomic gap in urban areas. Rapid population growth along with the lack of socioeconomic growth programs and urban development strategies have deteriorated unemployment, inflation, housing price, traffic congestion, and marginalization in cities, particularly in metropolises of Iran. This paper, using a descriptive-analytic research method and a meta-analysis technique, addresses the urbanization and urbanism changes in Iran. Using GIS technique and CV formula, the spatial distribution of urbanization and its rapid growth in Iran are depicted. The research data was derived from a systematic review of documents and techniques over 40 years. The results of the study demonstrated that Iranian cities have undergone an unsustainable growth trend and urbanization has overtaken urbanism. Over the past 4 decades, cities have been struggling with rapid growth and development. In this regard, development-oriented governments can play a significant role in tackling growth and urbanization problems. These problems are especially evident in the socio-economic, urban planning and urban ecology. The rapid growth of urba",0.0,https://www.nature.com/articles/s41599-021-00741-w.pdf2501447de314069d877546aa0dc1f932ada4198fcfa,10.1002/psp.70387,Divergent Paths of US Counties: Multiscale Drivers and Spatial Dynamics of Population Growth and Decline (2010−2020),2026.0,"Population, Space and Place",Mohammadreza Gholami Gowhareh,"251 252 Population change across US counties has taken sharply different paths, with some areas experiencing steady growth while others face persistent decline. These trends are not random; they reflect deeper shifts in the economy and uneven regional development. This study contributes to demographic and regional research by treating population growth and decline as two distinct demographic regimes, each influenced by its own set of factors. We examine which factors are associated with county‐level population change in the United States from 2010 to 2020, using data from 3130 counties with complete records across all explanatory variables, compiled from the American Community Survey, the Bureau of Economic Analysis and fiscal records. To capture both overall patterns and local variations, we combine standard regression methods (OLS, spatial lag and spatial error models) with Multiscale Geographically Weighted Regression (MGWR). Counties are explicitly divided into growing (253 N254 = 1548) and shrinking (255 N256 = 1582) groups, with models estimated separately for each. The findings show a moderate but consistent asymmetry between growth and decline. Declining counties tend to cluster toge",0.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/psp.70387257571a5ca7b0b9c123e1beb46b173b984f4ff333ee,10.31357/fesympo.v0i0.1524,PREDICTIve MODELS FOR MUNICIPAL SOLID WASTE (MSW) GENERATION AND COMPOSITION,2013.0,,N. Bandara; P. J. Hettiarachchl,"To develop an effective municipal solid waste management strategy for a givencommunity, it is essential to know the amount of waste generated and the composition of',the waste stream at a given time and to identify a mechanism for reliable estimation offuture waste quantities and compositions. It has been shown in literature that the amountof waste generated in a country is proportional to its population and the mean livingstandards of the people or their income in addition to many other demographic factors This paper presents the findings of a study carried out in a suburban Municipal area inSri Lanka to develop a predictive model to estimate waste generation patterns at a giventime. Here, an attempt was made to analyse the quantity and composition of wastegeneration in a sample of households in the study area over a time period and relate thisto various demographic factors. Over 300 households were studied for this purpose.Through regression analysis the ,amount of waste and waste composition was related tothe demographic factors. It describes the basis for the sample selection, socio-economicparameters used for modeling and the methodology adopted for effective data collection.S",0.0,http://journals.sjp.ac.lk/index.php/fesympo/article/download/1524/695258aff547b3c4285724199355d7ff2487b84945f5b4,10.22423/kreus.2026.18.2.43,Transmission Channels and State-Dependent Nonlinear Effects of U.S. Monetary Policy Shocks on the Korean Housing Market,2026.0,Konkuk Research Institute of Real Estate and Urban Studies,Junghwan Kim; Hyunseok Lee,"This study analyses the response of the Korean housing market using the monthly US 2-year Treasury bond yield as the US monetary policy shock variable and the nationwide house price index as the outcome variable, covering the period from January 2006 to July 2025. Local projection (LP) analysis reveals that the magnitude and duration of the impact on the Korean housing market vary depending on the direction of US monetary policy. An asymmetry is observed, whereby Korean house prices decline more significantly and for a longer period during monetary tightening than during monetary easing. To simultaneously account for asymmetry and state dependence, asymmetric threshold local projection (T-LP) was applied. Results indicate that under the low-interest-rate regime, the downward response to US tightening shocks is concentrated and amplified in the medium term, whereas under the high-interest-rate regime, the effects of easing shocks are relatively more pronounced. Thus, the spillover effects of monetary policy are significantly differentiated across regimes. This suggests that linear LP may underestimate the effect by averaging the opposing regime-specific responses or misinterpret it ",0.0,259b4adeab2433cc9821cce6c12f8379c94de0947e5,10.3390/su18062821,Spatial Clustering of Front Yard Landscapes: Implications for Urban Soil Conservation and Green Infrastructure Sustainability in the Río Piedras Watershed,2026.0,Sustainability,L. Sellés; Elvia J. Meléndez-Ackerman,"Current sustainability discourse promotes sustainable yard practices as a means for residents to contribute to urban environmental health and soil conservation. Social–ecological research suggests that yard practices are shaped by multiscale social drivers, including social contagion, whereby visible expressions of individuality in front yard design are copied by nearby neighbors. This study evaluated residential areas within the Río Piedras Watershed (RPWS) in the San Juan metropolitan area to assess evidence of social contagion in front yard configuration and vegetation structure, and to examine whether these variables were associated with socio-demographic and economic characteristics when spatial effects were considered. A total of 6858 front yards across six highly urbanized sites were analyzed using Google Earth Street View imagery. Housing lot sizes were quantified, and yards were classified into eight landscape configurations based on green and gray cover elements. Woody vegetation structures, including trees, shrubs, and palms, were also quantified to generate estimates of functional diversity and a front yard quality index. Significant differences in yard characteristics ",0.0,https://www.mdpi.com/2071-1050/18/6/2821/pdf260cc76b03c6f6ecf0621bfcee0a503f84fe4d25a05,10.61426/sjbcm.v6i4.1459,"DETERMINANTS OF HOUSE PRICE BY REAL ESTATE AGENCIES IN ELDORET MUNICIPALITY, KENYA",2019.0,Strategic Journal of Business & Change Management,Mordecai Otura Nyambane; W. Muturi,"Real estate pricing deals with the valuation of real estate and all the standard methods of determining the price of fixed assets apply. House prices are limited and determined by various factors. The purpose of this study was to assess the determinants of house prices of real estate agencies in Eldoret municipality in Kenya. The study was guided by three objectives; to establish the effect of location on house prices, to establish the effect of demand on house prices and to establish the influence of cost of production on house price. To achieve the objective of the study a descriptive research design was adopted. The target population was 30 respondents of real estate agencies in Eldoret Municipality. Both primary and secondary source of data were utilized. Inferential and descriptive statistics were used to analyze the data and the results were interpreted and presented using percentages and frequency distribution. Regression analysis was used to establish the extent to determinants influence house prices. The study found out that location has a positive influence on house prices, demand has a positive influence on house prices and real cost of production has a positive influenc",0.0,https://strategicjournals.com/index.php/journal/article/download/1459/14142617df8a4f2595bbe1e6ae88215fed9f1c2af7a574c,10.1108/pm-01-2025-0002,"Factors impacting housing prices in National Capital Region (NCR), India: a quantile regression approach",2025.0,Property Management,Ashish Kumar,"262 263 This study aims to empirically investigate the factors that impact housing prices in the National Capital Region (NCR), India, using ordinary least squares (OLS) and quantile regression estimation approaches.264 265 266 267 The data for the study were collected from various websites of various real estate intermediaries and through discussions with apartment owners’ associations in NCR. Quantile regression was used along with OLS to understand the impact of various factors on housing prices using a dataset of 529 residential properties.268 269 270 271 The study findings establish that size, age, number of bathrooms, floor level, security, parking, lift, power backup, waste disposal system and distances from hospital and shopping complexes have a positive impact on housing prices, while the number of rooms, number of balconies, distances from school, metro station and employment zone are established as negative factors for housing prices. The impact is not uniform across all quantiles for some factors. The impact of amenities was established to be the highest in deciding the housing prices in NCR, India.272 273 274 275 The research extends the ongoing empirical research on understanding “what impacts t",0.0,276bec725023b76ebf4c13947602fad899d23efd37c,10.11113/intrest.v20n1.436,Residential Price Forecasting in Shaoxing using Gaussian Process Regression with Bayesian Optimisation,2026.0,International journal of real estate studies,Bing-Zi Jin; Xiao-Jie Xu,"The Chinese residential property market has undergone pronounced cyclical shifts over the last decade, moving from sustained expansion to a sharp downturn after late 2021, which poses considerable challenges for investors, planners, and regulators seeking to anticipate urban housing price trends. While machine learning methods are increasingly applied to real estate forecasting, Gaussian process regression (GPR) remains underutilised in China’s spatially heterogeneous markets, and no prior study has deployed it to model price dynamics in Shaoxing—a representative, rapidly transitioning urban centre. To address this gap, the present study constructs a GPR forecasting framework with multiple kernel structures and adaptive basis functions, optimized through Bayesian inference and cross-validation, using monthly residential price data from January 2013 to July 2024. The model is trained on the period up to April 2022 and evaluated out-of-sample over May 2022–July 2024. For comparative benchmarking, identical test conditions are applied to a long short-term memory (LSTM) network, support vector regression, a regression tree, and a simple autoregressive model. The GPR model achieves a ro",0.0,https://intrest.utm.my/index.php/intrest/article/download/436/2072772a8ef2f7e74a127ef2e16d2ca3e4189f85a79895,10.1080/21681376.2025.2572482,"Housing in Slovakia: COVID-19 pandemics impacts on regions, affordability and residential development",2025.0,"Regional Studies, Regional Science",Ján Výbošťok; Dušan Paur,"ABSTRACT Housing affordability has become a critical challenge in Europe, particularly in post-socialist contexts with ownership-oriented tenure systems and weak rental sectors. Housing affordability in Slovakia ranks among the least affordable housing markets in the OECD (Organisation for Economic Co-operation and Development) countries, and it has worsened sharply following the COVID-19 pandemic, further intensifying pressures through rapid price growth and rising borrowing costs. This study examines district-level affordability dynamics from 2012 to 2023, focusing on its spatial variation, determinants and pandemic-related impacts. A housing affordability index (HAI) was calculated using regional property prices and income data, incorporating credit conditions and regulatory thresholds. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were employed to identify key socio-economic and housing-market determinants. Results show that urban cores and their suburbs experienced the largest post-COVID affordability declines, while peripheral eastern and southern districts remained relatively stable. On average, affordability dropped by 71 HAI points nation",0.0,https://doi.org/10.1080/21681376.2025.2572482278a08008d3d0d0437c8a43ea013517df2194654760,10.70382/caijeres.v10i4.050,RELATIONSHIP BETWEEN LOCATION OF A COMMERCIAL PROPERTY AND ITS RENTAL VALUE IN BAUCHI METROPOLIS,2026.0,International journal of environmental research and earth science,Jambil Suyudi Zubairu; H. Haruna,"This research examines the relationship between the location of commercial properties and their rental value in Bauchi Metropolis, with a specific focus on Laushi Shopping Complex and Dr. Suleiman Adamu NIPOST Shopping Complex. Location is a major determinant of property value as it influences accessibility, demand, business opportunities, and the willingness of tenants to pay higher rents. In many urban centers, including Bauchi, disparities in rental values of commercial properties often arise due to differences in location-related factors such as proximity to transport networks, customer traffic, infrastructure, and neighborhood characteristics. This study employs both primary and secondary data sources, with questionnaires administered to property owners, tenants, estate managers, and other real estate professionals. Data were analyzed using descriptive and inferential statistical tools to establish the relationship between location variables and rental values of the selected case study areas. It was the finding of this research that most of businessmen prefer to have the frontage shops space that can be easily accessible regardless of the size of the shop in that location. Ina",0.0,https://cambridgeresearchpub.com/ijeres/article/download/887/844279412b6c5b4d00ed3435305992ca11fb89f14f60ea,10.1017/cts.2020.356,"4265 Effect of individual characteristics, healthcare access, and built environment on care coordination outcomes related to cardiovascular disease risk factors",2020.0,Journal of Clinical and Translational Science,Sonal J. Patil; Yan Wang; Angela Johnson; D. Mehr; R. Foraker; R. Kruse,"OBJECTIVES/GOALS: We examined how individual characteristics and characteristics of the socioeconomic and built environment were associated with care coordination’s effect on cardiovascular disease (CVD) risks to identify geographic areas that may benefit from supplementary clinic-community linkages. METHODS/STUDY POPULATION: We analyzed data with geocoded residential addresses and data from electronic health records for 9946 adults from a Centers for Medicare & Medicaid Services funded innovation project from 7/1/2013 to 3/30/2015. Variables included patient-level demographics, Elixhauser comorbidity index, total time with a nurse care manager, and neighborhood factors such as poverty indicators, walkability, and social capital index. Outcomes were change in CVD risk factors, hemoglobin A1C, blood pressure (BP), and low-density lipoprotein (LDL). Generalized linear models were used to assess the effect of nurse care management program on outcomes after controlling for confounding factors. RESULTS/ANTICIPATED RESULTS: We report preliminary models that include patient demographics (age, sex, race), health care utilization, nurse care manager contact time, Elixhauser comorbidity inde",0.0,https://doi.org/10.1017/cts.2020.3562809e6dfb74d73a8f54ca1467507b46c94a3d146ec1,10.25034/ijcua.2026.v10n2-8,Urban Employment Growth and the Housing Price-Productivity Gap: Evidence from 35 OECD Functional Urban Areas,2026.0,Journal of Contemporary Urban Affairs,Kamyar Fuladlu; Hasim Altan,"Urban economic growth can enhance productivity and employment while intensifying pressure on housing markets, raising questions about whether housing-price appreciation remains proportionate to improvements in metropolitan economic performance. This study examines the relationships among employment, labour productivity, and house-price indices using an unbalanced panel of 600 city-year observations for 35 OECD functional urban areas in 11 countries between 2000 and 2023. A house-price-productivity gap is constructed from rebased house-price and labour-productivity indices to identify periods in which housing-price growth outpaces productivity growth. Two-way fixed-effects models, first-difference estimation, and sensitivity analyses examine within-city co-movement while accounting for persistent metropolitan characteristics and common temporal shocks. Annual house-price growth is positively associated with contemporaneous employment and productivity growth. In the levels specification, a 10% increase in employment is associated with 3.2% higher labour productivity and a 0.132-log-point increase in the house-price-productivity gap, equivalent to a 14.1% increase in the index ratio. ",0.0,https://ijcua.com/ijcua/article/download/674/769281bd0670dfe6c7a68b31620d4181ec80d750e884ad,10.1161/circ.133.suppl_1.33,Abstract 33: Instrumental Variables Regression to Account for Bias in Associations Between the Food Environment and Diet Quality in the Coronary Artery Risk Development in Young Adults (CARDIA) Study,2016.0,Circulation,Pasquale E. Rummo; D. Guilkey; S. Ng; Katie A. Meyer; J. Reis; J. Shikany; P. Gordon-Larsen,"282 Introduction:283 Although observational evidence suggests that the food environment is associated with diet behaviors, the literature is inconsistent. Most studies ignore potential bias due to healthier people selecting healthier residential neighborhoods and healthier food outlets locating in areas where there is demand.284 285 286 Hypotheses:287 Greater availability of fast food restaurants (vs. full-service restaurants) and convenience stores and grocery stores (vs. supermarkets) is associated with lower diet quality, accounting for potential bias.288 289 290 Methods:291 We used 20 years (1985-6; 1992-3; 2005-6) of data from CARDIA study (n=26,203 observations) with geographically-linked, time-varying neighborhood-level data. Using instrumental-variables regression with a generalized method of moments estimator, we estimated the longitudinal association between the percent of neighborhood fast food restaurants, full-service restaurants, convenience stores, grocery stores, and supermarkets (per total food outlets within 1-km network distance of respondents’ residences at each exam) and an292 a priori293 diet quality score, with higher scores indicating higher diet quality. We adjusted for individual",0.0,294a2d5cdf903d0bad0bc0c6afe0419b728d6acfa46,10.3390/urbansci9080293,Integrating Urban Factors as Predictors of Last-Mile Demand Patterns: A Spatial Analysis in Thessaloniki,2025.0,Urban Science,Dimos Touloumidis; Michael A. Madas; P. Kanellopoulos; Georgia Ayfantopoulou,"While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R2 to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks ge",0.0,https://www.mdpi.com/2413-8851/9/8/293/pdf29533e97fe7f23b6cee85356af11d275c3760ece7c2,10.68086/xefd4277,"Effect of Residential Developments in the Northern Industrial Zone of Calabar, Nigeria",2026.0,"International Journal of Environmental Sciences, Development & Sustainability",Ukpali Obongha; I. A. Ugbong; Christian N. Mfah,"The rapid expansion of residential development into planned industrial zones has become a major urban planning challenge in rapidly urbanizing cities of developing countries. This study examined the effects of residential development within the Northern Industrial Zone of Calabar, Nigeria, focusing on its implications for industrial productivity, environmental quality, infrastructure demand, and property values. A longitudinal research design was adopted using panel data collected over six years (2019–2024). Data were analyzed with a Fixed Effects Panel Regression Model, while spatial analysis assessed the pattern and extent of residential expansion. The findings showed that residential development intensity had a significant negative effect on industrial productivity (β = -0.432, p < 0.05), indicating that increased residential encroachment reduces industrial efficiency. Environmental pollution (β = -0.381, p < 0.05) and infrastructure demand (β = -0.264, p < 0.05) also negatively affected productivity. In contrast, the property value index (β = 0.215, p < 0.05) was positively associated with industrial productivity, suggesting improved investment attractiveness. The model explain",0.0,https://ijesds.org/wp-content/uploads/2026/08/16-Effect-of-Residential-Developments-in-the-Northern-Industrial-Zone-of-Calabar-Nigeria.pdf2963963eed55880dbed654b5fa5d5eeb97464e7549e,10.31891/2307-5732-2022-313-5-56-60,MULTISOURCE INTELLIGENT PARKING ASSISTANT,2022.0,Herald of Khmelnytskyi National University. Technical sciences,Mariia-Ruslana Novosad,"Problem searching for a parking space is time-consuming and highly relevant both in Ukraine and abroad. Time spent searching for a parking space leads to excessive traffic, more traffic jams, air pollution and increased fuel consumption. These factors also affect the daily stress levels of drivers. Due to this, the process of finding a parking space should be fast and convenient. At the same time, there has been significant development of real estate in Lviv over the past few years. Accordingly, the need for organizing the process of parking cars of residents of residential areas is growing. This paper presents the results of the development of an application for a quick and convenient search for a parking space. A review of similar software applications was conducted. Proposed solutions use various technologies to solve the problem of searching for a free parking space including IoT, sensors, machine learning for image recognition. Even though they solve the problem of searching for a free parking space, most of them can be expensive to implement, maintain, they don’t provide the ability to work with different data sources. An activity diagram of system is presented and it shows t",0.0,http://journals.khnu.km.ua/vestnik/wp-content/uploads/2022/12/vknu-ts-2022-n5313-56-60.pdf2975724dac66ae9c368f50833143a2ca285cba5669b,10.54660/.ijmrge.2023.4.2.671-686,Real Estate Portfolio Valuation Techniques to Unlock Funding for Affordable Housing,2023.0,International Journal of Multidisciplinary Research and Growth Evaluation,Adetola Adewale Akinsulire; T. Ohakawa,"Real estate portfolio valuation plays a critical role in unlocking funding for affordable housing projects. Investors, financial institutions, and policymakers rely on accurate valuation techniques to assess risk, determine asset value, and allocate capital efficiently. This study examines various valuation methods and their application in securing funding for affordable housing initiatives. Key valuation techniques include the cost approach, sales comparison approach, and income capitalization method. The cost approach estimates property value by considering the cost of land acquisition, construction, and depreciation, making it useful for newly developed affordable housing units. The sales comparison approach evaluates property value based on recent transactions of similar properties, providing market-driven insights that are essential for investor confidence. The income capitalization method, which determines value by analyzing expected rental income and operating expenses, is particularly effective in assessing large-scale affordable housing portfolios. Emerging techniques, such as automated valuation models (AVMs) and artificial intelligence-driven predictive analytics, are en",0.0,298fafa3a6c39577efbf9ac1d399f4c30d044d056f8,10.1007/s12061-026-10028-9,Spatial Associations Between the Neighborhood Built Environment and Mental Health: Evidence from Tehran,2026.0,Applied spatial analysis and policy,Pouria Boujari; M. S. Shahmiri,"Built environments influence mental health, yet evidence on how different dimensions of urban form relate to specific mental health outcomes remains mixed, particularly in high-density cities. This study examined these relationships in Tehran, Iran, while accounting for spatial dependence and exploring spatial heterogeneity. Using secondary neighborhood-level data from 356 neighborhoods, five mental health outcomes—anxiety, depression, social dysfunction, somatic symptoms, and overall psychological distress—were analyzed in relation to the built environment. Ordinary Least Squares (OLS), Spatial Durbin Models (SDM), and Geographically Weighted Regression (GWR) were employed to assess global associations, spatial spillover effects, and localized spatial variation. The findings indicate that residential density emerged as the built-environment characteristic most consistently associated with poorer mental health, whereas population density showed no significant direct association with any mental health outcome. Access to daily services were associated with lower somatic symptoms and social dysfunction. Anxiety and depression were explained mainly by neighborhood socio-demographic cha",0.0,https://link.springer.com/content/pdf/10.1007/s12061-026-10028-9.pdf299b08c79398f40f05b692f409bb0414d98e988a6cd,10.3390/land15060987,Do Greener Environments Support Better Business? An Empirical Study in Seoul’s Commercial Alleys,2026.0,Land,Kangjae Lee; Youngjune Kim; Ashraf Khadija; Eun Jung Kim,"This study investigates the association between urban greenness and sales in commercial alleys. We focus on 1090 commercial alleys in Seoul, South Korea, defined as neighborhood-scale open commercial streets or districts composed of small retail, service, cafe, and restaurant businesses, and combine spatially explicit measures of greenness with data on weekend sales to assess how variation in vegetation is associated with local economic performance. Greenness is measured by the normalized difference vegetation index (NDVI) derived from remote sensing imagery. We employ a set of global and spatially explicit models, including Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), Multiscale Geographically Weighted Regression (MGWR), and a Python Geographical Random Forest (PyGRF, v0.0.12), to capture both overall and location-specific relationships. The results show that higher levels of greenness are significantly associated with higher weekend sales, with spatial heterogeneity observed across different areas of the city. The green investment efficiency index (GIEI) results further identify clusters of high investment efficiency in areas characterized by strong gre",0.0,https://doi.org/10.3390/land150609873002d31b9af26f2db847c0ec33bec108bd673ca2cbc,10.35595/2414-9179-2025-1-31-638-653,Urbanization and housing infrastructure in Tashkent: an economic-geographic analysis,2025.0,InterCarto. InterGIS,Jahongir Abdazov; Pakhlavon Kurbonov; Nizomiddin Jaloliddinov; Ravshan Makhamadaliev,"This study examines the urbanization processes and the economic-geographic characteristics of housing infrastructure in Tashkent, Uzbekistan, from 2015 to 2024. As the capital of the Republic of Uzbekistan and one of Central Asia’s largest cities, Tashkent has undergone significant demographic and economic transformations in recent years. Over the nine years, the city’s population surged 28.2 % from 2 371 300 to 3 040 800. The population density is growing from 7 000 to 9 000 people per km2. This growth is driven by internal and external migration flows, a high birth rate (20–25 per 1 000 people), and economic reforms. The research employs various methods, including statistical analysis, cartographic modeling, Geographic Information Systems (GIS), and case study analysis. The escalation of housing prices and deficiencies in water supply, public transportation, and social services (e. g., schools and kindergartens) have intensified social inequalities. GIS-based analyses indicate that central districts (Shaykhontohur, Yakkasaray, Yunusobod) benefit from well-developed infrastructure, while peripheral areas (Sergeli, Yangihayot, Bektemir suffer from limited access to communal and tra",0.0,http://intercarto.msu.ru/jour/data/g3/article1343.pdf301e474ecb8231010471cfbe0ac87e2b02cbff19474,10.33137/utjph.v2i2.36806,Informing Policy from Prices: An Overview of California Nursing Homes,2021.0,University of Toronto Journal of Public Health,Karen El Hajj,"Introduction: The rising cost of healthcare along with the aging demographic requires the attention of policy makers. The United States’ nursing home industry is costly to older adults, requiring many to resort to government funded Medicare to offset these costs. This study aims to understand determinants of nursing home prices in the state of California. Variables included in the analysis are selected based on previous literature on the costs of nursing homes in the US. 302Methods: The data were analyzed using a multi-variable regression analysis. The analysis sample included 1,121 nursing homes across California, using facility level and governmental data that is publically available for the years of 2016-2017. Data collected included financial indicators (net income), ownership (for-profit, non-profit) represented as a dummy variable, occupancy rates, reimbursement rates (Medicare & Medicaid), staffing, quality and competition variables such as nursing homes per county. 303Results: The regression analysis indicated that ownership type (for-profit), competition and occupancy rates have a negative significant effect on nursing home prices. Whereas, reimbursement rates of both Medicare",0.0,https://doi.org/10.33137/utjph.v2i2.36806304aac84a927849409ac0f5ba740eac7a39290ffd92,10.3389/fclim.2026.1880823,The price of paradise: how communities tolerate chronic coastal flooding in rural North Carolina,2026.0,Frontiers in Climate,J. Collins; Ryan McCune; M. Hino; K. Anarde; K. Amspacher,"Sea-level rise (SLR) poses a growing threat to the livability of coastal communities, leading to many projections of out-migration in the coming decades. However, communities’ experiences with and responses to SLR-driven flooding remain poorly characterized, especially in rural areas. Here, we investigate flooding tolerance in Down East Carteret County, North Carolina, a region of 13 unincorporated communities experiencing chronic coastal flooding. We thematically analyze interviews with 36 residents and local experts from June 2024 to June 2025 and triangulate with planning documents and direct observations at community meetings and events. Community members reported wide-ranging impacts from chronic flooding to property, roads, infrastructure, children, and other emerging areas. Despite these impacts, 14 of 18 households wanted to stay forever, calling flooding “the price of paradise.” Leveraging their local knowledge, residents tolerated flooding cognitively, behaviorally, structurally, and institutionally, though they also recognized limits to that tolerance. Desires to stay reflected deep attachment to place and resistance to community change, especially an increase in second ",0.0,https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2026.1880823/pdf305fb0d5855e8ce92c9651f0577dbbfe3e389dca5a2,10.2139/ssrn.3483279,Heterogeneities in the House Price Elasticity of Consumption,2019.0,Social Science Research Network,Nazanin Khazra,"I provide new evidence on the house price elasticity of consumption by exploiting micro-level consumption data from the Nielsen consumer panel for 2004 to 2016. I estimate the elasticity as a non-parametric function of household characteristics using a newly developed causal machine learning model called Generalized Random Forest (GRF). I find substantial county and household level heterogeneities in the elasticity. At the county level, it ranges from 0.04 to 0.16 with some neighboring counties being up to eight standard deviations apart. On the household level, elasticity ranges from 0.01 to 0.21 in which household structure plays an important role in defining the heterogeneity. Among all characteristics, having a child, size of the household, and age of the heads of the household create substantial disparities. Therefore, basing policies on an average estimate and avoiding local and household level heterogeneities may result in unintended policy consequences. I find that locations with volatile housing markets are less elastic; thus, not accounting for local heterogeneities overestimates total consumption responses in booms and underestimates them in busts. This is the first pape",0.0,30691a01d44979596486a075e9c0330702f066bad2e,10.1108/ijbpa-07-2025-0164,"Private sector participation in water infrastructure provision: investigating users' preference in Lagos, Nigeria",2026.0,International Journal of Building Pathology and Adaptation,T. Ayodele; Olayemi O. Olatunde; Abel Olaleye,"307 308 There is a growing need for private sector participation (PSP) in the provision of water infrastructure to boost availability and efficiency, especially in developing economies. This study investigates users' preference for PSP in water infrastructure provision in Lagos, Nigeria.309 310 311 312 Using a multistage sampling, the study selected 57, 172 and 296 buildings in Shomolu, Surulere and Ikeja local government areas, respectively, in Lagos. Closed-ended questionnaires were administered to one adult resident in each building. From a total of 525 questionnaires, only 450 (85.71%) were returned and analyzed using descriptive and inferential statistical techniques.313 314 315 316 The analysis revealed that users had a higher level of awareness of PSP in water infrastructure provision, though preference for PSP was slightly lower. Three key determinants of PSP acceptance were identified: effective management/policy framework, service competitiveness/effectiveness and price increase. While users recognize the advantages of improved service delivery, the positive attitudes are offset by concerns of increased costs. Anticipated price increases and reduced affordability diminish perceived behaviour",0.0,https://doi.org/10.1108/ijbpa-07-2025-0164317e985991d4e40b21b9c74f8df8ddeab960837c4df,10.5194/egusphere-egu2020-7120,Test the Effectiveness of the Open Spaces Scenario in Promoting Socio-economic Development,2020.0,,Si Chen; Zi-Pan Cai; Brian Deal,"The preservation of open spaces is treated as an important policy in recent years as urbanization level is increasing higher in the world (Geoghegan, 2002). There are multiple positive effects associated with open spaces, including recreation, aesthetic and environment values (Geoghegan, 2002). The positive effects of open space as a nature-based solution on urban social, economic and environmental factors have been explored by a number of previous papers, such as housing price (Lutzenhiser & Netusil, 2001; Bolitzer & Netusil, 2000), spatial pattern (Lewis et al., 2009; Irwin & Bockstael, 2004), human health (Groenewegen et al., 2006; Irvine et al., 2013) and social safety (Groenewegen et al., 2006; Fischer et al., 2004). However, relatively less papers have predicted the open spaces’ influences on socio-economic development. This paper will firstly verify the open space influences on economic factor (housing sale prices) and social factor (sense of safety, residential agglomeration) using a linear regression model. We consider the housing attributes, urban form attributes (eg. population density, block size, road density), driving and walking accessibility to different types of pu",0.0,https://doi.org/10.5194/egusphere-egu2020-7120318549b234677608b26116a391a579e6b851f3ee0e0,10.22214/ijraset.2026.83379,From Disrepair to Disorder to Departure: The Infrastructure Quality Index to Crime Rate to Vacancy Pathway in Kolkata’s Housing Stock,2026.0,International Journal for Research in Applied Science and Engineering Technology,Sovan Biswas; Sankha Subhra Nath; Mr. H. D. Sonawane,"The broken-windows tradition holds that infrastructure decay leads to residential vacancy because it facilitates319neighborhood crime, which leads to residential departure. We test this in the Kolkata metropolitan housing stock using a320continuous, building-level vacancy rate – share of flats vacant within each building – rather than the binary occupied/vacant321indicator common in the literature. We use survey data on 1,416 buildings across 100 wards to construct an Infrastructure322Quality Index (IQI) from five service indicators, and separately estimate the IQI to crime to vacancy mediation for apartments (n323= 853), individual houses (n = 281), and gated communities (n = 282). The result is a clear pattern of differential. Crime, when324pooled, appears to mediate 82.6% of the IQI to vacancy association, but this is driven almost entirely by the non-apartment325typologies. Mediation is strong in gated communities and significant in individual houses, but is virtually absent in apartments326where the indirect effect is −0.015, with a confidence interval that includes zero (z = −0.53, p = 0.60). Instead, apartment vacancy327is driven by building age; neither IQI nor crime is significant with cont",0.0,https://doi.org/10.22214/ijraset.2026.8337932833e424089e98f8f99e302d450172d08a5e46e7c6,10.70382/bejasd.v7i2.017,"MARKET VALUATION OF HERITAGE PROPERTIES: A CASE STUDY OF OLD RESIDENTIAL QUARTERS IN ILE-IFE, OSUN STATE",2025.0,Journal of African Sustainable Development,T. Taiwo; Saheed Omobolaji Rahman,"This study investigates the market valuation of heritage properties, focusing on old residential quarters in Ile-Ife, Osun State, Nigeria. Ile-Ife, recognized as the cradle of Yoruba civilization, is home to numerous cultural heritage sites that significantly influence the city's architectural landscape and urban development. Despite the rich cultural heritage, there is a paucity of empirical research examining how these heritage sites impact the market value of residential properties in the area. The research employs a mixed-methods approach, integrating quantitative spatial analysis with qualitative assessments. Quantitative data were collected through property transaction records, spatial measurements of proximity to heritage sites, and property valuation reports. Qualitative data were gathered via semi-structured interviews with property owners, real estate professionals, and urban planners. The study area encompasses residential properties within a 2-kilometer radius of selected heritage sites, including the Ooni's Palace, the National Museum, and the Ifa Temple. Findings indicate a positive correlation between proximity to heritage sites and increased property values. Propert",0.0,https://berkeleypublications.com/bjasd/article/download/538/488329822be0d61812a243b53a9d84a37c867313c2592b,10.1007/s10389-022-01817-w,An exploration of structural determinants driving racial disparities in COVID-19 deaths in Michigan,2023.0,Journal of public health,Emily Clark; Erin N. Voichoski; Alexa Eisenberg; R. Mehdipanah,"Aim Racial disparities in COVID-19 death rates have largely been driven by structural racism in health, housing, and labor systems that place Black, Brown, and Indigenous populations at greater risk for COVID-19 exposure, transmission, and severe illness, compared to non-Hispanic White populations. Here we examine the association between taxable property values per capita, an indicator influenced by historical and contemporary housing policies that have disproportionately impacted people of color, and COVID-19 deaths. Methods Taxable values serve as a proxy for fiscal health providing insight on the county’s ability to address imminent needs, including COVID-19 responses. Therefore, higher taxable values indicate local governments that are better equipped to deliver these public services. We used county-level data from the American Community Survey, the Michigan Community Financial Dashboard, The Atlantic’s COVID Tracking Project, and the Community Health Rankings and Roadmap for this cross-sectional study. Maps were created to examine the geographic distribution of cumulative death rates and taxable values per capita, and regression models were used to examine the association betw",0.0,https://link.springer.com/content/pdf/10.1007/s10389-022-01817-w.pdf330cc825e51b86019d46084d726b5f5cf6d60b46886,10.18664/1994-7852.214.2025.352039,GEOGRAPHIC INFORMATION BASE FOR REAL ESTATE VALUATION,2025.0,Collection of Scientific Works of the Ukrainian State University of Railway Transport,O. Voronkov; A. Yevdokimov,"This article explores the application of Geographic Information Systems (GIS) in real estate valuation, demonstrating how spatial analysis can increase the accuracy and objectivity of property appraisal. Using the rental housing market in Kharkiv as a case study, the research develops a practical GIS-based methodology implemented in the open-source software QGIS. A geodatabase was created containing 80 rental listings geocoded by address, supplemented by infrastructure layers (metro stations, public transport stops, shops, pharmacies, parks, schools, kindergartens, and hospitals).Spatial analysis techniques, including density mapping, buffer zones, and visualization of accessibility, revealed two primary high-value rental zones: the prestigious central part of Shevchenkivskyi district and densely built-up neighborhoods of Saltivka with well-developed infrastructure. The study confirmed a strong correlation between rental prices and proximity to metro stations, while also showing that a dense and efficient ground public transport network can largely compensate for the absence of metro access in certain areas.The results highlight the effectiveness of GIS in visualizing spatial patte",0.0,http://csw.kart.edu.ua/article/download/352039/3388813319a0f1d2535f2f74fa8c16810ba267063d9b5a2a1,10.1051/itmconf/20257901023,A Robust Ensemble-Based Framework for House Price Estimation: Integrating XG-Boost with SHAP and Web Deployment,2025.0,ITM Web of Conferences,Ezil Sam Leni A; R. T; Sridhar Devarajan,"Accurate house price prediction is crucial for stakeholders in the real estate industry, including buyers, sellers, agents, and investors. Traditional valuation methods often rely on manual appraisal or basic regression techniques, which may lack scalability and the ability to capture complex relationships among features. With the rise of machine learning, especially ensemble models like XGBoost, there is a growing opportunity to improve prediction accuracy and robustness. This study presents a comprehensive framework for optimal house price prediction using XG-Boost, applied to the King County housing dataset comprising over 21,000 records. The suggested objective is supported by carefully conducted data preprocessing, which will imply outliers’ elimination, feature engineering, and normalization. Other important derived features that were included were the age of the house and the renovation status of the house as well as the price per square foot. The XG-Boost regression model was trained and validated, and the R-squared ratio was found to be 0.87, indicating decent model performance. The model was also deployed as an interactive web application with Streamlit to allow users to ",0.0,https://www.itm-conferences.org/articles/itmconf/pdf/2025/10/itmconf_keis2025_01023.pdf332766d53dbf6ae79a1ef460d6815ef54de34afa9bd,10.11648/j.sjams.20261401.12,Unveiling the Impact of Nonlinear Modeling in Housing Price Prediction: An Empirical Comparative Study,2026.0,Science Journal of Applied Mathematics and Statistics,Jie Wang; Qiu-Tong Yu; Xin-Tong Liu; Hongli Zhu; Qiuyu Ming; Jia-Tong Dai; Guan-Yu Sha; Hanyu Xu; Yan Zhong; Shan-Cheng Yu,"Regression analysis is a core analytical tool widely employed across diverse domains for predicting continuous outcomes, serving as a cornerstone of statistical inference and machine learning applications ranging from economic trend forecasting to healthcare risk assessment and real estate valuation. Choosing an effective regression technique is critical for accurate predictions, yet a daunting challenge for non-experts due to the wide variety of methods, each with distinct assumptions, tuning requirements and applicability boundaries. To address this dilemma, this study conducts a rigorous empirical comparison of five popular regression techniques—Ordinary Least Squares (OLS), Ridge regression, Lasso regression, Elastic Net, and Polynomial regression—applied to house price prediction using two benchmark datasets: the classic Boston Housing dataset and the comprehensive California Housing dataset. A multi-dimensional evaluation framework was adopted, including quantitative metrics (Mean Squared Error (MSE) and coefficient of determination () and qualitative diagnostics (residual analysis and Quantile-Quantile (QQ) plots) to assess prediction accuracy and error distribution. Results",0.0,http://article.sciencepg.com/pdf/j.sjams.20261401.123331c55251aab21f05e129c3df0f39fa8213d3e2312,10.36348/gajeb.2026.v08i03.001,Data-Driven Decision-Making for Real Estate Portfolio Optimization Using Cloud Analytics,2026.0,Global academic journal of economics and business,Syed Fakruddin Albeez,"Cloud analytics is actively applied in the field of real estate investment and portfolio optimizations in today's business environment. Solutions created by AWS, Microsoft Azure, and Google Cloud Platform (GCP) provide the opportunities to manage the portfolio by ingesting data, performing predictive modeling, and visualizing data sets. The current paper aims at analyzing the role of cloud analytics in data-driven portfolio optimization in seven markets: Saudi Arabia, UAE, Qatar, Oman, India, the United Kingdom, and the United States. There are a lot of developments concerning the application of predictive analytics and machine learning in recent years. The results of the JLL 2025 Global CRE Technology Survey report that 92% of the companies in the CRE sectors are running AI pilots, whereas the share was 5% in 2023 [13]. The accuracy rate of estimates made with machine learning algorithms in relation to property prices and rental demand stands between 82% and 91% [22]. It is claimed that there are potential profits worth $1.3 trillion in the global real estate sector [17]. IoT devices installed in smart buildings allow saving up to 25% on operating expenses [3]. Moreover, 60% of al",0.0,334be08a9b25b3a0c744a88854ef2a7e57cae0bab79,10.46799/adv.v1i12.157,"Analysis of the Feasibility of Investing in Taman Melati Dinoyo Apartments, Malang City",2023.0,Advances In Social Humanities Research,Bella Astrina Classinta Ard; Haris Muhammadun; Laksono Djoko Nugroho,"This research aims to investigate the investment potential of Taman Melati Dinoyo Apartments, Malang City as a promising form of property. Through an economic and property market analysis approach, this research explores the factors that influence investment decisions and the impact on local economic growth. An in-depth literature study was used to understand property investment trends and apartment market characteristics. The data analysis method includes collecting primary data through interviews with property developers and other related parties. Additionally, secondary data from financial institutions, local governments and other sources were used to validate the findings. The results of this research identify the main factors that influence apartment investment such as property prices, construction costs, occupancy rates and interest rates. Apart from that, this research can also be a reference for project developers to identify potential risks and stimulate effective mitigation strategies by using investment capital that is used in a ratio of 30% own capital and 70% loans.The analytical method used to analyze this research is data collection, data processing, income analysis,",0.0,https://adshr.org/index.php/vo/article/download/157/152335e86f8af42fdcba9f3f54268e3e760a47df0a8020,10.3390/su18147268,Exploratory Data-Driven Modeling of Macroeconomic Indicators Associated with Sustainable Housing Affordability: A Comparative Analysis of Construction Economics in Poland,2026.0,Sustainability,Aleksandra Kostrzanowska-Siedlarz; Kamil Roter,"This article employs exploratory data-driven modeling to examine the relationships between selected macroeconomic indicators, residential property prices, and housing affordability pressures in Poland between 2020 and 2024. This turbulent period was selected for analysis because of the unprecedented volatility triggered by the COVID-19 pandemic and the geopolitical shocks associated with the war in Ukraine, both of which severely disrupted macroeconomic stability and construction supply chains. The study examines how key economic variables—including inflation, gross domestic product (GDP), unemployment, and average and minimum wage dynamics—are associated with residential property price dynamics within the framework of construction economics. Using statistical modeling techniques, including linear regression and Pearson correlation analysis, the study quantifies the strength, direction, and dynamics of these relationships across primary and secondary housing sectors. Our findings reveal a distinct comparative pattern of associations: average wage growth and inflation emerge as the macroeconomic indicators most strongly associated with property valuations, while macroeconomic growth",0.0,https://www.mdpi.com/2071-1050/18/14/7268/pdf?version=17841911583362cb83e1bd5ce1628e0f9740f8d44c84151dfe224,10.1093/oso/9780198824305.003.0002,Introduction,2018.0,The AI Delusion,Gary Smith,"The Democratic Party’s 2008 presidential nomination was supposed to be the inevitable coronation of Hillary Clinton. She was the most well-known candidate; had the most support from the party establishment, and had, by far, the most financial resources. Two big names (Al Gore and John Kerry) considered running, but decided they had no hope of defeating the Clinton machine. That left an unlikely assortment of lesser-knowns: a U.S. Representative from Ohio (Dennis Kucinich), the Governor of New Mexico (Bill Richardson), and several U.S. Senators: Joe Biden (Delaware), John Edwards (North Carolina), Chris Dodd (Connecticut), Mike Gravel (Alaska), and Barack Obama (Illinois). The nomination went off script. Obama was a first-term senator, a black man with an unhelpful name, but he excited voters. He raised enough money to be competitive in the Iowa caucuses and he persuaded Oprah Winfrey to campaign for him. Obama defeated Clinton by eight percentage points in Iowa and the race was on. Obama won the Democratic nomination and, then, the presidential election against Republican John McCain because the Obama campaign had a lot more going for it than Obama’s eloquence and charisma: Big Dat",0.0,337b44b9bfe7022079272b30265c977affe0a9faa60,10.54691/v6hkjm84,Enhancing Housing Price Prediction Accuracy through Hybrid POI-XGBoost Models: A Case Study of Nanjing,2026.0,Scientific Journal of Economics and Management Research,Lei Sun," [Purpose] This study aims to improve the precision of second-hand housing price prediction by overcoming the limitations of traditional statistical data, such as poor timeliness and authenticity.[Method] Utilizing Python web crawlers, this paper collects housing data from Lianjia and processes neighborhood characteristics using Baidu POI (Points of Interest) data. Three models are constructed and compared: Vector Autoregression (VAR), Random Forest, and the proposed POI-XGBoost model. [Findings] The empirical results based on Nanjing 338 [Purpose] This study aims to improve the precision of second-hand housing price prediction by overcoming the limitations of traditional statistical data, such as poor timeliness and authenticity.[Method] Utilizing Python web crawlers, this paper collects housing data from Lianjia and processes neighborhood characteristics using Baidu POI (Points of Interest) data. Three models are constructed and compared: Vector Autoregression (VAR), Random Forest, and the proposed POI-XGBoost model. [Findings] The empirical results based on Nanjing data demonstrate that the XGBoost model, enhanced with POI features, achieves superior predictive performance (of 0.9",0.0,https://bcpublication.org/index.php/SJEMR/article/download/9337/9272339c6cba6244d71ef3d5bbaa6b2814779c96ce3eb2f,10.3390/ijgi12020069,Multi-Source Data and Machine Learning-Based Refined Governance for Responding to Public Health Emergencies in Beijing: A Case Study of COVID-19,2023.0,ISPRS Int. J. Geo Inf.,Demiao Yu; Xiao-Ran Huang; He-Cang Zang; Yuan-Wei Li; Yu-Chen Qin; D. Li,"The outbreak of COVID-19 in Beijing has been sporadic since the beginning of 2022 and has become increasingly severe since October. In China’s policy of insisting on dynamic clearance, fine-grained management has become the focus of current epidemic prevention and control. In this paper, we conduct a refined COVID-19 risk prediction and identification of its influencing factors in Beijing based on neighborhood-scale spatial statistical units. We obtained geographic coordinate data of COVID-19 cases in Beijing and quantified them into risk indices of each statistical unit. Additionally, spatial autocorrelation was used to analyze the epidemic risk clustering characteristics. With the multi-source data, 20 influencing elements were constructed, and their spatial heterogeneity was explored by screening 8 for Multiscale Geographically weighted regression (MGWR) model analysis. Finally, a neural network classification model was used to predict the risk of COVID-19 within the sixth ring of Beijing. The MGWR model and the neural network classification model showed good performance: the R2 of the MGWR model was 0.770, and the accuracy of the neural network classification model was 0.852. T",0.0,https://doi.org/10.3390/ijgi12020069340b7aeb8efe0485a234f2b131616c6f5ffb1494217,10.20472/efc.2019.012.023,MODELLING HOUSING MARKET AND HOUSING PRICE DYNAMICS IN CROATIA*,2019.0,"Proceedings of the 12th Economics & Finance Conference, Dubrovnik",Tamara Sli kovi,"Since the beginning of this century, the Croatian housing market has passed through various stages of development. Roughly, trends in this housing market could be observed through three different phases. The first period encompasses the first eight years of this century and is characterized by stable and positive movements of supply and demand indicators. The year 2008 represents the turning point towards negative trends on the Croatian housing market. This was the beginning of a crisis which lasted seven years. During this period, the demand for housing units has fallen dramatically and supply of new housing units has grown at low rates. As a result of such market developments, housing prices have decreased. Negative trends persist until 2015, when stable increase of supply indicators is perceived. Thereafter, demand side of the market starts to recover, along with housing prices which begin to rise.This research represents an attempt to identify the factors that have influenced the mentioned developments on the Croatian housing market. The significance of factors which are included in analysis is tested within the multiple regression framework. Special emphasis is placed on model",0.0,http://www.iises.net/proceedings/12th-economics-finance-conference-dubrovnik/table-of-content?cid=95&iid=023&rid=11828341dd6beb02684c433521263144a3f91a770f8d6460,10.15396/afres2016_164,"The Impact Of Religious Camps Developmenton Residential Property Values: A Case Study Of Lagos Ibadan Expressway, Nigeria",2016.0,,B. Ogunleye; Y. Adeniran; Olusegun,"This study examines the impact of religious camps developments on residential property with reference to selected areas along Lagos/Ibadan Expressway in Nigeria with a view to guiding property investors on the viability of residential property development in close proximity to religious camps. It assessed the types of residential property developments, rental values obtainable from such properties between the years 2005 -2015 and the impacts of these camps on the rental values of the residential properties. The register available at the Ogun State Branch of the Nigerian Institution of Estate Surveyors and Valuers were used to determine the population of registered firms of Estate Surveyors and Valuers practicing in Ogun State within the proximity of the study area which amounts to twelve (12) in numbers. Since they were manageable and could be traced, they were all taken for the study upon which structured questionnaires were administered. Data collected were analyzed using descriptive statistics and regression analysis. The study revealed that most of the investors prefer to build tenement buildings as it provides more accommodation that can house many population; findings also sh",0.0,https://afres.architexturez.net/system/files/non-refereed-AfRES_2016%20proceedings-10.pdf342564f09b28ddd5edad81b7845a3d2ff0db444e6bb,10.3390/ijgi15100460,Modeling the Relationship Between Ride-Hailing Demand Density and the Urban Built Environment on the Basis of the GNNWR and Dynamic Spatial Partitioning,2026.0,ISPRS International Journal of Geo-Information,Zhi-Wei Zhu; Yuxuan Li; Xin-Xin Zhang,"Understanding the complex spatiotemporal heterogeneity between ride-hailing demand and the urban built environment is crucial for optimizing urban space and allocating transportation resources. Previous analyses using fixed spatial scales (e.g., regular grids or administrative boundaries) often suffer from the modifiable areal unit problem (MAUP) and fail to capture the actual distribution of ride-hailing demand. Furthermore, traditional geographically weighted regression (GWR) models struggle with nonlinear spatial relationships. To address these challenges, this study uses Xiamen, China, as a case study. We employ the K-means++ clustering method on ride-hailing origin–destination (OD) data to generate dynamic spatial partitioning units, integrating them with points of interest (POIs), land use, and population data. A geographically neural network weighted regression (GNNWR) model is then introduced to quantify how built environment factors influence mobility, explicitly capturing nonlinear variations. Results show that dynamic spatial partitioning effectively mitigates the MAUP: the traditional GWR R2 improved from 0.414 (administrative units) and 0.577 (regular grids) to 0.649. ",0.0,https://doi.org/10.3390/ijgi1510046034378293c6915e7d64f019e50782d42b7d6526a9b05,10.1108/ijhma-05-2026-0144,"Labor market change, income polarization and urban housing inequality: a comparative study of Italy, Spain and the United Kingdom",2026.0,International Journal of Housing Markets and Analysis,Alessio Sardo; Daniela Arlia; G. Cerruti,"344 345 This paper aims to explore the interaction between labor market reforms and housing policies in Italy, Spain and the UK.346 347 348 349 By providing a comparative legal analysis of significant policy changes at the macro level and a descriptive overview of the evolution of their main economic determinants – wages and housing prices – at the micro level, the study highlights the socioeconomic impact of these reforms and their contribution to urban disparities.350 351 352 353 The analysis suggests that the implementation of labor market reforms aimed at increasing flexibility and competitiveness coincided with heightened precarity, particularly for low-wage and nonstandard workers across all countries.354 355 356 357 The analysis does not address causality.358 359 360 361 The paper suggests that stratified labor policy and work deregulation have an impact on urban inequality and wage distribution at sub-city level.362 363 364 365 The study argues that these reforms, coupled with macro-events such as the increase in global trades and the advent of ICT, might have exacerbated the socioeconomic inequalities at both national and local levels over the last decades by disfavoring less skilled workers, particularly in large c",0.0,https://www.emerald.com/ijhma/article-pdf/19/7/139/11873786/ijhma-05-2026-0144en.pdf366a75a7a1c1802a287533c34a3a85a164d018e345c,10.47191/ijmra/v6-i1-29,"Empirical Evidence on Analysing the Quality of Life of Informal Settlement for Regeneration Project: Case Study of Boa Wala Settlement, Lahore",2023.0,International journal of multidisciplinary research and analysis,Muhammad Ahsan; B. Ozbakir,"The empirical evidence from literature has disclosed that the extensive share of world’s population continues to live in informal settlements due to extensive economic, social, and welfare disparities. These informal settlements possess the attributes of poor infrastructure and utilities services. In reality, due to excessive housing prices, economic incapacity, and insufficient technical abilities among poor people, they are forced to live in such dilapidated settlements. However, over the times, governmental institutes have adopted several distinct strategies to enhance the quality of life (QOL) of inhabitants living in informal settlement. Therefore, this research study intends to examine the contemporary living conditions in informal settlement using multi-indicator analysis. To assess the QOL, five leading indicators including building density, utilities services, road infrastructure, urban furniture, and social services and their associated 29 sub-indicators was identified and Bao wala as informal settlement in Lahore, Pakistan has been selected as case study area. A semi structured questionnaire was formed and sample size of 99 was calculated to perform socio-economic survey",0.0,http://ijmra.in/v6i1/Doc/29.pdf3677237fefb29db85ecf2a905665032eccfde186623,10.35850/kjta.23.6.01,Analysis of the Effect of Strengthening the Comprehensive Real Estate Tax for Multi-house Holders: Focusing on Avoidance of Multiple Houses in Response to the 9.13 Policy,2022.0,THE KOREAN TAX ASSOCIATION,Sang-Bin Lim; E. Choi; So-Young Lee,"After Covid－19, the housing market has been experiencing abnormal overheating, and the housing market is facing an abnormal situation, such as a surge in housing prices over the past three years. In the housing market, the market is showing the characteristics of an investment goods market, similar to the Bitcoin market, as housing prices soar, such as the craze of “smart one－house” and the craze of “2030 generation housing gap investment”. The purpose of this study is to investigate the cause of the abnormal situation in the housing market as it is attributed to the tax system. 368In the comprehensive real estate tax, the strengthening of taxation on multi－house owners and special cases for single－house owners widen the gap between multi－house owners and single－ house owners in the tax burden. In this case, the taxpayer will make an effort to become a single －family house owner to avoid tax. If there is an expectation of increase in house prices, a tax strategy is established to become one house from multiple houses through gift after division of the household as a measure to reduce the tax burden rather than to contemplate the sale of the house. 369On the other hand, taxpayers who ca",0.0,37052bf45fa67dde97d9d0a9f6dad5cf3b2020a646d,10.24294/jipd.v8i5.2248,Investigating the government’s role in the boom-and-bust cycles of the housing market,2024.0,Journal of Infrastructure Policy and Development,Abolfazl Meshkini; Sadegh Nejati; Somayeh Alipour,"Governments intervene in the housing market via implementing various monetary, fiscal, foreign exchange and credit policies. By this, the housing market undergoes cycles of boom and bust as well as significant swings in value added and housing prices. Therefore, the main goal of this research is to consider the effect of the government’s change on the monetary and financial policy’s impact on the business cycles of the housing sector during the period of 1978–2020. On the other hand, we estimate the impact of monetary and fiscal policies on housing business cycles concerning government’s change. To calculate housing business cycles (boom and busts), the housing value added were initially de-trended using the Hodrick–Prescott filter. This paper takes a novel use of the threshold regression model with government’s change as threshold variable. According to the study’s findings, there are three threshold effects (two threshold levels or three regimes) of monetary and fiscal policy on housing business cycles. For instance, the money supply coefficient in the first regime was −1.68, indicating that the effect of monetary policy in this regime is countercyclical. in the second and third ",0.0,http://dx.doi.org/10.24294/jipd.v8i5.2248371b14a23a70a3b5c6eac4700bddc5b8c22fdbf7b77,10.66884/kesj.2025.15atrcmx,"EFFECT OF ENVIRONMENTAL QUALITY ON RESIDENTIAL PROPERTY RENTAL VALUE IN BIRNIN KEBBI, KEBBI STATE",2025.0,Kaduna State University Environmental Sciences Journal,H. O. Abdulazeez,"<p>372 <strong>Purpose:</strong> Environmental attributes are important determinants of real estate value and are a critical factor in determining the prices of residential property. This study aims to investigate the effects of environmental quality on the rental value of residential properties in Birnin Kebbi, Nigeria.373 </p>374375 <p>376 <strong>Design/methodology/approach:</strong> To achieve this aim, determinants of environmental quality were assessed alongside the rental value of residential properties in the study area. A stratified random sampling technique was employed in the administration of the questionnaire on a sample size of 1,067 out of the 304,113 sample frame of the study population. Of the administered questionnaires, 580 were retrieved (54.5%), which was adjudged adequate for the study. Both descriptive (mean, average weighted mean) and inferential statistical (Spearman's correlation) tools were adopted for analysis.377 </p>378379 <p>380 <strong>Findings:</strong> The result revealed that the correlation coefficient (r) obtained was 0.344, indicating a positive but weak relationship between environmental score and rental value. And the significant level (p) of 0.450 obtained wa",0.0,381631d8eb3d5d60934f2ed671d97ac3cf9f363ed89,10.3390/land14112228,Industrial Land Expansion as an Unintended Consequence of Housing Market Regulation: Evidence from China,2025.0,Land,Si-Xuan Li; Hangtian Xu; Wenzhuo Zheng,"China’s rapid urbanization, characterized by extensive land allocations, operates within a framework of binding quotas imposed by upper-level governments, while local governments exercise broad discretion over the zoning of newly transacted land parcels. In this context, investigating the evolving patterns of land supply structure during this period is therefore of critical importance. The central government’s 2018 articulation of the “Houses are for living in, not for speculation” (fangzhubuchao) sought to mitigate housing market speculation and curb potential asset bubbles, including through changes to residential land supply. Using a panel of 266 prefecture-level cities across China, this study employs a generalized difference-in-difference model to examine how housing market regulations affect the industrial sector through adjustments in land supply. To capture cross-city variations in local policy interventions, we construct a measure based on the land price wedge between residential (and commercial) and industrial land derived from a hedonic pricing model, which reflects underlying housing market conditions. The results indicate that a reduction in residential land supply cau",0.0,https://www.mdpi.com/2073-445X/14/11/2228/pdf?version=1762857820382d2dab7d26dfdfc69549a0ff7cdc6db7f60d0a708,10.1029/2026wr044077,Closing the Equity–Revenue Gap: Pricing Pathways for Unmetered Urban Water Utilities in the Global South,2026.0,Water Resources Research,H. Khan; J. Sears,"Urban water utilities across South Asia grapple with persistent financial shortfalls and significant inequities in water affordability, with limited tools for reform in largely unmetered systems. In this study, we evaluate non‐volumetric pricing strategies and service reforms capable of improving utility revenues while reducing the disproportionate financial burden of water expenditures borne by lower‐income piped households. Using detailed household‐level survey data and property value assessments from Karachi, Pakistan, we estimate a logistic regression model of bill payment behavior and train a local linear regression forest to model household reliance on expensive alternative water sources. Next, we build a simulation framework using these models to estimate how utility revenues and household water expenditures change for a range of tariff and service reforms, and how these reforms reshape inequality in water expenditures. Simulation results for the current tariff system indicate that coupling full bill distribution with improved service quality could increase utility revenues by approximately 80% by leveraging latent willingness to pay among currently‐unbilled households. Cons",0.0,https://doi.org/10.1029/2026wr044077383a9f23b99fd5ad5ed8cba26b0ebb2bdf1154ce6f0,10.1111/ijcs.70137,"Stylistic Signals in Professional and Amateur Language Used in384 C2C385 Markets",2025.0,International Journal of Consumer Studies,József Hubert; Zsófia Kenesei; A. Bauer,"386 In consumer‐to‐consumer (C2C) markets, individuals must decide whether to sell property directly or rely on professional intermediaries. While digital technologies increasingly enable direct transactions, the expertise and strategic communication skills of intermediaries continue to play an important role. Our objective in this research is to connect expertise theory with signaling theory by examining whether professionals employ stylistic elements in real estate listings that traditionally convey quality and trustworthiness, and how these differ from those used by amateurs. To address this, we introduce topic‐prevalent n‐grams to examine stylistic patterns across a large dataset of more than 300,000 real estate advertisements in the United States. Two consecutive studies apply natural language processing (NLP) techniques to uncover systematic differences in wording, tone, and rhetorical strategies between professional brokers and amateur homeowners. A third study, which involved a scenario‐based experiment with US homeowners, illustrates how potential buyers perceive and respond to the different linguistic styles. The results show that professional brokers use language strategic",0.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/ijcs.7013738734556515f99aab69038b49d0e1b14ce6da6c4e5a,10.61799/2216-0388.1885,Enhancing House Price Prediction Using Ensemble Machine Learning Models: A Comparative Study with U.S. Housing Data,2026.0,Mundo Fesc,A. Gbadebo; Abiola Olaide Ayodele,"Background: Proper prediction of house prices is one of the primary issues in the real estate market, especially under unstable political and economic conditions. Forecasting models must adapt to increasing market complexities, yet such adaptability is difficult to achieve using traditional prediction techniques.388Objective: This paper aims to compare the predictive accuracy of five machine learning models, including XGBoost, LightGBM, CatBoost, HistGradientBoosting, and Bayesian Ridge Regression, using a Kaggle U.S. housing dataset to determine which model performs best and which features contribute most to accurate predictions.389Methods: A dataset of 1,460 samples and 32 variables from 1991 to 2024 was used. Both regression and classification measures were applied to evaluate model effectiveness. Feature importance analyses were conducted to identify variables that consistently influenced predictions.390Results: CatBoost demonstrated the strongest performance, with a training accuracy of 0.94, a test R² of 0.86, and an MCC of 0.81. Variables such as OverallQual and GrLivArea were consistently identified as highly influential. Ensemble models, particularly CatBoost and XGBoost, outper",0.0,https://doi.org/10.61799/2216-0388.188539140696b19cda58c998a6d8cb8611fc04b2b3fdafb,10.3390/su18157710,"Fine-Scale Longitudinal Reconstruction of Urban Employment and Job–Housing Dynamics: Evidence of Bounded Decentralization, Dual-Core Divergence, and a Three-Year Exploratory Adjustment Lag in Tianjin, 2010–2023",2026.0,Sustainability,Li Yan; Li-Jian Ren; Jia-Zhen Zhang; Yingxia Yun,"Urban employment restructuring generates job–housing mismatches whose planning consequences depend on spatial scale and temporal trajectory, yet existing data sources rarely combine fine spatial resolution with long temporal coverage. This study applies multiscale geographically weighted regression (MGWR) to reconstruct annual employment surfaces at 1 km2 resolution for Tianjin, China, from 2010 to 2023, using a single anchor year of mobile signaling data. Spatial relationships estimated from 2020 records were transferred across time using annual updates of Points of Interest (POI) density, nighttime light intensity, and population density, with aggregate consistency enforced against official employment totals. Validation confirmed the spatial ordering of employment concentration in the calibration year (Spearman ρ = 0.509, RMSE = 2856 jobs/km2), temporal stability in an independent year (ρ = 0.522, RMSE = 2640 jobs/km2 for 2023), and consistency with district statistics (R2 = 0.718). Directional robustness to anchor-year selection was further supported by reverse-transfer validation (ρ = 0.652) and by a 2023-anchored reconstruction that replicated the principal directional finding",0.0,https://doi.org/10.3390/su1815771039297c242d2fce279611b4776acadd9ff169de1befa,10.29327/2565368.3.1-5,The Impact of Housing Prices and GDP Growth on Income Inequality in Canada: A National Study (1990–2022) with Unemployment as a Control Variable,2025.0,Socioeconomic Analytics,S. Mousavi,"This study examines the relationships among housing prices, GDP growth, and unemployment and their effects on income inequality in Canada from 1990 to 2022. Employing multiple regression analysis, the research reveals that rising housing prices are significantly associated with lower income inequality, contradicting the common assumption that higher housing costs exacerbate socio-economic disparities. In contrast, GDP growth appears to have little measurable effect on income distribution, challenging the Kuznets Curve Hypothesis, which posits that economic growth ultimately leads to reduced inequality. Unemployment shows minimal relevance as a mediating variable between housing prices, GDP growth, and inequality, suggesting that its role in shaping income disparities is limited. These findings emphasize the importance of government intervention and robust social policies to mitigate labor market shocks and their distributional consequences.393The study contributes to the literature by challenging conventional economic thought and highlighting the critical influence of housing markets and redistributive policies on income inequality. Although homeownership can serve as a short-run equ",0.0,https://periodicos.ufpe.br/revistas/SECAN/article/download/265829/5038839442b0b18de6fa311ac928b03dedc463405a243939,10.1108/ijhma-06-2026-0185,"The Japanese real estate conundrum: a panel data analysis of aging, population stagnation, and long-term house price trends",2026.0,International Journal of Housing Markets and Analysis,Zu-Guo Jia; Yang Cao,"395 396 This study examines whether Japan’s prolonged housing downturn reflects unique domestic factors or broader structural forces observable across advanced economies. By situating Japan within a comparative OECD framework, the paper evaluates the long-run roles of income growth, population dynamics and demographic aging in shaping real house price movements. The purpose of this study is to determine to what extent Japan’s post-1991 decline can be interpreted as an extreme case of general macroeconomic and demographic mechanisms rather than an isolated national anomaly.397 398 399 400 The study constructs a balanced panel of 19 OECD economies from 1990 to 2019 and estimates panel regressions with country and year fixed effects to identify long-run drivers of real house price growth. The preferred specification uses per capita real GDP growth, population growth, aging-ratio growth and CPI inflation as key determinants. These cross-country estimates are then applied to Japan to generate fitted values, residual patterns and counterfactual simulations, allowing assessment of how Japan’s housing market would have evolved under alternative demographic and macroeconomic scenarios.401 402 403 404 Cross-cou",0.0,405ebce56f2f9fc847f71c375be5636f32324e9d70f,10.32796/cice.2016.92.6133,Banking Stability and the Financial Cycle: The Spanish Experience,2016.0,,Ana Lozano-Vivas; Flores Martinez-Alba,"espanolEn el presente articulo se definen las caracteristicas del ciclo financiero en Espana durante el periodo 1995-2016 y, ademas, se analiza hasta que punto la estabilidad del Sistema Bancario atenua o exacerba el ciclo financiero. Los resultados obtenidos en estos dos analisis empiricos ponen de relieve que el ciclo financiero en Espana se define con precision por la relacion credito/PIB, el credito al sector privado no financiero y los precios de los inmuebles residenciales y que la brecha credito/PIB, respecto a su tendencia a largo, representa un buen indicador adelantado de la crisis bancaria en Espana. Por otra parte, los resultados indican que una desviacion exogena del valor real respecto al valor potencial del ratio provisiones sobre prestamos totales ayuda a suavizar el ciclo. Este ultimo resultado parece indicar que la regulacion espanola de las «provisiones dinamicas» puede ser considerada como una buena herramienta macroprudencial para combatir la prociclicidad del sistema financiero. EnglishThis paper defines the special characteristics of the financial cycle in Spain during the period 1995-2016 and investigates to what extent the stability of the banking system at",0.0,http://www.revistasice.com/index.php/CICE/article/download/6133/6133406bca36ad0747de469e8bb538c8621a62a24a022ea,10.54254/2755-2721/2026.35721,Research on Construction of Multi-Dimensional Evaluation Model for Realization Path of Spatial Justice in Urban Renewal Context,2026.0,Applied and Computational Engineering,Xiaoying Ou; Yu-Lu Zhou; Jianlun Wang,"Urban renewal has gradually shifted from physical redevelopment to a comprehensive governance process involving public resource redistribution, social equity protection, and civic participation. Traditional renewal evaluation usually focuses on land-use efficiency, construction intensity, and spatial form improvement, but it insufficiently explains equity issues related to public service opportunities, transport accessibility, housing pressure, resident participation, and benefit sharing. In response to this gap, this study constructs a multi-dimensional evaluation model for the realization path of spatial justice in the context of urban renewal. The study uses 500 m × 500 m grids as unified analytical units and integrates data on public service facilities, transport networks, housing prices, rents, population density, green-space resources, and resident perception questionnaires. A spatial, social, and perceptual evaluation database is then formed. Methodologically, the two-step floating catchment area method is used to measure public service accessibility, entropy-weighted TOPSIS is applied to calculate the spatial justice index, and random forest with SHAP interpretation is used",0.0,https://ace.ewapub.com/article/view/35721.pdf4075c6c6c1c7ec2a9a18281a51f49f5793e21e311d6,10.56409/kreis.2026.9.3.305,"Conservation and Value Formation of Historic Cultural Assets from a Real Estate Value Perspective : Urban Spatial Change, Conservation and Adaptive Reuse in The Rocks, Sydney",2026.0,KOREA REAL ESTATE INDUSTRY SOCIETY,Seung-Man Jin; J. Seo,"This study examines the relationship between the conservation and adaptive reuse of historic cultural assets and value formation in The Rocks, Sydney, from a real estate value perspective. The Rocks is a historic waterfront district with substantial development potential due to its proximity to the Sydney CBD and Circular Quay, while retaining long-established relationships among streets, lanes, buildings, topography, and the waterfront. Although large-scale redevelopment was proposed during the 1960s and 1970s, community action and the Green Bans contributed to a shift toward conserving the existing urban fabric while accommodating new uses. Based on documentary and public data analysis together with field observation, this study examines George Street, Nurses Walk and Playfair Street, the Museum of Contemporary Art Australia (MCA), and the Sirius Building. The findings show that the value of The Rocks is formed not only by individual buildings but also by the relationships among streets, lanes, lots, buildings, topography, and the waterfront. Conservation has not meant preserving the district in a fixed historical condition; rather, it has involved retaining major elements of the",0.0,408bdd578e2165920c852d058690325121aa82085e2,10.3390/su18062675,Quantifying the Cost of Delay in Floodplain Property Buyouts,2026.0,Sustainability,Tanjeel Ahmed Bin Zaman; Md Shoaib Mahmud; Himadri Sen Gupta; M. Harati; J. W. van de Lindt,"Flood hazard mitigation programs increasingly rely on property buyouts and home elevation, yet participation remains sensitive to program design details that affect household constraints. This study estimates homeowner preferences for buyout and elevation program attributes using a stated-preference discrete-choice experiment administered to N = 1560 homeowners, in which each respondent completed up to 4 choice tasks with 3 alternatives (Buyout, Elevation, and Neither). Choices are modeled in a random-utility framework with a multinomial logit as the primary specification and a mixed logit as a robustness specification. Observed choices favor Buyout (51.2%) over Elevation (29.6%) and the status quo (19.2%). In the estimated utility model, higher buyout offers increase acceptance, longer payment delays significantly reduce acceptance, and longer time to vacate increases acceptance; the acquisition option feature also raises buyout utility. These timing effects imply economically meaningful offer-equivalent tradeoffs: at representative baselines, an additional month of payment delay requires approximately a 6.45 percentage-point increase in offer (as a share of home value) to maintai",0.0,https://www.mdpi.com/2071-1050/18/6/2675/pdf?version=17731273384095cebf4171ab33d78f1774e47b906013d8103d51a,10.32628/ijsrst26133132,"SmartPrice: A Multi-modal, Explainable AI System for Predicting House Prices Using Structured Data and Property Descriptions",2026.0,International Journal of Scientific Research in Science and Technology,Avadhut R. Upadhye; Vivek R. Bhuravane; Gousiya A. Khanche,"Accurate residential property valuation is important to real-estate investment mortage approval, taxes and urban planning. Conventional house price prediction systems rely primarily on structured numerical attributes and human expertise, often ignoring unstructured property descriptions that contain valuable semantic indicators such as renovation quality, interior features, and neighborhood appeal. This paper proposes SmartPrice, a multi-modal explainable artificial intelligence framework that integrates structured tabular features with textual property descriptions for enhanced housing price prediction. The proposed architecture employs a dual-branch learning pipeline: structured numerical and categorical attributes are modeled using XGBoost, while unstructured text descriptions are encoded through a one-dimensional Convolutional Neural Network. The extracted representations from both modalities are fused at the feature level and supplied to a regression layer for final price estimation. To ensure transparency and trustworthiness, SHAP is incorporated to provide both global and local interoperability of model predictions. Experiments conducted on the Dataset collected from Kaggle ",0.0,https://ijsrst.com/index.php/home/article/download/IJSRST26133132/IJSRST2613313241028a1a5e1453616b480e490c1e1966efca65fe728,10.70382/ajsitr.v13i9.091,A Hybrid Consensus-Based Feature Selection Framework for Robust Housing Price Prediction,2026.0,Journal of Science Innovation and Technology Research,Josiah Afang Kalat; J. Alhassan; O. Ojerinde,"Accurate predictions are essential for investment planning and risk assessment in real estate markets. Machine learning techniques have increasingly been adopted to improve the accuracy of housing price prediction. However, numerous studies have reported the absence of comprehensive predictor sets, ineffective feature selection practices, and limited model reliability and interpretability. Accordingly, this study develops a hybrid feature selection framework that compiles filter, wrapper, and embedded regularization feature selectors to improve the reliability of predictors. The framework employs the Boston Housing benchmark dataset and applies consensus-based feature selection prior to training four regression models: Linear Regression, Support Vector Regression, Random Forest, and Artificial Neural Networks. Four regression models were evaluated using the Mean Absolute Error (MAE), Mean Squared Error (MSE), the Root Mean Squared Error (RMSE), and the coefficient of determination to assess performance, reliability, and overall soundness of the models. Random Forest Regression achieved the highest predictive accuracy followed by ANNs (Artificial Neural Networks), SVR (Support Vecto",0.0,https://africanscholarpub.com/ajsitr/article/download/1715/1541411eb6bf8aac98782b4dbe80f6888489132e66ab178,10.3390/realestate3030016,"Hysteresis in the Czech Housing Market: Price Dynamics over the First Complete Mortgage Interest Rate Cycle, 2018–2026",2026.0,Real Estate,E. Hromada,"The period 2018–2026 offers the first opportunity to observe a complete, closed mortgage interest rate cycle in the Czech Republic: rates fell to a historic trough of 1.98% (March 2021), rose to 5.37% (September 2023) and stabilized around 4.5% from 2025. This paper examines whether the relationship between mortgage rates and housing prices is reversible over such a cycle. The analysis draws on the EVAL software (version 2.0), which has continuously collected and evaluated real estate advertising across all Czech municipalities since 2007, combined with Czech National Bank and Czech Statistical Office data; methods include phase-specific logarithmic regressions, Chow tests, asymmetric difference regressions, cross-correlation analysis and a composite affordability indicator, the Mortgage Payment Burden (MPB). The point estimates are asymmetric: rate increases are followed by lower listing volumes within four months and lower prices within seven, whereas rate cuts show no detectable direct effect; the difference between the two responses is, however, not statistically significant at the available sample size, so the asymmetry is suggestive rather than conclusive. The price–rate traj",0.0,https://doi.org/10.3390/realestate303001641269a8fbb019943e92b1b955ab1de471d61c0df722,10.70864/joae.2025.v13.i7(1).pp247-253,A SCALABLE HYBRID MACHINE LEARNING FRAMEWORK FOR RENTAL PRICE CLASSIFICATION LEVERAGING SEMANTIC TEXTUAL EMBEDDINGS AND SENTIMENT-DRIVEN FEATURE ENGINEERING,2025.0,Scientific Digest : Journal of Applied Engineering,K. S. Kumar; R. Karthik; B. N. Kumar; B. S. Prakash; Banothu Mani,"In today’s digital era, rental listing platforms host thousands of property ads, making it increasingly difficult for users to assess price categories and for agents to market properties effectively. While structured features like location, number of bedrooms, and square footage are widely used, they often fail to capture the full context provided in descriptions and amenities, which influence viewer interest and pricing perception. Traditional systems primarily rely on rulebased filters or manual interpretation, which are not only time-consuming but also inconsistent and incapable of adapting to the growing scale and diversity of listings. The limitations of these traditional approaches include poor handling of textual data, low scalability, and limited accuracy in predicting appropriate price tiers. This leads to mismatches in user expectations, reduced engagement, and inefficiencies in rental market operations. To overcome these challenges, there is a clear need for a system that intelligently leverages both structured and unstructured data to predict the rental price category more accurately. This research proposes a machine learning-based classification system that incorporate",0.0,https://joae.org/index.php/JOAE/article/download/188/156/351413380c00383a8cf6a8ad7b7af8fc0f85ef8cb201f2,10.12691/jfe-2-1-4,Subprime Mortgage Delinquency and Default Rates by Metropolitan Area: An Analysis by Origination Vintages and Projections for 2007,2014.0,,G. Hanweck,"Subprime mortgage default rates have led to a crisis in the residential mortgage markets unprecedented since the Great Depression. Information reported on these defaults has demonstrated that they vary by location and region. To properly understand the causes of such high rates of mortgage defaults, we undertook this study. It reports on a cross-section analysis of default rates of residential, subprime and Alt-A mortgages aggregated to the metropolitan areas (MSA) level. The hypotheses tested here represent the effects of loan and borrower level characteristics and MSA economic factors, such as MSA employment growth, unemployment rate, household income and housing price changes and their volatility on the level of default rates on subprime mortgages. We test these by origination vintage for 2005 and 2006 and project default rates by MSA into 2007 using 2006 vintage estimated parameters. We find that loan and borrower level characteristics such as loan-to-value ratio weighted by original loan balances, the weighted proportion of loans that have no documentation or the borrowers’ weighted FICO score and MSA economic factors of housing price changes and employment growth are highly s",0.0,https://doi.org/10.12691/jfe-2-1-441415eca45627453423a3daa18b7f5d2ed5719c38fd,10.1016/j.healthplace.2026.103735,Structural inequality shapes spatiotemporal patterns of heat-related emergency calls.,2026.0,Health and Place,Joseph Karanja; Amy E. Frazier; J. Vanos; D. Hondula; Matei Georgescu,"Discrepancies between projected and observed impacts of heat hazard underscore the need to understand how heat exposure interacts with sociodemographic and environmental factors to shape health outcomes. However, current methodological approaches overlook unobserved spatial and temporal heterogeneity, limiting their policy relevance. This study examines how advances in heat vulnerability assessment can reveal key predictors of heat-related emergency calls for improved heat resilience planning. Using persistent change analysis, year-on-year difference maps, and a Poisson Pseudo-Maximum Likelihood spatial lag of X (PPML-SLX) model with two-way fixed effects, we identify which sociodemographic and environmental characteristics buffer or amplify heat risk over time in the City of Phoenix. Results reveal increasing emergency calls in south and central Phoenix, and decreasing in the north. Tracts with increasing call trends had higher rates of uninsurance, poverty, and disability, and lower educational attainment. They also had higher proportions of non-US citizens, individuals with limited English proficiency, households without a vehicle, and Hispanic residents (standardized mean diffe",0.0,https://www.sciencedirect.com/science/article/pii/S1353829226001309/pdf415dc6a4f43b241e5586e24d60cb83395488b720fe6,10.1007/s11252-026-01984-8,Native-plant landscaping enhances floral visitation by insects and birds in residential yards,2026.0,Urban Ecosystems,E. Reyes; Noriko L. Smallwood; Eric M. Wood,"Native plant landscaping is gaining traction in cities worldwide for its potential to enhance biodiversity and ecosystem function. However, it remains unclear to what extent native plants increase animal-floral interactions compared to conventional landscaping such as lawns and ornamental plants. We quantified insect and bird flower visitation networks in 31 residential yards across Greater Los Angeles, California. We also assessed whether native plants in low-income areas, often lacking green amenities (the luxury effect), can support diverse floral-visitation networks. From April to September 2023, we conducted three visits each to 18 paired yards (one native-planted, one conventional) and 13 additional native-landscaped yards. We used timed area searches to quantify insect and bird floral visitation and collected predictor data on air quality, temperature, plant cover, species identity, flower color, phenology, as well as housing prices. Native-landscaped yards supported 4× higher floral-visitor abundance and 1.5× higher richness than conventional yards, largely driven by bees (Apidae) and hoverflies (Syrphidae), which were positively associated with native plant richness, flowe",0.0,https://link.springer.com/content/pdf/10.1007/s11252-026-01984-8.pdf416c8d19cadc0b76c6c28e23e2c7b984195f9a9208c,10.1101/2024.08.22.24312324,Unraveling the impact of COVID-19 on urban mobility: A Causal Machine Learning Analysis of Beijing’s Subway System,2024.0,bioRxiv,Linmu Zou; Yan-Hua Chen; Rui Guo; Pei-Cheng Wang; Yan-Rong He; Shiyu Chen; Zi-Jia Wang; Ji-Ming Zhu,"The COVID-19 pandemic has drastically altered urban travel patterns, particularly in public transportation systems like subways. This study examines the effects of the pandemic on subway ridership in Beijing by analyzing the influence of 19 factors, including demographics, land use, network metrics, and weather conditions, before and during the pandemic. Data was collected from June 2019 and June 2020, covering 335 subway stations and over 258 million trips. Using a three-stage analytical framework—comprising Light Gradient Boosting Machine (LightGBM) for fitting, Meta-Learners for causal analysis, and SHapley Additive exPlanations (SHAP) for interpretation—we observed a substantial decline in ridership, with approximately 10,000 fewer passengers per station daily, especially in densely populated areas. Our findings reveal significant shifts in influential factors such as centrality, housing prices, and restaurant density. The spatiotemporal analysis highlights the dynamic nature of these changes. This study underscores the need for adaptive urban planning and provides insights for public health strategies to enhance urban resilience in future pandemics. Significance The COVID-19 p",0.0,http://dx.doi.org/10.1101/2024.08.22.2431232441750a1f55d2f9a3b9c0eaac60af67cf52a9bd67f80,10.31979/mti.2025.2450,A Comprehensive Study of Impacts of “Q” Bus Rapid Transit System on Blackstone Avenue,2025.0,,Yertai Tanai; K. Ciftci,"This study examines the dual impact of the Fresno Area Express (FAX) Q Line, a 15.7-mile Bus Rapid Transit (BRT) system launched in 2018, on the Fresno housing market and passenger satisfaction. The Q Line, designed to modernize public transit,features eco-friendly compressed natural gas (CNG) vehicles, real-time passenger information, and improved service efficiency.Housing market analysis focused on residential properties sold between 2012 and 2024, utilizing Geographic Information System(GIS) mapping to segment properties into three regions: the Q Line corridor, an outer buffer zone, and the rest of Fresno. Results indicate no statistically significant increase in property values within the Q Line corridor, challenging assumptions that public transit improvements substantially influence housing prices. Passenger satisfaction was assessed using FAX survey data and advanced machine learning models to identify key predictors. Statistical independence tests revealed no significant differences in satisfaction based on age, gender, employment status, or education. However, larger households reported higher satisfaction, while lower-income passengers expressed dissatisfaction. Machine ",0.0,https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1546&context=mti_publications4180f0a978a642f206a007b1a44aa36b3ff13fd2f6e,10.52436/1.jutif.2026.7.3.5517,Hybrid Unsupervised-Supervised Learning for Housing Submarket Segmentation and Price Prediction in Surabaya Urban Areas,2026.0,Jurnal Teknik Informatika (Jutif),Rinabi Tanamal; S. Nugraha; Nathalia Minoque Kusuma Salma Rasyid; Livanty Efatania Dendy; Jessica Theijer,"Surabaya’s rapid population growth, reaching 3.02 million residents, has intensified housing affordability challenges and increased structural variability in residential markets. This study proposes a hybrid machine learning framework that combines unsupervised clustering with supervised classification to identify submarket segments and predict housing price categories. A dataset of 490 properties containing structural, land, ownership, and contextual features was preprocessed and analyzed using K-Means. Cluster quality assessment through elbow inspection and a silhouette score of 0.45 indicated the presence of five meaningful market segments. These segments served as targets for a supervised classification stage that evaluated seven models, optimized via randomized hyperparameter search within a standardized preprocessing pipeline.419The RBF-SVM achieved the strongest performance, reaching 97 percent accuracy and a macro-F1 score of 0.97, representing an 8 percent improvement over non-hybrid baselines and outperforming boosted ensembles such as XGBoost. Permutation importance analysis identified number of floors, building orientation, position rank, and ownership status as dominant ",0.0,https://jutif.if.unsoed.ac.id/index.php/jurnal/article/download/5517/12494201fe15b0ce7fbbe848e0c0608b201118fe03f221c,10.2139/ssrn.3976059,A Traffic Prediction Using Machine Learning: Literature Survey,2021.0,Social Science Research Network,Ji yoon Kim,"The Bid-Rent theory suggests that prospective land users, including residential, retail, or office users, compete to get the location most accessible to the Central Business District (CBD) where users can decrease their commute cost [1]. However, as recent technology development enables telecommuting, some studies suggested that CBD no longer exists since people can now work from home, which can reduce or eliminate the commute cost. At the same time, many urban economics scholars still support the Bid-Rent theory, finding that commuting cost determines the housing prices [2]-[3]. Also, while various studies have suggested that employers offer higher wages for employees who have a longer commute to attract high-quality labor, others suggested that low-skilled workers are not likely compensated enough for the higher commute cost as they do not have strong bargaining power in the job market [4][5], [6]. According to the U.S. Department of Transportation’s research in 2003 [7], while middleincome workers use their car to commute to work and spend about 5% of their income on commuting, 66% of low-income workers commute by their car and spend more than 20% of their income on commuting. T",0.0,https://doi.org/10.2139/ssrn.397605942141ee521d3a2efa4da8a2cd264274156d613ba701,10.11113/intrest.v20n1.444,"University Proximity and Residential Rental Values in Peri-Urban Dar es Salaam, Tanzania: Spatial Pricing Dynamics in Emerging African Housing Markets",2026.0,International journal of real estate studies,Louis Peter Godwin; C. Lucian,"While the influence of universities on surrounding housing markets is widely recognized, evidence from African cities remains scarce. This study investigates the effect of university proximity on residential rental values in peri-urban Dar es Salaam, Tanzania, focusing on neighborhoods around Ardhi University and the University of Dar es Salaam. Data were collected through structured questionnaires administered to landlords and tenants. Out of 150 targeted respondents, 107 valid questionnaires were completed, resulting in a response rate of 71.3%. The analysis employed a mixed-methods approach, combining a household survey with GIS data. A suite of spatial econometric techniques, including Geographically Weighted Regression (GWR), quantile regression, and kriging interpolation were applied to assess the spatial impact of proximity, property attributes, and local infrastructure on rental values for the year 2024. The findings indicate that proximity to university campuses leads to a significant rental premium, with rental values decreasing by approximately 25% per kilometer from Ardhi University. This trend reflects high demand from students, staff, and associated services. Addition",0.0,https://intrest.utm.my/index.php/intrest/article/download/444/209422ce1d044d0a0f93dad5321069c70f02f316b07dfa,10.1134/s2079970525600441,Effects of Transport Accessibility on Residential Property Prices (A Case Study of Saratov),2025.0,Regional Research of Russia,P. O. Gonyukhov; A. Sheludkov,"Increasing spatial inequality is observed in Russia at many territorial levels, including within large cities. The most striking expression of the depth of social stratification in urban areas is differences in housing prices, which are often taken as an indicator of inequality due to the lack of other public data. In this article, we investigated the impact of transport accessibility and its characteristics on the socioeconomic status of urban areas, expressed through the average cost of residential real estate, with a case study of Saratov. For this, we aggregated data from 80 000 advertisements for the sale of apartments in the city in 2021, using network analysis methods to assess transport accessibility when traveling by vehicle and by public transport to the city center and with respect to the transport network of the entire city (integral accessibility). According to the results of the regression analysis, the characteristics of transport accessibility (together with the age of buildings) were able to statistically explain up to half of the differences between Saratov districts in the average listing price of residential real estate. Contrary to our expectations, the best pr",0.0,https://link.springer.com/content/pdf/10.1134/S2079970525600441.pdf423ea0eef073c11f96a51de2a6a9aff888673ce698a,10.47392/irjaeh.2025.0381,Precision in Real Estate: Hybrid Multi-Stage Predictions for House Prices,2025.0,International Research Journal on Advanced Engineering Hub (IRJAEH),A. Manasa; P. Chandana; Shoaib Ahmed; Kumar Baradur,"In the dynamic world of real estate, proper house price forecasting is extremely important for buyers, sellers, and policymakers. This work presents a hybrid multi-stage machine learning system that integrates diverse data modalities—numerical, textual, and visual—to achieve high-precision property price forecasting. The model is organized in three stages. Firstly, a Neighbourhood Scoring Module exploits geo-spatial and socio-economic features extracted from images with CLIP (Contrastive Language–Image Pre-training), giving contextual knowledge of the surroundings of the property. Secondly, the House Attribute Module exploits structured information—e.g., area, number of rooms, and amenities—coupled with text features such as property descriptions represented using SBERT (Sentence-BERT). The third and last stage, the Fusion Module, integrates predictions from the earlier stages through late fusion methods and XGBoost to provide the final estimate of the house price. The datasets are preprocessed meticulously to eliminate duplicates, manage missing values, and preserve image-text alignment. For training, the system is trained independently on every modality and then combines the pred",0.0,https://irjaeh.com/index.php/journal/article/download/890/816424be4e0de511d9810ff119e486834bdc97d88450b9,10.22214/ijraset.2026.78883,Intelligent Rental Valuation: A Full-Stack Approach to Multi-Modal Property Price Prediction Using Machine Learning and Deep Learning,2026.0,International Journal for Research in Applied Science and Engineering Technology,Abhishek Singh,"The rapid expansion of digital property rental platforms has intensified the challenge of determining fair and425competitive rental prices in highly dynamic markets. Property rental prices are influenced by a combination of structural426attributes, geographical location, rental duration, and latent market demand, making manual pricing strategies unreliable and427inconsistent. This research presents an intelligent, full-stack rental valuation system that integrates Machine Learning (ML) and428Deep Learning (DL) models into a real-world property rental platform to deliver accurate, transparent, and scalable price429predictions.430The proposed system introduces a three-tier price prediction framework:431manual feature-based prediction, where users provide explicit property attributes;432geospatial map-based prediction, leveraging Google Maps integration to extract latitude and longitude for location-aware433pricing; and CSV-based batch prediction, enabling bulk rental valuation for large property datasets. Ensemble regression434models including Random Forest and XGBoost are evaluated alongside a deep neural network architecture to capture nonlinear435relationships between property features and rental price",0.0,https://doi.org/10.22214/ijraset.2026.78883436286e95d18098223d6d48fd8260b3d0b03ce9756f,10.26731/1813-9108.2019.2(62).196-202,The impact of transport zoning on the city route network functioning,2019.0,Modern technologies System analysis Modeling,M. Sharov; O. Lebedeva,"Enhancing transport accessibility will improve the efficiency of the route network, which will result in the reduction of overall transport costs and will make it possible to implement other planning tasks. The competitive properties of urban public transport can be enhanced by improving the operational management efficiency and the transport planning quality. Transport zoning and accessibility indices are used to solve problems within the framework of transport network modeling and its optimization. In the process of performing this work, data on transport accessibility were systematized; the calculation of time costs was performed; assessment of transport accessibility by city zones; the dependence of the cost per square meter on the time spent on movement. The rules were identified that must be followed when conducting transport zoning, indices that must be considered when assessing transport accessibility. The more data is available, the higher the quality of the study is. When assessing the impact of transport accessibility on housing prices, it is important to pay attention to the distance from the central part of the city, the proximity of highways, the system of approach ro",0.0,https://doi.org/10.26731/1813-9108.2019.2(62).196-2024378e66994dede6511d39d572055c3b5b729b31c4da,10.3390/land15030446,Urban Structural Imbalance Under Rapid Expansion: Evidence from Service Accessibility and Housing Prices,2026.0,Land,Wen-Xuan Zhang; Jianguo Wang,"This research examines the structural evolution and functional performance of urban spatial expansion in Changchun, Northeast China. Utilizing an integrated framework of the Adjusted Sprawl Index, Gaussian two-step floating catchment area (Gaussian 2SFCA) accessibility modeling, and XGBoost-SHAP machine learning, the study identifies a decoupled growth pattern where land development and infrastructure construction proceed without a corresponding increase in population density, reflecting a structural-demographic divergence. Empirical results demonstrate that land expansion reached a significant peak between 2015 and 2020, followed by a transition toward morphological equalization and stabilization after 2020. This process manifests as asynchronous urbanism, where the strategic deployment of physical infrastructure frameworks systematically precedes the functional integration of essential social services. The analysis reveals the emergence of localized service-value misalignment clusters in peripheral zones. The phenomenon represents a deviation from the traditional monocentric paradigm toward McCann’s framework of modern urban economics, as high residential valuations are sustained",0.0,https://www.mdpi.com/2073-445X/15/3/446/pdf438bd313cbce779d7e26af2478147c7f26c075326e3,10.3389/fpubh.2026.1847064,"Assessing human-scale green equity in the 15-minute city using street-view-based visual landscape indicators and explainable machine learning: a case study of Chengdu, China",2026.0,Frontiers in Public Health,Si-Ya Yan; Yueyue Ma; Xi-Qian Wang,"Introduction Against the backdrop of rapid urbanization, identifying disparities in green environmental exposure among different socioeconomic groups is of great importance. Taking the central urban area of Chengdu, China, as a case study, this study assesses street-level green environmental equity within a 15-minute walking distance using Baidu Street View images. Methods A Mask2Former semantic segmentation model was employed to extract seven street-view visual landscape indicators, including Green View Index (GVI), Tree View Index (TVI), Sky View Factor (SVF), Building View Index (BVI), Hard Paving View Index (HVI), naturalness, and wildness. Second-hand housing prices were used as a proxy for socioeconomic stratification. Getis–Ord Gi*, global Moran’s I, local Moran’s I, bivariate local Moran’s I, random forest, Shapley Additive Explanations (SHAP), and partial dependence plots (PDPs) were applied to reveal the spatial relationships between housing prices and visual landscape indicators, as well as their nonlinear effects. Results The results show that (1) housing prices in Chengdu’s central urban area exhibit significant positive spatial autocorrelation, with both high-price an",0.0,https://doi.org/10.3389/fpubh.2026.184706443929e553097fe8638acf3e56fabbe8139a43d6404b,10.14434/artifact.v4i1.13372,Local Real(i)ties: A Contemporary Image of Thought,2017.0,,H. Frichot,"Noopolitics is a neologism that designates how minds (nous) come to think collaboratively at the scale of populations, a phenomenon facilitated by increasingly sophisticated information societies and their capacity for instantaneous electronic communications. Noopolitics complements the already well-established term biopolitics, which designates how the lives and deaths, and general health and well-being of individuals are managed at the scale of populations through practices of governance. What happens when a noopolitics rigidifies, what kinds of effects does it produce? A dogmatic Image of Thought understood as an ossified status quo takes hold, over-determining how people think together and about themselves, and about their worlds, including their local environment-worlds. In relation to an expanded understanding of the spatialities of feeling that architecture contributes to, this essay will focus in particular on the noopolitics at work in the production of architectural imagery where it becomes indistinguishable from real-estate imagery. The compelling case this essay will address is the emergence of the styled real-estate image in the Stockholm context where a large proporti",0.0,https://scholarworks.iu.edu/journals/index.php/artifact/article/download/13372/296514406f76b4e5e14509c5253760c0b2f476b839484d05,10.25264/2311-5149-2026-42(70)-274-281,SPATIAL ANALYSIS OF THE RELATIONSHIP BETWEEN SALE PRICES AND RENTAL RATES AS INDICATORS OF THE RESIDENTIAL REAL ESTATE MARKET,2026.0,"Scientific Notes of Ostroh Academy National University, ""Economics"" Series",O. Pomortseva; S. Lubenets; S. Kozyrenko,"This article investigates spatial patterns in the relationship between residential sale prices and rental rates across the vernacular neighborhoods of Kharkiv. The study establishes the nature and strength of the correlation between secondary market housing prices per square meter and rental rates. The empirical basis comprises a geodatabase covering 146 vernacular neighborhoods in Kharkiv, containing verified and harmonized data on average housing prices and rental rates for one-, two-, and three-bedroom apartments as of the first half of 2026. Unlike traditional studies constrained by administrative divisions, this research employs a vernacular approach that provides higher spatial resolution for real estate market analysis. Pearson and Spearman correlation coefficients, along with a linear regression model, were applied to evaluate interdependencies between indicators, complemented by calculations of gross annual rental yield and investment payback periods. Regression residual analysis was utilized to identify areas overvalued or undervalued relative to their rental potential. The results confirm a statistically significant positive relationship between housing sale prices and r",0.0,https://doi.org/10.25264/2311-5149-2026-42(70)-274-281441810ae02c263238d492017c9a7c15cbe61c74a161,10.3390/land15101870,"Housing Market Impacts in the Face of Bus Stop Consolidation-Spatiotemporal Evidence from Denver, CO",2026.0,Land,K. Black; Daniel Kolliner; Ever Croffoot-Suede; Cassidy Kaler,"The evolution of metropolitan transportation networks is an integral component of urban development. Policymakers are eager to prioritize sustainable development as their cities grow and progress. As such, academics have heavily turned their attention to the impact of providing more accessible public transportation, while significantly less academic work focuses on the reduction of public transportation accessibility. Our paper aims to contribute to the research gap surrounding bus stop consolidation. Public transportation efficiency theory posits that some bus stops with limited ridership should be consolidated with higher-demand stops. This paper evaluates the impact of bus stop consolidation on residents in Denver, Colorado by examining the effect of a consolidation project on surrounding residential property values from 2002 to 2022. We use a complete geospatial dataset of property values and bus stop closures during this two-decade span. Our project extends previous research by leveraging variation in the timing of the bus stop consolidations within a stacked spatial difference-in-differences model. Under the parallel trends assumption, we find the following results: (1) House",0.0,https://doi.org/10.3390/land15101870442968367a698556aa03981aea9a998930e1d43b718,10.54989/msd-2026-0006,Macroeconomic and Institutional Determinants of Sustainable Housing Markets in the European Union: A Dynamic Panel Analysis,2026.0,Managing Sustainable Development,Petros Gkiosis; X. Chapsa,"This paper examines the determinants of real house prices in 24 European Union countries using annual panel data for 2005–2024. The dynamic framework relates real house prices to GDP per capita in purchasing power standards, lagged inflation, employment as a share of total population, private-sector credit flows as a share of GDP, the logarithm of aggregate tourism receipts, annual changes in urban population, and property rights protection. A Fixed Effects specification serves as the benchmark. Tests reveal pervasive cross-unit interdependence, while Pesaran’s CIPS test indicates a mixed order of integration, with both I(0) and I(1) variables. These findings support second-generation panel estimators. The Dynamic Heterogeneous Panel Model is the main estimator, and the Augmented Mean Group estimator provides a robustness check. The baseline results show strong persistence in real house prices. GDP per capita, employment, private-sector credit flows, and annual changes in urban population have positive effects, whereas lagged inflation, tourism receipts, and property rights protection have negative effects. AMG estimates broadly confirm this pattern. However, annual changes in urba",0.0,https://msdjournal.org/wp-content/uploads/6.-Macroeconomic-and-Institutional-Determinants-of-Sustainable-Housing-Markets-pp.-106-125.pdf443878545101c3757f9256009596fb69ad07d1e092a,10.52152/22.2.50-56(2024),The impact of Global Economic Policy Uncertainty on the Chinese housing price,2024.0,Lex Localis-journal of Local Self-government,Zheng-Rong Cai; Rusmawati Binti Said; W. Ngah,"Global Economic Policy Uncertainty (GEPU) represents a critical determinant of housing market dynamics in increasingly integrated economies, yet its transmission mechanisms to Chinese provincial housing prices remain theoretically ambiguous and empirically underexplored. The research motivation centers on understanding how GEPU influences housing prices through multiple channels: financial transmission via interest rate adjustments affecting mortgage costs and investment decisions, trade channels through foreign direct investment flows altering property demand, and expectation channels whereby uncertainty dampens economic growth and consumer confidence, subsequently reducing housing market activity. This investigation becomes particularly compelling given China's unique institutional framework combining capital controls with managed exchange rates, creating potential buffers against external shocks while simultaneously experiencing growing integration with global markets. Recent events including Sino-American trade tensions and Federal Reserve policy volatility demonstrate GEPU's capacity to generate cross-border spillovers, making systematic analysis essential for understanding co",0.0,https://lex-localis.org/index.php/LexLocalis/article/download/3-899/5-234445b7e70820e3d6ce581231f4e596e0f8583216d6a,10.66884/kesj.2025.2wxwb4br,"EFFECTS OF HOUSING CONDITION ON RENTAL VALUES OF RESIDENTIAL PROPERTIES IN KADUNA METROPOLIS, NIGERIA",2025.0,Kaduna State University Environmental Sciences Journal,Matthew Mamman; M. S. Mohammad; Ishiyaku Bala; N. Iroaganachi,"<p>445 <strong>Purpose:</strong> A neighbourhood’s housing environment encompasses the social services and utilities that make a community liveable. Because housing quality significantly affects both environments and their populations, it is vital to understand its drivers and measurement metrics. This study aims to assess the effects of housing quality on the rental values of residential properties in the Malali area of Kaduna Metropolis.446 </p>447448 <p>449 <strong>Design/methodology/approach:</strong> The study adopted quantitative research approaches and survey strategy because of the nature of the research questions with a sample size of 277. Structured questionnaires were distributed to the respondents. The data was analysed using mean ranking, frequency distribution tables and multiple regression analysis.450 </p>451452 <p>453 <strong>Findings:</strong> The result revealed that, the average (Mean) rent paid in different types of rented residential properties in Malali area of Kaduna metropolis, Nigeria, stands for flat ₦436,093, bungalow ₦547,043 and tenement ₦116,083.454 </p>455456 <p>457 <strong>Research limitations/Implication:</strong> This study was limited to two categories of independent vari",0.0,4589f95edfd65bd72e9bb2829089b03d5a0ba9a0c20,10.12982/cmjs.2022.102,Housing Price Prediction by Divided Regression Analysis,2022.0,Chiang Mai Journal of Science,Y. Goh; Y. Goh; Chun-Chieh Yip; K. Ng,"Regression analysis is a statistical methodology to investigate the relationship between the dependent variable and the independent variables. In current era with the trend of big data, we might face some problems when performing statistical analysis for the massive volume of data. For example, the heavy burden of the computing load will cause the computation to be time consuming, the accuracy of the results might be affected in view of the vast volume of data. Hence, divided regression analysis is proposed to reduce the burden of the computing load. This approach performs subdivision of the dataset into several unique subsets, then the multiple linear regression is fi tted into each subset. The results obtained from each subset are then combined to obtain a divided regression model which is treated as the original overall dataset. The dataset used in this paper is KC Housesales Data, obtained from the Kaggle website. The dataset contains statistics information about the housing price, for example, size of lot, size of living area and selling price of the house. The goal of this paper is to predict the selling price of a house from the given attributes. The dataset is partitioned i",0.0,http://epg.science.cmu.ac.th/ejournal/dl.php?journal_id=1169445940f3b311a8a0e71352aac0bbda6a2c6c83ec031b,10.1177/23996544261490292,Zoned for privilege: The spatial injustice of elite school zones in Aotearoa New Zealand,2026.0,Environment and Planning C Politics and Space,Timothy Boyle Buckenham; T. Skelton,"This article examines the relationship between sought-after school zones and spatial in/justice in Aotearoa-New Zealand, challenging the country’s enduring self-image as an egalitarian society. It reveals how elite state schools, valued for their social prestige, drive socio-spatial divides that entrench privilege within specific urban spaces. By influencing housing markets and facilitating residential sorting, these schools contribute to the concentration of property wealth and social capital within their enrolment zones, transforming access to sought-after public education into an asset predominantly available to affluent families. This dynamic perpetuates cycles of advantage and exclusion, further complicating ideals of equality and fair opportunity. Mixed-methods were used with spatial analysis to examine property value patterns, community surveys to explore public attitudes, and qualitative interviews to capture lived experiences. This approach uncovers a self-reinforcing cycle in which zoning policies, housing demand, and school reputations perpetuate socio-economic stratification. The findings demonstrate that these interwoven systems of privilege deepen socio-economic inequ",0.0,https://doi.org/10.1177/23996544261490292460958371c2e96c95c6708f12de92e6e76e51e9be17,10.30564/jees.v8i8.13633,Efficiency Evaluation and Path Optimization of Regional Atmospheric Environment Governance from a Synergistic Emission Reduction Perspective Synergistic Control of PM2.5 and O3,2026.0,Journal of Environmental & Earth Sciences,Yi-Zhen Du; Chen-Chen Zhang,"There is a growing challenge posed by the coexistence of fine particulate matter (PM2.5) and ground-level ozone (O3). Despite the significant reductions in primary particles and a variety of gaseous pollutants achieved by conventional air pollution control, O3 pollution has remained or even increased in many areas, demonstrating the shortcomings of single-pollutant regulation. This review examines how the efficiency evaluation and path optimization of regional atmospheric governance can be viewed through the prism of synergistic emission reduction to coordinate control of PM2.5 and O3. It initially explains the chemical linkage between PM2.5 and O3 through common precursors, the atmosphere’s capacity for oxidation, photolysis, heterogeneous reactions, and regional transport. It subsequently discusses efficiency evaluation methods, such as indicator systems, data envelopment analysis, slack-based measure models, Malmquist indices, spatial econometric methods, and integrated benefit assessment. The review also discusses pathways for emission reduction across the industrial, power, transport, residential, and agricultural sectors, with a focus on differentiated VOCs-NOx sensitivity, N",0.0,https://doi.org/10.30564/jees.v8i8.136334615b2f7e7da4b6e4b5a804a95a6c3de9cfc6828708,10.3390/math13142287,Robust Variable Selection via Bayesian LASSO-Composite Quantile Regression with Empirical Likelihood: A Hybrid Sampling Approach,2025.0,Mathematics,Ruisi Nan; Jingwei Wang; Hanfang Li; Youxi Luo,"Since the advent of composite quantile regression (CQR), its inherent robustness has established it as a pivotal methodology for high-dimensional data analysis. High-dimensional outlier contamination refers to data scenarios where the number of observed dimensions (p) is much greater than the sample size (n) and there are extreme outliers in the response variables or covariates (e.g., p/n > 0.1). Traditional penalized regression techniques, however, exhibit notable vulnerability to data outliers during high-dimensional variable selection, often leading to biased parameter estimates and compromised resilience. To address this critical limitation, we propose a novel empirical likelihood (EL)-based variable selection framework that integrates a Bayesian LASSO penalty within the composite quantile regression framework. By constructing a hybrid sampling mechanism that incorporates the Expectation–Maximization (EM) algorithm and Metropolis–Hastings (M-H) algorithm within the Gibbs sampling scheme, this approach effectively tackles variable selection in high-dimensional settings with outlier contamination. This innovative design enables simultaneous optimization of regression coefficients",0.0,https://www.mdpi.com/2227-7390/13/14/2287/pdf?version=1752671446462be216a16ed3a8980c48ec3727e561aeb34cfac34,10.2196/preprints.16787,Impact of Built Environments on Body Weight (the Moving to Health Study): Protocol for a Retrospective Longitudinal Observational Study (Preprint),2019.0,,S. Mooney; J. Bobb; P. Hurvitz; Jane Anau; M. Theis; A. Drewnowski; A. Aggarwal; Shilpi Gupta; D. Rosenberg; A. Cook; Xiao Shi; P. Lozano; A. Moudon; David E. Arterburn,"463 BACKGROUND464 Studies assessing the impact of built environments on body weight are often limited by modest power to detect residential effects that are small for individuals but may nonetheless comprise large attributable risks.465 466 467 OBJECTIVE468 We used data extracted from electronic health records to construct a large retrospective cohort of patients. This cohort will be used to explore both the impact of moving between environments and the long-term impact of changing neighborhood environments.469 470 471 METHODS472 We identified members with at least 12 months of Kaiser Permanente Washington (KPWA) membership and at least one weight measurement in their records during a period between January 2005 and April 2017 in which they lived in King County, Washington. Information on member demographics, address history, diagnoses, and clinical visits data (including weight) was extracted. This paper describes the characteristics of the adult (aged 18-89 years) cohort constructed from these data.473 474 475 RESULTS476 We identified 229,755 adults representing nearly 1.2 million person-years of follow-up. The mean age at baseline was 45 years, and 58.0% (133,326/229,755) were female. Nearly one-fourth of ",0.0,https://www.researchprotocols.org/2020/5/e16787/PDF47784c3805a5b569aeb02802b8252f2de6668db13f6,10.55670/fpll.fusus.3.4.5,Examining the association between deteriorated urban fabric and socio-economic resilience in Tehran Metropolis,2025.0,Future Sustainability,Kooshiar Zebardast; K. Ziari,"Deteriorated urban areas usually face social, economic, and environmental problems. They often struggle with issues like poverty, inadequate housing, poor public spaces, social isolation and a sense of hopelessness, limited business opportunities, and a lack of investment. These complex problems cause significant disaster resilience challenges for these areas. This article investigates the association between urban deteriorated fabric (UDF) rate and socioeconomic resilience (SER) in the neighborhoods of Tehran Metropolis. Fourteen SER variables are identified through a literature review. Exploratory factor analysis is used to transform them into fewer factors. Four factors are extracted and are labelled as economic, social, economic-demographic, and community capital resilience. Similar extracted factors are combined to obtain social and economic resilience subcomponents. Jenks' Natural Break classification method is used to classify the UDF rate into five categories. Ordinary least squares (OLS) and Geographically Weighted Regression (GWR) are used to examine the association between UDF rate (dependent variable) and SER subcomponents (independent variables). The findings of the st",0.0,https://fupubco.com/fusus/article/download/478/230/1455478de71cc438506648df71d9ff158ceb4ada4551f08,10.26443/msurj.v17i1.177,"Spatial distribution and socioeconomic differences between urban farms' production and distribution points in Chicago, IL",2022.0,McGill Science Undergraduate Research Journal,E. Melis; E. Armitage; Yuxin Ma; Amelia K Weiss; Brian E. Robinson,"Urban farming remains popular as a potential sustainable replacement or supplement for traditional agricultural models, but little comprehensive research has been done regarding the socioeconomic characteristics of this food production and distribution model. Using the City of Chicago as a case study, this research seeks to understand where urban commercial farms are located and whether there exists a significant disparity between the social demographics of the neighbourhoods where these farms produce crops compared to where their goods are sold. The distribution of urban farm and sale points was determined by geocoding the location of all production (farms) and distribution points (sale points) for commercial urban farming companies in Chicago, then calculating spatial statistics and calculating the mean centers, standard distance, and standard deviational ellipses (SDE) for each. These were then overlaid onto choropleth maps containing socioeconomic indicator data derived from the US 2016 census. These socioeconomic indicators — median annual household income, mean home value, and percent racialized minority population — were analysed to determine if a correlation exists between ",0.0,https://msurjonline.mcgill.ca/article/download/177/15447991b36fd1b521ce5e6b2054e44543870e864797ca,10.55670/fpll.futech.4.3.22,AI-enhanced spatial value reassessment in digital transformation: impacts of smart eco-city management paradigms on housing price formation mechanisms,2025.0,Future Technology,Ping Li; Cheok Mui Yee; Wan-Yi He; Li-Juan Lu; Kaizhou Qin,"This study examines the transformative impact of artificial intelligence-enhanced smart eco-city management paradigms on spatial value assessment and housing price formation mechanisms. Through sophisticated mixed-methods analysis of 320 neighborhoods across five urban areas, employing advanced machine learning algorithms for pattern recognition, the research identifies significant synergistic relationships between digital infrastructure and environmental quality that profoundly influence housing valuations. Empirical evidence demonstrates that neighborhoods exhibiting high levels of both digital connectivity and environmental amenities command substantial price premiums of 60-100% above baseline areas, markedly exceeding the combined individual effects of digital (25-45%) and environmental (15-40%) factors alone. The strength of this synergistic relationship manifests in robust correlations between combined Digital-Environmental indices and housing prices (r = 0.83), with AI-driven predictive models achieving exceptional accuracy in forecasting spatial value shifts (R²=0.861). The study contributes a multidimensional analytical framework linking technological innovation, artificia",0.0,https://fupubco.com/futech/article/download/379/208/12914808392a5c77de21a6f7d3fc479d14609916a0ee42e,10.67763/jitsi.v1i2.66,Penerapan Metode Linear Regression Untuk Memprediksi Harga Rumah,2026.0,JITSI : Jurnal Informatika Dan Teknologi Sistem Informasi,Muhammad Arizal Dwisakti; Muhammad Ridho Pramana; Riski Juliandri; Taronisokhi Zebua,"House prices are an important indicator in the property sector and are influenced by various factors, including physical characteristics of the building and location. This study aims to analyze and predict house prices using a linear regression method by utilizing several variables, namely land area, building area, number of bedrooms, number of bathrooms, parking availability, and distance to the city center. The data used in this study are secondary data collected through a web scraping process and are focused on houses with a price range of 300700 million rupiah to represent the middle-market segment. The research stages include data preprocessing, Pearson correlation analysis, multicollinearity testing, multiple linear regression modeling, and model performance evaluation using the coefficient of determination (R²) and Root Mean Square Error (RMSE). The dataset is divided into 80% training data and 20% testing data. The results show that the constructed linear regression model achieves an R² value of 0.3078, indicating that the independent variables are able to explain 30.78% of the variation in house prices. The RMSE value of 117,482,242 indicates that prediction errors remain ",0.0,https://jurnal.interaksisaintek.com/JITSI/article/download/66/514815e3b8a386e25f44ebeb784f277ba8f9a886c4581,10.1088/1755-1315/1389/1/012015,Towards an artificial intelligence model for the automated prediction of building value,2024.0,IOP Conference Series: Earth and Environment,Simon Rubinstein; E. Witt; Kaleem Ullah,"Property markets are volatile, necessitating the constant recalculation of property values for a variety of reasons including for the efficient design and construction of buildings. This research is aimed at the automation of the property valuation process using Artificial Intelligence. In a three-part research effort, a Multiple Criteria Decision Method (MCDM) approach using Complex Proportional Assessment (COPRAS) is first applied to predict the property value of new residential units on the basis of a comprehensive list of building characteristic variables identified as relevant for describing a particular property type (in the test case, terraced houses). For initial testing and validation of the valuation prediction calculations, the weights and values of criteria are determined through experts’ opinions and the estimated value of a test property is derived. This first part of the research is described in this report. The second phase of the research involves the automatic acquisition of the variables’ values for any building from the recently digitalised Estonian Building Register. The third part of the research focuses on replacing the need for experts’ opinions of the relat",0.0,https://doi.org/10.1088/1755-1315/1389/1/012015482b4f2581ecafbb85d23f7610cc6131c2a102c92b1,10.37943/21ebbh5859,DEVELOPMENT OF MACHINE LEARNING METHODS FOR MARKET TRENDS,2025.0,Scientific Journal of Astana IT University,S. Sapakova; Zhansaya Bekaulova; A. Nurlanuly; Duriya Daniyarova; G. Ybytayeva; Kaldybayeva Aizhan,"In the rapidly evolving real estate market, the application of machine learning (ML) is crucial for understanding and predicting price trends. This study evaluates and compares seven ML models, including multiple linear regression, random forest regression, support vector regression (SVR), decision tree regression, and XGBoost, to determine the most effective predictor of real estate prices in Astana, Kazakhstan. The study focuses on the Yesil district, a key area in the city, utilizing a dataset of over 9,000 records extracted from a broader collection of more than 30,000 real estate transactions across Kazakhstan. Through rigorous experimentation, model performance was assessed using statistical metrics such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R-squared). The results indicate that the Random Forest Regressor and XGBRegressor models outperformed others, achieving the highest R-squared values (99.55% and 99.18%, respectively) and the lowest MAE and RMSE values. These findings highlight their robustness in predicting housing prices with high accuracy. The primary objective of this study was to develop a precise ML model ",0.0,https://journal.astanait.edu.kz/index.php/ojs/article/download/663/228483b70b4d4f07ced932c47b60d27ebb2d05f787eafc,10.1111/jors.70070,Local Crime Spillover Onto Public–Private Partnership Financing Commitment: Do Urban Proximity and Local Affluence Matter?,2026.0,Journal of Regional Science,King Yoong Lim; Lin Lang; Diego Morris; Waseem A. Toraubally,"484 While the relationship between public infrastructure and crime location choices is well‐studied in criminology, the impact of crime as a spillover externality on public–private partnership (PPP) infrastructure financing remains unexplored. Leveraging a data set of 542 Private Finance Initiative (PFI)‐funded infrastructures in England and Wales from 2011 to 2018 period, we find evidence that local crime negatively affects government's reimbursement commitment to its PFI's partner, that is, a novel crime–PPP underpayment nexus. We also find evidence that this adverse fiscal resource re‐allocation effect is moderated by both contemporaneous local affluence and the predetermined urban proximity of the infrastructure. Specifically, the former (measured by local house price), while exhibiting a direct “crime attractor” effect, reflects the “nondependent on government to provide security” effect, as rich neighborhoods can afford their own private security and therefore less reliant on public police spending. This mitigates the negative effect of crime on PFI underpayment. The latter, which concerns the physical location of the infrastructure built, is hypothesized to exhibit the “guardi",0.0,https://doi.org/10.1111/jors.7007048536eac2e74fd054fe7faa7ad4134c3aa524d5616d,10.29327/2290393.2.2-2,Mass appraisal for property tax purposes: approach using global and local spatial regression,2023.0,Revista Valorem,C. Zilli; Norberto Hochheim,"O IPTU é uma fonte de receita essencial para os municípios brasileiros, mas o sistema atual, baseado em valores de avaliação desatualizados, resulta em um tratamento desigual dos contribuintes. Para promovera equidade, são necessárias avaliações regulares de imóveis, especialmente no mercado imobiliário brasileiro, que evolui rapidamente. Um desafio chave neste processo é desenvolver um modelo que trate eficazmente os efeitos espaciais. Este estudo explora aaplicação de modelos de regressão espacial global e local para enfrentar a dependência e heterogeneidade espaciais em avaliações em massa de imóveis, visando garantir uma tributação justado imóvel. Analisando imóveis urbanos, avaliou-seo desempenho do modelo autoregressivo condicional (CAR) como um modelo de regressão global e o modelo de regressãogeograficamente ponderada(GWR) como um modelo de regressão local. As previsões de ambos os modelos são combinadas com a interpolação por krigagem ordinária para gerar uma Planta de Valores Genéricos (PVG)abrangente, representando a área de estudo. Nossos achados demonstram que a modelagem GWR supera o modelo CAR em precisão e sua capacidade de contabilizar a dependência e heterogeneida",0.0,https://revistavalorem.com/index.php/home/article/download/20/4948628bfe3b84e38fcfa8559d7a920b188a51b042630,10.11113/intrest.v20n1.483,Lifestyle Priorities and Homeownership Decisions among Malaysian Civil Servants: A Behavioural Finance Perspective,2026.0,International journal of real estate studies,Mohd Izwan Nuzaihan Jamil; M. Wahid; Azwan Abdullah; Nor Natasha Abd. Rashid,"The rising cost of living and property prices in Malaysia have intensified housing affordability challenges among civil servants. This quantitative cross-sectional study examines how lifestyle spending priorities, psychological and social factors, and perceived barriers and opportunities influence financial wellbeing and homeownership status, with financial management tested as a mediating mechanism. Data were collected through an online survey of 411 Malaysian civil servants and analysed using descriptive statistics, Pearson correlation, multiple regression, binary logistic regression, and Hayes's PROCESS macro with 5,000 bootstrap samples. The results show that 48.7% of respondents owned a home, while the largest income group earned RM3,000–RM4,999 per month. Financial management showed the strongest association with homeownership odds (OR = 2.18), whereas lifestyle spending priorities were negatively associated with homeownership (OR = 0.71). The financial wellbeing model explained 68.7% of the variance, while the homeownership model explained 29.4%. Mediation analysis indicated that financial management transmitted the effects of lifestyle spending priorities and perceived barr",0.0,https://intrest.utm.my/index.php/intrest/article/download/483/216487a488a5addaf7ba6973c76b5e81d8c6efeb4e0dfe,10.1200/jco.2026.44.16_suppl.e13643,Spatial neighborhood effects and geographic embeddings in random forest modeling of mammography screening rates.,2026.0,Journal of Clinical Oncology,Benjamin Nketsiah; Asabere Kwabena Asante; Nana Kweku Bentsi Essel,"488 e13643489 490 491 Background:492 Mammography screening rates vary geographically across the United States, yet standard machine learning models often fail to account for spatial dependence, limiting their ability to resolve local screening disparities. This study examined whether incorporating spatial neighborhood effects and geographic embeddings improves random forest (RF) prediction accuracy and reduces residual spatial autocorrelation in tract-level screening estimates.493 Methods:494 PLACES 2024 tract-level mammography screening data were linked with socioeconomic and healthcare access indicators, including poverty, educational attainment, race, insurance coverage, median home value, proximity to mammography facilities, and the Social Vulnerability Index. Three RF regression models were trained using 5-fold cross-validation: (1) a base model with socioeconomic and access predictors; (2) a spatial-lag model including lag_mammo, defined as the mean screening rate of adjacent tracts; and (3) a spatial-embedded model adding geographic coordinate terms (longitude, latitude, longitude², latitude², and longitude×latitude). Model performance was evaluated using RMSE, MAE, R², and Moran’s I of r",0.0,4954b369244d2927a31600a5733c54206c65d0a4272,10.46299/j.isjmef.20240303.05,Statistical analysis of main determinant’s role in the value formation of residential property assets in Ukraine,2024.0,"International Science Journal of Management, Economics & Finance",V. Yakubovsky; Kateryna Zhuk; Maria Ankudovych,"The objective of this study is to investigate the influence of both qualitative and quantitative factors on real estate valuation within the Ukrainian residential market. Leveraging a comprehensive dataset from the Ukrainian real estate market, encompassing essential property characteristics and historical pricing data, specific indicators were identified to discern primary determinants impacting residential property values. Through the development of multivariate regression models, this research ranks qualitative indicators to facilitate their integration into the analysis. Regression analysis conducted over data spanning from 2021 to 2023 reveals significant factors affecting housing prices across different segments, including apartments, land plots, and households. Notable determinants include area, building type, renovation quality, layout, heating system, and natural gas availability for apartments; area, distance to the nearest city, soil type, location, natural surroundings, and land use for land plots; and property type, location, and area for households. This study contributes to the understanding of real estate valuation dynamics by employing regression analysis to scruti",0.0,http://dx.doi.org/10.46299/j.isjmef.20240303.054965c2823f9fb79358e2fe37f19edf6c7208258f5ef,10.1016/j.jadohealth.2025.07.015,Adolescent Predictors of Financial Hardship in Young Adulthood: A Cross-National Comparison.,2025.0,Journal of Adolescent Health,J. Heerde; Jen Bailey; J. Toumbourou; Susan M. Sawyer,"PURPOSE497Economic factors are an important social determinant of health. Financial hardship in young adulthood is particularly concerning, given current inflation rates, high housing prices, and rising cost of living. Yet, we know remarkably little about the socioecological factors in adolescence that influence later risk for financial hardship in young adulthood above and beyond family socioeconomic circumstances.498499500METHODS501Multivariable ordered logistic regression analyses using cross-national longitudinal data from the International Youth Development Study explored associations between a range of socioecological factors in adolescence and financial hardship in young adults. Participants from Washington State (the United States) and Victoria (Australia) were surveyed at ages 13, 14, 15 (2002-2004), and 25 years (2014-2015; N = 1,945; 51% female).502503504RESULTS505At least two indicators of financial hardship were reported by 25% of young adults. Multivariable predictors of financial hardship in young adulthood included higher levels of family conflict, a history of school suspension, and academic underperformance. Early family economic disadvantage was no longer significant after adjustm",0.0,https://www.jahonline.org/article/S1054-139X(25)00320-9/pdf506db8b134c8df80df1a57308206c758f51d16e2f9a,10.3390/buildings16204013,Multidimensional Green Space Exposure and Housing Prices in High-Density Urban Areas: Spatial Evidence from 3729 Communities in Guangzhou,2026.0,Buildings,Wei-An Wang; X. Hong,"Green space in high-density cities is scarce and shared unevenly, and the question of whether its value affects housing prices and whether that effect holds between communitie is still unclear, as most studies measure one exposure dimension or report a single area-wide estimate. In this study, we examine 3729 residential communities in Baiyun District, Guangzhou, linking a composite green space exposure (GSE) index to second-hand listing prices. The GSE index equally weights three normalized indicators—the green coverage ratio, park accessibility index, and green view index—which describe availability, accessibility, and visibility. We fitted ordinary least squares, geographically weighted regression, and multiscale geographically weighted regression in turn, judging spatial variation through residual spatial autocorrelation and corrected local inference. The three dimensions of the index peak in different parts of the district: the composite GSE runs higher along the eastern foothills and in parts of Jinshazhou and lower across the central and southern built-up areas. High-GSE and high-price areas overlap only partially: the greenest communities are not, in general, the most expen",0.0,https://doi.org/10.3390/buildings16204013507ac526a1cb34b0a3c196ebeea38fdaa4f186c0fa3,10.1088/2752-5309/ae84f3,"Investigating patterns and sources of variability in children’s blood lead levels in Milwaukee, Wisconsin using a machine learning model",2026.0,Environmental Research: Health,Oluwatobi Oke; S. Magzamen; Geoffrey S. Siemering; S. Johnson; J. Auerbach; E. Carter,"Blood lead levels (BLL) in children in the United States have decreased in recent decades; yet, lead is toxic at any concentration, and disparities in lead poisoning persist across population groups. As a result, lead exposure continues to be a major environmental public health concern. Because the relative contributions of different lead exposure sources are rarely evaluated together, mitigation efforts may be fragmented, with limited resources not always directed toward the highest-impact interventions. The objective of this study was to investigate the combined impacts of housing characteristics, soil lead concentrations, and water service line materials on childhood BLLs using tree-based machine-learning methods. Milwaukee, Wisconsin, was selected as a case study due to its high prevalence of elevated pediatric BLLs and large population at risk from older housing stock. Using existing data, we applied extreme gradient boosting model with SHapley Additive exPlanation (SHAP) to quantify the relative contributions of multiple lead exposure sources among children aged 1–5 years and to assess whether exposure profiles interact to produce higher BLLs. Our results indicate that housin",0.0,https://iopscience.iop.org/article/10.1088/2752-5309/ae84f3/pdf5086c6fddeb9ee0e77036b53767483c42df84105b25,10.31004/joecy.v6i2.11288,Pemodelan Harga Properti Residensial di Jabodetabek Berbasis XGBoost-SHAP dan Risiko Banjir,2026.0,Journal of Innovative and Creativity (Joecy),Amara Thuridha; Dedi Trisnawarman,"The Greater Jakarta (Jabodetabek) property market is characterized by profound heterogeneity, with prices driven by physical building attributes, land values, accessibility, neighborhood amenities, and environmental risks. This study develops an explainable property price prediction framework utilizing Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). The dataset comprises 31,730 residential property listings enriched with sub-district-level assessed land values (NJOP), accessibility to train stations and toll gates, public amenities, and a 2025 news-based flood risk index. The flood risk index was constructed from an initial corpus of 3,810 flood-related news records, which were subsequently filtered to yield 1,470 unique news articles published in 2025 concerning the Greater Jakarta region. Following data preprocessing, 31,729 observations were retained for modeling. The XGBoost regression model yielded a cross-validated R² (CV-R²) of 0.902 ± 0.004, a hold-out R² of 0.908, and a Mean Absolute Percentage Error (MAPE) of 25.33%. Furthermore, the price segment classification model achieved an accuracy of 87.30% with a weighted F1-score of 0.872. The SHAP ",0.0,https://doi.org/10.31004/joecy.v6i2.11288509fa23395427b469513996ab97c81787c3c45253ea,10.1108/ijhma-05-2026-0170,Do housing price cycles reflect herd behaviour in developing and emerging economies?,2026.0,International Journal of Housing Markets and Analysis,Ezekiel Davies; Francis Dogbe; P. D. Agorsor,"510 511 This study aims to examine whether housing price cycles in 16 developing and emerging economies reflect herd behaviour.512 513 514 515 This study used annual panel data from 2005 to 2023 sourced from the Bank for International Settlements and the World Bank’s World Development Indicators. The main model uses real housing price growth (inflation-adjusted) as the dependent variable and nominal housing price growth as a robustness check. Grounded in herd behaviour theory and information cascade theory, the study uses two-way fixed-effects panel regression, an instrumental-variables fixed-effects (FE-IV) estimator related to System GMM, and a mean group estimator, complemented by the Chang et al. (2000) cross-sectional absolute deviation (CSAD) herding test.516 517 518 519 Macroeconomic fundamentals were found to be the dominant drivers of housing price growth, but the role of inflation shrinks sharply. In the preferred two-way fixed-effects specification, gross domestic product growth (β = 1.223, p < 0.01) is a robust predictor, while the coefficient of inflation falls from β = 1.816 (p < 0.01) under nominal growth to β = 0.388 (p = 0.015) under real growth, consistent with much of the previously",0.0,5203a671c8d04faeeed58d973840cccffbb6935a04b,10.17798/bitlisfen.1878248,The Impact of Crime and Spatial Features on Housing Prices: An Explainable AI Approach in New York City,2026.0,Bitlis Eren Üniversitesi Fen Bilimleri Dergisi,Özgür Özgenç; Esin Ayşe Zaimoğlu,"This study presents a machine learning–based framework that integrates spatially linked real estate transaction data and crime records to predict residential property prices in the New York City (NYC) housing market over the period 2010–2024. The motivation stems from the limitation of traditional valuation approaches, which primarily emphasize structural property attributes while inadequately accounting for environmental and neighborhood-level factors. To address this gap, crime data from the NYC Police Department and real estate sales records from the NYC Department of Finance were spatially combined using Neighborhood Tabulation Areas.The resulting dataset comprises annual neighborhood-level residential sales statistics and crime data categorized into nine major types. Seven machine learning algorithms—Linear Regression, Ridge, LASSO, Random Forest, XGBoost, LightGBM, and CatBoost—were trained and evaluated using 5-fold cross-validation. Model performance was assessed using R², RMSE, MAE, and MAPE metrics.The results demonstrate that the CatBoost model achieved the highest and most stable predictive performance, with an R² value of approximately 0.75. Further analysis indicates ",0.0,https://dergipark.org.tr/en/download/article-file/5668135521fff8ff2c2b6b74b00fbc6d1895b66c9a5303bbba,10.1108/ijhma-06-2026-0213,Housing price prediction in Ireland: a machine learning framework integrating geospatial encodings and macroeconomic indicators,2026.0,International Journal of Housing Markets and Analysis,Emmanuel Oluwapelumi Odedele; Oladele Bidemi Ajayi,"522 523 This study aims to develop and evaluate a machine learning framework for predicting residential property prices in Ireland using the complete Irish Property Price Register, a population-complete data set of 736,002 arm’s-length transactions spanning January 2010 to March 2026. It formally quantifies the marginal contribution of geospatial, structural and macroeconomic data modalities.524 525 526 527 A multi-modal predictive framework integrates structural transaction data, address-derived geospatial location signals via Bayesian out-of-fold target encoding and macroeconomic indicators from the Central Statistics Office. Five models are trained and evaluated, including Ridge Regression, Random Forest and three HistGradientBoosting variants on an 80/20 train-test split.528 529 530 531 Geospatial location features increase R² by 0.2552, from 0.3494 to 0.6046, reducing mean absolute error by €28,271 per property. Macroeconomic lag features covering the consumer price index, unemployment and earnings change R² by less than 0.0001. The best model, HistGradientBoosting v3, achieves R² = 0.6046 and MAE = €80,755 on 144,470 held-out observations.532 533 534 535 The study is constrained by the absence of floor ",0.0,https://doi.org/10.2139/ssrn.72443185368c36a3ed30080aced7b120ff6db4bb921488c392,10.61838/jmaaue.89,Investigating Social and Economic Factors Affecting Housing Prices (Case Study of Shiraz City),2025.0,Manifestation of Art in Architecture and Urban Engineering,Morteza Saeedian; A. Shahvandi,"The purpose of this study is to analyze the social and economic determinants of housing price dynamics in Shiraz over the past two decades. This research employed a descriptive–analytical design with an applied orientation. Annual time-series data for 2001–2022 were collected from official sources including the Central Bank of Iran, the Statistical Center of Iran, and the Housing and Urban Development Office of Fars Province. Variables included urbanization rate, rent index, per capita income, housing bank credits, housing tax, unemployment rate, gross national product, government expenditures on housing, construction input price index, and building permits. Data were analyzed using unit root tests, multiple regression, and error correction models. The results indicated that rent prices, per capita income, and urbanization rate had the strongest positive and significant effects on housing prices. In contrast, gross national product and government expenditures in housing showed significant negative impacts. The construction input index also exhibited a strong positive effect. The overall model demonstrated high explanatory power, accounting for more than 85 percent of housing price ",0.0,https://jmaaue.org/index.php/jmaaue/article/download/89/815379a2f3492f920b802a67096a527084262c30ee130,10.22215/cujs.v3i2.5126,A Systemic Functional Linguistic Analysis of Remote Real Estate Listings,2025.0,Carleton Undergraduate Journal of Science,Emily Udle; Rachelle Vessey,"In the aftermath of the COVID-19 pandemic, the rural population in Canada increased faster than in any other G7 country. The province of Newfoundland was a distinct outlier, seeing the most drastic decrease of all rural Canadian regions at 6.4%. This was no surprise to local residents. Since the 1992 Cod Moratorium, these populations have faced continued socioeconomic hardship and decline. While decades of outmigration from rural Newfoundland to urban centres have been studied, current research does not account for this post-pandemic phenomenon. From a linguistic viewpoint, the population decline in Newfoundland has been redirected from regional discourse to a perpetuation of tourism development. While tourist discourse represents one strategy for combating population decline, the linguistic implications upon real estate listings are not known.538This project investigated the linguistic implications of increased global mobility and urbanism on real estate listings. Drawing upon the theoretical framework of systemic functional linguistics (SFL), we analyzed the interpersonal, experiential, and textual metafunctions of a 2022 real estate listing from the remote town of Harbour Breton, ",0.0,https://ojs.library.carleton.ca/index.php/CUJS/article/download/5126/37205393e8e59a2f4d09f7e08345cc75f9b15ef6aa22cde,10.1145/3630106.3659027,"The Impact of iBuying is About More Than Just Racial Disparities: Evidence from Mecklenburg County, NC",2024.0,"Conference on Fairness, Accountability and Transparency",Isaac Slaughter; E. M. Brown; Nicholas Weber,"Instant buyers (iBuyers)—companies that buy and sell homes based on automated valuation models (AVMs)—now hold more than 5% market share in some USA cities. In this work, we investigate the fairness of iBuyers by constructing a dataset that links racial demographics from voter records with detailed property information on over 50,000 real estate transactions. Using Bayesian hierarchical modeling we find that: 1. iBuyers Decrease the Racial Sales Price Gap Between Black and White Home Sellers. Controlling for over 50 property features we find that iBuyers reduce the racial price gap that otherwise exists between homes sold by Black and White homeowners. This is not, however, a result of equity achieved through proprietary AVMs, but rather a result of both Black and White homeowners being similarly disadvantaged by iBuyers’ low purchase prices; and, 2. iBuyers Increase Property Conversion Rates from Individual to Institutional Ownership. We trace iBuyers’ purchases as well as their follow-on sales of homes in Mecklenburg County. In doing so, we show that iBuyers increase the rate at which properties are converted from being individually owned to institutionally owned. The eventual pu",0.0,https://doi.org/10.1145/3630106.36590275407aab2b9fc7f33002300442495874543f58b04798,10.66224/ihio.8.2.132,Modeling Unobservable Variables in Life Insurance Demand in Iran,2025.0,Iranian Journal of Health Insurance,Maryam Ghafourboroujerdi; M. Mousavi; Esmaeel Safarzadeh; A. Safari,"Introduction: Life insurance serves as a pivotal instrument for providing households with psychological and financial security against aging-related challenges and loss of breadwinners, while also functioning as a significant financial institution for large-scale economic investments. Effective planning and policymaking in life insurance require precise identification and analysis of demand determinants. These factors can be categorized into: a) Observable economic variables, and b) Unobservable factors such as risk aversion, preferences, culture and lifestyle, emotions, financial and insurance literacy. Methods: To estimate the impact of unobservable variables, we employed the “underlying trend” concept within a Structural Time Series framework for modeling life insurance demand. Results: Unobservable variables (underlying trends) exhibit statistically significant effects on life insurance demand in Iran; For the final year of the study, the significant trend level value is 13.23, and its constant slope is 0.085. Beyond premium prices and income, other significant variables include stock prices, free-market exchange rates, gold prices, and housing prices - all consolidated as a co",0.0,http://journal.ihio.gov.ir/files/site1/user_files_5aeb35/ghafour-A-10-275-2-c4496c0.pdf541e68c3f01b1fb04776197ee91103adc0033e33cc3,10.11606/d.11.2017.tde-20072017-170049,Os impactos do desmatamento na incidência de malaria na Amazônia: uma análise espacial,2017.0,,Augusto Seabra Santos,"IMPACTS OF DEFORESTATION IN THE INCIDENCE OF MALARIA IN BRAZILIAN AMAZON: THE SPATIAL ANALISYS The aim of this study was to analyze the relationship of cases of malaria with deforestation in the municipalities of the Amazon, between 2003 and 2012. The research also intends to contribute to the literature through of spatial econometrics models, enabling capture effects spillovers of several socio economic determinants on incidence of malaria in the region. First, it became clear that the cases of malaria in the Amazon region are not randomly distributed among the municipalities, meaning that the disease is spatially correlated. It was also found evidence that municipalities with higher annual rates of malaria parasites are surrounded by other municipalities that also have a higher incidence of malaria. Regarding to the econometrics estimates, it was found evidence that deforestation is strongly statistically correlated with malaria cases, where areas of up to 425 km of loss os forest cover has a positive relationship with the cases of the disease, while deforested areas above 425 km have negative relationship with contamination malaria. Notwithstanding, the study also found a positi",0.0,http://www.teses.usp.br/teses/disponiveis/11/11132/tde-20072017-170049/publico/Augusto_Seabra_Santos.pdf542b5b83085c50826574232a8ab7ecdfbd6928feb14,10.3390/realestate3030015,Beyond Prediction: A Generalized Explainable Hybrid Econometric-Machine Learning Framework for Real Estate Valuation,2026.0,Real Estate,Firangiz Mammadrzayeva; N. Jafarov; I. Aliyev,"Real estate valuation increasingly requires methodologies that combine predictive accuracy, economic interpretability, and model transparency. This study proposes a Generalized Explainable Hybrid Econometric–Machine Learning Framework integrating econometric modelling, machine learning, and Explainable Artificial Intelligence (XAI) within a unified analytical architecture. The framework is organized around a generalized conceptual valuation equation and implemented through a workflow comprising data quality assessment, econometric benchmarking, hyperparameter optimization, nonlinear machine-learning modelling, five-fold cross-validation, and SHAP-based explainability. The methodology is demonstrated using an internally constructed Azerbaijan commercial real estate dataset and independently assessed using the publicly available UCI residential benchmark dataset through an identical Python-based computational pipeline. Under the complete income-capitalization benchmark, the Artificial Neural Network achieved the highest predictive performance for the Azerbaijan dataset, whereas Random Forest performed best for the external benchmark. A leakage-reduced robustness analysis further show",0.0,https://doi.org/10.3390/realestate30300155432751361471b2ad9cbe3a30c20986bd86475b8e82,10.17576/jkukm-2022-si5(1)-03,"Cabaran Pemilikan Rumah untuk Generasi Milenium di Lembah Klang, Malaysia",2022.0,Jurnal Kejuruteraan,A. Musa; Norngainy Mohd Tawil,"The Millennium Generation is a cohort of generations classified as a group between the ages of 23 to 38 years in 2019, it can be a reference point to describe the characteristics and demographics of this generation. In this new era of 2020, the Millennium generation has faced great challenges in the issue of home ownership, to understand this issue this research will identify the factors that influence home ownership and its challenges for the Millennium generation in home ownership in the Klang Valley. The methodology of this study is conducted quantitatively through a questionnaire study conducted to 500 respondents and a total of 467 respondents who have provided feedback that is 94.3%. As a result of the analysis, factors that influence ownership and challenges are identified. The Statistical Package for Social Sciences (SPSS) version 22.0 was used to analyse the data. The level of respondents’ agreement with the factor instrument was determined using mean values. Age, marital status, educational level, employer status, duration of employment, number of households, household income factors, and expenditure are factors that influence home ownership, based on the findings of the ",0.0,https://doi.org/10.17576/jkukm-2022-si5(1)-0354417551476e7e992da557becfd862af426ffb7e58e,10.58631/ajemb.v4i12.392,Determinants of Market Value of Luxury Condominium Units in Medan City,2025.0,American Journal of Economic and Management Business (AJEMB),H. Hartono,"This study aims to analyze the determinants of market value of luxury condominium units in Medan City, Indonesia, by focusing on unit-specific physical attributes and building management quality. Rapid urbanization, land scarcity, and vertical housing development have positioned condominiums as an important segment of urban residential markets. However, despite regional economic growth, luxury condominium prices in Medan have shown relatively stagnant, fluctuating trends, indicating a discrepancy between macroeconomic performance and property market behavior. This research employs a quantitative approach using multiple linear regression analysis based on 96 condominium units from major luxury developments in Medan. The independent variables include unit size, floor level, view, facilities, and maintenance fee, while market value serves as the dependent variable. Prior to hypothesis testing, classical assumption tests were conducted to ensure the reliability of the regression model. The empirical results demonstrate that unit size, facilities, and maintenance fee have a positive and significant effect on the market value of luxury condominiums. In contrast, floor level and view are ",0.0,https://ajemb.us/index.php/gp/article/download/392/434545e10a51c0af5621a45541cc721eb7399865b8f1d3,10.1109/gtss68743.2026.11679646,AI-Based Real Estate Price Prediction,2026.0,2026 International Conference on Green Technologies and Sustainable Solutions (GTSS),Ankit Nishad; Ankleshwar Singh; Mayank Srivastav; Nikhil Choudhary; A. Srivastava,"The real estate business has a global presence as the second largest sector in the country and this will continue to grow however currently the major hurdle in this industry is determining how much a property actually costs to purchase or sell at fair market value. Various aspects dictate what determines the value of a property including; the size and location of a property, the age of a building, if there are any necessary amenities nearby, current sales trends in the market, and also social infrastructure available to citizens who may want to purchase or rent a real estate property. Many traditional methods of valuating real estate rely solely on human expertise, performing market research and using basic or simplistic statistical models for calculating the value of a property therefore it takes time to find an accurate assessment of a property's value and it does not take into account how many different variables interact with each other in a non-linear manner. With advances made in both Artificial Intelligence (AI) and Machine Learning (ML), new methods for forecasting and deriving pricing models have become widely accepted as powerful means of optimizing and streamlining the e",0.0,5462bbb324b94bc32bc38f14c86592b874cffef7e05,10.48550/arxiv.2506.12069,Algorithms for estimating linear function in data mining,2025.0,"Journal of Investment, Banking and Finance",Thomas Hoang,"The main goal of this topic is to showcase several studied algorithms for estimating the linear utility function to predict547the users’ preferences. For example, if a user comes to buy a car that has several attributes including speed, color, age,548etc in a linear function, the algorithms that we present in this paper help with estimating this linear function to filter out549a small subset that would be of best interest to the user among a million tuples in a very large database. In addition, the550estimating linear function could also be applicable in getting to know what the data can do or predicting the future based551on the data that is used in data science, which is demonstrated by the GNN, PLOD algorithms [1,2]. In the ever-evolving552field of data science, deriving valuable insights from large datasets is critical for informed decision-making, particularly553in predictive applications. Data analysts often identify high-quality datasets without missing values, duplicates, or554inconsistencies before merging diverse attributes for analysis. Taking housing price prediction as a case study, various555attributes must be considered, including location factors (proximity to urban centers, crime ra",0.0,55625a865c04021f148474133b95e1a63a3fe653fb6,10.5821/qru.13720,"Life Between Housing in Taiyuan,China",2025.0,QRU: Quaderns de Recerca en Urbanisme,Jing Ren,"This article provides an overview of Taiyuan's geographical, industrial background and urban development process, from a planned economy to a market economy. It focuses on analyzing the four main types of housing in Taiyuan: the quadrangle courtyard houses embody traditional family culture, the collective housing of units reflects the characteristics of a planned economy, the workers' social housing is a product of the transitional period, and the high-rise commercial housing marks the rise of a market economy. Housing prices are significantly influenced by public space, the completeness of facilities and the location of schools. With the advancement of urbanization, the housing structure in Taiyuan has been constantly evolving, from family communities to unit communities, and then to commercialized housing under a market economy, reflecting social changes and the changing needs of people's lives.557 This article provides an overview of Taiyuan's geographical and industrial background, as well as its urban development process, from a planned economy to a market economy. It focuses on analyzing the four main types of housing in Taiyuan: the courtyard housing embodies traditional famil",0.0,https://upcommons.upc.edu/bitstreams/a08a7bde-6639-4144-813f-170185d2ebc7/download5587541686fd00684b09d60f7969d7f158cec11c9f1,10.12775/eip.2022.020,Flat location and size as a determinant of homeownership duration,2022.0,Ekonomia i Prawo.,I. Foryś,"Motivation: Both the research on the residential market focuses primarily on analyses of the size and quality of the housing stock, or seeking dependencies with socio-economic factors. The second research area is the analysis of prices and construction of residential price indices. As an equally important issue is assessing the intensity of trade in particular types of flat and location. A standard view is that those small flats are more frequently traded on the market than large ones. 559Aim: The study concerns analysis of ownership duration of a flat by the same owner (from the day of purchase to the sale day). It depends on the characteristics of the dwelling, including the location and size. The research will verify the hypothesis of a shorter duration for small units and its location. The study relies on the example of one local housing market. The study use regression analysis to examine the property value on the city’s districts and duration analysis to explore ownership duration time, and nonparametric models of a proportional model Cox gambling with explanatory variables dependent on time. 560Results: This research is significant in socio-spatial connection to the housing mark",0.0,https://apcz.umk.pl/EiP/article/download/37602/3261856159ccd188a58010d138a76e36cae65ffbd79a4645,10.4314/hpjsmt.v6i2.12,Spatial impact of waste dumpsites on proximate housing values and household income in some selected metropolitan cities in North-western Nigeria,2025.0,"HAFED POLY Journal of Science, Management and Technology",A. A. Yakub; Aminu Mansir; S. D. Gimba; Z. M. Zaidu; Adebayo Falilat Folake,"This study investigates the consequence of waste dumpsites pollution on proximate housing property value on the one hand, and also its consequences on household earnings on the other hand in north-western Nigeria, thereby incorporating a 2-way relationship between pollution and real estate value, and pollution and income. Using relative distance from waste dumpsites, a survey of resident’s income level and an analysis of property data makes up the research technique. Thus, randomly selected houses situated around waste dumping sites within the selected survey area were administered structured questionnaires to elicit necessary information for the survey in order to ascertain the degree to which the neighborhood's residents' income level and the rent they pay for their houses are impacted by the proximity of a garbage dumpsite. Through a quantitative technique and cross sectional survey that includes questionnaire surveys and field research, the study gathered data, which was analysed to test the premise that neighborhoods that are exposed to high pollutants command lower property values, and that such neighbourhoods are often occupied by poorer residents than areas not exposed to p",0.0,https://www.ajol.info/index.php/hpjsmt/article/download/309044/290601562ed272879113dac24babaa4a9d01b919f86e8e306,10.65736/chs03012,Influencing Factors and Spatial Characteristics of Urban Coastal Space Vitality Based on MGWR model: A Case Study of Qingdao,2026.0,Coastal Human Settlements,Xiaoli Sun,"Urban coastal spaces serve as critical public resources, where spatial vitality acts as a primary indicator of environmental quality and social functionality. This study aims to quantify the spatial characteristics of coastal vitality and identify its multi-scale driving mechanisms to inform evidence-based urban planning. Focusing on the eastern coastline of Qingdao, we characterized spatial vitality using spatiotemporal crowd distribution data derived from Baidu Heatmaps. Spatial autocorrelation analysis was employed to detect clustering patterns, while a Multiscale Geographically Weighted Regression (MGWR) model was implemented to examine the heterogeneous influence of multi-source spatial factors. The analysis reveals a significant positive spatial autocorrelation in coastal vitality, characterized by a prominent ""east-high, west-low"" distribution pattern. The MGWR results indicate that the Normalized Difference Vegetation Index (NDVI) of open spaces, shoreline morphology, spatial compactness, transport accessibility, and surrounding residential density are the primary determinants, with their impact varying across different spatial scales. Compared to traditional global and loc",0.0,https://doi.org/10.65736/chs030125638506f16b0710a57ed835f9583338b207fd204258,10.62051/a4tma782,Analysis of Tianjin real estate market: multiple linear regression prediction method,2024.0,"Transactions on Economics, Business and Management Research",J. Yi,"In this paper, the price prediction of Tianjin real estate market is studied, and the key factors affecting housing prices are systematically analyzed by using the method of multiple linear regression prediction. The research data comes from the real estate transaction data of Tianjin from 2018 to 2022, covering independent variable information such as housing area, housing age, geographical location and supporting facilities. By constructing multiple linear regression model, it is found that housing area and supporting facilities have a significant positive impact on housing prices, while housing age and distance from the city center have a significant negative impact on housing prices. Specifically, for every 1 square meter increase in housing area, house prices are expected to rise by 450 yuan; Every year the age of the house increases, the house price is expected to drop by 200 yuan; Every kilometer away from the city center, house prices are expected to drop in 800 yuan; Every time the score of supporting facilities is increased by 1 point, the house price is expected to increase by 1200 yuan. The goodness-of-fit test of the model shows that the R value is 0.85, which shows th",0.0,https://wepub.org/index.php/TEBMR/article/download/3795/4145564b9ed7778be4b40dc1ed23274c0b06225e885cf9a,10.59952/tuj.v8i2.505,"Effect of Property Market Liquidity on Real Estate Investment Performance in Nairobi County, Kenya",2026.0,The University Journal,Said Aden; J. Muriithi,"This study examined the effect of property market liquidity on real estate investment performance in Nairobi County, Kenya. The study adopted a pragmatic research philosophy using an embedded mixed-methods research design. The target population comprised 129 real estate investment firms operating in Nairobi County. Quantitative data were collected from 215 respondents using structured questionnaires, while qualitative insights were obtained from key informants through interviews. Descriptive statistics, correlation analysis, and linear regression were used to analyze quantitative data, whereas qualitative data were analyzed using content analysis. The findings revealed that respondents perceived Nairobi’s property market to exhibit favorable liquidity conditions characterized by stable property prices (M = 4.03), accessible financing (M = 4.02), and active property turnover (M = 3.95). Real estate investment performance was also rated positively, with transaction volume (M = 4.01), capital appreciation (M = 4.01), and rental yields (M = 4.00) indicating strong market performance. Correlation analysis established a strong positive and significant relationship between property market",0.0,https://journals.daea.or.ke/index.php/tuj/article/download/505/24356585d7c2812036f9f54351dee08df7c5f83d99e95c,10.54254/2753-8818/2025.22048,Dimensionality-Prediction Dilemma: The Dual Effects of PCA in Housing Price Regression Modeling,2025.0,Theoretical and Natural Science,Tangrui Xiong,"This study evaluates the practical dimensionality reduction efficacy of Principal Component Analysis (PCA) and its consequential impacts on regression-based housing price prediction by comparing three modelsmultiple linear regression, ridge regression, and LASSO regressionapplied to California housing data, while preliminarily exploring the underlying mechanisms. The research first establishes a spatial-economic-social framework influencing housing prices through data dimensionality reduction. This process initiates with KMO-Bartlett validation of the dataset, followed by variable screening conducted under comprehensive consideration of variables practical significance, and subsequently implements Kaiser criterion-guided PCA to derive three primary factors governing housing prices within this spatiotemporal context: Spatial Distribution & Density, Geographic Location, and Economic Status, thereby yielding interpretable components reflecting spatial, geographic, and economic dimensions. Further findings reveal that all models exhibit quantitatively comparable declines in predictive performance post-PCA implementation, with ridge and LASSO regression demonstrating nearly equivalent p",0.0,https://www.ewadirect.com/proceedings/tns/article/view/22048/pdf566ac1fc670836c8610387893b256bd209434ab0888,10.3390/land15050705,Spatial Heterogeneity and Multiscale Effects of the Built Environment on Commuting Distance: MGWR Evidence from Residential and Employment Perspectives in Shanghai,2026.0,Land,Jingxian Wu; Xiao Li; Han-Ning Dong; Jing Zhao; Yi Zhang,"Rapid urbanization has intensified jobs–housing separation and increased commuting distances in megacities, posing challenges for sustainable urban development. Existing studies often examine commuting behavior at a single spatial scale or focus on either residential or employment locations. Using mobile phone signaling data, this study derives network-based commuting distances within the suburban ring of Shanghai and integrates multiple built environment indicators. A multiscale framework is developed using six spatial units, ranging from 2 to 4 km grids to street-level zones, to assess spatial scale effects and support the selection of an appropriate analytical unit. The 3.5 km grid was selected for subsequent analysis as a balance between spatial detail and statistical stability. Within this framework, Multiscale Geographically Weighted Regression (MGWR) examines the spatial heterogeneity and scale effects of built environment factors from both residential and employment perspectives. The results show: (1) The choice of spatial unit significantly affects model performance, with the 3.5 km grid providing a suitable balance between spatial detail and statistical stability. (2) Bui",0.0,https://www.mdpi.com/2073-445X/15/5/705/pdf?version=17769370595674592c7bd67fa7f80c2d69a6fec05ac128341bd51,10.3103/s0146411619070265,Building a Data Store with the Dynamic Structure,2016.0,Automatic Control and Computer Sciences,Y. Artamonov,"This article presents the analysis of approaches to data warehouse construction based on relational and NoSQL solutions and lists the limitations of the relational approach to data mining. The contradiction between data presentation in the real subject domain and the model of data presentation in the relational and NoSQL approaches is revealed. The revealed contradiction is related to the temporality of the values of individual data attributes, the variability of the composition of these attributes, and structure of connections between them. A new logical model of the data warehouse with dynamic structure is proposed. The model is based on the concept of the object as a container for properties storage. Each property of the object includes the property name and two property values—without reference and with reference, that are relevant at a given time. The reference property value points to an object whose name is interpreted as the value of the property at a given time. A formal description of the model with allocation of the necessary functionality to manipulate objects and their properties (selectors, predicates, constructors) is given and the necessary control structures are in",0.0,https://www.mais-journal.ru/jour/article/download/322/313568bcb4a81cfb3346fbef5e4a41bf9da4b7e2a194f6,10.62792/ut.evision.v13.i25-26.p3361,THE IMPACT OF MACROECONOMICS CONDITIONS ON INVESTMENTS IN NORTH MACEDONIA,2026.0,"International Scientific Journal in Economics, Finance, Business, Marketing, Management and Tourism-ECONOMIC VISION",Gazmend Dehari,"Knowing the right Macroeconomic conditions is of key importance for enterprises to make well-informed investments decisions. Regardless of the vast empirical studies on the predictability of these conditions, no consensus have emerged, on what is the most likely model to use for such forecasts. The purpose of this paper is to focus on certain macroeconomics indicators that will shape an environment at a degree of stability intended to enable investors i.e. enterprises to enhance their investments. The macroeconomic determinants considered are as following: GDP growth, inflation, unemployment rate, interest rate, trade and housing prices. This studies goal is to examine how the stated macroeconomic indicators fluctuations will affect the level of investment in North Macedonia. The sample includes all the available yearly observations data for the period 2000-2024. All the data come from the World Development Indicators database (World Bank 2025), except for the independent variable of housing price index, which came from the National Bank of the Republic of North Macedonia (NBRNM). The investment variable, depended variable, is measured as a foreign direct investment net inflow as p",0.0,https://doi.org/10.62792/ut.evision.v13.i25-26.p33615696af3b3b3f630242eb4c08960c861a2d05b037c36,10.1093/jue/juae020,House Price Index as an early indicator of development risk to biodiversity,2024.0,Journal of Urban Ecology,Marlen Acosta Alamo; L. Manne; Richard R. Veit,"570 Habitat loss due to changes in land cover is one of the main causes of biodiversity decline worldwide. Habitat loss occurs disproportionately in areas of high biodiversity because these same areas are particularly suitable for development. We assessed the effect of development risk on the biodiversity of breeding birds in the United States. We compared the effect of two predictors of habitat loss on the richness, abundance, and rarity of woodland, open-habitat, and urban birds at the local and regional levels. We used the House Price Index—as a measure of development risk—and primary productivity as predictors in simulations of habitat loss. For local scale analysis, we used generalized regression models. For regional-scale habitat loss simulations, we statistically compared the results obtained from each predictor. Locally, development risk and primary productivity interacted in their effect on the richness, abundance, and rarity index of all birds. At the regional level, development risk predicted larger declines in richness and abundance and increases in the rarity of open-habitat birds following habitat loss than primary productivity. For woodland birds, both risk of developm",0.0,https://doi.org/10.1093/jue/juae020571fe31ab90da398df50bc88eea94fb13f899b7e72a,10.54091/krepa.2025.26.3.23,Does the Policy Change across Administrations Induce Spatio-Temporal Spillover Effect in Housing Prices of Submarkets in Seoul? : Focusing on A Python-Based Numerical Analysis Model,2025.0,Korea Real Estate Policy Association,Bumseok Chun; Jaebum Jun,"This study investigates how shifts in real estate policy orientations across presidential administrations have shaped the spatio-temporal transmission of apartment prices across Seoul’s 25 districts. Although the literature frequently discusses ripple and balloon effects, few studies have simultaneously examined dynamic inter-district transmission and changing regulatory environments using long-term empirical data. By utilizing 254 monthly observations from November 2003 to December 2024, combined with presidential term information, this study identifies how the diffusion pathways and structural characteristics of price shocks differ across administrations. A stepwise Python-based analytical framework was employed, integrating correlation network analysis, a VAR(Vector Autoregression) model, Granger causality tests, and IRFs(Impulse Response Functions). Statistically significant correlations were used to refine the VAR specification, after which Granger causality allowed the identification of originating and receiving districts. IRFs were then used to quantify the propagation intensity, transmission lags, and temporal decay patterns of shocks across space. The results show that pri",0.0,5724e18956d31dcf155e6fa8f6389b91b5b4f1dccb7,10.47611/jsrhs.v13i1.6588,Monetary Flood Damage Prediction Based On Machine Learning Models,2024.0,Journal of student-scientists' research,Jaya Bijoor; Shreyaa Raghavan,"Flooding has increased by 50%, and for the past 25 years, the United States has experienced a flooding event every 2 to 3 days [1]. The Federal Emergency Management Agency (FEMA) is the only resource people utilize to determine whether their homes are at risk for flooding. FEMA maps are costly, outdated, and lack predictive accuracy, leaving homeowners unaware and unprepared for potential dangers [2]. Additional tools are needed to ascertain accurate estimates of flood damages, but computational methods require rich datasets, which are difficult to obtain for flooding events [3]. We create a model that predicts monetary damages using various factors related to the flood event, such as duration, location, and cause. We utilize a flood dataset from the National Oceanic and Atmospheric Administration (NOAA) and data pre-processing techniques to handle missing values and remove irrelevant features [4]. We generate aggregate predictions by leveraging linear regression, random forest regression, XGBoost regression, and neural networks. Our findings show we can effectively use upsampling techniques to combat limited flood data. We show that floods with higher monetary damages are easier t",0.0,https://www.jsr.org/hs/index.php/path/article/download/6588/3004573b4f063d53db959943350a92e7a396b3c45e9ff52,10.38124/ijisrt/ijisrt24may1134,"Environmental and Social Implications of Mixed Land use in Zambia: A Case Study of Makeni Area, Lusaka",2024.0,International Journal of Innovative Science and Research Technology,Emmanuel Chama Mulenga; E. Nyirenda,"This study aimed to explore the Environmental and Social Implications of mixed land use in Makeni, Lusaka, focusing on three specific objectives: understanding the social implications, determining environmental impacts, and assessing the management of mixed land use in the area. Employing a mixed-methods approach, the research incorporated both qualitative and quantitative data collection and analysis, utilizing secondary and primary data. The sample size of 385 participants included 375 occupants of commercial and residential properties in Makeni and 10 key informants from relevant environmental agencies. Findings indicated that mixed land use in Makeni had positive effects on housing prices, accessibility to social amenities, and property costs. It also contributed to improved road infrastructure and building quality, with limited evidence of increased social problems. However, diverse opinions among respondents suggested varying perceptions. Air sampling revealed concerning levels of carbon monoxide from industries, surpassing allowable limits, while noise levels exceeded WHO recommendations in residential areas. Water analysis was not conducted due to closed systems or lack of ",0.0,https://www.ijisrt.com/assets/upload/files/IJISRT24MAY1134.pdf574b0a5cae569ea21d6cd21a1263c8f83122a4a2f7d,10.31107/2075-1990-2025-1-92-107,Ruble Debt Burden,2025.0,Financial Journal,B. Alekhin,"The burden of ruble debt is measured by a ratio between the amount of rubles that Russians borrowed from their banks and their monetary income. Why ruble debt only? The share of foreign currency credit to Russian households has been declining quite steadily and remained insignificant in recent years. Even in the peak of 2010 it fluctuated around 10%. The purpose of this study is to analyze the size, dynamics and determinants of debt burden. To achieve this purpose, a panel for 81 regions of the Russian Federation for 2000–2022 was assembled on the basis of official statistics. All variables proved to be non-stationary which allowed to use panel dynamic least squares (DOLS) to estimate the parameters of the cointegrating equation. The estimates proved the existence of long-term, equilibrium relationship between debt burden and explanatory variables. To support our empirical search for drivers of debt burden we relied on the theories of life-cycle, liquidity constraints and behavioral finance. The first two proved to be useful, while the role of behavioral finance remained unclear as it was impossible to operationalize such a murky concept as financial literacy. In accordance with th",0.0,https://doi.org/10.31107/2075-1990-2025-1-92-1075755d4b4fb9b781f260dcefa056615c743aa5ec64f3,10.21837/pm.v23i38.1821,THE IMPACT OF MASS RAPID TRANSIT (MRT) ON RESIDENTIAL PROPERTY PRICES IN MALAYSIA: A CASE STUDY OF THE SG. BULOH – KAJANG (SBK) LINE,2025.0,PLANNING MALAYSIA,Puteri Ameera Mentaza Khan; A. MohdAini; Z. N. Musa,"The relationship between transportation infrastructure and property prices is crucial in urban studies and real estate research, particularly in rapidly urbanizing regions like Malaysia. One crucial factor influencing real estate pricing is its proximity to well-planned and effective transportation hubs. Research continuously shows that homes near major highways or public transportation frequently fetch higher prices, reflecting the premium price paid for convenience and shorter commute times. Despite several studies being undertaken on the correlation between railway stations and property values, Malaysia, as a developing country, still lacks studies in this area of research. This paper focuses on the impact of Mass Rapid Transit (MRT) stations on residential property prices along the Sg. Buloh – Kajang (SBK) line in Malaysia. Through observational methods of data collection, the characteristics of MRT stations, including station type, facilities, and connectivity, were identified. Of the 31 stations, 9 were selected to reflect the residential property prices in the neighbourhood. This study focused on analysing residential property prices as the dependent variable using a seconda",0.0,https://www.planningmalaysia.org/index.php/pmj/article/download/1821/149757602b3454d86261e8d989b5a85e19665d487fcbb27,10.21203/rs.3.rs-34863/v1,Associating the COVID-19 Severity with Urban Factors: A Case Study of Wuhan,2020.0,,Xin Li; Lin Zhou; T. Jia; Ran Peng; Xiong-Wu Fu; Yuliang Zou,"577 Wuhan encountered a serious attack in the first round of the COVID-19 pandemic which has resulted in serious worldwide consequences politically and economically. Based on the Weibo help data, we inferred the spatial distribution pattern of the epidemic situation and its impacts. Seven urban factors, i.e. urban growth, general hospital, commercial facilities, subway station, landuse mixture, aging ratio, and road density, were selected for validation with the ordinary linear model, and the former six presented globally significant association with the epidemic severity; thereafter, the geographically weighted regression model was further adopted for local test to identify their unevenly distributed effects in urban space. Among the six, the place where general hospitals exert effects on epidemic situation highly is associated with their distribution and density; commercial facilities appear the most prevalently distributed factor over the city; newly developed residential quarters with high-rise buildings face greater risks, mainly distributed around the waterfront area of Hanyang and Wuchang; the influence of subway stations concentrates at the adjacency place where the three tow",0.0,https://www.researchsquare.com/article/rs-34863/latest.pdf578419c959257365dddfb728bdf6a1a6424fc0a6b8f,10.1177/03611981261457410,Residential Price Modeling Using Spatially Validated Machine Learning Methods: A Comparison Across Geographical Contexts,2026.0,Transportation Research Record,Usman Ahmed; Jason Hawkins,"Land use (i.e., buildings) and transportation infrastructure are tightly coupled systems, such that residential property prices play a critical role in transportation planning. In a similar manner, transportation infrastructure influences property prices, such that accurate forecasts of both systems are related research problems. The objective of this study is to examine these interactions and evaluate the effectiveness of machine learning methods in modeling residential real estate prices across different urban contexts. Specifically, we first examine the impact of land and transportation infrastructure on residential real estate prices using Extreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning methods, and compare results between two cities representing diverse geographic and socioeconomic contexts. Second, we investigate the application of machine learning methods on spatial data and provide a comparison of non-spatial and spatial cross-validation on the performance of machine learning methods. We use SHapley Additive exPlanations (SHAP) values to study the impact of land use and transportation infrastructure on real estate prices. The models are applied, ",0.0,57949c7b158b15556bd5a94afc3bb71b338f842a385,10.15826/jtr.2025.11.4.234,"Assessing the of Fairness of Local Property Taxation: A Case Study of Tel Aviv, Israel",2025.0,Journal of Tax Reform,Mordekhay Mintz; Boris A. Portnov,"The ad valorem tax system, based on property values, is a common approach to municipal property taxation. However, it is unclear whether this tax approach results in greater environmental fairness and social justice at the neighborhood level, compared to e.g., less sophisticated property tax estimation methods, such as the Arnona taxation, which is based on property size and currently implemented in Israel. To answer this question, we conducted several tax simulations for Tel Aviv-Yafo, the second-largest city in Israel with about 450,000 residents. The simulation involved data on 2,000+ residential properties sold in 2022–2023. Our findings indicate that the tax rates, estimated using the Arnona system, correlate strongly with the socioeconomic status (SES) of the neighborhood (r = 0.787, p < 0.01), albeit this association is not either monotonic or linear. Currently, when the ad valorem tax system is used for tax rate simulations, the correlation between neighborhood SES and the estimated tax rates is found to be weaker (r = 0.475, p < 0.01), thus indicating even a less equitable tax pattern. These findings indicate that neither taxation system helps to achieve full socio-economi",0.0,https://doi.org/10.15826/jtr.2025.11.4.234580f1cf8db37dda24236d217ca77c2df7d64124221d,10.64189/ict.25313,Advanced Feature Engineering for Residential Property Valuation: A Case Study on King County Housing Data,2025.0,"Journal of Information and Communications Technology: Algorithms, Systems and Applications",Aditi Nagayach; Atul Samadhiya,"Accurate property valuation is critical for real estate markets, financial institutions, and urban planning. Traditional appraisal methods are time-intensive and subjective, while complex machine learning models often lack interpretability. This study addresses these challenges by developing an advanced linear regression framework that balances predictive accuracy with model transparency through systematic feature engineering. In this study, we present an advanced linear regression framework for residential property valuation using comprehensive feature engineering techniques. Utilizing the King County House Sales dataset comprising 21,613 transactions from May 2014 to May 2015, we developed 40 engineered features including interaction terms, polynomial features, ratio calculations, and location-based composites. After outlier removal using the interquartile range method, our dataset consisted of 20,467 properties with 55 total features. The optimized linear regression model achieved a test R² of 0.7198 with a normalized root mean square error (NRMSE) of 0.20 (20% of mean property value) and mean absolute error of 82,626. Feature importance analysis revealed that basement-to-living",0.0,https://gr-journals.com/Ictasa/pdf/ICT_25313.pdf581507d23d428115f097a78874790a633410e1a8376,10.18288/1994-5124-2020-4-110-137,Income Residential Demand Elasticities for Electricity: Do We Need to Differentiate the Tariff?,2020.0,,K. Tumanyants,"This study assesses how the Russian household welfare rate impacts electricity consumption volume. Modeling of Russia Longitudinal Monitoring Survey (RLMS) data provided by the Higher School of Economics (HSE) has revealed a heterogeneous dependency between average per capita family income and electricity consumption volume. Electricity demand is not elastic among both urban and countryside residents. However, urban consumption is better described by a linear function, while rural consumption is better described by a quadratic function. The electricity tariff is not statistically significant for urban residents, whereas the price elasticity of electricity consumption for rural residents is close to 1. Application of lifeline rates for electricity tariffs in certain regions did not affect the level of elasticity of consumption by income, but some anomalies have been identified that indicate forced savings of electricity in order to pay for it at a minimum price. The study provides a divided quantitative assessment of influence of some non-income determinants on electricity demand among urban and countryside residents: number and age of family members, residential property area etc. ",0.0,https://doi.org/10.18288/1994-5124-2020-4-110-137582f58dd3af44682887e3406f87babc6680cacdc854,10.22616/j.balticsurveying.2021.14.008,"Engineering Inspection Associated Artificial Intelligence for Appraisal of the Property in Niteroi, Rio de Janeiro, Brazil",2021.0,Baltic Surveying ..,Vladimir Surgelas; I. Arhipova; V. Puķīte,"The construction sector is linked to the general development of a country. There is a lot of data scattered and not properly explored in relation to the buildings constructed. However, if these scattered data on the behavior of the real estate market are organized, combined with knowledge of civil engineering, this merger of information can mitigate some evaluation problems, especially those that are overvalued for unknown or dubious reasons. Thus, there is a need for models capable of working with limited data to analyze the causal relationships between explanatory variables and sales prices and, from there, predict property values. The purpose of this article is the innovative use of simple building inspection strategies to predict the market price for residential apartments. For this, 19 samples of residential apartments are used in the city of Niterói, Rio de Janeiro, Brazil, in February 2021. The methodology uses the results of the survey of civil engineering and converts them into heuristic terms predicting the price of the property. With this, the imprecision, uncertainty, and subjectivity of human expression combined with the knowledge of civil engineering result in a plaus",0.0,https://doi.org/10.22616/j.balticsurveying.2021.14.008583f967745d6170767bb9697ec985dd4f2b299f9376,10.15641/jarer.v11i1.2029,Comparative Performance of Selected Machine Learning Algorithms in Predicting Residential Property Price Across Sub-Markets in Lagos Metropolis,2026.0,Journal of African Real Estate Research,T. Ashaolu; Amos Adewusi; A. Oyelakin,"Traditional techniques of property valuation are increasingly criticised for their subjectivity, vulnerability to data insufficiency, and other discrepancies, thereby fuelling a methodological shift towards automated and data-driven valuation frameworks. Hence, this study explores three selected machine learning algorithms - Random Forest (RF), Bayesian Ridge (BR), and LASSO Regression – in estimating residential property prices across three distinct sub-markets within Lagos Metropolis. The study seeks to identify the algorithm that achieves the highest predictive precision and to examine the extent to which sub-market heterogeneity affects model reliability. The dataset utilised for the study comprised 469 residential property transactions concluded over the years by thirteen registered Estate Surveying and Valuation firms operating within the Metropolis. The results revealed varied performance across the sub-markets, with Random Forest emerging as the most efficient model, demonstrating the model’s robustness and adaptability to nonlinear property data and spatial variability. The Bayesian Ridge Regression model produced moderate results, performing reliably in sub-markets charac",0.0,https://journals.uct.ac.za/index.php/JARER/article/download/2029/127458433e960fa7306c2392c38aedb2caccbdb60515e48,10.3389/fclim.2026.1892472,"Static–dynamic air pollution exposure differences among primary schoolchildren in Linyi, China",2026.0,Frontiers in Climate,Wenkang Gao; Fanhua Kong; Haohan Zhou; Heming Bai,"585 Assessing whether residence-based static exposure can represent activity-based exposure is important for refined air pollution exposure assessment, particularly for vulnerable populations such as primary schoolchildren. We quantified static–dynamic exposure differences among primary schoolchildren from 22 primary schools and 138 residential communities and examined their associations with housing price in Luozhuang District, Linyi, China. To support exposure estimation, we developed high-resolution land-use regression (LUR) models integrating dense monitoring observations, road networks, area-of-interest data, and other spatial predictors to generate 100 m × 100 m typical hourly concentrations of PM586 2.5587 , PM588 10589 , NO590 2591 , and O592 3593 . After incorporating children’s school-time locations, dynamic exposure estimates remained broadly comparable to static estimates but showed systematic pollutant-specific differences. Significant differences were found for NO594 2595 , PM596 2.5597 , and O598 3599 in both seasons, with small absolute mean differences of 0.04–0.19 μg/m600 3601 . Static estimates tended to overestimate exposure to NO602 2603 and O604 3605 but underestimate exposure to PM606 2.5607 . Housing-pr",0.0,https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2026.1892472/pdf6085a7aed55a0752c0e4f1732ed0d701ce57b9fe920,10.1088/1755-1315/1505/1/012019,Smart living housing valuation: conceptualising hedonic cost approach through elemental cost model,2025.0,IOP Conference Series: Earth and Environment,K. F. Chieng; Fara Diva Mustapa,"The integration of Smart Living concepts into conventional housing development aligns with global sustainability goals and revolutionises the Smart City initiative. This paper delves into the pivotal role of Smart Living within the broader Smart City framework, emphasising its significance in addressing residential community well-being. Smart Living housing features, such as assisted toilets, motion sensors, and CCTV systems, are meticulously designed to bolster environmental conservation, social inclusivity, and economic resilience, thereby addressing challenges such as ageing populations, physical disabilities, environmental sustainability, and safety concerns. However, challenges emerge in accurately valuing Smart Living properties due to a scarcity of comparable samples for traditional comparison-based valuation methods. To address this, the paper proposes leveraging a cost approach valuation facilitated by an elemental cost model developed through the Hedonic pricing method. This conceptual model serves as a decision-making process by delineating cost drivers quantitatively and cost data qualitatively, culminating in the development of an elemental cost model through the Hedon",0.0,https://doi.org/10.1088/1755-1315/1505/1/0120196096f5b9f30a27775af73db7d738f3e6e73c1d1580b,10.47191/jefms/v8-i9-40,Housing Affordability Trap and Labor Mobility: Evidence From Vietnam’s Urban Development,2025.0,"Journal of economics, finance and management studies",Quan Nguyen van; Bao Le Quoc,"Housing affordability is becoming a major challenge in rapidly developing countries, including Vietnam. Rapidly rising housing prices while incomes have not kept pace make it difficult for many residents, especially low-income and migrant groups to access suitable housing. This limits labor mobility to better places of employment, negatively affecting the labor market and socio-economic development. However, the relationship between housing affordability and labor mobility in Vietnam still remains gaps. The study aims to clarify the impact of housing affordability on labor mobility in Vietnam, and further examine the impact of demographic and housing characteristics. The study is based on Kain's (1968) Spatial Mismatch theory and adopts the quantitative research method with data collected from 525 residents from urban areas across Vietnam. The results show that housing affordability is the factor that has the strongest and most positive influence on labor mobility, which means when housing expenditures account for a larger proportion of income, workers tend to move more to find better areas or job opportunities, at the same time, education level and housing type also have a positiv",0.0,https://www.ijefm.co.in/v8i9/Doc/40.pdf610e1b5d8c3eb36545b5b00e3e3fcf509be5dbe963a,10.17831/enqarcc.v22i2.1254,"From School to Housing: A Typological Analysis for the Adaptive Reuse of Historic Public Schools in Oklahoma City, USA",2025.0,Enquiry The ARCC Journal for Architectural Research,Hiroki Mishima; Francesco Cianfarani,"Abandoned historic schools in the USA have become a growing concern for urban communities, presenting a multifaceted issue that impacts social, environmental, and economic aspects of public life. Several factors contribute to the abandonment of historical public educational facilities, including demographic shifts, economic changes, and funding cuts. Since the postwar era, the economic and demographic decline of U.S. urban cores has left many interwar-period schools unmaintained and vacant. This neglect has led to informal occupation, declining neighborhood property values, increasing public maintenance costs, and, in many cases, demolitions. Focusing on Oklahoma City as a case study, this paper investigates the adaptive reuse viability of pre-World War 2 public schools. By analyzing 18 schools in the city’s urban core, we explore their typological characteristics through historical research, cluster analysis, and precedent study. Given the current housing affordability crisis in the USA, this study explores the possibility of repurposing historic school buildings into affordable housing, discussing the main design challenges associated with such conversions. Specifically, the reus",0.0,https://arcc-journal.org/index.php/arccjournal/article/download/1254/96161109accb37ce495410d2dd2e40ea0893f90fb01fba,10.32996/bjes.2022.2.1.7,Socio-spatial Disparities of Tobacco Outlets in Edinburgh,2022.0,British Journal of Environmental Studies,Yiqun Wang,"This paper joins spatial autocorrelation models (Global Moran's index, Local Moran index) to analyse the distribution of cigarette vending outlets in Edinburgh and utilises global regression (OLS) models and Geographically Weighted Regression models (GWR) to examine the spatial heterogeneity of fiscal and social factors that affect the distribution of retail outlets in Edinburgh. The results demonstrate that Edinburgh has a considerable spatial agglomeration of cigarette retail outlets, with a clear trend towards clustering in high and low-value areas of retail outlet distribution, showing significant geographical characteristics. The impact of each element on the distribution of cigarette retail outlets was analysed utilising the ranking of the elements in SIMD (The Scottish Index of Multiple Deprivation) as explanatory variables. It was established that data regions with a high ranking in education, skills, and training and those with a high ranking in geographic access to services revealed a positive trend in the density distribution of cigarette retail outlets, while data regions with a high ranking in housing and those with a high ranking in crime revealed a negative trend o",0.0,https://doi.org/10.32996/bjes.2022.2.1.76126c279ae50bac4449574b3746ae8f16ea9bedc1a5,10.22555/pjets.v14i1.1478,Data-Driven Price Prediction for Airbnb Using Machine Learning Models,2026.0,Pakistan Journal of Engineering Technology & Science,Muhammad Suleman; Asma Qaiser; Mirza Adnan Baig; Muhammad Haris,"The real estate market in metropolitan cities is influenced by several factors, including economic trends, which affect house price estimates. Moreover, the rental industry has been transformed, be it for short-term or long-term rentals, by platforms such as Airbnb, particularly in regions such as Greater London, a tourist hub with dynamic housing demand. Price evaluation is a critical challenge, as hosts tend to overprice or sometimes underprice their property, whereas affordability is a major concern for guests. The lack of analytical exploration of raw and complex datasets makes it difficult for hosts and guests to align themselves with the right prices. This study establishes a machine learning predictive framework that estimates the property's listing price by employing environmental and listing-specific attributes. A Greater London scrape data from Inside Airbnb was utilized for analysis. The methodology includes data cleaning, feature engineering, and exploratory data analysis (EDA) techniques, essential for predictive performance. Supervised models, including linear regression, ridge regression, lasso regression, XGBoost, Gradient Boosting, LightGBM, and Ensemble approaches",0.0,https://journals.iobm.edu.pk/index.php/pjets/article/download/1478/891613ba50780b6392394b1c447f0f41a74dda6abb3b88,10.23900/2359-1552v14n8-21-2025,"USE OF SPATIAL REGRESSION MODELS IN THE MASS APPRAISAL OF REAL ESTATE IN THE COASTAL NEIGHBORHOODS OF MACEIÓ, ALAGOAS, BRAZIL",2025.0,Revista Políticas Públicas & Cidades,Carlos Alberto Peruzzo Trivelloni; Nelson Marisco; Norberto Hochheim,"This paper presents the results obtained in the research that has been carried out to study the price of apartments in the coastal neighborhoods of the city of Maceió, State of Alagoas, Brazil: Ponta Verde, Cruz das Almas, Jatiúca, Pajuçara. The research studies the price of real estate in these seaside neighborhoods as support for understanding the value of the square meter practiced. This research uses a comparative method of market data using spatial econometric models supported by Geographic Information Systems (GIS). The data on the properties on offer was collected from real estate agents and from property listings on the Internet. It was then systematized in electronic spreadsheets, with the plane coordinates georeferenced to the UTM system and the information imported into QGis. The computer programs GeoDa and GeoDaSpace were used to carry out Exploratory Spatial Data Analysis and classical and spatial regressions for the estimation process. The results obtained show that the Spatial Lag (SAR) model using two-stage spatially generalized Instrumental Variables (IV) estimators (S2SLS + MGM/HAC) was able to eliminate the effects of spatial autocorrelation of the residuals pres",0.0,https://journalppc.com/RPPC/article/download/2608/158261409c70e20c7c41d52ab52245bc7d7f39503bdb0bc,10.70121/001c.121732,Deriving and Verifying a Community Vitality Index through a Machine Learning Model Based on House Price Data,2024.0,Scholarly Review Journal,Mark Li,"Recognizing the complexity and limitations of the approach to produce a traditional Multi-dimensional Community Index (MD-CVI) as well as high cost and time-intensive, the study explores another approach that establishes a machine-learning model to derive a community index by leveraging contemporary house prices. This method has several significant advantages over traditional methods, providing a more efficient and reliable way to assess community well-being. The model produces a dynamic Community Vitality Index (CVI) that quickly captures changes within a community and enhances the MD-CVI’s sophistication by selecting and combining any geographic regions generated by clustering algorithms, employing machine learning model and house sold price reduces human errors from questionnaires, and potentially improves accuracy. The study also exemplified and verified the model via house data from Minnesota, Texas, and Connecticut, spanning the last five years. Subsequently, comparing the house-price-based CVI with traditional MD-CVIs reveals a consistent alignment in their community index rankings. The conclusion is that the house-price-based CVI, as second research, is a viable proxy and a",0.0,https://www.scholarlyreview.org/article/121732.pdf615dcd506a60259294d7d1b18cca84d967185ce8fdd,10.38104/vadyba.2020.2.12,MICROECONOMICS OF ARCHITECTURE: BETWEEN MARKET AND PUBLIC,2020.0,,V. Navickas; Tomas Skripkiūnas,"The position of architecture between market goods and public goods is addressed in this study. A transition of architectural objects of built616environment from market goods towards public or nonmarket goods is presented in literature review. The real estate market value is highly influenced617by concepts of externalities and public goods, therefore being highly spatially dependent and making the process of the real estate valuation more618complex. The internalization of these externalities and public goods is impossible because of the nature of public space in the city. The concept of619value and different types of value, like exchange, use, image, social, environmental, cultural value, are also presented in literature review. These620different types of value are transferred to value in exchange when estimating market value. The aim of research is to calculate the amount of the real621estate market value that is influenced by externalities, public or nonmarket goods. The process of value transfers between market and public is also622discussed in this study. In the research part prices of similar apartments623measure the coefficient of variance. Newly constructed apartment buildings with partial f",0.0,https://doi.org/10.38104/vadyba.2020.2.12624fc1b624cf4cfc4248359196532e8dc67accfcb49,10.1108/jerer-10-2022-0031,House price bubble detection in Ukraine,2023.0,Journal of European Real Estate Research,Alona Shmygel; Martin Hoesli,"PurposeThe purpose of this paper is to present a framework for the assessment of the fundamental value of house prices in the largest Ukrainian cities, as well as to identify the thresholds, the breach of which would signal a bubble.Design/methodology/approachHouse price bubbles are detected using two approaches: ratios and regression analysis. Two variants of each method are considered. The authors calculate the price-to-rent and price-to-income ratios that can identify a possible overvaluation or undervaluation of house prices. Then, the authors perform regression analyses by considering individual multi-factor models for each city and by using a within regression model with one-way (individual) effects on panel data.FindingsThe only pronounced and prolonged period of a house price bubble is the one that coincides with the Global Financial Crisis. The bubble signals produced by these methods are, on average, simultaneous and in accordance with economic sense.Research limitations/implicationsThe framework described in this paper can serve as a model for the implementation of a tool for detecting house price bubbles in other countries with emerging, small and open economies, due to",0.0,625da8cf0d2771a198804b8bef00a3bc974754eb2bc,10.1109/access.2026.3729262,Parsimonious Fusion of Geographically Weighted Neural Networks and Deep Learning for Spatial and Spatiotemporal Non-Stationarity,2026.0,IEEE Access,Yun Pu; Mei-Hong Gao; Xiang-Feng Liu,"High-frequency spatiotemporal data often exhibit pronounced non-stationarity that challenges conventional geographically weighted regression (GWR) and its neural network variants—existing models either remain locally linear or neglect long-range temporal dependencies, but rarely address both limitations simultaneously. This paper proposes a parsimonious fusion paradigm that couples deep learning with geographically weighted neural networks through a single end-to-end trainable coefficient. For spatial cross-sectional data, the geographically neural network weighted regression model with an artificial neural network (GNNWR-ANN) combines a spatially weighted neural network with an artificial neural network, using a learnable scaling coefficient $\alpha $ to balance local spatial adaptability and global nonlinear mapping. For spatiotemporal data, the geographically and temporally neural network weighted regression model with a long short-term memory network (GTNNWR-LSTM) incorporates a long short-term memory network into the spatiotemporal weighting framework to capture temporal dynamics under the same fusion principle. Extensive experiments on Beijing’s 2024 second-hand housing price",0.0,https://doi.org/10.1109/access.2026.3729262626cb9451bb712c3adbfc190c8db2f506f76537301e,10.2478/qic-2026-0028,Analysis of Quantum Machine Learning Models in Investment Decision Making in Real Estate Industry,2026.0,Quantum Information & Computation,Huiqing Liu; Tong Liu,"Abstract This paper uses quantum state coding and quantum-classical hybrid models based on quantum state coding and quantum-classical hybrid models to solve the portfolio optimization problem in real estate project investment. The article transforms classical real estate data, such as house prices and rental yields, into quantum states for processing through quantum state coding methods, and constructs a quantum-classical hybrid model to overcome current quantum hardware limitations. The empirical results show that the average relative error of the quantum machine learning model is 3.26% in terms of investment risk prediction, which demonstrates an obvious advantage compared with the 9.36% error of the traditional support vector machine model. The model also shows good anti-interference ability, and the prediction error can still be kept within 8% after adding noise data. The financial index simulation of a long term rental apartment project shows that the probability of the project’s NPV reaching the expected level is 75.61%, the probability of the payback period meeting the expectation is 86.6%, and the probability of the internal rate of return exceeding the expected value is 47",0.0,https://reference-global.com/download/article/10.2478/qic-2026-0028.pdf62743d121b4410960feedce4d0579aa8ee6bf77f969,10.2139/ssrn.3603327,AVM versus Appraisal-Based Underwriting in Refinance Mortgages: The Trade-off Between Noise and Bias.,2020.0,,George Krivorotov; Michael LaCour-Little,"Extensive research has confirmed that there is significant anchoring bias in purchase mortgage appraisals, with appraisers setting the value at or above the contract price the vast majority of the time. However, understanding appraisal bias in refinance mortgages has been significantly more difficult due to the absence of a sales transaction value, with most research relying on noisy valuations from AVMs or mark-to-marketing prior transactions to provide a baseline of value. To circumvent this problem, this paper utilizes a novel dataset of 9 million recent refinance appraisals from 2013-2017 that contain the initial estimate of value provided by the homeowner/loan officer in the loan application, a value that is not permitted to be shared with the appraiser. We find a limited amount of anchoring bias on this initial estimate of home value, with around 7% of appraisals hitting the mark exactly. We analyze some of the potential drivers behind the bias and find that it is concentrated among certain appraisers and occurs more at higher LTVs. Loans with collateral valued by appraisers that routinely hit the mark are observed to have slightly higher default risk and slightly worse risk ",0.0,https://doi.org/10.2139/ssrn.360332762885fb1a06b4006059894dfe51ba27a533ea0850c6,10.24136/cxy.2026.001,The impact of macroeconomic factors on residential property prices in the secondary market in Poland during the period 2013–2023,2026.0,Catallaxy,Maria Koziatek,"Motivation: One of the major issues facing the modern economy is the high cost of real estate, which significantly limits, or even prevents, many people, particularly those from the Z generation, from purchasing their own homes. As a result, the number of individuals who accept the prospects of renting for their entire lives is increasing. This marks a significant change in attitudes towards property ownership in Poland.Aim: The aim of this study is to identify and evaluate the impact of macroeconomic factors on the average price per 1 m2 of residential properties sold on the secondary market in Poland during the period 2013–2023.Materials and methods: The study utilised quarterly data from the Central Statistical Office (GUS) during the period from the I quarter of 2013 to the IV quarter of 2023. Based on these data, a regression analysis was conducted to determine the statistical significance of selected macroeconomic variables and to assess their impact on changes in property prices. The accuracy of the model was verified using statistical tests.Result: The analysis showed that, on average, the price of 1 m2 of residential properties sold on the secondary market in Poland during",0.0,https://journals.economic-research.pl/cxy/article/download/4118/259362943213ea4769e075ceaab2482694da722a4fa3add,10.55544/ijrah.5.5.14,The Endowment and IKEA Effects: Arranging Your Living Space in the Minimalist and the Feng Shui Styles,2025.0,Integrated Journal for Research in Arts and Humanities,Amalia Călinescu,"This study explores how self-involvement in interior design influences perceived value by examining the interaction of the endowment and IKEA effects. Using a two-stage thought experiment, the research simulated hypothetical buying and selling scenarios involving virtual one-bedroom apartments furnished according to two contrasting styles: Feng Shui and minimalism. Fifty imagined participants engaged in furnishing tasks while a second group evaluated pre-arranged spaces, allowing the study to compare valuation patterns under conditions of active participation and passive observation. The hypothetical results suggest that personalization combined with symbolic meaning amplifies attachment, increasing willingness to pay (WTP) and willingness to accept (WTA) beyond baseline estimates. Full engagement in furnishing, particularly when aligned with culturally significant frameworks such as Feng Shui, appeared to generate stronger emotional investment than minimalist arrangements. Differences between WTP and WTA persisted across all phases, thus reflecting valuation asymmetries often attributed to loss aversion. Although purely conceptual, this simulation provides a structured framework t",0.0,https://ijrah.com/index.php/ijrah/article/download/902/10656302585171f1a3eac48a288163697f96281a8092ff7,10.3390/urbansci10030165,"The Formation Mechanisms of Intra-Urban Commuting Flows from a Relational Perspective: Evidence from Hangzhou, China",2026.0,Urban Science,Jianjun Yang; Gula Tang,"Intra-urban commuting plays a fundamental role in shaping urban spatial structure and daily mobility patterns. Existing studies have largely explained commuting flows using attribute-based or distance-centred approaches. Such approaches overlook the interdependent and relational nature of commuting within complex urban systems. This study constructs a subdistrict-level commuting network using anonymised mobile phone signalling data from Hangzhou, China, and a valued exponential random graph model (valued ERGM) to examine how commuting flows are generated through the interaction of network self-organization, local job-housing conditions, and multi-dimensional proximity. The results reveal strong endogenous dependence exemplified by reciprocal commuting ties. Employment agglomeration and public rental housing provision are associated with stronger integration of subdistricts within the commuting network, while high housing prices and certain residential amenities are associated with reduced inter-subdistrict commuting. Beyond geographic distance, metro connectivity, administrative affiliation, and social interaction are significantly associated with commuting flows. This study advanc",0.0,https://doi.org/10.3390/urbansci10030165631b5543eaefe32a17ef6150e3eaee490f42fa6dc25,10.31092/jmkp.v8i2.2922,Property Valuation: Using Machine Learning for Rent Value Prediction,2024.0,JURNAL MANAJEMEN KEUANGAN PUBLIK,Cindy Clara Afrisca; Hanif Noer Rofiq; D. Atmoko,"632 633 634 635The rising demand for rental spaces, such as hotel lodging rooms, has led property owners in high-potential areas to begin renting out their properties to meet this need. The Directorate General of State Assets (DJKN) under the Ministry of Finance, which manages state-owned assets, oversees approximately 1,100 room units with potential for rental. In alignment on increasing non-tax state revenue from state-owned assets (BMN), offering lodging room rentals as a form of BMN utilization presents a promising opportunity for expansion. To facilitate efficient rental services for these BMN rooms, a quick and accurate valuation process is essential. However, establishing fair rental prices for both property owners and tenants poses a significant challenge. This study, therefore, aims to develop automated valuation model (AVM). Since rental price predictions depend on various bebas variabels, the study employs several machine learning algorithms, including Ridge, Linear Regression, Random Forest (RF), Lasso, SVR, Elastic Net, and Extreme Gradient Boosting. The research focuses on rental markets in Jakarta, Bogor, and Bandung, using a dataset of fewer than 500 rental room samples sou",0.0,https://doi.org/10.31092/jmkp.v8i2.2922636a9ab945c4e2ef5b6c53aa86a563efa728e5b5ebe,10.46793/csge5.44ds,THE RELATIONSHIP BETWEEN RESIDENTS’ INCOME AND IMMIGRANTS’ SETTLEMENT IN LARGE EUROPEAN CAPITALS,2024.0,The 5th Congress of Slavic Geographers and Ethnographers,D. Shatilo,"A study based the relationship between immigrant settlement and income in major European capitals using London, Paris, Madrid, and Berlin as examples. We also tried to study Moscow. The cities were selected based on the importance of immigration processes, the availability of regional statistics, and the capital’s roles in the country. These cities have attracted large numbers of immigrants, especially over the last 30 years (London and Paris earlier due to the influx of people from former colonies). The example of housing costs and income distribution shows socio-ethnic stratification. Because of the study, typologies of city districts were compiled, taking into account the share of immigrants, income levels, and housing prices, which demonstrate a greater degree of mosaic and heterogeneity of territories. For these European capitals, it was shown that the hypothesis about the connection between migrants’ income and microgeography is not always confirmed. The most difficult picture is in London and Paris. In Berlin and Madrid, sectors of migrant localization are noticeable, but there (not to mention Moscow) it is more difficult to identify their connection with income stratificati",0.0,637f740dd5a078a391303d16f4c8301b1a84253c99d,10.54254/2753-8818/2026.36726,Housing Price Modelling with Multiple Linear Regression and Regularisation,2026.0,Theoretical and Natural Science,Xiao-Yang Liu,"Housing price models in a feature-price system can provide strong support for the housing-purchase decisions of households, real estate appraisal, bank credit risk control and urban planning. To determine the added value of various factors in the construction characteristics, location features and neighbourhood environments on house prices, several regressions can be employed. Real-world housing datasets generally have the following statistical problems: multicollinearity, outliers and model instability. This paper systematically reviews the theoretical basis and empirical methods for multiple linear regression and regularisation (Ridge regression, Lasso and Elastic Net) in housing price modelling and introduces fundamental concepts such as the response variable, explanatory variables and design matrix, as well as data standardisation and pre-processing techniques. Cutting-edge empirical research based on public datasets, such as the Ames Housing, California Housing, and King County House Sales datasets, is reviewed in this paper, together with model diagnostics, evaluation metrics, and the strengths and weaknesses of least squares and regularised regression. The traditional linear",0.0,https://tns.ewapub.com/article/view/36726.pdf6384fba9d511c20aeea41634c720d5d167185e2590c,10.1140/epjds/s13688-019-0182-z,"Inside 50,000 living rooms: an assessment of global residential ornamentation using transfer learning",2019.0,EPJ Data Science,Xi Liu; Clio Andris; Zixuan Huang; S. Rahimi,"The global community decorates their homes based on personal decisions and contextual influences of their larger cultural and economic surroundings. The extent to which spatial patterns emerge in residential decoration practices has been traditionally difficult to ascertain due to the private nature of interior home spaces. Yet, measuring these patterns can reveal the presence of geographic culture hearths and/or globalization trends. In this work, we collected over one million geolocated images of interior living spaces from a popular home rental website, Airbnb (http://airbnb.com), and used transfer learning techniques to automatically detect the presence of key stylistic objects: plants, books, decor, wall art and predominance of vibrant colors. We investigated patterns of home decor practices for 107 cities on six continents, and performed a deep dive into six major U.S. cities. We found that world regions show statistically significant variation in decorative element prevalence, indicating differences in geographic cultural trends. At the U.S. neighborhood level, elements were only weakly spatially clustered and found to not correlate with socio-economic neighborhood variables",0.0,https://epjdatascience.springeropen.com/counter/pdf/10.1140/epjds/s13688-019-0182-z639005bd1896feb17019771cd5721b4ce254162019f,10.22271/maths.2024.v9.i4a.1769,Optimizing valuation accuracy in Kenya’s real estate market: Application of supervised machine learning models,2024.0,International Journal of Statistics and Applied Mathematics,Ruth Wangari Muchai,"The real estate sector in Kenya plays a pivotal role in economic development yet traditional valuation methods often struggle to accurately reflect dynamic market conditions. This research explores the transformative potential of advanced data-driven techniques to enhance real estate valuation accuracy focusing on residentials within Nairobi metropolis for the period between 2009 to 2024. The research assesses the effectiveness of supervised machine learning models. The models evaluated included linear regression, lasso regression, ridge regression, gradient boosting, and random forest algorithms. Data was sourced from Highlands Valuers firm covering 521 records of residential properties. The research employed exploratory data analysis and model development. Lasso regression emerged as the best model due to its significant improvements in RMSE and R-squared values after hyperparameter tuning and regularization. This demonstrated superior accuracy and predictive capability. The most significant features were comparable sales, socio-economic status of the area, built-up area, number of floors, total number of rooms, rent, building age, residential development type, number of bedrooms",0.0,https://doi.org/10.22271/maths.2024.v9.i4a.1769640ed537a6a82930e5844cd657f4218d82c34c502bf,10.5220/0012825400004547,House Price Prediction with Optimistic Machine Learning Methods Using Bayesian Optimization,2024.0,Proceedings of the 1st International Conference on Data Science and Engineering,Haolan Jiang,": Recently, housing has been a fundamental necessity for human survival. However, the challenge lies in the often-inflated housing prices, particularly in high-GDP cities. House price prediction is crucial for citizens as it aids in effective financial planning and contributes to social stability. This study delves into the factors influencing house prices, employing and evaluating four regression models: multiple linear regression, regression decision tree, Random Forest, and XGBoost. The focus is on optimizing the performance of the two most promising models. The study finds that there is no substantial positive or negative association between the prediction label, which is the average house price of a region, and any individual attribute in the dataset. Through model comparisons, it is observed that the decision tree model outperforms the regression model significantly, with the integrated models, specifically Random Forest and XGBoost, outshining the regular regression tree model. In 5-fold cross-validation, the Bayesian optimized XGBoost model yields the best results in this study. The post-optimization R2 value of XGBoost is 0.846, showcasing an improvement of 0.024 compared ",0.0,http://dx.doi.org/10.5220/00128254000045476417e7dbe863611949eb743f3642a656e8d65103218,10.47611/jsrhs.v12i3.4723,California Affordable Housing Policies Fail in Wealthy Towns: A Case Study of the CityWalk Project,2023.0,Journal of student-scientists' research,Aria Capelli; Deland Chan,"To remedy its lack of affordable housing, the state of California has passed legislation aimed at implementing fair housing principles, including rules that require the creation of low-income housing in cities throughout the state. In San Ramon, a new development plan has been approved which will create thousands of housing units, retail developments, parks, a new hotel, and parking amenities. Called CityWalk, the new development projects a vision of a “walkable city,” one in which people can live and work in the same district, never having to commute by car again. The reality, however, is that San Ramon’s housing prices are likely inaccessible for the workers that CityWalk is hoping to attract -- the labor force composed primarily of retail salespeople, restaurant cooks and servers, hotel maids and janitors, among other low-wage employees, who might actually work in the immediate vicinity. CityWalk meets the state’s 15% affordable housing allocation requirements, but upon further inspection, it may not meet the needs of the workers who need housing. Did San Ramon build housing units affordable enough to not only satisfy the state government’s mandates on low-income housing but als",0.0,https://www.jsr.org/hs/index.php/path/article/download/4723/217164280536004af08c987cfaa78c1c53fda96cafab4e3,10.1145/3819836.3819950,Establish smart cities based on service indicators of different cities,2026.0,"Proceedings of the 2026 5th International Conference on Cyber Security, Artificial Intelligence and Digital Economy",Feng-Han Zhang; Tian-Yu Shen; Ling-Zi Wang; Xiao-Meng Liu; Shi-Yuan Xu,"Housing price forecasting and its influencing mechanisms are critical issues in urban studies and policy-making. Existing studies often rely on either single-city case analyses or complex model structures with limited interpretability. To address these limitations, this study proposes a unified framework that integrates time-series forecasting with multi-source urban service analysis. First, the autoregressive integrated moving average (ARIMA) model is employed to forecast housing prices based solely on historical price time series, ensuring model simplicity and robustness. Forecasting performance is evaluated using standard error metrics and compared with a machine learning benchmark. Subsequently, urban service levels are quantified using multi-source point-of-interest (POI) data, covering infrastructure, business services, transportation operations, and sociocultural services. An entropy-weighted TOPSIS method is applied to construct composite urban service indices through a standardized and reproducible procedure. The proposed framework is empirically validated using data from two representative cities, where identical indicator systems and weighting rules are applied to ensure",0.0,https://doi.org/10.1145/3819836.38199506437d58335e698835ed8664becc2d809591dcf87634,10.3390/jrfm18030119,Is There a Common Financial Cycle in Systemic Economies?,2025.0,Journal of Risk and Financial Management,Khwazi Magubane,"Countries such as advanced systemic economies (ASEs) and systemic middle-income countries (SMICs), considering macroprudential policy coordination, must ensure that their financial cycles are sufficiently synchronized. However, differences in the features and significance of financial cycles between ASEs and SMICs pose challenges in determining the extent of their synchronization. Accordingly, this study assesses whether a common financial cycle exists between these types of economies. The point of departure for this analysis is to examine the characteristics of the common financial cycle. To this end, this study employs data on capital flows, credit, house prices, share prices, and policy rates, utilizing the Markov switching dynamic regression model and the dynamic factor model to identify and analyze the cycle. The findings reveal strong evidence of a significant financial cycle, which explains 83% of the total variation across countries. This cycle is characterized by longer durations compared to domestic financial cycles and occurs less frequently than domestic cycles. Moreover, it exhibits high persistence in its contractionary and expansionary phases, with greater volatility",0.0,https://doi.org/10.3390/jrfm18030119644b72781aed2cf6317bb1f29020627545b870f8605,10.54097/q8cwsx22,The Investigation of Rental Market Rent Models Based on Machine Learning,2024.0,"Highlights in Business, Economics and Management",Shiqi Tang,"In terms of the renting field, landlords often set rental prices subjectively based on market trends, lacking standardized criteria. This can lead to arbitrary pricing and potential market instability. In contrast, machine learning, with its advancements across various industries, offers a solution to model these complex, non-linear relationships. Although numerous studies have investigated the factors that impact rental prices, there is a scarcity of research dedicated to predicting rental prices, in contrast to the extensive body of work centered on forecasting property prices within the real estate sector. Firstly, this study examines key factors influencing rental prices. Utilizing a Kaggle dataset containing statistical information on Italian housing rentals, including architectural and geographical features, this study processes the data, handles missing values, removes outliers, and analyzes feature correlations. Various machine learning models, including Backpropagation (BP) neural networks, decision trees, random forests, gradient boosting regression, and multiple linear regression, are employed for rental price prediction. The study concludes by comparing model performanc",0.0,http://dx.doi.org/10.54097/q8cwsx22645d48660307d71d7c7229f6907c01df2b31ff67be6,10.2991/febm-19.2019.17,An Empirical Analysis of the Factors Affecting Real Estate Price under the Background of Loose Monetary Policy: Based on the Statistics of Chengdu in the past 15 Years,2019.0,Proceedings of the Fourth International Conference on Economic and Business Management (FEBM 2019),Boya Zhang,"Chengdu, one of the fastest growing cities in China and the world, has seen a steady rise in housing prices, but in the past year it has seen explosive growth, with prices in the main urban areas almost doubling from the end of 2016 to the end of 2018. As a new first-tier city, the fluctuation of housing price in Chengdu will also reflect the change of housing price in various provinces and cities in China to some extent. Based on the price of commercial housing in Chengdu, this paper conducts a corresponding analysis and research. The purpose is to have a more comprehensive and objective understanding of the factors affecting the housing price in Chengdu, and to provide a reference for the factors affecting the housing price in various provinces and cities across the country. In this paper, first of all, by consulting relevant books on the impact of housing prices, reference to the research results at home and abroad, to find out the factors that affect the change in housing prices, and then select the factors that have a universal impact on housing prices, and combined with the current situation of Chengdu for analysis. Finally, this paper USES multiple linear regression to analy",0.0,https://doi.org/10.2991/febm-19.2019.17646e1eb5b8de2095cc61265f79d90a2976b58a67664,10.34028/iajit/22/3/1,Explore the relationship between house prices and crime rate in the UK using machine learning techniques,2025.0,˜The œinternational Arab journal of information technology,Soudabeh Motevali; Hamzeh Aljawawdeh; Sami Abuezhayeh; Eman Qaddoumi,"In the UK, house price estimation is an important topic. It has been discussed in numerous academic research publications and official and business reports. A house’s price can change depending on size, age, and location. One of the main characteristics of a property’s neighbourhood is the crime rate; it is crucial to consider how it may affect the home’s price. If crime rates rise, a neighbourhood may lose appeal to potential purchasers in favour of comparable-priced neighbourhoods. This study employs data from January 2020 to April 2022 to examine the relationship between crime rates and house prices in Bristol, UK. Crime datasets were taken from the UK Crime Stats website, while data on house prices was taken from the Price Paid Data of the HM Land Registry. 34,000 transaction records and criminal statistics are combined in the study to provide a comprehensive dataset for analysis. Two tree-based machine learning algorithms-Decision Tree (DT) and Random Forest (RF)-and Exploratory Data Analysis (EDA) were used to model and evaluate the data. The results show that the Random Forest outperformed the Decision Tree regarding prediction accuracy. According to the results, there is a ",0.0,https://doi.org/10.34028/iajit/22/3/16472b0c246599edab8527ee37110e0c2856c6e8bc7a,10.1109/i-coste60462.2023.10500769,A Data-Driven Approach to Predicting Melbourne Housing Prices Using Advanced Machine Learning Models,2023.0,2023 International Conference on Sustainable Technology and Engineering (i-COSTE),Jesmin Nahar; C. Yun,"This research rigorously explores the intricate correlations between property attributes and housing prices in Melbourne, aiming to refine price prediction accuracy. Positive correlations observed with ‘Rooms,’ ‘Bedroom2,’ ‘Bathroom,’ ‘Landsize,’ and ‘BuildingArea’ underscore the intuitive link between property size and price, while negative associations with ‘Distance,’ ‘Y earBuilt,’ and ‘PropertyCount’ emphasize the influential role of location, age, and market demand. Multiple modeling approaches were employed and compared for their predictive prowess. Among these, the Random Forest model (Approach 5) stands out with an exceptional R-squared value of 0.81, showcasing its adeptness in capturing complex data relationships effectively. Notably, the inclusion of categorical variables and leveraging localized data in conjunction with Random Forest (Approach 6) also yielded an impressive R-squared value of 0.81, underlining the pivotal role of nuanced information in precise predictions. Furthermore, XGBoost (Approach 7) demonstrated commendable performance, achieving an R-squared value of 0.80, showcasing the versatility of boosting techniques. The comparison of these approaches empha",0.0,6482ff1cc364240166b028fc5361498fbd2d8b3ac28,10.59075/ijss.v3i2.1527,Enhancing Real Estate Price Forecasting Using Advanced Machine Learning Algorithms: An Empirical Evaluation with Real Market Data,2025.0,Indus Journal of Social Sciences,Jamshaid-ul-Hassan; K. Ullah; Humera Hayat; Shamim Jhatial; Kainat Yousuf,"Predicting real estate prices accurately has become crucial for buyers, investors, and legislators to make wise choices. A viable strategy for identifying the complex patterns and trends in an ever-changing market is to use cutting-edge machine learning algorithms. The purpose of this study is to offer reliable conclusions about the dynamics of the Lahore, Pakistan, real estate market, especially as it relates to inflation and other economic shifts. The goal is to improve forecasting techniques in order to promote a real estate market that is more open and effective. The results are intended to assist in making decisions and to provide guidance for policies that encourage steady, sustained market growth. To forecast housing prices, a number of parametric regression models were used, such as the Extra Trees Regressor, XGBoost, Random Forest, Gradient Boosting, Decision Tree, and CatBoost Regressor. As of June 26, 2023, data set was collected from a public website. Data set comprises on 9,539 listings from 6 districts. Additionally, As of June 26, 2023 in Pakistan real estate market, the average inflation rate in 2024 was 24.76%. The models that performed the best among the ones t",0.0,https://induspublishers.com/IJSS/article/download/1527/1367649439b77617ca394cffcd8ffe88c31aba83fc51d3b,10.3390/land15040659,Unveiling the Spatial Non-Stationarity Between Built Environment and External Relations in Small Towns Using MGWR and Mobile Phone Data: Evidence from the Yangtze River Delta,2026.0,Land,Yang Li; Yao Wang; Mingmei Han; Yu Xia; Yanran Ma,"The external relations of small towns are an important dimension in the regional urban system. However, the “metropolitan bias” in existing studies results in a lack of empirical verification of their characteristics, hindering effective regional policymaking. Applying Central Flow Theory (CFT), mobile phone data, and a multiscale geographically weighted regression (MGWR) model, this study investigates the spatially non-stationary associations between built environment factors and the “city-ness” and “town-ness” of small towns in the Yangtze River Delta. The results show: (1) Enterprise density in metropolitan shadow areas is positively associated with cross-city jobs–housing separation; in peripheral areas, both enterprise density and housing prices exhibit a strong correlation with intra-municipal jobs–housing separation. (2) Middle schools consistently correlate with localized intra-municipal flows, suggesting a plausible spatial anchoring role; around metropolises, medical and commercial facilities link to recreational flows and commuting town-ness, while in distal small towns, medical facilities coincide with intratown jobs–housing balance, and commercial facilities correlate ",0.0,https://www.mdpi.com/2073-445X/15/4/659/pdf?version=177635374065077d50f4073bf17848214c21c7e7821574a67783c,10.54097/xze3as16,The Investigation and Prediction of Influencing Factors of Australian Housing Price Based on Machine Learning Models,2024.0,"Highlights in Business, Economics and Management",Chongsen Ma,"With the ever-changing economic environment in recent years, people pay more and more attention to the real estate market, and different house configurations affect house prices. In this paper, house features such as bedroom count and bathroom count are analyzed as factors influencing house prices. Analyze the relationship between influencing factors and real estate prices and predict the trend of housing prices. First, constant values and values that have no impact on the research results should be selected and deleted, and effective influencing factors such as bedroom count and bathroom count should be left. Then, Exploratory Data Analysis (EDA) analysis was carried out on these effective influencing factors, and range, mean, standard deviation and quartiles of the data were read. Then the scatter plot is made to observe whether there is a correlation between the influencing factors and real estate prices. In the part of linear regression, the least squares method was first used to establish Ordinary Least Squares (OLS) model for R square, F-statistic and other calculations, observe the values, and further understand the correlation between influencing factors and housing prices.",0.0,https://drpress.org/ojs/index.php/HBEM/article/download/24523/24027651d87a7fa0cf1ac256dfdf41ea2dfab1378b1633b7,10.70382/hijbems.v010i7.062,DETERMINANTS OF PRIVATE REAL ESTATE INVESTMENT DECISIONS IN NIGERIA’S SOUTH-SOUTH REGION,2026.0,International Journal of Business Economics and Management Science,Ofonime Akpan; E. G. Udo,"This study investigates the determinants of private real estate investment decisions in the South-South geopolitical zone of Nigeria, a region characterized by economic dynamism yet persistent development challenges. Utilizing a cross-sectional survey design, primary data was collected from 500 real estate professionals and investors in Akwa Ibom and Rivers States. The analysis, employing multiple regression techniques, examined the influence of economic factors (interest rates, inflation, income levels), government policies (land-use regulations, property taxes, building approvals), infrastructural development (road networks, electricity, water supply), and market factors (demand, rental yield, property price trends). Findings reveal that all four categories of factors exert statistically significant and positive influences on investment decisions, with government policy variables and market fundamentals demonstrating particularly strong effects. The results confirm that cumbersome regulatory processes, high compliance costs, inadequate infrastructure, and volatile market signals collectively shape investor behaviour. The study concludes that fostering a conducive investment envir",0.0,https://harvardpublications.com/hijbems/article/download/582/56765284f9425c554ff8adf4736887984b947819d27ea6,10.3846/tede.2026.24967,"Assessing the impact of economic variables on Romania's real estate market evolution: risk, uncertainty, and investment dynamics",2026.0,Technological and Economic Development of Economy,Mioara Chiriţă; Georgel Chiriță; Nicoleta Bărbuță-Mișu; D. Şarpe; Cristina Maria Enache; V. Antohi; Mihaela Neculita,"This study examines Romania’s real estate market (2003–2023), analyzing economic variables’ impact on housing demand and investment trends through PLS-SEM and regression analysis. It addresses housing affordability challenges, particularly for younger generations, highlighting rising prices, increasing costs, and limited mortgage access as key barriers to homeownership. Furthermore, it explores the socio-economic implications of affordability issues, emphasizing their links to inequality and financial vulnerability. This study examines the shift from consumption to investment in the market, emphasizing the rising demand for real estate. It explores the impact of the sharing economy, particularly short-term rental platforms like Airbnb, on housing prices and affordability. Analyzing economic variables within a supply-demand framework, it distinguishes between new and existing housing stock while incorporating a LCC perspective. The research identifies high-growth market segments, providing insights for policy formulation and investment optimization. It contributes to the debate on government interventions in housing, evaluating whether support should target supply or demand for affo",0.0,https://journals.vilniustech.lt/index.php/TEDE/article/download/24967/132866535bf64415c2a1c1d6d8f623b2f4511938d549d5e4,10.35970/jinita.v8i1.3010,"Comparison of Multiple Linear Regression, Random Forest, and Decision Tree Methods for House Price Prediction in West Nusa Tenggara",2026.0,Journal of Innovation Information Technology and Application (JINITA),Mindi Richia Putri; Asmaul Husna Rs; Ahmad Fatoni Dwi Putra; M. Rifqi; Syaidatussalihah,"This study aims to develop an accurate housing price prediction model for West Nusa Tenggara (NTB) by comparing three machine learning approaches: Multiple Linear Regression (MLR), Random Forest (RF), and Decision Tree (DT). The research adopts a quantitative experimental design consisting of data collection through web scraping, preprocessing, model training, and performance evaluation. Secondary data were gathered from Lamudi, Rumah123, and 99.com, containing 641 housing records with key features such as land area, building area, number of bedrooms, number of bathrooms, and price. Data preprocessing involved handling missing values, normalization, and data augmentation to improve model robustness. Model performance was assessed using Root Mean Square Error (RMSE) and the coefficient of determination (R²). The results show that non-linear models outperform linear ones, particularly after data augmentation. Without augmentation, Random Forest achieved the best performance (R² = 0.890, RMSE = 5.6×10⁹), while with augmentation, the Decision Tree model achieved the highest accuracy (R² = 0.999, RMSE = 1.1×10⁹). In contrast, MLR exhibited poor performance and sensitivity to data comple",0.0,https://ejournal.pnc.ac.id/index.php/jinita/article/download/3010/1110654bebffedf4a4f34aeb7767230b9c7e6f615cda720,10.54254/2754-1169/96/2024mur0111,A Critical Analysis of the Standard Urban Model in the Context of Greater Manchester,2024.0,"Advances in Economics, Management and Political Sciences",S. Wang,"This study critically examines the applicability of the Standard Urban Model (SUM) in the context of Greater Manchester, utilizing empirical data and spatial analysis to explore its operational limitations and potentials. Originally conceptualized by William Alonso, the SUM provides a theoretical framework to understand intra-urban economic dynamics predicated on a monocentric city structure. Through a comprehensive dataset encompassing census demographics, housing prices, and GIS-mapped land-use patterns from 2015, this paper investigates the models ability to predict and describe urban land rent gradients and residential distribution relative to the citys Central Business District (CBD). The findings indicate that while the SUM may provide a generalized view of urban structure, it significantly underestimates the socio-economic diversities, technological advancements, and cultural influences that are inherent in urban development. Particularly, the analysis reveals that rental price declines with distance from the CBD are not as pronounced as predicted by the SUM, with a low coefficient of determination (R=0.2551). Furthermore, the study highlights how urban development in Manche",0.0,https://aemps.ewapub.com/article/view/13864.pdf65560baaaab9e349694711826de7788e8ea4b27978d,10.47611/jsrhs.v13i3.6683,Making Housing Predictions Using ML Without Live Market Data,2024.0,Journal of student-scientists' research,Justin Chen; Soyoun Choi,"This paper explores the importance of machine learning and the benefits machine learning can bring to the housing industry. The housing industry makes up an average of more than 15% of the US’s yearly GDP and plays a significant role in the US economy. Additionally, the housing industry, specifically house prices, determines the environment each person grows up in by filtering out those who are unable to afford the costs of living in any given area. Having accurate house price information on hand can save many hours spent looking for affordable housing. After a brief introduction of Zillow, a house listing website that also contains a feature that provides highly accurate predictions of its users’ house prices, the central question, “How high a degree of accuracy can house prices be predicted without information regarding the state of the current economy or house market?” is explored. While Zillow uses data such as market trends and past selling prices, only information regarding the physical traits of the house, such as its geographical location and its room count, are considered within this paper (Note that the scaling factor for many variables is messed up, meaning that despite ",0.0,https://www.jsr.org/hs/index.php/path/article/download/6683/32116562c3ee0993ca658d314dfdffe2e1fdd1581e41a3e,10.24815/jaroe.v5i3.31261,"The Demand for Subsidized Housing: A Case of Banda Aceh City, Indonesia",2022.0,"Journal of Accounting Research, Organization and Economics",B. Nazamuddin; Fadhillah Masri; F. Fitriyani,"Objective – The Indonesian government has introduced the Housing Financing Facility (FLPP) scheme. The subsidized housing scheme is intended to help low-income families improve their welfare. Understanding how mortgage subsidies affect the demand for new houses can provide a better understanding of how housing policy affects the number of people who afford to own homes, house prices, and other social welfare outcomes. This study investigated the effects of income, housing value, education and family size on the demand for subsidized housing in Banda Aceh.Design/methodology – The methodology used in this paper is multiple regression method which was collected from household data of 60 respondents from six sub-districts in Banda Aceh City, Indonesia.Results – The study found that only income and housing values were statistically significant in determining the demand for subsidized housing. Meanwhile, the purchase of subsidized housing does not depend on the size and education of the family. These findings suggest that households tend to buy larger size homes as incomes rise and seek to maximize their benefits from buying subsidized homes even at higher prices, which is proxied by the",0.0,https://jurnal.usk.ac.id/JAROE/article/download/31261/18191657d7d39b99e1ff6e58e3528204c9cd64ee6903e452,10.26789/jsupp.2022.01.002,Airbnb’s Growth Changes Residential Property Prices in Adelaide,2022.0,Journal of Sustainable Urbanization Planning and Progress,Haiting Zhu,"In this research, the impact of Airbnb on residential property prices in Adelaide is assessed. Many previous European and American research have connected short-term rental, Airbnb and property market and made conclusion that short-term rentals (STR) are increasing housing prices and rents. However, there is no research about Airbnb and its effects on residential property prices in Adelaide. This research aims to fit this gap and makes Plan SA know more about Airbnb’s effects. This research used multiple linear regression model to examine the Airbnb activity’s effects on housing prices in Greater Adelaide during a specific time period before COVID-19. This regression model has been utilised in 3 Airbnb hotspot suburbs, Adelaide, North Adelaide and Glenelg and 3 adjected suburbs with few Airbnb listings, Thebarton, Prospect and Glengowrie in Greater Adelaide; different results in these 3 pairs suburbs has been employed to understand Airbnb’s different effects in housing prices. According to the results of regression model in different suburbs, Airbnb hotspot areas, have higher R squares and unpopular areas have smaller R squares. Considering coefficients in Airbnb hotspot areas, the",0.0,https://www.udspub.com/ajj/public/index.php/jsupp/article/download/1718/1565658e8fc0639c10125b90362c5f5efd1e372d6b25fd8,10.59141/jrssem.v5i10.1495,"Temporal Analysis of Property Values Before and After the Revitalization of Depok City Square, 2015–2025",2026.0,Journal Research of Social Science Economics and Management,Areez Rachmawati; A. G. Malau,"Public space revitalization aims to improve environmental quality and drive regional economic growth. Depok City Square, revitalized in 2020, has undergone significant physical and social changes. This research analyzes property value changes around the square from 2015 to 2025, identifies spatial patterns of value increases, and evaluates the influencing factors. This quantitative study used secondary data (NJOP and property portals) and primary data from field surveys at 120 points within 0–2 km of the square. Analysis employed multiple linear regression (five variables: revitalization dummy, distance to square, distance to main road, commercial intensity, observation year) and GIS-based spatial mapping. The results showed that average property value increases ranged from 48.4% to 87.9% depending on the distance zone, with the highest increase occurring within a radius of 0–500 meters from the square. The regression model yielded an R² of 0.782, with the revitalization dummy variable as the largest determinant (? = Rp1,924,300/m²). Spatial patterns revealed an uneven distribution of increases, with commercial corridors on the west and north sides benefiting more than residential ",0.0,https://jrssem.publikasiindonesia.id/index.php/jrssem/article/download/1495/20076599e93998e6ce89570566d507bbe4716c34d1b5301,10.56028/aemr.15.1.1012.2025,The Influence of Proximity to Public Transit on Land Prices in Urban Areas,2025.0,Advances in Economics and Management Research,Shangheng Li,"This paper investigates the influence of proximity to public transit on land and property values in urban areas. While prior studies generally present positive correlations, the extent of value appreciation varies significantly across transit modes, socioeconomic contexts, geographic contexts, and general urban settings. Through a global review of empirical evidence, this research examines residential and commercial property price premiums associated with bus rapid transit (BRT), light rail, and heavy rail systems. The findings indicate that properties located within 400 meters of transit stations typically capture considerable price premiums, with commercial properties often experiencing more premiums due to increased customer access and foot traffic. However, negative externalities such as noise, congestion, pollution, and construction-related disruptions can diminish or even reverse these gains in some neighborhoods. The analysis also reveals that value premiums are not uniform: lower-income areas may sometimes experience stronger positive effects, while in other cases externalities outweigh benefits, suggesting that development of public transit is more influencial in lower-inc",0.0,https://madison-proceedings.com/index.php/aemr/article/download/4146/41616608d729ce10d345ad1bd79a88638ccb4bd594090ea,10.1080/24694452.2026.2639721,Quantifying Uncertainty in Spatial Prediction for Nonstationary Spatial Processes,2026.0,Annals of the American Association of Geographers,Peng Luo,"Uncertainty quantification for geospatial prediction models plays a crucial role in evaluating model confidence and supporting informed decision-making. Conformal prediction (CP), as a model-agnostic framework, has recently been introduced into geospatial settings, leading to the development of geospatial conformal prediction (GeoCP). GeoCP considers spatial dependence and incorporates a geographic weighting mechanism into CP to capture spatially varying uncertainty, satisfying the localized exchangeability assumption. In many real-world scenarios, however, spatial processes could exhibit abrupt transitions, such as neighboring regions with distinct land-use types, resulting in significant differences despite close geographic proximity. To address this limitation, we propose geospatial similarity conformal prediction (GeoSIMCP), an extension of GeoCP that jointly considers both geographic distance and feature-space similarity when estimating local uncertainty. We further develop a parameter optimization framework to ensure robust model tuning. Through comprehensive simulation studies under varying spatial structures, we demonstrate the advantages of GeoSIMCP over GeoCP. Additionall",0.0,https://www.tandfonline.com/doi/pdf/10.1080/24694452.2026.2639721?needAccess=true6614091cc14f8aecac9350d12dc4c94d223d6385fd6,10.70382/ajbegr.v13i4.065,"Managing Cooking Gas Explosion Risks in Residential Properties: Empirical Evidence from Abuja, Nigeria",2026.0,Journal of Built Environment and Geological Research,A. Achoru,"The study examined the incidence of cooking gas (LPG) explosion in residential properties in the city of Abuja, Nigeria in terms of residential property management. The study adopted a mixed method approach and data were collected from fire service agencies, property managers, tenants and residents in Gwarinpa, Kubwa, Wuse, Maitama and Lugbe through the use of questionnaires, interviews and secondary sources. Analysis was performed using descriptive and inferential statistics. Chi-square test and one-sample t-test was used to analyse the study objectives. Frequency tables, percentages and mean scores, were used to summarize respondents' demographic characteristics and responses to the research questions. The findings identified poor maintenance practices, poor gas cylinders and accessories, poor ventilation, tenant negligence, weak regulatory enforcement and poor safety awareness as the major causes of gas explosions. The study also found that gas explosions negatively affect tenant safety, property values, insurance costs and the reputation of property managers. The study recommends that proper management of residential properties, routine safety inspections, strict compliance wit",0.0,https://africanscholarpub.com/ajbegr/article/download/1500/151466230d6d58a31ee1e77a1ad6bfdde1c298421cef866,10.3390/su181910082,Satellite-Based Analysis on the Associations of Neighborhood Greenness with Demographic and Socioeconomic Characteristics in Seven Korean Metropolitan Cities,2026.0,Sustainability,Jiyoon Moon; Hyungjoo Yim,"Urban greenness is unevenly distributed, but its associations with social and demographic conditions vary across urban settings. Using Landsat imagery and neighborhood statistics, we examined greenness in 1154 neighborhoods across seven Korean metropolitan cities. Spatial models used standardized values of greenness and the continuous predictors, with city fixed effects. In the reference spatial error model, child share was positively associated with greenness (β = 0.250) and log-transformed population density negatively associated (β = −0.593; both p < 0.001). Older-adult share (β = 0.056, p = 0.090) and assessed housing price (β = −0.084, p = 0.052) had fewer certain associations. The National Basic Livelihood Security (NBLS) recipient rate had little marginal association with greenness (r = −0.008) and an uncertain negative estimate when density was omitted. Its jointly adjusted association was small and positive among similarly dense neighborhoods (β = 0.064, p = 0.004), but depended on age-composition adjustment and weakened when large administrative units were excluded. Supplementary spatial screening identified 91 exploratory candidate neighborhoods combining low greenness a",0.0,https://doi.org/10.3390/su181910082663ae50a3ce2963341c6d8be4416d87393789ad47c1,10.54254/3029-0880/2025.27554,Prediction of the median value of owner-occupied homes (MEDV) in the Boston housing dataset using regression models,2025.0,Advances in Operation Research and Production Management,Xiaojin Zhou,"The Boston housing dataset is one of the significant tools used to examine the influencing factors of housing price. Meanwhile, housing price prediction is crucial for government regulation, business decision-making, and individual homebuyers. Existing studies fall short in balancing model interpretability, computational efficiency, and generalization ability. Hence, this study, based on the Boston housing dataset, focuses on the prediction of the median value of owner-occupied homes (MEDV). It constructs three models, including linear regression, decision tree regression, and Bayesian regression, and evaluates their performance using mean squared error (MSE), mean absolute error (MAE), and the coefficient of determination (R2). It examines the effects 13 features, including per capita crime rate by town (CRIM) and average number of rooms per dwelling (RM) on MEDV. Furthermore, it trains and visualizes the results of each model. The results show that decision tree regression achieves the highest R2, effectively capturing nonlinear relationships but being prone to overfitting. Linear regression and Bayesian regression perform better in terms of MSE and MAE; the former is simple in s",0.0,https://www.ewadirect.com/journal/aorpm/article/view/27554/pdf6645c5212d081e145c5fadf1b5671e2d2595429c919,10.54254/2754-1169/2026.lh34984,The Impact of U.S. Housing Price Fluctuations on Economic and Social Development,2026.0,"Advances in Economics, Management and Political Sciences",Xin Liu,"As a vital component of the national economy, the real estate sector sees housing price fluctuations that not only on the functioning of the property market itself but also, through channels such as the wealth effect, credit constraints, further influence economic development. This paper focuses on the U.S. housing market and discusses how changes in housing prices may affect economic growth, people's daily lives, social inequality, population movement, and regional development. Housing prices are not only a market indicator. To examine this issue, the paper reviews existing studies, compares relevant indicators, and uses quantitative analysis based on publicly available data. A time-series regression model is also built to explore the possible links between housing prices and broader economic outcomes. The results show that rising housing prices may support economic growth in the short run through the wealth effect and real estate investment. But if housing prices increase too quickly, they can also create heavier housing burdens for families and reduce their ability to spend on other goods and services. High housing prices may also affect young families' willingness to have child",0.0,https://aemps.ewapub.com/article/view/34984.pdf6659b9ecbeb8eddb13aaf5cfcc70cd2f036768b1f21,10.1108/ijhma-04-2025-0085,Urban spatial analysis of the profitability of housing rental in Bogotá,2025.0,International Journal of Housing Markets and Analysis,J.S. Azcarate-Romero; J. Toloza-Delgado; N. A. Cruz Gutiérrez; P.A. Mahecha,"666 667 This study aims to analyze the factors influencing rental rates in Bogotá, focusing on apartments and houses across different zones of the city. This study explores the relationship between physical property characteristics and spatial patterns affecting rental profitability.668 669 670 671 This research uses generalized additive models (GAMs) to estimate rental rates while accounting for spatial dependencies. Data is collected from rental advertisements across Bogotá, including physical attributes and geographical coordinates. A spatial interpolation is applied to visualize rental rate behavior across the city.672 673 674 675 The analysis reveals that the number of rooms and property age positively influence rental rates for both apartments and houses. Stratum (socioeconomic level) has an inverse relationship with profitability, especially for apartments. Built area has a positive effect for apartments but a negative one for houses. The spatial analysis shows higher rental rates on the periphery of the city, particularly in areas such as Usme and Bosa. In addition, property usage regulations in some areas affect profitability, with commercial properties having a different behavior from purely",0.0,676821f5925da69a8ceb8db09953f76b732358215a3,10.3390/buildings16173365,“La Mano De D1OS” on the Real Estate Market of Naples: The Maradona Mural Between Street Art Cultural Icon and Housing Values,2026.0,Buildings,P. De Paola; Fabiana Forte; Yvonne Russo; Hugo Castro Noblejas,"In recent years, the city of Naples has seen increasing attention focused on the mural dedicated to Diego Armando Maradona, located in the Spanish Quarter. Originally a spontaneous expression of street art and initially perceived by the local community as a simple artwork, the mural has gradually transformed into a powerful symbol of identity, becoming a true cultural icon of the city. This process has generated effects that transcend the artistic value of the artwork, contributing to the enhancement of the entire surrounding urban context and altering the social and economic dynamics of the quarter. By considering the Maradona Mural as the epicentre of a range of territorial and socio-economic effects, this paper aims to explore the issue of measuring the influence exerted by its presence on the market values of residential properties located in the Spanish Quarter of Naples. From this perspective, street art can be interpreted as a positive externality capable not only of fostering processes of urban regeneration and the redevelopment of formerly marginalised or degraded areas but also of influencing the dynamics of the local real estate market. Property values reflect not only t",0.0,https://doi.org/10.3390/buildings161733656779ee9b14c4df3a57a922c3b2bf54fca6165300bc6,10.3390/ijfs14060164,Macroeconomic Drivers of House Price Cycles in the EU: Are They Synchronized Across Member States?,2026.0,International Journal of Financial Studies,Vytautas Snieška; Daiva Burkšaitienė; V. Navickas,"This paper examines the drivers of house price cycles across EU countries between 2005 and 2024 and measures their synchronicity. We used panel data methods—fixed effects, dynamic panel models (Arellano–Bond GMM), and a pooled VAR framework—to capture static and dynamic relationships between house price growth and key macroeconomic variables. The results show that the dynamics of house prices are highly persistent. GDP growth has a clear positive effect, while higher unemployment and interest rates push prices down. Migration flows, however, are not statistically significant at the EU aggregate level. Property taxation shows a positive coefficient, which probably reflects structural and institutional differences rather than a direct dampening effect on prices. Dynamic analysis suggests that macroeconomic shocks have persistent and economically meaningful impacts on house price growth. Hierarchical cluster analysis revealed three distinct groups of countries, meaning that house price cycles are only partially synchronized across the EU. Unlike previous studies that typically examine individual determinants or synchronization separately, this study integrates panel econometric method",0.0,https://www.mdpi.com/2227-7072/14/6/164/pdf?version=17812428136782230c2f1fd867adfd695333dd9cf8c4a827c4ce2,10.3390/books978-3-7258-3780-9,Trends in Real Estate Economics and Livability,2025.0,,,"This Reprint compiles peer-reviewed articles from the Buildings Special Issue Trends in Real Estate Economics and Livability, exploring urban development, housing markets, and spatial dynamics. Key themes include spatial disparities in housing conditions, urbanrural integration, housing affordability, and socio-economic drivers of residential segregation. Advanced methodologies like spatialtemporal modeling and street-view image analysis are employed to study complex urbanhousing interactions. For example, research on Chinas floating population reveals how housing burdens influence migration, while studies on Riyadhs real estate market propose strategies to optimize off-plan sales. The Reprint also examines regional contrasts, such as Polands young renter preferences and South Koreas retail resilience during COVID-19. Urban heritage preservation and gentrification are recurring topics, with case studies on Chinas historical districts and the psychological drivers of architectural preferences. Additionally, the role of social capital in urban renewal, particularly in Chinas aging communities, is explored, advocating participatory approaches for green development. Articles on multi-s",0.0,https://doi.org/10.3390/books978-3-7258-3780-9679fdd6c712b38a9bc5fced99d78fa8b071d1d14e75,10.31705/icfmf2026.17,Enhancing resident satisfaction through effective common area management in Sri Lankan high-rise condominiums,2026.0,International Conference on Facilities Management Futures (FMF),H. N. M. Dayarathna; U. Rathnayake; A. S. Fernando; A. S. W. Karunarathna,"High-rise condominiums have become a more sought-after residential real estate investment option due to the rapid urbanisation of Sri Lanka, where effective common area management (CAM) is crucial to enhance the satisfaction of the resident community and sustain property values. However, limited research has been conducted on CAM within the Sri Lankan condominium context. Therefore, this study aims to identify how effective CAM practices influence resident satisfaction and to propose recommendations for improvement. A mixed method approach based on the SERVQUAL model was used to explore the satisfaction of residents of high-rise buildings in Sri Lanka. Data collected from residents through surveys were analysed using statistical methods using SPSS software, while qualitative data obtained through structured interviews were analysed using content analysis. The findings revealed that tangibility, reliability, responsiveness, and assurance significantly influence resident satisfaction, with tangibility emerging as the strongest predictor. Qualitative findings further highlighted the importance of well-maintained facilities, security, cleanliness, and recreational amenities, while iden",0.0,https://dl.lib.uom.lk/bitstreams/da74364f-d423-4b23-8348-b36c612151df/download68054776f85a695ece2a2db9c949e25e73897fa9485,10.3390/mca31030111,Evaluating Regularization Estimators Under Severe Multicollinearity: A Simulation and Empirical Study on Housing Prices,2026.0,Mathematical and Computational Applications,O. Ekiz; Meltem Ekiz,"Accurate housing price prediction is important for market efficiency and purchasing decisions. However, multicollinearity among independent variables remains a major challenge in linear regression, causing variance inflation and reducing the reliability of the ordinary least squares (OLS) estimator. Although regularization methods such as ridge regression, least absolute shrinkage and selection operator (LASSO), and elastic net (EN) are widely used, evidence regarding their variance behavior under controlled multicollinearity structures remains limited. This study addresses this gap through simulation experiments conducted under controlled correlation structures with sample sizes ranging from 100 to 2000, 5 to 70 independent variables, and correlation coefficients between 0.50 and 0.99. The findings are further validated using the California Housing Dataset, where mean squared prediction error (MSPE) is computed on the full dataset, while root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2) are evaluated on a hold-out test set. Simulation results show that LASSO generally yields the lowest variance estimates under moderate multicollin",0.0,https://www.mdpi.com/2297-8747/31/3/111/pdf?version=1781866908681f8429402a854824d8588eb3b12f1f88d4cd2f94f,10.46609/ijsser.2026.v11i09.034,"Real Estate, Gold, and Equity as Inflation Hedges : Evidence from India",2026.0,International journal of social science and economic research,Praful Gupta,"This study examines whether Indian residential real estate, gold, or equity provides a682statistically significant inflation hedge over the period 2013 to 2025. Fifty two quarterly683observations are analysed using four econometric specifications: the classical Fisher regression,684a corrected specification with differenced inflation to address integration order mismatch, the685Fama and Schwert decomposition into expected and unexpected components, and a distributed686lag model to accommodate index smoothing in the Reserve Bank of India House Price Index. The687Engle and Granger cointegration procedure tests for a long run equilibrium between each asset688price and the consumer price index. Standard errors follow Newey and West with four lags.689Every specification is subjected to the full battery of ARDL style diagnostic tests, including690normality, serial correlation, heteroskedasticity, ARCH, Ramsey RESET, and CUSUM691parameter stability. Three central findings emerge. First, no asset class provides a statistically692significant contemporaneous inflation hedge at the quarterly frequency. Second, real estate693returns display a delayed response to inflation, consistent with the smoothing bias in the RB",0.0,https://doi.org/10.46609/ijsser.2026.v11i09.03469463c9a382e4cc7217ffccc3e103bf39793d8cd62e,10.1177/0739456x19890654,"Review: Pragmatic Justifications for the Sustainable City: Acting in the Common Place, by Meg Holden",2019.0,Journal of planning education and research,Jared Enriquez,"infrastructure as an antipoverty and social justice strategy in Cully, Portland. We wondered whether Wilson had considered discussing how the creation of green infrastructure and amenities, which are oftentimes taken for granted in many better-off communities, could become precursors of displacement. Is Cully the only neighborhood in the city that could have sidewalks, lighting, and green infrastructure after the implementation of the plan, or was this set up as a pilot study for an antipoverty project? This case study, in many ways, was an antithesis in itself; since the introduction of green infrastructure had increased the housing prices in Cully from 2010 to 2015 by almost 57 percent, we wondered whether these activities might have further displaced many in low-income vulnerable communities? Finally, chapter 7 (“Conclusion: Toward Design Justice”) concludes by highlighting the seven important lessons learned from the four case studies and numerous vignettes elaborated in this book. These are summarized in seven subsections (pages 170–174) that largely comprise the gist of creating a sustainable and resilient community through community-driven designs. Resilience for All: Strivi",0.0,695d84d94c36bcf79ab29cc69177849e3b0f53ec5ac,10.25134/ilkom.v19i2.411,A Data-Driven Approach to Comparative Evaluation of Regression Models for Accurate House Price Prediction,2025.0,Nuansa Informatika,Tiara Permata Hati; B. Budiman; Imannudin Akbar; Nur Alamsyah,"This study aims to develop and evaluate a property price prediction model in Bandung by applying machine learning (ML) algorithms. The need for more accurate property price predictions is increasing due to fluctuations in the property market. This study analyzes property characteristics, including the number of bedrooms, bathrooms, land area, building area, and location, as well as their impact on house prices. The study evaluates four regression algorithms, including linear regression, K-Nearest Neighbors (KNN), Random Forest, and XGBoost. Finally, this study proposes price_per_m2 and building_land_ratio as new features recommended for improvement in accuracy. The bottleneck method is derived from the data collection area of the Rumah123.com website, encompassing data preprocessing and data exploration. The following metrics will be used to evaluate each model: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). Based on our study, we conclude that both Random Forest Regression and XGBoost Regression achieve the highest accuracy, with R² values of 0.9941 and 0.9955, respectively, after adjustment. Conversely, Linear Regression a",0.0,https://journal.fkom.uniku.ac.id/ilkom/article/download/411/119696e8ba610649b3c8fb157685ea48daaa022295edf6,10.23977/jeis.2025.100217,A Comparative Study on the Training Effects of Different Optimizers for Deep Learning Models,2025.0,Journal of Electronics and Information Science,Peng Yin,": The training efficiency and generalization performance of deep learning models are highly dependent on the selection of optimizers. Differences in gradient update strategies among various optimizers directly affect the model's convergence speed, final accuracy, and training stability. Taking the house price prediction task as the research carrier, this paper constructs a fully connected neural network model based on the Boston Housing Dataset to systematically compare the training effects of three classic optimizers: Stochastic Gradient Descent (SGD), Adaptive Moment Estimation (Adam), and Root Mean Square Propagation (RMSprop). By controlling irrelevant variables such as model structure, learning rate, and batch size, quantitative analysis is conducted from three core dimensions: convergence speed, final prediction accuracy, and training stability. The applicable scenarios of each optimizer are discussed in combination with experimental results. Experiments show that the Adam optimizer has the fastest convergence speed and can quickly reduce the loss value in the early stage of training; the SGD optimizer, although converging slowly, can achieve the optimal final prediction accu",0.0,http://www.clausiuspress.com/assets/default/article/2025/12/01/article_1764600476.pdf697f1f0d3a4671db42dea5d9c7857013733da29881d,10.1109/icmsci67830.2026.11469614,A Data-Driven Computational Approach for Property Valuation and Market Trend Analysis,2026.0,2026 Second International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI),R. Varaprasad; G. Khatri; Khan Farhan Ali Khan; Kattela Prashanth; Mallela Suresh,"Studying homes, apartments, and other structures in Taiwan's cities is still a significant field of study. Techniques like statistical analysis and questionnaire data collecting are frequently employed in real estate forecasting and analysis. However, these approaches are insufficient and time-consuming when multidimensional features are taken into account. Building a real estate forecasting model that can adjust to a changing environment was the goal of this project. Public government databases provided the data, which were then standardized for precise grouping. A suitable data clustering technique was then used to the standardized data, and cross-statistical analysis was carried out to confirm the chosen algorithm. To improve the clustering analysis's accuracy, we employed an autoencoder-based deep learning system. The best clustering solution was then found using a double-bottom map particle swarm optimization (DBM-PSO) clustering technique. Data gathered from public websites was subjected to cluster analysis and deep learning in order to identify the causes of Taiwan's house prices' steady rise over the last ten years. The study's findings show that three important variables h",0.0,698d436e3a1b30f49b512bb65b77936bd0dcd943388,10.55041/ijsrem42310,"Estimation Residential Real Estate Price Considering Spatial Variables using Machine Learning in GIS: A Case Study in Chon Buri, Thailand",2025.0,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,Kamonchanok Tonlo,"This research investigates the effectiveness of machine learning (ML) techniques in predicting residential real estate prices, with a specific focus on integrating spatial variables within a Geographic Information System (GIS) framework. A case study in Chon Buri, Thailand, is utilized to compare the predictive accuracy of the Forest-based Classification and Regression (FBCR) algorithm, a GIS-specific ML tool, against established algorithms such as XGBoost (XGB) and Random Forest (RF). The methodology includes data collection through web scraping, rigorous preprocessing using the Interquartile Range (IQR) method for outlier detection, and spatial data integration via ArcGIS Pro. Model performance is evaluated using R², Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results demonstrate that FBCR outperforms both RF and XGBoost in predicting real estate prices, evidenced by a higher R² value of 0.6412, lower RMSE of 1.529, and lower MAE of 1.131 on the testing dataset. This superior performance highlights FBCR's ability to effectively capture and model the complex spatial relationships that influence property prices. The study underscores",0.0,https://doi.org/10.55041/ijsrem42310699329258cc685812b876c11a76ec856edabbf485ca,10.70917/ijcisim-2026-4497,Predictive Modeling of Real Estate Prices Using Machine Learning Techniques,2026.0,International journal of computer information systems and industrial management applications,Mahesh Mahajan; Makrand Shahade; Aishwarya Nikam; Paresh Wani; Bhagyashri A. Patil; C. Patil,"Accurately estimating residential property prices is a challenging yet essential task in today’s changing market envi-ronment. Conventional manual valuation methods often result in inconsistencies; therefore, data-driven approaches have increased importance. This research focuses on developing a regression model using machine learning that can predict house prices and assist in informed decision-making within the real estate industry. The Ames, Iowa dataset from Kaggle contains 1,460 houses with 81 features that include lot size, neighborhood, construction year, and overall quality. Data pre processing involved handling missing values in numerical fields with median imputation and in categorical fields with mode imputation, followed by the application of one-hot encoding and standardization of features using the z-score.The regression algorithms Random Forest, Linear Regression, Decision Tree, Support Vector Regression, and K-Nearest Neigh-bors were trained on an 80/20 train-test split and evaluated using the metrics R2, RMSE, and MAE. Among these, the Random Forest model performed best with R2 ≈ 0.891, RMSE ≈ 28,872, and MAE ≈ 17,614. On the other hand, the weakest performance was",0.0,https://cspub-ijcisim.org/index.php/ijcisim/article/download/4497/3613700ac95b6e1009ce1d13d77855e1e6f783e4ee924e7,10.1177/21582440251408850,The Changes in Housing Prices and Fertility Levels in Chinese Cities—From the Perspectives of the Incentive Effect and the Crowding-Out Effect,2026.0,SAGE Open,Weiwei Zhang; Sun Pu,"Quantitative analysis of the impact of housing prices on fertility levels in Chinese cities is an important step for promoting high-quality economic development. Achieving balanced urban population growth in China will also contribute to sustained and healthy social and economic development. This paper presents empirical research based on panel data from 284 prefecture-level and higher cities in China, covering the period from 2010 to 2022. The research examines both incentive effect and Crowding-out effect. It uses regression models, mediating effect models, and threshold effect models. The study reaches the following conclusions: First, the increase in housing prices initially leads to a decline in fertility rates, followed by an increase. This suggests a positive “U”-shaped relationship. The declining part of the curve is significantly larger than the increasing part, and the results have passed rigorous robustness and endogeneity tests. Second, from the income perspective, in cities where housing prices have risen, labor, capital, and technology have grown significantly, helping to mitigate the decline in fertility. The contribution of the mediating effect of technology expendi",0.0,https://doi.org/10.1177/2158244025140885070125bbac9ea7797b2fb56ffd521bfbc2203cea2540,10.54517/cd.v5i1.2742,Prediction of new housing prices in Changsha urban area based on multiple machine learning algorithms: A comparative analysis,2024.0,City Diversity,Jun-Jia Yin; A. Alias; N. A. Haron; Nabilah Abu Bakar,"As China’s pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are ",0.0,https://aber.apacsci.com/index.php/cd/article/download/2742/34157023c81e7dc6f6a0c16d4c0960e1a6e3620f9a93a76,10.2478/remav-2026-0015,Socio-Political and Economic Determinants of Residential Property Performance Volatility in Emerging Real Estate Markets,2026.0,Real Estate Management and Valuation,Nasiru Salihu; I. Daniel,"Abstract This study investigates the determinants of residential property performance volatility in an emerging African real estate market, with particular emphasis on the interplay between socio-political unrest, demographic dynamics, and macroeconomic factors. Using a purposive sampling approach, data were obtained from 327 estate surveying and valuation firms across major Nigerian cities through structured questionnaires and expert interviews. Descriptive statistics and Principal Component Analysis (PCA) were employed to identify key drivers of volatility, following a three-stage analytical process involving factorability assessment, component analysis, and eigenvalue validation using Kaiser’s criterion. The findings reveal youth restiveness as the most dominant factor influencing residential property performance, followed by locational characteristics, structural attributes, population growth, and disposable income. Youth restiveness (driven by socio-political instability, unemployment, and economic uncertainty) was found to significantly undermine investor confidence and property values. The study contributes novel empirical insights into how socio-political volatility shapes ",0.0,https://doi.org/10.2478/remav-2026-00157038b49de98072903d72495214521e44306ccf98ebd,10.1109/dsaa65442.2025.11247972,An Event Study Framework for Analyzing Bidirectional Causality Between House Prices and Premium Café Entry,2025.0,International Conference on Data Science and Advanced Analytics,Sachin Date,"The relationship between cafes and house prices is an emerging field of study in urban data analytics. Recent research suggests that homes near cafes, especially premium cafes, start out dearer and appreciate faster than the broader U.S. housing market. However, significant endogeneity due to omitted variables and the possibility of bidirectional causality has made interpretation problematic. This uncertainty is compounded by limited methodical transparency, inadequate falsification criteria, and absence of rigorous analysis of bidirectional causation in the published work on this topic. Our study aims to fill these gaps. We present an analytical framework based on a Two-Way Fixed Effects (TWFE) event-study regression model (i.e., a generalized difference-in-differences design for staggered entry) combined with matching and covariate adjustment, to simultaneously study forward and reverse causality between house prices and premium café entry. Using Starbucks as a case study, we performed robust empirical analysis of the association between Starbucks' entry, and pre- and post-entry house price growth in over 500 U.S. neighborhoods across 45 states from 2012 to 2022. We controlled fo",0.0,7043f0e74a8cee961733ed1ebcdf19705aee98137f9,10.51244/ijrsi.2026.1304000192,An Ensemble Learning-Based Data-Centric Geospatial Framework of Property Value Index Estimation Based on Satellite-Derived Features,2026.0,International journal of research and scientific innovation,Vaishnavi Shastri,"The proper estimation of property value is an intricate issue that is affected by socioeconomic, environmental, and infrastructural variables. Conventional methods of valuation are based mainly on formal housing information, including income, population, and property type, and usually do not reflect the spatial and environmental context that has a great influence on the desirability of real estate. 705This paper suggests a data-driven model to make a Property Value Index (PVI) estimate based on incorporating structured housing data from the California Housing dataset and satellite-based geospatial attributes. This study is in contrast with the traditional methods of optimization of the models, it is more about the systematic construction of a spatially enriched dataset based on the remote sensing data sources. The most important attributes, such as vegetation density (NDVI), light intensity at night, road density, and the presence of water, are derived using the geospatial computing and aggregated across the localized spatial buffers using Google Earth Engine.706Ensemble learning models such as a baseline Random Forest, an enhanced Random Forest and XGBoost are used to evaluate the pro",0.0,70774d4f131b6ad2422b51ed7cf88bf2cd59da98bec,10.3390/e28050537,Finite-Length Spatiotemporal Modelling for Housing Price Network Spillovers,2026.0,Entropy,Lu Qiu; Yanzhe Jiao; Gege Dong; Guangcan Cui,"Mapping directed spillover pathways in urban housing prices is essential for monitoring the contagion of housing prices across cities. However, existing studies typically rely on either spatial gravity models or time-series models in isolation to analyze intercity connections, thus failing to simultaneously capture the spatiotemporal integration characteristics of housing price contagion. To address this, we embed a finite-length sequence correlation analysis (Correlation-Dependent Balanced Estimation of Diffusion Transfer Entropy, CBEDTE) into the gravity model, yielding the CBEDTE-GM integrated model. Using housing price data from 296 Chinese cities, we construct a spatiotemporal correlation matrix and employ the directed minimum spanning tree algorithm to extract core directed spillover pathways. Results reveal that China’s urban housing price spillover network exhibits a hierarchical architecture with pronounced ripple effects, where eastern coastal cities and the national core city serve as dominant radiation hubs. The East China sub-network occupies a distinctive net spillover position. We identify heterogeneous structural evolution patterns across regional sub-networks: (1) ",0.0,https://www.mdpi.com/1099-4300/28/5/537/pdf?version=17790973747088f8eedeb456cab4a0678ca7dd126b9bc1e2942d5,10.58915/ijbt.v16i2.3276,Household Income and Housing Price Movements in Malaysia: Reassessing the Role of Interest Rates and Inflation,2026.0,International Journal of Business and Technopreneurship,H. Musaddad; Muhammad Khuzairi Mohd Tarmizi; Wan Anis Wan Mohd Assrudin; Muhammad Fhadli Syakirin Mohd Jamalludin; Nurmasturah Mokhtar; Nur Akmarini Che Mat Razali,"This study examines the influence of macroeconomic factors on housing prices in Malaysia, with particular emphasis on household income, interest rates, and inflation. Housing affordability has become an increasingly important socioeconomic concern due to the persistent increase in residential property prices relative to household income growth. Using annual time-series data from 2002 to 2022 obtained from the National Property Information Centre (NAPIC), Department of Statistics Malaysia (DOSM), and Bank Negara Malaysia (BNM), this study applies Ordinary Least Squares (OLS) regression analysis to evaluate the relationship between selected macroeconomic variables and housing prices in Malaysia. The findings indicate that household income has a significant positive influence on housing prices, suggesting that stronger purchasing power contributes to higher housing demand and property price appreciation. In contrast, interest rates and inflation were found to have statistically insignificant relationships with housing prices during the study period. These findings highlight the dominant role of income growth in shaping housing market dynamics in Malaysia. The study contributes to the ",0.0,https://ejournal.unimap.edu.my/index.php/ijbt/article/download/3276/1729709d111b2e40e4222ef43f07bd4749ef24dd9d0c1d0,10.3390/jop1020008,Do Urban Parks Pay for Themselves? Property Value Capitalization and Municipal Fiscal Returns from Chicago’s 606 Trail,2026.0,Journal of Parks,Duane Ebesu,"Investments in urban parks are frequently justified not only in terms of their environmental and recreational amenities but also in terms of their potential to fuel appreciation in neighborhoods and corresponding increases in local fiscal revenues. There is a substantial body of literature indicating that proximity to parks and other forms of urban open spaces can be capitalized into surrounding residential property values; however, there is relatively limited research examining whether these effects plausibly translate into fiscal revenues that could offset the costs of urban park projects. This study seeks to address this question in the context of Chicago’s 606 Trail, a 2.7-mile elevated rail-to-trail conversion completed and opened on 6 June 2015, after a publicly disclosed investment of $95 million in the project at the time of opening. Using property transaction data and a difference-in-differences event-study approach, this study estimates the impact of park completion on surrounding residential property values and converts these amenity capitalization effects into corresponding municipal fiscal revenues. The main contribution of this paper is to demonstrate that amenity cap",0.0,https://www.mdpi.com/3042-5689/1/2/8/pdf?version=17763343687104e0d23fed68dafc3900a26993e6079a826b0beec,10.38027/iccaua2026en0373,Assessing Socio-Economic Centrality Indicators in Urban Research: A Bibliometric and systematic LLM-Assisted Review (2016–2026),2026.0,Proceedings of the international conference of contemporary affairs in architecture and urbanism-ICCAUA,Khelil Cherfi Khadidja; Merzelkad Rym; H. Ahmadnia; Hamza Benacer,"Socio-economic centrality indicators are widely used to explain the spatial concentration and interaction of economic activities, populations, and services within urban and regional systems. Between 2016 and 2026, rapid methodological diversification and interdisciplinary adoption have generated a fragmented and conceptually heterogeneous body of research. This paper presents a bibliometric and systematic review of socio-economic centrality indicators published between 2016 and 2026, combining classical bibliometric techniques with LLM assisted semantic synthesis. The combined approach enables the identification of dominant indicator families and their conceptual linkages. Eight socio-economic centrality indicators were identified: (1) accessibility and transport connectivity, (2) mobility and flow-based centrality, (3) economic and commercial activity, (4) amenities and urban attractors, (5) housing prices and investment dynamics, (6) population concentration and demographic indicators, (7) mixed-use development and activity intensity, and (8) innovation and knowledge networks. Results reveal a clear shift from traditional graph-based centrality measures toward multidimensional so",0.0,https://journal.iccaua.com/jiccaua/article/download/921/860711aa22e7ab448ac7e9d883345a8727b546b71652b8,10.37407/kres.2025.43.4.195,Analyzing the Linkage Between Urban Landscape Elements and Property Value Formation in Residential Complexes,2025.0,Korea Real Estate Society,H. Yun,"In contemporary urban environments, landscaping has emerged as a core component that extends beyond aesthetic enhancement, contributing significantly to the improvement of residential quality, the physical and psychological well-being of residents, and the strengthening of local identity and urban competitiveness. Urban landscape resources—such as parks, green spaces, and view corridors—are closely associated with residential satisfaction and play a critical role in shaping real estate values. As demand for pleasant living conditions and environmental welfare increases in densely populated cities, the need for systematic research on the relationship between landscaping and real estate value has grown substantially. 712This study aims to empirically analyze the impact of urban landscaping factors on real estate values and to identify the key elements influencing value formation. To achieve this, the Analytic Hierarchy Process (AHP) was applied to evaluate the relative importance of landscaping attributes, based on survey responses from apartment residents, real estate practitioners, and landscape experts. 713During the analysis, landscaping factors were structured into a hierarchical fr",0.0,714c27bb162a21e02e24af6ca45e7eacffb91547138,10.30606/rjti.v4i3.3993,Application of Multiple Linear Regression Algorithm for House Price Estimation Based on Building Location and Area to Improve Predictive Accuracy in Real Estate Valuation,2025.0,Riau Jurnal Teknik Informatika,Kecitaan Harefa,"Significant differences in home prices, even on properties with similar building sizes and locations, pose a major challenge in accurately determining property valuations. The discrepancy between the actual market price and the estimated value makes it difficult for potential buyers, sellers, and developers to make the right decision. To overcome these problems, this study applied the Multiple Linear Regression (MLR) algorithm in the Decision Support System (DSS) to estimate house prices based on the location and area of the building. The dataset used consists of 545 housing data points with variables such as house prices, locations, and building areas. The research stages include data collection, pre-processing (data cleaning and normalization), model development using MLR, and model performance evaluation. The evaluation was carried out using the division of trained data and test data with an 80:20 ratio, so that the model was tested using data that was not previously trained. The results showed that the model produced a Mean Absolute Error (MAE) of 1,474,748.13, a Root Mean Squared Error (RMSE) of 1,917,103.70, and a coefficient of determination (R²) of 0.273. A relatively low R",0.0,https://journal.upp.ac.id/index.php/rjti/article/download/3993/21297159499528ec92cc12ee81cf3bc35e3006d72001be4,10.48175/ijarsct-29515,A Systematic Review- House Price Prediction Using Machine Learning,2025.0,"International Journal of Advanced Research in Science, Communication and Technology",Rohan Bamane; Diganta Gavhane; Paras Igave; M. M. Zade,"Abstract: As the real estate industry increasingly relies on data-driven insights, predicting house prices accurately has become crucial for both buyers and sellers. Traditional valuation methods often fail to account for multiple nonlinear factors influencing property value, making machine learning-based solutions more reliable and efficient. The project titled “House Price Prediction Using Machine Learning” aims to create a forecasting model that can estimate716property prices based on various influencing parameters such as location, area, number of rooms, furnishing status, and nearby amenities.717The project is implemented using Python, leveraging tools as NumPy for numerical calculations, Pandas for data management, and Scikit-learn for building and evaluating machine learning models. The methodology includes data preprocessing involving data cleaning, normalization, handling missing values, and feature encoding. Various regression methods like Gradient Boosting, Random Forest, Decision Trees, and Linear Regression718are trained and evaluated to determine the most precise predictive model for price estimation.719The trained model predicts housing prices for new data entries, providing",0.0,https://doi.org/10.48175/ijarsct-295157204fa0e3f6df7de77c0eda332cc9c406d53dcde32f,10.55606/jtmei.v5i2.6237,Optimasi Random Forest untuk Prediksi Harga Rumah menggunakan Seleksi Fitur dan Penyetelan Hyperparamater,2026.0,"Jurnal Teknik Mesin, Industri, Elektro dan Informatika",Roberto Reyna Wijaya; M. E. Purbaya; Rozzan Zhofrul Islam,"House price prediction has become an important need in the property sector because selling prices are influenced by various factors, such as building quality, living area size, garage capacity, and physical property conditions. The complexity of relationships among these variables makes machine learning approaches, particularly Random Forest Regression, relevant for developing accurate and stable prediction models. This article discusses the optimization of Random Forest Regression for house price prediction through feature selection and hyperparameter tuning. The dataset used in this study is the House Prices: Advanced Regression Techniques dataset from Kaggle, with SalePrice as the target variable. The analysis process includes data exploration, data preprocessing, feature selection, train-test data splitting, Random Forest model development, and hyperparameter optimization using Randomized Search. Model performance was evaluated using R-Squared, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results show that the optimized model achieved an R-Squared value of 0.8927 and an RMSE value of 28,687.08. The most dominant features influencing house prices were Overa",0.0,https://doi.org/10.55606/jtmei.v5i2.623772148e2f0585e181a915613f0662c44f24ce334187b,10.1109/icet66147.2025.11321431,"House Price Prediction using Linear Regression, Random Forest, and Gradient Boosting",2025.0,International Conference on Emerging Technologies,Sabeel Ahmed; S. Bazai; Paras Pervaiz; U. Bhatti; Muhammad Imran Ghafoor; Laveeza Rehman,"Accurate house price prediction is essential in real estate planning, investment analysis, and housing policy. This work investigates three machine learning models: Linear Regression, Random Forest, and Gradient Boosting. It uses an extended feature set comprised of property age, spatial location proxy, renovation index, and amenities score. Structured preprocessing, feature engineering, and a subsequent 80/20 traintest split were performed on the dataset. Experimental results indicate that Gradient Boosting yields the lowest prediction error and offers more stable generalization compared to the other benchmarked models. An ablation study showed that the engineered features significantly improve performance, while permutation importance underlined the most influential predictors. Although R2 values remain modest, findings highlight the potential of incorporating richer spatial and socioeconomic attributes. The study contributes a reproducible, improved pipeline for house price prediction and provides practical insights relevant to real estate stakeholders. The study makes use of a dataset containing both categorical and numerical housing attributes from Quetta, Pakistan. Prior to m",0.0,7225f7941e93f4ba1777a496de0a2ed6426cc00c903,10.21837/pm.v24i44.2293,YOUTH HOMEOWNERSHIP READINESS AND ACCESS TO GOVERNMENT HOUSING PROGRAMMES IN MALAYSIA,2026.0,PLANNING MALAYSIA,Nur Arnie Aziera; Ezdihar Hamzah; Nurul Ain Najwa,"This study examines factors associated with homeownership readiness among young adults in selected urban areas of Malaysia, focusing on perceived homeownership barriers, financial capacity and access to government housing programmes. A quantitative cross-sectional research design was adopted using a structured questionnaire, with 551 valid responses collected. Descriptive statistics, reliability and factorability analysis, and multinomial logistic regression were employed. Among the 235 non-homeowners, only 21.7% intended to purchase a house within the next five years, while 57.4% had no intention and 20.9% were uncertain. Deposit requirements, location and house price emerged as the most prominent perceived barriers. The six barrier items demonstrated strong internal consistency and were consolidated into a Perceived Homeownership Barriers (PHB) construct. However, PHB was not statistically significant in explaining homeownership intention after controlling for age, monthly income and residential location. Monthly income showed significant associations, particularly between respondents with no purchase intention and those who were uncertain. Although awareness of government housin",0.0,https://www.planningmalaysia.org/index.php/pmj/article/download/2293/1856723583c5256c1682e2ee29b90edfab6fc1e2a41e32c,10.18503/1995-2732-2026-24-1-176-191,SPATIAL PRICES VARIATION IN THE REGIONAL CONTEXT: A SYSTEMATIC REVIEW OF FOREIGH LITERATURE,2026.0,Vestnik of Nosov Magnitogorsk State Technical University,D. Krasnoselskaya; V. Timiryanova; V. Prudnikov; Ekaterina S. Gaintseva,"The spatial variation in prices depends on the influence of various factors. In the existing literature, empirical studies on this topic differ significantly depending on the type of goods, the objectives of the research and its methodology, and the level of spatial hierarchy under consideration. In this paper, we analyze the studies devoted to the spatial variation of prices of the most common goods, their main determinants, the analytical methods used, and the presence or absence of a spatial component in price changes. Using a systematic approach and the PRISMA method, we have reviewed 278 articles and conducted a content analysis based on the Scopus database and the Tandfonline journal resource. The results have shown that the contemporary literature is dominated by studies related to variations in housing and land prices. The most significant factors influencing the difference in housing prices include architectural characteristics, location (geographical variables), social and economic factors, the presence of local amenities or disamenities, as well as the specifics of housing purchase transactions. The determinants of land price variation are closely related to the type of ",0.0,7242a18cecd245593dfe8060041a9e3ab58f814a184,10.21837/pm.v24i44.2295,"FACTORS INFLUENCING RUMAH SELANGORKU PURCHASE-RELATED DECISIONS AMONG YOUNG PROSPECTIVE BUYERS IN SELANGOR, MALAYSIA",2026.0,PLANNING MALAYSIA,Nurul Ain Najwa; Ezdihar Hamzah; Nur Arnie Aziera,"Home ownership among young adults in Malaysia, particularly in Selangor, has become increasingly challenging due to rising property prices, income imbalance, and financial commitments. Although the Rumah Selangorku Scheme was introduced by the Selangor State Government to provide affordable housing for the B40 and M40 groups, concerns remain regarding its ability to meet the needs and expectations of young prospective buyers. This study aims to identify the factors associated with Rumah Selangorku purchase-related decisions among young prospective buyers and to examine differences in perceptions according to their application status. A quantitative approach was adopted using questionnaire data collected from 325 respondents aged 18 to 35 who live or work in Selangor. Data were analysed using descriptive statistics, the Relative Importance Index (RII), reliability and item analysis, exploratory factor analysis, Welch’s ANOVA, Games–Howell post-hoc analysis, and multinomial logistic regression. The findings indicate that respondents generally placed high importance on housing price, facilities, location, developer reputation, government incentives, and related housing information. A ",0.0,https://www.planningmalaysia.org/index.php/pmj/article/download/2295/1858725d922b923f6add38ad1d815fe471e6103c379128a,10.3390/su18104986,Decoding Spatial Non-Stationarity in Coastal–Mountainous Housing Markets: A Sustainable Urban Informatics Framework Using Explainable STGCN,2026.0,Sustainability,Jong-Hwa Lee; Sung Jae Kim,"Traditional linear models in urban informatics struggle to capture the complex, non-linear spatial non-stationarity inherent in metropolitan housing markets. To overcome these constraints, this study introduces a data-driven computational framework integrating a Spatio-Temporal Graph Convolutional Network (STGCN) with gradient-based Explainable Artificial Intelligence (XAI) and Geographically Weighted Regression (GWR). This framework is empirically tested using 217,598 apartment transactions in Busan, the Republic of Korea, augmented with high-resolution micro-demographic grids and Digital Elevation Model (DEM) topographical data. Utilizing unsupervised K-Means clustering, the region is spatially stratified into a dense Urban Core and a dispersed Suburban Periphery. The STGCN demonstrates overwhelming predictive superiority (R2=0.802) over the traditional Spatial Error Model (R2=0.437). Crucially, gradient-based XAI and localized GWR coefficients successfully unspool the deep learning “black box,” visualizing hyper-localized economic realities that global linear models obscure. The analysis expose stark regional market segmentation driven by environmental topography, mathematically",0.0,https://doi.org/10.3390/su1810498672663870974b1c2c2d5022574752da6ac2e80a6b5c8,10.54097/rwv9m728,Data-driven urban Development Prospects Based Research,2025.0,"Frontiers in Business, Economics and Management",Haobo Zhang; Yang-Yang Li; Xuan Cao; W. Qiu; Jixi Zhang; Changgen He; Wenbo Song,"This study evaluates the future development prospects of Changchun and Hohhot through a data-driven approach, focusing on key factors such as housing prices, service levels, urban resilience, and sustainable development. The research integrates advanced modeling techniques including multiple linear regression, random forest, and XGBoost to analyze various urban indicators. In housing, the models identify price fluctuation factors and estimate housing stock using indirect metrics, with gradient boosting regression emerging as the most effective model. Changchun is found to excel in retail, finance, and lifestyle services, while Hohhot excels in public infrastructure, though both cities require balanced development to address disparities in facility distribution. Urban resilience is assessed by examining response capabilities to extreme weather and emergencies, with targeted investments suggested to enhance resilience and service accessibility. Sustainable development strategies for both cities are proposed, with Changchun focusing on green transportation and mixed-use developments, and Hohhot emphasizing healthcare and tourism infrastructure. Both cities are encouraged to adopt smar",0.0,https://drpress.org/ojs/index.php/fbem/article/download/29211/286637276fc4753d2ddfac2a7ebab8eae416c140b8061a1f,10.3390/su172310462,Heterogeneity of Influencing Factors for Informal Commercial Spaces in Communities from the Perspective of Right to the City: A Case Study of Harbin,2025.0,Sustainability,Han Wu; Chun-Yu Pang,"Effective governance of informal commercial spaces is a common challenge faced by cities globally. To break through the superficial governance mindset of traditional spatial regulation, this study focuses on clarifying the spatial distribution characteristics and influencing factors of such spaces. By integrating the theory of “The right to the city” with the “7D” principles of New Urbanism, and focusing on the Jinxiang Street area in Harbin, a representative zone combining traditional industrial and modern residential communities, this study constructed a multidimensional indicator framework including population factors, functional diversity of facilities, accessibility of the built environment, spatial suitability, and intensity of community management, extracting 17 significant variables. Through spatial autocorrelation analysis (Moran’s I), multiscale geographically weighted regression (MGWR), and geographic detector analysis, the results show that informal commercial spaces exhibit clustered yet uneven characteristics between aging and upscale communities; the MGWR model reveals significant spatial heterogeneity in influencing factors; and geographic detector analysis shows th",0.0,https://doi.org/10.3390/su17231046272879ee0e1e6de51b2bdeb2361d43b3dc9dfaac577f,10.54097/hset.v47i.8183,Factors Affecting China Birth Rate: Taking Shanghai as an Example,2023.0,Highlights in Science Engineering and Technology,Zhong-kun Zhu,"China's birth rate has fallen off a cliff in the past few years, and the total birth rate has fallen below 10 per thousand. On January 17, 2022, the Chinese National Bureau of Statistics released China's economic data for 2021. The annual birth population was 10.62 million, and the birth rate was 7.52‰. But in 2016, that figure was still 13.57‰. Previous studies have explored various influencing factors, but further research is needed on the degree of influence of multiple factors on the birth rate. Based on previous research, this study adopts mathematical-statistical methods such as principal component analysis, correlation analysis, stepwise multiple regression, and ridge regression to analyze more influencing factors to explore and test the effect of these factors on the birth rate and uses the data of the Shanghai Bureau of Statistics from 2002-2021 for 19 years to try and judge which factors significantly affect the birth rate under the joint action. The validity of the judgment conclusion is tested by various data from Beijing and Guangzhou in 2021. Results of the study found that the per capita GDP, unit housing price, per capita disposable income, and resident consumption ",0.0,https://drpress.org/ojs/index.php/HSET/article/download/8183/79587298816310ac987663357cb1f56d65de89adc937ff6,10.53106/054696002023120114003,「生不如死」能「起死回生」嗎？臺灣地區各縣市生產補貼政策對生育率之影響,2023.0,應用經濟論叢,許義忠 許義忠; Shou-Xia Zheng,"730 「生不如死」能「起死回生」嗎？面對生育率愈來愈低的趨勢下，臺灣地區各縣市紛紛祭出許多有關生產前與生產後的生育補貼政策。本文首先彙整有關影響生育率的決定因子，包括經濟因素、人口因素與環境因素，並建構生育率決定因子模型，同時在經濟因素中加入房價所得比，藉以觀察近年來房價高漲的問題對生育率所造成的影響效果，並透過迴歸分析找出重要的顯著影響因子。其次，透過差異中之差異法針對近年來各縣市政府為了提高生育率所推出的各項生產補貼政策進一步驗證，實證結果指出在不考慮龍虎年效應時，新北市的「婚後孕前健檢」有效，而在固定效果模型中，臺中市的「婚後孕前健檢」與「到宅坐月子服務」亦為有效，除此之外，各縣市的其它政策幾乎對總生育率的變動率無法有效提高。此外，經濟因素中的失業率與房價所得比以及人口因素中的女性勞動力參與率愈高，則生育率的成長率將愈低；而人口因素中的粗結婚率則具有正向影響效果。731 Can “Worse than death” become “Back from the dead?” Faced with the trend of declining birth rates, several countries and cities in Taiwan have introduced numerous policies related to pre- and postnatal subsidies. This article initially consolidates factors influencing birth rates, encompassing economic, demographic, and environmental aspects. It constructs a model of birth rate determinants, integrating the housing-to-income ratio as part of the economic factors. This study aimed to observe the impact of the recent surge in housing prices on birth rates and identify significant influencing factors through regression analysis.732Furthermore, utilizing the difference-in-difference method, this study validates the effectiveness of various pro-natal subsidy policies implemented by local governments in re",0.0,733c8de299007bc939aa48777d41102a6e31563bce3,10.33119/gn/100924,The Impact of Banking Regulation on the Economic Performance of EU Countries in 2007-2009,2013.0,,Katarzyna Sum,"The article investigates the role of a broad array of banking regulatory features in shaping the resilience of European Union countries to the latest financial crisis. The study considers the level of output, the yearly averages of the month-to-month government bond yield spreads, and the yearly variance of these spreads as performance measures. Moreover, the author considers house price growth, public debt levels, financial openness and the extent of private credit as basic factors determining the vulnerability of EU countries to the crisis. The empirical specification is based on dynamic panel data models, which are estimated using the Generalized Method of Moments (GMM). The basic regressions including the main predictors of the crisis are expanded to include interaction terms between banking regulatory measures and the specified predictors. This approach is aimed at seizing the marginal effect of banking regulation on the impact of the respective factors of the crisis. The sample encompasses 25 EU countries during the period of 2004-2009. The choice of the time sample is aimed at capturing the developments in the economic cycle preceding the crisis, which affected the performan",0.0,http://gospodarkanarodowa.sgh.waw.pl/pdf-100924-33208?filename=The Impact of Banking.pdf734ceadd302511bfdcbeebb266937f5d9e55078c990,10.22399/ijcesen.2870,Evaluating Machine Learning Models for House Price Prediction with Different Sampling Techniques,2025.0,International Journal of Computational and Experimental Science and Engineering,A. Çetin,"This study investigates the interplay between advanced sampling techniques and machine learning models to predict residential property sale prices using a diverse dataset encompassing structural, locational, and economic attributes. Emphasizing Stratified Extreme Ranked Set Sampling (SERSS), the research systematically evaluates the impact of five sampling methods—SERSS, Cluster, Bootstrap, Systematic, and Random Sampling—on various machine learning algorithms, including CatBoost, Random Forest, ElasticNet, and FIkNN. The findings reveal that SERSS significantly enhances the generalizability and robustness of predictive models by capturing both central and extreme data tendencies, outperforming traditional methods in preserving dataset variability. Ensemble methods like CatBoost, Random Forest and similarity algorithm like FIkNN consistently demonstrated superior predictive accuracy, achieving the Mean Absolute Error (MAE) between $85 and $650, and high R² values across structured sampling techniques. Conversely, unstructured methods such as Random Sampling introduced biases, leading to substantial deviations in predictions. These results underscore the critical importance of align",0.0,https://ijcesen.com/index.php/ijcesen/article/download/2870/826735316120d10b70732213f35397c4e8fddab26c55f6,10.1108/ijhma-08-2025-0188,The impact of increased inflation on residential real estate investment in the Czech Republic,2025.0,International Journal of Housing Markets and Analysis,Petr Šuleř; Jiří Máška; Jan Vrba,"736 737 This research paper aims to examine the impact of increased inflation on residential real estate investment in Prague, Czech Republic, over the period 2019–2024. The objective is to measure the relationship between inflation and housing prices and to evaluate the influence of monetary policy on market dynamics.738 739 740 741 Quantitative time-series analysis was applied to inflation and housing price data. Correlation and regression models were used to assess direct and lagged effects (including a one-quarter lag). The asset-based valuation method was implemented to detect property overvaluation, while the delayed effects of the Czech National Bank’s (CNB) monetary tightening were also analysed.742 743 744 745 A strong positive relationship between inflation and housing prices was identified (r = 0.75), which strengthened with a one-quarter lag (r = 0.80). In 2022, residential property prices were overvalued by up to 10% relative to their fundamental values. Monetary tightening by the CNB led to a temporary decline in investment activity and a delayed correction of market prices.746 747 748 749 The analysis is limited to a five-year horizon, a single urban market (Prague) and partial qualitative data",0.0,750592f93b86b5d6e73799d6fe7241aa3c6b6eb5cdc,10.71146/kjmr357,"THE IMPACT OF INTEREST RATES ON MORTGAGE, LEASING DEMAND, HOUSE PRICES AND ARBITRAGE OPPORTUNITY: A CASE STUDY OF PAKISTAN",2025.0,Kashf Journal of Multidisciplinary Research,Noman Nazir; Sohaib-Uz-Zaman; S. Alam,"In this study, it is attempted to study how the interest rates have influence on key real estate market variables, including mortgage rates, leasing demand, house prices and arbitrage opportunities in Pakistan. The dataset used for the research is from 2014 to 2023 and the research employs a Simple Linear Regression (SLR) Model using SPSS software. The inclusion of inflation rates and GDP growth form controls variables to explain what drives the property sector more comprehensively.751The findings show that interest rates have a large positive effect on mortgage rates thus changes in monetary policy are directly reflected in borrowing cost. However, interest rates do not explain leasing demand or house prices very well, emphasizing that there are other significant factors influencing lease demand and home prices than interest rates. Also, the study explains how the interest rate has a negative impact on arbitrage opportunities against the prevailing financial theories that higher interest rates diminish the feasibility of the investment in real estate arbitrage.752The implications provided by these results are important for policymakers, investors, and financial institutions. However, ",0.0,https://kjmr.com.pk/kjmr/article/download/357/455753cf83506af1bc3041e26fec98e23b8ad885860f69,10.61173/xd7s4x66,Research on Machine Learning based House Price Prediction and Stock Data Visualization,2025.0,"Science and Technology of Engineering, Chemistry and Environmental Protection",Jiayi He; Ke-Er Wang,"The mechanism and prediction of stock market volatility constitute a long-standing core research focus within financial engineering. Machine learning stands out due to its exceptional capacity for modeling nonlinear structures and has consequently become a principal technique in stock price prediction. It effectively uncovers complex market interrelations and underlying trends. A common limitation in existing research, however, lies in inadequate model interpretability and a lack of integrated visualization support. To bridge these gaps, this research selects Vanke A (000002)—a representative real estate firm—as the case study, builds a stock prediction model using the random forest algorithm, embeds visualization features, and ultimately develops an end-to-end decision support system. Empirical findings reveal that the model successfully tracks short-term stock price fluctuations and exhibits strong predictive consistency and robustness in stable market climates. Further analysis of feature importance underscores the persistent influence of volume-price indicators—such as opening price, historical trading volume, and turnover rate—as pivotal drivers of stock price movement, reaffi",0.0,https://lseee.net/index.php/te/article/download/2070/TE013702.pdf754670c60f27b11c7d86ea3f794dd5bd42b87888e9b,10.47405/mjssh.v10i8.3570,"The Relationship Between Housing Type, Housing Prices, and Strategic Location on Housing Affordability in Malaysia",2025.0,Malaysian Journal of Social Sciences and Humanities,M. Hassan; Muhammad Danish Asyhraf Shaharudin; N. Zamani,"The issue of housing affordability in Malaysia, particularly in Johor, has become increasingly critical due to rising housing prices and the imbalance between household income and market prices. This study assesses the factors of housing type, housing price (housing market and construction costs), and strategic location (neighbourhood and public amenities) on housing affordability. This study aims to identify the levels of housing type, housing price, and strategic location in relation to housing affordability and to analyse the relationship between these variables. The study employs a quantitative descriptive design using a questionnaire as the data collection instrument. A total of 385 respondents in Bandar Sri Alam, Pasir Gudang, were selected through simple random sampling. Data were analysed using descriptive statistics, Pearson correlation, and ANOVA. The levels of housing type, housing price, and strategic location were found to be high. A significant relationship was identified between housing type, housing price (housing market), and public amenities with housing affordability (p < 0.01). However, construction costs and neighbourhood factors did not show a significant rela",0.0,https://www.msocialsciences.com/index.php/mjssh/article/download/3570/2303755ee55a4ae3dba2687de3e4885176c2db671a51fee,10.54097/e4e0tn53,Study on the Transmission Effect of Interest Rate Decline on Housing Price in Structural Deflation Cycle (2023-2025),2025.0,Highlights in Science Engineering and Technology,Hao-Lin Li,"During economic downturns, particularly deflationary periods, the transmission of interest rates to real estate prices poses a complex and challenging issue for central banks worldwide. The intertwining of various economic issues and black swan events continuously hinders this transmission mechanism, repeatedly weakening the effectiveness of interest rate policies and making it difficult for single-rate measures to effectively break the downward spiral in housing prices. This paper focuses on the specific context of China's economic downturn since 2019, examining the transmission effect of interest rate policies on real estate prices through regression analysis. It explores how interest rate policies interact with property values while simultaneously influencing investment and consumption patterns. The research finds that during structural deflation, interest rate cuts have a short-term positive impact on housing prices, with current and one-period lagged rates showing significant effects. However, the transmission mechanism is severely weakened in the long run. This inefficiency is attributed to three key bottlenecks: high household debt saturation, liquidity traps for developers,",0.0,https://drphset.com/index.php/ojs/article/download/65/63756b0ea2895141bef0f68a4118ea68d02a4fc27bce6,10.15396/eres2013_51,The Non-linear Ripple Effect of Housing Prices in Taiwan: A Smooth Transition Regressive Model,2013.0,,Mei-Se Chien,"Being different from past research of regional housing prices, this paper employs smooth transition regression model, derived in Terasvirta (1998), to investigate ripple effects among four regional house prices in Taiwan. The aim of this paper is to test whether a smooth transition regression model, which is capable of capturing this non-linear behaviour, can show a better characterisation of regional housing prices than a linear model. This empirical analysis applies the four regional house prices of Taiwan, including the capital in Taiwan, Taipei City, and its suburban area, New Taipei City, and the other two mega cities of Taichung City and Kaohsiung City, from the first quarter of 1998 to the second quarter of 2011. Using the changing rate of housing price of Taipei City to be the threshold variable, the empirical results of the smooth transition regression model show that the ripple effect exists between housing prices of New Taipei City and Taipei City, while there is no ripple effect between housing prices of New Taipei City, Taichung City and Kaohsiung City. Besides, this paper has presented evidence of a non-linear relationship between housing prices of New Taipei City and",0.0,https://eres.architexturez.net/system/files/pdf/eres2013_51.content.pdf757602584c64cbc827c89094115fa518259ed05af26,10.70382/ajaias.v10i2.058,The Impact of Tertiary Institution on Property Rental Value,2025.0,Journal of African Innovation and Advanced Studies,Muhammad Mukhtar Muazu; A. Yusuf,"This study seeks to examines the impact of tertiary institutions on property rental values in Fatara community Azare, Bauchi, Bauchi State. The presence of tertiary institutions often influences the socio-economic structure of surrounding communities by increasing housing demand, changing land use patterns, and affecting rental prices. Primary data were collected through structured closed-ended questionnaires administered to landlords, tenants, and estate agents within the study area, while secondary data were obtained from relevant publications and property records. The data were analyzed using descriptive statistical tools such as frequency tables and mean scores. The findings revealed that the existence of tertiary institutions in Fatara community has significantly increased the demand for rental accommodation, leading to higher property values and rapid urban development. The study also found that proximity to the institution, accessibility, and the availability of basic amenities as key factors influencing rental variations in the area. The study concludes that tertiary institutions contribute positively to property market growth in Fatara community by stimulating demand and e",0.0,https://africanscholarpub.com/ajaias/article/download/940/921758b016358b1d6d1b0e28b14a1bab3abd3080ab8933,10.17762/turcomat.v12i11.6053,Prediction Of The Accuracy Of Student Boarding House Prices Around Widyatama University Using Neural Network Backpropagation Algorithm,2021.0,Turkish Journal of Computer and Mathematics Education,Ari Purno Wahyu; Raka Maheka Auramuwahid,"Boarding room rental services are a business that has been around for a long time. One of the target consumers of this business is from students who plan to stay temporarily during the lecture period so that they are close to the campus location where the student is studying. A strategic location such as in the area of one of the campuses in Bandung, namely the Widyatama University Campus, provides added value for boarding owners and also consumers who will rent, but there are several problems in this boarding rental business, namely the lack of knowledge of information on boarding rental prices around the area. campus due to the lack of effort made by boarding house owners in surveying boarding rental price data. In addition, boarding house owners find it difficult to determine the range of rental prices in accordance with boarding facilities. This problem is also experienced by prospective boarding house tenants in estimating whether the rental price offered is ideal and comparable to the facilities that will be obtained. This research conducts trials and predicts the range of rental prices in accordance with the parameters of the facilities that have been defined based on a su",0.0,https://turcomat.org/index.php/turkbilmat/article/download/6053/5050759533b1bf0696280d56d4907f5cb4687bca72d5889,10.54254/2753-8818/2026.dl34391,Housing Prices and Marriage Rates Across OECD Countries,2026.0,Theoretical and Natural Science,Rui Luo,"In recent years, housing affordability has become an urgent concern for many OECD economies, raising questions about its impact on family formation and marriage behaviour. This study examined the relationship between real residential property prices and crude marriage rates using an unbalanced panel dataset (N = 749) from 37 countries during the period from 2000 to 2022. House price data are from the OECD Analytical House Prices Indicators database, and marriage rate data are from Our World in Data. Four increasingly strict regression norms are estimated: aggregated OLS, OLS with macroeconomic control, national fixed effects, and bidirectional fixed effect models including national and annual effects. If there is no fixed effect, there is a significant negative correlation between house prices and the marriage rate, which is consistent with the assumption of affordability constraints. Once the national fixed effect is introduced, the negative correlation becomes insignificant, which shows that transnational structural differences promote set correlation to a large extent. The bidirectional fixed effect model produces a significantly positive coefficient, which may reflect the procy",0.0,https://tns.ewapub.com/article/view/34391.pdf760b88b256df73c60110f396557e880a3a3ea612baf,10.3126/jopls.v2i1.98210,Reducing Overfitting Problem by Hyperparameter Optimization of Different Regularization Techniques using Linear Regression,2026.0,Journal of Physical and Life Sciences,Nawaraj Paudel; Manoj Saru," 761In machine learning, regularization refers to the techniques for calibrating learning models that prevent overfitting and lower the adjusted loss function. Regularization is useful for attaining the perfect fit between overfitting and underfitting. This research focuses on identifying the problem of overfitting on linear regression model and also aims to solve that problem by implementing different regularization technique like Lasso (L1 regularization), Ridge (L2 regularization) and Elastic-Net (L1_L2 regularization) which are applicable on regression model. Optimization of the hyperparameter is also carried out for all the regularization techniques in order to find the ideal value of the hyperparameter to minimize the MSE and to enhance the model performance and efficiency. The dataset used for this research is the Nepali Housing Price Dataset which has been downloaded from Kaggle repository. MSE is used as performance metrics for to analyze the performance of regularization techniques. From the experimental results, it is observed that the Elastic-Net regression outperformed both Lasso and Ridge regression with the minimum MSE of 3.5541 at lambda (λ) value of 0.9. Also, the M",0.0,https://doi.org/10.3126/jopls.v2i1.98210762cca9d50f45a2c765a71aba58cd8577afa3545042,10.32473/edis-fe990-2016,Consumer Perceptions of Lawn Fertilizer Brands,2016.0,EDIS,H. Khachatryan; A. Rihn; M. Dukes,"Widespread urbanization in the United States has increased the number of lawns. A healthy lawn provides many benefits, including urban heat dissipation, water quality protection, erosion control, carbon sequestration, community safety, aesthetics, and property value growth. Many homeowners maintain their healthy lawns by applying fertilizers throughout the growing season, but excess fertilizer runoff and leaching have received much attention recently because of waterway pollution and algae blooms, leading many states to place restrictions on what chemicals can be used in lawn fertilizers.763Consumers’ increased environmental awareness creates a niche opportunity for the fertilizer industry to promote environmentally friendly lawn fertilizers, but in order to effectively exploit it, industry stakeholders must understand consumer purchasing behavior before expending labor, time, and money creating products for sale. The following 3-page report covers research methodology and the existing brand awareness and selection of consumers in the lawn fertilizer industry. It also discusses homeowners’ preferences for certain fertilizer attributes. Consumer awareness, selection, and attribute pre",0.0,https://doi.org/10.32473/edis-fe990-20167642496df96e711af71e0ee891ef853f03e7e12bc6f,10.53560/ppasa(63-2)708,A Robust Stacked Ensemble Approach for House Price Prediction,2026.0,Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences,Muhammad Khateeb Khan; Asad Ullah Khan; Salman Ahmed; Wajid Ali,"Real estate price prediction plays a fundamental role in decision-making and ensuring market stability for buyers, sellers, and property owners. Since the real estate sector has a significant impact on the global economy, accurate prediction models are crucial. In this study, we used the Ames Housing dataset and performed thorough preprocessing to handle missing values, reduce multicollinearity, and enhance feature quality. We introduced three new features, total square footage, total porch area and the age of the house to capture the better property characteristics. After these steps, we evaluated a range of machine learning (ML) models, including Linear Regression, Ridge, Lasso, Decision Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost and explored hybrid combinations with weighed ensemble and stacked ensemble strategies. We proposed CXL Stacked Ensemble (CatBoost + XGBoost + LightGBM) model with ridge regression as meta-learner and the model outperformed with R² of 0.9333 and a normalized RMSLE of 0.1132, demonstrating the superior model predictive capability. To validate its robustness fold-wise RMSE comparisons and significance tests (paired t-test and ",0.0,https://www.ppaspk.org/index.php/PPAS-A/article/download/1660/1117765f0af66f2c63cacb40d74ced6f2b5db6cc0067674,10.3390/su18010476,How Urban Distance Operates: A Nonlinear Perspective on Talent Mobility Intention in the Yangtze River Delta,2026.0,Sustainability,Xing Yan; Ji-Zu Li,"Based on micro-level job seeker data from 41 cities in China’s Yangtze River Delta, this study employs threshold regression to examine how inter-city distance influences talent mobility. The results reveal that distance exerts a negative impact on mobility intention and moderates the relationship between a destination’s economic level and mobility. Notably, significant threshold effects are identified at 164.1 km and 271.5 km, delineating three spatial regimes. Short-distance flows (<164.1 km) show the highest intensity, driven by strong economic incentives and high mobility. In contrast, medium-distance flows (164.1–271.5 km) prove least attractive due to offsetting effects, while long-distance flows (>271.5 km) rebound slightly as talent selectively targets major economic hubs, with distance exhibiting only weak inhibition. Crucially, these nonlinear patterns remain robust after addressing endogeneity concerns via the 2SLS method, substituting spatial distance with temporal distance, and controlling for housing prices and cultural factors. Heterogeneity analysis further indicates that individuals with bachelor’s degrees, those above age 30, and talent in labor-intensive industrie",0.0,https://www.mdpi.com/2071-1050/18/1/476/pdf766c08f9b77b9a321bb9ac0d345e76a0d260a53dbf5,10.21776/ub.rekayasasipil.2025.019.01.15,Domain of Property Value Determinants in Urban Areas,2025.0,Rekayasa Sipil,G. Artiani,"The construction industry has a strategic role in supporting the development of urban areas through the development of infrastructure, housing, and commercial space. However, rapid urbanization and the complexity of managing construction projects create challenges in determining optimal property value. Although many studies have been conducted on property values, studies specifically addressing the interaction between construction management and the determinants of property values in urban zones are still limited, especially in Indonesia. This study aims to identify and analyze various factors that affect property value from the construction management perspective through integrative literature studies. The analysis revealed 62 factors grouped into four main domains: types, causes, impacts, and solutions. Dominant factors such as land prices, transportation accessibility, and project sustainability significantly influence property value dynamics. In addition, the quality of construction implementation, technology selection, and project risk management are also key elements that affect the success of regional development. This identification provides strategic insights for construct",0.0,https://doi.org/10.21776/ub.rekayasasipil.2025.019.01.157679ffffe0721c52b2ba25a802ac4f09ff4886bb22f,10.4236/ojbm.2022.105129,The Crowding out Effect of China’s Real Estate Price Rise on Processing Trade,2022.0,Open Journal of Business and Management,Yan Xiao; Xinyi Yuan,"In view of the central government’s proposal that “The house is used for liv-ing, not for speculation”, the rising market of China’s real estate market has been flattened. However, the reflection on the impact of China’s real estate investment on the real economy cannot be ignored. Through the econometric regression model, this paper analyzes the impact of China’s real estate prices on processing trade by using random and double fixed effects. The results show that: there is a strong correlation between housing prices and processing trade; there is a significant inverted U-shaped relationship between house prices and processing trade, and the corresponding inflection point prices of the negative impact of house prices on processing trade are calculated; and the impact of house prices on processing trade in the east, the middle and the west is significant. The crowding out effect of housing prices on processing trade in the east is the most significant, followed by the west, and finally in the central region. Therefore, the central region has formed a good low-lying land effect of processing trade transfer. The high quality destination of processing trade should be the central regio",0.0,https://doi.org/10.4236/ojbm.2022.105129768bfc188982bf9550c8a1189743a55de7f9cc1948e,10.26466/opusjsr.1638632,Speculative House Price Bubble Dynamics and Bursting Mechanisms in Türkiye: A Threshold Regression Model Proposal,2025.0,OPUS Journal of Society Research,Mehmet Kuzu,"One of the most current social and socio-economic problems in Türkiye is the speculative bubbles that have formed in the housing markets due to the current economic and financial crisis, making it difficult for households to own a house. This study analyzes the dynamics and bursting mechanisms of speculative price bubbles in the Turkish housing market. The GSADF test was applied using monthly data for 2012-2024 to identify bubble periods in the housing market. In the determined bubble periods, robust regression analysis was performed to determine the main factors affecting these price formations. In addition, the Kalman filtering method was used to examine the dynamic coefficients of housing price bubbles that change over time. The findings show that falling interest rates trigger housing price bubbles in Turkey in an inflationary process; the bursting of the bubbles is associated with increasing domestic and foreign risk premiums, interest rate expectations, and realized interest rate increases. In addition, it investigated how the current housing bubble can burst in a controlled manner through the threshold regression model. The study results reveal that maintaining the interest ",0.0,https://dergipark.org.tr/en/download/article-file/46039877695b5e2976379a85f5f812cb8768013d369656f0c3,10.1108/ijhma-12-2025-0311,Exploring the macroeconomic and demographic determinants of housing price dynamics in BRICS nations,2026.0,International Journal of Housing Markets and Analysis,Chanchal Dey,"770 771 This study aims to explore the macroeconomic and demographic factors that influence housing price dynamics in the BRICS (Brazil, Russia, India, China and South Africa) nations. In these emerging economies, stakeholders must understand the macroeconomic and financial factors driving housing markets.772 773 774 775 This study includes a balanced panel data set of five BRICS countries spanning 11 years (2014–2024). It uses static panel data econometrics to evaluate the determinants of Housing Price Index (HPI) as specific explanatory variables. These include Gross Domestic Product (GDP) per capita, inflation rates, unemployment rates and population changes. Diagnostic tests for endogeneity and serial autocorrelation were conducted to ensure reliability.776 777 778 779 GDP per capita has an insignificant effect on housing prices across BRICS countries, even after controlling for country-specific factors and endogeneity. Inflation exerts a positive and statistically significant impact on housing prices. It supports the view that housing serves as a hedge against inflation in emerging markets. Unemployment shows a negative and statistically insignificant relationship with housing prices. It suggests",0.0,7809fc7f273148f53dfdadaf431a4a601086ec65656,10.1088/1757-899x/688/5/055082,Spatial Changes of Urban Housing Prices: Analysis of Traffic Costs Based on Taiyuan,2019.0,IOP Conference Series: Materials Science and Engineering,Hua-Ping Zhao; Xu Wei,"In the modern society, traffic costs have become a key factor for residents to choose housing with increasing congestion for urbans. The interaction between housing prices and traffic costs is getting closer. The convenience of traffic and the distance from the schools, hospitals and commercial centres will affect the traffic costs of residents, which in turn will affect the housing prices in urban. In terms of traffic convenience, this paper not only uses the number of bus stations within 1km to measure the bus convenience of housing, but also uses the number of lanes adjacent to the main street to measure the accessibility of street frontage. In terms of medical facilities, while considering the number of surrounding hospitals, the quality of hospitals is distinguished, which makes the traffic convenience of housing and the level measurement of medical facilities more comprehensive and the research results more reasonable and reliable. Through the empirical study of Taiyuan, Shanxi Province, the results show that the traffic costs can explain the price level of 41.7%. For each additional bus station near the residential area, the housing price will increase by 2.1%. For every str",0.0,https://iopscience.iop.org/article/10.1088/1757-899X/688/5/055082/pdf7814346833a1ebd266fd76fc9820eb1c5c2addd6748,10.1093/sleep/zsae067.01094,1094 Who Gets the Stim?: A Demographic Analysis of Hypoglossal Nerve Stimulator Patients,2024.0,Sleep,L. Magaña; R. Soose; Kristen Kurland; T. Harrison; Angela L. Mazul,"782 783 784 In addition to traditional demographic analysis, Geographic Information System (GIS) tools, provides a unique methodology that allows us to spatially analyze data and capture geographic data related to HNS patients. This study seeks to create geographic maps related to socioeconomic attributes of HNS patients in addition to relevant demographic and clinical data to better understand potential barriers to care for those seeking treatment for obstructive sleep apnea (OSA).785 786 787 788 A retrospective study was performed of patients who underwent HNS implantation at a tertiary care academic hospital from August 2011 to October 2021. Relevant demographic and clinical characteristics were captured for each patient, both pre- and post-operatively. GIS was used to geocode patient addresses and perform spatial analyses to capture socioeconomic dynamics that may produce barriers to care for vulnerable populations. Maps were produced identifying distribution of patients relative to median household income, Area Deprivation Index (ADI), and drive times to the hospital.789 790 791 792 229 patients were included in this study. 167 patients were male and 62 were female. 222 patients implanted during th",0.0,793a9391d84db81da6886df645e89d087203c33c309,10.3390/su18031580,Airport Proximity Effects on Residential Property Values: Market Benefits of Multimodal Accessibility,2026.0,Sustainability,Edmund Zolnik; Rio Schondelmeyer,"Proximity to transportation infrastructure is an amenity that adds market value to residential property. However, not all infrastructure adds market value equally. For example, the empirical evidence suggests proximity to rail infrastructure adds more market value than proximity to pedestrian infrastructure, even though surveys show a willingness on the part of buyers to pay for the latter. Further, proximity to transportation infrastructure such as airports may subtract market value from residential property due to the disamenity of noise. In order to isolate the amenity or disamenity effect of proximity to airports, this study adopts a multilevel approach to residential property transactions, which encompasses the complement of air, bus, pedestrian, and rail infrastructure in an entire region. Analysis of thirteen years of residential property transactions near two international airports advances knowledge on the amenity effect of proximity to airports in light of the amenity effect of pedestrian infrastructure. Unsurprisingly, subregional differences in the amenity effect of proximity are evident due to differences in land use near the respective airports. The latter result sugg",0.0,https://www.mdpi.com/2071-1050/18/3/1580/pdf?version=17702805137943b38c838e386ece3d532ae0dacb0719ea3e19efd,10.56201/ijgem.vol.11.no11.2025.pg153.166,"Geospatial Analysis of Residential Land Value Distribution for Urban Planning, Development, And Equity in The Federal Capital City, Abuja, Nigeria",2026.0,IIARD International Journal of Geography and Environmental Management,Dr. Innocent E. Bello,"This research addresses gaps in the study of property value determinants in Abuja, Nigeria, where previous analyses have focused on other cities like Lagos and Port-Harcourt, usingstatistical techniques. These studies overlooked geospatial analysis, which is critical forunderstanding land value distribution in Abuja, given the absence of comprehensivenationwide land transaction data. This study aims to map and analyze the geospatial795distribution of land values in Abuja, incorporating the concept of land value increment tax, central to equitable resource sharing. Key factors influencing land values examinedincludeland use, proximity to the Central Business District (CBD), amenities, infrastructure, andhousing quality. These factors were examined using advanced geospatial tools to determinetheir impact on land values across Abuja’s cadastral zones. The study used GeographicallyWeighted Regression (GWR) analysis to explore the relationship between land values andthese determinants, of ering insights into ef ective land value taxation policies. By mappingspatial land value distribution and identifying neighborhood attributes af ecting landprices, the research provides a foundation for ",0.0,https://iiardjournals.org/get/IJGEM/VOL. 11 NO. 11 2025/GEOSPATIAL ANALYSIS OF RESIDENTIAL 153-166.pdf796ed99b37c7af3ace53f774ae8ae7a8a0c3470df05,10.3390/buildings16193975,Whose Streets Are Greener? Assessing the Effects of Housing Attributes and Property Management on Green View Index in Urban Neighborhoods,2026.0,Buildings,Yi-Zhuo Sun; Yu-Wei Zhai,"Street-visible greenery is a routine form of environmental exposure, yet its unequal distribution is often interpreted mainly through contemporary socioeconomic or built-environment conditions, with limited attention to historical housing development and community management. This study investigates how unequal green exposure emerges from the combined influence of historical housing development and community management. Using street-view imagery, household statistics, housing provision attributes, property management structure, and built-environment data for 155 communities in Xiamen, China, we combined spatial mismatch analysis with bivariate tests and multivariable regression. Results reveal a pronounced spatial mismatch: 23.9% of communities contained 42.3% of households but exhibited below-median GVI. Furthermore, modeling demonstrated that neither communities’ mean housing price nor commodity housing share was significantly associated with GVI. Instead, higher GVI was associated with older building age (β = 1.90 per decade, p < 0.05), work-unit housing (β = 1.58, p < 0.01), and market-oriented property management (β = 0.64, p < 0.001), whereas road density was negatively assoc",0.0,https://doi.org/10.3390/buildings16193975797f201b4fe78ca8bee03ebccb60167a51b1fe7f5d0,10.54254/2754-1169/2026.ld36663,A Review of House Price Regression Prediction Methods,2026.0,"Advances in Economics, Management and Political Sciences",Xiao-Fei Cai,"Real estate is an important sector of the national economy. Fluctuations in housing prices affect residents' lives, financial security and the overall economy. Accurate prediction of housing prices can provide support for market entities' decision-making and government housing regulation. With the development of econometrics and machine learning, housing price regression prediction has gradually evolved from traditional characteristic price models to ensemble learning. This paper classifies existing prediction methods into traditional econometric methods, classical machine learning, hybrid and deep learning. It analyzes the principles, applicable scenarios and advantages and disadvantages of each method, and summarizes empirical conclusions from feature engineering - multi-source data fusion and the model applicability in different market environments. The results show that tree ensemble models such as random forest and XGBoost generally have better prediction effects than traditional linear regression. Hybrid models and multi-source data fusion further enhance the ability to predict housing prices. Currently, there are still problems in this field such as limited data quality, wea",0.0,https://aemps.ewapub.com/article/view/36663.pdf7988df71f818af16b5ce055a4247e58d7652980bffb,10.1109/icrito66076.2025.11241262,"CRISP-DM-Based Residential Property Valuation in Bolton, UK: Comparative Analysis of Supervised Machine Learning Models",2025.0,"2025 12th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)",E. G. Nwibo; Anchal Garg; Pradeep Hewage; Sarika Jain,"The boom in the real estate market necessitate easier and modern prediction models to accurately estimate properties' values. In this research, seven machine learning algorithms for house price prediction on residential houses in Bolton, Greater Manchester, were evaluated to cover the research gap in local UK market research. Following the CRISP-DM guideline, analysis of 13,933 transactions of 2,930 unique properties over a period of 12 months based on 17 property attributes with GDPR compliance were made. The pipeline for preprocessing end-toend comprised correlation analysis, detection of outliers using IQR, log transformation for skewness correction, and Min-Max normalization. Seven different models were experimented with: Linear SVR, Polynomial SVR, RBF SVR, K-Nearest Neighbors, Decision Tree, Random Forest implementations (RF-100, RF-200, RF-500), and XGBoost. Performance assessment with 70/30 traintest split showed Random Forest with 500 estimators yielded superior results ($R^{2}=0.93$, MAE $=\mathbf{0. 1 0 8 6}$, MSE $=\mathbf{0. 0 3 3 2}$, RMSE $=0.1822$), then XGBoost ($R^{2}=0.92$). Total Living Area was the superior predictor ($r=0.67$), confirming rudimentary real esta",0.0,https://ub-ir.bolton.ac.uk/view/delivery/44UOBO_INST/1210424020008841/1310948490008841799759847f2564665b7b3cb3d4850594bd12a1f9759,10.15678/pg.2025.72.2.00,Introduction,2025.0,Journal of Public Governance,Angelika Wodecka-Hyjek; Paweł Chlipała,"Urban management encompasses many problem areas that shape residents’ quality of life. A focus on the quality of life is gaining importance alongside the growing awareness of public administration employees, managers, as well as residents of urban agglomerations in the field of sustainable development and social responsibility. Government institutions, local authorities, and enterprises increasingly recognise the benefits associated with societal well-being. A well-managed city provides its residents with opportunities for comfortable living, rest, and personal development, while these factors enhance employee productivity and attract investors and tourists, too. At the same time, residents’ expectations are rising with regard to access to housing, adequate transport infrastructure, educational opportunities, cultural offerings, leisure and recreational services, and many other aspects. The voices of residents, voters, and stakeholders – all of whom may shape the tone of the debate on urban management strategies – are becoming increasingly significant.800This issue of the Journal of Public Governance addresses three areas of urban management, covering topics related to social servic",0.0,https://publicgovernance.pl/zpub/article/download/773/53480190373a6e92b60a58ff91517033ea0a101729345b,10.53383/100390,Equity Release Mortgages in the UK: Regional Characteristics of Demand and Supply,2024.0,International Real Estate Review,Norman E. Hutchison; A. Koblyakova; Bryan MacGregor,"We consider the factors that affect regional variations in equity release mortgages (ERMs) in the United Kingdom (UK). These are a growing section of the market, but with regional differences in factors that affect demand and supply. We estimate a time series cross-sectional (TSCS) model within a simultaneous equation framework to analyse the number of ERMs (a proxy for the demand for ER debt) and the average loan-to-value (LTV) ratios, a proxy for supplied loan terms. We include variables that cover demographics, demand, pricing, risk, macroeconomic factors and regional dummies. For the number of ER loans, we find positive effects from net pension income and holiday costs, and negative effects from the average LTV ratio levels, state benefits, first-time buyer interest rates, female life expectancy, mortgage price differential, house price growth and gross value added (GVA). For the average LTV ratios, there are positive effects from gross pension income, average borrower age, holiday costs, and the risk premium, as well as negative effects from the number of ERM loans, female life expectancy, and the level of real house prices. Overall, households who are living in southern, weal",0.0,https://doi.org/10.53383/100390802f4b430fcdfa0fbfbd31927f00f573185ada8d92a,10.20319/icssh.2026.272303,IMPACT OF SPATIAL FACTORS ON REVENUE AND EXPENDITURE OF HOUSING MAINTENANCE FUND: A CASE STUDY IN BEIJING,2026.0,PEOPLE: International Journal of Social Sciences (ISSN 2454-5899),Wei-Zhen Zhao,"Housing maintenance is an integral component of sustainable urban governance. However, the revenue and expenditure of housing maintenance fund often vary across regions due to socioeconomic, built-environment, and ecological settings. The study of its spatial heterogeneity can be critical for understanding regional disparities, optimizing resource allocation, and improving governance efficiency. This study proposes categorizing research samples based on functional zones, 275 subdistricts/townships within three functional zones of Beijing were examined to explore the influence mechanisms of spatial factors on maintenance fund revenue and expenditure, then compares ordinary least square (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) models in analyzing the impact mechanisms of spatial factors. The conclusions are as follows: (1) Functional zones coincide with sample categories, showing significant differentiation and spatial autocorrelation, which underscores the role of Beijing's master plan. (2) MGWR performs better than other models: influence factors exhibit spatial heterogeneity in maintenance fund, GDP exerts the most s",0.0,https://grdspublishing.org/index.php/people/article/download/3135/2958803f7b72366e648e4477be61d38a2eab9263211f43a,10.22214/ijraset.2023.50307,Dream House Price Predictor,2023.0,International Journal for Research in Applied Science and Engineering Technology,Abhiraj Singh; K. Chand; Susheela Singh; Kamal Soni,"Abstract: The dream house price predictor project aims to build a machine learning model that can predict the selling price of a house based on various features such as location, number of bedrooms, square footage, and other relevant factors. The model will be trained on a dataset of historical housing prices and features, and will use regression techniques to make predictions on new, unseen data. The project will also explore the impact of different features on house prices and provide insights into which factors are the most important in determining the value of a property. The goal of the project is to provide a tool for homebuyers, sellers, and real estate professionals to better understand the market and make informed decisions. The Dream House Price Predictor project is aimed at predicting the prices of residential properties based on various features such as location, size, number of bedrooms, and other amenities. The project uses a dataset of real estate transactions and employs machine learning algorithms to build a predictive model. The model is trained on the historical data and tested on a validation set to ensure its accuracy. The results of the project can be used by ",0.0,http://dx.doi.org/10.22214/ijraset.2023.50307804475749267b1a9e1b1a237153939e2077d6cbbb7b,10.1061/jtepbs.teeng-9862,Spatial Heterogeneity and Scale-Dependent Determinants of Public EV Charging Station Utilization: An Integrated Semantic-Spatial Approach,2026.0,Journal of Transportation Engineering Part A Systems,Peng-Fei Zhao; Xu-Yang Li; Jian-Xin Lin; Jie Liu,"805 806 The growing gap between the rapid expansion of public electric vehicle (EV) charging infrastructure and its realized utilization efficiency poses a critical challenge to sustainable urban mobility. Existing studies’ reliance on macrolevel aggregates or single-dimensional metrics fails to capture fine-grained spatial heterogeneity or disentangle the interacting multiscale mechanisms that drive charging demand. This study proposes an integrated semantic–spatial analytical framework to quantify the determinants of utilization at the subdistrict scale. Using a multisource dataset for Beijing, China, we first identify urban functional zones through a Word2Vec-based semantic classification approach. We then combine the Optimal Parameter Geodetector and multiscale geographically weighted regression (MGWR) to detect dominant factors, interaction effects, and scale-dependent spatial relationships. The results show that charging station utilization is jointly shaped by demand intensity, urban functional context, and charging-supply configuration rather than by facility supply alone. A larger number of charging stations does not necessarily correspond to higher utilization. The subdistric",0.0,8073fb09abb92f29e4cd3cd190b8cba4423417bb9f0,10.1007/s42413-026-00296-1,Northend Neighborhood Livability By Residents’ Life Conditions,2026.0,International Journal of Community Well-Being,Andy J. Kim; Jason Ware,"Policymakers hoping to revitalize struggling communities often struggle with a similar concern: high rates of mobility among dissatisfied resident obscure the true effectiveness of new policies and community initiatives. In order to circumvent this issue, this study in partnership with the City of Lafayette observes a sample of Northend residents from 2018 to 2024 including residents that have moved out of the Northend and residents that have stayed. Analyses includes a simplified classification system modeled after a similar study by the Annie E. Casey Foundation and linear regressions comparing life conditions over move status. By identifying the various types of Northend residents and comparing the differences in resident status between 2018 and 2024, these analyses can indicate how the livability of the Northend is impacting its residents. Results indicate that most movers can be classified as up-and out movers, improving their life conditions, and most stayers can be classified as long term stayers that do not intend to move. While the vast majority of residents experienced improvements to life conditions between 2018 and 2024, mover residents saw significantly greater increas",0.0,https://link.springer.com/content/pdf/10.1007/s42413-026-00296-1.pdf808855b11a877501420b04b4cbea1c2aa69d7f41c35,10.15316/sjafs.2019.170,The Approaches of The Contracting Companies to The Housing Landscape in Konya Province,2019.0,,Bahadır Ekici; A. Polat,"Today's residences show highly functional and qualified formations and offer many functions to their users. One of the most important factors that increase the housing function and aesthetics is the housing landscape. In this study; the opinions and experiences of the contractor companies that have implemented housing projects in line with the supply and demands of the users have been determined. A survey was conducted through face-to-face interviews with 97 house producer company officials affiliated to Konya Contractors Association. In the first part of the questionnaire, the approaches to projecting the housing landscape and the factors that affect the project process are questioned. In the second part, landscape application approaches, various professional groups in the application stages, quality and characteristics of the application are presented. In the third section, the economic and financial evaluations of the residential landscape are made and the values that the landscape adds to the housing through projects and applications are determined. In the last part of the questionnaire, the satisfaction levels of the contractors and their customers were measured based on the e",0.0,http://sjafs.selcuk.edu.tr/sjafs/article/download/1111/898809978f9121061a15392bd14d6b0dab647085c86b41,10.46336/ijqrm.v6i4.1143,The London Exodus: A Spatial Analysis of Public Health Shocks and Economic Drivers of Internal Migration During the COVID-19 Pandemic,2026.0,International Journal of Quantitative Research and Modeling,Alexander Eko Nugroho; Zaki Ahmad; Muhammad Rizan Ramandika,"The COVID-19 pandemic in 2020 triggered a significant urban exodus in major global cities, shifting internal migration patterns in unprecedented ways. This study investigates the determinants of internal net migration across 33 London boroughs to understand the drivers behind this population displacement. Utilizing a spatial econometric framework, the research integrates public health indicators (COVID-19 cases), economic variables (housing prices and GDP), and geographic characteristics (population density and distance to the city center) into an enriched Ordinary Least Squares (OLS) model. The methodology is validated through rigorous diagnostic testing, including Heteroskedasticity-Consistent (HC1) standard errors and spatial autocorrelation checks (Moran's I), confirming that spatial feature engineering sufficiently captures geographic dependencies. The results reveal that the public health shock was the primary driver of the exodus; total COVID-19 cases exhibited a highly significant negative correlation with net migration, indicating that boroughs with higher infection burdens experienced larger population outflows. Population density acted as a secondary push factor, while t",0.0,https://journal.rescollacomm.com/index.php/ijqrm/article/download/1143/904810721444f5149e0a7acb0af49f01833e82b405ae18,10.1108/jes-12-2024-0856,Enhancing property value prediction: a regression model with fine-grid geographic segmentation,2025.0,Journal of Economic Studies,Hongjai Rhee,"PurposeWe propose a novel scalable regression model that incorporates two-dimensional location dummies based on longitudinal and latitudinal coordinates. This approach easily achieves comparable levels of explanatory power to ML models while avoiding the interpretability challenges inherent in non-parametric ML techniques.Design/methodology/approachRegression models have long been a fundamental and effective toolkit in economic analysis, facilitating the validation of theoretical predictions and the examination of policy effects. However, their explanatory power is often constrained by linearity constraints or limited information, which can, in turn, distort the estimated marginal effects of explanatory variables. While machine learning (ML) models can serve as an alternative, their improved fit often comes at the cost of increased complexity in interpretation and inference. This paper proposes that incorporating geocoded information into the regression models for property values can significantly enhance explanatory power to a level comparable to ML models while preserving the simplicity of regression approaches. As a case study to validate the proposed method, we examine the impa",0.0,811995bafb8146275783f886ceafdc03ff93dcaa4bd,10.47194/ijgor.v6i1.362,Analysis of the Influence of Inflation on the Real Value of Deposits on Property Purchase Accessibility,2025.0,International Journal of Global Operations Research,M. Zuhri; Fidela Wanda Ammarilia,"Inflation is an economic phenomenon that has a significant impact on people's purchasing power. When the inflation rate increases, the real value of money saved in the form of savings, including deposits, tends to decrease. However, when inflation exceeds the deposit interest rate, the real value of these savings will be eroded, affecting customers' ability to invest, including in purchasing property. The increase in property prices that has continued to occur in recent years has added to the challenges for people in obtaining a house or other property. Uncontrolled inflation can affect the accessibility of property purchases, because property prices often rise faster than the growth rate of deposit savings. Therefore, this study was conducted which aims to analyze the relationship between inflation and deposit interest rates offered by banks and how inflation affects the real value of money saved in the form of deposit savings over a certain period of time. This study uses a quantitative method with descriptive analysis techniques and multiple linear regression for analysis purposes. The results of the study show that high inflation reduces people's purchasing power and reduces th",0.0,https://doi.org/10.47194/ijgor.v6i1.362812b1a372bfad6e8b9663c651a4111d1c4487c5da12,10.62311/nesx/rp-8-jan-2024,Evaluating Housing Development through the Lens of Climate Vulnerability and Energy Sustainability,2024.0,International Journal of Academic and Industrial Research Innovations(IJAIRI),Murali Krishna Pasupuleti,"Abstract:813814Urban housing decisions face twin pressures of escalating climate hazards and rising energy burdens. This study develops an integrated evaluation framework that couples spatially explicit climate vulnerability with energy sustainability metrics for new development and retrofit pathways. Conceptually, exposure–sensitivity–adaptive capacity constructs are synthesized with building- and grid-level performance indicators to form a joint Housing Climate–Energy Index (HCEI). The central problem addressed is the absence of policy-ready tools that compare candidate projects on resilience, efficiency, affordability, and emissions under real-world constraints. Methodologically, a mixed, data-integrative design is employed: hazard layers, parcel and permit records, utility billing aggregates, and socio-economic statistics are linked; multi-criteria decision analysis is combined with spatial econometrics to estimate joint burdens and scenario effects, with uncertainty and equity stratifications. Results from the empirical application indicate that code-plus design standards and targeted retrofits in heat- and outage-prone tracts can reduce the HCEI by roughly one-quarter to one-thir",0.0,815d24d9bc96f77f2391589f95e444baa19bd7bea70,10.1111/1467-8462.70055,Temporary Employment and First‐Time Homeownership in Australia,2026.0,Australian Economic Review,Inga Laß,"816 817 818 How does temporary employment, that is, fixed‐term contract and casual employment, affect the transition into first‐time homeownership among young people in Australia? Does the effect differ by employment type, gender, relationship situation, or parents' socio‐economic status?819 820 821 822 Both fixed‐term and casual contracts are associated with a reduced likelihood of entering homeownership; however, the association is significantly stronger for casual contracts. Associations are also significantly stronger among unpartnered people and (in the case of casual work) among men. By contrast, there are no differences by parental socio‐economic status.823 824 825 826 The employment and income insecurity associated with temporary contracts in combination with high house prices are a significant contributor to young people's low homeownership rates. Policies fostering transitions into permanent employment besides measures supporting first‐time homebuyers, increasing housing supply and strengthening renters' rights could help improve the housing situation of young workers.827 828 829 830 The study used longitudinal data from 12,689 individuals aged 18–39 who were interviewed in the Household, Income and L",0.0,https://doi.org/10.1111/1467-8462.700558312e52b8120d504cddfa568dcf1f968646f2fe6dea,10.62051/e9x5cc73,Predicting House Prices Using Machine Learning Models,2025.0,Transactions on Computer Science and Intelligent Systems Research,Tianyi Zhao,"Predicting house prices is crucial for stakeholders such as buyers, developers, financial institutions, and policymakers. This paper explores how machine learning models can help with house price prediction. Using the Ames Housing Dataset, we compare three models: linear regression, decision tree, and random forest. The goal is to understand which model gives better results in terms of accuracy and how well it works with new data. Data preprocessing, including imputation for missing values, standardization, and feature selection, was performed to ensure compatibility with the models. The models were tested using three common evaluation tools: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the R² score. The results show that the random forest model performs the best. It has the lowest error and the highest R² score among the three. This is because random forest uses many decision trees and combines their results, which helps reduce overfitting and improves prediction. The study highlights the potential of machine learning in improving house price prediction accuracy and discusses future avenues for model optimization. These include incorporating real-time data, enhanc",0.0,https://wepub.org/index.php/TCSISR/article/download/5534/606583205df52818e23dcb4bcedaf3dde62b94b9112b483,10.1002/psp.70347,"Late‐Life Mobility in a Rapidly Ageing Middle‐Income Country: Spatial Patterns and Local Demographic Change in Brazil, 1980–2010",2026.0,"Population, Space and Place",F. A. I. González; Jean Sleifer,"How do older adults reshape the spatial demography of a middle‐income country undergoing one of the fastest population‐ageing transitions on record? Brazil offers a particularly informative case: its share of population aged 60 or above rose from 6.2% in 1980 to 10.8% in 2010 and is projected to reach 25% by 2050, and a nationally administered pension regime makes benefits fully portable across the country's 5565 municipalities. Using large‐sample microdata from four Brazilian Demographic Censuses (1980–2010, 84 million records harmonised to 4298 stable comparable areas) linked to municipal economic accounts, we document a senior counter‐current with the hallmarks of counter‐urbanisation. Senior inter‐area mobility declines with age yet remains substantial through retirement ages, and it is not local boundary‐hopping: the median senior move covers roughly 100 kilometres and nearly a third crosses state lines. Older Brazilians move against the working‐age tide—the largest metropolitan areas lose retirees on net, while small and medium‐sized interior destinations receive them at rates reaching 5%–6% of initial population per 5‐year window. Receiving areas expand demographically—in to",0.0,833e06a8e55f9314e39810755069c2b422ccbe42087,10.26459/hueunijard.v129i3c.5986,"ASSESSMENT OF FACTORS AFFECTING REAL ESTATE PRICE IN HUE CITY, THUA THIEN HUE PROVINCE",2020.0,HUE UNIVERSITY JOURNAL OF SCIENCE AGRICULTURE AND RURAL DEVELOPMENT,Bình Văn Nguyễn; Hoang Dao Nhat Linh; Tuyết Thị Ánh Trần,"This study focuses on determining the impact of factors on property prices in Hue city, Thua Thien Hue province. The study used quantitative and qualitative analysis in SPSS to statistically characterize the investigated subjects; using Likert scale with five levels and regression method to evaluate the influences of relevant factors on real estate; surveying 115 samples in 03 wards of An Tay, An Cuu and An Dong of Hue city. The results showed that, 6 factors affecting real estate prices were identified, including: (i) Location, (ii) Topographic and shape, (iii) Legal, (iv) Social factors, (v) Infrastructure and (vi) Environment. The impact level of 6 factors on real estate prices are: Environment (0.416), Terrain (0.408), Location (0.225), Infrastructure (0.197), Legal (0.195) and Social factors (0.120). For factors affecting real estate prices, depending on the type of street that the factors affecting real estate prices have different roles. For streets with good business potential, the important factor is that the location of the land plot provides high profitability and stability leading to high buyer demand, followed by parcel shape factors, infrastructure conditions, living ",0.0,http://jos.hueuni.edu.vn/index.php/hujos-ard/article/download/5986/1162834285a73d537c17031d8625078e8935466f65472cc,10.62383/polygon.v1i2.854,Prediksi Harga Rumah dengan Regresi Linier,2023.0,Polygon,Fathoni Dwi Atmoko,"Property price determination is a complex challenge influenced by various factors, thus requiring an effective method for accurate prediction to support investment decision-making. In the current digital era, conventional approaches are being replaced by data-driven and artificial intelligence methods, where Linear Regression remains a popular choice due to its simplicity and effectiveness in modeling linear relationships. This study aims to analyze the relationship between the physical characteristics of a house and its selling price, and to build an accurate predictive model using the Linear Regression algorithm. A quantitative method was used, focusing on Building Area , Number of Rooms, and Building Age against the House Selling Price. Correlation analysis results show that Building Area has the strongest correlation (0.81) with price, while Building Age shows a negative correlation (-0.52). The Linear Regression model demonstrated very strong and stable performance. The model achieved an R² Score of 0.9396 on the testing data, meaning 93.96% of house price variability can be explained by the model. Furthermore, the low MAE of only 11.31 million rupiah indicates a small predic",0.0,https://journal.arimsi.or.id/index.php/Polygon/article/download/854/87883525c8f3b6a6b05bd6d7d06e05e7be9b6fca8327c8,10.55186/2413046x_2025_10_9_207,MATHEMATICAL METHODS FOR ANALYZING SUPPLY AND DEMAND FACTORS AND THEIR CONTRIBUTION TO HOUSING PRICE DYNAMICS IN RUSSIA,2025.0,Moscow Economic Journal,N. Muzylev; A. Starostina; L. Belousova,"Over the past few years, the cost of residential real estate in the Russian market of new buildings has shown rapid growth, significantly outpacing such economic indicators as the general inflation rate, the dynamics of income of citizens, rental prices and costs of construction companies. This trend has led to a doubling of average prices in the primary housing market since the beginning of 2020. The formation of prices for residential real estate is a complex process caused by the complex impact of numerous determinants. 836This study uses a modified version of the methodological approach originally developed by A. Shapiro in order to quantify the contribution of supply and demand factors to the observed housing price growth in the regions of the Russian Federation. This work is the first attempt in the academic literature to decompose the price changes for residential real estate in Russia from 2016 to 2024 into components determined by supply and demand, with a comparative analysis in the regional context. 837The calculations performed and the subsequent analysis provide new opportunities for interpreting the reasons for the rise in housing prices in various regions of the Russian ",0.0,https://ecience.ru/en/storage/download/2283048382d1b4cdf96ecdad48544e9f6c5eecc46d0834b70,10.15405/epsbs.2019.08.6,Assessment of Equilibrium Housing Prices in Penang After the Global Financial Crisis,2019.0,,Tze-Haw Chan,"Penang – a unique island-mainland state in the northern region of Malaysia, experienced an abnormal growth of 205.7% in the past 27 years. This has raised the concern of housing affordability and market disequilibrium. An equilibrium model is developed to assess the Penang housing prices in the post-global financial crisis period. Both fundamental factors and speculative demand are taken into accounts. In the long run, the ARDL cointegration estimation reveals that housing prices are mainly driven by building cost and speculative demand, whereas fundamental factors such as household income, inflation and interest rate are trivial in decision of property investment. In the short run, only speculative demand is of significant. The cointegration regression further detects multiple market disequilibrium during the study period of 2009-2016. Nevertheless, the speed of adjustment is slow, suggesting that market corrections will take about 3 years to complete. The empirical results have provided a concrete diagnosis of Penang property market that market disequilibrium is on the rise due to demand-supply imbalances and speculative investments. This shall provide an early warning to both th",0.0,https://doi.org/10.15405/epsbs.2019.08.6839f22377622bf5738294e171035bed6a58b5eb8373,10.1038/s41598-026-52995-6,IALA-BPNN: superior house price prediction through multi-strategy optimized Artificial Lemming Algorithm and BP neural network,2026.0,Scientific Reports,Qi-Feng Guo; Sheng-Peng Li; Tianqi Xia; Da-Ren Chen,"Against this backdrop of a global economic downturn, the real estate market exhibits characteristics of both a consumer good and an investment asset, with housing prices displaying fluctuating nonlinear trends. Predicting these trends holds significant importance for governments, developers, and individuals. Addressing the vulnerability of BP neural networks (BPNN) to initial weights and biases in housing price forecasting, this study enhances the Artificial Lemming Algorithm (ALA) by incorporating pinhole imaging learning, evolutionary mutation perturbation, and golden sine development strategies. This improved ALA is applied to update the BPNN network structure, establishing the novel IALA-BPNN housing price prediction model. Experiments using four public datasets compare IALA-BPNN against eight comparable hybrid models, validating the model’s success and the effectiveness of the three improvement strategies. Further comparisons with four machine learning models (RF, SVM, LSTM, CNN) demonstrate the model’s strong competitiveness in housing price forecasting. Finally, a Shanghai housing price dataset is constructed. Comparing IALA-BPNN with the top-performing models from the previ",0.0,https://www.nature.com/articles/s41598-026-52995-6_reference.pdf840ee55b5018946c3740b08987e6ce1cb0beb52cbd2,10.30871/jaic.v10i1.10718,The Comparative Analysis of K-Nearest Neighbors Algorithm and Random Forest Regressor for House Price Prediction in Bandung City,2026.0,JOURNAL OF APPLIED INFORMATICS AND COMPUTING,Dimas Yudhistira Ananda; Ferian Fauzi Abdulloh,"The rapid population growth and continuous urban expansion in Bandung have contributed to volatile and escalating housing prices, creating significant challenges for market transparency and affordability. This study aims to develop and evaluate machine-learning models to predict house prices in the Bandung region using a publicly available dataset consisting of 7,609 property records. Following the CRISP-DM methodology, the research includes data exploration, preprocessing (outlier handling using IQR, one-hot encoding, and feature standardization), model training, and performance evaluation. Two regression models K-Nearest Neighbors (KNN) Regressor and Random Forest (RF) Regressor—were compared through systematic hyperparameter tuning using Grid Search and Random Search techniques. The experimental results show that the Random Forest Regressor achieves the best performance with an R² score of 0.7838 and a mean absolute error (MAE) of approximately Rp 399.7 million, outperforming the optimized KNN model. Feature importance analysis also indicates that land area, building area, and location are the most influential predictors of property prices. The findings highlight the effectivene",0.0,https://jurnal.polibatam.ac.id/index.php/JAIC/article/download/10718/3427841871e09a43b07cddac0d3c99559043f1bd6c1a614,10.54254/2753-8818/2026.dl34409,Feature Attribution and Error Diagnosis of a Random Forest Housing Price Prediction Model Integrating SHAP and LIME,2026.0,Theoretical and Natural Science,Ruoxiang Wang,"Housing is one of the most basic needs of humanity globally, and fluctuations in housing prices have garnered significant attention. Currently, the scale of China's second-hand housing transaction market is rapidly expanding. Understanding the factors influencing second-hand housing prices can enhance transaction efficiency and achieve a balance between supply and demand. This article obtains the Ames dataset from Kaggle and selects the Random Forest (RF) model, which is a relatively mature model in the field of housing price prediction, for training. It integrates Shapley Additive exPlanations (SHAP) and the Local Interpretable Model-agnostic Explanation (LIME) to analyze the importance of housing features. The results indicate that the RF model exhibits good performance in predicting housing prices. The SHAP analysis reveals that the most significant global features influencing second-hand housing prices are the quality of building materials and decoration, followed by living area, and there is an interaction between these two factors. Furthermore, it utilizes LIME to perform local attribution on high-quality small-area samples in the test set. The results revealed that the local",0.0,https://tns.ewapub.com/article/view/34409.pdf8423abde54bc49f35be9716074955139ab78a65a9bf,10.2139/ssrn.4178922,Till Debt Do Us Part: Strategic Divorces and a Test of Moral Hazard,2022.0,Social Science Research Network,Yeorim Kim; M. Mastrogiacomo; Stefan Hochguertel; H. Bloemen,"We test whether households that face prospective home equity losses during a house price downturn use divorce to shed debt. We study the Dutch context, where qualifying homeowners can buy into a mortgage guarantee scheme that insures the lender against borrower default and transfers the risk to the public. Divorce is one of the major events that obliges the guarantor to repay outstanding residual debt after (foreclosure) sale. We argue in this paper that divorce is endogenous to holding underwater mortgages, and hence constitutes a choice that can be used for strategic use of the insurance. Using administrative data, we find a significant, 44% increase in the probability to divorce for households with an underwater mortgage. This effect is causal to being insured. The identification relies on a regression discontinuity design, that exploits the fact that the insurance is only available for properties with values below a legislated qualification threshold. The house price crisis (2008-2013) provides an unexpected shock to house values, leaving about 40% of owners with an underwater mortgage. Their home equity averages to about €-50.000. Couples with similar characteristics just abov",0.0,https://doi.org/10.2139/ssrn.4178922843f1763967be078080c87c32f4145271609164269e,10.55763/ippr.2025.06.03.001,Tales of a few cities,2025.0,Indian Public Policy Review,B. Debroy; Devi Prasad Misra,"844This paper extends a previously developed model for analysing passenger movement using Indian Railways Unreserved Ticketing System (IR-UTS) data to examine urban growth in Mumbai, Chennai, Delhi, and Kolkata. The study analyses suburban travel patterns on the Indian Railways network as an indicator of spatial urban expansion. While suburban travel differs from migration, it effectively reveals urban growth patterns. The research integrates geospatial earth observation data with housing property prices to explore relationships between population dispersion and real estate values. Combining processed satellite imagery with Land Use Land Classification (LULC) data enables mapping of urban growth directions. Key findings show suburban travel rebounded post-COVID lockdowns but remains below pre-pandemic levels, potentially due to changed transport preferences or emerging counter-magnets. The analysis examines passenger arrival trends from top origin districts for each metropolitan city to understand urban growth patterns and LULC changes in suburban areas. The methodology demonstrates how high-frequency railway passenger data can effectively track urban spatial expansion when combined ",0.0,https://doi.org/10.55763/ippr.2025.06.03.001845424587ce711a8485255c13fb37972200bd20ca9a,10.1080/09599916.2023.2175712,Empirical effects of the designation of milieu protection areas on the residential property market in Berlin,2023.0,Journal of Property Research,Lion Lukas Naumann; H. Lischke; M. Nadler,"ABSTRACT Milieu protection areas (MPAs) are a frequently used urban policy regulation method in large German cities such as Berlin or Munich. MPAs protect residents from displacement by restricting property rights in designated zones through limitations on modernisation and the conversion of rental apartments into condominiums. Policymakers expect this to have a price-dampening effect and slow gentrification while also anticipating corresponding effects on property markets. This study investigates the long-term empirical effects of these restrictions on the Berlin residential property market and examines how milieu protection affected the purchase prices and transactions of condominiums within Berlin’s MPAs and their surroundings. We relate transaction data from 1991 to 2019 with other neighbourhood characteristics, regress prices, and the number of transactions using geographic information systems and regression difference-in-differences models in different spatial submarkets both inside and outside of the MPAs. Results indicate that milieu protection reduces transaction activity in property markets and regulation has been ineffective in curbing price increases. Meanwhile, limitat",0.0,https://www.tandfonline.com/doi/pdf/10.1080/09599916.2023.2175712?needAccess=true846723d07b0b27f1f233211f481a27fa22d2ea80030,10.54097/fbem.v6i1.2423,Analysis of Cultivated Land Area and Urbanization Rate Based on Spatial Coordination,2022.0,"Frontiers in Business, Economics and Management",Guo-Jiang Zhang,"The study of the regional effects of urban competitiveness on housing prices and their impact is of great significance to real estate market operations, consumer purchases and government decision-making. This study takes 32 major cities as research samples, draws on the more mature urban competitiveness index system, selects 8 representative factors, collects panel data such as statistical yearbook, and uses principal component analysis regression equation to establish the influence of urban competitiveness on housing price. The model analyzes the extent to which major competitiveness factors affect housing prices. The results show that: 1 The regional GDP and the balance of household savings play a more important role in the change of housing prices. The impact of environmental factors on housing prices is gradually expanding. The impact of urban real estate development investment on housing prices is getting smaller and smaller. The tertiary industry creates comfort for urban life and has a greater impact on housing prices. The total population at the end of the year and the number of students in colleges and universities have a negative impact on housing prices; the total popula",0.0,https://drpress.org/ojs/index.php/fbem/article/download/2423/232484721a30395c73abf85ee06634a1732ff4a03f25e8e,10.33411/ijist/2025718397,Comparative Evaluation of Machine Learning and Deep Learning Models for Real Estate Price Prediction,2025.0,International Journal of Agriculture & Sustainable Development,Asad Ullah Khan; Muhammad Khateeb Khan; Usama Khan; Faheem Shaukat,"Accurate real estate price prediction is vital in informed decision-making for investors, policymakers, and stakeholders. This study evaluates various machine learning and deep learning models for predicting real estate prices using the House Prices 2023 dataset which contains 168,000 entries of Pakistani property data. In our proposed methodology we performed data preprocessing and features engineering to standardize the data. We performed extensive experiments by using Machine Learning (ML) and Deep Learning (DL) models on our preprocessed data. The model’s performance was evaluated based on the R-squared (R²) score and Mean Squared Error (MSE) metrics. Based on the provided metrics, the Decision Tree achieved the highest performance with an R² of 0.9968 and an MSE of 0.0021, followed by Random Forest with an R² of 0.990 and MSE of 0.0007. Similarly, other ML models like Gradient Boosting and XG Boost also outperformed by achieving (R² 0.9959, MSE 0.0028 R² 0.9747, and MSE 0.0170) respectively. In contrast,models like AdaBoost, Neural Network, and Convolutional Neural Network (CNN) showed comparatively lower performance due to the nature of the data. The study emphasizes that ens",0.0,https://journal.50sea.com/index.php/IJIST/article/download/1180/1700848ced04cd119a87e17bcde34c03e72cb757a32fda1,10.5220/0010074713601364,Analysis of Physical Factors and Housing Reputation Effect to Property Value in Citra Wisata Residence,2018.0,,C. D. Malahati; Khaira Amalia Fachrudin; Hilma Tamiami Fachrudin,": Housing has become a valuable marketing target nowadays, because housing can be used as an investment in the future in a relatively long time. Deciding the value of a housing property is based on physical attributes of a housing, such as location, facility, design, environment and housing reputation. Citra Wisata Residence is one of the oldest housing estates in Medan Johor Subdistrict, with its well-equipped and well managed housing, the values are increasing. By the increasing growth of new housing in this research area, Citra Wisata Residence still has a good image and keep its existence until today. The purpose of this research is to find out and analyze how big the location, facility, design, environment and housing reputation affect the property’s value. The population of this research were housing residents of Citra Wisata Residence, and based on Slovin formula, the number of samples are 82. Hypothesis testing uses multiple linear regression analysis using SPSS and STATCAL at a significance value of 5%. The result of this research indicate that location, facility, design, environment and housing reputation simultaneously have a positively and significantly affect the value",0.0,https://doi.org/10.5220/001007471360136484991097390bd6e3b4a1f3c6b1a1ad6d7ba687bb79a,10.3846/ijspm.2026.25982,"Strategic housing satisfaction and preferences among U.S. military personnel: Evidence from Camp Humphreys, Korea",2026.0,International Journal of Strategic Property Management,Heejung Park; Saejoon Oh,"This study explores off-post housing preferences and satisfaction among U.S. military personnel and civilian employees stationed at Camp Humphreys in South Korea—the largest overseas U.S. military installation. Using the Importance–Performance Analysis framework, the research identifies key factors shaping residential experiences and strategic housing needs in host communities. While attributes such as English communication and clarity of lease agreement support resident satisfaction, unmet expectations regarding parking and shared facility management highlight critical service gaps. However, beyond individual preferences, the findings carry broader implications for real estate market responsiveness and urban policy. The structured nature of military housing allowances minimizes price sensitivity, suggesting that quality, service reliability, and cultural adaptability hold greater value for this tenant segment. These insights point to opportunities for local governments and developers to differentiate through certified multilingual real estate services and tailored infrastructure planning. By aligning investment strategies with the unique needs of foreign military populations, stak",0.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/25982/133078509673a80ee471309f5c9590eb6d49ac41f23e3fb0,10.12928/optimum.v14i2.9451,COVID-19 pandemic and housing demand mortgage: Partial adjustment model approach,2025.0,Optimum: Jurnal Ekonomi dan Pembangunan,F. Ayuningtyas; Anggrismono Anggrismono,"One of the efforts to encourage the fulfillment of housing needs is the provision of housing both from the government and the private sector by considering the community's purchasing power. The background of this research is that the fulfillment of housing needs by both the government and the private sector has yet to be maximized while the demand for housing is relatively high. This research is still necessary because the demand for housing is still high. Increased housing demand will push house prices due to limited housing land. The problems faced focus on the relatively high demand for mortgages because even in the conditions of the COVID-19 pandemic, people still make houses their top priority. This study aims to analyze the effect of people's income, loan interest rates, the amount of housing finance, and dummy before and during the COVID-19 pandemic on housing demand mortgage. The data analysis technique in this study used multiple regression Partial Adjustment Model (PAM). The results showed that the variables of income as proxy for GDP and loan interest rates significantly affected housing demand mortgage. In contrast, the amount of housing finance and the COVID-19 dummy h",0.0,https://doi.org/10.12928/optimum.v14i2.94518513d2efe2a44db6b03a37ca2503734feff28fe676a,10.1007/s44199-025-00122-1,Conditionally Autoregressive Models for House Price Data: Insights from a Comparative Simulation Study,2025.0,Journal of Statistical Theory and Applications (JSTA),I. P. Kinasih; Haziq Jamil; Elvynna Leong,"The modelling of property prices has been extensively studied in econometrics, with widely used approaches including generalised linear regression and geographically weighted regression. These models commonly address local spatial correlations observed in property price data. However, despite its potential to capture spatial effects, the conditional autoregressive (CAR) model remains underutilised for this purpose. This study examines the robustness and predictive power of the CAR model, comparing it with established spatial models across three different datasets generation. An illustrative case study on Lombok house price data is also included. Simulation results showed that the CAR model demonstrates a distinct advantage, achieving lower bias and variability compared to other spatial regression models, effectively capturing neighbourhood-based spatial relationships, and exhibiting strong predictive power across various scenarios. In the Lombok case study, the CAR model outperformed other models, providing more precise estimates for property-related factors such as land size and built-up area. The results confirm that CAR’s spatial framework enables a nuanced analysis of property ",0.0,https://link.springer.com/content/pdf/10.1007/s44199-025-00122-1.pdf85289064e14ca81778fe397c9a655a930b5be3c503d,10.31539/costing.v7i5.11658,"Home Ownership Dynamics: Analysis of The Impact of Gold, Bonds and Stocks on Property Investment Moderated by Loan Interest Rates",2024.0,Journal of Economic Bussines and Accounting (COSTING),Harsono Yoewono,"The purpose of this study is to examine how shares, bonds, and gold affect real estate investment, which is influenced by loan interest rates. In this study, the data type used is quantitative. Secondary data in the form of residential property price index data, bonds, stocks, loan interest rates and gold prices were sourced from Bank Indonesia. On a monthly basis, sampling employed a time series data method. Multiple linear regression is the data analysis technique employed in this study, which makes use of the E-views software. The study's findings indicate that gold has little bearing on real estate investments. Bonds have an impact on real estate investing. This demonstrates how interest rates or the inclination to invest in real estate can be impacted by an increase in bonds. Increase in shares can influence the interest or tendency to invest in property. Loan interest rates cannot moderate the influence of gold on property investment. Loan interest rates can moderate the effect of bonds on property investment. The results of this research show that there is a negative correlation, meaning that loan interest rates can weaken the influence of bonds on property investment. Loan",0.0,http://dx.doi.org/10.31539/costing.v7i5.11658853ba20c45aaae6b08ad9628c0fd3a3b1ee9357a8ce,10.1109/icaice54393.2021.00083,Nonlinear fitting based on BP-neural network for prediction of commodity house prices in Beijing,2021.0,2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE),Diqin Jiang; Ran Biao,"This paper uses the price of commercial housing in Beijing as its focal point. It attempts to identify the factors influencing the price of commercial housing during the real estate development process, as well as to determine the extent to which these factors influence the price of commercial housing and make house price predictions based on this information. This study utilizes principal component analysis to discover the key influencing elements of home prices, find the weight connection between house prices and each primary influencing factor, and develop a house price prediction model using the BP neural network modeling technique using the principal component analysis technique. Using the principal component analysis method, we establish the main influencing factor indexes of house price determination by comparing the contribution rate of each index in the load matrix to the principal component and use multiple linear regression to get the functional relationship between the price of commodity house and its influencing factors for house price and regional prosperity, supporting facilities, transportation, housing design, education, property, and environment. Based on the comp",0.0,854fcd4a4c655a1f1430c2d0181ad348e4bf6aaf098,10.15793/kspr.2018.97..004,Analysis of Determinants for Mixed-use Land in Land Development Project Area in New City by Price Level: Evidence from Gimhae,2018.0,,Taeyun Jeong,"The purpose of this study is to construct a pricing model for the store residence land for residential and non-residential mixed use in Jangyu New City in Gimhae, Gyeongsagnam-do and analyzed the physical and environmental characteristics expected to act differently according to the price level through quantile regression analysis. In its analysis, the study applied quantile regression using a 2-stage regression method by the spatial lag model which is a regression model reflecting spatial dependency. As a result of the analysis, regression analysis by spatial lag model was found to give better explanation and represent inherent characteristics of land well in quantile regression. Physical factors were found to have bigger effects on the store residence land for residential and non-residential mixed use than environmental factors, especially in high-price land. Store residence land is likely to reflect the characteristics of the use of non-residential real estate more than the environmental factors of residential real estate due to its possibility of commercial use. The spatial correlations appeared higher in the middle and high price quantile of store residence land and physical c",0.0,https://doi.org/10.15793/kspr.2018.97..004855f642a74030b00c2347a2ffb8325d1b7fa0c9913f,10.1051/itmconf/20257803005,Predicting Housing Prices in Sweden: A Comparative Study of Linear Regression and Machine Learning Models,2025.0,ITM Web of Conferences,Lu Feng,"This study investigates the factors influencing the asking prices in the Swedish housing market, focusing on property-specific variables such as the number of rooms, land area, living area, and price per square meter. Using a linear regression model, the analysis reveals that these factors explain 18.9% of the variation in asking prices, with all coefficients showing significant positive relationships. To address the limitations of linear models, this study further employs nonlinear machine learning approaches, including decision trees and random forests, to capture complex interactions in the data. The decision tree model achieves perfect fit on training data (R² = 1.000) but shows reduced generalization on test data (R² = 0.800), suggesting potential overfitting. In contrast, the random forest model demonstrates robust performance, with high explanatory power (R² = 0.892 on test data) and minimal prediction errors, highlighting its superiority for housing price forecasting. Diagnostic tests confirm the absence of multicollinearity and autocorrelation in the linear model, while the machine learning models provide deeper insights into nonlinear relationships. These findings offer v",0.0,https://www.itm-conferences.org/articles/itmconf/pdf/2025/09/itmconf_cseit2025_03005.pdf856bcf8f764df95a216ad7246e32d3d7eee1a8ca3d1,10.3390/su18010041,Estimation of Bus Passengers’ Residential Locations Based on Morning Rush Hour Travel Data and POI Information,2025.0,Sustainability,Ling-Xiang Zhu; Qipeng Xuan; Liang Zou,"To address the issues of inefficiency and high costs in obtaining data on the residential distribution of public transport passengers at present, this paper proposes an approach of “estimating the residential distribution of public transport passengers based on characteristics such as housing prices of residential Point of Interest (POI) and the convenience of public transport and its stops”. First, from two aspects—public transport travel and the selection of public transport stops—eight influencing factors for the selection of public transport stops during travel are identified. Based on these factors, a regression model for the number of public transport passengers from residential POI to their corresponding stops is constructed, through which the number of passengers traveling from each residential POI to all accessible public transport stops is obtained. This number is then used as a weight to allocate the actual passenger flow of each public transport stop to the respective residential POI, thereby realizing the estimation of the residential distribution of public transport passengers. Furthermore, this approach enables the estimation of the proportion of trips made from resi",0.0,https://www.mdpi.com/2071-1050/18/1/41/pdf?version=1766149578857b9e1d09ff13f801ef90f3cd15f899f008911b19e,10.4314/just.v43i3.7,Exploration of Machine Learning Algorithms for Housing Price Prediction in Megacities,2025.0,Journal of Science and Technology (Ghana),R. Ogundeji; N. Badmus; Malik Olasubomi Akintola,"Effective predictive models are good for tackling pressing issues faced by residents and stakeholders regarding the cost of houses in cities. Thus, buyers and sellers of houses have challenges in making decisions due to a lack of adequate data-driven decision-making. This study explores the feasibility of using machine learning techniques to predict house prices in Lagos and Abuja, Nigeria. The data used for this research were obtained from a property listing website and encompass key features such as location, number of rooms, property type and other basic characteristics. Several models including Linear Regression, Bayesian Linear Regression, Support Vector Regressor and Ridge Regression, were trained using these data. The performances of models considered for this study were evaluated using the Mean Absolute Error and the R-Squared scores, and the Support Vector Regressor performed best. Also, the model predicted Lagos house prices better than Abuja house prices with the highest R-squared and lowest Mean Absolute Error (MAE) values. An independent t-test was also conducted to test for significant differences in the price of houses in Lagos and Abuja. The results indicate there i",0.0,https://www.ajol.info/index.php/just/article/download/302800/28467885866d02c03404dfe9e1a87bd369745b6af2f6492d9,10.1038/s41598-026-60742-0,The impact of housing cost burden on self-rated health among urban renters in China,2026.0,Scientific Reports,Dong-Sheng Zhan; Ying He; De Wang; Zi-Cheng Wang,"Since China’s market-oriented housing reform, rapidly rising urban housing prices and declining affordability have made housing cost burden a critical factor affecting the health and well-being of urban residents, particularly vulnerable groups such as renters. While previous literature has examined the relationship between housing costs and residents’ health, few studies have comprehensively assessed the effects of both subjective and objective housing cost burdens on the health of urban renters in China, and examined their underlying mechanisms. Using large-scale survey data from the 2021 City Health Examination social satisfaction survey, this study employs binary logistic regression and instrumental variable methods to investigate this relationship. The results show that both subjective and objective housing cost burdens are significantly negatively associated with self-rated health among Chinese urban renters. Furthermore, these negative associations are stronger among renters living in high-rent cities, males, local Hukou holders, and those in larger families. Housing cost burden is linked to lower self-rated health through longer commuting time, lower perceived community hea",0.0,https://www.nature.com/articles/s41598-026-60742-0_reference.pdf859f7abfd50f776482aa9f778c822c16c48373b848f,10.3791/71567,"Spatial Analysis of the Impact of Urban Spatial Structure on Urban Vitality in Yantai, China Using Multiscale Geographically Weighted Regression.",2026.0,Journal of Visualized Experiments,Xin-Yao Sun; Xiao-Hui Wang; Yu Ye; Long-Sheng Wang; Yi-Ge Zhang; Tian-Wen Li; Yan-Feng Zhang; Wei-Shan Qin,"Understanding how urban spatial structure influences urban vitality is essential for developing human-centered urban planning strategies. However, the spatial heterogeneity of these relationships and the relative contributions of different urban spatial structure elements to daytime and nighttime vitality remain insufficiently understood. This study aimed to quantify urban vitality and investigate how urban spatial structure affects its spatiotemporal variation in the main urban area of Yantai, China. Multisource spatial datasets, including Baidu heatmap data, points of interest, street-view imagery, road networks, building information, and land-use data, were integrated to characterize urban vitality and urban spatial structure. Kernel density estimation, spatial autocorrelation analysis, and multiscale geographically weighted regression (MGWR) were employed to evaluate the spatial distribution of urban vitality and the scale-dependent effects of urban spatial structure variables. The results demonstrated that urban vitality exhibits a pronounced spatial polarization characterized by a composite ""center-periphery"" and ""one core with multiple points"" pattern, together with signific",0.0,8609994490f1de546d1e6bd44050988431feba1e676,10.3846/ijspm.2023.20362,EFFICIENCY AND CHALLENGES IN PUBLIC REAL ESTATE MANAGEMENT: INSIGHTS FROM LITHUANIA,2023.0,International Journal of Strategic Property Management,Viktorija Cohen; Evelina Bereznauskė; Arūnas Burinskas; Saulius Raslanas,"Public real estate is considered an integral part of national wealth. It generates a wide range of economic activities that contribute to creating and providing common public goods. This study examines the efficiency of public real estate management in Lithuania. Our findings show that public property use, disposal, and management strongly depend on the managerial approach. We apply data envelopment analysis (DEA) in addition to the income value, regression, and correlation methods. We find that there are certain biases present due to the exclusion of some state-owned properties from the public register. Furthermore, we identify the need for greater precision in the indicators used for financial reporting of state-owned assets. The excessive number of vacant asset units poses a challenge, as it requires substantial maintenance expenditures. Moreover, the appraisal of alternative use of these assets is lacking, thereby limiting the potential for maximizing public benefits. Our analysis reveals that the rental price per 1 m2 and the residual value of the leased premises are the most critical determinants influencing the management efficiency of publicly owned property. Furthermore, i",0.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/20362/11998861de185a9af9fed3bd4c56a0f57812fc2cc38627ac,10.61730/z80c4140,The Influence of Purchasing Power and Economic Value of Property on Housing Sales Strategy,2026.0,Outline Journal of Economic Studies,T. Handayani,"The purpose of this study was to analyze how purchasing power and the economic value of property influenced housing sales strategy within a dynamic real estate market. The research method employed a quantitative approach using primary data collected from 150 respondents and secondary data from industry and economic sources, which were then processed using multiple linear regression to examine both partial and simultaneous effects of the variables. The results indicated that purchasing power served as the most dominant factor shaping sales strategies, as consumers’ financial readiness strongly affected pricing policies, payment flexibility, and promotional approaches used by developers. The economic value of property also played a significant role, particularly in determining product positioning, perceived attractiveness, and the strategic emphasis placed on location, facilities, and investment potential. The conclusions drawn from this study emphasized that developers needed to integrate both consumer financial conditions and property value characteristics in order to formulate adaptive, targeted, and sustainable marketing strategies. The originality/value of this research lay in i",0.0,https://journal.outlinepublisher.com/index.php/OJES/article/download/602/375862e01c12ae22ad2adc518256d512931744a73824dd,10.47772/ijriss.2026.100700105,"A Study of Real Estate Assets Pricing in Ibadan Metropolis, Nigeria",2026.0,International journal of research and innovation in social science,Oyebola Isaac Abiodun; Salami Henry Taiwo; Oyewole Oyebisi Olaleye,"This study examined the determinants of residential real estate asset pricing in Ibadan Metropolis, Nigeria, with emphasis on the influence of macroeconomic, microeconomic, and property-specific factors. A quantitative research design was adopted using structured questionnaires administered to private estate developers and residents of selected housing estates. Descriptive statistics and multiple regression analysis were employed to analyse the data. The descriptive findings revealed that respondents perceived macroeconomic factors as the most influential determinants of property prices, with inflation rate ranking highest among macroeconomic variables. Earnings and profitability emerged as the leading microeconomic factor, while location was identified as the most influential property-specific characteristic. However, regression results showed that interest rate, inflation rate, money supply, gross domestic product, and capital flow did not significantly influence the prices of detached, semi-detached, terrace, and four-bedroom residential properties. The study concludes that residential property prices are better explained by a combination of property-specific, neighbourhood, ins",0.0,https://rsisinternational.org/journals/ijriss/uploads/vol10-iss7-pg1453-1464-202607_pdf.pdf8638f95d4fe9344071d712022566f4ea8d32672f838,10.31083/jeems40169,Research on the Impact of Housing Prices on the Innovation of Manufacturing Enterprises in China,2025.0,Journal of East European Management Studies,Ying Xiong; Xiaomeng Liu,"The manufacturing industry is the “ballast stone” of China’s economy, and innovation in the manufacturing industry plays a key role in promoting high-quality economic development and propelling the modernization drive. Based on relevant theoretical analysis, this study uses data from 441 manufacturing companies (ME) listed on the Shanghai and Shenzhen A-shares in China from 2013 to 2022 to empirically analyze the impact mechanism of housing prices (HP) on innovation in manufacturing enterprises (IME) by constructing an econometric model. The results of the benchmark regression model indicate that in the short term, the rise of housing prices is beneficial for manufacturing enterprise innovation. However, as housing prices continue to rise, high housing prices can have a suppressive effect on innovation, indicating an inverted U-shaped relationship between the two variables. Mediating effect analysis shows that housing prices have different effects on enterprise innovation through resource allocation (RA), financial constraints (FC), and real estate investment (REI). Heterogeneity analysis reveals that in the long term, the inhibitory effect of housing prices on enterprise innovatio",0.0,https://www.imrpress.com/journal/JEEMS/30/3/10.31083/JEEMS40169/pdf86453dcbcedb8620855af45abe7362ca4af0fd5f86a,10.2991/aebmr.k.220307.193,The Impact of National Policies on the Real Estate Industry in China,2022.0,Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022),Yan-Shu Ma; Yi-Feng Yue,"China's real estate has always been a very important driving force of China's economy. The proportion of real estate investment in GDP increased from 5% in 1995 to more than 13% in 2019, of which more than 70% was used for residential construction. Real estate not only accounts for 23% of household consumption but also connects with all sectors of the economy through investment, construction and financial system. The role of real estate in China's economic growth has been criticized for many years because of high house prices. Getting rid of excessive dependence on real estate has become the direction of many local governments. China's government has imposed a lot of regulations and policies on the real estate industry recently. Moreover, it will affect the price of housing in China and also affect the future development of the real estate industry in China. The characteristics of the real estate industry, the policy, and the influences of the real estate industry and the future expectations of the industry will be discussed in the paper. According to the history of China's real estate development and the latest policies, this paper analyzes the impact of China's new real estate po",0.0,https://www.atlantis-press.com/article/125971890.pdf865f18d727f7ec59f17ff6f8de5a2eb2c8d1e7101b7,10.1145/3821835.3821841,MIC-BiLSTM: An Interpretable Deep Learning Framework for Real Estate Asset Pricing in High-Dimensional Markets,2026.0,Proceedings of the 2nd International Conference on Smart City and Sustainable Development,Nuerbiye Xiemuxiding; Xing-Long Li; Xiao-Ye Du; Liang Zhao; Maimaitiaili Maimaitimin; Lin Tang,"In recent years, Machine Learning (ML) and its subfield Deep Learning (DL) have flourished, providing powerful tools for complex data modeling. However, high-dimensional data in house price prediction poses significant challenges due to varying feature importance and redundant information. Existing studies often overlook complex nonlinear dependencies among features, temporal evolution patterns in data, and model interpretability. To address these issues, this paper proposes a novel framework integrating Maximal Information Coefficient (MIC) feature selection, Bidirectional Long Short-Term Memory (BiLSTM), and Local Interpretable Model-agnostic Explanations (LIME). The MIC method effectively screens 40 key features from 79 original dimensions, reducing computational complexity while preserving critical nonlinear relationships. BiLSTM captures bidirectional temporal dependencies among construction and remodeling years, improving prediction accuracy. LIME provides fine-grained feature contribution analysis, enhancing model transparency. Experimental results on the Kaggle House Prices dataset demonstrate that the proposed BiLSTM-MIC model achieves R² of 0.8269, representing a 57.41% r",0.0,https://doi.org/10.1145/3821835.382184186663c98402bec1d98255a7046e0ebd29193c04cdd0,10.56028/aemr.1.2.71,Research on Housing Price in Boston,2022.0,Advances in Economics and Management Research,Zhi-Heng Ma; Tan Wang; Yubo Zhang,"In the United States, due to the characteristics of sparsely populated areas, its housing prices are not quite the same as those in China. At the same time, due to factors such as the freedom of firearms in the United States, many people consider more factors in purchasing houses, resulting in great differences in housing prices in the United States. The housing price problem has also become a major concern. 867This paper first analyzes fourteen variables, respectively, thirteen independent variables were analyzed to find the relationship between independent variables and housing prices, establish regression model analysis, and then through the frequency histogram to find the relationship between independent variables and frequency, through the relationship between frequency and housing prices to illustrate the relationship between independent variables and housing prices. By analyzing the correlation to find out the relationship between the variables and housing prices. 868Through the statistical description and analysis model of data, we can establish the normal distribution relationship between independent variables and dependent variables, and approximate the normal distribution pa",0.0,https://madison-proceedings.com/index.php/aemr/article/download/323/267869ddffe6716da41b4374d9f4f04db231565547be91,10.61091/jcmcc127a-160,Supportive Strategies and Empirical Research on Housing Policies for the Improvement of the Living Environment of Special Needs Groups under the Social Security Framework,2025.0,Journal of Combinatorial Mathematics and Combinatorial Computing,Yu Wang,"Due to the continuous increase of housing prices in recent years, many special groups of low and middle income do not have enough financial ability to pay for the high housing prices, and the problem of living environment is becoming more and more prominent. Based on the utility function in economic theory, this paper constructs a utility function model under the constraints of household budget income and price, and determines the income line of housing security households. The distributional efficiency of the implementation of the guaranteed housing policy is estimated through both in-kind rent allocation and rent subsidy. Based on the empirical distribution characteristics and public opinion surveys, a rational distribution model for the current stage of sheltered housing is proposed. Taking Singapore's guaranteed housing policy as a case study, combining empirical evidence and simulation experiments, the effect of improving the living environment of special needs groups under the framework of social security is explored. The results show that: using a 10% allocation ratio of subsidised housing (5% each for affordable housing and public rental housing), the vacancy rate of public",0.0,https://combinatorialpress.com/article/jcmcc/Volume%20127/Volume%20127a/Supportive%20Strategies%20and%20Empirical%20Research%20on%20Housing%20Policies%20for%20the%20Improvement%20of%20the%20Living%20Environment%20of%20Special%20Needs%20Groups%20under%20the%20Social%20Security%20Framework.pdf870f1b6476338f5019117373175ffd70bd559a040e9,10.5901/ajis.2016.v5n3s1p351,Macroeconomic Determinants of Households’ Credit Risk: Empiric Evidences from Macedonia,2016.0,,Lindita Muaremi; D. Salko,"In general, the credit risk is considered as more problematic risk for the banking system. Credit given by banks is divided into loans for business and loans to individuals. The effects of the financial and debt crisis are also felt in Macedonia. This is especially noticeable after 2010, from the growth of non-performing loans against total loans granted. The non-performing individuals’ loan is a measure of credit risk of individuals. It is of particular interest to investigate the factors that determine the behavior of non-performing loans to individuals. This study sheds light on the effects that macroeconomic factors have on the credit risk of individuals. The analysis of time series and the rigorous application of the econometric model challenged the strong and significant connection of the risk free interest rate, the index of house prices, the exchange rate and economic growth of the credit risk of individuals. All these factors have a positive relationship with this risk, with the exception of economic growth. It is more than logical that an improvement of economic growth will lower the credit risk of the individual, because the rate of non-performing loans to individuals wi",0.0,https://www.richtmann.org/journal/index.php/ajis/article/download/9805/9443871c017f9b954a31390ec9e1cd94008a1e9615cbf38,10.54691/bcpbm.v38i.3753,The Real Estate Price Prediction Based on Multivariate Linear Regression Models,2023.0,BCP Business & Management,YuChen Zhang,"The price of households had increased greatly as the demands in the market increased. Real estate had become the most popular method to resist inflation. Thus, more experts and scholars are working on the research in this area, including but not limited to find the future trends of housing prices and the possible bubble of housing prices. This paper will use the basic data regression model in Excel, powered by Microsoft, to find the factors that lead to the appreciation or depreciation of real estate, and three different methods of dealing with the data will be applied which are standardization, interception, and normal normalization. As a result of complex processes, normalization is the best way to deal with the data that was chosen from Kaggle. In the meantime, a linear correlation was done for checking the independency of each variable, and turned out to be successful. According to the analysis, an inverse relationship between the variables house age and distance nearest to the store was found, and the lower the house age is, the higher the price is, higher the house age is, lower the price is. The main aim of this paper is to investigate the relationship of the factors and the",0.0,https://doi.org/10.54691/bcpbm.v38i.3753872cdc9c76812284f1b97fe8ca60d5f1c963bf029f9,10.54254/2755-2721/2025.gl23414,House Price Prediction Using Machine Learning: Analysis of XGBoost and Gradient Boosting Models,2025.0,Applied and Computational Engineering,An Li,"This study investigates the optimization and application of Distributed Machine Learning (DML) techniques, essential for addressing the computational challenges of large-scale datasets in modern applications such as autonomous driving and natural language processing. The primary objective is to evaluate the performance and characteristics of key DML methods, including MapReduce, Parallelized Stochastic Gradient Descent (PSGD), and Federated Learning (FL). A comprehensive analytical framework is employed to explore the underlying mechanisms of each method. For MapReduce, the study examines data partitioning, mapping, shuffling, and reducing processes to assess its efficiency in managing large datasets. In PSGD, the research analyzes the methods of data allocation, local gradient computation, and parameter aggregation to highlight its parallelization benefits and identify potential bottlenecks. FL focuses on its privacy-preserving model training process, which includes client-server communication, local updates, and global aggregation. Experimental results using the ImageNet dataset and Gboard user log data show that while MapReduce offers fault tolerance, PSGD accelerates training o",0.0,https://www.ewadirect.com/proceedings/ace/article/view/23414/pdf8737c3a4bee2424ba8617a7df2d3a67edf0f6d5f73e,10.5040/9780755603565.0010,Annotated Bibliography,2022.0,Technology in Modern German History,Silva Mathema,"This paper evaluates the impacts of new housing developments funded with the Low Income Housing Tax Credit (LIHTC), the largest federal project based housing program in the U.S., on the neighborhoods in which they are built. A discontinuity in the formula determining the magnitude of tax credits as a function of neighborhood characteristics generates pseudorandom assignment in the number of low income housing units built in similar sets of census tracts. Tracts where projects are awarded 30 percent higher tax credits receive approximately six more low income housing units on a base of seven units per tract. These additional new low income developments cause homeowner turnover to rise, raise property values in declining areas and reduce incomes in gentrifying areas in neighborhoods near the 30th percentile of the income distribution. LIHTC units significantly crowd out nearby new rental construction in gentrifying areas but do not displace new construction in stable or declining areas. Edmiston, Kelly D. “Nonprofit Housing Investment and Local Area Home Values.” Economic Review Q I (2012): 67–96. Available at: http://www.kansascityfed.org/publicat/econrev/pdf/12q1Edmiston.pdf Abstra",0.0,874d175482c6883af1ef2ade5665ad10f5bb8e70b6c,10.19168/jyasar.923817,Konut Finansmanında Katılım Bankalarının Etkinliği,2021.0,,Mehmet Sengur; Müslime Sözen,"Turkey in the housing finance market, conventional banks, participation banks and some private institutions provide different conditions and opportunities for their customer through home ownership by. The Islamic banks operating in Turkey offer Housing Finance products which are identical with the conventional mortgage products. Islamic home financing products are certainly different from conventional home loan. Conventional provided home loan is based on lending of interest which is prohibited by Shari’ah. On the other hand, having the big majority Moslem population, Islamic finance is an alternative to be developed in Turkey. All housing finance is a large share of public income and covers long-term payments. This paper, will focus on two selected participation banks in Islamic banking institutions. This research aims to investigate the relationship between alternatives offered by two participation banks in choosing Islamic home financing products. In this study, between the quarterly 2010-2019; Multiple linear regression analysis was conducted by using asset size of Kuveyt Türk and Albaraka Türk banks, housing price index, inflation rate, housing loan interest rates and GDP. It ",0.0,https://doi.org/10.19168/jyasar.9238178753d4f46343401573ef97e4a1fc906db43635cfdac,10.3390/buildings16173381,Assessing the Spatial Heterogeneity of Village Homestead Improvement Potential: Village Environment and Multidestination Urban Housing Purchases,2026.0,Buildings,Chengxiang Wang; Chi Chen; Sai-Zu Wang; Wei-Ling Hsu,"Understanding the interactions between rural household decision-making behavior and the environment is critical to promoting sustainable rural development. Analyzing the interrelationship between multidestination urban housing purchases, homestead improvement potential, and village environments can provide a theoretical basis for formulating rural revitalization policies. This study uses full-sample household survey data, applying multiscale geographically weighted regression to assess spatial variability and K-means clustering to categorize effects. The findings reveal significant spatial heterogeneity in village homestead improvement potential, most strongly associated with locational attributes, water network density, residential quality, and social factors. The association between multidestination urban housing purchases and improvement potential varies by destination: township purchases are positively associated with it, whereas purchases in county centers and beyond show negative associations. The spatial variability of different factors is significant, allowing villages to be classified into five zones based on dominant factors. Tailored, zone-specific policy directions—deve",0.0,https://doi.org/10.3390/buildings16173381876bc3bcb61cf6790285b43f938ce70f6bb341202cf,10.35136/krer.36.3.4,Persistence and Heterogeneity of Residuals in Officially Assessed Commercial Land Prices: Dynamic Analysis of a Standard-Lot-Based Mass Appraisal System,2026.0,Korea Real Estate Institute,Da Hoon Kim,"In Korea, a legal basis for a non-residential real estate price disclosure system has been established; however, the system has yet to be implemented. Moreover, despite the integrated nature of land and building values in commercial real estate, land values are still estimated indirectly using officially assessed standard land prices and the Land Price Comparison Table. Focusing on Korea, this study consolidates transaction parcels for commercial and mixed-use residential properties in seven major metropolitan cities at the unique-parcel level. These parcels are combined with officially assessed land-price data for 2015–2024, yielding an unbalanced panel of 19,084 parcels and 186,948 parcel-year observations. Residuals in officially assessed land prices are derived from out-of-sample predictions generated through five-fold parcel-level group cross-validation, with all annual observations for each parcel assigned to the same validation fold. AR(1) specifications are estimated using the difference generalized method of moments, revealing positive persistence of approximately 0.72–0.78; however, no single specification satisfies both the serial correlation and overidentification tests",0.0,877ef7a5b7e886618eece6be16ddff155ae51bfbffc,10.54097/pqmcfy41,Urban Housing Price Prediction and Service Level Evaluation Based on Machine Learning,2025.0,Highlights in Science Engineering and Technology,Jiaye Chen; Jia-Rui Huang; Hua-Wei Huang; Ali Zulfiqar,"With the acceleration of urbanization, the sustainable development of cities and the improvement of residents' quality of life have become major challenges facing the world. Taking Wenzhou City and Hohhot City of China as examples, this paper uses machine learning method to build urban housing price prediction and service level evaluation models. First, the stochastic forest regression and ridge regression models are used to predict the housing price, and the forecasting effect of the two models is compared. The results show that the stochastic forest regression model has better performance in capturing nonlinear relationship and forecasting accuracy. Secondly, a multi-index comprehensive evaluation model is constructed to evaluate the urban service level, and the factors affecting the service level are determined by weighted summation method. The results show that optimizing the greening rate and property management fees can effectively enhance the service level and improve the quality of life of residents. The research results of this paper provide a scientific basis for urban planning and decision-making, and provide a reference for the sustainable development of other cities. F",0.0,https://hsetdata.com/index.php/ojs/article/download/597/557878f5d851aa205f9a3835454624d12f0b137883e616,10.14293/s2199-1006.1.sor-.pp0tp9i.v1,Property valuation by machine learning for the Norwegian real estate market,2021.0,,Amandip Sangha,"We train a machine learning model on large data set for predicting property values in the Norwegian real estate market. Our model is a gradient boosted regression tree. The data set is the largest market data set of properties in Norway considered in the research literature. We achieve state of the art accuracy.879 A large scale market data set of real estate properties is collected from sales and rental ads on publicly accessible internet sites. The property advertisements show property features and appraisal values made by real estate brokers. We train a gradient boosted regression tree model on selected features of the data set. This is a multivariate regression model built with supervised learning. We do 5-fold cross validation to assess the accuracy and robustness of the model.880 The gradient boosted regression tree models are already known to give the best prediction accuracy on real estate price valuations. We achieve state of the art pre- diction accuracy using a minimal feature set and only publicly and freely available sales advertisement data.881 The novelty of our work lies in the fact that we use a minimal feature set in our model, and we have the largest data set in the re",0.0,https://doi.org/10.14293/s2199-1006.1.sor-.pp0tp9i.v1882e78e1ed0381cfce6524686274bc5e28c10d62dd1,10.1108/ijhma-04-2026-0100,"Economic shocks and housing market dynamics: evidence from Shelby County, Tennessee",2026.0,International Journal of Housing Markets and Analysis,Fatemeh Kamkar; Jason De Freitas; Mark A. Sunderman,"883 884 This study aims to examine housing market dynamics in Shelby County, Tennessee, during the COVID-19 period (2020–2022) to determine whether house price growth reflected speculative behavior or fundamental economic drivers.885 886 887 888 Using granular multiple listing service transaction data merged with macroeconomic indicators, including mortgage rates, inflation, unemployment and construction costs, the study uses fixed-effects regression models, propensity score weighting and nonlinear specifications to assess the relative importance of demand fundamentals, supply constraints and speculative activity. Market liquidity measures, including sales turnover and months’ supply, are incorporated to complement price-based analysis.889 890 891 892 House price appreciation during the pandemic was primarily driven by fundamental demand factors, particularly historically low mortgage rates, increased household liquidity and shifting housing preferences. Speculative activity, measured by non-owner-occupied and out-of-town purchases, played a limited role and did not amplify price growth. Liquidity indicators further show that price increases were closely associated with demand pressure rather than spe",0.0,893799db8c3e5db1067f56df44451b9408775675af5,10.33411/ijist/20246310311044,Real Estate Price Prediction,2024.0,International Journal of Innovative Science & Technology,R. Naz; Bushra Jamil; Humaira Ijaz,"Real estate price predictions are critical for stakeholders, including investors and developers, because they have a considerable impact on investment decisions and market stability. In order to fill in the shortcomings in earlier approaches, this work presents a novel methodology by utilizing Deep Learning(DL) and Machine Learning (ML) techniques to improve real estate price forecast accuracy. We used the ""House Prices 2023 Dataset"" from Kaggle, which contains 168,000 entries of Pakistani property data. Our methodology included extensive data preparation, feature engineering, and the use of various algorithms, including Linear Regression, Gradient Boosting, Random Forest, Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN). The models were tested using MSE, RMSE, R-squared, and accuracy. KNN outperformed the other models, with a lower RMSE of 13.79 and a higher R-squared value of 0.85, indicating improved predictive accuracy. RF also produced impressive results, with an accuracy of 80%. Handling complicated feature interactions, guaranteeing model scalability, and controlling hardware resources were all challenges that suggested possibilities for future improvement.",0.0,https://journal.50sea.com/index.php/IJIST/article/download/951/1498894bebfbba6d38c6386c102ea6d55d923dbe0a94a2b,10.51583/ijltemas.2026.150400100,Impact of Property Management Practices on Rental Value Stability in Urban Housing Markets in Lagos,2026.0,International Journal of Latest Technology in Engineering Management & Applied Science,Adah Okechukwu Uchenna; Ingonabo Idholo; Adarugo Elohor; I. Chukwuma; Idisi Benjamin Evi,"Rapid urbanization has intensified pressure on housing markets in fast-growing cities such as Lagos, leading to rising demand for residential accommodation and unstable rental values. Property management practices are increasingly recognized as critical determinants of rental housing market performance and stability. However, inconsistent management approaches, poor maintenance, and ineffective tenant management often contribute to rental price disparities across urban residential areas. This study examines the influence of property management practices on the stability of residential rental values in Lagos. It investigates commonly used management methods, the role of maintenance practices, tenant management strategies, and the contribution of professional property management. The findings show that regular property inspections (30%) and routine maintenance and repairs (25%) are among the most widely used management practices. Maintenance activities contribute 35% toward maintaining stable rental values, while proper tenant screening accounts for 32.5% in ensuring consistent rental income. Professional property managers also enhance operational efficiency and maintenance performan",0.0,https://www.ijltemas.in/submission/online/article/download/4640/62688958fc4cbe95b54c9a2454c4d572640c043dfe5a5b2,10.1093/jncics/pkaf074,Individual income and race-associated differences in prostate cancer mortality in a statewide registry,2025.0,JNCI Cancer Spectrum,Alec Zhu; Stephen Rhodes; B. Al Hussein Al Awamlh; Randy A Vince; Nicholas G Zaorsky; D. Spratt; Camilo Arenas Gallo; Anyull D. Bohorquez Caballero; Mollie Goldman; Jonathan E. Shoag,"Abstract Background Prior studies evaluating racial disparities in cancer outcomes used regional measures of deprivation when accounting for socioeconomic status, which lack granularity. We evaluated differences in prostate cancer mortality between Black and White men using individual home prices in addition to regional metrics to understand the impact of individual wealth on prostate cancer outcomes. Methods Individuals diagnosed with prostate cancer between January 2004 and December 2016 in the Ohio Cancer Incidence Surveillance System were included. Individual home addresses were linked to the Area Deprivation Indices and home prices using data from an online real estate marketplace. Using inverse probability weighting to balance patient characteristics, we assessed differences in prostate cancer–specific mortality or other-cause mortality between Black and White men after accounting for clinical characteristics and social determinants of health (insurance, area deprivation, and home price). Results We identified 70 660 (85%) White and 12 192 (15%) Black men with prostate cancer. Black race was associated with a higher risk of prostate cancer–specific mortality in models that ad",0.0,896ef2cb3f293961452a8e01523d3c40623e3dc6956,10.29227/im-2025-02-02-125,Development of Apartment Prices in the Cadastral Areas of Brno Between 2014 - 2024 with Regard to Ownership Structure,2025.0,Inżynieria Mineralna,Pavel Juříček; Maxmilian Wittmann; Tereza Ježková,"Over the past decade, Brno has experienced a significant increase in apartment prices, with values at least doubling in most of the observed areas. This study analyzes real transaction prices obtained from the cadastral register and provides a clear overview of price developments in different parts of Brno. The data is categorized by cadastral areas and compared, with a particular focus on differences between specific locations such as Brno - střed (city center), Veveří (a prestigious address), Zábrdovice (a less sought - after area), and Lesná (a panel housing estate). Additionally, actual sale prices are compared with listing prices from publicly available real estate platforms (Sreality). The price trends show a consistent upward trajectory, with a slight dec line during the pandemic, followed by a renewed increase in property values. The article further examines three main forms of ownership: private (including units in homeowners' associations), cooperative, and municipal. Another goal is to estimate the prop ortion of investment apartments based on the registered permanent residence of their owners. The study also explores regional differences in apartment prices and the impa",0.0,https://inz-min.online/index.php/im/article/download/1851/3553897b10e6e847c3949f8dabb17251296f60b4b0531e7,10.2991/iea-15.2015.148,Do House Prices Deliver Misleading Information,2015.0,Conference on Industrial Electronics and Applications,Rui-Hu Kuang,"Identifying whether housing prices have misled the market or not at present is an important topic in real estate regulation management in China. Many scholars believe that house prices cannot mislead the market, and try to attribute the rapid rise of house prices to factors such as excessive investments and less municipal governments’ regulation, which evidently is not completely convincing. The significance of this paper is to give a new scientific answer to the questions above through rigorous empirical analyses. The methodologies for analyzing the relationships between house prices and determinants were based on a fix effects model and a dynamic model considering cross section dependence, using fix effects regression and the augmented mean group (AMG) estimation. The estimation results demonstrate that the average residential house prices do not response to any change in different variables (determinants), which are average incomes, real estate investments and construction costs except the house bubbles. Thus, house prices deliver misleading information, which proves that the scholars’ viewpoint is false. Excessive investments from institutions and less regulation from municipal",0.0,https://download.atlantis-press.com/article/20584.pdf8986ddd2a78ed71117753ca02750caa06192f794918,10.62051/dpz1db11,Predicting the Rise in California Home Prices and Factors Affecting,2024.0,Transactions on Computer Science and Intelligent Systems Research,Yufan Bai,"California’s real estate market has been in the spotlight for its unique geography and economic advantages, and home prices have been volatile. Therefore, accurate prediction of house price increases is of great importance to home buyers, investors and policy makers. This paper utilizes the California house price dataset, combines macroeconomic indicators and micro property attributes, and analyzes house price prediction through random forest, decision tree and neural network models. The study results show that the Random Forest model performs the best in predicting house prices with an R²value of 0.817210, which is significantly higher than the other models at 0.817210. the influence of R²leads to a slightly poorer neural network performance (CNN). Further through visualization studies and feature importance analysis median income, longitude, latitude, and distance from the sea are the key factors affecting the house price in California. House prices are strongly positively correlated with median income, while distance from the sea is negatively correlated with house prices. These key factors can also directly affect future home buying trends and house price direction. The researc",0.0,https://wepub.org/index.php/TCSISR/article/download/4160/46788992836909adcc1e08639e37f96317167fcfe0cc9be,10.54097/ztf2nr57,Urban Housing Price Prediction Based on Multiple Linear Regression Mode,2026.0,Journal of Innovation and Development,Yu-Tao Zhang,"The real estate market, as a crucial component of the national economy, directly affects residents' quality of life and economic development stability through its price fluctuations. Research on housing price prediction holds significant practical value for government policy formulation, developer investment decisions, and consumer home-buying choices. This study utilizes urban second-hand housing transaction data to construct a housing price prediction model using multiple linear regression, systematically analyzing the mechanisms of seven core influencing factors: housing area, building age, floor level, distance to city center, number of bedrooms, number of bathrooms, and number of parking spaces. Using 200 sets of real housing transaction data, this study establishes a multiple linear regression model with high prediction accuracy through correlation analysis, regression modeling, and residual diagnosis. The results demonstrate that the model achieves an R² of 0.972 on the training set and 0.974 on the test set, with a Root Mean Square Error (RMSE) of 152,300 yuan and Mean Absolute Error (MAE) of 124,100 yuan, showing excellent fitting performance and generalization ability. Am",0.0,https://drpress.org/ojs/index.php/jid/article/download/35293/34479900f4296d6c1755260f9ddb8e27694234a0fb369991,10.25034/ijcua.2026.v10n2-9,"Urban Accessibility, Housing Prices and Socio-Economic Status: A Spatial Analysis of Istanbul Neighbourhoods",2026.0,Journal of Contemporary Urban Affairs,Murat Şeker; Arif Saldanlı,"Multidimensional accessibility shapes both housing markets and the socio-economic composition of neighbourhoods, yet fine-grained evidence from rapidly urbanising contexts remains limited. This study examines how pedestrian accessibility to 88 urban services relates to listed housing prices and socio-economic status across 816 Istanbul neighbourhoods with at least 1,000 residents. Indicators from the municipal 34dakika.istanbul platform, measured as the population share reaching each service within a 17-minute walk, are aggregated into ten equally weighted categories and a Combined Accessibility Index. Least squares residuals display pronounced spatial dependence (Moran's I = 0.340, p = 0.001), so spatial lag and spatial error models are estimated and selected on robust Lagrange multiplier tests and information criteria. In the preferred spatial lag specification, only Transport and Mobility accessibility remains associated with higher prices after Holm correction (β = 0.397, p = 0.001); a 0.10 increase corresponds to a 4.4 per cent direct and a 9.1 per cent total price difference. For socio-economic status, transport (β = 13.35), health (β = 11.74) and economy (β = 11.51) accessib",0.0,https://ijcua.com/ijcua/article/download/607/77190175451ab63cef0589ed5990f732a3842cff0e0516,10.62802/sgawt064,"Quantitative Analysis of the Future of the Sharing Economy, Housing, White-Collar Labor Markets, and Problems in Big Cities",2024.0,Next Generation Journal for The Young Researchers,Elif Ekmekçi,"This research project explores the future of the sharing economy and its profound effects on housing markets, white-collar labor dynamics, and socio-economic challenges in large metropolitan areas. Using quantitative economics and econometric techniques, the study evaluates how platforms such as Airbnb, Uber, and coworking spaces are reshaping urban life, exacerbating housing affordability issues, and altering labor market structures. By analyzing housing market data, labor statistics, and platform economy data from major cities like New York, London, and Istanbul, this research provides empirical insights into how the sharing economy influences urban economic landscapes. The study employs difference-in-differences (DiD) and multivariate regression models to assess causal relationships, particularly in terms of housing market disruptions caused by short-term rental platforms and labor market fragmentation driven by the rise of gig and remote work. Spatial econometric techniques are also used to explore geographic disparities in housing and employment impacts across urban districts. Preliminary findings indicate that the proliferation of sharing economy platforms has contributed to ",0.0,902a445cec2157f296de6977076ce989983a366537b,10.1007/s44192-026-00383-x,The widening mental health gap between American homeowners and renters: 2013–2023,2026.0,Discover Mental Health,Masanori Kuroki,"This research aimed to investigate the evolving disparities in mental health between homeowners and renters in the United States between 2013 and 2023. This study analyzed trends in self-reported poor mental health days using a nationally representative dataset spanning from 2013 to 2023. The changes in reported poor mental health days between homeowners and renters over this period were compared. Multivariable regression models were employed to adjust for potential confounding demographic and socioeconomic factors and to quantify the widening gap in poor mental health days between the two groups. The findings revealed a significant divergence in mental health trends between homeowners and renters. After adjusting for demographic and socioeconomic characteristics, the adjusted difference in reported poor mental health days between renters and homeowners widened from 0.4 in 2013 to 1.3 days in 2023. This disparity was significantly more pronounced in states with high housing price growth compared to states with lower growth. This study highlights a widening mental health gap between American homeowners and renters. The results suggest a potentially increasing protective association ",0.0,https://doi.org/10.1007/s44192-026-00383-x903b89d88154c28d683f9430579bc5867448b380ea2,10.62486/agma2025246,A Study of Housing Price Drivers in China's New First-Tier Cities,2025.0,Management,Zhuoxi Chen; Baitong Zhong; Cheng-Ke Ran; Zi-Xin Li,"Over the past two decades, urban housing prices in China have surged dramatically, prompting extensive research into the underlying causes. Despite scholarly efforts, a comprehensive analysis of the factors influencing housing prices in newly-designated first-tier cities remains elusive. This study aims to bridge this gap by examining a range of determinants impacting housing prices within these rapidly growing urban areas.Utilizing a detailed conceptual framework, the research identifies key influences such as financial expectations, market demand, and the availability of public services. The findings suggest that these factors play a substantial role in driving housing prices upwards, reflecting the rising purchasing power and urbanization trends in these cities. Interestingly, the study also highlights that limited land supply—often considered a primary driver of housing costs—does not significantly impact pricing in the context of new first-tier regions.This analysis underscores the complexity of the housing market dynamics in China, suggesting that policymakers should consider a broader range of influencing factors when addressing housing affordability and stability in these b",0.0,https://managment.ageditor.uy/index.php/managment/article/download/246/4739047f9c6fa802316bb3ab2a8b38e15f25c33bfa3df7,10.37034/infeb.v6i2.914,Influencing Factors Analysis IoT Adoption in Indonesian State Housing Management Information System,2024.0,Jurnal Informatika Ekonomi Bisnis,Irfan Murtadho Agtyaputra; Teguh Raharjo; Ibnu Pujiono,"The Internet of Things (IoT) has revolutionized asset management in Smart Homes. In Indonesia, one of the biggest struggles in implementing emerging technology in the e-government system is connecting the users' behavioral intention with the suitability of the type of work and the technological convenience offered. A technology acceptance and use model is proposed to address this issue by analyzing the correlation between IoT adoption in asset management. The model draws insights from two complementary methods: user behavioral intention predicts utilization, and task-technology fit predicts performance with moderating factors analysis. The study finds that social influence, hedonic motivation, and price value factors are compelling users' intentions to adopt the Internet of Things in asset management. The IoT technology is deemed worth the price for asset management, particularly for state residences, due to its automation, accuracy, and real-time features. It also enhances decision-making, as the asset information is more reliable and secure. This study enriches moderating factors analysis to study the effect of technology adoption. Additionally, research on asset management syste",0.0,https://infeb.org/index.php/infeb/article/download/914/410905a62c916bb9855e16442c5e11e7fbe5462554a4c4,10.1109/icccnt61001.2024.10723986,Harnessing Artificial Neural Network Model to Anticipate Housing Market Rates,2024.0,International Conference on Computing Communication and Networking Technologies,K. Karthika; P. Balasubramanie; K. Dharshini; P. Shanmugapriya; T. Ramya,"Predicting house prices is a vital issue in real estate market analysis, having important ramifications for homeowners, buyers, and investors. Amidst this exploration, we utilize an innovative artificial neural network (ANN) approach for predicting house prices, applied to the housing sales data specifically gathered from Chennai. The information includes many parameters, including area, the number of lavatories and bedrooms, the house’s quality rating, and other features. Our initial steps involve data preparation, encompassing tasks such as managing missing data, encoding categorical variables, and standardizing numerical features. Next, we use a sequential architecture with multiple dense layers and dropout regularization to build a deep learning model. Metrics like mean absolute error, mean squared error, root mean square error, mean absolute percentage error, R-squared, and root mean squared log error are used to assess the algorithm’s efficacy after it has been built with the Adam optimizer with mean squared error loss. The results of our experiments underscore the efficacy of the proposed methodology in precisely forecasting house prices. We achieve superior performance leve",0.0,906ecb5a7e047ecc8eaecd0dbae9ebc62bf6d675388,10.59934/jaiea.v5i2.2073,Predicting House Prices in South Jakarta using Linear Regression and Decision Tree Regressor Methods,2026.0,Journal of Artificial Intelligence and Engineering Applications (JAIEA),Frendy; Neviasary; Nursiah; Laura Vidiani; Siti Nurdiani,"The development of digital technology and data analytics has significantly improved predictive capabilities across various sectors, including the property market. House price prediction is a critical element in decision-making for buyers, sellers, investors, and developers, as prices are influenced by factors such as location, building size, land area, number of rooms, and available facilities. This study aims to build a house price prediction model in South Jakarta using Linear Regression and Decision Tree Regressor machine learning algorithms and compare their performance based on regression evaluation metrics. The dataset consists of 1010 entries, divided into 80% training data and 20% testing data. The experimental results show that Linear Regression produced the best performance with an R² score of 0.7713, meaning that the model can explain 77% of the variance in house prices. The model achieved a prediction error of approximately MAE Rp 1.98 billion and RMSE Rp 3.26 billion. Meanwhile, the Decision Tree Regressor obtained an R² score of 0.5560 with higher prediction errors, indicating a tendency toward overfitting and weaker generalization on testing data. Therefore, Linear R",0.0,https://ioinformatic.org/index.php/JAIEA/article/download/2073/140190775b911d696c736bb0ab8781323efb3d1643ec743,10.24269/ekuilibrium.v18i2.2023.pp149-155,The Influence of Sleman City Hall on Housing Price: A Case Study using OLX Data,2023.0,Ekuilibrium : Jurnal Ilmiah Bidang Ilmu Ekonomi,Muhammad Naurul Muna; Romi Bhakti Hartarto; Ibnu Hajar,"The Special Region of Yogyakarta (DIY) is one of the provinces in Indonesia with rapid development. This is indicated by the increasing number of shopping centers and the development of tourism and educational activities. Sleman Regency is a business center in DIY Province because it has the largest number of shopping centers in DIY Province compared to other districts. This study aims to determine whether the house adjacent to Sleman City Hall, the largest shopping center in Sleman Regency, has a higher selling price. While similar studies use primary data collected in the field through questionnaires, this study contributes to the property economics literature by utilizing secondary data from the OLX dataset to estimate the selling price of the house. A total of 229 data obtained through the OLX website related to selling prices, proximity to Sleman City Hall, and property characteristics were processed using multiple regression analysis with the ordinary least squares (OLS) technique. The finding of this study is that the distance from Sleman City Hall has a negative and significant effect on the selling price of the house. Another finding from this study is that the building ar",0.0,https://journal.umpo.ac.id/index.php/ekuilibrium/article/download/6622/27839082f9f80e3d0f62119c82ff3c4b9067b54c0a6d8e7,10.1108/ijse-09-2025-0818,"Human capital agglomeration, urbanisation and housing prices in China: evidence from quantile regression",2026.0,International Journal of Social Economics,Li-Ping Lan; C. Tang; Xiaojuan Pi,"909 910 We examine how human capital agglomeration and urbanisation jointly shape housing price dynamics in China. We aim to disentangle their distinct mechanisms and assess whether the effects of human capital concentration are consistent across different market segments.911 912 913 914 Using balanced panel data from 31 Chinese provinces between 2002 and 2021, this study applies quantile regression techniques to capture the heterogeneous effects of human capital agglomeration and urbanisation across the housing price distribution.915 916 917 918 The results reveal that human capital agglomeration significantly increases housing prices in lower-to-middle quantiles, reflecting agglomeration externalities and productivity gains in moderately priced markets. However, its influence weakens in the upper quantiles, where rapid urbanisation and congestion externalities dilute these benefits. Conversely, urbanisation exerts negligible effects at the lower end but increasingly negative impacts in high-priced regions, suggesting the presence of urban diseconomies and inefficiencies in spatial allocation.919 920 921 922 The findings underscore the need for differentiated and integrated policies aligning human capital cl",0.0,923964c651b1cccf2f8594e499d45e6cc3765f93aa6,10.5539/jsd.v17n3p28,"Modeling the Economic Value of Green Spaces in Residential Areas of Dar Es Salaam City, Tanzania",2024.0,Journal of Sustainable Development,N. Mwageni,"The economic value of green spaces in residential areas has been advocated to raise the land and property values. However, the establishment of the economic value of green spaces need many data such that it is costly and needs expertise. The objective of this study was to develop a user-friendly mathematical model that employ few data to determine the economic value of green spaces in residential areas in Dar es Salaam City. The study was cross sectional and employed structured questionnaires as a data collection method. Linear regression model was developed by relating economic values of green spaces to various environmental and socio-economic conditions of households. The study revealed that the net economic value of home greenery was significantly influenced by income of the households, area covered, age of the respondents and green space type. The average net benefit calculated from the model (1,317USD per household per year) was not far from the normal arithmetic mean calculated through traditional methods (1,369USD per household per year). This implies that the model can best predict the overall mean of the net benefit by 96.2%. It is therefore, recommended that the model can",0.0,https://ccsenet.org/journal/index.php/jsd/article/download/0/0/50123/54238924cf9f8418aa27028f4bf6b13e43509a10a8a67d26,10.5592/co/senet.2022.17,The Importance of Urban Spatial Grid Data in the Building Valuation Process,2023.0,Proceedings of the 6th IPMA SENET Project Management Conference “Digital Transformation and Sustainable Development in Project Management”,D. Kliment; Sofie Pokorná; Monika Doležalová,The aim of this paper is to structure information on environmental characteristics that influence housing prices so that it can be effectively used in the valuation process through digital visualization. A suitable data platform for real estate valuation entities should contain information about the spatial data of the urban grid in a structured form that is stored digitally and accessible to all. OpenStreetMap can be considered as such a platform. It is a relatively user-friendly open tool that can be used for spatial and market analysis. This activity is very necessary in the valuation process as it is very time consuming and requires extensive data collection. This paper examines the environmental spatial characteristics in the urban grid to see whether or not they have an impact on the price of housing. These influences are examined using a non-parametric test that allows two groups or conditions to be examined. The Mann-Whitney U test is a test for ordinal data that is similar to a two-sample t-test. Nonparametric tests are used when a certain distribution of the data cannot be assumed. The paper concludes by presenting a systematic approach to incorporating information on the,0.0,https://publications.ipma.world/wp-content/uploads/2023/12/17-6th-IPMA-SENET-2022.pdf92583b73b12db5defc6adb808a0f172e71e1b653a22,10.5194/isprs-archives-xlviii-4-w8-2023-211-2024,"TOWARD SPATIAL AND SOCIAL EQUALITY: INVESTIGATING AND MODELING THE LINK BETWEEN OPPORTUNITY AND URBAN INTENSITY IN METRO MANILA, PHILIPPINES",2024.0,"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences",M. A. B. Ensina; J. P. Tabernilla; B. Carcellar III; M. Martinez,"Abstract. This paper employed an interdisciplinary mixed-method approach combining geomatics, geography, and sociology to investigate the link between opportunity and urban intensity across the 32 legislative districts of Metro Manila. Eighteen opportunity indicators, weighted within their opportunity dimensions using Analytic Hierarchy Process (AHP) were used to output Opportunity Scores, while annual VIIRS DNB nighttime light was used as proxy data to approximate the region’s Urban Intensity Score. Two regression models were developed with different sets of explanatory variables: an AHP-based model that used the top two weighted indicators from each opportunity dimension, and a model that used Exploratory Regression Analysis (ERA) to determine the appropriate variable combination. Modeling via Ordinary Least Squares Regression (OLS) produced a properly specified global model, while OLS modeling of the ERA-based model exhibited non-stationarity between variables which necessitated performing Geographically Weighted Regression (GWR). Our quantitative findings, combined with insights from our mini focus group discussion (FGD), provided evidence of the unequal distribution of opportu",0.0,https://isprs-archives.copernicus.org/articles/XLVIII-4-W8-2023/211/2024/isprs-archives-XLVIII-4-W8-2023-211-2024.pdf926df3b01049852298482773dcc72913d319ba7796d,10.47264/idea.nasij/4.1.7,"Effects of road infrastructure development on residential property value and land development shifts in a peri-urban area of Karachi, Pakistan",2023.0,Natural and Applied Sciences International Journal (NASIJ),Humaira Nazir; Reena Majid Memon; M. Yousuf; Maryum Sajid Raja,"The introduction of road infrastructure development projects often triggers expectations of shifts in property values. Extensive research has already been conducted regarding the impact of such projects on property values and land development in developed regions. However, there is a research gap when it comes to developing countries like Pakistan. Therefore, this study aimed to investigate the influence of the road infrastructure development project, the M-9 Motorway, on residential property values and land development in a mega residential project in Bahria Town Karachi (BTK), Pakistan. This study employs a mixed-methodology approach, incorporating qualitative methods (interviews) and quantitative analysis (questionnaire surveys and property data analysis). Statistical tools such as Statistical Package for Social Sciences (SPSS), the Pearson correlation coefficient, and the Chi-square goodness of fit test were used for correlation and hypothesis testing. This research reveals that proximity to a major road infrastructure project directly impacts property purchase prices, rents, and land development in BTK, either positively or negatively. The obtained results align with both expe",0.0,https://www.ideapublishers.org/index.php/nasij/article/download/933/396927dd785bf30fa4e655690ff985a8b4e95f050b6a93,10.52436/1.jpti.846,Optimasi Prediksi Harga Rumah dengan Random Forest dan Optuna Hyperparameter Tuning,2025.0,Jurnal Pendidikan dan Teknologi Indonesia,Reza Muhammad Fadzryan; Eka Angga Laksana,"Prediksi harga rumah merupakan aspek penting dalam pengambilan keputusan investasi di sektor properti. Penelitian ini bertujuan untuk membandingkan beberapa algoritma Machine Learning dan Deep Learning dalam memprediksi harga rumah, serta mengoptimalkan Random Forest Regressor menggunakan Optuna dengan implementasi Tree-structured Parzen Estimators (TPE). Dataset yang digunakan adalah House Price 2023 Dataset dari Kaggle, yang mencakup 168.000 entri data properti di Pakistan. Metodologi penelitian ini meliputi tahap preprocessing data, rekayasa fitur, serta penerapan beberapa algoritma prediksi, yaitu Artificial Neural Networks (ANN) dengan model Feedforward Neural Network, KNeighborsRegressor, Linear Regression, dan Random Forest Regressor. Model-model ini dievaluasi menggunakan metrik MAE, MSE, RMSE, R-squared, dan Akurasi. Random Forest Regressor memberikan hasil terbaik dengan R-squared 0.91 dan Akurasi 91.33%. Untuk meningkatkan performa model, diterapkan optimasi hyperparameter menggunakan Optuna dengan pendekatan TPE yang berbasis Bayesian Optimization. Hasil model yang dioptimalkan mencapai peningkatan performa dengan R-squared 0.92 dan akurasi 91.75%. Hasil ini menunjukkan",0.0,https://jpti.journals.id/index.php/jpti/article/download/846/508928d811b6629dc28520a63cd5f26f8a571d4f4de886,10.31098/ijmesh.v9i1.3567,Improving Housing Price Prediction with Machine Learning: Insights from Yogyakarta,2025.0,"International Journal of Management, Entrepreneurship, Social Science and Humanities",Galuh Sudarawerti; Fahmi Arif,"Predicting housing prices accurately remains a major challenge in the real estate industry, especially in fast-urbanizing areas where both structural and locational factors are at play. This study focuses on the Special Region of Yogyakarta, Indonesia—a city with varied land use and dynamic housing market conditions—to explore how machine learning (ML) can support better price forecasting. Using the CRISP-DM framework, we analyzed data from 2,020 residential listings, incorporating variables such as building area, land size, number of rooms, and district. Among the several classification models tested, Random Forest achieved the highest accuracy and F1-score. According to the feature importance analysis, building area, land area, and district emerged as the strongest predictors, while vertical features like the number of floors had relatively little effect. These findings suggest that in Yogyakarta’s market, observable physical features may play a bigger role in price determination than location-specific factors. While the study offers a practical framework that real estate professionals can apply, its reliance on structural data and a single-region focus does limit how broadly the",0.0,https://journals.researchsynergypress.com/index.php/ijmesh/article/download/3567/1882929056678cc4f500b4e1c8725f9d8280ce0a5af2b64,10.52783/jisem.v10i41s.7980,Property Price Prediction Using Machine Learning,2025.0,Journal of Information Systems Engineering & Management,Armaan Siddiqui; Gayathri Thekkayil; R. Verma; V. Verma; Dr. Rajni Kaushik,"To make smart investment choices in the rapidly evolving property sector market of today, it's critical to have reliable tools for estimating property prices This study aims to come up with a comprehensive machine learning-based system for forecasting property prices. The proposed solution incorporates historical property data and takes into consideration the future development plans to enhance prediction accuracy. 930Using crucial factors that effects the property prices such as location, square footage, count of bedrooms, and bathrooms, this study presents a machine learning model that assists in estimating residential sector pricing. The model also accounts for actual developments that may impact property value, such as new highways, train stations, airports, retail centres, and other public infrastructure. The system also integrates financial tools, including an EMI calculator and a rent estimator based on user-defined profit margins. In the advanced phase, a decade’s worth of historical data, combined with categorized property classes and reasons for annual price shifts, was used to further enhance accuracy. A “Future Factors Database” was incorporated to account for upcoming i",0.0,https://jisem-journal.com/index.php/journal/article/download/7980/3645931dcebaba2290f2b160bba96f8383d76309062cdb3,10.55041/ijsrem30682,Machine Learning based Predicting House Prices using Regression Technique,2024.0,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,Kunal Sapkal,"Predicting the price of a house helps for ascertain the house's selling price in a specific area and assist individuals in determining the ideal moment to purchase a home. Our goal in this machine learning task on house price prediction is to use data to develop a machine learning model capable of predicting housing values in the specified area. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to increase the model's efficiency for this project we are using supervised learning, which is a part of machine learning. We have to go through different attributes of the dataset. This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This suggested model is thought to be the most accurate one for estimating home prices and makes the most accurate predictions. This offers a succinct overview, which is necessary in order to forecast the price of the home. This project consists of what and how the house price model works with the assi",0.0,https://ijsrem.com/download/machine-learning-based-predicting-house-prices-using-regression-technique/?wpdmdl=29996&refresh=665b4aae3cd61171725892693286c565ab964cee381f1e9eb28e73c1ff1dbd1728,10.3390/buildings12071021,Schools’ Capitalization into Housing Values in a Context of Free School Choices,2022.0,Buildings,Mohammad Ismail; Abukar Warsame; Mats Wilhelmsson,"The issue of schools and their capitalization in property values has been analyzed extensively. Our contribution is to analyze this effect in an alternative institutional context. In this case study, we analyzed the housing market in Stockholm, Sweden. What distinguishes the Swedish school system is that we have a free choice of schools, which means that a family does not necessarily have to live in a school district to access the schools in that area. This means that families do not have to move to the district to which they intend to send their children but can apply to send them there regardless of whether they live there or not. Nevertheless, families might be interested in living close to good schools to be within walking distance of these schools. This is especially true at the primary school level. Therefore, we analyzed schools’ capitalization in property values in the context of free school choice. We used data on transaction prices for condominiums in Stockholm’s inner city. The results indicate a capitalization of living close to good schools, but this capitalization is limited. We can show that schools’ capitalization depends partly on the quality of the schools and par",0.0,https://www.mdpi.com/2075-5309/12/7/1021/pdf?version=165787793593330ee417f35fae286e354b8696a870dca54aee886,10.54254/2754-1169/50/20230580,House Prices Prediction Advanced Regression Techniques,2023.0,"Advances in Economics, Management and Political Sciences",Yue Zong,"In recent years, with the development of the real estate industry, housing prices have continued to rise. The nation, society, and individuals are all concerned about these prices. For commodity housing prices, there are many factors that influence the housing prices. Apart from national regulations, factors such as lighting, layout, and environment of the houses themselves also have a certain impact on the prices, leading to significant fluctuations in the real estate market. Therefore, researching an accurate model for predicting housing prices has practical significance. It can guide residents in housing consumption and provide policy recommendations for government price regulation. Machine learning methods have become a new type of prediction method in this regard. Based on the theories of data analysis and machine learning, a dataset consisting of 2920 data points with 81 attributes was selected from the publicly available Kaggle housing dataset. The data was normalized and analyzed for feature selection. The ranking of attributes most correlated with housing prices was obtained. Subsequently, a neural network model was built, parameters were adjusted, and the trained network ",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/7085/pdf934eb20d6eaa9f35ff701a1badb2aa1c88015de9c5d,10.22214/ijraset.2026.80865,House Price Prediction Using Customer Needs and Financial Analysis,2026.0,International Journal for Research in Applied Science and Engineering Technology,Aman Singh,"The prediction of house prices has become one of the important uses of machine learning in the real estate field.935Accurate price predictions based on data help buyers, sellers, real estate agents, and investors to make informed financial936choices. In this research project, a model based on machine learning is created to estimate house prices by using various937property-specific features. These features include location type, area measured in square feet, number of bedrooms and938bathrooms, number of stories, type of road access, availability of amenities such as guestrooms, basements, hot water heating,939air conditioning, parking availability, preferred area designation, and the status of furnishing. The dataset is sourced from940publicly accessible housing records and goes through a detailed preprocessing pipeline that involves dealing with missing values,941eliminating outliers, encoding categorical variables via label encoding and ordinal mapping, and normalizing numerical942features to ensure the model trains consistently. Techniques for feature engineering are utilized to create meaningful943representations from the original attributes. Several machine learning algorithms are put into pract",0.0,https://doi.org/10.22214/ijraset.2026.80865944c7dbeff268cb294509ab735f32ca5aa11d5d1f84,10.3846/jbem.2026.23923,The spatial relationship between innovation capacity and housing affordability in Chinese cities,2026.0,Journal of Business and Economic Management,Juan-Feng Zhang; Ning Hao; Dong-Sheng Zhan; Lele Li; Danxia Zhang; Rui Han,"With China’s rapid urbanization, innovation capacity and housing affordability have become important factors affecting sustainable development. This paper aims to address this research gap by examining the spatial characteristics and correlation between the housing market and urban innovation capacity. With statistical analyses of the housing market and innovation indicators in China’s major cities, there are following findings. Firstly, it is found that there are obvious spatial characteristics between urban innovation ability and housing affordability. Housing supply has a significantly positive effect on urban innovation, particularly in central regions. Similarly, housing demand also positively impacts urban innovation. The impact of the housing supply-demand ratio on urban innovation exhibits regional variation. Secondly, the impact of housing affordability on urban innovation levels exhibits a nuanced pattern, resembling an inverted “U” shape, particularly pronounced in the eastern region. When the housing price-to-income ratio remains below a critical threshold, its growth positively contributes to enhancing urban innovation levels. Thirdly, an increase in the housing supply",0.0,https://journals.vilniustech.lt/index.php/JBEM/article/download/23923/1331694564b1bda38786678275a764034582ddd52712905b,10.54254/2753-7048/2025.21584,Research on the Impact of Real Estate Commercial Housing Prices on Societal Marriage RatesAn Empirical Analysis Based on Provincial Panel Data,2025.0,Lecture Notes in Education Psychology and Public Media,Jia-Cheng Zhao,"With the rapid rise in housing prices in China, young people face increasing financial pressure, especially when it comes to purchasing homes and establishing families. As a result, an increasing number of individuals are choosing to delay or forego marriage altogether. This study investigates the impact of real estate prices on marriage rates, based on provincial panel data in China from 2005 to 2022. By employing various regression models, including log-linear and polynomial regression models, the study analyzes the dynamic relationship between housing prices and marriage rates. Several control variablessuch as per capita GDP, divorce rates, birth rates, and education levelsare incorporated to enhance the explanatory power and robustness of the results. Findings show that rising housing prices significantly suppress marriage rates, particularly in economically disadvantaged areas or regions with high price volatility. Specifically, a 1% increase in housing prices is associated with an average 0.36% decrease in the marriage rate. This research not only provides a new perspective on the relationship between housing prices and marriage rates but also serves as an important reference",0.0,https://doi.org/10.54254/2753-7048/2025.2158494627a9730fb824550cf0026c2f99e01c0133473367,10.1108/pm-06-2025-0078,Integrating occupant sentiment into multi-unit high-rise residential property valuation,2025.0,Property Management,Ishaan Thakur; D. Patel,"947 948 This study introduces a framework to formalise and consider occupant comfort perceptions in the property valuation process. It aims to quantify occupant perceptions to assist real estate valuers in valuing the “comfort” aspect of residential properties.949 950 951 952 The study initially employs the Delphi process to shortlist occupant comfort factors and subfactors. It then uses structured occupant surveys to gather occupant perception scores and applies the best holistic adaptable ranking of attributes technique to quantify occupant perception scores and ultimately derives the facility Occupant Sentiment-Centric Apartment Rating System (OSCARS) score (FOS). The comparison of the FOS value with the prevailing average OSCARS score (AOS) yields the occupant-based property value (OPV) correction factor. The OPV correction factor is then proposed as a diagnostic tool to adjust the market value of a residential apartment.953 954 955 956 This study develops and presents a novel framework to integrate occupant sentiment into the property valuation process and also proposes a continuous improvement mechanism for multi-unit high-rise residential buildings.957 958 959 960 The findings of this study offer a pra",0.0,961bcffab4fd928bac1e53a886d61e6bc54da1d9f01,10.36825/riti.14.33.006,"Inteligencia artificial en la valuación inmobiliaria: Avances, desafíos y perspectivas",2026.0,Revista de Investigación en Tecnologías de la Información,Iván Humarán Nahed; Pedro Alfonso Aguilar Calderón; Julio Ernesto Osuna Covarrubias,"Artificial intelligence (AI) has acquired an increasing role in real estate valuation using automated valuation models (AVM), designed to streamline processes and analyze large volumes of data. This article examines recent advances in the application of AI to real estate valuation, as well as its main technical, ethical, and regulatory challenges, and, above all, the accuracy of its estimates in relation to the sale values found in the real estate market on a given date. With an emphasis on emerging markets, the study combines a review of specialized literature with a case study of a residential property valued using an automated platform, comparing the result obtained with a reference value based on publicly available market information. The results of the case study show an absolute percentage deviation of 38.89%, highlighting significant limitations of AVM when applied in contexts with high urban heterogeneity, a scarcity of reliable transactional data, and limited submarket segmentation. Based on these findings, the role of AI as a support tool in valuation practice is discussed. It is concluded that AI does not replace the professional appraiser, but rather complements their w",0.0,https://doi.org/10.36825/riti.14.33.0069628e049f6a152545a8d92132eca7bb29eb25090558,10.1108/ijhma-07-2025-0178,"Solo demand, rising rents: how young singles shape urban housing costs",2025.0,International Journal of Housing Markets and Analysis,Bilal Çalışkan; Ziya Usta; Masud Hatami,"963 964 This study investigates how the concentration of young single-person households (aged 24–35 years) influences small-unit rental prices in Istanbul’s fragmented housing market. Amid rapid demographic shifts and urban restructuring, this paper aims to provide robust causal evidence on the demand-side pressures driven by young singles in shaping rental dynamics, particularly in the increasingly competitive one-bedroom apartment segment.965 966 967 968 Using a novel cross-sectional data set covering 605 neighborhoods in Istanbul, this study applies a two-stage least squares instrumental variable strategy to address endogeneity, particularly simultaneity bias. The spatially lagged share of young singles is used as an instrument. The model controls for a rich set of neighborhood-level variables, including accessibility, amenities, income, education, population structure and housing stock age.969 970 971 972 A one-percentage-point increase in the share of young single-person households leads to a 4.2% increase in average rents for one-bedroom apartments. These effects are more pronounced in neighborhoods near employment centers, with strong transit access and lifestyle amenities. The results of this ",0.0,973d88066da23d0c5a23c8989816131abd4e4ebd915,10.55186/25880209_2025_9_4_12,THE CURRENT STATE OF ASSESSMENT ZONING OF TERRITORIES ON THE EXAMPLE OF THE CITY OF KRASNODAR,2025.0,International Agricultural Journal,Y. Zaitseva; Yuri Savchenko,"The article is devoted to a comprehensive study of appraisal zoning of the territory as a fundamental method of spatial differentiation of real estate value. The essence of appraisal zoning as an interdisciplinary category at the junction of cadastre, economics, geography and land law is revealed, emphasizing its key role in the system of mass cadastral valuation for the purposes of taxation, land management and territorial planning. Based on Russian regulatory documents, the methodology of appraisal zoning, including a chain of successive stages, is analyzed in detail. Particular attention is paid to the factors determining the cost and the critical role of GIS technologies in ensuring the quality and visibility of the process. The practical significance of appraisal zoning is confirmed by an original study on price zoning of land plots for individual housing construction in Krasnodar. Using the Avito platform data, based on the principles of mass appraisal, the authors identified 9 statistically significant price zones, revealing significant spatial heterogeneity of the market. The conducted research substantiates the fundamental importance of constantly improving, scientifically",0.0,https://iacj.ru/en/storage/download/227059974908cb1f7de6418955aafb03a45211fce8ae7cf06,10.15680/ijmrset.2025.0808003,"Entrepreneur and Broker at Sotheby’sInternational Realty, Kirkland, USA",2025.0,"International Journal of Multidisciplinary Research in Science, Engineering and Technology",Aditya Kasturi,"Predicting the housing prices correctly is one of the major problems in urban analytics, especially when975the area experiences high micro-geographic heterogeneity. The spatial, structural, and socioeconomic relationship is976often nonlinear and is complex and traditional models do not give an accurate picture of the relationships of variables at977the level of the neighborhood. In this paper, a deep learning architecture is proposed to accommodate hyper-local data978sources and multi-dimensional sets of features to predict the prices of housing in the Tri-County Region of Seattle979(which includes the counties of King, Pierce, and Snohomish).980The goal is to build a data driven model which could advance the accuracy of price (forecast) with higher precision at981the sub regional level to provide finer granularity of valuations. We use deep neural network (DNN) model that is982trained on property-level data presented by Zillow and Redfin which are supplemented with demographic, locational983and temporal features provided by the U.S. Census Bureau and OpenStreetMap. The locational resolution is ensured984with the help of data normalization (date normalization and feature encode nodes), and geospatial ",0.0,985da88ef8e3cfc6b2448b50502622c009fefeb1578,10.63887/jtie.2026.2.4.13,A Study on House Price Prediction Based on Multi-Model Fusion: Centered on PCA and Random Forest,2026.0,Journal of Technology Innovation and Engineering,Rui-Qi Zhou; Yi-Ming Xu,"Based on the Ames housing price dataset, this paper constructs and compares the performance of various machine learning models for real estate price forecasting. To address the challenges of high-dimensional features and missing values, we first improve data quality through data cleaning and feature selection. Building on this foundation, we propose a modeling framework that combines principal component analysis with regression trees, effectively achieving feature dimensionality reduction and model complexity control. Additionally, a random forest regression model was introduced to enhance the model’s generalization ability and stability through multi-tree ensemble and random feature selection mechanisms, with performance evaluated using out-of-bag error. Furthermore, log-transformed linear regression and ridge regression models were constructed as controls to analyze the impact of linear relationships and regularization on prediction performance. Experimental results indicate that ensemble learning methods possess distinct advantages in capturing nonlinear relationships and feature interactions, with the Random Forest model demonstrating the best performance in terms of prediction",0.0,https://www.sci-open.net/index.php/JTIE/article/download/17341/18247986f1023c931d10dcea94362fa5c5fcdcdec0916c51,10.55041/ijsrem26175,House Price Prediction using R language,2023.0,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,P. Gawali,"The purpose of this project is to use the R language to predict house prices based on various features, such as location, size, number of rooms, and other characteristics, using a dataset from Kaggle. In order to gain a better understanding of the data, we conducted exploratory data analysis, which included assessing the distribution of variables, identifying missing values, and detecting potential outliers. Additionally, we examined the correlation between the features and the target variable After performing data cleaning and pre- processing, we employed multiple machine learning algorithms, such as linear regression, random forests, and XGBoost, to construct predictive models. We evaluated the performance of these models using various metrics, such as mean absolute error, mean squared error, and root mean squared error. Our findings revealed that XGBoost had the best performance, achieving a root mean squared error of 0.13, indicating high predictive accuracy. We also performed a feature importance analysis to identify the most critical variables for predicting house prices. In summary, our project highlights the effectiveness of using R language and machine learning techniques ",0.0,https://ijsrem.com/download/house-price-prediction-using-r-language-2/?wpdmdl=24443&refresh=65c20f0e372451707216654987d06fc4aa383758554a6afa97931f1ea615750202,10.47127/ijtmr.v8i1.152,The The Real Estate Market in Ghana: The Driving Factors and Housing Price.,2023.0,International Journal of Technology and Management Research,Firmin Anewuoh; W. Gyadu-Asiedu; Kennedy Appiadu-Boakye,"Abstract 988The pricing of real estate houses in Ghana is a great concern to most Ghanaians. Numerous factors contribute to this. This study aimed to investigate the driving factors that influence Real Estate pricing of houses in Ghana. A total of 196 set of structured questionnaires were administered to members of the Ghana Real estate Developers Association (GREDA) and comprised of contractors, consultants, the client’s team, and others. Out of these questionnaires distributed, 130 completed questionnaires were returned. The results of the analysis showed the following as the main factors driving real estate prices: construction cost, financial market, structural features, government policies, macroeconomic determinants, and shifting population demography to be the major factors driving the pricing of real estate residential houses in Ghana. The value of R (the correlation coefficient of the regression analysis) was 0.846, indicating a very high positive correlation existing between the dependent and independent variables. This suggests that the two variables are moving in the same direction. The study concludes that 62.9% of the variations in the pricing of real estate residential",0.0,https://ijtmr.ktu.edu.gh/index.php/ijtmr/article/download/152/123989ce686a8609714c436a9dbe1ee091848ab891f6be,10.31577/sn.2025.3.31,Proximity to Kindergartens and Housing Price: The Case of Bratislava Hinterland,2025.0,Slovenský národopis / Slovak Ethnology,Ján Výbošťok; F. Križan; Michala Sládeková Madajová,"The paper aims to explore the relationship between proximity to kindergartens and housing prices in the suburban hinterland of Bratislava and to identify the nature of this relationship in the context of current education policy. Using a log-linear regression, this analysis examines whether kindergarten accessibility – measured through distance and capacities – contributes to higher housing prices. Contrary to prevailing assumptions, the results reveal that the influence of kindergartens on housing prices is negative. Although statistically significant, the effect sizes are negligible, suggesting that kindergartens do not have a decisive impact on shaping residential property prices in the Bratislava hinterland. Additionally, the negative association may suggest that new development, characterised by higher housing prices, is located in areas with limited accessibility to kindergartens. The results challenge established paradigms of amenity capitalisation and emphasise the need for context-sensitive interpretations. They also underscore the importance of integrated urban planning that aligns housing development with timely public service provision. This study offers new empirical i",0.0,https://www.sav.sk/journals/uploads/11022151SN3_2025_31.pdf990a18940da89b0d3fb5b6f41be77978f744228c5a3,10.1145/3833541.3833605,Predicting UK House Price Growth Using Random Forest Regression: Evidence from Mortgage Rate Dynamics,2026.0,Proceedings of the 2026 3rd International Conference on Digital Society and Artificial Intelligence,Zi-Yu Hao,"This study uses random forest regression to predict monthly UK real house price growth from lagged mortgage interest rate changes. The data span February 1995 to December 2023, a period that includes the 2008 financial crisis, the COVID-19 pandemic, and the post-2021 tightening cycle. House prices come from the Office for National Statistics; mortgage rates from the Bank of England. Both series are transformed into stationary growth rates via log-differencing and first-differencing, then organized into a lagged feature matrix with four lags per variable. The model is trained on the first 80 percent of the sample and tested on the remainder. On the test set, predictive accuracy is low (R-squared of 0.05), and feature importance analysis shows that past house price growth accounts for roughly 80 percent of total importance, leaving mortgage rate lags with about 20 percent. Partial dependence plots confirm only a weak, negative link between rate increases and predicted price growth. These results echo recent findings that structural supply constraints, regional heterogeneity, omitted macroeconomic fundamentals, and possible regime shifts weaken the national-level transmission from int",0.0,https://doi.org/10.1145/3833541.38336059919379a831b3b2f7db97097fae845b5170f9090a31,10.47127/ijtmr.v8i1.162,The Real Estate Market in Ghana: The Driving Factors and Housing Price.,2023.0,International Journal of Technology and Management Research,Firmin Anewuoh; W. Gyadu-Asiedu; Kennedy Appiadu-Boakye,"Abstract 992The pricing of real estate houses in Ghana is a great concern to most Ghanaians. Numerous factors contribute to this. The purpose of this study was to discover the key variables influencing Ghanaian real estate pricing. Members of the Ghana Real Estate Developers Association (GREDA) were given a total of 196 sets of structured questionnaires. The members comprised of contractors, consultants, the client’s team, and others. Out of these questionnaires distributed, 130 completed questionnaires were returned. The results of the analysis showed the following as the main factors driving real estate prices: construction cost, financial market, structural features, government policies, macroeconomic determinants, and shifting population demography to be the major factors driving the pricing of real estate residential houses in Ghana. The value of R (the correlation coefficient of the regression analysis) was 0.846, indicating a very high positive correlation existing between the dependent and independent variables. This suggests that the two variables are moving in the same direction. The study concludes that population demographics, construction cost, structural feature, macroe",0.0,https://ijtmr.ktu.edu.gh/index.php/ijtmr/article/download/162/125993a49c53108de47077ee7958e78c549b84f1972fde,10.4314/jasem.v29i2.19,"Evaluation of the Impact of Impact of Traffic Congestion on Commercial Property Rental Value in Mararaba, Karu LGA, Nasarawa State, Nigeria",2025.0,Journal of Applied Sciences and Environmental Management,B. Ayuba; S. Adamu; K. Muhammad; M. Dauda,"The objective of this paper was to evaluate the Impact of Traffic Congestion on Commercial Property Rental Value in Mararaba, Karu LGA, Nasarawa State Nigeria using appropriate standard procedure after collecting data from 127 respondents through a structured questioner administration. The study concluded that the traffic congestion has effect on the commercial property rental value in the study area. The study further established that: the problems including air pollution, crime activities and waste of productive hours were prevalent issues caused as a results of traffic congestion in the study area; main causes of traffic congestion in the study area are proximity of commercial properties (market) to the highway, early morning and evening hours rushing to and from work, and nonchalant attitude (behavior) of road users; traffic congestion has effect on the rental value of commercial properties in the study area hence business activities were found to increase as a result of traffic congestion and Estate Surveyors charged more rent as a result of this; Therefore, the study concluded that the traffic congestion has an impact on commercial properties value in the study area. The st",0.0,https://www.ajol.info/index.php/jasem/article/download/289383/27231599454acc71437b75348f43b0cba594fd0d879334b50,10.12692/ijb/22.3.92-101,"Residential property price forecasting model for central Pangasinan, Philippines: Input to enhancing resilient planning and disaster mitigation strategies",2023.0,International Journal of Biosciences,Jr Fernando S. Viray; Xin Liu,"This quantitative-experimental study aims to create a price forecasting model for the residential real properties of the fourteen municipalities and cities of central Pangasinan using supervised learning classification algorithms (linear regression and decision tree) to predict whether the real property’s value will increase or decrease in the future and classic statistical forecasting techniques (straight line, moving average, simple linear regression, and multiple linear regression) to predict the rate of increase or decrease using a -+5% margin of error. Data used were derived from the Residential Real Estate Price Index (RREPI) of the Banko Sentral ng Pilipinas (BSP) from 2016 to 2021, Zonal Valuations (ZV) from the Bureau of Internal Revenue (BIR) from 1990 to 2023, and the Housing Cost Construction Index (HCCI) from the Philippine Statistics Authority (PSA) from 2006 to 2021 following an 80:20 training-testing data split ratio. The resulting model utilizing the RandomForest algorithm shows a significant 93% accuracy rate and 93% precision rate. Results further showed that machine learning-based algorithms performed better than the four classic statistical forecasting techniqu",0.0,https://innspub.net/wp-content/uploads/2023/08/IJB-V22-No3-p92-101.pdf995738bf39f1bf0152f03044b4f2c8c39aaa0cac91f,10.52903/wp2026359,Threshold-based policies and distortions in housing markets,2026.0,Working papers,D. Karamanis; Christos Kotsogianis; Evangelia Papapetrou,"This paper examines how investment thresholds embedded in residence-by-investment programmes shape housing market outcomes. The analysis exploits a reform of Greece’s golden visa scheme that selectively doubled the minimum real estate investment requirement - from €250,000 to €500,000 - across municipalities. Using comprehensive administrative data on property transactions from 2017-2024, the study combines a bunching framework with a difference-in-differences design to identify behavioural responses to eligibility thresholds and their spatial equilibrium effects. Under the initial regime, pronounced bunching in transaction prices is observed around the €250,000 cutoff, indicating that the threshold was a salient determinant of pricing behaviour. Following the reform, this bunching disappears in municipalities subject to the higher threshold, while persisting in unaffected areas. Consistent with this pattern, treated municipalities experience a relative deceleration in price growth, alongside increased transaction activity in nearby markets that remain eligible under the lower threshold. The results demonstrate that residency-investment thresholds may operate as active policy instr",0.0,https://doi.org/10.2139/ssrn.6441680996dfcdccec55fa476feaef6eaa9a2a79d1a9c3707d,10.17265/1934-7359/2020.09.002,"“Dangerous” Buildings and Urban Planning: Locus, Legal Framework and Assessment for the City of Larissa",2020.0,,M. Markatou,"Dangerous, vacant and abandoned buildings traditionally have been considered as negative elements in the urban environment. Their “harmful” properties include hosting “nests” of criminality (drug trafficking etc.), becoming a threat to public safety (easy to catch fire, collapsing building materials etc.), degrading conditions of public health (rubbish damp, rats infesting etc.), affecting property values in the surrounding areas, loading their owners with taxes (property tax etc.) and no revenues, imposing negative impacts on the aesthetics and the quality of the urban environment. On the other hand, they might trigger opportunities for urban regeneration, provide new available spaces for urban uses if demolished, and provide a stock of urban elements of special characteristics, to be used for the formulation of housing policies. The present article reviews urban policies focusing on these properties and assesses existing implementations. The various factors characterizing the above initiatives constitute challenging planning and legal cases. The complexity of the issue of abandoned buildings in the urban environment, is to be tested in the case of the city of Larissa, Greece. Leg",0.0,https://doi.org/10.17265/1934-7359/2020.09.002997a362e342d478ec7efe873228840ed32d58bbfec9,10.64149/gjaets.13.2.1-6,A MACHINE LEARNING-BASED HYBRID FRAMEWORK FOR REAL ESTATE PRICE PREDICTION,2026.0,Global journal of advanced engineering technologies and sciences,Shyam Patidar; Arpit Solanki,"Accurate prediction of real estate prices plays a significant role in financial planning, investment decision-making, urban development, and smart city management. Traditional property valuation approaches mainly rely on statistical regression techniques and expert judgment, which often fail to model nonlinear market behavior and heterogeneous housing datasets. To overcome these limitations, this research proposes a novel Hybrid Adaptive Real Estate Prediction Algorithm (HAREPA) within a machine learning–based hybrid framework. 998The proposed system integrates Linear Regression, K-Nearest Neighbor, and Support Vector Regression models through adaptive ensemble learning. Feature engineering techniques are employed to extract meaningful attributes and improve learning efficiency. Adaptive weights are automatically computed using inverse error analysis, enabling dynamic contribution of each model during prediction. 999Experimental evaluation is conducted using a real-world real estate dataset obtained from Kaggle. Performance comparison using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and prediction accuracy demonstrates that the proposed HAREPA model significantly outperf",0.0,https://gjaets.com/index.php/gjaets/article/download/402/3831000f506aa6caa08e347b34a7d9e6e3046803e908d80,10.5220/0013988000004916,Localization Marketing and Brand Building: A Comparative Study on Real Estate Marketing Strategies Between Vanke and Capitaland Group,2025.0,Proceedings of the 2nd International Conference on Public Relations and Media Communication,Xi-Yu Long,": In the context of intensifying competition in the real estate market and shifting consumer focus from hard factors to soft values, brand marketing has become crucial for real es-tate companies to build their competitiveness. This paper delves into the brand building and mar-keting strategies of Vanke and CapitaLand. Vanke focuses on residential development, achieving a light-asset transformation through ""small equity management."" By enhancing product quality and optimizing services—such as the rapid certification and transparent construction of the Zhongshan Vanke Central Park project to boost customer trust, and creating an eco-community in Suzhou projects to enhance reputation—Vanke strengthens community operations and property management, emphasizing management capabilities and local adaptability. CapitaLand, centered on commercial real estate, leverages the ""private equity fund + REITs"" model to achieve asset circulation. By adjusting marketing strategies, improving self-channel capabilities, developing distinctive product lines, and enhancing customer service, CapitaLand focuses on capital opera-tions and cultural scene innovation, aiming for internationalization and premium",0.0,https://doi.org/10.5220/0013988000004916100170063f8826172165e64de3b8296be64bdb1442f0,10.18698/2542-1468-2022-5-40-53,Geoinformation analysis of changes in Stavropol urban forestry borders,2022.0,FORESTRY BULLETIN,D. Lipilin; D. Gura; E. Yarotskaya; N. Naumenko,"A study was carried out by comparing the spatial data of the project for the organization and management in the Stavropol forestry enterprise. The total area of the Stavropol urban forestry from 1997 to 2021 increased by 564,7 hectares due to the addition of a steppe landscape territory. It was established that 429,75 hectares of forested areas were excluded from forestry. Based on the information from the Unified State Register of Real Estate, the areas that were excluded from the forestry are currently used for residential development. Satellite images show the boundaries of the territories of the stows in 1997 and 2021, marked with different colors of the territory excluded from the total area of forest areas, as well as added areas. The comparative analysis of data for 1997, 2013 and 2021 allows us to identify all the changes in the qualitative and quantitative aspects. In accordance with the results of the study, during the study period, there is a positive trend in the increase in the area of the Stavropol urban forestry. However, it has been established that, based on the results of a detailed analysis of changes in the area of natural boundaries and regional reserves, the a",0.0,https://doi.org/10.18698/2542-1468-2022-5-40-531002d442f211310c545ee19312ea6a87fa7e19e4a4ba,10.54254/2754-1169/2025.20677,"The Impact of Talent Attraction Policies on Housing Prices in Haikou, Nanjing, and Wuhan",2025.0,"Advances in Economics, Management and Political Sciences",Y. Han,"In the context of escalating global urban competition, attracting top talent has become essential for cities to boost economic competitiveness. Talent attraction policies significantly impact local economies and real estate markets by driving population growth and enhancing urban innovation. Talent attraction programs profoundly influence local economies and real estate markets by stimulating population expansion and fostering urban innovation. This study focuses on Haikou, Nanjing, and Wuhan, using empirical methods to analyze the impact of talent attraction policies on housing prices. Through parallel trends tests and group regression analyses, results show that these policies significantly affect housing prices, with policy implementation leading to notable price increases. Additionally, mediating effects analysis indicates that research and development levels and patent grants mediate the relationship between talent attraction policies and housing prices, highlighting innovation as a key mechanism. Placebo tests confirm the validity of these policy effects, revealing no significant impacts outside the implementation periods. This paper advises governments to refine policy measu",0.0,https://aemps.ewapub.com/article/view/20677.pdf1003c5e5700da734e69b04f37bcc3357c2eb0a0e803f,10.2991/aebmr.k.220307.257,Analysis of the Impact of Population Structure Change on Housing Pricei China,2022.0,Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022),Zih-Yuan Zeng,"It is very important to discuss the impact of China's demographic structure change on the real estate market in the process of gradually disappearing “demographic dividend” and increasing aging. In this paper, unitary linear regression model was adopted to explore the impact of dependency ratio changes on housing price based on panel data of Provinces in China from 2002 to 2019. Further heterogeneity analysis method was adopted to perform grouping regression from different dependency ratios, regions and time perspectives. The results of this study are as follows: (1) the total dependency ratio has a negative relationship with housing price. (2) There is a positive relationship between elderly population dependency ratio and housing price, and a negative relationship between children dependency ratio and housing price. (3) There is a negative relationship between the total dependency ratio and housing price in eastern and western China, a positive relationship between the total dependency ratio and housing price in northeast China, and no significant relationship between the total dependency ratio and housing price in central China. (4) Before the census year 2010, dependency ratio ",0.0,https://www.atlantis-press.com/article/125971666.pdf10042d7f2324a3689374bb67d6fd0cfb8c1d11fabe1b,10.3846/ijspm.2025.24010,Estimating spatiotemporal heterogeneous effects of haze pollution on housing rents in China,2025.0,International Journal of Strategic Property Management,Dong-Sheng Zhan; Xuan Zhou; Tian-Han Yang,"Severe haze pollution significantly affects urban life quality and employment location choices, which in turn impact rental housing demand and housing rents. While numerous studies have delved into the effects of haze pollution on the real estate market, with a particular focus on housing prices, there is a notable scarcity of research on its impact within China’s rental housing market. This study employs spatial panel data encompassing 289 Chinese cities from 2015 to 2021 and applies a Geographically and temporally weighted regression (GTWR) model to investigate the spatiotemporal heterogeneous effects of haze pollution on housing rents in urban China. Our results indicate that the GTWR model’s goodness-of-fit surpasses that of the OLS, GWR, and TWR models. The results of the GTWR model reveal that haze pollution has a negative effect on housing rents in China, with the eastern region experiencing a notably stronger negative impact than the western region. Moreover, this negative impact becomes increasingly stronger over time. In addition, population, economic, and social factors significantly impact housing rents in Chinese cities. These findings offer valuable insights into the ",0.0,https://doi.org/10.3846/ijspm.2025.240101005410a60cd8d3a1858b21795bf3732a4a69fedede5,10.3390/buildings16040770,Spatially Optimized Multi-Level Modeling of Housing Price Differentiation and Resource Allocation Strategies,2026.0,Buildings,Zihan Xu; Yu-Ze Dan; Jia-Xin Zhang,"China’s rapid urbanization has intensified its intra-urban differentiation, with housing prices increasingly reflecting the uneven distribution of public resources and development opportunities. Taking Nanchang as a case study, this study examines the spatial structure of housing prices and the heterogeneity of their driving mechanisms. By comparing ordinary least squares and geographically weighted regression models, we identify a strong spatial non-stationarity in the determinants of housing prices, with key factors exhibiting location-dependent effects and, in some cases, directional reversals. To enhance spatial interpretation, Kriging interpolation is applied to local coefficients, revealing continuous spatial gradients in the factors’ influence. Building on these findings, a capitalization potential index is simulated under standardized resource-improvement scenarios to diagnose potential mismatches between market price responsiveness and spatial equity. The results indicate that areas with high capitalization potential often coincide with relatively low housing prices, suggesting a structural misalignment between market efficiency and spatial equity. This study contributes t",0.0,https://www.mdpi.com/2075-5309/16/4/770/pdf?version=17710413541006ac7cd6338878838a6234c75c0deb65407aac5ea3,10.22616/j.balticsurveying.2022.17.004,Modelling residential property values in Bida using geographic information system,2022.0,Baltic Surveying ..,Dauda Yunusa; Katun Mohammed Jibrin; Hauwa L. Etsu-Ndagi; Nwoye Isreal Izuchukwu,"Many studies have unveiled the importance of variation in residential property values overtime, but failed to cover different types of residential property value and location. The aim of this research is therefore to model residential property rental value in Bida from 2015 to 2020 with the aid of Geographic Information System (GIS). The study focused on the rental values of the residential property and rental value variation across space. Data collected for this paper includes residential rental values and geographic coordinates from 196 residential properties in the study area, comprising 101 one-bedroom, 80 two-bedroom and 15 three-bedroom apartments. Inverse Distance Weighted (IDW) interpolation tool of ArcGIS was employed in analyzing the data. It was found out that the core areas of the town commands lower rental values while the southern part of the town commands higher rental values. It was also found out that one bedroom apartment is the most dominant residential rental property followed by the two bedroom apartments and three bedroom apartments respectively. Geospatial database was produced for each model in a bid to ascertain the level of changes with time. The benefits ",0.0,https://doi.org/10.22616/j.balticsurveying.2022.17.00410072b3e74fe448bc6dd1ff3985b74450d6f8b5a6fe1,10.1093/geroni/igae098.1669,RESIDENTIAL PREFERENCES FOR CURRENT AND FUTURE RESIDENTIAL ENVIRONMENTS AMONG KOREAN MIDDLE-AGED ADULTS,2024.0,Innovation in aging,Mi-Chin Hong; Sang E. Lee,"Abstract Midlife adults often plan for life after retirement, forming their own preferences for where to live after retirement. Their residential preferences for life after retirement may be different from the past as they consider their well-being in later life. This study compared middle-aged adults’ preferences for residential features between current and future residential environments (i.e., past and current residential preferences) and examined how differences in preferences for current and future residential features were associated with health and social characteristics. A sample of 1,651 Korean middle-aged adults (aged 49–64) rated their preferences for each of 12 residential features (e.g., proximity to family/relatives, low living expenses, accessibility to health services) regarding their current and future residential environments, separately. Respondents considered “house price” and “safe and clean environments” most important for both current and future residential environments, while “accessibility to health services” and “opportunity for leisure activities” were more important for future residential environments. Regression results showed that respondents with poor",0.0,100833b3eac49485d60c8f642fb7471eed1bfe7c20f3,10.54097/hbem.v16i.10532,"Research on Influencing Factors of Second-Hand Housing Price in Haidian District, Beijing",2023.0,"Highlights in Business, Economics and Management",K. Han,"The housing issue is an economic issue as well as an issue of people's livelihood, especially in megacities like Beijing, where housing prices in the second-hand housing market fluctuate greatly, so its influencing factors have become the focus of attention. Studying the influencing factors of housing prices in the second-hand housing market can provide reference for the government to formulate appropriate real estate regulation policies and predict the development trend of housing prices in the future. This article collects nearly 3,000 groups of public second-hand housing individual transaction data from the Lianjia website in the domestic second-hand housing market. The time span of the data is from June 2020 to June 2021. After data cleaning and descriptive statistical analysis of the acquired data, we set the total price of the house as the explained variable, and the number of rooms, the area of the house, the decoration, the floor, and the unit price of the house are used as the explanatory variables. We establish a multiple linear regression model, then test and optimize it. The results are as follows: in the second-hand housing market of Haidian District, if the second-han",0.0,https://drpress.org/ojs/index.php/HBEM/article/download/10532/10248100957a52907519ba9ffcaec92bb4a656bdb885b4c64,10.22363/2312-8143-2025-26-3-258-265,Regression Neural Networks Advantage over Classical Regression Analysis,2025.0,RUDN Journal of Engineering Researches,O. Saltykova; V. D. Saushkin,"In this study, two analyzing methods are used to predict housing prices in California: neural network forecasting methods and methods based on regression analysis. Using the example of individual forecast indicators produced on the basis of two methods, the forecast results are compared. The purpose of this study is to show that the accuracy of prediction by neural networks is higher than that of the classical method. The assessment is carried out by creating a product in Python, which was chosen for reasons of ease of implementation of this analysis, ease of implementation of the product, as well as ease of constructing a graphical analysis of the results obtained. An open data source consisting of sixteen thousand items, which includes a number of housing criteria and prices based on these criteria, was used as resources for training the neural network. A broad review of studies comparing the predictive performance of artificial neural network-based methods and other forecasting methods is conducted. Much attention is paid to comparing artificial neural network methods and linear regression methods. Based on the results of this study, it was revealed that the accuracy of the neur",0.0,https://journals.rudn.ru/engineering-researches/article/download/47076/258301010059e12f0ab02f0c00a98508d6916a002e1ac8409,10.12928/optimum.v16i1.13186,Reducing property valuation bias through random forests: Predicting prices for public asset optimization,2026.0,Optimum Jurnal Ekonomi dan Pembangunan,Edy Riyanto; Imanishi Dwi Alfianyah,"This study applies supervised machine learning, specifically the Random Forest regression algorithm, to predict office rental prices in DKI Jakarta. A dataset was compiled via web scraping of online property listings, incorporating features such as location, office area, number of floors, lifts, parking capacity, and building grade. Data preprocessing involved handling missing values, removing outliers, applying one-hot encoding, and normalizing the data to ensure consistency. The model was developed using the CRISP-DM framework and evaluated through an 80:20 train-test split and 10-fold cross-validation. Performance metrics included Root Mean Squared Error (RMSE) and R². The Random Forest model achieved high accuracy, with cross-validation yielding an R² of 0.934 and an RMSE of Rp16.288 per m²/month. SHAP analysis revealed that lifts, floors, parking, office area, and building grade significantly influenced predictions. Bias analysis indicated a tendency to underestimate rents for grade B and C buildings. The model was also simulated to estimate rental values of underutilized government-owned offices, supporting asset optimization amid the planned capital relocation. These results",0.0,https://doi.org/10.12928/optimum.v16i1.1318610117d203a85d6a6be41ddd39abf0b3e534049daf1dd,10.2139/ssrn.4166936,It's the Smell: How Resolving Uncertainty About Local Disamenities Affects the Housing Market,2022.0,Social Science Research Network,Jeff Chen; Gary Cornwall; Scott A. Wentland,"This study examines how the housing market responds to closing a major, local environmental disamenity when the policy announcement resolves years of uncertainty. Specifically, we explore this question in the context of a surprise announcement to close the Fresh Kills Landfill on Staten Island, New York, which was, at one point, the largest landfill in the world. Empirically, we exploit both spatial variation and the timing of this policy shock, finding the long-awaited announcement to curb this notorious disamenity (i.e. its smell), counter-intuitively, decreased nearby home prices by about six percent relative to the control group. This occurred as homeowners of the previously illiquid homes nearest to the landfill rushed to sell their homes after this uncertainty was resolved, where weekly volume of transactions nearly doubled directly following the announcement. As an additional control, we observe thousands of properties near landfills elsewhere in the U.S. experienced no similar pattern over the same period. Our results suggest that when policymakers resolve uncertainty about (dis)amenities, the immediate effect on prices might be more ambiguous than a demand-oriented story m",0.0,https://doi.org/10.2139/ssrn.41669361012d0ff96d37e73ec095d1498279a0ecfe7f53ed3b3,10.1145/3778450.3778468,House Price Prediction and Interpretability Analysis Based on Automated Machine Learning,2025.0,Proceedings of the 2025 International Symposium on Machine Learning and Social Computing,Wen-Hao Tang,"Purchasing a home is not only a major life decision but also one of the most critical investments in household wealth allocation. In recent years, with the advancement of data science, machine learning (ML) methods have become important tools for studying and predicting housing prices, owing to their strengths in feature processing and pattern recognition. However, automated machine learning (AutoML) techniques, which are designed to automatically generate high-performing models for specific tasks and enable non-experts to conduct ML, have seen limited application in the domain of housing price prediction. Moreover, many ML algorithms do not prioritize interpretability, making it difficult to identify which features drive the predictions of housing prices. This study employed four AutoML tools, including a commercial web-based platform and several open-source libraries. Housing price data from King County, Washington (2014–2015) was used to investigate the predictive performance of AutoML models in housing price prediction and to conduct interpretability analyses of these models. The results suggest that AutoML models are capable of achieving promising predictive performance in thi",0.0,https://doi.org/10.1145/3778450.37784681013f21c80b2c9f4740843f3376a322452ea527a4fb9,10.5220/0013937800004915,The Impact of New Urbanization Transformation in Urban Planning on Housing Price,2025.0,Proceedings of the 3rd International Symposium on Humanities and Social Sciences,Hanning Xu,": Under the impetus of the new urbanization strategy, China's real estate market exhibits prominent policy-driven characteristics and spatial heterogeneity. This study constructs a ""policy-capital-space"" tripartite analytical framework grounded in Marxist land rent theory, systematically investigating the operational mechanisms of urbanization transformation on housing prices from 2015 to 2024. The research reveals three critical findings: (1) The efficiency of monetary policy transmission diminishes with city hierarchy (Shenzhen's mort-gage rate elasticity coefficient at 0.32), while dependence on land finance (exceeding 50% in Hangzhou) exacerbates regulatory efficacy attenuation; (2) Infrastructure investments generate a ""rent dilution"" effect (metro systems' marginal effect on commercial land prices declined by 9%), whereas the ""price-capped land + affordable housing"" model (Changsha's price-to-income ratio at 8.3) and industrial coordination planning (15% of Suzhou's industrial revenue subsidizing rental housing) effectively mitigate regional disparities. The study proposes establishing a land value appreciation redistribution mechanism, allocating 20% of land transfer revenue",0.0,https://doi.org/10.5220/001393780000491510142ef944ce1f120aa4b5e2531d4a299827659dd9f1,10.1007/978-3-032-37936-8_19,LEON: Location Embeddings from OSM Neighborhoods via Hexagonal Graph Masked Autoencoders,2026.0,,Szymon Sołtysiak; Radosław Małek; Jedrzej Kusnierz; Miłosz Chojecki; Piotr Szyma'nski; Aleksandra Kawala-Sterniuk,"Geographic information systems increasingly rely on sophisticated spatial representation learning techniques to extract meaningful patterns from complex geospatial data. This paper introduces LEON, a novel self-supervised framework that adapts Graph Masked Autoencoders (GraphMAE) for geospatial region representation learning. Our method leverages the inherent spatial structure of geographic data by constructing hexagonal grid graphs using H3 indexing and applying masked autoencoding techniques to learn robust spatial embeddings from OpenStreetMap (OSM) amenity distribution patterns. We evaluate LEON on multiple real-world datasets including EuroSAT satellite imagery classification and various geographic prediction tasks (housing prices, crime prediction, and urban analytics). Experimental results demonstrate that LEON achieves significant improvements in spatial understanding, with up to 1.87% accuracy improvement on EuroSAT classification and consistent performance gains across geographic prediction benchmarks. The learned embeddings exhibit highly structured and distinct properties, making them particularly suitable for downstream spatial analysis tasks. Our findings suggest that",0.0,https://arxiv.org/pdf/2610.054971015bcb8fc58a0bfe6444a3a192745687553d510518b,10.24843/jnatia.2023.v01.i03.p18,Uji Performansi Algoritma Linear Regression dan Random Forest Regression pada Implementasi Sistem Prediksi Harga Rumah,2023.0,Jurnal Nasional Teknologi Informasi dan Aplikasnya,I. Wijaya; Ida Bagus Gede Dwidasmara,"Currently the house has become one of the needs that must be met. The price of a house is the main parameter that determines whether a person or organization buys or invests. In general, house prices are influenced by several factors, including building area, land area, number of bedrooms, number of bathrooms and number of garages. Currently, there are many websites devoted to providing information about buying and selling houses. This of course makes it easier for someone when looking for a house with the desired specifications without the need to come directly to the location. However, the house buying and selling platform does not provide a house price prediction feature that is in accordance with user specifications. This means someone who is planning to buy a house does not get an initial idea of the costs that must be spent to own the desired home. Therefore, in this study, researchers will design a web app-based house price prediction system that can make it easier for users to get predictions of the desired house price. In this study the prediction algorithms to be used are linear regression and random forest. Both algorithms will be analyzed for their performance and then ",0.0,https://ojs.unud.ac.id/index.php/jnatia/article/download/102444/506541016c775f7c2ce1f69bfc07e7dd798cbc84d0b955971,10.1177/0971685819890183,"Of Modernity, House Prices and Suspending Singularity of Time",2020.0,,Yugank Goyal,"Why do we buy houses as opposed to renting one? This question, in its simplistic formulation captures, inter alia, some of the most fundamental emotions of temporal values that we impose on ourselves. Yet, the question has attracted little scholarly scrutiny. The article, using this question as a case, attempts to excavate the silences of our imagination of time in the cacophony of modernity. Time has had varying versions of existence in the modern world. When time is singular, it has the same meaning attributed to by everyone in the same community. A pluralistic conception of time is the exact opposite. I use discount rates as a unique entry point to understand how people view their future (time), and thereby a conceptual aperture to see if time is losing its singularity or not. More importantly, how so. I collected data on house prices in India in five major metropolitan cities in India and compared those prices with rental values. The crude estimation is a useful proxy to observe discount rates, and consequently, varying conceptions of time. I show that time has become a homogenized entity for people falling in similar economic class while it has lost its singularity for those w",0.0,https://doi.org/10.1177/097168581989018310174dce929b50af3a8b07f57e5120374b8cd886427d,10.1108/ijhma-01-2026-0006,Integrating multi-source data and explainable AI for housing market analysis in Indonesia,2026.0,International Journal of Housing Markets and Analysis,Ivan Aldy Ganesen; Muhamad Farhan Syakir; Fawwaz Abrial Saffa; Ayu Purwarianti; N. Utama,"1018 1019 This paper aims to address limitations in real estate valuation in Indonesia arising from regional heterogeneity, fragmented data sources and opaque automated models. Specifically, it seeks to develop a more accurate and transparent comparative market analysis framework by integrating multisource property data and explainable machine learning, thereby improving both valuation reliability and interpretability for heterogeneous market segments.1020 1021 1022 1023 The study integrates property listings from multiple major Indonesian online platforms into a unified dataset with over 70 spatial, structural, facility and pricing attributes. After extensive preprocessing and normalization, properties are grouped into low, medium and high price classes. Multiple regression-based machine learning models are evaluated, with hyperparameter optimization applied to tree-based models. Explainable analysis is used to examine feature contributions across price classes.1024 1025 1026 1027 Extreme Gradient Boosting demonstrates the strongest overall performance, achieving Mean Absolute Percentage Errors of 26.53% for medium-priced properties and 14.60% for high-priced properties. Explainable analysis indicates that spa",0.0,10285f6993c88106444e475e2bb0fc794bf72bd782ac,10.1177/02750740251340063,The Hidden Household Water Affordability Burden: An Examination of Municipal Ordinances and Racial Equity,2025.0,American Review of Public Administration,Laura Medwid; Dan Huang; Deborah A. Carroll; Christelle Khalaf; Kate Albrecht; Jun Li,"The provision of affordable, high-quality residential water services is necessary for community health and well-being. Yet, the time-intensive nature of data collection and inconsistent public availability of ordinances online make research on water policy an often overlooked aspect of municipal governance. This study investigates the intersection of municipal water policy, equity, and accessibility by analyzing the hidden household burdens associated with establishing water service, nonpayment penalties, and assistance programs. By systematically analyzing municipal ordinances, this study uses regression analysis to better understand the relationship between these fees, penalties, and affordability programs in reference to community demographics, water system characteristics, and municipal financial indicators. Extant research reveals social and demographic disparities in water rates. This study extends those findings, highlighting inequities in water provision policies beyond routine billing. Communities with higher percentages of minorities have more burdensome water provision policies including higher water service deposit requirements, fewer days to pay before disconnection of",0.0,1029398fa6c4f5f77786a030859c7b7874bc382e598c,10.22214/ijraset.2019.4622,Housing Prices Prediction using Linear Regression,2019.0,International Journal for Research in Applied Science and Engineering Technology,Dhikhi T,": Individuals hoping to purchase home will in general be increasingly traditionalist with their financial plans and market systems. The present structure incorporates figuring of house costs without the fundamental estimate about future market examples and cost increase. It predict the capable house esteeming for land customers concerning their money related plans and needs. By breaking down past market patterns and value ranges, and furthermore up and coming improvements future costs will be anticipated. The entire work will be done using linear regression algorithm of supervised learning. It includes a site which acknowledges client's determinations and after that joins the utilization of various direct relapse calculation of information mining. This application will assist clients with investing in a home without moving toward a specialist. It likewise diminishes the hazard engaged with the exchange. The cost function equation is expressed as C(x)= FC + V(x), where C equals total production cost, FC is total fixed costs, V is variable cost and x is the number of units. Gradient Descent Algorithm is an advancement calculation used to limit some capacity by iteratively moving towa",0.0,https://doi.org/10.22214/ijraset.2019.4622103075bf4d5eb1a3c9c8e597c504767178ef60b1295e,10.5220/0013814000004708,Study for Factors Influencing Pre-Owned Housing Prices in China,2025.0,"Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy",Mu-Ge Zhao,": Against the backdrop of the sustained development of China's real estate market and active pre-owned housing transactions, it is crucial to explore the factors influencing pre-owned housing prices. This article focuses on this and uses a review method to integrate literature from multiple fields. Research has found that a combination of multiple factors influences pre-owned housing prices. In the inherent properties of a house, factors such as age, decoration, layout, and floor level directly affect its price. In terms of the external environment, location factors have a significant impact, and the completeness of supporting facilities such as education, healthcare, commerce, and transportation in the surrounding area greatly affects its market value. The supporting services, such as property services and greening rates within the community, will also have an impact on housing prices. In addition, the article summarizes existing research methods, such as multiple linear regression, geographically weighted regression, and random forest models. A deep understanding of these factors and mechanisms can not only help homebuyers and investors make scientific decisions but also provide ",0.0,https://doi.org/10.5220/001381400000470810313c713257d4dda387c43f2bc6ab7c5adc9744adde,10.9734/ajpas/2025/v27i5759,Impact of Government Environmental Regulations and Foreign Investment on Urban Housing Affordability: A Longitudinal Econometric Analysis (2014–2023),2025.0,Asian Journal of Probability and Statistics,S. O. Are; T. Oguntola,"This study examined the complex relationship between environmental regulation policies, foreign direct investment (FDI), and housing affordability in urban real estate markets over a ten-year period (2014–2023) with the environmental regulation index as the independent variable. Utilizing a normal-power regression model, the analysis integrates macroeconomic indicators, including property tax rates, interest rates, inflation, unemployment, and government spending to control for broader market influences. The results revealed that stricter environmental regulations are associated with increases in average housing prices, primarily due to their impact on construction costs and development constraints. Meanwhile, FDI shows a dual effect: in some cases, it stimulates housing supply and infrastructure development, but in others, it drives up property values, particularly in high-demand urban centers, thereby contributing to reduced affordability. Diagnostic tests confirm the robustness of the model, with an R² of 0.7984 and satisfactory forecast accuracy. The findings underscored the need for integrated policy frameworks that reconcile the goals of sustainability, economic growth, and s",0.0,https://doi.org/10.9734/ajpas/2025/v27i57591032f2ba89d1f52f857945edd3ad9b8c62c5f09080dc,10.22178/pos.125-70,"An Empirical Assessment of Road Network Influence on Commercial Property Values in Ojo LGA, Lagos, Nigeria",2025.0,Path of Science,A. Abidola; Sunday Philip Akingbemisola; Olabimpe Bilquees Balogun; A. Adebayo; Mutiurahmon Opeyemi Raji,"This paper is an empirical investigation of how the infrastructure of a road network affects the commercial property values in the Ojo Local Government Area in Lagos State, Nigeria; this is to determine the different levels of impact of road proximity, quality, connectivity and access to public transport on commercial rental performance in a fast-growing metropolitan setting. The researchers collected primary data through a cross-sectional survey of 473 commercial property owners, tenants, and real estate agents across nine major road corridors. Data analysis was conducted using descriptive statistics, correlation analysis, and multiple regression models. The findings indicate that residential homes close to arterial roads (within 100 meters) have average rental prices of about 25%, whereas immediate proximity to BRT corridors yields price increases of 30-68%. The most potent predictor of commercial property value was road quality (r = 0.72), and the regression equation explained 70 % of the variation in rental value. The paper finds that road network infrastructure is a dominant force in Lagos's commercial property markets. It suggests long-term investment in road maintenance, tra",0.0,https://pathofscience.org/index.php/ps/article/download/3502/187410333e960f5acd5e75d16ae1c87949b91202c6001aa2,10.31410/itema.s.p.2023.133,The COVID-19 Pandemic and Its Effects on the United States Housing Market,2023.0,International Scientific Conference ITEMA 2023: Vol 7. Selected Papers,Glen Liperi; Egla Mansi; Nerajda Feruni,"This research examines the impact of the COVID-19 pandemic on the housing market in the United States, with a specific focus on housing prices and their relationship with COVID-19 infection rates. The study utilizes a panel-data regression model, using monthly county-level data spanning from January 2020 to December 2022. Two separate models were employed, one for urban counties and another for rural counties, determined by population data. These models employ a fixed effects regression approach with robust standard errors and incorporate various independent variables to explore the association between county-level economic activity, unemployment rates, long-term interest rates, COVID-19 infection cases, and national housing market prices. The findings of this investigation reveal that the prices of homes in the United States were influenced by both the number of COVID-19 infection cases and the economic factors under consideration. Interestingly, urban counties experienced more pronounced fluctuations in housing prices compared to their rural counterparts. Moreover, there was a noticeable shift in housing demand, with a preference for less densely populated areas over densely ",0.0,http://dx.doi.org/10.31410/itema.s.p.2023.13310345104048bf1bd5a1ad7e0fa432e80b814d70004f3,10.1057/s41599-024-03627-9,A Lucas island model to analyse labour movement choice between cities based on personal characteristics,2024.0,Humanities and Social Sciences Communications,Tian-Ge Qi; Yuning Gao; Yong-Jian Huang,"The labour movement has been a key factor for cities’ development and caused regional inequality between cities. Although empirical studies have been conducted to investigate it, little theoretical evidence has been provided to find out the underlying mechanism. This paper describes and derives a Lucas-Prescott style island model to study the location choices of the heterogeneous agents by utilising endogenous technology growth, which in turn influences personal human capital growth. It leads to the U-shape curve of the inequality of wage income with the technology of these islands but not in terms of total income. In the extended two-goods model, the magnitude of the implications is increased by the impact of non-tradable goods price. Together with empirical research using the US census data, this paper finds that skilled labours with less endowed wealth tend to live in large cities for its high salary. On the other hand, those less-skilled but with more endowed wealth tend to live in cities with better environment, which drives up the price level of non-tradable goods in these cities. This explains the population concentration in the super cities and the high housing price-wage r",0.0,https://doi.org/10.1057/s41599-024-03627-91035d60d2f47db541d062f072a5a4e2f7e8f8eec61cf,10.3390/su17167459,A Preliminary Analysis of the Relationships Between Rising Temperatures and Residential Rental Rates in the USA,2025.0,Sustainability,Michael Garvey; Tony G. Reames,"Climate change poses significant challenges to the economic and social sustainability of urban dwellers, particularly in the real estate market, where rising temperatures are affecting property values. While most research focuses on how climate change impacts buyers and sellers, this study shifts attention to renters, who may be more vulnerable to climate-induced price increases. By analyzing rental price and climate data, this study uses ordinary least squares (OLS) and fixed-effects regressions to assess the impact of temperature fluctuations on rental rates across 50 major U.S. metropolitan areas. The findings reveal a positive and significant relationship between rising temperatures and rental rates, particularly in the Northeastern and Southern U.S. These results suggest that targeted policy interventions may help ease financial pressures on vulnerable renters and support more sustainable urban development over time. The analysis also highlights the potential role of energy efficiency measures in rental housing to lower energy costs and alleviate rent burdens. Additionally, the findings indicate that local policymakers may consider rent stabilization strategies and investments",0.0,https://www.mdpi.com/2071-1050/17/16/7459/pdf?version=175552047110368e33cdccf36ccb40edb3c6204893e13bece7e55a,10.38100/jhuf.2025.10.2.29,"The impact of regional housing price changes on voluntary1037 termination of reverse mortgages: Focusing on age-group differences1038 controlling for mortality risk",2025.0,Journal of Housing and Urban Finance,Seong-won Lee,"This study aims to overcome the limitations of previous research by distinguishing between terminations due to death and those due to voluntary choice and by addressing the insufficient reflection of micro-level price changes in regional housing markets. We construct quarterly panel data at the subscriber level, utilizing the complete dataset of reverse mortgage subscribers for apartments up to the end of June 2025 and incorporating sales price indices at the district level. Furthermore, we aim to identify the pure effect of economic factors on voluntary termination by controlling for the possibility of attrition due to death by setting age-specific mortality probabilities as an offset in the model. Using a discrete-time hazard model, first, the analysis confirmed that both long-term cumulative housing price changes in enrollment and short-term price changes from the immediately preceding quarter were key drivers that increased the risk of voluntary termination. Second, the impact of housing price appreciation on termination was heterogeneous across age groups, showing a tendency to be most sensitive among the younger group and weaken with increasing age. This suggests the need for",0.0,http://www.ejhuf.org/download/download_pdf?pid=jhuf-10-2-29103988af5986d31944f366ab7070ec51a76e205d1a0f,10.5220/0012973500004508,Advancing House Price Forecasting: Linear Regression and Deep Learning Models Analysis,2024.0,"Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence",Feng Tian,": The prediction of housing prices has received widespread attention from researchers due to its importance. This study offers a comprehensive analysis of house price forecasting, employing both traditional linear regression models and advanced deep learning techniques to enhance prediction accuracy. Through meticulous comparison and experimentation, deep learning methods, particularly feedforward neural networks, emerged as significantly superior in capturing complex nonlinear relationships and high-dimensional data patterns compared to linear regression models. In order to improve prediction performance, the research integrates data preparation, feature selection, and model evaluation as it methodically investigates different aspects of the dynamics of the housing market. Results highlight the potential of deep learning techniques to offer substantial improvements over conventional models, particularly in recognizing spatial and temporal trends in house pricing data. Future research aims to integrate external factors like economic indicators and urban development parameters to refine and expand predictive capabilities. It is anticipated that this strategic approach will improve t",0.0,http://dx.doi.org/10.5220/0012973500004508104065bb118a3409a8fbc1b111cd380333c1519f7c75,10.4995/vibrarch2022.2022.15291,Measuring Gentrification in the Russafa Neighborhood,2022.0,Proceedings,Alicia Llorca Ponce; G. Rius-Sorolla,"Gentrification is a global phenomenon, affecting a large number of cities. It involves the displacement of neighbourhood resident by new residents of a higher socioeconomic status. This article aims to highlight the characteristics of the phenomenon in the Russafa neighbourhood, in the city of Valencia. The paper focuses on two aspects: changes in the socioeconomic characteristics of the resident population and physical changes in the housing stock. The data were collected from the Population and Housing Census, the Municipal Register of Inhabitants and other statistical information compiled by the Valencia City Council. The neighbourhood’s socioeconomic status is approximated through three variables: educational level, vehicle fleet and income, based on the analysis of housing prices. The results obtained show physical and social transformations typical of gentrification phenomena. On the one hand, from the physical point of view, the data indicates that an important real estate activity of renovation, rehabilitation and construction of new housing is carried out. On the other hand, the results indicate that at the end of the analysed period, the socioeconomic level of the populat",0.0,https://ocs.editorial.upv.es/index.php/VIBRArch/VIBRArch2022/paper/download/15291/78861041a8a9eb92ea683796c6c8dbc3345d9c8eb3a12754,10.19172/kreaa.24.4.1,Determinants of Chonsei Deposit to Housing Sales Price Ratio Based on Rental Housing Market Equilibrium Condition: Focused on Housing Price Volatility,2018.0,The Journal of Korea Real Estate Analysists Association,Eui-chul Chung,"Traditional approach to analyze the determinants of Chonsei deposit to housing sales price ratio is based on asset market equilibrium condition. However, this approach does not fully account for complexity involved in Korean rental housing market, where Chonsei, Monthly-rent-with-variable deposit, and pure monthly rental contracts coexist. In this paper, we utilize each rental type’s different rate of return structure and examine the determinants of Chonsei deposit to housing sales price ratio based on rental housing market equilibrium condition. We further incorporate housing price volatility into the analysis as a critical variable related to housing investment risks, which has been ignored in previous studies. Our empirical analysis using a sample of the apartment sales price and rent in Seoul confirm our theoretical predictions: Chonsei deposit to housing sales price ratio is positively linked to the rent-to-price ratio and housing price volatility, and the ratio is negatively affected by the expected housing price appreciation rate. The relationship between the Chonsei deposit to housing sales price ratio and housing price volatility was robust to different measures of housing",0.0,1042e12e9e7a7afff082a72aa02890516b4c3637869b,10.3390/jrfm19080595,Determinants of Mortgage Loans in Bulgaria and the Euro Area: A Comparative Analysis,2026.0,Journal of Risk and Financial Management,Gergana Mihaylova-Borisova,"This article investigates the factors that determine the dynamics of mortgage lending in Bulgaria and the Euro area by using ordinary least squares (OLS) regression models based on stationary time series over the period 2010–2025. The results show that in Bulgaria, the dynamics of mortgage lending are determined primarily by wage growth, inflation, and the high liquidity of the banking system, which increases banks’ capacity to extend new loans. In contrast, in the Euro area, the main factor driving mortgage lending trends is interest rates on mortgage loans, with the development of the real estate market, as measured by house price index, also exerting a significant influence. The findings further indicate that, despite the high degree of economic integration between Bulgaria and the European Union, the factors determining mortgage lending differ, which justifies the need for separate modeling of mortgage loans in the two economies. Moreover, mortgage lending transmission mechanisms differ substantially across the two economies despite their close monetary integration, highlighting the importance of country-specific institutional characteristics. The faster growth of mortgage lend",0.0,https://doi.org/10.3390/jrfm190805951043424b316c5c0b7301550392219db18a1a9d2d249e,10.2478/remav-2026-0032,Exchange Rate Volatility and House Prices in the Eastern European Countries,2026.0,Real Estate Management and Valuation,Viktor Shevchuk,"Abstract We analyzed the causal relationship between exchange rate volatility and house prices in 11 Eastern European countries. Exchange rate volatility was modelled using a GARCH framework, while the determinants of house prices were estimated with the GMM method. Based on quarterly data covering the period from 2004 to 2024, we established that exchange rate volatility effects on house prices are quite heterogeneous, with a statistically significant positive effect in 7 countries and a negative effect in 4 countries. However, there is a predominantly dampening effect of exchange rate depreciation on house prices, except Slovenia and Latvia. Among other findings, house prices exhibit inertia, which could be interpreted as the presence of speculative motives among market participants. An inverse relationship between interest rate and house prices is a distinct feature of countries with a floating exchange rate regime. Higher output abroad exerts an upward pressure on house prices in 5 countries, with the opposite effect in 3 countries. The real exchange rate misalignment is significant for house prices in eight countries. Joining the eurozone is likely to increase house prices. Ma",0.0,https://reference-global.com/download/article/10.2478/remav-2026-0032.pdf104431a75c18fb9a7bbb19f45217cecf82672bb5bca0,10.48550/arxiv.2605.08660,Optimised Support Vector Regression for California Housing Price Prediction: The Critical Role of Feature Engineering and Hyperparameter Tuning,2026.0,arXiv.org,Emmanuel Adutwum,"In the recent literature, Support Vector Regression (SVR) has been cited as one of the weakest performers on the California Housing benchmark dataset, with Preethi et al. (2025)specifically ranking it last among the algorithms they tested, reporting an R2 of only 0.60. This paper examines whether the previously reported performance reflects experimental configuration choices rather than an inherent algorithmic limitation. A structured experimental workflow is applied: ten domain-motivated derived features are constructed from the eight raw inputs, an exploratory ensemble feature importance analysis identifies the most predictive candidates, and a randomised search over hyperparameter combinations with three-fold cross-validation selects the optimal SVR configuration within a leakage-safe scikit-learn Pipeline. A formal four-stage ablation study isolates the contribution of each component: scaling alone accounts for +0.744 in R2 (from -0.054 to 0.690), feature engineering adds +0.026 (to 0.716), and hyperparameter tuning contributes +0.008 (to 0.723). The resulting tuned SVR achieves a test R2 of 0.723, a 0.123-point absolute improvement over the previously reported SVR result (from",0.0,10459b799df6640a19c01c9d7d8282ed86cd7c37843d,10.54254/2754-1169/94/2024ox0208,A Study of Property Intrinsic Value Prediction Based on Machine Learning Modeling,2024.0,"Advances in Economics, Management and Political Sciences",Wentao Zhang,"When purchasing goods, buyers consistently prioritize the fairness of the price to make informed decisions, particularly for high-value items. This consideration becomes even more crucial in the real estate market, where verifying the fairness of a property's asking price through conventional methods can be challenging. This study addresses this issue by aiming to develop a functional predictive model that can estimate a property's intrinsic value using various machine learning techniques. By analyzing the California Census Data published by the US Census Bureau, this research investigates the potential for determining a property's intrinsic value based on readily available information about the block in which the property is situated. The study's objective is to create a model that provides accurate and reliable property valuations, helping buyers make more informed decisions and promoting transparency in the real estate market. This model leverages demographic, economic, and housing data from the census to predict property values, potentially transforming how properties are appraised and traded. By offering a method to verify asking prices, the research seeks to enhance fairness ",0.0,https://doi.org/10.54254/2754-1169/94/2024ox020810468ce294f03fbf873fbe5423743ce5c5b37ab1e8b0,10.24957/hsr.2021.29.1.79,An Analysis of Determining Factors for Re-subscription after Early Termination of JooTaekYeonKeum,2021.0,Korean Association for Housing Policy Studies,Byeng Kuk Kim; Byungkwon Lim,"Amid the ultra-low birth rate and rapid aging population, the role and importance of the JooTaekYeonKeum in driving the improvement of retirement income and housing stability using housing is growing, but there are a number of cases of re-subscription after termination, with limited definitiveness. Therefore, this study analyzed the impact on re-subscription after early termination of JooTaekYeonKeum, targeting 6,200 actual cases of JooTaekYeonKeum handled by 12 domestic financial institutions. In addition, a multilateral analysis was conducted by categorizing the location characteristics of the JooTaekYeonKeum collateral (metropolitan and non-metropolitan areas), institutional characteristics of re-subscription of JooTaekYeonKeum (whether re-subscription is restricted), and characteristics of the relative age group (low and high). The main analysis results show that in the case of the housing price change rate variable, it was an important factor that had a positive (+) effect on re-subscription despite the age and collateral location classification. According to the equity theory of termination, the cost of re-subscription was excessive when the LTV was high, and the utility was ",0.0,10476471f3a1122f91db8c389bd8fbee73d5e0b30ac8,10.37134/geografi.vol12.1.10.2024,Preliminary Survey of The Influence of RURBAN Phenomenon on Rural Development in Mukim Telok Panglima Garang,2024.0,Geografi,,"The development of cities and the process of urbanisation have caused urban development boundaries to be exceeded. The trend of development shifting away from cities has put pressure on peri-urban and rural areas. This study aims to examine the phenomenon of RURBAN afftecting villages in Mukim Telok Panglima Garang, Kuala Langat. Secondary and primary data were analysed to assess the extent of the impact of the RURBAN phenomenon on these villages. RURBAN is the phenomenon where rural areas undergo gradual urbanisation. Urban-like amenities equip villages to meet the needs of their residen. Due to urbanisation, distance is no longer a barrier, and transportation systems have facilitated daily activities. Observations and field studies indicate a current trend of converting agricultural land into residential areas to meet housing demands in the market. Factors such as industrial area development, high property prices in cities, and comprehensive road networks have further accelerated the construction of residential estates developed through pocket developments. Consequently, the growing population has led to shortage of basic amenities. To balance urbanisation and rural development, ",0.0,https://ejournal.upsi.edu.my/index.php/GEOG/article/download/9683/53061048ec2208172c9b83ab0a99cc124641e0e75c9cf818,10.1007/s11150-026-09854-0,Who gets the Keys? Single-family homeownership trajectories in Sweden from gender and regional perspectives,2026.0,Review of Economics of the Household,Helena Bohman; Martin Grander,"1049 Research shows that housing situations, as well as housing trajectories, differ between women and men. We also know that income inequalities in many countries have increased over the last decades. Patterns of wealth accumulation however – which often are based around homeownership – remain less studied. Understanding homeownership is therefore key to understand also other aspects of economic development. We use Swedish register data on single-family home ownership to follow property value and ownership trajectories between men and women. The very strong local characteristics of housing markets also motivates integrating regional perspectives. Using cohort analysis, we address the following research questions; (i) how do single-family homeownership trajectories differ between men and women, (ii) are there signs that gender-based differences are changing over cohorts, and (iii) how do the patterns change in a regional context. Our analysis shows declining ownership for younger cohorts. While women enter housing ownership earlier than men, ownership rates of men surpass women over time. This raises questions on perceived advantages or early entry to the housing market. We find persi",0.0,https://link.springer.com/content/pdf/10.1007/s11150-026-09854-0.pdf1050cd6056e1d83d70847ef50a9005df6bd3db34d66d,10.54097/ncz9xh08,Comparative Analysis of Linear and Polynomial Regression Models in Predicting Shanghai Housing Prices,2026.0,Journal of Innovation and Development,Zheng-Nan Wang,"This study presents a comparative analysis of linear and polynomial regression models for predicting housing prices in Shanghai's dynamic real estate market. Utilizing a dataset of 100 second-hand housing listings sourced from Lianjia between 2020 and 2023, with living area as the primary predictive feature, we rigorously train and evaluate both models. Our findings demonstrate that a second-degree polynomial regression model (MSE = 0. 58, R² = 0. 89)significantly outperforms a simple linear regression model (MSE = 0. 97, R² = 0. 78)in predictive accuracy and explanatory power. Residual analysis further reveals that the linear model suffers from heteroscedasticity, indicating its inadequacy in capturing the market's complexity, particularly for high-value properties. The results underscore the importance of selecting appropriately complex models for economic forecasting. We conclude that polynomial regression offers a superior fit for the non- linear trends prevalent in Shanghai's housing market. This study provides practical insights for stakeholders in real estate investment, policy formulation, and urban economics, while also acknowledging limitations related to dataset size and",0.0,https://drpress.org/ojs/index.php/jid/article/download/33674/3295010510d252dab8069e8951a9f67aa3e07244a74154b05,10.54254/2753-8818/2025.dl27131,"Equipping AI with an ""Economic Sensor"": Enabling Loan Prediction Models to Perceive Macroeconomic Changes",2025.0,Theoretical and Natural Science,Jia-Ming Ma,"Traditional credit risk models, focusing primarily on borrower-specific features, often fail to capture the impact of macroeconomic fluctuations, leading to systemic misjudgment during downturns. This paper proposes enhancing loan prediction models by integrating an ""economic sensor"" a module incorporating key macroeconomic indicators (GDP growth, unemployment rate, interest rate, Housing Price Index, Industry Index) processed via a temporal sliding window mechanism. We develop an economic shock simulator for stress testing. Using a synthetically collected 5-year data set of loan applications and macroeconomic conditions, we train Random Forest models. Results show the enhanced model (individual and macro features) outperforms the baseline (individual features only), with accuracy increasing from 83.13% to 85.21% and AUC from 0.8993 to 0.9217. The model demonstrates heightened sensitivity to economic shocks, evidenced by a rightward shift in the predicted default probability distribution and an increase in the mean predicted default rate from 21.5% to 31.8% post a simulated 20% housing price crash. Crucially, it provides early warnings, identifying 2,873 clients (75% SMEs) with sig",0.0,https://www.ewadirect.com/proceedings/tns/article/view/27131/pdf105247b15d1c041d16e71023960b6fa659d9c6a0436a,10.54254/2754-1169/66/20241235,House Price Prediction Based on Joint Clustering and Linear Regression,2024.0,"Advances in Economics, Management and Political Sciences",Guanyu Chen,"The corona virus disease (COVID-19) epidemic has a great impact on the real estate industry. The epidemic has caused a downward trend in housing values. It is essential for those who want to buy property during this period of economic recovery to understand the key factors influencing house pricing. In order to identify the factors that influence real estate prices and make house price prediction, this study uses a mathematical model called linear regression to analyze the data. The model is further improved and streamlined by focusing on essential elements. The study successfully pinpoints the most critical factors affecting home prices and makes relatively accurate house price prediction. Notably, the house's number of levels and bedrooms are essential determinants for house price prediction. Understanding these variables enables real estate agencies to concentrate on particular areas for improvement, thereby optimizing the total housing market, which gives buyers a clearer idea of the types of homes they can pick. Real estate brokers can work more productively, increase their competitive edge, and guarantee long-term success in the post-pandemic recovery phase. Furthermore, by b",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/9364/pdf1053f7934fe22ac3dd5ca749a57d3390aaa269dcc299,10.30574/wjarr.2025.26.2.1826,The technological transformation of real estate: How AI is reshaping the industry,2025.0,World Journal of Advanced Research and Reviews,Abhimanyu Bhaker,"The residential real estate industry is experiencing a profound technological revolution driven by artificial intelligence systems that are fundamentally restructuring how properties are researched, evaluated, and transacted. This article examines the technical architecture behind six key AI innovations transforming the real estate landscape. From conversational interfaces powered by large language models and knowledge graphs to comprehensive API ecosystems that democratize previously restricted data, these technologies are shifting power from information gatekeepers to consumers. The article further explores computational visualization tools enabling virtual property redesign, sophisticated machine learning models for market forecasting, and computer vision systems that extract structured insights from property imagery. Despite remarkable advances, significant challenges remain in data standardization, privacy protection, model explainability, and bias mitigation. By providing a technical foundation for understanding these systems, this article illuminates not only how AI is currently reshaping real estate processes but also how emerging technologies like blockchain, federated lea",0.0,https://journalwjarr.com/sites/default/files/fulltext_pdf/WJARR-2025-1826.pdf10543377a45e3e46d34bbbfab97cbf64ced59fc0e5f0,10.22260/isarc2024/0168,Training and Generating Apartment Plan Graphic Images for Commercial Uses,2024.0,Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC),Taesik Nam; Youngchae Kim; Sumin Chae; Y. Yoo; J. Lee,"– This paper introduces a methodology utilizing image generation AI to create 3D floor plan images for Korean-style apartments. The growing interest in spatial dynamics, driven by changes in residential preferences, has elevated the importance of widespread comprehension and the demand for floor plans. Specifically, apartment floor plans, crucial for commercial purposes, require visually compelling representations that are both diverse and detailed yet easily comprehensible. Traditional 2D floor plans face limitations in public understanding due to the use of architectural terminology and specialized symbols. As an alternative, 3D floor plan images offer a comprehensive understanding of three-dimensional structures and arrangements, with potential applications across various commercial sectors. The recent emergence of image generation AI presents opportunities for the automated creation and alteration of architectural images. Fine-tuning the generated results can be achieved through detailed prompt settings and additional training techniques. The methodology for constructing a 3D floor plan image generation model for Korean-style apartments involves: 1. Creating a 3D Building Infor",0.0,1055a2b5565473fa7c72b9963899dce8e8c94c20422a,10.1007/s44327-025-00109-8,"Predicting housing price, housing density, and green area coverage from combined satellite and street view imagery using deep learning",2025.0,Discover Cities,Ana Beatriz de Figueiredo Oliveira; Mauro Castelli; Esra Suel,"Rapid urban expansion and rising housing prices have created significant social and economic challenges in many cities, exposing the limitations of traditional, resource-intensive data collection methods for urban planning. Remote sensing technologies, such as satellite imagery, offer a cost-effective alternative for data collection. Incorporating street view imagery can enhance the quality of collected information by providing a human perspective on the urban environment, often inaccessible through standard remote sensing methods. This research aimed to develop a deep learning classification model using a multi-modal fusion of satellite and street view imagery to predict urban metrics of housing price, housing density, and green area coverage. Focusing on Lisbon, a European city facing rising housing prices, this research used EfficientNetB0, a pre-trained model originally used for object recognition on the ImageNet dataset, which successfully generalized its learning to interpret urban imagery. The findings highlight the potential of integrating pre-trained models within deep learning frameworks for urban analysis. This approach leverages low-cost, readily available data, providi",0.0,https://doi.org/10.1007/s44327-025-00109-81056bb94a1d3a15e06eb47d52178dc4e79b172333d96,10.55632/pwvas.v92i1.737,Forecasting California Housing Prices Using a Linear Regression Model,2020.0,Proceedings of the West Virginia Academy of Science,Luisely Doza; Jason Rafe Miller,"With the prevalence of big data, we are able to implement simple Machine Learning techniques against datasets to solve many present issues. Utilizing the California Housing Prices dataset, we can apply linear regression models to forecast districts’ future median housing prices. As a result, homebuyers could predict if they are getting a good deal on their home, investors could predict their potential return on investments and capitalize on undervalued properties, and sellers could gauge what they could potentially sell for on the open market. To find a starting base, our research began as an independent study guided by a textbook that provides hands-on machine learning training, including one that uses the California Housing Prices dataset. From here, we are able to choose the Least Square Regression Line as our performance measure in the model—which will result in a linear equation that looks for minimizing the variance between the training set’s predictions and the actual points produced on the regression line. Once we are able to train our model to make accurate predictions from the testing set, we can expand upon this research by moving beyond linear regression and investigati",0.0,https://doi.org/10.55632/pwvas.v92i1.73710573a6e641610ab34a70e7d089687e1b1e83743b2db,10.1029/2024gh001140,Effect of Regional Housing Hardship on Spatial Variation in Cancer Incidence: Does Housing Stress Increase Cancer Incidence?,2024.0,GeoHealth,Hai-Shi Yu; Jinyu Huang; Yang Wang; Xiao-Li Yue; Ying-Mei Wu; Hong'ou Zhang,"Housing hardship can create a range of health issues. However, little attention has been paid to the relationship between housing hardship and cancer incidence. This study examines the Yangtze River Economic Belt (YREB) in China as a case study to develop a model of factors related to housing hardship that can affect cancer incidence. This study employs spatial regression models to investigate the correlation between housing hardship and cancer incidence and further explores the variation in the correlation between urban areas (UAs) and non‐urban areas (NUAs). The research conclusions are as follows: (a) a palpable correlation exists between housing hardship and cancer incidence. The housing price‐to‐income ratio (HPIR) and the rental household proportion (RHP) are positively correlated to cancer incidence, whereas the per capita living area (PCLA) has a negative correlation with cancer incidence. (b) The differences in the impact of housing hardship on cancer incidence between the UAs and the NUAs are reflected mainly in the differences in the PCLA and the RHP. The PCLA has a strong association with cancer incidence in the UAs, whereas the RHP demonstrates a strong correlation wit",0.0,https://doi.org/10.1029/2024gh0011401058ca70739fecc59d91bc7eb046c19576980e64468f,10.1038/s41598-026-62854-z,House price prediction using a hybrid GRU–MLP based on binary whale optimization algorithm and ant colony optimization for hyperparameter tuning,2026.0,Scientific Reports,Sarah M. Alhammad; Yasser Fouad; Amira A. Mahmoud; Ahmed M. Osman; Hazem M. El-Bakry; Ahmed M. Elshewey,"Accurate house price prediction is essential for real estate valuation, investment planning, and intelligent property decision-support systems. This study proposes an optimized hybrid deep learning framework that integrates a Gated Recurrent Unit and Multilayer Perceptron model with the Binary Whale Optimization Algorithm for feature selection and Ant Colony Optimization for hyperparameter tuning. The proposed framework was evaluated using a publicly available Kaggle house price regression dataset containing 500 housing records with structural, locational, and amenity-related attributes. The dataset was divided into training, validation, and testing subsets using a 70:20:10 ratio, and leakage-free normalization was applied using only the training data. Experimental results show that the proposed BWOA–ACO–GRU–MLP model outperformed standalone GRU, MLP, CNN, LSTM, and BiLSTM models. It achieved an MSE of 0.0146, MAE of 0.1051, RMSE of 0.1208, MAPE of 0.0112, MedAE of 0.0969, and an R2 of 99.04%. These results demonstrate that combining feature selection, hyperparameter optimization, and hybrid neural regression improves prediction accuracy and model stability for house price estimati",0.0,https://www.nature.com/articles/s41598-026-62854-z.pdf10597100da25dd6367202063f88fb76e8803b327218a,10.47772/ijriss.2025.908000076,Socioeconomic and Locational Determinants of House Buying Behaviour in Bintulu: A Micro-Level Urban Housing Analysis,2025.0,International journal of research and innovation in social science,Abdul Hayy Haziq Mohamad; Abdul Rahman Edruce Kushairi; James Ho Chun Bui,"This study explores the key determinants shaping consumer house buying behaviour in Bintulu, Sarawak, an urban area experiencing rapid development and rising property prices. Despite mid-range apartment prices exceeding RM400,000 and detached homes in prime locations priced above RM800,000, housing demand in the area remains high. Using a structured questionnaire distributed to 130 respondents, this research examined five core factors: price, location, environment, house features, and living space. Data were analysed through descriptive statistics, the Kruskal-Wallis H test, and Spearman correlation analysis. The findings reveal that location and environmental factors exert the greatest influence on purchase decisions, while price and living space were comparatively less significant. Moreover, income and occupation levels significantly affect how buyers evaluate housing attributes. Individuals with higher incomes and those working in the private sector or self-employed demonstrated stronger preferences for strategic location and spacious housing. These results suggest that lifestyle needs and urban convenience are taking precedence over traditional affordability concerns. The study",0.0,10606fcf7ac157823343420ef213ef6aaa8747488570,10.1108/ijhma-09-2025-0200,Power in the pitch: narratives and market value of solar PV homes in New Zealand,2026.0,International Journal of Housing Markets and Analysis,Y. Matthews; C. S. Harland,"1061 1062 Residential solar photovoltaics (PV) have expanded rapidly in many countries, yet little is known about how solar features are communicated in property marketing or how they are capitalised in housing markets with low incentives and limited uptake. Existing international research primarily focuses on price effects in higher-penetration settings and has not included large-scale analysis of listing narratives. This study aims to address these gaps by examining how solar homes are described in real estate advertising and whether solar installations influence sale prices and days on market in Aotearoa New Zealand.1063 1064 1065 1066 The analysis draws on a four-year set of 79,944 matched listings and sales, combining nearest-neighbour propensity score matching, fixed-effects regression and topic modelling of listing descriptions.1067 1068 1069 1070 Results show that solar homes are marketed through lifestyle, rural and quality cues rather than explicit sustainability framing, suggesting that PV is bundled with broader amenity signals rather than positioned as a distinct attribute. PV-equipped homes command an average sale price premium of 1.34%, with no significant difference in days on market. Effects do",0.0,10715c2f2e23059ed3d2e81ffbc95555ba0e8b3e32a3,10.54097/w83cv695,Research on Urban Resilience and Sustainable Development of Changchun and Hohhot Based on Multi-Dimensional Analysis,2025.0,"Frontiers in Business, Economics and Management",Tianshu Wang; Xi-Cheng Ning; Jia Zhao,"In the context of population aging and increasing global extreme climate events, this study focuses on evaluating the urban resilience and sustainable development capacity of two cities (Changchun and Hohhot). By integrating data from multiple sources and applying techniques such as big data statistical analysis, random forest, and decision tree, the research addresses several key aspects. Firstly, housing prices in different areas of the two cities are predicted, and the total existing housing stock is estimated. Secondly, the service levels of various departments in the two cities are quantitatively analyzed to identify commonalities and differences. Thirdly, the resilience of the two cities in coping with extreme weather and emergencies is evaluated, along with their sustainable development capacity. Based on these analyses, future development plans are formulated for each city, including investment areas, amounts, and expected improvements in smart city development. The results show differences in housing prices, service levels, and resilience between the two cities, with Changchun generally showing more balanced development and higher resilience in some aspects. The study also",0.0,https://drpress.org/ojs/index.php/fbem/article/download/31988/3131510723833581f473938c4cedb56851659c6a077a1b694,10.20944/preprints202107.0152.v1,Analysing the Residential Market in Trójmiasto Using Self-Organizing Map,2021.0,,Olgun Aydın; Krystian Zieliński,"Although the residential property market has strong connections with various sectors, such as construction, logistics, and investment, it works through different dynamics than do other markets; thus, it can be analysed from various perspectives. Researchers and investors are mostly interested in price trends, the impact of external factors on residential property prices, and price prediction. When analysing price trends, it is beneficial to consider multidimensional data that contain attributes of residential properties, such as number of rooms, number of bathrooms, floor number, total floors, and size, as well as proximity to public transport, shops, and banks. Knowing a neighbourhood's key aspects and properties could help investors, real estate development companies, and people looking to buy or rent properties to investigate similar neighbourhoods that may have unusual price trends. In this study, the self-organizing map method was applied to residential property listings in the Trójmiasto area of Poland, where the residential market has recently been quite active. The study aims to group together neighbourhoods and subregions to find similarities between them in terms of price",0.0,https://doi.org/10.20944/preprints202107.0152.v11073a1bb31c9c16f51d36faeed8168584d107ddf711a,10.1108/ijhma-11-2025-0270,To become a homeowner: the dream that faded away for Mexican workers,2026.0,International Journal of Housing Markets and Analysis,Jacques Lartigue-Mendoza; Saul Quintana-Navarro; Mateo Lartigue-Mendoza,"1074 1075 This paper aims to examine the evolution of housing affordability for workers in Mexico between 2005 and 2020, emphasizing regional disparities and identifying the main determinants that have widened the gap between housing prices and wages.1076 1077 1078 1079 Using cross-sectional time-series data for Mexico’s 32 states, the study applies feasible generalized least squares econometric models to analyze the divergence between the housing price index and the wage index. Explanatory variables include demand and supply factors.1080 1081 1082 1083 Housing affordability has deteriorated substantially, especially in Mexico City (the country’s capital), where housing prices increased 155.57% more than wages over the analyzed period. The real interest rate, which affects both the demand and supply side, is the strongest determinant: a one-point increase raises housing prices by 1.91% relative to wages. A one percent reduction in available surface, after discounting ejidal and built surfaces, increases housing prices by 0.48% more than wages. High migration, population density and growing remittances also exacerbate the problem.1084 1085 1086 1087 The SHF (Sociedad Hipotecaria Federal) housing price index used covers only",0.0,10886b88f5d714daf00791cfb4bc9639a28e8351615e,10.22447/jatb.10.1.202306.1,"Consumer Preferences for Residential Real Estate Properties among Interested Buyers in Laguna Province, Philippines, before and during the COVID-19 Pandemic",2023.0,Asian Trade Association,A. Alyssa; Angeline B. Madamba Jeanette; Pura T. Depositario Dinah; Roldan S. Reyes James,"Purpose – This study assessed the consumer preferences for residential real estate properties among interested buyers in Laguna, Philippines, before and during the COVID-19 pandemic. Design/Methodology/Approach – Interested property buyers in Laguna were randomly selected and characterized based on their socio-demographic and economic characteristics, and their COVID-19 experiences were described. Consumer preferences for different aspects of housing units were examined descriptively and using McNemar’s test, changes in preferred attributes of residential units before and during the pandemic were compared. Findings – Single-detached economic housing with three bedrooms was the most preferred type of unit of interested buyers. It was found that the higher the monthly income of respondents, the higher the property value they preferred to purchase. Los Baños is the preferred municipality to live in because of its proximity to the metropolitan capital. The preferred mechanism of ownership is either rent-to-own or via a loan. Those earning a monthly income of PhP 131,484 (USD 2,708.77) to PhP 219,140 (USD 4,514.63) preferred to purchase residential property in cash. As for attributes, p",0.0,1089fbb61aeb7411f58d93b8bbf6f634cf7c8d88091a,10.54254/2755-2721/2026.gu34304,A Comparative Study of House Price Prediction Using Multiple Models,2026.0,Applied and Computational Engineering,Yang Chen,"Predicting housing prices, therefore, has important implications for real estate management, financial risk assessment, and urban planning. The rise of big data has further opened up new tools for housing price prediction, enabling the use of more complex and data‑driven approaches alongside traditional methods. This study applies linear regression, decision tree regression, and random forest regression to the Boston housing dataset. Model performance is evaluated using MAE, MSE, and R². The random forest model achieves the best performance (MAE=2.05, MSE=7.93, R²=0.89), outperforming linear regression (R²=0.67) and decision tree regression (R²=0.86).Correlation analysis reveals that the number of rooms (RM) shows the strongest positive correlation (r=0.70), while the lower-status population proportion (LSTAT) exhibits the strongest negative correlation (r=–0.74), together with crime rate (CRIM), these features form a core explanatory framework. The proportion of low-income population (LSTAT), and the distance to employment centers (DIS) are the most significant factors affecting housing prices. This study not only provides effective machine learning methods for housing price predi",0.0,https://ace.ewapub.com/article/view/34304.pdf10906537655754e35592a0d55c4331031ccfb7d0ac6c,10.54091/krepa.2023.24.3.69,Analysis of the Impact of Age and Regional Characteristics on Population Change: Focusing on the Resident Population Census from 2009 to 2020,2023.0,Korea Real Estate Policy Association,Hyun-Tae Joo,"According to the population projections by Statistics Korea, the working-age population is expected to decline by over 3.5 million people from its peak in 2019 to 2030. Additionally, Republic of Korea has the lowest total fertility rate of 0.84 births per woman among OECD countries. The population decline in Korea is a significant challenge accompanied by population aging. However, there are some regions in the metropolitan area where population growth is observed. In this study, we aim to identify the key factors influencing population change using machine learning analysis. The factors consider include population composition by age group and various social factors such as employment, housing, culture, health-care, and welfare at the community level. The analysis results show that changes in population size in the age groups of 50∼54 and 45∼50 had a high degree of influence on the change in the population of the region. These age groups have a high proportion in the population composition of Korea and are associated with active economic activities in the working-age population. On the other hand, age groups with older individuals have less influence on population change. In terms ",0.0,1091c60736e351c796b9ed4faf578cdf80f8a3fb88d0,10.54254/2754-1169/2026.gt35154,An Analysis of the Impact of Net Population Inflow on Commercial Housing Prices Based on Panel Data Models—A Case Study of Hangzhou,2026.0,"Advances in Economics, Management and Political Sciences",Zimo Zhang,"Cross-regional population movement and the linkage of the housing market are currently the core topics widely discussed in regional economics. Existing studies have mostly focused on cross-sectional data analysis of urban clusters and regions, but there is still a research gap in understanding the individual heterogeneity and temporal dynamics of a particular city. Consequently, this paper utilizes panel data from 10 districts and counties in Hangzhou, China, from 2020 to 2024, and validates the direction and intensity of the net population inflow's impact on housing prices by constructing a two-way fixed effects model. The study first conducted correlation matrix analysis of variables, then used a panel model for baseline regression, and finally used a two-stage least squares method to mitigate endogeneity bias in the results. Empirical results show that net population inflow has a significant positive impact on housing prices in each district and county of Hangzhou. In addition, economic development level and industrial structure have a significant positive impact on housing prices, while income level hurts housing price growth. The above conclusions provide strong evidence for t",0.0,https://aemps.ewapub.com/article/view/35154.pdf10928926a379d8bc0c969d50dcdc80b7b3976b6a6a55,10.5220/0013883100004919,AI-Powered House Price Estimation Using Machine Learning,2025.0,"Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies",P. Kumar; C. Reddy; S. Basha; U. Siva; R. Mukesh,": The exact estimation of house prices plays a vital role in property decision-making to benefit all real estate market parties including buyers and sellers as well as investors. This research investigates the applicability of data-driven methods in property value estimation, with the support of artificial intelligence and machine learning. Sophisticated predictive models analyze different factors including geographic location and size of property structure and economic condition and market trends within a broad range of variables. This method depends on regression models as well as decision trees among ensemble learning techniques and deep neural networks to achieve better price estimation results. Research shows that price forecast accuracy success depends on selecting the right features which encompass property characteristics together with neighborhood variables and financial variables. Predictions generated from analyzing real estate data using deep learning combined with ensemble learning outperform conventional statistical methods in accuracy levels. The research explores additional approaches to improve accuracy which combine the analysis of external economic facts and sent",0.0,109349afbdfab03ddb471d9fb568d0e110755f9f301d,10.55248/gengpi.5.0324.0798,Accurate House Price Projections,2024.0,International Journal of Research Publication and Reviews,Ravindra Turpati; S. Krishna; Ajit Kumar Rout,"— This study delves into the dynamics of housing prices in Visakhapatnam, employing a multifaceted approach that combines linear regression modelling, geocoding, and K-means clustering. Leveraging a comprehensive dataset encompassing diverse locations, area (in square feet), and bedroom-bathroom configurations, we construct a robust linear regression model for price estimation. The dataset is further enriched through geocoding, introducing latitude and longitude coordinates to each location. Expanding our analysis, we employ K-means clustering to unveil spatial patterns and groupings within the housing market. Our findings reveal intricate relationships between location, property attributes, and housing prices, providing valuable insights for both prospective buyers and real estate stakeholders. Visualizations on the Visakhapatnam map illustrate the spatial distribution of housing clusters, shedding light on localized market trends. This integrated approach not only refines price predictions but also enhances our understanding of the spatial dynamics shaping the real estate landscape in Visakhapatnam. The outcomes of this study contribute to informed decision-making in the housing ",0.0,http://dx.doi.org/10.55248/gengpi.5.0324.07981094a400741ea6ce40409f2a98a82d6f8c59ada608b9,10.53106/221915772024120030003,疫情爆發期間社區居住特徵對南京市居民COVID-19防疫行為之影響,2024.0,建築科學,Ji-Zi-Guo-Ji-Zi Guo; 黃溦溦 黃溦溦; Guo-Ruo-Zhou-Guo-Ruo Zhou; 陳柏翰 陳柏翰; Zhang-Ying Chen,"自2019年12月底新冠病毒（COVID-19）爆發以來，截至2023年7月19日，全球確診病例已超過7.6824億例，死亡人數超過695萬人。住房和鄰里的物理和社會特徵被認為對人類健康至關重要。然而，關於社區居住特徵對降低新冠肺炎風險影響的數據需求尚未得到充分滿足。本研究在中國南京共收集並分析了814份有效問卷，旨在考察社區住房和人口因素對居民預防行為的影響。研究結果顯示，女性、年長者、在職人員、受教育程度較高的人群更有可能採取當局建議的預防行為；聘有物業管理、房價較高的中小型社區以及屋齡較新的社區更有利於改善居民的防範行為；這些社區住房、人口因素共同對居民的防範行為產生了重大影響，其中社區住房特徵比人口因素的影響更大；房價和社區規模是最重要的解釋變數，其次是物業管理和社區屋齡。因此，本研究建議，社區住房特徵和適當的物業管理應作為緩解流行病爆發風險的投入。Since the coronavirus disease 2019 (COVID-19) outbreak in late December 2019, there have been more than 768.24 million confirmed cases and over 6.95 million deaths worldwide by July 19, 2023. Housing and neighborhoods’ physical and social features are considered essential for human health. There is an unmet need for data on the effects of community housing characteristics to mitigate the risk of COVID-19. A total of 814 valid questionnaires were collected and analyzed in Nanjing, China, to examine the effects of community housing and demographic factors on residents’ precautionary behaviors. Our results indicate that being female, older, employed, and better educated are more likely to adopt the precautionary behaviors suggested by authorities. Small or medium-sized and younger communities with property m",0.0,109551a1059b59d4b43da8e865be40b3f370d986a54a,10.32890/jcia2026.5.2.5,"House price prediction in Ajah, Lagos using a random forest regression model",2026.0,Journal of Computational Innovation and Analytics (JCIA),Ayodeji M. Obaude; E. Oyekanmi; A. Adegboye; Oluwaseyi Abe,"Housing is one of the most essential need to commoner but several factors contribute to acquiring a suitable home, factor such as low income, high material cost and most importantly the presence of middleman in acquiring suitable house posed significant challenges thereby increases the cost, this study addresses this problem by developing a random forest regressor model to predict house price in Ajah Lagos, a dataset of 24,326 instances was sourced from Kaggle and filtered to 2142 instance containing Ajah listing. The data underwent several preprocessing steps; outliers were detected using the interquartile range, and Recursive Feature Elimination was used to identify relevant features that contribute to the model while eliminating less important ones. The RF model demonstrates a robust performance with an R-squared of 68.5%, a Mean Absolute Percentage Error of 18.96%, and a Root Mean Squared Error of ₦11.1 million. Compared with existing models in the literature, the model achieved a mean absolute error of ₦8.09 million, representing a 4.82% improvement and outperforming the ridge regression models. This research contributes to the field by providing stakeholders with a data-driv",0.0,https://e-journal.uum.edu.my/index.php/jcia/article/download/27168/5244109678275fd0eaf66fb56137b4c71d1c9dd2719494f5,10.38124/ijisrt/ijisrt24oct1873,House Price Pridiction Using Machine Learning,2024.0,International Journal of Innovative Science and Research Technology,Dev Mulchandani; Swati Shamkuwar,"This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This proposed model considers as the most accurate model used for calculating the house price and provides a most accurate prediction. This provides a brief introduction which will be needed to predict the house price. This project consists of what and how the house price model works with the assistance of machine learning technique using scikit-learn and which datasets we will be using in our proposed model. Predicting the price of a house helps for determine the selling price of the house in a particular region and it help people to find the correct time to buy a home. In this task on House Price Prediction using machine learning, our task is to use data to create a machine learning model to predict house prices in the given region. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to improve the efficiency of the model. for this project we are using supervised learnin",0.0,https://www.ijisrt.com/assets/upload/files/IJISRT24OCT1873.pdf10975c6283b04171a3d88a6ed11c99a471946c157118,10.54097/5811g474,Research on Housing Price Prediction Based on Machine Learning,2024.0,Highlights in Science Engineering and Technology,Zi-Rui Huang,"The stability and long-term growth of the real estate industry depend heavily on the capacity to forecast house price trends with accuracy. The purpose of this research is to evaluate the benefits and drawbacks of several machine learning models for housing price prediction. First, the factors influencing housing prices in various ways are explained. Next, the multiple linear regression, backpropagation neural network, and random forest model, respectively, are introduced. In the meantime, the paper analyzes their forecasting shortcomings and provides examples of other scholars' optimization experiments on the aforementioned three models. The findings indicate that these three types of optimized models can obtain more stable and accurate prediction results when housing prices are predicted. Housing price forecasting can lower investment risks for real estate developers by assisting them in creating more effective project development plans and sales tactics. The government can better grasp the real estate market's development trend, create pertinent regulations and control measures, and support the stable and sustainable growth of the real estate market with the aid of home price pr",0.0,http://dx.doi.org/10.54097/5811g47410984ef7bbb869ece488800b8516b39f7e082883729f,10.54254/2754-1169/2025.21165,"Predicting Gold Prices: Interactions with Energy Markets, Currencies, and Equity Indices",2025.0,"Advances in Economics, Management and Political Sciences",Yuhang Lu,"This study investigates the dynamic relationships between Gold and several key financial and economic variables, including Crude Oil, Natural Gas, the NASDAQ 100 Index, U.S. Treasury Bonds, the U.S. Dollar Index, and the Housing Price Index. The research uses advanced statistical techniques such as Vector Auto Regression (VAR) to capture the complexities of these interactions and assess how fluctuations in Gold price can influence other variables and overall economic performance. Significant findings indicate that Gold prices are primarily affected by their lagged values and the U.S. Dollar Index, with strong relationships confirmed by high statistical significance. Notably, a rise in the dollar's value correlates with a decrease in Gold prices, while past Gold prices substantially influence current values. The study highlights the interdependencies among these financial indicators, providing valuable insights for investors and policymakers. By understanding these relationships, stakeholders can make more informed decisions in an increasingly interconnected economic landscape. This research contributes to the existing literature on asset correlations and enhances the comprehension ",0.0,https://doi.org/10.54254/2754-1169/2025.2116510995944306425279be569dd860b5dc0561ba29744fe,10.1145/3748636.3762799,RailEstate: An Interactive System for Metro Linked Property Trends,2025.0,SIGSPATIAL/GIS,Chen-Wei Chang; Yu-Chieh Cheng; Yun-En Tsai; Fanglan Chen; Chang-Tien Lu,"Access to metro systems plays a critical role in shaping urban housing markets by enhancing neighborhood accessibility and driving property demand. We present RailEstate, a novel web-based system that integrates spatial analytics, natural language interfaces, and interactive forecasting to analyze how proximity to metro stations influences residential property prices in the Washington metropolitan area. Unlike static mapping tools or generic listing platforms, RailEstate combines 25 years of historical housing data with transit infrastructure to support low-latency geospatial queries, time-series visualizations, and predictive modeling. Users can interactively explore ZIP-code-level price patterns, investigate long-term trends, and forecast future housing values around any metro station. A key innovation is our natural language chatbot, which translates plain-English questions (e.g., ""What is the highest price in Falls Church in the year 2000?"") into executable SQL over a spatial database. This unified and interactive platform empowers urban planners, investors, and residents to derive actionable insights from metro-linked housing data—without requiring technical expertise. A demon",0.0,https://dl.acm.org/doi/pdf/10.1145/3748636.37627991100cfb31eecdc43e62b8a87d7fecdffb6aa2043938f,10.58837/chula.the.2015.1614,The Effects of Macroprudential Policy on Housing Market: Evidence from 30 Provinces in China,2015.0,,Lina Wang,"The housing market plays a significant role in Chinese economic growth. However, a house price inflation and the high credit growth could affect the stability and the safety of the financial system. In order to manage the potential risks from the housing market, macroprudential policy has been popularly used in recent years as both a regulatory and supervisory instruments, including China. The main objective of this thesis is to evaluate the effects of macroprudential policies on house price stability management of both newly constructed housing market and second-hand housing market in 30 provinces of China and to find out which instrument is more effective by using the fixed-effect panel model. By the way, to point out the different effectiveness between the different individual macroprudential policy instruments on newly constructed houses price (overall) and second-hand houses price (overall) by using the pooled panel model. Also, to compare the different effectiveness of the reserve requirement ratio (RRR) on three different housing space types of newly constructed houses and second-hand houses. Using panel regression, it is found that most of macroprudential instruments are si",0.0,1101c454d39fa3bad62281f5c28185fa67a0c2e1b79d,10.2139/ssrn.4178207,"Fiscal Monitoring, School District Outcomes, and Residential Choice",2022.0,Social Science Research Network,Travis St. Clair,"Under the canonical model of local political economy, taxpayers vote with their feet to find the community that provides the optimal bundle of taxes and public goods (Tiebout, 1956). One common objection to this model is that taxpayers lack perfect information about their choice sets, and consequently rely on heuristics or third-party rating agencies. This paper exploits New York state’s fiscal monitoring program to examine the effect that quality labels have on school district enrollment and outcomes. Taxpayers may be especially sensitive to labels providing information about the management of school districts, as studies have shown that parents of school-age children pay close attention to information on school quality. Using a regression discontinuity design, I examine the effect of fiscal stress and “environmental” stress labels on property values, school enrollment, demographics, and test scores. While fiscal stress labels have no effect, environmental stress labels – indicating social, economic, and demographic pressures, such as a high percentage of disadvantaged students – cause enrollment and property values to decline, especially in wealthier districts. Taxpayers are more sensit",0.0,https://doi.org/10.2139/ssrn.4178207110279f4aed15e0ac08ea43c3de2d283c604e5a2a5b3,10.62051/7gzntt12,Utilize the Machine Learning Models to Forecast Home Values.:Seattle U.S.,2024.0,Transactions on Computer Science and Intelligent Systems Research,Yunqi Zhang,"Understanding housing price predictions assists the government in better adjusting and formulating relevant policies to promote economic stability and sustainable social development. In this study, researchers collected a large amount of data on the Seattle real estate market, including housing characteristics (such as area, number of bedrooms, number of bathrooms), geographical location (such as neighborhood, nearby facilities), and historical price data. This paper used this data to train and test linear regression, and KNN prediction models to forecast future housing price trends. The linear regression model, on the other hand, models the linear relationship between a single independent variable and the dependent variable, predicting housing prices by fitting an optimal line. The KNN prediction model, based on the nearest neighbor algorithm, predicts by searching for the K nearest neighbor samples closest to the target sample. Researchers will compare the accuracy and effectiveness of these three methods in predicting Seattle housing prices to determine which method is most suitable for housing price forecasting. Through this analysis, they aim to provide a reliable housing pric",0.0,http://dx.doi.org/10.62051/7gzntt1211032ba20a88e43703e3c456f3dc2c6891b7c5ac372b,10.18267/j.pep.823,Are the Effects of Opening New Mass Rapid Transit Segments in Taiwan on Nearby Housing Prices Positive?,2023.0,Prague Economic Papers,Ming-Te Lee; Ming-Long Lee; Yahang Cheng,"This study evaluates the effects of opening new segments of mass rapid transit (MRT) lines on housing prices near the MRT stations in Tucheng District and Xinzhuang District, New Taipei City, Taiwan. The effect of proximity to each MRT station is estimated separately with difference-in-differences regressions integrated with spatial econometrics with heteroscedasticity-robust standard errors. The opening of the new segment of the Blue Line, also known as Bannan Line of the Taipei Metro, does not significantly influence housing prices within 600-metre road network distance of the MRT stations, compared to prices outside the distance range. In contrast, and also unlike the findings of prior studies, although the segment and the stations are underground structures, the opening of the new segment of the Orange Line, also known as the Zhonghe-Xinlu Line of the Taipei Metro, significantly decreases housing prices within 600-metre road network distance of the MRT stations, compared to prices outside the distance range, perhaps because the opening of the stations is delayed about one and a half years to be used as a temporary storage area for MRT trains. The findings have implications for ",0.0,http://pep.vse.cz/doi/10.18267/j.pep.823.pdf1104796696c73fa998b433b099374e172b6ed882dfbf,10.61173/ebwh1d62,Factors affecting housing prices in Yangtze River Delta Cities during 2000 to 2023,2025.0,Interdisciplinary Humanities and Communication Studies,An-Jie Sha,"This thesis aims to develop a framework for identifying and evaluating the important factors affecting housing prices in the Yangtze River Delta region. These factors include urbanization rate, population, debt ratio, GDP, and resident income over the past 20 years for four cities: Shanghai, Hangzhou, Suzhou, and Nanjing. The Pearson correlation analysis and linear regression analysis are methods used to find and analyze the factors with the strongest correlation. These two methods enable a direct identification of the factors that most significantly affect housing prices, providing insights into their impact and predicting trends for these factors in future years. To investigate key drivers of housing price surges in China's Yangtze River Delta (2000–2023), I analyzed historical data (avg. price ↑490% to 10,400¥/m²; Shanghai ↑12x to 50,000¥/m²) from authoritative sources (Zhang, 2024; Anjuke, 2024), designed a quantitative framework using Pearson correlation and linear regression to test five factors (population, urbanization rate, GDP, disposable income, debt ratio) across Shanghai, Hangzhou, Suzhou, and Nanjing, and established predictive models for future trends. This empirical",0.0,https://doi.org/10.61173/ebwh1d62110591b91138778cdbcd3ed778a59483d30d91d25d95,10.18860/cauchy.v10i2.36937,Mathematical Modeling of the Decoy Effect to Shift Consumer Preferences in House-Type Purchases,2025.0,CAUCHY,Sentot Eko Baskoro; I. N. Sutapa; S. Suhartono,"This study discusses the design of a Mechanism Design-based Decoy Effect Mathematical Model to shift consumer preferences from small-type houses to large-type houses. The model uses area-per-price utility that combines the ratio of building area and land area to price, and formulates the optimization of decoy attributes so that large-type houses dominate but remain close as comparators. The model is explained with a case study, namely large houses are set at a price of IDR, land area of m², and building area of m²; small houses are priced at million IDR, building area of m², and land area of m²; then two types of decoy house prices are set, namely million and million IDR. With the ratio of building and land area to price, namely and as well as and , large-type houses excel in terms of building-per-price and are equivalent in terms of land-per-price. Asymmetric decoy houses can be constructed by selecting and . The results of the mathematical model calculations obtained the closed solutions 'adjacent' and 'strong' for the two types of prices. Sensitivity analysis shows that the target margin advantage increases linearly with and the context scale , above the base margin",0.0,https://ejournal.uin-malang.ac.id/index.php/Math/article/download/36937/pdf1106c6992539a199b8c8018a213de0838b425d87ced8,10.11606/d.12.2021.tde-19012022-184059,Migration and crop change: evidence from Brazil using a spatial equilibrium model,2021.0,,G. Monteiro,"Climate-change forecasts for Brazil points to sizable and spatially heterogeneous changes in temperature. This is likely to spur migration and spatial changes in crop patterns. We develop a unique tractable model that integrates both migration and agricultural adaptation as responses to climate change. Our model considers that workers’ locational choice is impacted by climate change through three channels: a direct amenity-value channel, as well as the indirect agricultural wages and housing prices channels. The impacts from indirect channels are affected by how farmers adapt to the new climate. Our simulations predict up to an 18.25% increase in micro-region migration rates, with barely no effect from crop change. On the other hand, agricultural adaptation has important impacts in employment in agriculture. The South macro-region is predicted to have an increase in agriculture share of employment when farmers change crop patterns, this includes a reduction in agriculture area dedicated to the production of Maize and an increase the area dedicated to Coffee in the region. Our results help to identify populations most vulnerable to climate change and the regions where most gains fro",0.0,http://www.teses.usp.br/teses/disponiveis/12/12138/tde-19012022-184059/publico/GabrielFacundesMonteiroCorrigida.pdf1107bf1d83a5ab390e936a7ac45432acc25b8cd4e548,10.21203/rs.3.rs-537516/v1,"Housing Situation, Residential Relationship And The Prevention Of Chronic Diseases Of The Elderly",2021.0,,Ya-Jun Duan; Juan Luo,"1108 Background:To understand the impact of housing and living-related factors on the chronic diseases of the elderly floating population, and to provide preventive measures for reducing the risk of chronic diseases among the elderly floating population in my country.Methods: In this paper, we used the data from the 2017 National Floating Population Health and Family Planning Dynamic Detection Survey, selected housing and housing-related factors as explanatory variables, and adopted a binary logistic regression analysis method.Result: People living in free housing are more mobile and more susceptible to chronic diseases; most people in self-owned housing need to repay their mortgages in their daily lives, and there is a certain life pressure that affects them to enjoy a high-quality life; elderly floating population who migrate with spouses or children Life pressure is heavier, and there is a greater probability of chronic disease risk.Conclusion: This article proposes that all localities should strictly control housing prices, increase housing supply for the floating population, improve the service level of the elderly floating population, improve the social security system and mecha",0.0,https://doi.org/10.21203/rs.3.rs-537516/v11109ab0b2680d1ff367332b99dfcf7636dd5f7ee0fad,10.32628/ijsrset2411263,Enhancing House Price Prediction and Accuracy using Particle Swarm Optimization,2024.0,International Journal of Scientific Research in Science Engineering and Technology,M. Rao; B. Rao; M. Yamuna; G. Revanth Mehatha; G. Bhavya Nagasri,"Predictive models for determining the sale price of houses in cities like Bengaluru is still remaining as more challenging and tricky task. The sale price of properties in cities like Bengaluru depends on a number of interdependent factors. Key factors that might affect the price include area of the property, location of the property and its amenities. In this research work, an analytical study has been carried out by considering the data set that remains open to the public by illustrating the available housing properties in machine hackathon platform. The data set has nine features. In this study, an attempt has been made to construct a predictive model for evaluating the price based on the factors that affect the price. Modeling explorations apply some regression techniques such as multiple linear regression (Least Squares), Lasso and Ridge regression models, support vector regression, and boosting algorithms such as Extreme Gradient Boost Regression (XG Boost). Such models are used to build a predictive model, and to pick the best performing model by performing a comparative analysis on the predictive errors obtained between these models. Here, the attempt is to construct a pred",0.0,https://ijsrset.com/index.php/home/article/download/IJSRSET2411263/IJSRSET24112631110ea2569a40c28d3872ad58bfa06e5f2001dedf617,10.54254/2753-8818/2025.22572,Hybrid House Price Prediction Model by Integration of Simple Linear Regression and Cubic Spline Interpolation,2025.0,Theoretical and Natural Science,Yiwen Tang,"In this day and age, house-purchase has become a crucial consideration for almost everyone, whether seeking a residence or making an investment. Therefore, analyzing the relationship between these factors and house prices is vitally important for both buyers and sellers to make informed decisions. Some researchers have used a linear regression model that can predict the house price for a company or individual. This paper focuses on a target sample data set of Houses in London from Kaggle. The author firstly provides an analysis of two methods to model the relationship between the dependent variable, House Price, and an independent variable, Square Meters. These methods are the Simple Linear Regression Model (SLR) and Cubic Spline Interpolation polynomial (CSI), respectively. Then, a Hybrid House Price Prediction Model is established to predict the house price with specific Square Meters by integrating SLR and CSI. Finally, the author uses Multiple Linear Regression to model the effects of various independent variables from the target sample to the House Price. The research significance of this paper mainly includes increasing the accuracy and comprehensiveness of the House Predicti",0.0,https://www.ewadirect.com/proceedings/tns/article/view/22572/pdf1111f24082bb64ae2c233e6bb06987971d245e880500,10.1145/3772726.3772759,A Machine Learning-Based Study on the Risk Identification and Countermeasures of China's Real Estate Bubble: A Comparative Analysis with Typical International Cases,2025.0,Proceedings of the 2nd International Conference on Intelligent Computing and Data Analysis,Daixuan Xu; Fu-Kuan Li,"This paper aims to use machine learning method to build a model that can quantitatively assess and warn the risk of China's real estate bubble. The research selects the multi-dimensional quarterly data of macro-economy, financial credit and real estate market from 2005 to 2023, and constructs a characteristic system including key indicators such as house price income ratio, credit gap and real estate development investment. This paper uses logistic regression, xgboost, LSTM and xgboost/LSTM hybrid model to train the model of supervised learning algorithm to identify the potential bubble risk state. The empirical results of the simulation show that the hybrid model shows good early warning ability (AUC reaches 0.96), and identifies that the speed of credit expansion, the deviation between housing prices and residents' income are the core factors driving the bubble risk. Through in-depth comparison with the cases of Japan and the United States, this paper analyzes the uniqueness of China's real estate market. Finally, based on the model analysis and international experience, this paper puts forward a set of systematic policy recommendations to achieve “soft landing” from the aspects ",0.0,https://doi.org/10.1145/3772726.37727591112440a633cf2ee4480fc4a5c5fd0f8f18ff4622023,10.54254/2754-1169/49/20230502,Analysis of Regional Differences and Influencing Factors of Real Estate Prices: An Empirical Study Based on 31 Provinces in China,2023.0,"Advances in Economics, Management and Political Sciences",Xiaodi Liu,"Currently, the continuous rise in housing prices in China has brought about social issues regarding housing fairness. Therefore, it is of practical significance to study the influencing factors of China's real estate prices and the effects of these factors. This article first identifies ten variables that have a significant impact on housing prices through analysis, and then conducts empirical analysis separately for the eastern and central-western regions. Based on the correlation and model multicollinearity tests, two corresponding models are established. Subsequently, this article uses a multiple linear regression model for regression and uses a test set to verify the model's effectiveness, conducting quantitative analysis. Combining the analysis results, the factors that have a significant impact on housing prices in the eastern region of China are identified as the sales area of commercial housing, rental prices, and unemployment rates. In the central-western region, the factors with a significant impact on housing prices are disposable income and real estate development costs. Based on these conclusions, this article proposes relevant policy suggestions, which are expected to",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/7040/pdf1113f50dfca228d69931f114bb1d7e327eeeff6b8775,10.65102/is20261176,Explore driving factors of vacant housing in England: taking cases of growing and shrinking urban areas,2026.0,Ingegneria sismica,Ruidan Sun,"This article aims to analyze the potential determinants of residential vacancy rates in different urban environments in England, using Blackpool (representative of declining cities) and Liverpool (growth city model) as examples for comparative research. This article uses a multiple linear regression model to conduct correlation analysis on population, socio-economic, housing market, and built environment factors, and evaluates whether the spatial distribution of vacant housing exhibits clustering or dispersion patterns using global and local autocorrelation analysis. Research has found that the increase in the number of small households is negatively correlated with housing vacancy rates, but only significantly in shrinking cities such as Blackpool. Among socio-economic factors, employment in the tertiary industry is only related to job vacancies in declining cities, while unemployment becomes a common catalyst. The impact of median housing prices on vacancy rates in these two types of cities can be ignored. From the perspective of urban form, the density of commercial facilities is the only building environment factor related to vacancy rate, but its impact varies depending on the",0.0,1114c17ce8cb0d0915a4b3df200560c6e6761ead6de1,10.35940/ijitee.i1103.0789s419,Recent Advancements in Various Machine Learning Techniques,2019.0,International Journal of Innovative Technology and Exploring Engineering,,"The field of machine learning is witnessing its golden era as it turnout to be the leader in this field of artificial intelligence. This paper presents a comprehensive study of recently developed machine learning techniques especially for house price prediction. Due to the lack of sufficient knowledge required to train the machine learning models, existing techniques usually use various attributes and assign constant values to these attributes. Unsuitable assignment to these attributes does not provide desired results. The primary objective of this review paper is to provide a structured outline of some well-known machine learning techniques. This paper also focuses on the methods which can assign optimal values to the existing techniques. The review has revealed that the meta-heuristic techniques can attain the optimistic parameters for the machine learning techniques. However, metaheuristic techniques still suffer from the poor convergence speed and stuck in local optima kind of issues. Finally, this paper describes the various issues and challenges of image machine learning techniques, which are required to be further studied. The various challenges with existing machine learnin",0.0,https://doi.org/10.35940/ijitee.i1103.0789s41911158b3e220c29b7f84a7cbf9f575a36724c62008c3e,10.22214/ijraset.2019.5554,Proposal for Slum Rehabilitation in Anandwali: A Case Study of Nashik,2019.0,International Journal for Research in Applied Science and Engineering Technology,Radhika Patil,": Rising population with immigration to urban area are one of the main reason to form a substandard, dingy houses of high density and congestion, overcrowding, absence of basic amenities like water supply, drainage, sewage line and disposal of garbage, in simple word it called as slum. Especially in large cities housing price has increased speedily, so it is very difficult for the people to buy a house with their own preference. Shortage of affordable houses and infrastructure respect to demands are responsible for formation of slums near to their working places. Slum dwellers are important contributors to the development of the city, by providing their services to the citizens and organisations like industrial workers, construction site labours, domestic savants, rag- pickers and small scale businesses etc. countries have help to slum dwellers in rising their standard of living and confidence level, increase healthy environment, hygiene and cleanliness. The paper studied policies which area formed for slum upgradation, rehabilitation and redevelopment. It also analyse the slum scenario in Nashik city and prepared a proposal of Slum Rehabilitation of Anandwali, Satpur which is loca",0.0,https://doi.org/10.22214/ijraset.2019.5554111638bf59597966e99f31288b3b3bfbe1d0b22ac0b5,10.52324/001c.127962,Spatial Discontinuities in Water Rights: Estimating Household Willingness-to-Pay for Unmetered Irrigation Water,2024.0,The Review of Regional Studies,Matthew K. Gnagey,"House prices capture the value households place on environmental attributes and amenities. Access to an unlimited water right for irrigating lawns in an arid region is a prized environmental amenity. The distribution of this amenity varies across space, and often houses with these water rights are located in neighborhoods that differ substantially from other neighborhoods. This study aims to disentangle these neighborhood differences to identify the value that households place on unmetered water rights for lawn irrigation. Identification relies on a regression discontinuity design exploiting the variation in unmetered water access that occurs based on historical land use and geographic features. By examining houses on the border of the water rights zones, the analysis isolates houses that only differ by their access to unmetered irrigation water, thereby eliminating potentially correlated variation with this access, such as differences in socioeconomic attributes, local tax rates, school quality, and environmental amenities. The findings indicate that households are willing to pay a premium of 2% for access to unmetered irrigation water. This premium is not driven by factors such a",0.0,https://rrs.scholasticahq.com/article/127962.pdf11175db1e2745b54f690925e50b88ff1e1328a7d5be9,10.1177/00111287261425622,The Long-Term Effects of Structural Discrimination on Public Safety: The 1930s Redlining Maps,2026.0,Crime & Delinquency,David Mitre-Becerril,"Enduring disinvestment can lead to different socioeconomic trajectories and the clustering of distressed, blighted urban areas. Do these differential trajectories explain present-day public safety? This study examines the long-term public safety impacts of the residential security maps, the once-legal 1930s racially discriminatory maps used to determine the real estate market’s “risk” of lower property values, limiting residential loans to racial-minority individuals. This research collects incident-level data from 48 police departments and compares areas near different color-grade boundaries around a small bandwidth in a spatial regression discontinuity design. Neighborhoods differed along sociodemographic dimensions before the introduction of redlining maps. There are significant increases in present-day violent, property, low-level, and weapon-related offenses in redlined areas, even after controlling for historical differences. There is some evidence that present-day law enforcement, as measured by arrests, police stops, and calls for service, was also affected. Redlining maps may have exacerbated neighborhood disadvantages, potentially preventing safer neighborhoods in the lon",0.0,https://journals.sagepub.com/doi/pdf/10.1177/00111287261425622?download=true11186e1d5336bff1f5714826bf133088500f6acf78e5,10.62422/978-81-981590-7-6-011,"San Diego Home Prices and Proximity to Colleges, Military Bases, and U.S./Mexican Border",2025.0,"Abstracts of the 7th World Conference on Business, Management, Finance, Economics, and Marketing",Andrew Narwold; Al-Bustan Ma,"1119Purchasing a home is often the single most important investment an individual makes. Aside from the structural characteristics of the home, a potentially more significant factor in the decision-making process is the location as many newly purchased homes are remodeled or expanded prior to the new homeowners moving in. In this paper, in addition to structural characteristics and proximity to amenities such as the ocean and open spaces, we examine distances to colleges, military bases and the U.S./Mexican border in relation to the value of single-family homes in San Diego County over the years 2000-2023. The County of San Diego is unique in its topography as well as economic diversity. Moreover, the City of San Diego ranks eighth among most populous cities in the United States and while home prices are above the national average, the values are less than other California cities such as those in the Bay Area and San Francisco. San Diego also experiences enviable weather conditions year-round. As such, San Diego is ideal for examining the market value of external factors on the price of a home. The findings suggest evidence of a dis-amenity to military bases and the U.S./Mexican port ",0.0,https://eurasiaconferences.com/storage/attachment/wFBT75jruiUSQLirLNXX6wxqWuZx2EurxASP9smA.pdf1120983d749ed9703ef4fdf843f2f7c7fb749fe7eada,10.1007/s10663-026-09676-4,Asset prices and durable spending in Central and Eastern Europe,2026.0,Empirica,Florina Burdet; Monica Ioana Pop Silaghi,"This paper analyses the effect of asset price changes on durable consumption in 11 Central and Eastern European economies from the European Union from 2000Q1 to 2023Q2. Using the method of moments quantile regression for panel data, we find that declines in house prices lead to decreases in durable consumption spending, which are more pronounced among lower-spending households who also show greater sensitivity to stock price declines than higher-spending ones. In contrast, higher-spending households react more prominently to house price increases, underscoring the asymmetric nature of wealth effects. Additionally, monetary and fiscal indicators, such as rising interest rates and public debt, are associated with reduced consumer confidence, further dampening consumption across all durable spending levels. Wealth inequality constrains durable spending, especially in the lower quantiles, while an ageing population slightly boosts consumption in lower expenditure quantiles, reflecting a more cautious approach to spending. These findings highlight the importance of targeted policy measures to stabilise consumption, address wealth inequality, and mitigate the financial vulnerabilities of",0.0,https://link.springer.com/content/pdf/10.1007/s10663-026-09676-4.pdf1121b7625e673e9d0c426f325655681e3f17080269d1,10.65286/icic.v22i2.44137,Data-Driven Office Rental Price Prediction: An Empirical Study of Deep Learning vs. Gradient Boosting,2026.0,"Poster Volume 0008 The 2026 Twenty-Second International Conference on Intelligent Computing July 23-26, 2026 Toronto, Canada",Yi-Xuan Yang,"The ability to predict office rental prices is very important in urban planning, investment strategy and regulation of the market. Nevertheless, it is still difficult to value commercial real estate because of extreme data heterogeneity. Predictive models should simultaneously be able to handle structured attributes that are rigid (e.g., floor area and level) and unstructured textual information (e.g., location tags and amenity descriptions), and there is no trivial way to reconcile the two.11221123To address this, we built a new multi-modal dataset that focuses on commercial office listings in Hangzhou, China. We directly combine web-scraped transaction data with rich geospatial Point of Interest (POI) measures obtained through mapping APIs.11241125In the recent past, the scholarly world has been overwhelmingly in favor of multi-modal deep learning models to process such heterogeneous data. However, our empirical results lead towards a different direction. We prove that Gradient Boosting Decision Trees (GBDT), namely CatBoost, not only significantly outperforms traditional statistical approaches in the commercial real estate field, but also decisively defeats complex deep learning baselines, ",0.0,11264c31e259cf5625db8bd0cbbcae78c68fed2c8164,10.36253/aestim-19824,Text-augmented house valuation: using embedding vectors and SHAP values,2026.0,Aestimum,Lee Changro,"With the recent advent of large language models, text data can now be readily processed and analyzed across various sectors; traditionally, this was not the case, and text data had proven challenging to analyze. The housing market uses and features a vast amount of textual data, such as property descriptions and neighborhood trend reports, but has yet to fully exploit this potential. This study has two objectives: first, to enhance house valuation accuracy by integrating text information into valuation models; and second, to interpret how text information contributes to improved performance. We convert the environmental descriptions of each house into embedding vectors, which are then incorporated into regression and extreme gradient boosting (XGB) models to estimate house prices. Additionally, we interpret the XGB model’s results using Shapley Additive explanations (SHAP) values. The embedding vectors significantly improved the performances of both the regression and XGB models, and the embedding vector values aligned well with the semantic meaning of the environmental descriptions of each house. This study contributes to the literature by deepening our understanding of embedding ",0.0,https://oaj.fupress.net/index.php/ceset/article/download/19824/15077112749a131b337966ee8aff2dd20022ae66e4666e6b7,10.18374/jabe-20-3.8,"DETERMINANTS OF U.S. OUTBOUND TOURISM TO CANADA, MEXICO AND WESTERN EUROPE: EMPIRICAL EVIDENCE FROM AN AUTOREGRESSIVE DISTRIBUTED LAG MODEL",2020.0,Journal of Academy of Business and Economics,Macki Sissoko; John Kamiru; James Corprew,"This article investigates U.S. outbound tourism demand for selected major foreign destinations, namely Mexico, Canada and Western Europe, using the Autoregressive Distributed Lag model (ARDL) and quarterly data during the period 1995Q4 - 2016Q4. No study in the tourism literature conducted an empirical analysis on U.S. outbound tourism demand, the largest source of tourist arrivals in foreign destinations in the world, using ARDL modelling. Results from the Augmented Dickey-Fuller unit root testing methods ruled out that none of the variables under consideration, in this study, is in the order of I(2): both indicated that all the time series are integrated in the order of, either I(0), or I(1). The ARDL bounds tests reveal the existence of a long-run equilibrium relationship between the number of U.S. tourists' arrivals, relative prices of tourism in these selected foreign destinations, transportation costs, real U.S. personal disposable income, and real U.S. median home price as proxy for wealth. The estimated coefficients of the short-run dynamic ARDL models are negative as expected, and significant; thus, indicating that short-term deviations, due to shocks, are restored back in",0.0,https://iabe.org/iabeX/abstracts/JABE-20-3.8_Abstract.pdf11285551fe68f083e53aded85fca7c9779c3ce00133b,10.1109/iciccs65191.2025.10984632,AR Portal for Enriching Education and Business,2025.0,International Conference Intelligent Computing and Control Systems,Parvathavarthini S; L. K; P. M; Harshini J; Manjarika B. S.; Yuthithra M,"Augmented Reality is a booming technology that can be used to bring Augmented characters to physical world. The overlayed images can be seen through a mobile phone itself. ""Augmented Reality for Enriching Education and Business Experiences"" is used to enhance a multi-door augmented reality application that provides customers to realize the diverse virtual experiences in online business and educational systems. With Unity, augmented reality enables the development of complete experiences by combining virtual and physical things. Technologies like plane recognition, AR interactions and Raycasting using AR Foundation for smooth interaction and integration. In order to improve interaction and immersion, the AR Portal establishes an interactive doorway to virtual worlds that allows users explore areas like the home or laboratory and can engage with 3D objects inside the Portal. The AR Portal can be visualized through the smart phones. Utilizing ARCore for Android, the application visualizes multicasting virtual doors, each leading to an unbelievable and immersive environment. The real estate environment offers valuable buyers or renters the ability to feel virtual realization of detaile",0.0,1129e6791dc6106aa6a7174cd58c50e5f94de067bd51,10.1080/09599916.2025.2542209,Private speculation versus public inefficiency: which delays housing development more?,2025.0,Journal of Property Research,Dian Rahmawati; D. A. Samsura; E. van der Krabben,"ABSTRACT Housing supply in urban areas is often delayed due to various factors, which leads to persistent backlogs. While theories on development delay, such as regulatory inefficiencies and speculative developer strategies, have been extensively examined in Global North contexts, their relevance in rapidly urbanising environments remains underexplored. This study investigates how project characteristics and market conditions influence development timelines. Drawing on a dataset of 214 housing projects in a major Indonesian city, we examine the influence of land area, number of built units, building size, housing location, house price, and land price increases on the time taken for permit issuance and project completion. Our findings indicate that project characteristics exert a stronger influence than market dynamics. The analysis also reveals a distinctive sequencing inversion in which construction frequently precedes permit issuance. This finding highlights how discretionary enforcement and informal practices reconfigure conventional models of development timing. Overall, the study contributes to a more situated understanding of housing supply processes, emphasising the need to ",0.0,https://www.tandfonline.com/doi/pdf/10.1080/09599916.2025.2542209?needAccess=true113060cd085daa47e5c5af4aad106e91c07a606ef9d6,10.3390/jrfm19030171,Why Market Prices May Not Be the Best Benchmark for Automated Valuation Models: Empirical Evidence of Ex Ante Unobservability of Gender-Associated Price Discrepancy in the Auckland House Market,2026.0,Journal of Risk and Financial Management,C. Yiu; Ka-Shing Cheung,"Automated Valuation Models (AVMs) are typically trained by learning to replicate observed housing transaction prices. This paper argues that such benchmarking is theoretically debatable. Market transaction prices are not direct measures of underlying property value but are realised outcomes of exchange processes that involve buyer-specific attributes that are unobservable prior to sale. Using residential housing transactions from Auckland, New Zealand, and buyers’ gender inferred from unstructured purchaser name data via artificial intelligence-based natural language processing, we provide empirical evidence that buyer attributes systematically affect transaction prices. Specifically, gender composition is shown to influence the discrepancy between AVM estimates and transaction prices, while no corresponding effect is found when AVMs are compared with capital values, which are the Council’s appraisals for rating purposes. This asymmetry reflects the shared information set of AVMs and professional appraisals, as both are based only on property and market information available prior to sale and do not incorporate buyer identity. The findings provide initial evidence for valuers to ad",0.0,https://www.mdpi.com/1911-8074/19/3/171/pdf?version=17722700781131762dd45bb9b7ff53a2a7ac9734f4360da93d0466,10.46793/urbanizam24.217vm,THE ROLE OF URBAN PLANS IN SOCIAL HOUSING PROVISION IN SERBIA,2024.0,Urbanism and sustainable development,Zlata Vuksanović-Macura; A. Jevtic,"In this paper, we investigate the role of urban planning in providing social housing in Serbia. Social housing is essential in providing affordable and sustainable housing options for low-income individuals and families. In recent years, the issue of social housing has gained traction as cities around the world face rising housing prices and a growing number of individuals experiencing homelessness. Urban planning encompasses the design and development of cities and communities to create environments that are functional, sustainable, and conducive to quality of life. Through land use regulations, transportation planning, and infrastructure development, urban planning aims at creating neighborhoods that are well-connected, economically viable, and socially inclusive. Furthermore, the position of social housing within urban plans is critical in ensuring access to essential services and amenities. This paper explores the intersection between planning and construction laws and the contemporary notion of social housing. By analyzing the general plans of the major cities Belgrade, Kragujevac, Novi Sad, and Niš, we investigate the extent to which urban planning has influenced the provisio",0.0,https://unicloud.kg.ac.rs/index.php/s/WaKanBmgNeFP7bd1132f5b6372eb26ce015257382bb717937d6878dd85c,10.21776/csefb.2025.04.3.11,Analysis the Effects Macroprudential Policies Through Macroeconomic Variables on Housing Credit Growth,2025.0,"Contemporary Studies in Economic, Finance and Banking",S. Indrayanti; Yenny Kornitasari,"The increase in population without accompanied development and provision of affordable housing will become a major problem due to the unfulfilled need for housing. The increasing demand causes a surge in housing prices. With this problem, banks as intermediary institutions develop their activities by providing facilities to meet housing needs through Home Ownership Credit (KPR) products. This study aims to determine the effect of macroprudential policy through macroeconomic variables (JUB, IHPR, Inflation, and Economic Growth) on housing credit growth in Indonesia from 2005-2023. The method used is Multiple Linear Regression Analysis, with interest rates as a control variable. There is a need to control credit growth to prevent a surge in housing asset prices and maintain inflation to remain under control, thus creating financial system stability. The results of this study indicate that the first objective of macroprudential policy has a significant effect on credit growth. In addition, the second objective of the macroprudential policy through variables such as JUB, Inflation, Economic Growth, and Interest Rates has a significant effect on housing credit growth. Meanwhile, through",0.0,https://csefb.ub.ac.id/index.php/csefb/article/download/428/2611133b900cadade4f47fa0ecef4d09a7e1013ed1dc389,10.46793/urbanizam25.019dj,PERCIPIRANI FAKTORI KVALITETA SAVREMENIH STAMBENIH KOMPLEKSA U BEOGRADU,2025.0,Urbanizam i održivi razvoj,A. Đukić; Jelena Brajković; Jelena Marić; Emilija Jović,"Belgrade, the capital and largest city of Serbia, represents a dominant real estate market, especially residential, with a constantly high demand from buyers. Until 2000, the market was shaped by socialist and post-socialist housing policies, which made housing provision a central priority, while after that it transitioned to a market-driven system with continued population growth fuelling demand, outstripping supply, and increasing property prices. At the same time, customer profiles and their motives have become increasingly diverse, which has affected the breadth and quality of the offer of modern residential complexes. The transformation of society and globalization brings more complex demands of buyers and new criteria for assessing the quality of real estate. Factors such as location, connectivity, equipment, efficiency in daily activities, and immediate availability of services and goods stand out as key. Modern customers have increasingly sophisticated expectations, including multifunctional spaces, complex and environment maintenance, security systems, greenery, and attractive views. New trends in the way of life and work further shape the attractiveness and value of real ",0.0,11347c00623922d619dfa7d8c1785abf150a72309e91,10.36347/sjahss.2024.v12i07.007,Evaluation of the Spatial Pattern of Fitness and Leisure Venues at the City Scale,2024.0,Scholars Journal of Arts Humanities and Social Sciences,Fan-Cheng-Xiang X. Fan; Ningning Wei; Lingfeng Hu,"Reasonable layout of fitness and leisure places is an important part of the implementation of the national fitness strategy. The analysis of the spatial pattern and influence mechanism of fitness and leisure places from the perspective of city has certain guiding significance for optimizing urban planning and construction and rationally allocating sports resources. This paper uses kernel density, buffer zone, inverse distance interpolation, Kriging interpolation, geographical detector and other geographical spatial analysis methods to analyze the spatial distribution pattern and distribution causes of five types of fitness and leisure places in counties (cities, districts) under the jurisdiction of Jinhua City. It is found that the overall spatial distribution characteristics of fitness and leisure places in Jinhua City are ' core-periphery ', and different forms of fitness and leisure places show different distribution characteristics. The spatial pattern of fitness and leisure places in Jinhua City has the characteristics of relying on traffic arteries and high-density population areas, and has a positive correlation with the spatial layout of housing prices. Jinhua fitness and l",0.0,https://doi.org/10.36347/sjahss.2024.v12i07.00711358ffc448ce6e46fbcf5437c444d77a6fea653c29c,10.70693/itphss.v2i3.182,Analysis of the Impact of Stock Prices and House Price Fluctuations on Resident Consumption in China,2025.0,International Theory and Practice in Humanities and Social Sciences,Hui Hu; Yifan Lei,"The relationship between stock prices, house prices and resident consumption has long been a focus of the financial research with changes having an impact on the country’s macroscopic stability and fiscal stability, as well as affecting some economic activities to some extent. Research has found that there is a significant wealth effect of stock price and house price fluctuations on resident consumption. The theoretical analysis of the effects of stock price and house price fluctuations on resident consumption is presented in this paper. In addition, an empirical analysis of the effects of house price and stock price fluctuations on resident consumption is conducted based on regression analysis using quarterly data from 2013 to 2021. 1136The results show that the negative crowding-out effect of stock prices and house prices on resident consumption is weaker than the positive boosting effect and the positive effect of stock prices on resident consumption is slightly weaker than that of house prices. Although the resident consumption is significantly affected by fluctuations in stock prices and house prices in the short term, the resident income is still the main influencing factor and ",0.0,https://doi.org/10.70693/itphss.v2i3.18211376e342a40589949a89eec6acad53cbc31179cc2bb,10.1007/s10902-026-01079-3,The Impact of Household Housing Unaffordability on Subjective Happiness: an Analysis of the Moderating Role of Amenities,2026.0,Journal of Happiness Studies,Liya Zhang; E. Hui; Jianfu Shen,"This paper investigates the relationship between household housing unaffordability and subjective happiness in urban China. We propose a novel framework to measure household housing unaffordability as a form of long-run affordability pressure, by comparing the relative persistence of housing prices and resident incomes dynamics using fractional time series analysis. Using panel data from the China Household Finance Survey (CHFS) in 2015, 2017, and 2019, we employ the multilevel ordered logistic regression model to estimate the relationship between unaffordability and happiness. Our findings reveal a negative impact, suggesting that the deterioration of household unaffordability may reduce per capita living space and increase residential crowding, resulting in decreased levels of subjective happiness. Moreover, the level of local amenities is shown to exert a positive moderating effect on this impact. Analysis of different households indicates that highly educated households exhibit greater resilience to housing unaffordability, while high-income and local households are more sensitive to local amenity levels. These insights offer a fresh perspective for policymakers to effectively ",0.0,https://link.springer.com/content/pdf/10.1007/s10902-026-01079-3.pdf1138fb592b6008ce998aa61c76480ca5b79ad288526d,10.21834/ajqol.v3i12.137,Characteristic Qualities Affecting Landed House Prices,2018.0,Asian Journal of Quality of Life,A. Nawawi; Edie Ezwan Mohd Safian; A. Saleh,"The research was conducted to find the characteristics quality of houses in Malaysia. The aim of this research was to produce the ranking characteristics of house qualities and determining its weightages. The methodology used in this research was primarily by using the Delphi Method to ascertain characteristics quality and also Analytical Hierarchy Process to determine the ranking of the characteristics quality. Data collection gathered through a structured questionnaire that incorporates the element of Saaty’s Scale using variables outlined from review and interview. A theoretical framework of the research was established from this that will lead to future research model.Keywords: House Quality; House Price; AHP; DelphieISSN 2398-4279 © 2018. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Plan",0.0,https://ajqol.e-iph.co.uk/index.php/ajqol/article/download/137/pdf1139a1cf0482877f8f53a4698218224a11df272ce0b5,10.4028/p-pk3iuw,Applying AI to Predict High-Cost Rental Prices: A Comparative Modelling Approach,2026.0,Advances in Science and Technology,Mohammad Amir Parvez; S. Dadvandipour,"Accurately predicting real estate values remains challenging in volatile and rapidly growing real estate markets, especially for expensive homes worldwide. Traditional statistical and economic models frequently miss the temporal dynamics and nonlinear dependencies affecting changes in property values. This study uses a large-scale, multi-market dataset to anticipate real estate values utilizing sophisticated deep learning architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Dense Neural Networks (DNN). To increase learning efficiency, data pretreatment techniques included time-series sequencing, categorical encoding, and normalization. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2) were used to assess the model's performance. The findings show that the LSTM model obtained the lowest average prediction error (MAE) and produced extremely accurate forecasts by effectively incorporating long-term temporal dependencies. According to the study's findings, deep learning gives a solid, scalable foundation for accurate property price predictions. It also has practical ramifications for urban planners, ",0.0,https://doi.org/10.4028/p-pk3iuw1140d569711ac8975457534266850b0643e222b549f9,10.54254/2754-1169/2025.bl28844,The Relationship Between Housing Prices and Various Factors in ChongqingAn Empirical Study Using Linear Regression,2025.0,"Advances in Economics, Management and Political Sciences",Q. Shi,"The present work examines the correlation between the housing price and other economic and spatial aspects in Chongqing City through the linear regression analysis. The study is expected to measure the effect of major determinants like income, distance to the city center, housing size and mortgage rates on housing prices. Simple and multiple linear regression models are used to analyze data obtained based on government statistics and records of real estate transactions to the period 20222024. The findings show that there is a positive correlation between income and size of housing and housing prices but not between distance to the city center and interest rates. A large part of the variation in housing prices can be explained by the model with an R 2 of 0.85 indicating the strength of the regression model in explaining the principal factors influencing the changes in prices. This paper will make a contribution to the literature since it will present an empirical study of the housing market of Chongqing and will present policy suggestions to improve affordable housing in the city and market stability. Study limitations are that it could contain missed variables, and the linearity as",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/28844/pdf1141467a8caae9c59caa776449771f6525986d15978b,10.54097/bynj1947,Study on Factors Influencing the Prices of Houses in California Based on Factor Analysis Method,2023.0,Highlights in Science Engineering and Technology,Ruxian Huang,"This research investigates the factors influencing housing prices in California through a comprehensive analysis using the factor analysis method. The study explores various dimensions of the housing market, examining specific periods, environmental factors, transportation infrastructure, gated communities, city formation dynamics, and broader economic and market dynamics. The integration of machine learning techniques for housing price predictions is also explored. The dataset utilized is the ""California Housing Prices"" dataset obtained from Kaggle, comprising 10 types of metrics for each block group in California. Rigorous preprocessing was conducted, including handling missing values, removing outliers, and normalizing features. The factor analysis identified three significant factors explaining 76.588% of the variance in house prices. These factors represent geographical location, housing size, and population density. The weight analysis further reveals that factors associated with housing size and geographical location significantly impact housing price fluctuations. The findings offer valuable insights for homeowners, real estate professionals, and policymakers, enabling info",0.0,https://drpress.org/ojs/index.php/HSET/article/download/13443/1306911423d5a7599b2d5e603e03859d43d566ec86686fddb,10.23977/msmee.2018.72142,Implementation of Multi-layer Switching and Loading Technology for 3D Panorama Platform,2018.0,,Jiaying Wang; Heping Wang; Xianwen Xiu; Wen Jiang,"Three-dimensional panoramic technology is an important area for the application of new technologies in mapping geographic information, and is the direction of the current development of urban informatization. The establishment of three-dimensional panoramic views of the city can intuitively and truly provide people with all kinds of information for a full range of urban scenes. It subverts traditional urban planning construction concepts and management methods by integrating high-tech tools such as GIS, network, multimedia and virtual simulation. The three-dimensional panoramic technology provides more control and management methods to the city's planning and construction department, and then predicts the future development trend of the city. The three-dimensional panoramic view can display all the image information of the 360-degree scene in a real-time manner, which can give the viewer an immersive feeling. Because of these characteristics, digital 3D panoramas have begun to be applied in such fields as urban planning, product display, construction real estate, clothes display, hotel and guesthouse display, tourism scenery display, automobile display, corporate and academy displa",0.0,http://www.clausiuspress.com/conferences/AEASR/MSMEE 2018/G3987.pdf114351643209bc3940389d09a78a9f938d7cefeb7e25,10.1109/icaice63571.2024.10863902,Big Data and Spatial Econometric Analysis of the Layout of Public Transport Stations in Wuhan and Zhengzhou,2024.0,2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE),Hao Ding; Zhi-Ye Guan; X. Ruan; Ping Zhang,"Along with the rapid development of urbanisation in China, the development pressure of urban public transport is increasing day by day. As an important infrastructure and component of the urban public transport system, the rationality of the spatial layout of bus stops is of great significance in improving the efficiency of the urban public transport system. Most of the studies on the layout of public transport are about how urban public transport can serve the urban residents in terms of living, socialising, commuting, consumption and recreation. This paper proposes that public transport facilities in cities (especially megacities) should also match the spatial distribution of residential areas, companies, catering services, attractions, and recreation. Different from the traditional methods of spatial statistical analyses, such as Moran index, nuclear density and so on. This paper adopts big data spatially econometrically analyse the layout of public transport stations in Wuhan and Zhengzhou from the perspective of multi-source data. The results of the case study show that compared with the obvious inner ring monocentric agglomeration layout of bus stops in Zhengzhou, the spatial",0.0,1144bf43ea3d946653dcb26b953367439d4173a335d2,10.30672/njsr.177940,Differentiated New Housing: Implications for Market Equilibrium and the Effect of Shocks,2026.0,Nordic Journal of Surveying and Real Estate Research,A. Emblem; T. Borgersen,"It is widely established that housing supply is differentiated across multiple dimensions, and a substantial body of empirical research has explored how various housing and locational characteristics influence prices. While the responsiveness of housing supply to price changes is of both theoretical and practical importance, the supply side of the housing market remains relatively underexplored. This paper revisits core economic insights on product differentiation and examines their implications for housing prices and supply elasticity. We question the common assumption that increased housing supply necessarily dampens prices. Instead, we propose that higher supply, when accompanied by increased differentiation, may indeed reduce the elasticity of supply, making supply less responsive to rising demand. By means of a simple theoretical model, we show that when housing supply is more differentiated, demand shocks tend to produce sharper price increases and more limited changes in housing quantity, compared to markets with more uniform supply. These insights are highly relevant to housing market debates and should be considered, despite the methodological challenges of estimating pric",0.0,https://doi.org/10.30672/njsr.1779401145dcde1eba0618c187cba1d25ee4d291721a6c03bb,10.55041/isjem06368,Leveraging Deep Learning Intelligence for Real Estate Analytics,2026.0,International Scientific Journal of Engineering and Management,Ratna Kumari S.; Pinninti Pujita,"Predicting the price of a house is a hard problem. Many things affect the final number at the same time, such as location, the size and condition of the building, nearby amenities, the state of the local economy, and how buyers happen to feel about the market that month. Older valuation methods, which are mostly built on straight-line regression, tend to miss the bends and jumps that show up in real data. In the last few years, deep learning has given researchers new tools to deal with this kind of messy, high-dimensional information. In this paper, we develop a price prediction model around a Convolutional Neural Network (CNN). CNNs were first made famous by image tasks, but they also work well on well-organised tabular data once the columns are arranged with some thought. Our system pulls together property attributes, basic location details, and market signals, and learns the hidden patterns that push prices up or down. Structured real estate records are reshaped into small feature maps, and the CNN then extracts layered representations that a simple regressor cannot easily pick up. The goal of this work is to help buyers, sellers, investors, and local authorities by giving them ",0.0,1146a52eb01ba5d53d13826ad6de88af1a45a84d3269,10.1109/ccnis69465.2025.00007,Research on Urban Real Estate Price Forecasting Model Based on Multi-Source Data Fusion and Artificial Intelligence Algorithms,2025.0,"2025 2nd International Conference on Computer Communication, Networks and Information Science (CCNIS)",Cui-Ju Bao; Huan-Huan Ding,"Fluctuations in urban real estate prices have profound economic and social impacts, but traditional forecasting methods are limited by data homogeneity and insufficient dynamic modeling. To address these challenges, this study proposes an AI-based hierarchical multi-source data fusion framework that integrates spatiotemporal data, economic indicators, and emerging data sources, effectively combining structured and unstructured information to mitigate data heterogeneity. Based on this framework, the ST-GATAD (Spatio-Temporal Graph Attention with Anomaly Detection) model architecture is constructed. Combining a graph attention network (GAT), a long short-term memory network (LSTM), and an anomaly detection mechanism, it leverages the strengths of graph neural networks (GNNs) and attention mechanisms in modeling spatial dependencies, temporal changes, and sudden events (such as policy shocks), improving dynamic forecasting capabilities and adaptability. Utilizing SHAP value analysis and attention weight visualization, the model's interpretability is significantly enhanced, enabling the quantification of the impact of key factors on housing prices. Experiments using multi-source data f",0.0,1147943935deb210ee45e0967593dc28c89504ada494,10.48175/ijarsct-29499,A Comprehensive Review - Smart House Price Estimator Using Artificial Intelligence,2025.0,"International Journal of Advanced Research in Science, Communication and Technology",Anuja Pawar; Kirti Pujari; P. Pawar; A. Harale,"Abstract: The real estate market is growing fast, and knowing the right value of a property has become very important. Traditional ways of estimating house prices often take time and depend on human judgment, which can sometimes lead to errors or inconsistencies. This project aims to solve that problem by using machine learning to predict house prices more accurately and efficiently. The system focuses on key factors such as location, total area, number of rooms, and other important features that influence property value.1148In this project, a linear regression model is applied to analyze past housing data and find patterns between different features and their corresponding prices. The model learns from historical data and can then predict the price of a new house based on its characteristics. Various evaluation methods are used to test how accurate and reliable the predictions are, ensuring better performance.1149This approach helps homeowners, buyers, and real estate professionals make smarter financial decisions by providing quick and data-based price estimates. Overall, the project shows how artificial intelligence and data science can make the process of estimating house prices fast",0.0,https://doi.org/10.48175/ijarsct-2949911503b5d4a173189d979ea9cc4e605d149a3c6311452,10.54097/y4m3bs57,House Price Prediction Taking Account of the Influence of COVID-19,2024.0,"Highlights in Business, Economics and Management",Mingyu Bi,"Machine learning algorithms are utilized in various practical applications and academic research. Due to the advent of digital technology, there is now a greater availability of massive, organized, and georeferenced datasets. This has simplified the use of algorithms for trend analysis and detection as well as forecasting, which helps users make decisions. The purpose of this study is to determine which Machine Learning (ML) algorithms are most effective in projecting housing price and to assess how the COVID-19 has affected these prices. The method includes the stages of data preprocessing, feature manipulation, hyperparameter tuning, evaluation, selection, and, in the end, interpretation of the model. Another objective is to assess and compare several ensemble learning strategies, comprising bagging techniques like random forest, and boosting techniques like Gradient Boosting Regressor, using a linear regression model. The results of this study confirm that the real estate sector is very resistant to pandemics, as the decline in prices was not as severe as initially anticipated. Among the several models that were examined, extreme gradient boosting demonstrated the highest level ",0.0,https://drpress.org/ojs/index.php/HBEM/article/download/24520/240251151eef6e7515041e510be00392c2759d3c70dc2801a,10.35671/jmtt.v4i2.104,Predicting Greater Jakarta Area House Prices Using Random Forest and Linear Regression,2025.0,Journal of Multimedia Trend and Technology,F. Agustin; F. Zaki,"This study focuses on analyzing and predicting house prices in the Greater Jakarta Area using a machine learning approach, specifically comparing the performance of random forest regression and multiple linear regression. the increasing demand for adequate housing in Greater Jakarta Area, coupled with fluctuating house prices influenced by factors like land size, building size, number of bedrooms, bathrooms, and other facilities, necessitates an accurate price prediction system to assist both the public and businesses in decision-making. data was collected from Rumah123.com via Kaggle, followed by pre-processing and exploratory data analysis (EDA). the models were built using both algorithms and evaluated through 10-fold cross-validation, with an 80% training and 20% testing data split. the results demonstrate that random forest regression outperforms multiple linear regression, achieving a correlation coefficient of 0.5043 and a mean absolute error of 157,698,532. in contrast, multiple linear regression (m5p) yielded a correlation coefficient of 0.4895 and a mean absolute error of 209,890,933. therefore, random forest regression is recommended as a superior model for house price p",0.0,https://doi.org/10.35671/jmtt.v4i2.10411529af783c67accfa53a943e59e8e56589f721d145e,10.1111/1540-6229.12462,"Credit supply shocks, home purchase volume, and borrowing behavior",2023.0,Real Estate Economics,James N. Conklin; Haoyang Liu; Calvin Zhang,"We exploit the 2007 private label securitization (PLS) freeze as a quasi‐experiment to study the impact of a negative credit supply shock on home purchases and borrowing behavior. Using a difference‐in‐differences estimator, we show that a negative supply shock to first‐lien mortgages has little impact on the volume of purchases financed with a mortgage, but significantly reduces the average first‐lien loan balance. Much of this reduction in loan balances is the result of increased bunching at the conforming loan limit that is achieved through a combination of greater second mortgage utilization and larger downpayments. Importantly, we find significant heterogeneity in the response to the mortgage supply shock across borrower characteristics and house price levels. Home purchase volume does decline after the shock for less creditworthy borrowers and in expensive locations. The reduction in first‐lien balances is fairly uniform across borrower types, however, the effect is slightly more acute in less expensive areas. Our results suggest that financial market frictions (e.g., downpayment constraints, imperfect credit) play an important role in determining how credit supply shocks imp",0.0,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1540-6229.1246211539963cda2bc738552467123a0d6ea41689294751a,10.54254/2754-1169/2024.ld19609,Research of the Influence Factors of Housing Price-Take Singapore as an Example,2025.0,"Advances in Economics, Management and Political Sciences",Zi-Rui Liu; Yi-Zhi Pan,"This paper is to identify the influencing factors which impacts the house resale price in Singapore. 3000 samples from 2022 to 2023 had been selected from the data set. The relationship between 6 variables and the resale price of houses is analysed by the Multiple Linear Regression model. The correlation analysis and VIF value check are introduced, aimed to detect insignificant or highly correlated factors. To eliminate the potential interaction effect, the correlation is carried out. The result shows that the remaining lease and the commence lease are highly correlated to each other. One of them is eliminated in the improved model. By doing VIF check, the result shows that the flat model is an insignificant factor which will not influence the resale prices of houses. The floor area, flat type, also known as room allocation, storey, remaining lease and commencing lease of houses are significant and can influence the resale prices. To improve the model, the house model, the insignificant factor, and the commencing lease of the houses, the correlated factor are eliminated. The final model explains how the four factors, floor area, room allocation, storey and remaining lease can influ",0.0,https://aemps.ewapub.com/article/view/19609.pdf11547f339c1ab989769c86c527166573bc6b3ea01d2f,10.54254/2753-8818/2025.dl27031,Applying Hypothesis Testing and XGBoost to Analyze and Predict Housing Price Trends in Guangzhou,2025.0,Theoretical and Natural Science,Junyang Liu,"With the increasing application of machine learning technologies in the real estate sector, traditional models face challenges in terms of prediction accuracy and capturing nonlinear relationships. This study aims to integrate classical statistical methods with advanced machine learning algorithms to thoroughly explore the core factors influencing housing prices in Guangzhou and achieve high-precision regional price predictions. By employing statistical hypothesis testing to preliminarily screen factors, the significance of their relationships with housing prices is verified. On this basis, an XGBoost learning model is constructed to perform modeling and prediction, leveraging its strong capabilities in handling nonlinear relationships, interaction effects, and missing data. The study identifies the number of new houses, GDP per capita, the number of subway stations, and the number of large supermarkets as key factors verified to have a significant positive impact on housing prices in Guangzhou. Housing prices in Guangzhou's core districts have taken the lead in recovery, while development zones have shown steady growth with the greatest potential. In contrast, the peripheral subur",0.0,https://www.ewadirect.com/proceedings/tns/article/view/27031/pdf115573274579fc23a002132e61e32aa7835203d7c03d,10.25236/ajee.2025.070508,"Study on Spatial Distribution Characteristics and Influencing Factors of Housing Prices in Longgang District, Shenzhen",2025.0,Academic Journal of Environment & Earth Science,Xiaomin Lao; Xiaohui Zhuo; Bo Xiong; Hui Yin,": Housing prices have a direct impact on both the national economy and people's livelihoods. An in-depth exploration of the spatial distribution and influencing factors of housing prices is crucial for promoting the healthy development of the real estate industry. This study utilizes housing price data and POI (Point of Interest) big data, employing GIS spatial analysis methods to examine the distribution characteristics and spatial correlations of housing prices in Longgang District, Shenzhen. Furthermore, it explores the mechanisms by which various factors contribute to housing price heterogeneity. The findings are as follows: (a) Longgang District exhibits a polycentric spatial structure, with the housing prices peaks located in the western urban core and the eastern Longgang Central City; (b) Housing prices display significant spatial agglomeration, with high-value clusters located in Longcheng, Bantian, Buji, Jihua, and Nanwan Subdistricts, while low-value clusters are found at the junctions of Pingdi, Baolong, Longcheng, and Longgang Subdistricts; (c) The relative influence of factors on housing prices within the district ranks as follows: economic factors > transportation fa",0.0,http://francis-press.com/uploads/papers/9aBVmz1ZgJHPApzc55hQCmF7sIBwi6tlGWbaFH6C.pdf1156ec6f2ce063def49d2b2b6ad4ace4ef9c8886e4fb,10.61173/39609t15,Analyzing Influencing Factors and Inter-city Differences of House Prices Based on Statistical Methods,2024.0,Interdisciplinary Humanities and Communication Studies,Ying Xu,"With the acceleration of urbanization and the improvement of residents’ living standards, house prices have become a key factor affecting the quality of life of urban residents. Differences in house prices between cities can be attributed to a variety of factors, including, but not limited to, the level of economic development and residents’ incomes. To gain a deeper understanding of the variability of house prices and its determinants across cities, this study selects five representative cities in the UK for analysis. It explores whether there are statistically significant differences in house prices through Analysis of Variance and applies multiple linear regression modeling to assess the specific roles of each influencing factor on house prices. This analysis aims to provide policymakers and market participants with an in-depth understanding of the trends in house price changes, thereby supporting the effective allocation of real estate resources and the balanced development of the economy. The results not only reveal significant differences in house prices across cities but also identify key factors that significantly impact house prices, providing insights to promote the healt",0.0,https://www.deanfrancispress.com/index.php/hc/article/download/766/HC001502.pdf11578cd54b989f456e0f10412d61dc28aab22bd816b1,10.4028/www.scientific.net/amr.433-440.2342,Mathematical Modelling Analysis of Credit Risk in Home Loan,2012.0,,Po-Sheng Ko; Cheng-Chung Wu; Chun-Feng Chang,"Our country generally has voluntarily buys custom of the room, but house price actually non- each individual people all can bear its amount, therefore all relies on the mortgage loan which the financial organ provides. However the financial organ after the loaning out fund, will be able to face the loan person in also the funds behavior irregularity, will break a contract the situation influence to be biggest, further inquired into its influence factor has its necessity. Breaks a contract influence factor the situation to be possible to differentiate for the interest rate with the non- interest rate two aspects, tended to by the recent years market rate undulation relaxes the tendency to look, analysis the non- interest rate influence factor appeared is important. This research directly embarks from the loan characteristic, the analysis contains the loan amount, the loan interest rate, period of payments and so on three variables to break a contract influence the situation, and returns (Logistic Regression , LR) the model makes the real diagnosis analysis, the real diagnosis result also demonstrated the non- interest rate influence factor reveals the influence to the loan person ir",0.0,https://doi.org/10.4028/www.scientific.net/amr.433-440.234211589170a7fab9d882152c28fff91ab9215487dc2100,10.52783/cienceng.v11i1.261,House Price Prediction Using Texture and Visual Features,2023.0,Proceeding International Conference on Science and Engineering,M. Sweety; G. Jachak; Dr. Sayantani Nath,"Real estate sector has been growing at a rate never seen before. For this sector, a key role is played by the pricing of the property. Gone are the days when the price of the property was based on whims and facies of the real estate dealers. Machine learning has numerous applications in the domain of real estate, and one of the most popular ones is predicting house prices. The application of machine learning in house price prediction involves training a model on a dataset that includes a variety of visual and texture features related to the property. The model is then used to predict the price of a new property based on its features. 1159This paper successfully explores machine learning based house price prediction. The methodology followed was to first use data sets to train the model. Later, using correlation-based hybrid GA-reinforcement strategy, a suitable set of features has been selected. In the end, these features are applied to a XG boost regressor to get results. The accuracies are compared with the cases of without feature selection of different regressors. This algorithm, if successfully deployed will be beneficial to both sellers and buyers, because it sets a data-based b",0.0,http://seer-ufu-br.online/index.php/journal/article/download/261/1911160b63709a719976d279c74cb2862ab60a038834681,10.54254/2754-1169/2025.bj30052,Research on the Formation Mechanism and Monetary Policy Drivers of Spains Real Estate Bubble (1999-2007),2025.0,"Advances in Economics, Management and Political Sciences",Haohang Shi,"Spain experienced a severe real estate bubble from 1999 to 2007, driven by monetary policy and non-monetary factors. The study focuses on two core themes: exploring the formation mechanism, characteristics, and economic impacts of the bubble, and analyzing the transmission paths of monetary policy in influencing housing prices. The research uses data from sources like the World Bank and integrates qualitative analysis of theoretical frameworks and quantitative references to key indicators. The results indicate that the European Central Bank's unified monetary policy, particularly prolonged low interest rates, was the core driver, reducing mortgage costs, stimulating speculative demand, and leading to excessive credit inflows into real estate. Spain, having lost independent interest rate control after joining the Eurozone, passively adopted mismatched policies that exacerbated the bubble. Non-monetary factors, such as a 13% population increase from immigration and an 85% homeownership rate, further amplified the bubble. The conclusion emphasizes that Spain's real estate bubble was a result of the interaction between ECB monetary policy mismatches and non-monetary factors, with monet",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/30052/pdf1161a79caad4f097dfd2f331e74499f98038b67f1b4d,10.11606/d.12.2019.tde-28062019-165256,Does brazilian monetary policy respond to financial stress,2019.0,,F. M. D. de Lima,"Taylor Rules are an easy alternative to parametric model the response function of monetary authority to inflation as interest rates are the most common instrument for monetary policy. Nonetheless, a lot of research has been done on the subject: for example, Clarida, Gali and Gertler (2000) have incorporated the role for forward looking behavior through condition expectation operators, and results suggest that it is a useful benchmark in different regions. Although, this parametric relation was primarily suited for maintenance of low volatility and control of inflation, some authors argue that this setup can be extended to accommodate financial stresses. In this sense, we estimate an augmented Taylor Rule for Brazil from 2001 to 2018 by GMM, with moment conditions following closely Castro (2011). Our results suggest that the Brazilian Central Bank did not respond to the FCI proposed in the literature using a vector of house prices, stock market returns and exchange rates. However, our regressions with Taylor Rule model setups augmented only by with stock market returns shows a modest response of the Brazilian Central Bank to financial stress. Key-words: monetary policy. financial ec",0.0,http://www.teses.usp.br/teses/disponiveis/12/12138/tde-28062019-165256/publico/CorrigidoFernando.pdf1162a17282c811db23c1573c77d57156fa3a96a1e997,10.70382/tijbees.v08i4.053,"ASSESSING THE URBAN PLANNING AND PUBLIC SAFETY IMPLICATIONS OF UNCOMPLETED RESIDENTIAL BUILDINGS IN HIGH-DENSITY NEIGHBOURHOODS OF ETI-OSA, LAGOS",2025.0,International Journal of Built Environment and Earth Science,O. K. Olaifa; Samuel Daramola; Micheal Adegbile,"The increasing number of unfinished residential structures in high-density neighborhoods in Lagos' Eti-Osa Local Government Area creates serious difficulties to urban safety, spatial planning, and long-term housing delivery. This research studied the planning and public safety consequences of these abandoned structures by conducting field assessments of 52 buildings in five villages, including Sangotedo, Jakande, and Ikate-Elegushi, as well as stakeholder interviews and satellite imagery analysis. The findings show that 65% of these unfinished projects are in residential areas and are frequently related with increased crime, mosquito breeding, and deteriorating property values. Financial restrictions (80%), land ownership issues, weak enforcement of planning standards, and title verification delays are all major causes of project abandonment. Interviews with planning officials revealed that more than half of the properties required final development permissions. The report suggests stricter enforcement of development control regulations, regular community safety assessments, and the implementation of urban regeneration initiatives to turn abandoned structures into habitable are",0.0,https://doi.org/10.70382/tijbees.v08i4.0531163e2cc5795f59d6d54877b1c37f6c03fbf26e0bd73,10.66445/twe.v44i2.276004,Does Monetary Policy Affect Income Inequality? Evidence from Regional-level data in Thailand,2026.0,Thailand and The World Economy,Nattawan Pitakkochakorn; Saran Sarntisart; Md. Nasir Uddin,"This study investigates whether monetary policy affects regional income inequality in Thailand through different channels. The study uses regional-level data from 2011 to 2022 and employs a panel ARDL approach, FGLS and SUR estimates. Monetary policy effectively propagates regional income inequality through aggregate income channels, housing price channels, and interest rate channels. Those channels show that easing monetary policy can mitigate regional income inequality. A negative coefficient is found for the housing price channel, indicating that the easing of monetary policy induces an increase in housing prices but a decrease in rental prices, so the low-end distribution, who mostly cannot afford the house would benefit from a lower rental rate. In addition, Bangkok Metropolitan Region is more sensitive to changes in monetary policy than other regions. These findings suggest new insights for policymakers and the government an easing monetary measure can mitigate regional inequality via economic stimulation through various channels. Therefore, the central bank may need to consider the impacts of monetary policy on regional disparities, as each region has distinct characteristic",0.0,https://so05.tci-thaijo.org/index.php/TER/article/download/276004/1898591164ff555f032d8e8740f36974470cafa19677c6f5d8,10.1332/17598273y2026d000000061,Comparing local-level housing affordability – the case of Finnish low-wage private rental sector tenants,2026.0,Journal of Poverty and Social Justice,Elina Sutela,"Housing is a unique basic need due to its location specificity: house prices and rents differ across municipalities and neighbourhoods, and location affects access to work, education, amenities, and other aspects of daily life. Still, housing affordability is often assessed at the national level, which risks overlooking local variation. Local-level comparisons are especially vital in countries with diverse housing markets, but they are often hampered by data limitations. This article addresses this gap by exploring a simplified microsimulation, the Model Family Method (MFM), to compare housing affordability across cities. Using Finland as a case study, the analysis focuses on low-wage private rental sector tenants in 11 cities in 2019. It combines the MFM with two common affordability measures: rent burdens (rent-to-income ratio) and housing-induced poverty (residual income). The results show that no family fell into housing-induced poverty when social security was included, but moderate rent overburdens were common, especially in the Helsinki Metropolitan Area. However, the findings also indicate precarity in affordability: sudden changes in expenses or income could render housing",0.0,1165beadaee0db99b04cf1fc57de6aea4662413c88cd,10.1108/jfmpc-03-2024-0017,Predicting apartment prices in Prishtina: insights from gradient boosting modeling,2025.0,Journal of Financial Management of Property and Construction,Visar Hoxha; Dhurata Hoxha,"1166 1167 This paper aims to apply gradient boosting (GB) to forecast apartment prices in Prishtina, evaluating its effectiveness through established evaluation metrics and identifying key price determinants via variable importance analysis.1168 1169 1170 1171 The research method comprises analyzing a dataset of 1,468 property transactions from 2019 to 2023, attained from the Kosovo Department of Property Taxes. The data were split into training and testing subsets, with normalization applied to provide consistency, and the GB model's performance was evaluated using Mean Squared Error, Coefficient of Determination, Mean Absolute Error and Root Mean Squared Error.1172 1173 1174 1175 Findings point out that the GB model shows noteworthy predictive accuracy, with the distance from the Central Business District identified as the primary influencer of apartment prices in Prishtina. These findings also point out the model's capability to effectively capture and reflect the nuances of the local real estate market.1176 1177 1178 1179 Implications of this study advise the practical utility of machine learning, specifically GB, in enhancing real estate valuation and decision-making processes. By providing accurate and efficient mark",0.0,118042f737c1bf1c3f08ed10b7e17eaf0789673a4ab1,10.15611/fins.2024.1.04,Examining the Determinants of Residential Real Estate Prices. Evidence from Poland,2024.0,Financial Sciences,Michał Ruszuk; Krzysztof Spirzewski,"Aim: The aim of the research is to provide a comprehensive account of the determinants of residential property prices in particular the financial ones. This is an important issue for the Central Bank as well as for commercial banks, as the financing of the housing market is an important factor for conducting monetary policy as well as for assessing the level of credit risk. Methodology: An empirical survey method based on the pooled least squares (OLS) estimation model was used. Data published by the Central Statistical Office and the AMRON-SARFiN Centre were used. They were aggregated for voivodeships and quarters for the period 2010-2019. Findings: The result of the analysis allows researchers of this topic as well as practitioners to identify the priority determinants of residential property prices in Poland, which turned out to be the financial factor, i.e. income (average monthly earnings) and demographic factor (new marriages). Originality/Value: The main research’s value is the identification of the other analysed determinants, such as the cost of construction, interest rates and the supply of houses, as insignificant determinants of prices in the Polish real estate market i",0.0,https://dbc.wroc.pl/Content/130873/Ruszuk_Spirzewski_Examining_the_Determinants_of_Residential.pdf1181bc55467fd9f1063647b01b200414773eff2e20c2,10.61173/fh3z9657,The Impact of Urban Housing Purchase Restrictions on Real Estate Prices and Household Consumption: A Difference-in-Differences Approach,2025.0,Finance & Economics,Long Ma,"This study investigates the impact of urban home purchase restriction policies implemented in China’s real estate market in recent years on housing prices and household consumption, employing a difference-in-differences (DID) methodology. The study expands the interdisciplinary intersection of economics and sociology, enriching the empirical literature on this topic. Furthermore, the findings are translated into practical applications to enhance residents’ quality of life through threefold value creation. The comprehensive empirical analysis yields the following conclusions: First, standalone home purchase restrictions failed to significantly reduce housing prices or consumer price levels in the short term within the treated cities. Second, a significant bidirectional positive correlation exists between housing prices and consumer prices. Overall, the interplay between the real estate market and the consumption market reveals notable inertia and lag in policy transmission effects. Consequently, policymakers must adopt more comprehensive policy combinations when regulating the real estate market and fully anticipate the lagging characteristics of policy impacts to achieve long-term ",0.0,https://lseee.net/index.php/fe/article/download/1034/FE006512.pdf11822a2b8b3d5a0c865db3abf48e108b2dbbbb2ceeff,10.24002/kinerja.v30i1.11677,Factors Influencing Non-Performing Loans in State-Owned Banks’ Consumer Loans in Indonesia,2026.0,Kinerja,Ellen Mulyono; L. Pimada,"This research aims to examine the factors that influence Non-Performing Loan (NPL) consumer credit at state-owned banks in Indonesia. In this study, consumer credit refers to loans granted by banks to consumers, while Non-Performing Loan consumer credit refers to consumer credit that experiences repayment problems. Consumer credit base interest rate, the amount of consumer credit distribution, the consumer price index, the residential property price index, and the debt service ratio act as independent variables, and the Non-Performing Loan consumer credit variable stands as the dependent variable. The data used are annual panel data from the period of 2011–2023, and the analysis uses the panel-data regression method of the common effect model. The results of the study indicate that the consumer credit base interest rate, the consumer price index, the residential property price index, and the debt service ratio do not have a significant effect, while the amount of consumer credit distribution and the residential property price index have a significant effect on Non-Performing Loan consumer credit. This study provides the importance of state-owned banks to maintain the quality of con",0.0,https://ojs.uajy.ac.id/index.php/kinerja/article/download/11677/44781183aa25d09549854ebf9a674207f5f912ade4aacdbd,10.62051/ijgem.v9n3.06,Research on the Interactive Relationship between Housing Price Fluctuations and Macro Policy Regulation,2025.0,International Journal of Global Economics and Management,Longqian Li,"This article systematically explores the interactive relationship between housing price fluctuations and macroeconomic policy regulation in the context of China's real estate market. The current market is in a stage of deep adjustment, showing characteristics such as overall decline but local marginal improvement, significant structural differentiation, and sustained weakening of expectations. In the face of this situation, China has formed a multidimensional policy regulation system that covers demand side management, supply side optimization, and financial and tax coordination. Research has shown that policy regulation affects housing prices through short-term expectations guidance and liquidity effects, long-term structural adjustments and development model transformations. However, there are still challenges in the interaction between the two, such as policy lag, diminishing effectiveness, insufficient coordination, and multiple goals. This article suggests that in the future, a dynamic, precise, forward-looking and coordinated policy framework should be established, and basic institutional reforms should be deepened to promote the long-term stable and healthy development of th",0.0,http://ijgem.org/index.php/ojs/article/download/252/2501184a2b234b3d78580079ab28badc6608d74dbf040ef,10.37355/acta-2023/2-03,Linkages Between Property Prices and Macroeconomic Determinants: Evidence from Germany,2023.0,Acta VŠFS,Frederik Schröder,"The global financial and economic crisis has led to significant efforts to better integrate property price channels into macroeconomic models and to improve models. This paper deals with property prices in Germany and their determinants in the period Q1 2000‒Q4 2022. Since then, property prices in Germany have risen considerably. The models regularly use indicators that are aligned with the specificities of property, but less macroeconomic determinants. From economic models, theoretical modes of action between macroeconomic determinants and property prices are generally known. Regarding Germany, there are only limited studies on the direct influences of economic determinants. In the context of a regression analysis, this paper examines the relationship between the selected variables that directly drive house prices. On the one hand the results confirm a positive relationship between property prices and real GDP. On the other hand, a negative correlation was found for real short-term interest rates and inflation. In this study, no significant relationships were found between house prices and unemployment rate and long-term interest rates, despite a culture of long-term fixed interes",0.0,https://doi.org/10.37355/acta-2023/2-03118592b3d38be3dbff32b1c04e414ae4c9d017636b4b,10.21017/rimci.2023.v10.n20.a136,UNA APLICACIÓN DEL MODELO PROBIT SOBRE EL EFECTO VECINDARIO EN ENTORNOS EDUCATIVOS,2023.0,"Revista Ingeniería, Matemáticas y Ciencias de la Información",Charli Jhoan Benavides Parra,"Being able to rely on statistical analysis methods that allow describing and predicting results in the educational field under technological support has become a necessity at all levels, from primary to higher education, since it allows identifying shortcomings and opportunities in a local or general area, such as dropout, repetition, and student advancement or failure. That is why it is intended in this article to observe and study the multiple variables that could be related to the performance of students in educational centers, considering the quality of the neighborhood in the first instance. Estimating a spatial model of house prices to characterize which micro-neighborhoods the homes of the students are exposed to. The quality of the neighborhood will be used as an important explanatory variable to analyze the probability of influence on the performance of the students of the educational centers, in which it is intended to use geographic location data to apply spatial econometrics models. The objective is that this methodology can be used in different academic environments, for example, in higher education classrooms where more variability is perceived in the distribution of ",0.0,http://ojs.urepublicana.edu.co/index.php/ingenieria/article/download/929/650/11867eeea5a4a3845d4e45fad42bad570fad9bc3cc01,10.3390/land14051125,Spatial Differentiation Mechanism of Urban Housing Prices from the Perspective of Amenity: A Case Study of Nanjing,2025.0,Land,Guang-Yuan Feng; X. Bi; Jing-Xiang Zhang; Tianhan Cheng,"New economic development trends have brought challenges and transformation directions to China’s urban planning process, in which the relationship between supply and demand of urban housing needs urgent optimisation. Using data on multiple types of facilities and housing price information, this paper analysed spatial differentiation characteristics of housing prices in Nanjing. An evaluation indicator system of human environment quality was established under the amenity connotation based on three dimensions of natural amenity, artificial amenity and social atmosphere amenity, and the Gradient Boosting Decision Tree (GBDT) algorithm was applied to investigate the impact of different amenity factors on housing prices. The findings revealed that amenity factors have a positive impact on housing prices, with artificial amenity as the most influential. Partial amenity factors demonstrated nonlinear relationships with housing prices with obvious threshold effects. Based on these findings, this paper proposed targeted supply and demand optimisation strategies in accordance with the above three dimensions, aiming to offer practical recommendations and guidance for improving the quality of ",0.0,https://www.mdpi.com/2073-445X/14/5/1125/pdf?version=174783540911875f345c47e2f9b660f768d077cd90b359c0b19103,10.36347/sjet.2021.v09i10.001,Analysis and Prediction of Housing Prices in Shenyang City Based on Elman Neural Network,2021.0,Scholars Journal of Engineering and Technology,Zhi-Rui Zhao,"In recent years, housing prices across the country have been on a slow upward trend, and the real estate market is crisscrossed, and Shenyang is no exception. This paper chooses the Elman neural network that can process dynamic time series information to predict the trend of average housing prices in Shenyang. The article selects the average house price of Shenyang for 72 months from September 2015 to August 2021 in Shenyang, using 7 months as a set of training samples, the first 6 months as input data, and the seventh month as output data .There are 66 groups in total, the first 58 groups are used as training sets, and the last 8 groups are used as test sets. At the same time, the BP neural network and the RBF neural network are used for the same prediction, and the prediction results of the three are compared and analyzed. It was found that both the Elman neural network and the BP neural network are better than the RBF neural network in predicting performance. In terms of error details, since the Elman neural network can better process time series data, compared to the BP neural network, the predicted value of the Elman neural network is closer to the true value, and the predicta",0.0,https://doi.org/10.36347/sjet.2021.v09i10.001118819815f376352d9dcc6623af7578cc22212c73353,10.30871/jagi.v9i1.7663,"Navigating Campus Traffic: Analysing the Impact of Land Use on Congestion at King Abdulaziz University, Jeddah, Saudi Arabia",2025.0,Journal of Applied Geospatial Information,A. Hardi; Jamal Abdulmohsen Eid Abdulaal,"The present research examines the correlation between land use and traffic congestion at King Abdulaziz University in Jeddah, Saudi Arabia. The authors analyze the Congestion Index Value (CIV) data and land use proportions to determine the main factors that contribute to congestion. Additionally, the authors examine how different land use arrangements affect traffic flow on the campus. The analysis employs both Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to consider spatial variability. The findings demonstrate that specific purposes, such as hospitals, housing, areas designated for females, and central administrative structures, have a substantial impact on traffic flow. Furthermore, the presence of educational structures and their closeness to different entrances on campus are crucial determinants of congestion levels. The research indicates that strategically locating facilities and optimizing infrastructure near important entrances can reduce congestion and improve movement on campus. These insights offer essential direction to urban planners and university administrators in formulating efficient traffic management and sustainable urban deve",0.0,https://jurnal.polibatam.ac.id/index.php/JAGI/article/download/7663/32781189e71acbd173dd1a7f8c5efc62e0b857f3d8c39e99,10.5220/0014400900004861,Machine Learning Approaches for House Price Prediction: Methods and Comparative Analysis,2025.0,"Proceedings of the 3rd International Conference on Data Science, Advanced Algorithms, and Intelligent Computing",Jiayi Fan,": In real estate analytics, accurately predicting housing prices is essential, as it directly influences market regulation, policy planning, and investment decisions. Machine learning has become an effective technique to improve prediction performance in this field as a result of the development of data-driven approaches. This paper reviews and compares the development of major machine learning models used for housing price prediction, including traditional regression models, tree-based ensemble approaches, and neural network architectures. Although the ease of use and interpretability of linear regression make it valuable, it struggles to capture complex non-linear interactions. Tree-based models, such as Random Forest, offer greater accuracy and flexibility, but often lack transparency and require careful hyperparameter tuning. Neural networks, although computationally intensive, excel at modeling intricate feature relationships and are well-suited for large-scale or unstructured data. By analyzing the strengths and limitations of each model type, this review provides a clearer understanding of their applications and offers insights for selecting appropriate methods in housing pr",0.0,https://doi.org/10.5220/0014400900004861119097188e4c6e76070966d42d2358a5cd905b29f66f,10.7494/geom.2026.20.4.33,A Novel Housing Price Estimation Model Integrating GIS and DNN: A Case Study of Istanbul,2026.0,Geomatics and Environmental Engineering,Y. Çoruhlu; M. Dihkan; Okan Yıldız; M. Çelik; Hossein Azadi,"Housing valuation is a concrete reflection of socio-economic inequalities in urban space. Particularly in densely populated, spatially fragmented large cities like Istanbul, the current official mass appraisal system used for property taxation fails to reflect market reality. This situation results in revenue losses in property taxes and spatial injustices. This study develops a Deep Neural Network (DNN)-based model that integrates spatially derived variables from Geographic Information Systems (GIS) and incorporates 24 objective variables related to location, structure, and access in Istanbul. The model, trained on3,757 samples created using open-source big data, estimated housing values with high accuracy (R² = 0.979). The findings show that spatial differences in housing values are strongly related to urban variables such as accessibility and proximity to infrastructure. This approach not only produces housing value estimates but also provides a theoretical and methodological framework for spatial analyses of how value is produced in urban space. The study has the potential to support the development of a fair, transparent, and updatable mass appraisal system, especially for dev",0.0,https://doi.org/10.7494/geom.2026.20.4.33119145c5e38becd9cb448da6eb30f029022b6d34d602,10.55041/ijsrem38407,Impact of Natural Disasters on House Prices: A Global Analysis and Policy Implications,2024.0,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,Arpita Jha,"This study addresses the limited research on how natural disasters affect house prices by analyzing data from 117 countries between 2000 and 2018, using a panel regression approach to assess these impacts. The results reveal that natural disasters tend to decrease house prices, a finding that holds strong across multiple robustness checks. Specifically, geological disasters are found to have the most significant negative impact on property values. The study also distinguishes between the effects of small and large disasters, with both showing a clear downward effect on house prices. These insights carry important implications for policymakers and investors, as lower house prices in disaster-prone areas may indicate reduced consumption and investment due to the wealth effect, potentially leading to wider economic downturns. To support these regions, economic policymakers might consider measures such as low-tax policies or quantitative easing. The findings also highlight the urgent need for governments to address climate change impacts on the housing market by adopting sustainable technologies and renewable energy sources. Keywords Natural Disasters, House Pricing, Climate Change, St",0.0,https://ijsrem.com/download/impact-of-natural-disasters-on-house-prices-a-global-analysis-and-policy-implications/?wpdmdl=39687&refresh=672ecea6887e11731120806119297c15cfffa40422a5169adda49b90ebfa3545fd6,10.5220/0012990800004601,Predictive Models for United States House Prices,2024.0,"Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy",Hongyi Yang,": House prices are a crucial indicator affecting citizens' lives, directly impacting individuals' and families' financial situations, as well as the stability and development of entire communities. Therefore, it is imperative to conduct in-depth research on the societal impact of house price prediction models, exploring their effects on housing markets, economic development, social welfare, and potential challenges and issues. This study addresses the issue of accurate house price prediction by conducting extensive analyses on four ensemble learning models: Random Forest, XGBoost, AdaBoost, and Stacking. The selected metrics for assessing model performance in this experiment include RMSE, R-squared, Explained Variance Score, and MAPE. The results demonstrate that the Random Forest model excels across multiple evaluation metrics, outperforming other models with the lowest RMSE and MAPE values. XGBoost shows strong competitiveness, providing accurate predictions and effectively capturing nonlinear relationships in the data, albeit slightly inferior to Random Forest. AdaBoost and Stacking exhibit moderate performance, possibly limited by their ability to handle complex relationships a",0.0,http://dx.doi.org/10.5220/0012990800004601119335b22c4edd44922572e1faff76ab88c58ae890f0,10.54254/2754-1169/2024.ld18184,Dynamic Impact of Macroeconomic Indicators on U.S. Housing Market: A Panel Data Analysis from 2003 to 2022,2024.0,"Advances in Economics, Management and Political Sciences",Jun-Tao Wang,"This research examines the effect of several economic factors, such as the unemployment rate, producer price index, bank benchmark lending rate, mortgage interest rate, and S&P/Case-Shiller house price index, on the median home price in the United States. By employing panel data spanning from 2003 to 2022, this study utilizes multiple regression models to uncover the influence of various factors on house prices over several economic cycles. The findings indicate a strong and positive correlation between the S&P/Case-Shiller home price index and median house prices. Simultaneously, there is a strong and negative correlation between the unemployment rate, bank lending rates, and mortgage rates and median home prices. Additionally, rises in the producer price index are the main driver of house price increases. The study emphasizes the critical need for dynamic adjustment of economic policies and suggests policy recommendations such as focusing on interest rate policies, promoting employment and controlling construction costs to achieve a stable and sustainable housing market. At the same time, the study also suggests that other factors that may affect house prices be further explored ",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/18184/pdf1194fc0adf5d4d84d9dd152ad081fcf8579e83b80c20,10.3390/ijerph120606388,State-Issued Identification Cards Reveal Patterns in Adult Weight Status,2015.0,International Journal of Environmental Research and Public Health,Daniel S. Morris; Eric C. Main; Jenine K. Harris; Abraham Moland; C. Cude,"Background: State-issued identification cards are a promising data source for neighborhood-level obesity estimates. Methods: We used information from three million Oregon state-issued identification cards to compute age-adjusted estimates of average adult body mass index (BMI) for each census tract in the state. We used multivariate linear regression to identify associations between weight status and population characteristics, food access, commuting behavior, and geography. Results: Together, home values, education, race, ethnicity, car commuting, and rural-urban commuting area (RUCA) explained 86% of the variation in BMI among tracts. BMI was lower in areas with higher home values and greater educational attainment, and higher in areas with more workers commuting by car. Discussion: Our findings are consistent with other research on socioeconomic disparities in obesity. This demonstrates state-issued identification cards are a promising data source for BMI surveillance and may offer new insight into the association between weight status and economic and environmental factors. Public health agencies should explore options for developing their own obesity estimates from identificat",0.0,https://www.mdpi.com/1660-4601/12/6/6388/pdf?version=14337590951195855824fdf145e264dcd7ea3763b92d162e717a70,10.54091/krepa.2026.27.2.24,"How Does Short-term Volatility in Housing Policy Affect House Prices and Transaction Volume : Evidence from the Lifting and Reimposition of the Land Transaction Permit System in Seoul, South Korea",2026.0,Korea Real Estate Policy Association,Seorin Jang; Euna Kim; Junghoo Park,"This study uses the 2025 lifting and reimposition of Seoul's Land Transaction Permit System (LTPS)—two policy changes 39 days apart—as a natural quasi-experiment examining how a regulatory instrument's coercive force affects a behavioral variable (transaction volume) versus an outcome variable (price), and how an abrupt reversal affects market trust. Following Vedung (1998), the LTPS is a Stick instrument that directly governs transacting, not the price emerging from market aggregation—a gap compounded here by target mismatch: the instrument was designed for land, an exchange-value good, but is now applied to apartments, which combine use and exchange value. The study tests two hypotheses: volume and price will respond differently to the lifting and reimposition (policy congruence), and the reversal will erode policy credibility, causing price to move against the policy's intent and persist over time (policy credibility). Both are tested via difference-in-differences and negative binomial regression using Ministry of Land, Infrastructure and Transport transaction data. Volume rose about 119.6% after the lifting and fell about 67.9% after the reimposition, robust across comparison g",0.0,11969010a7849ddf7dd9531b5932820f4d9bcce4a137,10.1145/3772726.3772869,Resale House Price Prediction Based on RN-LGBM,2025.0,Proceedings of the 2nd International Conference on Intelligent Computing and Data Analysis,Zi-Long Wang,"In the current economic climate, accurately forecasting resale housing price trends and fluctuations has become a critical research focus in the real estate sector. This study introduces a hybrid prediction model, termed RN-LGBM, which integrates a ResNet-based feature extractor with a LightGBM (LGBM) predictor. In the RN-LGBM framework, the feature extractor first generates refined feature representations from the input data, which are subsequently used by the LGBM module to produce the final price predictions. Experimental results validate the effectiveness of the proposed model for resale housing price forecasting. Specifically, the RN-LGBM model achieves a Mean Absolute Error (MAE) below 0.11 and a Mean Absolute Percentage Error (MAPE) under 8%, outperforming both standalone ResNet and LGBM models. These results highlight the advantage of combining deep learning-based feature extraction with gradient boosting techniques, leading to superior predictive performance compared to conventional machine learning methods. Overall, the RN-LGBM model offers a valuable tool for accurate price prediction in the resale housing market and provides meaningful insights for future research in th",0.0,https://dl.acm.org/doi/pdf/10.1145/3772726.3772869?download=true1197877cbdabc2aba0920a744f15d91bf843fa770f5c,10.1007/s11146-020-09751-y,The Road to Higher Prices: Will Improved Road Standards Lead to Higher Housing Prices?,2020.0,Journal of real estate finance and economics,Theis Theisen; A. Emblem,"We study the impact of improved road standards on housing prices in a region of Southern Norway, where towns are located along the coast, like pearls on a string, with sparsely populated areas between the towns. In this region towns have since long been linked together by a road, but in 2009 a new highway, roughly parallel to the old road, was opened. The new highway runs Eastwards from the main town in the region and reduced one-way commutes to the main town by 10–15 min. We examine how this change in transportation infrastructure has affected housing prices, using difference-in-difference regressions to identify the impact of the new highway. Three smaller towns located East of the main town constitute the treatment region, while four towns and communities West of the main town form the control region. We find that the new highway on average increased housing prices in the treatment region by about 5%, but the effect on price differed substantially between the towns. The full impact on housing prices materialized when the new highway was opened. We estimate that the increase in total housing capital in the treatment region amounts to almost 84% of the construction costs of the ne",0.0,https://link.springer.com/content/pdf/10.1007/s11146-020-09751-y.pdf1198a5d6155b695026a4cc765721547bcbda1c77b130,10.15396/eres2019_107,"Research on the Impact and Mechanism of Talent Introduction Policy on Urban Housing Price - An Empirical Analysis Based on Nanjing, Wuhan and Xi'an",2019.0,26th Annual European Real Estate Society Conference,Yuan-Rui Hu; P. Lyu,"Research purposes: Starting from the relationship between population policy and housing market, this paper explores the impact and mechanism of talent introduction policy on local housing price, so as to provide basis for the corresponding policy adjustment. Research methods: theoretical mechanism analysis, quantitative empirical analysis. Research content: From the perspective of human capital theory, this paper explores the mechanism of talent introduction policy on housing price, including ""government-oriented effect"" and ""human capital effect"". Based on the macro-data of the National Bureau of Statistics, Wind, Habitat and other institutions, through the analysis of DID-PSM, we find that the policy of talent introduction has a net effect on the local housing price. The urban housing price increase of the policy of talent introduction is obviously higher than that of other cities of the same grade in the same period, showing the characteristics of unstable rise. The conclusion is that the policy of talent introduction has led to the realization of quantity growth and spatial agglomeration of human capital in the cities where the policy was issued in a short period of time, which",0.0,11993269024106c3c5c1b096e9638a30cfe4f5ab29de,10.55186/2413046x_2025_10_8_190,DEVELOPMENT AND TERRITORIAL DIFFERENTIATION OF THE HOUSING MARKET IN PERM CITY,2025.0,Moscow Economic Journal,German Pushkarev; Y. Ovchinnikov; Egor Anders,"This study is motivated by the pronounced spatial differentiation of the housing market in Perm and the need to develop strategies to reduce this inequality and improve access to quality housing. The aim is to analyze the patterns of development and spatial differentiation of Perm’s housing market in order to identify the factors behind price disparities and to develop recommendations for mitigating these imbalances. The study uses data on the housing market of Perm for 2020. Methods include geoinformation analysis (producing housing price heat maps) and statistical techniques such as analysis of variance (ANOVA) to reveal spatial pricing patterns. The results confirmed a significant influence of infrastructural differences on housing prices: central districts with well-developed infrastructure command much higher prices than peripheral areas. The market is segmented into classes (“Premium”, “Business”, “Comfort”, “Economy”) with different price levels. However, the analysis showed that segmentation by class alone is not the sole determinant of pricing due to high price variation within segments and the influence of other property characteristics. For example, in the mass-market se",0.0,1200b193e7bd2f9749a7e807a9de8c3138bdd7da7eb9,10.3390/urbansci9080318,Administrative Boundary Effect of Housing Prices in Hangzhou City and Changes Under District Adjustment Policies: Applying a Spatial Discontinuity Regression Method,2025.0,Urban Science,Ling Zhang; Ya-Peng Yang; Lifei Zhu,"The continuous expansion of China’s cities has led to a divergence in economics, population, and public service levels among different districts within the city. This has led to different housing prices, due to the resulting impact on housing supply and demand. Previous studies, although taking into account the possible differences in housing prices among different districts, have not focused on the extent to which districts affect housing prices. This study analyzes the housing price boundary effects among different districts in Hangzhou, China, using spatial discontinuity regression methods and data on newly built housing transactions from 2010 to 2021. This study also examines the impact of the integration policy, which acts to integrate suburban counties with the main urban area of Hangzhou, and whether that policy decreases the district boundary effect. The results show that the administrative boundary effect of housing prices in Hangzhou is significant, with most districts experiencing a house price boundary effect exceeding 10%. Encouraging regional integration policies effectively reduces the housing price gap that results from internal administrative divisions within the c",0.0,https://www.mdpi.com/2413-8851/9/8/318/pdf1201da788359b5464c70f1b6731b6a730154e5359237,10.53730/ijhs.v6ns1.6426,"Analysis of factors affecting gross income multiplier of warehouse property in Medan City and Deli Serdang Regency, North Sumatra Province",2022.0,International Journal of Health Sciences,A W Razali; Khaira Amalia Fachrudin; Hilma Tamiami Fachrudin,"The transformation from traditional trade patterns to digital trade has impacted practically every element of life. E-commerce, or digital trade, indirectly impacts the property industry's development. The increasing demand for modern warehousing is one of them. The Gross Income Multiplier (GIM) is the ratio of a property value to its annual gross income potential. GIM can be a simple and easy method for calculating a property value. This research aims to determine the GIM of warehouse properties in the research area and investigate the factors that influence it. These factors are the distance to Belawan Port, building area, frontage, building age, road width, position/location, and building design. Purposive sampling was used in this study, with a total sample of 81 warehouse units in 30 warehouse complexes. Multiple linear regression analysis with observational data was the analytical approach employed in this study. According to the findings, the average GIM calculation for warehouse properties in the research area was 26.32. The regression analysis revealed that the distance to Belawan Port and the building age have a negative and significant effect on the GIM index of warehous",0.0,https://sciencescholar.us/journal/index.php/ijhs/article/download/6426/27581202c6eae1c25e778430da557eefb4c403911468d0ee,10.24891/fc.30.1.194,Strategic aspects of OTSW analysis of the residential real estate market in Moscow during the contingency period,2024.0,Finance and Credit,P. Petrova,"Subject. The article addresses the OTSW analysis of the residential real estate market of Moscow during the contingency period caused by cyclical growth of housing prices, speculative expectations, and an increase in the debt burden of the population.1203Objectives. The aim is to identify strategic opportunities that form the potential development of all regional economic actors in the housing market. For each strategic opportunity of the actors, the paper aims to identify threats formed by the contingency period, reveal strengths and weaknesses that would help realize opportunities and minimize the effect of existing and potential threats to the development of the housing market.1204Methods. The analysis rests on the methodology of strategizing by academician V.L. Kvint.1205Results. Based on the strategic analysis of the residential real estate market that confirms the presence of characteristics of the contingency period in the Moscow housing market, I formulated strategic opportunities of involved actors that reduce the negative impact of emerging threats of the phenomenon under consideration. The findings can be used by government agencies to identify and implement strategic opportuniti",0.0,12068b58ad50395fc6baa885eaf915d02865c29e0ac0,10.61173/f8ddc398,Factors Affecting Changes in House Prices: A Multiple Linear Regression Analysis,2025.0,"Science and Technology of Engineering, Chemistry and Environmental Protection",Yuehan Chen; Zihan Guo; Sijia Song,"Housing prices, as a key indicator of macroeconomics and livelihoods, are influenced by a variety of economic factors. Exploring the impact of unemployment, building permits, housing subsidies, and mortgage rates on the US housing price index helps shed light on the mechanisms of the real estate market and provide insights for policymaking. This paper, utilizing US housing-related economic data from the Kaggle platform, employs a multivariate linear regression model to investigate the impact of GDP, building permits, housing subsidies, and mortgage rates on the housing price index (HPI). The results showed that in the univariate regression, the unemployment rate had a significant negative impact on the housing price index (R²=0.256), while building permits and housing subsidies showed significant positive effects, with housing subsidies having the strongest explanatory power (R²=0.687). Mortgage rates exhibited a significant negative correlation (R²=0.048). In the multivariate regression, all four variables maintained statistical significance, and the overall model fit was high (R²=0.945), indicating that the model’s predictive power is enhanced when multiple factors are considered",0.0,https://lseee.net/index.php/te/article/download/2071/TE013712.pdf12077d2894011a7c40caa92c799e64f5bdf4b1f8f3a6,10.1109/aic70896.2026.11717331,PropInsight: A Machine Learning and Visual Analytics Framework for Real Estate Price Prediction,2026.0,International Workshop on Artificial Intelligence and Cognition,Shresth Srivastav; Priya Mathur; Amit Kumar Gupta,"Estimating the prices of a homes is a very hard problem because there are so many different factors that an effect it. These factors include the home's location, size, furnishings, floor level, and a luxury grade. Standard valuation techniques that rely on a manual expert evaluation and subjective market comparison often produce inconsistent results, especially in a rapidly changing urban markets. This paper presents the PropInsight, a comprehensive machine learning pipeline and an interactive visual analytics dashboard designed to an automate and enhance real estate valuation for the Gurugram residential market. The system that scrapes property listings from the 99acres goes through a long preprocessing pipeline that gets rid of the duplicates, logically fills in a missing value, handles outliers, encodes features, and extracts useful engineered features. Then, the five regression models are trained and the tested: the Linear Regression, Random Forest, XGBoost, Gradient Boosting, and AdaBoost. The R2 score and the Mean Absolute Error (MAE) are used to measure performance. The experimental results show that the XGBoost has the best predictive accuracy (R2 = 0.908) followed by the l",0.0,1208384bac8c2360bb22e0244360bc839302bbc09a23,10.55214/25768484.v8i5.1881,Performance evaluation of housing price index prediction model using DNN and SVR,2024.0,Edelweiss Applied Science and Technology,Chang-Ho An,"This study used the base interest rate, mortgage interest rate, deposit interest rate, consumer price index, exchange rate, and composite stock price index as variables to predict the housing price index. The variables were normalized using the min-max normalization method. Machine learning methods, Deep Neural Networks (DNN) and Support Vector Regression (SVR), were employed to construct a prediction model, and the fit of the model was evaluated using the Root Mean Square Error (RMSE). The analysis results of the DNN model indicated that when applying the softplus activation function, the RMSE was 0.1298, resulting in the smallest prediction error, and the correlation coefficient between the predicted and actual values was highest at 0.9342. The analysis results of the SVR model using the Gaussian RBF kernel showed that the RMSE was smallest at 0.2781 when the parameters were set to C=100 and Gamma=0.1. Therefore, the DNN model was found to be more fit for predicting the housing price index. Additionally, it was confirmed that the consumer price index had a non-linear effect with a positive relationship, and the base interest rate had a linear effect with a negative relationship.",0.0,https://learning-gate.com/index.php/2576-8484/article/download/1881/70312097560172f142513865c7c51097d4d9d77f7bd6e7f,10.54254/2753-8818/26/20241061,Research on changes in housing price and exploration of its influencing factors,2023.0,Theoretical and Natural Science,Shi-Yu Yao; Jin Feng,"With economic development, peoples living standards have been gradually rising. As a necessity of life, the price of housing has been fluctuating throughout the years. By showing that, this paper aims to study the relationship between house prices and external factors such as income. Meanwhile, this paper have simply discussed their impact on micro-macroeconomics as well. In addition, this summary addresses the relationship between house prices and various economic indicators and the need for sustainable valuation methods to support informed decision-making in the housing sector. Time series and linear regression have always been the most suitable observational tools to find out the relationship between two variants. Thus, the author has actively used them in the research. Through this study, it is easy to find that house prices are strongly correlated with incomes even when the data are not so complete. With the inseparable correlation between incomes and house prices, both of them will affect economic development and their stability is significant for financial well-being. In conclusion, it is essential for people to indicate the possible risks and development of house pricing.",0.0,https://doi.org/10.54254/2753-8818/26/20241061121075dc231dde5373a533a4cbad0a39ada6bf28babb,10.1109/isssc66652.2025.11388391,Accurate House Price Prediction using Ensemble Machine Learning Model,2025.0,"2025 IEEE 3rd International Symposium on Sustainable Energy, Signal Processing and Cybersecurity",Bharti Thakur; A. Bisht,"In today's dynamic real estate industry, accurately predicting home prices is more important than ever. In this study, we developed a machine-learning-based system for estimating home prices using the Ames Housing Dataset. The system is capable of providing predictions from regression models (predicting exact price) and classification models (grouped into price ranges) comprising multiple models from both regression and classification categories, including single regression techniques: linear regression, lasso, and ridge regression, and advanced techniques such as support vector machines (SVM) and random forest. A key contribution of this study is the development of a unified system that leverages Principal Component Analysis (PCA) for efficient dimensionality reduction and simultaneously addresses both the exact price prediction (regression) and price categorization (classification) tasks to provide comprehensive real estate insights. Overall, the random forest provided the best estimates for price across all of the models tested, and SVM was classified into the best price groups. Multiple graphs were employed, including heatmaps, scree plots, and price prediction graphs, to visua",0.0,121191fdcf614a24045acac762ca9487768d983f3ff2,10.70731/mzqz2z57,Impact of Short-Haul High Speed Rail Commuting on Land Use and Spatial Structure in a Multi-Centre Metropolitan Area,2024.0,Journal of Sustainable Built Environment,Ming Xie; Tetsuya Yaguchi," Owing to the enhanced accessibility afforded by high-speed rail, the relationship between megacities and their neighboring cities has grown closer. Megacities exert a significant influence on the land use of neighboring cities. This study employs a spatial econometric model to investigate the direct impact and spatial spillover effects of high-speed rail on urban land values, based on urban panel data from China’s Yangtze River Delta region (2006-2020). The results revealed that shorter commuting times and improved frequency of service had a more substantial effect. Additionally, due to alterations in land values, there is a distinct preference tendency for urban land use attributes. Therefore, the city’s spatial organization has transformed, and its center of gravity has shifted as well. This study provides crucial theoretical justifications and empirical evidence to fill the gaps left by previous research on the effects of short-haul high-speed rail commuting on urban housing prices and land use. The study also offers valuable insights for strategic planning and implementation aimed at promoting urban development and urban agglomeration construction through HSR construction.",0.0,http://doi.org/10.70731/mzqz2z571212c90c720ddfcafc56e0c6d4ac2937c39d9bf51bf6,10.3846/ijspm.2026.26795,Impacts of urban renewal on housing prices: A systematic review and meta-analysis,2026.0,International Journal of Strategic Property Management,Minghao Liu; Gabriel Hoh Teck Ling; Xuerui Shi; Tongtong Hu; Zhen Guo,"Urban renewal is widely recognized as a driver of urban transformation, yet its impacts on housing prices remain contested. This study systematically reviews 153 peer-reviewed publications (2013–2023) using the PRISMA framework, integrating bibliometric mapping, text mining, and article-level statistical testing. The analysis shows that while descriptive evidence suggests variation across renewal types, scales, and contexts, chi-square tests confirm that only context is significantly associated with housing price outcomes. Urban-core projects are more often linked to appreciation, whereas peri-urban initiatives display mixed or negative effects. By contrast, project type and scale do not yield statistically significant associations, underscoring the primacy of location and local conditions. Beyond empirical results, the study highlights critical social implications, including affordability pressures, displacement risks, and uneven community stability. These findings provide a more differentiated and context-sensitive understanding of the renewal–price nexus, offering valuable guidance for policymakers and researchers concerned with equitable and sustainable urban development.",0.0,https://journals.vilniustech.lt/index.php/IJSPM/article/download/26795/134301213408d3bf9cac6f3c6436c9a7bfa765b8d468b9ea8,10.54097/k95s1e12,Impact of School Quality on House Prices in London and Bristol: Shifts in Urban Housing Dynamics During the COVID-19,2024.0,"Highlights in Business, Economics and Management",Wanhe Li; Yuanzhe Li,"This study examines the impact of school quality on house prices in London and Bristol before, during, and after the COVID-19 pandemic. Using cross-sectional data, the analysis highlights the relationship between proximity to high-quality state schools and residential property values, revealing shifts in buyer priorities influenced by pandemic-related factors such as urban exodus and changing educational dynamics. The findings demonstrate that while primary school quality positively correlates with house prices, the strength of this relationship has weakened during the pandemic as buyers increasingly prioritize property-specific characteristics. In contrast, secondary school quality shows varied effects across large and small cities, reflecting differences in school admission criteria and urban characteristics. The persistence of a house price premium near good primary schools suggests ongoing educational inequalities, as access to high-quality education remains limited to wealthier families. These results underscore the need for targeted policy interventions to address the entrenched disparities in educational opportunities and housing affordability in post-pandemic England.",0.0,http://dx.doi.org/10.54097/k95s1e12121476d731042387004ec5b89c4d4e87b6aaa94e36a8,10.3390/urbansci10080459,Use of Geographically Weighted Regression and Multiscale Geographically Weighted Regression to Account for Spatially Heterogeneous Property Value Impacts of Heavy Rail Transit Stations,2026.0,Urban Science,S. Mathur,"This study provides evidence on the impact of a heavy rail-based suburban metro station in Fremont, California, on house prices using geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The dataset comprises sale price, sale date, and property and locational characteristics for single-family houses sold during the January 2000–April 2018 period within two miles of the Warm Springs Station on the San Francisco Bay Area Rapid Transit system. The results demonstrate that, relative to the referent period (2000–2001): (a) the station-led house price impacts are spatially heterogeneous—the house price increased in one pocket only, comprising less than one-tenth of the dataset, not across the entire 0–2 mile station area, (b) the price increase in that pocket began right after the project announcement period, and (c) the price impacts indicated by the MGWR model markedly differ from baseline ordinary least square regression (OLS) estimates. Overall, the study’s findings highlight the need to move beyond average estimation techniques, such as OLS, to those that account for spatially heterogeneous price impacts, such as GWR and MGWR.",0.0,https://doi.org/10.3390/urbansci100804591215db3684d67741de25ae92064ef093bf9c1d604ef8,10.54254/2754-1169/2025.bj25752,Analyzing Key Determinants of Property Valuation in Boston Neighborhoods,2025.0,"Advances in Economics, Management and Political Sciences",Shuo Xin,"The current research examines the effects of particular attributes of dwellings on property valuation in neighborhoods of Boston. Given the increased sophistication of urban property markets, particularly in historically dense cities such as Boston, this study aims to offer data-based insights into factors most influential in property valuation. Employing the Boston 2024 property assessment data set from the Boston open data portal, this research utilizes multiple linear regression and K-means cluster analysis to conduct a broad investigation. Multiple linear regression measures the relationship between physical attributesland value, number of bathrooms, and living spaceand appraisal value. In contrast, K-means clustering identifies latent property groupings involving similarities in structure and market value level. Results demonstrate that area size, number of full bathrooms, and land value are the main contributing factors in higher values, while other factors, such as number of kitchens and some ZIP codes, negatively impact property value. These findings offer actionable information to homeowners, builders, and investment, urban design, and housing policy in Boston.",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/25752/pdf1216eb0710e7681f532d2049c35a7fc124e14d9cd8bc,10.1108/ijhma-07-2025-0162,Homeownership affordability: data-driven methodology for effective housing policy,2025.0,International Journal of Housing Markets and Analysis,Larisa Fleishman; Doron Sayag; A. Koblyakova,"1217 1218 The purpose of this paper is to contribute to the development of practical housing policy tools by presenting an algorithm that, unlike most existing approaches, enables the evaluation of homeownership affordability gaps across a broader range of household groups and locations.1219 1220 1221 1222 This paper presents the homeownership affordability matrix, which considers age-duration effects and various demographic characteristics, along with house prices and household incomes, within a single algorithm. It considers variations in housing size, matching the needs of different households, corresponding to loan terms, and examines the impact of mortgage duration on the affordability of mortgage payments across various age and income groups.1223 1224 1225 1226 The findings indicate that applying the suggested algorithm facilitates informed policy decisions by helping to identify and target household groups and regions that most need assistance with accessing affordable homeownership. Based on Israeli data, this study reveals that large households and younger populations face ongoing challenges in obtaining mortgage financing and achieving homeownership, with a significant decline in homeownership afforda",0.0,1227388e783e75a4e2730131b81f4fb9a30c53272d53,10.66884/kesj.2025.aalxujqg,"ASSESSMENT OF THE IMPACT OF OBSOLESCENCE ON RESIDENTIAL PROPERTY VALUES IN SULEJA, NIGER STATE",2025.0,Kaduna State University Environmental Sciences Journal,Jamila Usman; Wali Rakiya Ibrahim; Z. Usman; S. M. Nasir,"<p>1228 <strong>Purpose:</strong> The study assessed the impact of obsolescence on residential property values in Suleja with a view to evaluating the dimensions of physical, functional, and economic obsolescence and their impacts on residential property values.1229 </p>12301231 <p>1232 <strong>Design/methodology/approach:</strong> Primary data were collected through structured questionnaires administered to 120 respondents (property owners, tenants, and estate surveyors) across four neighbourhoods in Suleja: Kwanba, Second Gate, Angwan Bayi, and Emines. Respondents provided information on building condition, functionality of design, neighbourhood infrastructure, and rental values. The study employed mean score and the Pearson correlation method to determine the average condition of obsolescence and the relationship between obsolescence and property value, respectively.1233 </p>12341235 <p>1236 <strong>Findings:</strong> the result revealed poor physical attributes, low functional services and poor economic activities in housing across the study areas due to high obsolescence. Furthermore, there is a negative impact of the relationship between obsolescence and property value.1237 </p>12381239 <p>1240 <strong>Research li",0.0,12417859adb95b45d6bc914e4522831e79f12fd4110e,10.56065/cerp2024.1.2.40,Detecting Housing Bubbles Throughthe Assessmentof House Price Deviations in Bulgaria,2024.0,Construction Entrepreneurship and Real Property,Petar Ivanov,"This article attempts to assess the real deviations of house prices in Bulgaria from their long-term equilibrium levels. Moreover, we research the characteristics of house price bubbles on a global scale and introduce diagnostic tools for their identification on a local level. We compare the current price trends in Bulgaria versus those that already took place during the last documented house bubble that burst in 2008. For assessment of the price deviations, we evaluate a vector error correction model (VECM) that projects a curve for the equilibrium levels of the national house price index (HPI) for the last two decades. Hence, we could determine whether the domestic real estate market is overheating or cooling down. The model itself is built upon the interactions of the HPI with a few macro-financial indicators that as we have found out exercise influence on house prices in Bulgaria. The main conclusion is that despite the serious growth of house prices in Bulgaria during the recent years and the common public perception that the real estate market is overheating, no convincing evidence was found to prove the hypothesis of an inflating bubble in national terms.",0.0,https://cerp.ue-varna.bg/index.php/ojs1/article/download/31/161242b3ee52db1e3f6d4eef4c80288d9b715717092372,10.54097/faph1n29,Research on the Influencing Factors of Beijing Housing Price,2025.0,Highlights in Science Engineering and Technology,Zi-Han Wang,"This paper aims to develop a house price prediction system using a multiple linear regression model to improve the accuracy of predicting housing prices by analyzing the linear relationships among various influencing factors. Fluctuations in the real estate market have significant impacts on urban development and residents' quality of life, making precise house price forecasting essential for both real estate research and informed decision-making. Traditional prediction methods are often rooted in economic theories, but advancements in data science have introduced more data-driven approaches that can leverage vast datasets to enhance predictive power. Multiple linear regression, a classic statistical technique, is particularly suitable for examining the linear connections between multiple independent variables and a dependent variable, in this case, housing prices. This study incorporates factors such as location, infrastructure, economic indicators, and property characteristics to construct a predictive model, subsequently validating its effectiveness through real-world data analysis and the effect of Variance Inflation Factor (VIF) on the model is discussed.",0.0,https://hsetdata.com/index.php/ojs/article/download/243/220124364e6e0d0e56399a90ea269a5db31660877659764,10.54254/2754-1169/2025.cau26835,Analysis of Real Estate Market Pricing Rules and Construction of Prediction Model from the Perspective of Big Data,2025.0,"Advances in Economics, Management and Political Sciences",Xinyi Cao,"Amidst the digital revolution, data resources are surging exponentially. Cutting-edge technologies like big data and AI are rapidly evolving, creating new opportunities for industries. Yet, the real estate market is becoming more complex and unstable. Frequent policy changes, supply-demand imbalances, and economic volatility are heightening industry uncertainty. Meanwhile, consumers are demanding better living experiences, smarter amenities, and greener homes. These diverse and refined needs are driving a major shift in the real estate landscape. This paper employs a method of literature review and case study. It investigates the influence of big data in the prediction of price and the construction of the real estate market. This big data analysis on housing prices holds significant value by empowering homebuyers to understand explicit/implicit factors for informed decisions, guiding real estate firms on land/pricing strategies, supporting government policy formulation with data, aiding macroeconomic stability through risk identification in price patterns, and enriching academic theory in real estate economics through systematic analysis and novel methods.",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/26835/pdf1244d14b9a74252b0ef8c2c76a474680a11785c7faca,10.54691/bcpbm.v38i.3714,Real Estate Prediction of House Price in Text of Multivariate Linear Regression,2023.0,BCP Business & Management,Yongxuan Zhang,"During the recent decades, the estate market of China reflects dramatic increasing speed which shocks the whole world. Among various indicators, house price is one of the important indexes reflecting the fast raising price. In this study, the elements which connect to the house price are investigated. To be specific, this paper picks up the typical indexes to construct the model through the Multifactorial linear regression approach. Based on the method, this research discoveries the relationship between the house price with the other related macro-economic index new housing index, citizen wages, resident population, and currency of country. According to the analysis, there is slightly connection between them, and this connection is varying between different cities. While providing the reference to the following research, it also presents that the different development level for Chinese city have the different route to development. Thus, when enterprise or government make the decision or policy, it is necessary to consider its own conditions, not just copy other’s experience. These results shed light on guiding further exploration of the city development.",0.0,https://bcpublication.org/index.php/BM/article/download/3714/36301245ee7734939116d6f22eab74c9f163e632478428ed,10.55905/oelv22n2-068,Residential submarkets in traditional central areas: new spaces and property obsolescence in Recife,2024.0,Observatorio de la Economía Latinoamericana,Iana Ludermir Bernardino,"The central areas of Brazilian cities have acquired an increasing importance in the dynamics of capitalist production of space, either through the construction of new real estate or through the ressemantization of old real estate. In Recife, the intensification of the production of residential real estate in the immediate surroundings of the historic sites listed over the decades of 2000 and 2010 has added, to the stock built up in the central area, real estate with typologies similar to those already practiced in other districts. The present paper shows how the real estate market of new residential properties - corresponding to contemporary consumer desires - and the used real estate market are articulated in order to unveil how the residential real estate submarkets are configured. The behaviors of the bidders and consumers, the means of disclosing the available properties, pricing, the strategies for comparing the available properties and the attributes praised by bidders and valued by consumers are substantially differentiated so that, even located in proximity condition, units can enter into different submarkets with their own operating mechanisms.",0.0,https://ojs.observatoriolatinoamericano.com/ojs/index.php/olel/article/download/3183/22481246a6b7be12c5485726891ca3625471e23814fee1f8,10.71222/1s8mmk10,Comparative Study of Machine Learning Models for U.S. Housing Price Prediction,2026.0,"Journal of Computer, Signal, and System Research",Wen-Guang Zhou; Wen-Jiao Zhou,"This study compares machine learning models used to predict US house prices using a large-scale real estate dataset covering all US states, cities, and zip code regions. The goal is to evaluate prediction accuracy and identify effective preprocessing strategies for high-cardinality location features. After cleaning the data through missing value handling, deduplication, and outlier removal based on interquartile range (IQR), leakage-aware feature engineering was applied, including aggregating zip codes into ZIP3, grouping by the top K cities, and date decomposition, while excluding target derived variables. This study trains and evaluates three models-linear regression, random forest, and XGBoost-on a reserved test set using mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²). The results show that XGBoost achieves the best performance, outperforming linear regression and random forest, and feature importance indicates that location indicators play a dominant role in prediction gain. The findings demonstrate that the XGBoost model outperforms linear regression and random forest models in predicting US house prices.",0.0,https://www.gbspress.com/index.php/JCSSR/article/download/613/6221247b2367716adbb2c099895d56da225a9e7108de17f,10.48175/ijarsct-4854,House Price Prediction based on ML using Regression Techniques,2022.0,"International Journal of Advanced Research in Science, Communication and Technology",Archit Sisodia,"Guessing models for determining the sale price of houses in cities like Bengaluru still serve as a challenging and deceptive task. The retail price of buildings in cities such as Bengaluru depends on the number of other items. Important factors that may affect the price include the location, location and location of its facilities. In this research project, analytical research was conducted by considering a data set that is always open to the public by displaying available housing structures in the form of a machine hackathon platform. The data set has nine features. In this study, an effort has been made to develop a predictive model for price analysis based on price factors. Modeling tests use some retraction techniques such as multi-line retrieval (Small Squares), Lasso and Ridge retrieval models, vector retrieval, and reinforcing algorithms such as Extreme Gradient Boost Regression (XG Boost). Such models are used to create a predictive model and to select the most efficient model by performing comparative analysis and predictor errors found between these models. Here, the effort is to build a pricing model for price analysis based on price factors.",0.0,https://doi.org/10.48175/ijarsct-485412486f3fc655280b317ad9bea310a9f26c65e9e9af81,10.2139/ssrn.3663266,Securitization of Assets with Payment Delay Risk: A Financial Innovation in the Real Estate Market,2020.0,Social Science Research Network,Chao Ma; Hao Zhang; Hong-Biao Zhao,"We study a new type of securitization, mortgage-receivable-backed securities (MRBSs) issued by real estate developers. Unlike traditional mortgage-backed securities (MBSs), the major risk of underlying assets of MRBSs is payment delay instead of default and prepayment. Using unique loan-level data, we estimate proportional hazard models and detect factors that affect the risk of underlying assets of MRBSs, including bank characteristics, property-loan-household characteristics, local market conditions, and macroeconomic conditions. Especially, we find that the effects of house prices and LTVs on MRBS risk are the opposite of those on traditional MBS risk. Based on the estimates, we simulate cash flows of an underlying-asset pool and analyze the shortfall risk of the corresponding security tranches. We find that the securitization process imposes a natural adverse selection on the underlying assets. Our analyses provide a benchmark for conducting appropriate security designs based on the composition of the underlying asset pool, increase the transparency for investors on the risk pattern of MRBSs, and provide implications for pricing and regulation.",0.0,https://doi.org/10.2139/ssrn.36632661249f9062aba37b68908c676eaa58b80bd5eacac4baf,10.55681/jige.v5i2.2794,Comparative Analysis of Multiple – Linear Regression Algorithm with Random Forest Regression for Prediction of House Plot Prices,2024.0,JURNAL ILMIAH GLOBAL EDUCATION,Sigit Auliana; B. Permana; Gagah Dwiki Putra Aryono,"Humans basically have a basic need to have a place to live, which can be a house or shelter. Along with the rapid population growth in Indonesia, which continues to increase every year, many people do not have or have a decent place to live. Therefore, careful planning is needed so that every family can have a decent home. One very important aspect in planning investment in the form of property is predicting future house prices. One approach that can be used is to use a Random Forest and Multiple Linear Regression algorithm, which is an algorithm from Machine Learning. There are several factors that can influence the price of a house, including land area, building area, number of bedrooms, bathrooms and garage. In this research, multiple linear regression and random forest regression methods were chosen. The aim of this research is to find the best prediction results between the two methods. To achieve accurate predictions, research was carried out repeatedly by dividing the dataset into 80% for training and 20% for testing. The research results show that the random forest regression algorithm provides the best results, with an accuracy of 81.6%.",0.0,https://ejournal.nusantaraglobal.or.id/index.php/jige/article/download/2794/3006125060aa1aaaf9ff016a9f4ade7b8268722fa324348c,10.25236/icbdem.2020.066,Spatial Location Competition Strategy of Real Estate Enterprises from the Perspective of Classic Hotelling Model,2020.0,,Chao Zhou; Jie Luo; Peng-Fei Zhou,"Spatial location is an important factor in the market competition of real estate enterprises. Hotelling model is one of the most important models, which is based on different spatial locations of firms and provides an analytical framework for firms to determine their location and the nature of their spatial equilibrium in spatial location competition. The market of the real estate market is very similar to the hypothetical market of the classical Hoteling model, which has typical characteristics of spatial competition. Therefore, this paper uses the classical spatial competition model Hotelling model to analyze the competition of real estate developers, and draws the corresponding conclusions. It is considered that the transportation cost is an important factor affecting the competition and pricing among real estate developers, and the market entry cost and the fixed cost of development also have a significant impact on the competition of developers and the pricing of housing. If the impact of transportation cost on consumers is low enough, the price of housing will be close to the cost price of development, which helps to stabilize house prices.",0.0,http://webofproceedings.org/proceedings_series/ECOM/ICBDEM 2020/ICBDEM20066.pdf1251cf49e7304f2b83ef90170802b85037fb606ddc49,10.65805/g0xjvsa6cc88,EFFECTS OF LAND USE REGULATIONS ON HOUSING PRICES IN LAGOS STATE,2022.0,Journal of Environmental Sciences YABATECH,A. Ozugha,"The living standard of the residents in an urban area could be influenced either positively or negatively by land use regulations through its impact on property value. This paper examined the effects of land use regulations on residential property prices in Lagos State. The town planners in Lagos State planning office indicated the land use regulations adopted in the State and these were incorporated into the housing price determinants questionnaire administered to the real estate developers in the State. The methods of analysis adopted wer e correlation and multiple regressions. The result s revealed that there is increasing difficulty of obtaining regulatory approval for building or redeveloping old h omes. These have significant effect on housing prices. Given the acute shortage of housing in Lagos State, the policy makers should factor the adverse effect of the identified land use regulations so that it would not be counterproductive on shelter provision. By implication, the effects of land use regulation on the rental market might have repercussion on housing. In view of the above findings, there is need for innovative planning regulations.",0.0,https://doi.org/10.65805/g0xjvsa6cc88125290baa1932ef9ec70c41fd4493d28d7943af75d5a,10.2139/ssrn.3815426,Extreme Conditional Quantiles for Panel Data Model with Individual Effects and Heteroscedastic Extremes,2021.0,,Yanxi Hou; Xuan Leng; Ying-Gang Zhou,"Panel quantile regression models play an important role in real applications of finance, econometrics, insurance and risk management. However, direct estimates of the extreme conditional quantiles may lead unstable results due to data sparsity on the tail regions. Moreover, the presence of individual effects complicates the inference for extreme quantiles and a study on their theoretical properties is necessary. This paper proposes a two-stage method to estimate/predict the extreme conditional quantiles where an intermediate quantile is first estimated according to panel regression models and the extrapolation of the intermediate quantile to an extreme quantile is carried out in the second stage. Under a set of second-order regular variation conditions of heteroscedastic extremes, we establish the asymptotic theories for the two-stage prediction while its finite sample performance is demonstrated and compared to the direct prediction by simulations. Finally, we apply the two-stage method to the macroeconomic and housing price data, and find strong evidence of housing bubbles and common economic factors as well as the cross country heterogeneity.",0.0,https://doi.org/10.2139/ssrn.3815426125308067dc7927c424b7319f21e03ddc9b808cd1e62,10.36871/2618-9976.2024.07.005,APPLICATION OF MACHINE LEARNING IN REAL ESTATE MARKET RESEARCH,2024.0,SOFT MEASUREMENTS AND COMPUTING,Hoang Hieu Tran,"This article discusses the use of machine learning for analysis and forecasting in the residential real estate market. The authors emphasize various machine learning methods, including supervised, unsupervised, and semisupervised learning. These technologies are used to work with real estate data to improve the efficiency of price forecasting, analyzing market trends and optimizing marketing strategies. One of the significant benefits of machine learning is its ability to recognize complex patterns and relationships in large volumes of data, which is invaluable for indepth analysis of the real estate market. The article also discusses the main challenges associated with using this approach, including possible data quality issues and the risks of overfitting models, which can lead to biased analysis results. The author provide specific examples of the successful use of machine learning in this area, demonstrating how these technologies can effectively cope with the task of identifying nonobvious patterns in data. This opens up new perspectives for developing marketing strategies and improving the accuracy of real estate price forecasts. The final part of the article highlights the i",0.0,12544d21a0015220409cff549696893bcfc72c3d66da,10.59994/ajbtme.2026.3.29,California Housing Price Prediction Using Machine Learning: A Comparative Study Using Feature Engineering and Ensemble Methods,2026.0,Ahliya Journal of Business Technology and MEAN Economies,Mohammad Siwad; Mutaz Rasmi Abu Sara; Ahlam Awwad,"This study aimed to develop an accurate housing price prediction model for California by comparing multiple machines learning algorithms and evaluating the impact of feature engineering and ensemble techniques on predictive performance. The study utilized the California Housing Dataset, comprising 20,433 observations after data cleaning and preprocessing. A five-stage methodology was implemented, including the evaluation of seven baseline regression models, generation of engineered features, feature selection using the F-statistic method, hyperparameter optimization of the best-performing models through GridSearchCV, and the construction of Voting and Stacking ensemble models. The findings revealed that linear models achieved limited performance due to the nonlinear relationships among variables, whereas tree-based and ensemble methods demonstrated superior predictive capabilities. The Stacking Ensemble model achieved the highest performance with an R² value of 0.8431, an RMSE of $46,317, and an MAE of $30,150. Furthermore, the results confirmed that engineered features, particularly rooms per household, played a significant role in enhancing prediction accuracy. The scientific co",0.0,1255790369d3164a5c321a374c363e609d631b94c01e,10.54097/qbqype86,Analysis on Housing Price Using Macroeconomic Indicators in China,2024.0,"Highlights in Business, Economics and Management",Zihan Zhao,"In China, one of the most important stimulations in economic development has belonged to real estate market after the reform and opening, and it also contributes a lot to the financial market. However, there are many arguments nowadays, believing that Chinese real estate market is on the process of bubbles, almost about to burst. This study aims at discussing the influences of some macroeconomic indicators on price of commercial housing in China between 2013 and 2022. GDP, interest rate, inflation rate and money supply are chosen to be the independent variables. Using correlation and regression models, the results show that the indicators are correlated with each other. The analysis shows that there is a strong positive correlation between China’s GDP and commercial housing price which is the key for future economic development. Inflation positively correlates with the commercial housing price and a strong negative correlation is found between commercial housing price and interest rate and money supply. This research suggests that government should regulate and control macroeconomic indicators to protect the development in real estate market.",0.0,https://drpress.org/ojs/index.php/HBEM/article/download/16563/160811256cd6eec970b6a59ea8dee045617f97e2087086ea1,10.54254/2754-1169/2026.bl34118,The Linkage Analysis of Real Estate and Securities Markets Integrating Machine Learning and Data Visualization,2026.0,"Advances in Economics, Management and Political Sciences",Jie Bing,"This paper selected monthly time series data from both markets from January 2018 to December 2023. Data preprocessing and feature design were performed, and several experiments were carried out with LSTM, CNN-LSTM and Transformer as models. MAE, RMSE and LCE, which can achieve a lower 0.023, 0.031 and 0.018, respectively, with R2 =0.942, are achieved by TLAEFNA. The prediction accuracy and linkage feature capture are much superior to mainstream time series models, and are stable and robust when the data is insufficient and outlier interference is present. By implementing a visualization module, our algorithm intuitively presents linkage trends, attention weights and factor importance, which reveal the temporal and regional differences in the linkage between the real and securities markets and identifies the principal linkage factors such as housing price changes, real estate stock transaction volumes and funds and expectations transmission paths. This algorithm solves the black-box problem of machine learning that provides accurate and interpretable technical support for cross-market investment decisions, risk warnings and policy regulation.",0.0,https://aemps.ewapub.com/article/view/34118.pdf1257ddd1d5f9acd1640abf349a35eb11e60e8bf03eb4,10.1177/23998083251339296,The social stratification of homeowners’ housing wealth: Bringing a dynamic approach through the price gap index,2025.0,Environment and Planning B Urban Analytics and City Science,J. Ay; Thibault Le Corre,"The spatio-temporal variation of housing prices is central to unveiling the distribution of housing wealth (HW) across social groups. This paper presents a price decomposition framework to analyze the social differentiation of HW in terms of housing characteristics, location, and transaction date across labor-based occupational categories (OCs) of homeowners. Beyond variegated housing prices and capital gains, we highlight the importance of the concept of Price Gap Index (PGI)—the difference between average buyer and seller prices for each OC—to capture the redistribution of HW induced by housing transactions. Using data over two decades (1998–2017) for the French region Bourgogne-Franche-Comté, we find that static HW differences reflect the usual social hierarchy of the labor market. However, the PGI from a dynamic decomposition shows more regressive results of HW accumulation across OCs than the capital gains computed from the usual housing price index over time. These results confirm that spatial dynamics in the housing markets are an important generator of inequality, acting through the redistribution of HW during housing transactions.",0.0,https://doi.org/10.1177/239980832513392961258b8fc055e9dd25a6fd022fc749da1694e2e1750df,10.15641/jarer.v9i1.1394,Value Determinants in Mixed-Income Housing Developments,2024.0,Journal of African Real Estate Research,Dumisani Ndumo; C. Okoro,"While some recent studies have focused on the effect of mixed-income housing on the property values in the surrounding neighbourhoods, there is gap in the knowledge on effect determinants of value have on various components of value. Understanding the relationship between value components and determinants of value is imperative to attaining sustainable housing in South Africa. The determinants and value components were established from literature. The relationship between the two variables was evaluated in this study. A questionnaire was used to collect data from 82 participants experienced in valuing property within mixed-income housing developments. Multiple regression analysis showed that neighbourhood determinants significantly influenced monetary value and non-monetary value while environmental determinants had a significant influence on social benefits accruing to other stakeholders in mixed-income housing. As mixed-income housing gains traction in South Africa, the results of this study will serve as development guidelines on critical determinants of value and the extent of their influence on property value in mixed-income housing.",0.0,https://doi.org/10.15641/jarer.v9i1.139412592d87c288ee6d85af5f3c980498d8aa6d765c81f9,10.53106/054696002022060111005,臺灣地區地方不動產稅收因素之研究,2022.0,應用經濟論叢,許義忠 許義忠; Chung-Yi-Yu-Zheng-Yu Hsu; 沈政安 Cheng-Yuh Yu; 歐俊男 Jenq-An Shen,"1260 地方財政良窳，攸關地方政府施政績效能否展現，而地方不動產稅收乃地方財政收入主要來源之一，亦為財政得否健全之關鍵。本研究以臺灣地區19縣市2001年至2018年的追蹤資料，藉由最小平方法與分量迴歸進行實證分析比較。分量迴歸分析研究結果顯示，首先，以整體經濟因素角度觀察，平均每人GDP僅對低分量及中分量之契稅收入與土地增值稅收入呈現顯著正向影響；貨幣供給M2則對中分量及高分量的房屋稅，以及低分量和高分量的地價稅具有正向影響效果，然而對低分量及中分量的契稅與土地增值稅影響效果顯著為負。1261再者，就各縣市區域特性而言，分量迴歸分析的實證結果也指出，建物移轉登記棟數對不動產持有稅在低分量至中分量較具顯著正向關係。最後不動產稅基政策部分，分 量迴歸法結果卻顯示，公告地價調整比率對地價稅在低分量及最高分量有顯著正向關係；公告土地現值調整比率對土地增值稅在各分量則具有正向影響效果；房屋標準單價調整對不動產稅收則不具任何影響效果。1262 his study empirically investigates the factors affecting local real estate taxation in 19 municipalities in Taiwan. The factors are determined using panel data over the period from 2001 to 2018. In several cases, the estimates obtained through quantile regression are notably different from those obtained through ordinary least squares (OLS) regression. The quantile regression of per capita gross domestic product (GDP) reveals a significant and positive influence of GDP on the lower tail of deed taxation and land value increment taxation. Furthermore, the quantile regression estimates have a positive influence on the middle and higher tails of house taxes and on the lower and higher tails of land value taxes.1263The quantile regression estimates are significant in the upper tail",0.0,12646886753812b3d5005e2285868c53a7f3ec7c12f2,10.37407/kres.2026.44.2.95,Long-Run and Short-Run Effects of Inflation on the Price-to-Income Ratio (PIR) in Korea,2026.0,Korea Real Estate Society,Hyunsung Cho; Jae Tae Kim,"This study analyzes the long-run and short-run effects of inflation on the Price-to-Income Ratio (PIR) in Korea using annual data from 2003 to 2025. While previous studies have mainly focused on housing price movements or asset-market responses, this study reinterprets the role of inflation by using PIR, which reflects households' housing purchase burden relative to income, as the main dependent variable. To do so, the study employs the consumer price index (CPI) as the key explanatory variable and applies a long-run regression model and an error correction model (ECM) to analyze long-run equilibrium relationships and short-run adjustment processes. The study also conducts supplementary analysis to examine whether inflation shocks may be transmitted to housing burden with a time lag. 1265The empirical results show that, in the long-run regression, the consumer price index has a statistically significant positive effect on PIR, suggesting that rising prices may structurally increase households' housing burden. By contrast, in the error correction model, the short-run change in CPI does not move in the same direction as the long-run effect. Moreover, although the error-correction term h",0.0,1266d1229569c6973d9c15fbf58c5853c868844048e1,10.33480/jitk.v11i2.7256,COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND RANDOM FOREST ALGORITHM FOR PREDICTING HOUSING PRICES,2025.0,JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer),Dahlan Susilo; Diyah Ruswanti; Supriyanta; Wawan Nugroho,"House price predictions are an important thing in the property industry and are useful for buyers in making decisions. Principal Component Analysis (PCA) and Random Forest (RF) methods were used for accuracy analysis in predicting housing prices. Purpose of this research is to measure the accuracy of both methods also to compare RF method optimized with PCA and the one that has not been optimized. The data used is house prices in Karanganyar city based on data scraping results on the rumah123.com site. The analysis reveals that Jaten has the highest number of house sales, and sales of houses with land ownership certificates are also the highest. Of the 10 variables used, land area and buildings have the most influence on selling prices. The model training results show that the RF and PCA methods combination has more optimal value than only using the RF method. The error rate of the PCA method is smaller, averaging 0.0257, making its value more consistent than using only the RF method, which has a larger error value with an average of 0.0332. The model training time using PCA is faster (5005.75) than only using the RF method (6099.25)",0.0,https://doi.org/10.33480/jitk.v11i2.725612678878a91aeefd2cc17d2956f65192f1f5b7ba07c4,10.5220/0014363000004718,Predicting Second-Hand Housing Prices in Beijing: A Comparative Study of Machine Learning and Ensemble Models,2025.0,"Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence",Jia He,": In recent decades, the real estate market has always been an imperative force boosting China’s economic development. Accurate housing price prediction plays a key role in policymaking and investment decisions. Nonetheless, the traditional models often fall short when dealing with complex and nonlinear relationships. This study focuses on predicting second-hand housing prices in Beijing using machine learning techniques. The web crawler is used to obtain historical transaction data, then yield a dataset that includes 56,793 records with 23 features after pre-processing in Beijing. Three models-Random Forest, XGBoost, and LightGBM-were trained using grid-search and five-fold cross-validation. A combined ensemble model was also built to improve the overall robustness. Evaluation and visualizations were used to compare performance. The ensemble model outperformed the single model, followed by XGBoost, Random Forest, and LighGBM. This paper aims to provide a new idea for house price prediction research through machine learning methods, hoping to bring some inspiration to theoretical research and practical applications in related fields.",0.0,https://doi.org/10.5220/00143630000047181268bc3c653045e72a1a1465e1c5cc7c968ad5384756,10.65760/sjgs.v1.i1.6,"SPATIAL VARIATIONS OF HOUSING RENTAL VALUES IN BIRNIN KEBBI METROPOLIS, KEBBI STATE",2025.0,Sokoto Journal of Geographical Studies,I. M. Shera; I. U. Kaoje; S. Wali; A. Usman; G. B. Wurma; I. M. Dankani; M. Mohammed,"Housing is an essential human need that shapes the well-being of all citizens, hence, accessibility to decent and 1269affordable housing has emerged as one of the most daunting challenges of the contemporary century in many 1270countries. Nigeria is perhaps the fastest urbanizing country in the African continent and one of the 1271fundamental challenges facing the country is provision of affordable housing. This study assessed the spatial 1272variation in housing rental values in Birnin Kebbi metropolis, Nigeria with a view to identifying the socio1273economic characteristics of tenants in Birnin Kebbi. The study adopted the use of mixed method approach. 1274Data were collected through questionnaire and interview survey from 322 respondents, which were randomly 1275selected from the target population in the study area. The data collected were subjected to analysis using 1276descriptive analysis and inferential statistics with frequency distribution table. Findings revealed that two1277bedroom apartment was in higher demand within the period of 33years. It demonstrated that houses in low 1278density normally have higher value and vice-versa because of accessibility to and availability of social 1279facilities among",0.0,12808f0887019b71b743e0ba610bc730c830ae8e9fad,10.15381/risi.v18i2.32316,Evaluación experimental de hiperparámetros de un árbol de regresión mediante un diseño de parcelas divididas,2025.0,Revista de investigación de Sistemas e Informática,Roberto Carlos Evaristo Broncano; Frank Kaleb Chuctaya Quispe,"Hyperparameter tuning in regression tree models is a critical stage that directly influences their predictive capacity, interpretability, and generalization. Although automatic methods such as grid search or random search exist, these approaches can be computationally expensive and, more importantly, lack an interpretative statistical framework. This study proposes an experimental approach based on a split-plot design, which allows for the evaluation of the main effects and interactions of regression tree hyperparameters, incorporating the variability introduced by different random partitions as a main plot effect. Combinations of two key hyperparameters—maximum tree depth (max_depth) and minimum number of samples per leaf (min_samples_leaf)—were analyzed in terms of model performance, measured by the coefficient of determination (R²). The experiment was implemented on a housing price dataset. The Analysis of Variance (ANOVA) results showed significant individual effects and interactions between hyperparameters, supporting the usefulness of this approach for efficient, interpretable, and statistically grounded hyperparameter tuning.",0.0,https://doi.org/10.15381/risi.v18i2.323161281c6c5373e7d621a44459cbf98dd26ba0e9250a689,10.5220/0012797700003885,Analysis and Forecast of Influencing Factors of House Price in Boston,2023.0,Proceedings of the 1st International Conference on Data Analysis and Machine Learning,Boyao Zhang,": The cost of housing is a significant indicator of economic activity. Both buyers and developers of real estate closely monitor fluctuations in home prices. As a result, developing an accurate housing price forecasting model is critical for the financial market and people's livelihood. Boston dataset is used for data analysis in this paper. Firstly, this paper conducted a correlation study using Pearson's r and created scatter plots between each variable and the house price. In the multiple linear regression model, this study employed techniques including outlier removal, collinearity, and variable transformation to optimize the model for a more accurate prediction, finally getting an R-squared of 0.7927. In random forest model, this research finds the optimal parameter value and then compares the importance of predictors using the indicators %IncMSE and %IncNodePurity. Finally, this study determines that the random forest regression model is superior through the investigation of the factors that influence home prices, and this paper also analyze the elements that influence house prices and consider the shortcomings of this paper.",0.0,http://dx.doi.org/10.5220/001279770000388512828a163c9eded939c047511cb8811bb2c54f9defb2,10.54254/2754-1169/20/20230199,Research on the Influencing Factors and Prediction of Housing Prices Based on Regression Analysis --- Taking California as an Example,2023.0,"Advances in Economics, Management and Political Sciences",Kunlong Zhang,"A house is a necessity for everyone's life. But in today's world, with the continuous reduction of land area, the continuous increase of population, and the continuous maturity of the real estate industry, people have to consider more carefully what factors are the most important in the house. Based on the background of housing prices in California, this paper uses linear regression, random forest and principal component analysis to determine which variables have the greatest impact on housing prices. The reason for using three methods is to obtain more accurate results. According to the results, linear regression shows that income is the most relevant variable to housing prices. Random Forest shows an R square of 75.7%, meaning the predictions fit the data fairly well. Principal component analysis also shows that income is the most important variable. At the same time, house prices will be predicted based on the data obtained, which is about 116598.48588. Some suggestions for those who want to buy a house - which factors of the house are more important. Second, this article will also analyse how to face the rising housing prices.",0.0,https://aemps.ewapublishing.org/media/a02f966289d94d4cb7b2f45cd878fd8c.marked_Z9JVjK9.pdf1283370b8400308f80b885669b1887e6ee459daa30f0,10.1007/s00168-019-00919-w,Testing for spatial group-wise heteroskedasticity in spatial autocorrelation regression models: Lagrange multiplier scan tests,2019.0,The annals of regional science,J. le Gallo; F. López; C. Chasco,"The aim of this paper is to develop a spatial group-wise heteroskedasticity test based on the scan approach, specifically developed for spatial autocorrelation regression models (spatial lag and spatial error models): the “scan-LM test.” Based on the Lagrange multiplier (LM) principle, its main advantage lies in its comparative ease of implementation as it is not necessary to obtain the maximum likelihood estimations for the alternative hypothesis. Moreover, when rejecting the null hypothesis, this test identifies the shape and size of the spatial clusters with different residual variance, a feature which proves very useful for specification search of the regression model. Another important benefit of the scan-LM test is that it does not require the specification of a spatial weights matrix. An extensive Monte Carlo simulation confirms the good properties of the scan-LM test in terms of size and power. This test is also robust in the presence of non-normality and other forms of a spatial heteroskedasticity. We finally propose an application on housing prices in the agglomeration of Madrid for a specific submarket: the attics.",0.0,https://repositorio.uam.es/bitstream/10486/709487/1/5705952.pdf1284cab0699702ad84dc3c8767d17f723cde0abb13b0,10.31390/gradschool_dissertations.2463,Essays on risk and volatility,2011.0,,Jun-Yue Xu,"In this work, we begin with an investigation into the temporal correlation in default risk. We first establish a link between the dynamics of house price changes and the dynamics of default rates in the Gaussian copula framework by specifying a time series model for a common risk factor. We show that the serial correlation propagates from the common risk factor to default rates. In the second essay, we specify a model where the default correlation is stochastic. We find the distribution of expected value of cash flows received by securitized investment vehicles is distorted by the dynamics of default correlation. The third essay provides an empirical study on variance risk premium, which is defined as the difference between implied variance and ex post realized variance. We show that an individual stock’s variance risk premium and its two components can be used to predict future equity premium. In the fourth essay, we derive asymptotic properties of the quasi maximum likelihood estimator of smooth transition regressions. We show that the estimator converges at the usual √ T -rate and has an asymptotically normal distribution.",0.0,https://repository.lsu.edu/cgi/viewcontent.cgi?article=3462&context=gradschool_dissertations128544d9c9de089a2aa76fb13464c64bfd1a37dd7269,10.22214/ijraset.2023.52578,House Price Prediction System Using Machine Learning,2023.0,International Journal for Research in Applied Science and Engineering Technology,Amol Dhakne,"Abstract: When individuals are in the market for a new home, they often display a more cautious approach when it comes to their budgets and overall market strategies. However, the current system for determining house prices typically lacks the essential element of predicting future market trends and potential price increases. The existing system Give the functionality for buyers, allowing them to search for houses by features or address. Machine Learning has significantly contributed to various areas such as natural language processing, product recommendations, healthcare, customer service, and automotive safety. Its widespread adoption in these fields highlights its trendiness and its potential for transformative impact. In light of this, we are incorporating Machine Learning into our project. Predicting the selling price of houses in cities such as Bengaluru remains a complex and intricate task. Numerous interrelated factors influence the property prices in these cities. Essential elements that impact the price comprise the property's size, its location, and its characteristics, including the number of rooms and bathrooms.",0.0,http://dx.doi.org/10.22214/ijraset.2023.52578128660ea52359288bfc4dfc545bb97a5c6d051035d16,10.37407/kres.2026.44.1.289,A Study on the Efficiency of Housing Tax Policy in the Real Estate Market,2026.0,Korea Real Estate Society,H. Yun; Jin Kim,"This study examines the efficiency of housing tax policy in Korea by applying Data Envelopment Analysis (DEA) to 17 metropolitan and provincial governments from 2018 to 2024. Acquisition tax revenue and property tax revenue were selected as input variables, while housing transaction volume and the rate of change in housing sale prices were used as output variables. Regional efficiency was assessed using the CCR, BCC, and SBM models. Because the housing price change rate may include negative values that restrict DEA computation, a price stability index was constructed by transforming the price change rate so that all observations satisfy the non-negativity requirement of the model. The empirical findings reveal substantial regional disparities in the efficiency of housing tax policy. Even under the same policy framework, efficiency levels differed across regions depending on market structures and transaction characteristics. In 2018 and 2019, relatively high efficiency appeared only in a few regions, whereas the number of efficient regions increased in 2020 and 2021. However, after 2022 most regions showed relatively low efficiency despite tax relief measures, indicating that tax po",0.0,128771f2cba94670cd3e414be2a6dedf7b181d8e4d0c,10.36787/jti.v19i1.1858,Prediction of House Price using the Multivariate Adaptive Regression Spline Method,2025.0,Jurnal Tekno Insentif,Aysha Alia Iskandar; D. F. Al Husaeni; M. Z. I. Sahidin; L. Riza; W. Wahyudin,"Kekritisan lingkungan adalah kondisi suatu wilayah yang menunjukkan tingkat kerusakan atau kekritisan yang dapat memengaruhi kemampuan lingkungan dalam mendukung kehidupan manusia dan ekosistem. Faktor penyebabnya meliputi rendahnya vegetasi (NDVI), meningkatnya lahan terbangun (NDBI), suhu permukaan tinggi (LST), dan kepadatan penduduk. Tujuan penelitian ini menganalisis tingkat kekritisan lingkungan di Kota Bandar Lampung serta hubungan antara parameter tersebut terhadap kekritisan lingkungan. Metode yang digunakan adalah algoritma Environmental Critical Index (ECI) yaitu pendekatan integratif yang menggabungkan beberapa parameter lingkungan untuk menilai tingkat kekritisan wilayah secara spasial dan kuantitatif dengan data citra Landsat 8 (2014) dan Landsat 9 (2023). Hasil penelitian menunjukkan adanya peningkatan kekritisan cukup signifikasan sebesar 50% terjadi pada area sangat kritis seluas 1.447,74 ha. Sebaliknya terjadi penurunan pada area tidak kritis sebesar 35% atau seluas 1.029,16 ha dan pada area kritis sebesar 15% atau seluas 418,58 ha. Faktor yang paling berpengaruh terhadap kekritisan lingkungan adalah NDBI.",0.0,https://jurnal.lldikti4.or.id/index.php/jurnaltekno/article/download/1858/3021288c43f9eb0a27f512dde6f9261012f5c01eea04239,10.5220/0013829300004708,The Research on the Factors Influencing Housing Prices,2025.0,"Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy",Qianyu Shao,": This article aims to identify the extent to which certain factors influence housing prices. The author compares the accuracy and effectiveness of two methods Multiple Linear Regression and Random Forest in analyzing the significant factors with 1500 samples of the USA in 2014. Based on the multiple linear regression model, stepwise regression was used to remove the insignificant factor sqft-lot and analyze it to ensure the robustness of the model. Cross-validation is used to modify the random forest model’s parameters to ultimately produce the optimal model. The two models present different results. The 7 factors have different impacts on housing prices in different models. Random Forest is finally shown to be more accurate, the total impact of these seven factors on housing prices is relatively large. Bathrooms, sqft-lot and city have the biggest effects on home cost in the more precise random forest method. This study shows that in the future, more timely data can be selected and control variables can be used to further optimize. This study provides important reference and guidance for people to predict housing prices.",0.0,https://doi.org/10.5220/00138293000047081289f2d895b966896b33294c749c51879082a23846b3,10.70849/ijsci03042639097,An Intelligent Real Estate Price Prediction Framework Using Geospatial and Socioeconomic Features,2026.0,International Journal of Sciences and Innovation Engineering,E. P. Kumar; S. M. Kumar,": Intelligent price prediction systems have been developed as a result of India's growing need for trustworthy property appraisal. Based on important variables like location, area, number of rooms, infrastructure quality, crime rate, and socioeconomic conditions, this project offers a data-driven method for estimating home values. To determine the connection between property attributes and pricing, a dataset of 5000 real estate records with various qualities was examined. The system achieves great reliability in estimating property values by identifying patterns in the data to produce precise predictions. Using Flask, the solution is developed as an easy-to-use web application that allows users to enter property details and obtain real-time price estimates. For buyers, sellers, and real estate analysts, the program guarantees usability, consistency, and usefulness. This research shows how predictive modeling and data analysis may help make better decisions in the real estate industry. To further increase accuracy and practicality, future improvements might incorporate location-based intelligence and real-time market data.",0.0,https://ijsci.com/ijscipaperm/uploads/113667939097.pdf12906ac9658b7e8d06a4856baaded7047cf2200da810,10.22214/ijraset.2023.52048,Image Based Appraisal Real Estate Properties,2023.0,International Journal for Research in Applied Science and Engineering Technology,E. Sankar; M. D. P. Varma; M. M. Varma,"Abstract: Real estate appraisal, which is the process of estimating the price for real estate parcels, is vital for both deals and merchandisers as the base for concession and trade. Traditionally, the duplication deals model has been vastly espoused to estimate real estate price. still, it depends the design and computation of a complex profitable affiliated indicator, which is challenging to estimate directly. moment, real estate brokers give easy access to detailed online information on real estate parcels to their guests. We're interested in estimating the real estate price from these large quantities of fluently entered data. In particular, we dissect the vaticination power of online house cinema, which is one of the vital factors for online addicts to make a implicit visiting decision. In this work, we employ a intermittent Neural Network( RNN) to prognosticate real estate price using the state- of- the- art visual features. The experimental results indicate that our model outperforms several of other state- of- theart birth algorithms in terms of both mean absolute error( MAE) and mean absolute chance error( MAPE).",0.0,http://dx.doi.org/10.22214/ijraset.2023.520481291e6d26849df778b53b5e2eab2586a309ce203311f,10.1109/isdfs69419.2026.11459038,On Impact of Ensemble Strategies in Tabular Data Regression,2026.0,International Symposium on Digital Forensics and Security,Ivona Brajević; Una Tuba; Milan Tuba,"Ensemble learning methods have demonstrated remarkable success in housing price prediction due to their ability to combine multiple models and achieve superior predictive performance. This study evaluates tree-based ensemble models, Random Forest, Extra Trees, and AdaBoost, alongside a Voting ensemble strategy for housing price prediction on the Boston Housing dataset. The experiments employ a rigorous repeated hold-out evaluation protocol combined with cross-validation on the training set to ensure stable performance estimates across multiple random splits. Additionally, Random Search-based hyperparameter optimization is applied to assess how systematic tuning influences model performance and robustness. Our results demonstrate that Extra Trees consistently achieves strong performance with excellent stability. The Voting ensemble, combining two Extra Trees models and one AdaBoost model, achieves superior overall performance with an MSE of 8.113, representing a 29.8 % improvement over the best individual model. Hyperparameter optimization substantially improves all models, with the most pronounced gains observed for AdaBoost, where optimized configurations reduce MSE by approximate",0.0,1292366afdb36deb552049d1bcb75a35cf4d175384c1,10.54254/2754-1169/2026.nj30809,A Comparative Analysis of Machine Learning Algorithms for Forecasting Housing Prices in Boston,2025.0,"Advances in Economics, Management and Political Sciences",Yingxi Chen,"Real estate is a crucial and multifaceted field that encompasses property valuation, investment, urban development, and even aspects related to the surrounding regional economy. Accurate predictions of housing prices in Boston are therefore of great significance to its various stakeholders. To address this issue, in this study, after thorough data preprocessing and detailed feature engineering, multiple machine learning models were employed to predict the housing prices in Boston. These models include linear regression (LR), random forest (RF), and extreme gradient boosting (XGBoost). These models cover linear algorithms, ensemble methods, and tree-based techniques, and can comprehensively assess their ability to simulate the relationship between the features and prices. Among these models, XGBoost performed the best, achieving the highest R-squared value (0.920), the lowest root mean square error (RMSE = 2.7831), and excellent cross-validation performance. This indicates that XGBoost is highly effective in predicting housing prices in Boston and can provide feasible and practical prediction solutions for stakeholders.",0.0,https://aemps.ewapub.com/article/view/30809.pdf129329a9b86b0a981f834fb9d832e1cdb36b8071caf9,10.38100/jhuf.2026.11.2.97,"Analysis of the determinants of home sale decisions by mortgage1294 borrowers: Focus on shared equity/appreciation mortgages",2026.0,Journal of Housing and Urban Finance,Doyun Lim; Sunju Kim,"This study uses individual loan data for Shared Equity/Appreciation Mortgages contracted between October 2013 and December 2020 and repaid early after at least three years, with observations extending through December 2023. The study analyzes the factors determining home sale decisions by mortgage loan borrowers using the Cox proportional hazards model. The results of the Shared Appreciation Mortgage analysis show that, first, the Bank of Korea mortgage loan interest rate had the largest positive effect on the probability of selling a home, and declining market interest rates acted as a strong incentive for home sales. Second, the average annual housing price fluctuation rate had a negative effect, indicating a greater tendency to hold onto a home rather than sell when prices rise. Third, the loan-to-value ratio (LTV) had a positive effect, while borrower age and annual income had negative effects. The results of the Shared Equity Mortgage analysis show that the LTV had a positive effect on the probability of selling a home, while the average annual housing price fluctuation rate and annual income had negative effects.",0.0,http://www.ejhuf.org/download/download_pdf?pid=jhuf-11-2-9712958f33acc3c9eb567cde24ae3fe6775d2110a3ed79,10.61173/k9x9cm13,Study on the Influencing Factors of Housing Price,2024.0,"Science and Technology of Engineering, Chemistry and Environmental Protection",Tinhou Ouyang,"This study delves into the multifaceted dynamics of housing prices by applying a multiple linear regression model to assess the impact of various macroeconomic and social variables on housing price growth in the United States from 2010 to 2023. The research scrutinizes the influence of GDP growth, interest rates, unemployment rates, inflation, and population growth on the likelihood of housing price increases. The findings underscore the significant positive effects of economic growth and population expansion on housing prices, while concurrently highlighting the suppressive impact of rising interest rates and unemployment on the real estate market. Interestingly, the study reveals that inflation does not significantly influence housing prices over the studied period. These insights are pivotal for policymakers, investors, and homeowners, providing a nuanced understanding of the factors that can potentially sway housing market trends. The research concludes by emphasizing the complexity of housing market behavior and the importance of considering these economic indicators in strategic planning and investment decisions.",0.0,https://www.deanfrancispress.com/index.php/te/article/download/2562/2599129692eb58d807cc7cdd4dd724bc9cd9ea2b2c61811c,10.1108/jfep-05-2025-0184,Transforming real estate bubble risks into financial crisis: the moderating effect of bank credits,2025.0,Journal of Financial Economic Policy,N. Nguyễn; N. B. Do; Trang Thi Thu Nguyen; H. Le,"1297 1298 This study aims to investigate how risks arising from real estate bubbles evolve into financial crises, with a particular attention to the moderating role of bank credit across countries with different levels of financial development.1299 1300 1301 1302 Using a panel data set covering 21 countries from 2003 to 2024, this study uses logistic regression models to estimate the probability of financial crises, incorporating macro-financial indicators and cross-country heterogeneity.1303 1304 1305 1306 The findings reveal that while short-term increases in housing price may temporarily reduce crisis risks, lagged housing price significantly heightens systemic vulnerabilities, especially in advanced economies. In addition, bank credit shows a nonlinear relationship with crisis risk, but does not significantly moderate the impact of housing bubbles on financial crises.1307 1308 1309 1310 The study’s scope is limited to equity market crashes as a proxy for financial crises. Moreover, it uses a quarterly framework for identifying financial crises, which may mask important short-term fluctuations and misrepresent the true severity of market downturns.1311 1312 1313 1314 This research highlights the need for stronger financial supervis",0.0,13152f424ef47bc504de4db37062a0a7df65b6c0552f,10.54254/2754-1169/118/20242043,Predicting Housing Prices Using Supervised Machine Learning Models,2024.0,"Advances in Economics, Management and Political Sciences",Haiying Zhang,"Abstract: Variations in housing prices exert a profound impact on economic policies and personal financial decisions. This study aims to delve into the factors that affect housing prices and construct a predictive model using machine learning techniques. Machine learning enables computers to acquire knowledge from data and make predictions or decisions. This study forcasts house prices by analyzing the characteristics of data from Seattle, Washington using machine learning multiple linear regression, polynomial regression, and K-nearest neighbors regression (KNN). The findings of this investigation demonstrate that the polynomial regression model is the most accurate in predicting housing prices and can capture nonlinear relationships in the data more effectively than multiple linear regression and K-nearest neighbors regression. The main factors affecting housing prices are interior living space and the construction and design of buildings. These insights hold potential for enhancing government policies, facilitating effective land use decision-making by planners, and enabling investors to make more informed choices.",0.0,https://aemps.ewapub.com/article/view/16015.pdf1316abfcd4aa3c8d3ce0e734973fd4e146e4660588aa,10.2991/iccessh-17.2017.190,Research on the Causes and Countermeasures of China 's Real Estate Market Bubble,2017.0,,Xiang Xie,"In 2016, the Central Economic Working Conference of China pointed out that we must adhere to sound monetary policy and guard against economic and financial risks, while at the same time maintaining a reasonable and adequate liquidity. At present, the asset price bubble is mainly manifested in the real estate, the bond market, the stock market and the foreign exchange market. This paper uses the three indexes of house price income ratio, multiple linear regression method and local equilibrium method to measure whether there is a bubble in real estate price. Then it analyzes the causes of the bubble in the real estate market from the aspects of monetary policy, asset shortage, land finance and financial support. At last, it proposes normalizing shadow banking supervision, urging financial institutions to deleverage, reforming the land transfer system, implementing tight monetary policy, promoting tax reform and other measures to curb the real estate market bubble, in order to guide stable and healthy development of the real estate market. Keywords—real estate; bubble; deleveraging; land transfer system; taxation reforms",0.0,https://download.atlantis-press.com/article/25878721.pdf1317a9bddd929e93912b85946c70ca05b923eafb8987,10.48550/arxiv.2511.19845,SX-GeoTree: Self-eXplaining Geospatial Regression Tree Incorporating the Spatial Similarity of Feature Attributions,2025.0,arXiv.org,Chaogui Kang; Li-Jian Luo; Qing-Feng Guan; Yu Liu,"Decision trees remain central for tabular prediction but struggle with (i) capturing spatial dependence and (ii) producing locally stable (robust) explanations. We present SX-GeoTree, a self-explaining geospatial regression tree that integrates three coupled objectives during recursive splitting: impurity reduction (MSE), spatial residual control (global Moran's I), and explanation robustness via modularity maximization on a consensus similarity network formed from (a) geographically weighted regression (GWR) coefficient distances (stimulus-response similarity) and (b) SHAP attribution distances (explanatory similarity). We recast local Lipschitz continuity of feature attributions as a network community preservation problem, enabling scalable enforcement of spatially coherent explanations without per-sample neighborhood searches. Experiments on two exemplar tasks (county-level GDP in Fujian, n=83; point-wise housing prices in Seattle, n=21,613) show SX-GeoTree maintains competitive predictive accuracy (within 0.01 $R^{2}$ of decision trees) while improving residual spatial evenness and doubling attribution consensus (modularity: Fujian 0.19 vs 0.09; Seattle 0.10 vs 0.05). Ablation ",0.0,1318f870a2d97f24564c545a70499dbb596627b9f607,10.54254/2754-1169/2025.cau27161,"The Impact of Shopping Malls on Surrounding Housing Prices: Evidence from Beijing, China",2025.0,"Advances in Economics, Management and Political Sciences",Yi-Xuan He,"This study investigates the impact of shopping malls on the price of residential housing in the neighborhood, focusing on how the distance of shopping malls to residential housing affects the price of housing. This study uses statistical regression analysis via STATA 17 data from six selected urban districts and finds that housing prices of neighborhoods with higher accessibility to shopping malls and convenience stores improve significantly. The findings reveal that proximity to shopping centers significantly increases housing values, with homes closer to these centers becoming more expensive, primarily due to increased accessibility and improved amenities. It was also established that the quality of school districts has a significant effect on House prices, especially in high-demand income areas. Subsequently, neighborhoods in top-ranked districts possess higher prices because of the value that families attribute to educational assets. Further studies should generalize the research area, use wider sets of control variables, and make use of long-run models to reflect the dynamic tendencies of housing price changes.",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/27161/pdf131945d1afd6b1505bb377789cffd81e03cf1e6f8407,10.54097/8vj00x60,U.S. Housing Prices Before and After COVID-19: Shifts in Market Dynamics and Key Drivers,2026.0,Journal of Innovation and Development,Jia-Yu Shen,"Restrictions to International travel during COVID-19 and the unrelated adjustments to the policies resulted in permanently changing the United States market ever since the work-from-home movement gained traction. This article focuses on the use of fixed effects panels as well as breakpoints to analyze the housing prices changes every metro performed from pre-pandemic to post-pandemic. It uses data from Zillow and Freddie Mac from 2015 to 2024 and distinguishes the following changes: decreased price sensitivity to raised mortgage rates after 2020, the large price geographically located in the suburbs for the work-from-home option, and the inefficiently proportioned supply and demand for rent in the stricter market regulations states such as Massachusetts and California. Machine learning, in the form of random forests, further confirms the elevated importance of price predictors such as net migration and available housing units. These shifts indicate a change in the available housing policies, stressing that with the current trends in migration and native supply changing, the monetary systems may have lost relevance.",0.0,https://drpress.org/ojs/index.php/jid/article/download/32978/3226813209992d1b651ce3778faeee64b0115f6f013f36131,10.63620/mkjfisbm.2026.1010,A Model of Macroprudential Frictions for Indirect Monetary Policy in The Bahamas,2026.0,"Journal of Financial Intermediation, Sustainable Banking and Marketing",Sharon Branch,"The paper proposes a dynamic stochastic general equilibrium (DSGE) model of indirect monetary policy for The Bahamas. The model consists of ‘macroprudential frictions’ including a financial accelerator mechanism for the contract between entrepreneurs and financial intermediaries; a borrowing constraint for the contract between credit-constrained households and financial intermediaries; and ‘distance-to-default’ as a proxy for financial intermediaries’ balance sheet strength. The data used to estimate the model are a mix of U.S. data—a proxy for financial-sector shocks—and Bahamian macro aggregates. The results show that macroprudential shocks are substantial drivers of welfare during recessions, while standard productivity, monetary policy, and investment-specific shocks drive welfare outside of recessions. Housing price dynamics, banking sector risk premiums, and discount factor shocks account for most of the variance in the output and consumption decompositions. Comparing the model to one without macroprudential frictions reveals that ignoring relevant policy specifications leads to significant ‘policy mistakes",0.0,https://doi.org/10.63620/mkjfisbm.2026.10101321c755a9400eb0b2ef80c9292e669331ffeb078501,10.1109/tqcebt59414.2024.10545253,Predictive Machine Learning Approaches for Estimating Residential Rental Rates in India,2024.0,2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies,P. Jayadharshini; R. Ragunath; Prabhakaran D; Lalitha Krishnasamy; N. Abinaya; S. Priyanka,"As urban areas like Chennai and Bangalore witness a continuous surge in land and housing prices, accurately estimating the market value of houses has become increasingly crucial. This presents a formidable challenge, prompting a growing demand for an accessible and efficient method to predict house rental prices, ensuring dependable forecasts for future generations. In response to this need, this study delves into the core factors influencing rental prices, with a keen focus on location and area. Leveraging a dataset comprising ten essential features tailored for detecting Rental Price in Metropolitan cities, the research meticulously preprocesses the data using a Python library to ensure data cleanliness, laying a robust foundation for constructing the predictive model. Employing a diverse range of Machine Learning algorithms, including Random Forest, Linear Regression, Decision Tree Regression, and Gradient Boosting, the study evaluates their efficacy in forecasting rental prices. Notably, feature extraction underscores the significance of area and property type in shaping rental prices. In comparison with existing methodologies, this research adopts gradient boosting as its pref",0.0,1322c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,10.62051/1jq1vs36,Research on the Influencing Factors of House Prices in Chicago,2024.0,"Transactions on Economics, Business and Management Research",Pin-Yao Liu,"This study provides a comprehensive analysis of the factors that influence housing prices in suburban Chicago. Using detailed regression analysis, several key variables were identified as significant predictors of property value. The study included variables such as the number of bedrooms, square footage, number of rooms, size of the lot, annual taxes, number of bathrooms, number of garages, and the condition of the house. The R-squared value of the model is 0.732, indicating that approximately 73.2% of the house price variance can be explained by the selected variable. Houses with more rooms and more square footage tend to cost more. In addition, characteristics such as the number of garages and bathrooms can also have a significant impact on house prices. These findings provide valuable insights for real estate professionals, buyers and sellers, enabling them to make informed decisions regarding real estate investment and pricing strategies. By understanding the key drivers of house prices, stakeholders can better navigate the suburban housing market and ensure more accurate assessments and strategic planning.",0.0,https://wepub.org/index.php/TEBMR/article/download/3862/42231323995c6d0294f4fd90344c55b53cd2efbc1e99aff3,10.70517/ijhsa4541,A Study on Spatial Differentiation of Urban Land Prices Based on Geographically Weighted Regression Models,2024.0,International journal for housing science and its applications,Qiuyu Liu; Lina Zhang; Hui Wang; Huangzhen Lv; Yakai He; Yan Wang,"Urban residential land price is related to the basic life of residents as well as the healthy and stable development of society. In this paper, the residential land price in QZ city was selected as the research object, and the residential land price data of 107 sample points in the built-up area were collected for the 10 years from 2013 to 2022, and the Exploratory Spatial Data Analysis (ESDA) method and Geostatistical Kriging Spatial Interpolation (GSI) method were combined to investigate the law of spatial heterogeneity of urban land price. Finally, general linear regression model and geographically weighted regression model are constructed to investigate the influencing factors of urban land price spatial differentiation by selecting suitable influencing factors. The sample data of residential land price in the study area basically conforms to normal distribution, and the land price shows a U-shape in the latitudinal direction, and in the horizontal direction, the land price shows a trend of decreasing from the city center to the periphery, and a “mono-core” spatial structure of decreasing from the center to the periphery. There are large regional differences in the contribution",0.0,1324f235f28a97f34a9d838db94b7565e5fee975353b,10.54254/2754-1169/64/20231543,Influence Factor Analysis and Forecast of US House Prices Based on Linear Regression and Time Series,2023.0,"Advances in Economics, Management and Political Sciences",Qi-Hang He,"In the United States, house prices remain a perennial topic of interest for every resident. Beyond influencing individual housing choices, fluctuations in house prices play a pivotal role in shaping pleasant lifestyles and ensuring the nation's stable economic growth. The housing market, in fact, underpins the entire U.S. economic system. This study employed a multifaceted approach, utilizing both multiple linear regression models and time series analysis, to delve deeply into the factors influencing U.S. house prices. The research identified that residential construction spending and GDP are the most significant drivers leading to an increase in the American House Price Index (HPI). Additionally, through time series analysis, various graphical representations were constructed to visualize the quarterly trends in the American HPI, rendering the findings more comprehensible. In essence, this research offers a scientific foundation for future adjustments in housing price policies. It holds substantial relevance for all stakeholders in the real estate market, from buyers and sellers to developers and policymakers.",0.0,https://www.ewadirect.com/proceedings/aemps/article/view/8759/pdf132596081d3dce3003e8577bd939129a8bb2ac3b7b06,10.31559/gjeb2023.13.5.5,Determinants of Housing Prices in South Africa: The Impact of Covid-19,2023.0,Global Journal of Economic and Business,P. Brijlal; Brandon Hareb,"Objectives: The present study aims to determine the relationship between selected macroeconomic and market variables, and South African housing prices, and the influence that Covid-19 had on South African housing prices, after holding for the effect of the change in selected macroeconomic, and market variables on housing prices. Method: Housing prices were determined via the macroeconomic and market variables of economic growth, unemployment, inflation, household debt-to-income, interest rates, foreign exchange rates, stock market returns, and Covid-19. Data collected were analysed using correlation and hierarchical multiple regression tests. Results: The findings indicated that pre-existing relationships between housing prices and traditional determinants of unemployment and household debt-to-income have changed. The results also indicated that housing prices increased during Covid-19 over-and-above the expected increase attributed to changes in the other selected predictor variables. Conclusion: These results provide an updated collection of housing price predictors for practitioners to utilise post-pandemic.",0.0,http://www.refaad.com/Files/GJEB/GJEB-13-5-5.pdf13268748370c9c78d825ab2285095495d8953f7e7d82,10.47363/jesmr/2025(6)263,From Data to Decision: Leveraging Machine Learning for Crisis Response,2025.0,Journal of Economics & Management Research,N. Hasanli,"This study explores the integration of machine learning (ML) techniques in early warning models (EWMs) for financial crises, emphasizing decision-making1327in policy contexts. By com paring traditional statistical models such as logistic regression and advanced ML techniques, including boosting methods such1328as AdaBoost, Gradient Boosting, XGBoost, LightGBM, and CatBoost, the research evaluates predictive accuracy and decision-making efficiency. Utilizing1329a dataset spanning 13 countries over 49 years, this paper highlights key economic indicators such as Account-to-GDP, Inflation, and Housing Price Cycles1330as critical predictors. The findings underscore the superior performance of boosting models and provide actionable insights for policymakers on optimizing1331thresholds (τ) and balancing predictive error through relative preference (μ). Specifically, the analysis demonstrates how varying τ and μ influences model1332effectiveness, highlighting the trade-offs between Type I and Type II errors. This research contributes to enhancing financial stability through informed1333crisis anticipation and proactive policy interventions",0.0,https://doi.org/10.47363/jesmr/2025(6)2631334709a4c3e872e50eff17150efe514f06c8a7c1418,10.3390/app13127110,Deriving Controllable Local Optimal Solutions through an Environment Parameter Fixed Algorithm,2023.0,Applied Sciences,Ohtae Jang; Sangho Jo; Sungho Kim,"This paper addresses the challenge of optimizing objective functions in engineering problems influenced by multiple environmental factors, such as temperature and humidity. Traditional modeling approaches often struggle to capture the complexities of non-ideal situations. In this research, we propose a novel approach called the Environment Parameter Fixed Algorithm (EPFA) for optimizing the objective function of a deep neural network (DNN) trained in a specific environment. By fixing the environmental parameters in the DNN defined objective function, we transform the original optimization problem into a control parameter optimization problem. We integrate EPFA-CLS (Controllable local-Optimal Solution) with Gradient Descent and algorithms such as Adagrad to obtain the optimal solution. To demonstrate the concept, we apply our approach to an optimal course model and validate it using optimal course and Boston house price datasets. The results demonstrate the effectiveness of our approach in handling optimization problems in complex environments, offering promising outcomes for practical engineering applications.",0.0,https://www.mdpi.com/2076-3417/13/12/7110/pdf?version=16867351521335d73db8e541080423b134044d2118691ddf867f62,10.24957/hsr.2026.34.2.101,Determinants of Time to Close in Housing Transactions: Evidence from Survival Analysis,2026.0,Korean Association for Housing Policy Studies,Hyungkeun Kim,"This study examines the factors influencing the time to close in residential property transactions. While time on market and time to close both affect housing transaction outcomes, they reflect different economic mechanisms. In particular, time to close may involve a range of economic costs borne by sellers, including opportunity costs arising from housing price appreciation, overlapping mortgage payments, and temporary housing expenses. Particularly, this study conceptualizes time to close not as a simple administrative interval, but as an independent contractual component with important economic implications. Using nationwide transaction data on apartments, townhouses, and multi-family housing units in Korea since 2023, this study employs Cox proportional hazards models and accelerated failure time (AFT) models to analyze the determinants of the contract completion period. The findings reveal that, beyond macro-market conditions such as interest rates and housing price growth, transaction-specific characteristics-including housing price levels, relative price discounts or premiums, and whether transactions were broker-mediated or direct-significantly influence the duration of con",0.0,1336e56439108f1b9f4b91c8f17d6e58616efa44992d,10.55057/ajress.2022.3.4.9,Determinants of Housing Prices,2022.0,Asian Journal of Research in Education and Social Sciences,Muhammad Azlan; Yvonne Lee,"The purpose of this research is to study the determinants of housing prices in Malaysia and Singapore. The research analyses the relationship between the housing price index with four other macroeconomic variables which are gross domestic product, interest rate, inflation rate and unemployment rate. The methodology that is used in this research is the multiple linear regression analysis. This research also performs diagnostic checking through normality test and multicollinearity test. The data is collected from the World Bank and the Bank for International Settlements covering the period of 1979 to 2019. The results revealed that for both Malaysia and Singapore, gross domestic product is significantly related to housing price. Apart from the relationship between inflation rate to housing price, Malaysia and Singapore’s macroeconomic variables (GDP, interest rate, and unemployment rate) are similarly related to housing price. Policymakers in charge of macroeconomic policies should take heed that these macroeconomic indicators have an impact on housing price, as shelter is one of the basic needs of citizens.",0.0,https://myjms.mohe.gov.my/index.php/ajress/article/download/16771/8920133743809ce634697aab7dff414691799271eca470ec,10.54097/d5b4r638,Research on the influencing factors of housing prices in Beijing,2024.0,Highlights in Science Engineering and Technology,Tu Bai,"With the development of the times, people's living standards have improved, and housing prices are constantly rising with the increase of price levels. The overall increase in people's demand for houses and the consideration of more environmental and cultural factors have brought huge challenges to the real estate industry. This paper aims to explore various factors that affect housing prices, including floors, construction time, and the presence of elevators. By comprehensively analyzing factors at multiple levels such as policy, economy, market, and society, we can better understand the fluctuations and trends of housing prices, providing decision-making references for the government, homebuyers, and real estate market participants. This study used various methods, including data analysis, literature review, quantitative and correlation analysis, as well as multiple linear regression analysis, to reveal the main influencing factors on housing prices. The main analysis method is linear regression analysis, which specifically analyzes the linear overlap rate of various factors through previous years' data.",0.0,https://drpress.org/ojs/index.php/HSET/article/download/19357/18916133825d02f4b22954faacce875002c2eabd4644dc44d,10.1387/inecs.28108,Crime and Perceived Insecurity as Determinants of Housing and Tourist Accommodation Prices: A Case Study of Barcelona,2026.0,International E-Journal of Criminal Sciences,Diego J. Maldonado Guzmán; Jonathan Torres-Tellez,"This study examines how crime rates and perceived insecurity influence the prices of Airbnb accommodations and the price per square meter of traditional housing in the city of Barcelona (Spain). We estimate two ordinary least squares (OLS) regression models and two geographically weighted regression models. All regression models include additional control variables grouped into three categories: insecurity, household or population structure, and the socio-spatial transformation of the neighborhood. 1339The results indicate that crime is not associated with housing prices, whereas perceived insecurity, national heterogeneity, and the arrival of residents from high-income countries play a key role in both tourist accommodation prices and price per square meter. The influence of these factors on housing prices varies substantially across neighborhoods. These findings suggest that the effect of crime on housing prices may be mediated by other structural neighborhood factors. This underscores the need for a neighborhood-centered approach, rather than analyzing the effect of each individual variable in isolation.",0.0,https://ojs.ehu.eus/index.php/inecs/article/download/28108/26062134036ad1f674de54e1ecb64d16215afd62268e4b9f5,10.1051/itmconf/20269102007,Determinants of Second-hand Housing Price Premiums in Shanghai: Evidence from MLR and XGBoost Models,2026.0,ITM Web of Conferences,Zi-Xian Yang,"With the deepening of China's urbanization process, secondhand housing has begun to attract more buyers. The paper uses multiple linear regression and the XGBoost machine learning algorithm to examine 5850 second-hand housing transaction records in Shanghai obtained from the Kaggle platform. An analytical model composed of the above two models was constructed to study the influencing factors of the price premium of second-hand housing in Shanghai and their nonlinear relationships. The results show that the floor area of a house is the primary factor driving second-hand housing premiums, with the highest gain score in the XGBoost model. MLR results indicate that floor area is negatively associated with the premium rate, whereas the number of rooms has a small but statistically significant positive effect. Besides, orientation and floor position have complex nonlinear moderating effects on the premium rate. The paper identifies the key factors affecting Shanghai's second-hand housing price premiums, which can provide advice on buying second-hand house for residents and on optimizing urban housing policies.",0.0,https://doi.org/10.1051/itmconf/202691020071341ba5888e5375daceadfdec830d1897161de0f895c,10.1051/itmconf/20269102003,Ranked Factor Contributions: Random Forest Insights Into Beijing’s Housing Prices,2026.0,ITM Web of Conferences,Chi Zhang,"1342 This paper is based on Beijing's housing data and uses the random forest regression model to analyze the main influencing factors of housing prices. The USA's housing data is minor and are only compared with those of the Beijing area. The model performance is evaluated by the coefficient of determination (R1343 21344 ) and the mean square error (MSE). The results show that the model has good predictive ability and generalization performance. The feature importance analysis based on the mean decrease in impurity (MDI) indicates that structural factors such as housing area play a dominant role in the formation of housing prices, followed by location factors and educational facilities. The residual analysis results show that the prediction errors are mainly concentrated around zero and follow an approximately symmetrical distribution, indicating that the model does not have obvious systematic deviations. The research results emphasize the significant role of housing physical attributes and urban amenities in the formation of housing prices, and provide empirical support for the urban spatial structure theory.1345",0.0,https://doi.org/10.1051/itmconf/202691020031346